# Mapmaking Discussion & Philosophy (WIP/Critique) > Regional/World Mapping >  The KöppenGeiger climate classification made simpler (I hope so)

## Azélor

*
Prerequisites to this guide*
    I made this guide for earth-like planets.  
    This tutorial is mostly a follow-up of the tutorial  made by Pixie : https://www.cartographersguild.com/s...ad.php?t=27118
    To get a better understanding on climates you really need to read this, the climate cookbook: http://web.archive.org/web/201306191..._cookbook.html
*

Software*: 
You can use any software you like to do this but using multiple layers  is  a necessity. I would recommend Photoshop or Gimp. Personally, I used  Photoshop CS3. 
I have worked on a script to make step 7 easier.  Its not impossible to  do without the script but its much faster/easier since its automated.  I works well with Photoshop. 

*
Map projection:* 
Examples are made with the Winkle triple projection, its the same  projection used by National Geographic. Its useful because it minimizes  all the types of distortions. But you can use other projections too.  The euqirectangular is useful if you want to convert it to other  projections later. Winkle triple cannot be converted into another  projection, use it only if youre sure you wont need to convert it  later.  Otherwise, youre better to use equirectangular. 
*

Informations:* 
I used this as the main source of information : Köppen climate classification - Wikipedia, the free encyclopedia
But also other pages and some scientific articles. 

The first part I did was to collect the info on the wiki and also elsewhere to get a clear definition for each climatic zone: what it is but also where to place them: 
where they are the most likely to appear. 

A reference for climates, may contain some inaccuracies: 



Real world data : http://www.cartographersguild.com/sh...l=1#post277812
Wind map and pressure : https://www.cartographersguild.com/s...l=1#post279343

*The tutorial

*Step 1,2,3(elevation, currents, air pressure)
Step 4, winds
Step 5, temperatures
http://www.cartographersguild.com/sh...l=1#post285140
Step 6, precipitations
Step 7, climates 

Python script for generating climates (made by AzureWings)
*More about the script.*
*

Other threads about climates :* 

https://www.cartographersguild.com/s...ad.php?t=27118
https://www.cartographersguild.com/s...932#post289932
https://www.cartographersguild.com/s...ad.php?t=30482

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## Iggy

I tried to open the file but OpenOffice tells me it's damaged. Does that document come in any other format?

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## Iggy

OpenOffice Writer can read and write .doc files just fine. Except for this one.

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## Pixie

I've read the whole thing, it is helpful indeed Azelor.

Being a science teacher and an amateur climatologist/geologist (in fact, an amateur world-builder), I can follow all of it pretty easily. Dunno how less science savvy folks will manage it, but it doesn't seem complicated.
I think it is a very valuable add-on to the tutorial I (we) are currently building.

It made me think of one more climate map that would be very useful: a cross-reference between rain pattern and temperature, to make a two-colored map separating areas where evapotranspiration is greater / lower than precipitation. This could perhaps be helpful (do you think it would be helpful?)

Picking up the word usage in my tutorial, what do you think of this?
*Lower precipitation than evaporation (DRY seasons)*
Very Hot + Moderate/Low/Dry
Hot + Low/Dry
Warm + Dry
*Roughly equal precipitation to evaporation (MODERATE seasons)*
Very Hot + Wet
Hot + Moderate
Warm + Low
Mild + Low
Cold + Dry
Very Cold + Dry
*Higher precipitation than evaporation (WET seasons)*
All remaining combos

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## Corvus Marinus

Breaking down the meaning of each of the letters is really helpful in understanding the details of the system. And I think Part II: Climate Zones will be very useful; it's a lot clearer than Wikipedia. The formulas in Part I, for me (not very science-literate), do not translate into immediate usefulness in worldbuilding; but I can easily take Part II and use it to "proof" my map after it has gone through Pixie's system.

If I am still around when you have a final draft, I will happily check it for spelling/grammar/formatting, if that is something you'd appreciate.

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## Azélor

> I've read the whole thing, it is helpful indeed Azelor.


Good to know.

Were you considering a map or some info graphic like this one : http://powerfulinfographic.com/wp-co...parency-11.jpg
I don't know about the idea. There is a relation between temperature and minimum precipitations to avoid desertification. Did you know that the Sahara would need between 3000mm and 6000mm of rain per year to become a moderate climate similar to Spain? That's a lot of water and it's just the minimum. 

There is a part in the guide where I talk about yearly precipitation not seasonal. The problem with the classification is that it compares the driest month with the wettest without taking in consideration if the driest month is really dry. Sometimes, it's not the case. It's considered dry only because the wet month receive a lot more rain. So the letters s and w are more or less valuable here. At the equator, at least we know that under 60mm it's considered dry.



edit : this ! http://en.wikipedia.org/wiki/Aridity_index

and this : http://upload.wikimedia.org/wikipedi...dity-index.png

I'm now using this from the Trewartha wikipedia page: BW and BS mean the same as in the Köppen scheme, with the Köppen BWn climate sometimes being designated BM (the M standing for "marine"). However, a different formula is used to quantify the aridity threshold: 10(T − 10) + 3P, with T equaling the mean annual temperature in degrees Celsius and P denoting the percentage of total precipitation received in the six high-sun months (April through September in the Northern Hemisphere and October through March in the Southern).
If the precipitation for a given location is less than the above formula, its climate is said to be that of a desert (BW); if it is equal to or greater than the above formula but less than twice that amount, the climate is classified as steppe (BS); and if the precipitation is more than double the value of the formula the climate is not in Group B. Unlike in Köppen's scheme, no thermal subsets exist within this group in Trewartha's, unless the Universal Thermal Scale (see below) is used.

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## Azélor

Hey, I got some numbers!

Replacing this :

	If less than 30% of annual precipitation occurs in the summer : Annual precipitation (mm) < 20 × average annual temperature (°C)
	If more than 70 % of annual precipitation occurs in the summer: Annual precipitation (mm) < 20 × average annual temperature + 280 
	Else : Annual precipitation (mm) < 20 × average annual temperature + 140
o	If annual precipitation is  < 50 % of the threshold = BW: desert climate
o	If annual precipitation is between 50 and 100 % = BS: steppe climate
by this:

 if the annual precipitation (in centimetres) 

are Greater than R= humid
are Smaller than R but greater than R/2= semi-arid
are Smaller than R/2= arid

R=2 x T if rainfall occurs mainly in the cold season (s=summer dry)
R=2 x T + 14 if rainfall is evenly distributed throughout the year (f)
R=2 x T + 28 if rainfall occurs mainly in the hot season. (w= winter dry)
(T= mean annual temperature)

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## Pixie

Hmm, this complicates things a little bit. But at the same time, it helps. It helps because now we can get a workflow that will yield more accurate climate maps and it complicates because that workflow will be a little more messy now.

I tried to build a humidity map based on this info and on the scheme I mentioned earlier. It doesn't fit with climate predictions made like I suggested in the other thread in some places - namely, areas classified as savanna/monsoonal close to the tropics now seem Arid (desert?) all throughout the year and steppes at higher latitudes now have a properly Humid season, making them maritime/mediterranean.

Azelor, you have definitely raised a point that can't be overlooked. I will need to review my stuff  :Smile:  Thanks for that (or not! no, seriously, thanks for that)

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## Azélor

You also need to be cautious with the numbers. For example, Jaipur is classified a steppe but receive a little more than R. Inside one climate one can see huge differences. Lisbon (Csb) is a Mediterranean climate but R=4 it's pretty wet. While Los Angeles (Csa) is barely above 1.  It's not always clear because categories includes a broad range of possibilities. 

I like these formulas because they take into account that precipitation have a different impact depending when it fall.
Logically, if precipitation evaporate at a slower rate in winter, the water (or snow) will stay longer in the environment and thus will have a bigger impact on nature. In theory. 

Two cities receive the same amount of precipitation for the year. City A is summer dry and City B is winter dry. Over the course of the year, which of the two cities will be the driest? 







> areas classified as savanna/monsoonal close to the tropics now seem Arid (desert?) all throughout the year and steppes at higher latitudes now have a properly Humid season, making them maritime/mediterranean.


 plausible, but having a wet season does not always make the steppes a maritime/mediterranean climate. Only if they are not too far from the water.

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## Pixie

> I like these formulas because they take into account that precipitation have a different impact depending when it fall.
> Logically, if precipitation evaporate at a slower rate in winter, the water (or snow) will stay longer in the environment and thus will have a bigger impact on nature. In theory.


Yeah, that's the basic reasoning I think. That's what made me review the whole process - I am now going combo by combo, it becomes a huge table. 
5 january temperature levels x 6 january rain levels x 5 july temperatures x 6 july rain levels.... 900 entries. 
The original idea is to simplify climate prediction, 900 entries isn't simplifying. Still a work in progress...




> Two cities receive the same amount of precipitation for the year. City A is summer dry and City B is winter dry. Over the course of the year, which of the two cities will be the driest?


Did I say I am a science teacher? This is easy, when most of the rain falls in winter, moisture is available for longer. Thus, the location with rain in the summer is the driest.
However... plants metabolism is very dependent on sunlight, so the location with rain in summer may have more vegetation cover as both factors for plant growth coincide, and a dry hot summer requires plants adapted to drought, which normally means smaller leaves and slower growth rate.

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## Azélor

> Yeah, that's the basic reasoning I think. That's what made me review the whole process - I am now going combo by combo, it becomes a huge table. 
> 5 january temperature levels x 6 january rain levels x 5 july temperatures x 6 july rain levels.... 900 entries. 
> The original idea is to simplify climate prediction, 900 entries isn't simplifying. Still a work in progress...
> 
> 
> Did I say I am a science teacher? This is easy, when most of the rain falls in winter, moisture is available for longer. Thus, the location with rain in the summer is the driest.
> However... plants metabolism is very dependent on sunlight, so the location with rain in summer may have more vegetation cover as both factors for plant growth coincide, and a dry hot summer requires plants adapted to drought, which normally means smaller leaves and slower growth rate.


I think we need to make things complicated in order to understand the simples rules that makes the system. With some advanced statistics, we might be able to find interesting informations. I would like to see the file when it's done if possible.  

I got 10 temperature levels, what are your temperature levels?

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## Pixie

Table isn't finished, but here's what I mean:


Table is being made in an excel file and the result exported to pdf like you see here. Starting point should be january temperature, then using magic wand on intercept, user would shorten selection with january rain, then july temperature, then july rain... (and, to cover the whole map, repeat that 900 times!)

Any ideas are very welcome at this point.

Also, as you can see, there are lots of combos which I have doubts about or which, even if I apparently don't have doubts I am plainly wrong about.
If you have time, please give it a look - so far I only have these.

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## Azélor

I'm trying to classify the Dsb climate using your classification but I'm not sure how to. 

•	Precipitations: moderate
o	Summers = wet 
o	Winters= dry
•	Average monthly temperature between -25 °C and 28 °C
o	Summer: mild to hot
o	Winter: very cold to cold

the problem I'm having is that I know how much precipitation are required yearly but not for the specific seasons. 
I think I might have an idea, but it could make the numbers above useless.

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## Pixie

> I'm trying to classify the Dsb climate using your classification but I'm not sure how to. 
> 
> •	Precipitations: moderate
> o	Summers = wet 
> o	Winters= dry
> •	Average monthly temperature between -25 °C and 28 °C
> o	Summer: mild to hot
> o	Winter: very cold to cold


You're doing it again... Dsb means "dry summer"  :Wink: 
I would make it warm summer with arid or semi-arid conditions (low/dry rain patterns) and very cold winter with humid conditions (any kind of rain pattern except "dry" gives humid conditions in a very cold season)
A mild summer would make it Dsc... A hot summer would make it Dsa... this is the sort of reasoning I am making in building the table.

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## Azélor

I have another idea. It will make it easier to decide if one climate is winter/summer dry or forever wet.

first we need to set these assumptions:

Its in the northern hemisphere: july is in summer and january is in winter
January is always the coldest month
July is always the hottest month
January is the driest month of the year (w=winter dry)
July is always the driest month of the year (s=summer dry)



f: precipitation levels are either on the same category, 1 category down or up.
w and s: they are separated by at least one category

example: Pixiland receive 30mm of rain in January and 70mm in July. =f because they are just 1 category apart. 
Azelor Town receive 15mm in July but 75 mm in January. It's a dry summer, category 20-40 is separating each seasons.  

Both climate could be considered humid, maybe one is more humid than the other but that's not too important.
That way, it's simpler than the : precipitation < 1/3 of the wettest winter month
and the numbers are not very different.

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## Pixie

I see what you mean, and that makes it very easy to decide between a s-climate, a w-climate or a f-climate. But, that's 10 levels of rain.

Say.. we keep the 6 levels of temperature as the current system gave pretty matching results in my test with ascanius and add more levels of rain.
Instead of the existing 7, we add two more levels (could we merge the 0-5 with the 5-10?). The current process gives 7 levels, but I ignore the 6th and 7th.
This can be done adding two layers in the present composition of rain patterns and I think it can be done in a few different ways.
This could work, but it now becomes a 6 x 6 x 9 x 9 set of combinations.... 2916 different combos. That's complex enough, but we're getting to the point where it is impractical.

If I may say, Azelor, you are focused on getting accurate at a given point, knowing the exact conditions, whereas I am focused in getting an overall map of the land. What are we trying to reach here?

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## Pixie

This post got me thinking about how short is a 6 tier classification for mean temperature... You are using 11 different ratings in this and yet, there are some temperature combos present in more than one kind of climate, which means it becomes useless for classification means if there isn't more information as the same temperature range can be found in two of the climates...

So I thought the way to counter this sort of lists where we get confused is to use a 2-entry table. So I sat down and made one, I went for an 8 tier classification, including your terms "cool" and "severely cold". More than 8 is too much in my opinion.
So this is it. This table would be the source for classifying the entire thing according to temperature (precipitation/humidity) would come at a later stage.



question 1: 
what do you guys think of the use of a 2-entry table?

question 2: 
and what about the actual key used to fill in each position?

note: 
cold deserts and cold steppes are a miss in this table, as their temperature ranges would be more in the tune with a D-climate - they would have to be determined separately.

note 2: 
instead of terming the table X/Y axis as july and january, it could be termed as hottest month vs. coldest month, and then the classifications in each position wouldn't have to be symmetrical, allowing more flexibility - but at the same time, doubling the workload for the user, as it meant working the two hemispheres in separate.

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## Azélor

I do like your table, it look simple and clean. 

The keys look alright to me so far.




I need to go back to what I said initially that was : when climates get colder, less precipitation is require to stay wet.

We have that formula: Precipitation= temperature/2  (+something)
So, every time you move by 2 temperature categories, the minimum rain required move by one
In photoshop, this could translate in having another layer. The original if for the total precipitation, we don't change that. The second and new layer is a modifier added to the original. It take in consideration that more rain is required in hotter climate to stay wet. The first serves only as a reference representing total precipitations and the second represent the ''wetness level'' or ''relative precipitation''? 
It could be done by making colder climates appear wetter using the same color scheme as the original.

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## Pixie

> We have that formula: Precipitation= temperature/2  (+something)
> So, every time you move by 2 temperature categories, the minimum rain required move by one


It doesn't. It is a linear relationship, it changes the scale (temperature and precipitation are measured in different units anyway), but not the progression. The graphs you showed earlier (temperature vs. precipitation) had that straight line. Twice as much temperature requires twice as much precipitation for the same level of "wetness".




> In photoshop, this could translate in having another layer. _(...)_ . The first serves only as a reference representing total precipitations and the second represent the ''wetness level'' or ''relative precipitation''?


That's what I was trying with the "available humidity" map and the column called "humidity" in that reference table. Still, I admit having only arid/semi-arid/humid is too short to accurately classify climates - it's well enough to determine deserts but insufficient for anything else.




> What should we use for precipitation level? 
> 
> if I use the holdridge precipitation on the right combine with a possible equivalent on the left. 
> very wet/	super humid
> wet/	        per humid
> moderate/	humid
> steppe?/	sub humid
> steppe/	semi arid
> steppe/	arid 
> ...


I'll try to come up with a second 2-entry table adding up mean temperature and precipitation pattern. I mean, if I understand your idea (and if this is it I am for it), we will have three maps:
1. mean temperature
2. precipitation pattern
3. "wetness level" / "available humidity" / "humidity" (pick your preferred denomination, I vote for "humidity")

Climate regions would then be determined by finding particular combos of mean temperature and humidity.

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## Pixie

Great effort there, Azelor. That will help a lot in developing that reference table I mentioned earlier. I am still most worried with the sheer number of possible combinations...

But, I thought of a possible solution. This is a question for expert PSP/Gimp users, as well... If there was a way to set up a filter, in photoshop, that would do a look up based on the color of a pixel in different layers and paint the result of the lookup in a separate layer (did I explain myself in a legible way?) - then we would automate a part of the process.

I mean, automating this tedious task:
- if #color in layer "january mean temperature" is X and #color in layer "january precipitation" is Y and #color in layer "....  (etc.) then #color in layer "CLIMATES" becomes ZZ.

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## Pixie

Well, if it is a script, a script it is. I might google for a "how-to" later on. But first need to work on that table..

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## Pixie

I like your idea.

We would need to have a key "color to climate type", wouldn't we?

I'm off on holidays in a couple of hours, but will get back to this in September. That is, if you didn't fully solve the problem by then.

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## groovey

Oh wow! That's a lot of good work you did to try to figure out climates and how to represent them. 

Unfortunately, you already know, I have no idea about it all, so I can only share my absolute wonder on your job. I wish Pixie was around to offer real input.

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## groovey

Any breakdowns on this? I'm redoing the heightmap to my world and felt brave enough for a second to try and do the new climate myself, but I've re-read this thread and wow, it's scary. Any chance of a version for dummies? Is it even possible?

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## Azélor

I'm still working on it but it's taking more time than expected.


I might need a guinea pig, are you interested?
Your using photoshop right?

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## groovey

I do. I'm more than willing to give it a try, but keep in mind how very challenged I am with all the climate stuff, so you'd have to be patient with me.

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## Pixie

I, for one, am very curious to see what you two come up with.  :Smile:

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## groovey

If you still interested Azelor, my new height-map is almost ready. But for the climate stuff I have to redo the winds and the other stuff right (redo is a funny word, since you did it for me the first time)? That'll take me some time to figure out so I hope the thing I'd try for you isn't for this month's challenge.

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## Azélor

In my attempt to work on a method based on Pixie's tutorial I came up with the idea of recreating the climates of Earth using the tutorial to see what needed improvement. 
What model could be better than Earth?

The source of the data is include in these pictures.
*
Real world data:*

The elevation map.


Temperatures for January and July. No data for Antarctica.  



Precipitation for January and July.




The rest of this post is not mandatory for the tutorial. 



*How to determine if one area is dry ? Using Excel* 

I changed the precedent formula to this from the Trewartha Wikipedia page:

2(10(T − 10) + 3P) 

with T equalling the mean annual temperature in degrees Celsius and 
P denoting the percentage of total precipitation received in the six high-sun months (April through September in the Northern Hemisphere and October through March in the Southern)

if evaporation is smaller than the average precipitation = humid            
if evaporation is bigger than the average precipitation but smaller than the max = steppe        
if evaporation is bigger than max = desertThus if one area has an average temperature of 35°C : severely hot + severely hot (probably impossible on Earth) the default minimum precipitation required to assure it's humid is 10(35 − 10) + 0 = 250
Then, we add the % of yearly  precipitation falling in the 6 hottest months and multiply by 3. If it fall evenly during the year, the % is 50. So ... 250 + 3(50) = 400
But we still need to multiply by 2, to get 800. 
So if precipitation are under 800, it's not humid.
Details: 
The average yearly temperature is based on the fact that January and July are always the coldest/hottest months and that the temperature change at a constant rate at each month (of course, it's not like that in real life). Thus, in order to get the yearly average temperature I make the average of the lowest and the highest. As simple as that. 

For the precipitation, in order to have the yearly precipitations, I make the summation of the precipitations for summer and winter (by previously multiplying the precipitation of January and July by 6). 


Each temperature combo has a different aridity threshold but many do share the same numbers because it's based on the average temperature. Once we have the individual threshold for each (250 for severely hot + severely hot),

we need to figure how much rain each precipitation combination will give and how they are sprayed during the year.For example, a very wet area receive between 100 and 200 mm of rain in summer but less than 10mm in winter. I need to find the average precipitation here. To do that : min + max/2

min = (6*100mm) + (6*0mm)
min = 600mm
Max=1260mm
600+1260/2 = 930mm that's the average yearly precipitation for one of the 36 rain combinations.
When we have all the 36 averages, we need to find out the T from the formula. Average summer precipitation/ total precipitation.
Total= avg summer + avg winter
avg for a particular season = min+max/2
The example above would give : (100+200)/2 + (0+10)/2   
or just divide 930 by 6 = 155
Now (100+200)/2= 150mm falling in summer (July)
so 155/150 = 97% of the precipitations fall in the summer

Thus the total of the Threwartha formula is 2(10(T − 10) = 800
and we add 800+3P = 800 + 3(97) 
= 1091mm , that is the precipitations required to keep the place humid.
All that give us the requirements for severely hot + severely hot but just for 1 or the 36 rain combo...
When all the combos are done, we need to do it for the other temperature combinations ( some will share the same numbers)
IF THIS IS NOT CLEAR, I WILL POST THE TABLE MADE WITH EXCEL. It's not quite huge as it look. 

With that table, we compare the numbers with the previous table (the one indicating the total yearly precipitation, to find when a temperature combination is humid, steppe, or desert.
When that is done, it gives me a nice huge table with 3600 little square full of colors!



Next, it's with Photoshop. Ideally this process from here can be automated with actions/scripts. That's the step 7 of Pixie's tutorial. A brief description:

*What the script does*: 

-It start by making some dummy layers and adjustments to the different maps for technical reasons in order to be able to select all possible combinations.
-Select the different temperature combination and create a new layer for each.
-Using the temperature layer, he regroup them to create the major temperature zones from Koppen: A,Ca,Cb,Cc,Da,Db,Dc,Dd, tundra and ice caps. The last two don't need to take in consideration the precipitation, so they are done at this stage.
-Select the different rain combination and paint them in black on one layer each.
-Regroup the rain combo with all that share the same characteristic for when a specific temp combo is arid/or not. 
       Most of the rainy combo will always stay wet no matter how hot they are, I put them aside on a specific layer
       Others are selected one by one (or several if they share same characteristics) and then, intersect them with the temperature combination that are not humid in order to separate them on a new layer. So, the part that was not selected should be made of only humid      
       climate. When we are done with this, we can put the remaining areas with the always wet layer created earlier.
-Now we know where the desert, steppe and humid climate are. We can separate the cold from the hot arid climate or not because I don't think it's really a big deal.
-Taking the wet climates, we  intersect the layer with the temperature groups (A,Ca,Cb...) and create a new layer for each. Or we could just delete the arid part based on the arid layers, but it's the same result.
-When it's done, we still have to figure whether it's summer dry, winter dry or normal. Normal (f) have no dry season and  it's when the precipitation for each season is no further than 1 category apart. For example: Category 5: over 200mm and Category 4: 100-200mm. 
        I made a simple table for this and it's nothing complicated. Winter dry have very low precipitation in their dry season, often lower than the lows of summer dry. 
-With these w,s and f layer, we separate the temperature groups into Cfa,Csa.Cwa,Cfb... 
        Now we have the actual climates
-Some corrections to the map seems inevitable. Some deserts are surrounded by humid climate with no transition. Usually it's because the transition would occur inside the driest precipitation category. But since I'm using the average, there is only one value abd the transition is sometimes impossible. We need to add the steppe generally outside of the desert. I will come back to this later but the process is not perfect and still need some tweeking.

*The result*, I had to make the map 20% smaller to make it fit:

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## groovey

The result is pretty damn close to the Koppen maps of Earth, almost exactly! So you're definitely on the right track!

I'll wait for your instructions to do the temperature and precipitation layers then. I'm still finishing the redo of the coastal shelves.

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## Akubra

I agree completely with groovey. The differences with the real climate map of Earth are minimal. Astonishing!

Do you need another guinea pig? I have height, ocean currents and wind maps ready, but I'll have to redo temperature and precipitation maps. I'd be interested to follow your procedure and see what the result of it is.

Cheers - Akubra

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## Pixie

You are clearly aiming at a high level of accuracy. That is awesome but makes me wonder how do you produce temperature and rain maps with that level of certainty for an imaginary world.. can't wait to see the rest of your tutorial.

Cheers!!

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## Azélor

Can't wait either, it's better be amazing! 
The problem is that I started with the last part of the process (your step 7 if I recall). 
Now I got to figure how to make the first part fit.

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## Ilanthar

You're doing an impressive and very detailed work on this Azelor!

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## Azélor

> You're doing an impressive and very detailed work on this Azelor!


Thanks! Hopefully, it will be simple enough to be considered manageable for those interested. 


I'm now working of the altitude vs temperature. It does not give precise results and I don't think it will. Explanation:

I'm using temperature average, not the exact values. The temperature decrease slowly with elevation. If a category includes temperatures between 22 to 28, and the other is between 18 and 22 (for example), the change in the temperature color represents the change from 22 to 21. It's a small change normally. But since I'm using categories, I have no idea what the exact temperature is. Therefore, it's a change between the average of 22-28 (25) and 18-22 (20). From 25 to 20. 

It means that the "lowest" temperatures of the first category sometimes appear hotter that what they should be in reality and some areas of the latter category will appear colder. But I have no idea how to do otherwise. 
I was thinking I could select an elevation and expand the selection but some area are steep, others are flats, I have no idea what the exact temperatures are and the temperature variation is not the same everywhere. 

What I'm going to do is just to use this simple formula: *sea level temperature - (6.49 °C/1,000 m)
*

Using the average of each temperature category, lower the temperature according the the altitude. When the temperature reaches the average of a lower category, lower the temperature to that category. 


Unless someone has a better idea?

EDIT: I'm mostly done with the temperature part. I just need to figure how to place some of the most extreme categories and make a final version of the instructions.

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## Azélor

This is the result. It still need to patching but it's looks pretty god. I just need to make a final version of the instructions and maybe some adjustment to normalize things. 
Then, it's onto the precipitations but I think I will take a break before starting the next step. 

temperature maps for January and July

----------


## Ilanthar

I'm a bit surprised, are the temperature that hot in January, in the south Bolivia/NW Argentina?

----------


## Azélor

Yes but actually, it's hard to find data to confirm it. Several places in Argentina are on the fringe between 27 and 28.  Paraguay has a few over 28 but barely (28.2).

----------


## Mysterious Mapmaker XXIII

Hey!

I'm a conworlder looking to decide my conworld's climates some day, and I love what you're doing with this tutorial! I can't wait to see the full, re-worked version! Especially since it'll be the first version I actually see.  :Razz:  But honestly, this looks like what I've been looking for in regards to actually doing my climates.

Just for the record, are the bits on page 4 actually part of the tutorial, or just notes? Just trying to get a better idea of what's going on here.

----------


## Azélor

It's more like a draft.

----------


## Mysterious Mapmaker XXIII

Ah, okay! Looking forward to the full version!

----------


## Azélor

Some details, some are more evident than other but might still be educative:








Rossby wave. While not unique to America, it have a unique characteristic one this continent as explained in this article : http://www.americanscientist.org/iss...mild-climate/3 


The Arctic is cold and trapped in ice with low evaporation. A high albedo: reflect a lot of energy to space instead of absorbing. This result in low humidity.  

In summer:  

One things I haven't really figured out is where the influence of the front ends. I believe that it extends farther south that it's counterpart in Europe, but I don't know where it ends.
America is large enough to create his own modest monsoon. Mexico can be pretty rainy. There is the altitude but there is also a low pressure system around. It's not the ITCZ but possibly and extension of it? Anyway, all of central America is really wet.



July: during the cold season, the coasts receive some rain but it's not much and it's mostly if they are at right angle with the winds coming from the sea. The rest is dry except for the ITZC. Again, the rain belt is mostly a straight line. The Horn is still dry because it receive winds from the interior of the continent blowing toward Arabia...



South America:

January: A lot of things are still obscure about this continent. It receive more rain than Africa. Larger landmasses tend to become hotter and become larger low pressure center. This and apparently, the Amazon forest generates 50-80% of it's the region's precipitations. I've found this data on Earth Stackexchange but, I'm not sure the source is good.  The forest must help keeping the place more humid but maybe the influence is smaller. 

The reason why there is a larger forested area in SA compared to Africa is probably because the variation of the ITCZ is smaller than in Africa, the center of the continent seems closer to the equator. Just some theories.

Aside from that, Eastern Brazil is drier because it receive drier air from the high pressure system. That relation is inversed in winter but I'll talk about it later.

----------


## Mysterious Mapmaker XXIII

Interesting.

Is this to be part of the guide, or just ancillary information?

----------


## Azélor

> Interesting.
> 
> Is this to be part of the guide, or just ancillary information?


I don't know yet. 


I might have figured something out. Basically, we can estimate the quantity of rain mostly based on the air pressure and direction of the winds. And with a combination of other factors such as the distance from the water bodies.

----------


## Azélor

*Step 1 basic elevation map*
The first step is to make the elevation map. I made one level of elevation for each 1000m above sea level. 
I added several others layers of elevation under 1000m but they are mostly aesthetical.  

And by the way, it will also help a lot if you have latitude/longitude lines since we will refer to them all the time. 

Attachment 75636

Attachment 79116


*
Step 2 oceanic circulation, surface currents:*

Reference maps:

A simple one:


And another, more detailed: https://upload.wikimedia.org/wikiped...erless%293.png

Color signification:


·         Blue: Polar currents are cold between the poles and the polar circle.  Currents flowing toward the equator are cold if they originate from these regions.·         Red: Equatorial currents are hot, including those flowing from the equator toward the poles.·        White: Mid latitudes currents are mild. Hot current flowing poleward eventually get mild. And cold current become mild when they get closer to the equator. Mild currents also happen when cold and hot water are mixed. 

  Close to the equator, you can omit the transition to mild because at this latitude, water temperature has little impact unless its very cold.  


Mapping the currents:

1. Close to the equator there are two currents flowing westwards. 

2. As these two currents meet a large landmass they diverge away from the equator, toward the poles. 
They will stick to the eastern coast of that continent approximately until 40º to 45º of latitude. They are hot: draw them in red.

3. At about 45º, the Westerlies (West -> East winds) are strong enough to create an eastward current. It gradually shift direction toward the east.
 As it cross the ocean it can sometimes be tilted a little toward the north east. Draw in red and use white if the current drifts far enough from the continent.

4. When this current meets continental shores, it spreads north and south following the coasts. 
The currents moving back to equator will get slowly warmer. The part that flows north continues to cool slowly. 

5. Poles:  If you have polar oceans, you need to close the loops. 
Cold current flowing back to mid latitudes tends to stick to the coast until they encounter the hot currents mentioned at point 2.
 They fill the void left by the north-eastward movement of this hot current and then turn abruptly to the east. In the current map: see the Labrador Current east of New England. 

Near the poles, the currents are flowing from the east to the west. This only happens at very high latitudes (over 70 degrees approximately), where the polar easterlies are the dominant winds. 




*
Step 3 : Atmospheric pressure systems*

The hottest place is near the equator. Its the Intertropical convergence zone, ITCZ for short. The air rises.
The poles are the coldest, and the cold air sinks. 

The position of the pressure systems changes over the course of the  year, with the ITCZ roughly located where the planet receive the most  energy from the star. It should be close to the tropic n the summer but  the position is influenced by the actual temperatures. Land heat up more  than water and large landmasses will pull it to them. This mechanism is  the main engine of the atmospheric circulation.

Look here for a detailed explanation of atmospheric circulation: https://en.wikipedia.org/wiki/Atmospheric_circulation


*
The ITCZ, a low pressure system*

Close to 10-15° normally.  During the summer, the movement of the ITCZ is strong in Asia but limited elsewhere. In order to have an impact, continents need to be large, hot, with significant landmasses around the tropics.
The position of the ITCZ is not always clearly defined. The Intertropical latitudes are always hot and therefore, there is a consistent low pressure system. 

*
A. High pressure center*
*
Oversea:*
Cold season: 30° in a more or less continuous line
Hot season 35° separated, mostly on the eastern side of the oceans

Tend to be located on the eastern side, close to the continents because its where the cold currents are flowing. 
In summer, the high pressure system breaks apart as the continents are affected by low pressure systems due to hotter temperatures.

*Inland:*
Cold season: high pressure systems develop over the continents (including the poles). Larger continent = higher pressure. 
Hot season: hot temperatures prevent the formation of high pressure systems.

The inland systems tends to be poleward of the high pressure systems that are over the ocean. 
Areas between the tropics are not cold enough to become high pressure but it does not mean that the pressure is low. 

*
B. Low pressure systems:*

*
Oversea:
*Cold season: Centered around 55°
Hot season: Move 5 to 10° closer to the poles

They tend to disappear over the land. 

*Inland:*
Cold season: No low pressure overland.
Hot season: large landmasses become hot and the low pressure can cover most of the continent.


January



July

----------


## Pixie

Right on, Azelor! There are valuable additions to the method and wording so far. I'm certain this will turn out (finally) to be a self standing complete tutorial.

... And there's a good chance it will become a sort of Geoff's Cookbook 2.0 over the internet  :Wink:

----------


## Azélor

*Step 4, winds:* 

Use the pictures to figure out how the winds are blowing. *Figure A*
Or use the main map at the bottom at the page. 
All the figures are from the North Hemisphere except E. 



*
Dominant winds*

    Near the equator, the dominant winds are usually blowing to the west (Trade Winds)
    In mid latitudes, its blowing to the east (Westerlies)
    And close to the poles they are blowing to the west again (Polar Easterlies)


Wind usually flow from the highest pressure to the lowest. The larger the difference in pressure between two areas, the stronger the winds will be. 
Inside a large high or low pressure zone, the winds can be very weak. *Figure B*

*Mid latitudes winds*: Starting with your low pressure bands at high latitudes, the Westerlies should blow from west to east where its blue, and where there are no colors. Avoid the red. 

*Low pressure* have 2 types: Hot season: converge like the ITCZ in Asia, see *figure F*
 And those of the mid and high latitudes, the North Pacific (round , isolated) and the North Atlantic (continuous band of low pressure)  * Figure G*
*The high pressure systems at mid latitudes* (also called subtropical highs): Draw the poleward winds first. They have a curved shaped because they quickly change direction when encountering the Westerlies.  *Figure C*
 Winds blowing from the equatorward side of the system tend to blow toward the equator, or if any, toward the closest low pressure center. 
  Winds are converging near the equator; they tend to blow to the west. *Figure D
*
*Polar highs:* The high pressure systems on the poles brings dry and extremely cold temperatures. *Figure E* (South Pole)

*Overland highs* are spinning according to the schema. See *Figure A*, or look at Eastern Asia in winter. 

January winds



July winds

----------


## groovey

Excellent! I guess it's time I get my height-map ready...again. Will let you know when I'm ready to start trying these steps.

EDIT TO AVOID DOUBLE POST:

Hi Azelor.

I've started running your revised tutorial. I'm quite confused at "Step 3 : Atmospheric pressure systems":

For example, at:

"B. Low pressure systems:

Overland:
Cold season: No low pressure overland."

Bu then, in the January map you do paint low pressure (blue) on land.

By the way, I didn't catch anywhere that altitude has much of an influence, so I wonder if these old preassure maps you made long ago are still valid?

----------


## Azélor

Sorry, I just noticed this message of yours.

No, I haven't painted blue overland during the cold season. Unless you meant the southern tip of Greenland. It's blue but just because it's surrounded by water and by the low pressure system nearby. Coastal locations can be blue for this reason. But not inland, because the low temperatures are not prone to a low pressure system.

Other than that, I'm gonna say this because maybe it was not obvious:  The cold season refer to January in the north but to July in the south. That's why South America is all blue in January
I thought that using cold/hot season was better than using winter/summer since some places have only 1 or 2 seasons but everywhere has a coldest/hottest month around either January or July. (Almost everywhere)

And no, i don't think your pressure maps are still good.

----------


## groovey

D'oh! *massive facepalm*. 

I forgot to consider cold/hot seasons are opposite in the hemispheres. Now it makes much more sense.

Since your tutorial is bound to stick around as reference for future projects of this kind, you might consider adding the reminder in the step. I'd bet I might not be the only one on the long run who completely forgets about that important detail...

----------


## Azélor

Step 5 Temperatures

Temperature categories are loosely based of the Thewartha system
Monthly mean temperatures of each categories

*Attachment 79531*

Dark magenta - Severely hot: over 35°C
Red  Very hot: 28 to 35°C
Dark orange  Hot: 22 to 28°C
Orange  Warm: 18 to 22°C
Peach  Mild: 10 to 18°C
Yellow  Cool: 0 to 10°C
Green  Cold -10 to 0°C
Turquoise  Very cold -25 to -10°C
Blue  Severely cold: -38 to -25°C
Violet  Deadly cold: under -38°C


*5.1 Zone of temperature* (also called influence):

We separate the world into several zones to make the distribution of temperature according to the influence each region is subject to. 

The different zones are:*Hot current (red)* : areas affected by winds blowing from a hot current. Hot current have no impact in summer since the land is hotter than the water and its considered normal instead. 
Also, they have no impact between the tropics either. 

*Mild current (green):* Includes
Mid latitude currents that have cooled, as a transition from hot to cold.
Where cold and hot currents meet. Usually around 40-45 degrees or elsewhere like South Africa.

*Cold current (blue):* These are the coldest currents. They make the land colder compared to other locations at the same latitude. 
*Normal:* the default temperature at a given latitude.  *Continental:* Occur on large landmasses, under high pressure systems in winter but under low pressure systems in summer. 
Stronger  in the center or the eastern side of the continents if the winds have been blowing overland for a long time. 
Areas trapped in a sea of ice like the Canadian and Russian Arctic are also considered continental: ice limits the heat exchange and reflect light back to space.
*
Continental plus:* Is an extreme version of the above. It requires larger landmasses. The only known case is Central Asia in summer.
 
January




July

----------


## ascanius

Honestly all your work is making me want to go back and redo my climate map with your guide to get everything just perfect.  I may do anyway just to get a very detailed climate map, when i have time, probably tomorrow.  Keep up the good work, between you and Pixie I think the forum will have two great tools to make extremely realistic, detailed maps.

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## Pixie

I still didn't understand how temperature is supposed to be worked out. I think we need clearer guidelines, although I understand that you are trying to make the tutorial less "strict" in guidelines than it was before.

In terms of continental influence, one should lower the temperature level/color, right - one step for continental, two steps for continental plus.
But before this, how does one decide what is the level for each area? This isn't clear.

----------


## Azélor

> But before this, how does one decide what is the level for each area? This isn't clear.



I based my "model" on pure observations. 
Take an example, the border between the color yellow and green on that map http://www.cartographersguild.com/at...7&d=1440511271
The 0°C isotherm is located roughly at 35°N is Asia and North America. But much further north in Europe and on the American west coast. Thus these area have different influences (ALSO CALLED TEMPERATURE CATEGORIES), the first two are continental, and the others are oceanic.

I am aware that my method feels clumsy (clumsy for lack of a better word). 
But it's the best i manage to get. 
The ideal solution would involve mathematic algorithms, like that maybe : http://www.cartographersguild.com/sh...t=26931&page=2

----------


## johnvanvliet

> I still didn't understand how temperature is supposed to be worked out. I  think we need clearer guidelines, although I understand that you are  trying to make the tutorial less "strict" in guidelines than it was  before.



the problem with local area temperatures 
is the SYSTEM FEEDBACK 

generally the nearer the poles the cooler 
BUT not always 

the tilt of the planet plays a HUGE role 
the earth is a rather mild tilt  of about 23 degrees 

if this was say 45 Degrees 
summers in the poles will be VERY HOT 

the entire summer hemisphere will be HOT 

and the winter hemisphere will be COLD
-- OR NOT!!!! 

if the water currents move a lot of heat around 
you might have a rather mild winter in one hemisphere
-- think of England 

it SHOULD be covered with SNOW like Michigan and New York 
but it is NOT 

the ocean current feedback keeps them warm just like Anchorage Alaska 


also you have wind patterns 

here in the north of the USA  the "jet stream" dips 
and dips BIG TIME 

but just look at a map 
it moves back to the north as it approaches the east coast 


system feedback as rather complex 
it is so complex that the weather models often get some things a bit WRONG

----------


## Azélor

Er, I don't understand what you mean by system feedback. 

The point of using he influences I mentioned earlier is to take these differences (between Europe and the North American East coast for example) 
into consideration. Different areas will be categorized into different influences according to certain criteria, resulting in different temperatures for the same latitude (in some cases).

----------


## Pixie

The point about "pure observations", Azelor, is that we don't have them for any other planet, namely and specially, for staggering obvious reasons  :Very Happy: , for an imaginary one.

So, at the very least, you should add an indication about where one can find the 28ºC, 22ºC, 18º, 10º, etc.. isotherms on Earth. (In terms of latitude, that is)

Like groovey, I'm volunteering "my world" for a testdrive of this method, once it's a bit more fleshed out.

----------


## Azélor

Yep, we only have Earth as a reference. Meaning that there is no way to verify if one rule is a general one or a specific one. But we got several different continents. We can see weather a phenomenon occur everywhere on just on one of them and try to understand why. One thing I could do is too look into paleoclimatology but I fear I might have a hard time finding good data since I already had to search a lot to find quality data for our own era.  I'm not saying the information is not reliable but it's much more limited. 

Still, we got the problem of having pretty rigid rules. Which , in theory, work great for an almost 100% Earth-Like planet but what if :The land placement is really different? (Blocking the oceanic circulation) more land or more water % ...

A different axial tilt (more extreme temperature variation but one would just have to move the important features according to solar radiation, I guess)

Different gravity (might have an impact on the raincycle?)

Thicker/thinner atmosphere (I remember reading that Earth had only 1 air cell, from the equator to the poles, during the Mesozoic. Resulting in a much more temperated planet overall)  

Different speed of rotation (Mentioned in Geoff cookbook, faster=more air cells, slower=less air cells. It would impact the winds and thus on the surface currents)

Hotter or colder average temperatures (the obvious consequences would be more low pressure areas if it's hot and more high pressure areas when it's cold but it's only relevant to mention if the differences are greater compared to Earth. Otherwise, it doesn't have any direct impacts)
 
The model is not general enough to cover all this. It might give some insight but one would need a lot of guesses. We could try to give some answers to the most common changes encountered in fictional world tough.  

For fun, I tried to make sense of the climate in A song of ice and fire. I really like the series but I got to admit that it makes no sense from a climatic point of view (I know about the irregular seasons but there is an explanation for that at least). For example, trees grow far beyond the Wall in the north. A wall of ice cold enough not to melt overtime, yet large forest of tree can grow beyond that.  The the other thing is the difference of climate between Dorne and the Stormlands.  Which is roughly the same as the differences between Morocco and Ireland but they are next to each other with no transition zone.  

I will try to provide more information on the isotherms once I sort things out.

Edit: sorry for the confusion, I deleted that part earlier because isotherms are in step 5. It's not clear, I might make a graphic to help visualize the transition. 

General temperature placement: should be at 10º N during the northern summer and 10º S during the northern winter.
it seems to work fairly well if the oceanic currents and major winds are done correctly.

*areas between the tropics are not always mentioned
*extreme temperatures are not included 

Winter temperature placement: the numbers between the color names indicate the boundary between the two.


Hot current: 20 orange 30 peach 40 yellow 65-70 green
Mild current: peach 35 yellow 55 green 65-70 yellow
Cold current: dark orange 5 orange 10 peach 35 yellow 55 green
Normal: dark orange 20 orange 25 peach 35 yellow 55 green ...
Con: peach 30 yellow 40 green 45 turquoise
Con+: dark orange 15 orange 20 peach 25 yellow 35 green 40-45 turquoise 50-55


Summer temperature placement: red are only guidelines

Hot current: Is considered normal
Mild current: Red 25 dark orange 35 orange 40 peach 60? yellow
Cold current: dark orange 20 orange 35 peach 55 yellow 
Normal: Red 30 dark orange 40-45 (med) orange 45-47 peach 65-70 yellow 70? green 
Con: Red 35 dark orange 45 orange 55 peach 0= 
Con+: Red 45 dark orange 50 orange 60 peach 70 yellow

----------


## Azélor

So, I edited this post http://www.cartographersguild.com/sh...l=1#post280821

I improved the influence maps and improved their descriptions. I still need to add more precisions on how to place the continental influences but there are already some guidelines. 
Can anyone tell me if it's easy to follow?

Also, about Western Australia, should it be considered a cold current? My logic tells me that it should, but not the temperature maps.

----------


## Pixie

From what I understand, you are advocating a more refined mapping of influences, and then use them to adjust a general temperature rule based on latitude. Pretty much the same system as before, is this right?

As for Western Australia, I looked it up - and here's what I found: https://en.wikipedia.org/wiki/Leeuwin_Current
I would say this return current is formed only in Australia because the continent doesn't block the incoming equatorial current from the Pacific, which, on top of it all, is pushed southwards by Indonesia/Borneo. West Australia is the only continental west coast without a cold current with upwelling and it is the only neighbouring a east-west through.

My logic and limited oceanography knowledge wouldn't let me predict this either, but it really makes sense (and I shall be updating my currents map accordingly  :Wink:  )

----------


## groovey

I'm a dense person, but even I can follow what your saying in step 5, so that's an achievement for both you and me.

Interesting, will you edit the currents step then? To reflect the new toughts, or is not necessary?

----------


## Azélor

> From what I understand, you are advocating a more refined mapping of influences, and then use them to adjust a general temperature rule based on latitude. Pretty much the same system as before, is this right?
> 
> As for Western Australia, I looked it up - and here's what I found: https://en.wikipedia.org/wiki/Leeuwin_Current
> I would say this return current is formed only in Australia because the continent doesn't block the incoming equatorial current from the Pacific, which, on top of it all, is pushed southwards by Indonesia/Borneo. West Australia is the only continental west coast without a cold current with upwelling and it is the only neighbouring a east-west through.
> 
> My logic and limited oceanography knowledge wouldn't let me predict this either, but it really makes sense (and I shall be updating my currents map accordingly  )


Yes, It's probably very similar to the previous version but also different.

So I guess it make sense for Australia. 




> I'm a dense person, but even I can follow what  your saying in step 5, so that's an achievement for both you and me.
> 
> Interesting, will you edit the currents step then? To reflect the new toughts, or is not necessary?


I didn't knew this expression... Glad to hear it's not too complicated but it's not over yet. 
I don't understand, is there something wrong with the currents?


I'm almost done with the influence/temperature part. I just need to write it properly and provide some images for each steps.

Then, it will be the precipitation part, that I've already started but I got a couple of things to sort out. The model is not too complicated, for the moment.

----------


## groovey

With the currents I meant... well I'll be damned, I can't find the exact text that made me ask that question. 

I would swear I read somewhere in one of your latest posts how the currents we got with the currents step weren't always the right color/general temperature, because other factors influenced them. But I can't find it so never mind.

And by the way, the climate in A Song Of Ice and Fire makes no sense at all indeed, "it's magic". I was a bit dissapointed when I researched that and found out.

EDIT: a-ha! Found it.

"Colors of the currents appear to be a poor indicator to determine the impact on temperature. 

Polar currents are cold (including everything between the poles and 66°) , including currents flowing toward the equator if they have been mixed with polar waters.
Equatorial currents are hot, including those flowing toward the poles from the equator. 
Mid latitudes currents are mild, including those flowing toward the poles and the equator. The Gulf stream is an exception : it hasn't cooled enough to be considered hot"

----------


## Azélor

Ok, I see now. Yes, I don't know if I should change that. I start saying that equatoward currents are cold and then tell that they can be mild or hot is some cases.  That might not make a lot of sense. 
Perhaps I should just say it's mild because of x,y,z... in the first place?

To be more specific, we should use a gradient (or a map just like for the land temperature) for the water temperature from 0 to 30... something (although surface temperatures at sea level near Antarctica are much colder). That could be doable I guess.

----------


## groovey

I'd say editing the currents step would be due only if being more specific with the currents temperatues/colors makes it faster, easier and/or more precise to figure out climate, if it doesn't help much I'd say keeping the indications in mind would suffice.

----------


## Facubaci

Hi, I don't have idea about climates, but I'm interested in them. I'm following this thread!

Greetings and thanks for start this discussion.

----------


## Azélor

Still haven't edited the current part but I will keep it in mind. 

About the precipitation: I got a model, but haven't tested it much to see if it's good. 

Quite simple at its roots (until I have to add a bunch of exceptions) 

Here's a map to give an overview. It's a nice map to visualize:


So, by combining several already made map (pressure and winds) we can estimate what regions will be wet and those that are expected to be dry. (The map is probably imprecise over the oceans due to a lack of information) 
Basically, High pressure are dry as well as the winds coming from them. Shown in white on the map
The opposite, Low are wet. Shown as green on the map. 

But not all High are dry and not all Low are wet.
H: the eastern and poleward sides are prone to storms due to the mixing of hot and cold air. 
L: can be dry when too far from the water (Central Asia) or when blocked by mountains (Patagonia) or when receiving dry air (Somalia)

Now I can guess what place are wet/dry but I still need to figure out the approximate guidelines. (effect of altitude, distances...)

----------


## Deadshade

An advice for precipitation.
I have created a (simplified) model but can tell you that you won't get any realistic values if you don't work in 3 dimensions.
E.g the vertical movements of air are paramount.
The reason for that is that the lapse rate for saturated air such as what exists at ocean surface is about 6°C/km what is huge.
For example if you move 30°C saturated air only 3 km higher like what happens in the Hadley cell at equator, you lower its temperature to 12°C. And that means that you obtain for a single m^3 of deplaced air some 3 mm of precipitation on 1 m². Now multiply by the flow rate of air and you'll see what it gives.
With a 5 k mountain it is even more dramatic.
The vertical movements of saturated air have a much stronger impact on precipitations than horizontal movements which are rarely working with air mass temperature differentials like 15-25°C.

----------


## Azélor

I know about the orographic lift. That's why Iran is not completely a desert.  

Or does it have something to do with the western disturbances?

----------


## Deadshade

Orographic lift is only a small part of the vertical air mass movements because it's localized. Monsoon precipitation regimes are another local example which are also driven by the vertical circulation.
However what is by far the biggest part is the circulation in Hadley and Ferrel cells (ITCZ and around 50° - 60°). It is in those bands that most precipitations take place due to the massive adiabatic cooling of the rising air masses.

----------


## Azélor

Ha yes, the "convergent" systems. They are the green bands on the map above. (more or less)

----------


## Dagann

Amazing work Azelor.
I can't wait to see the last steps of your tutorial.


Btw, what are the red areas in your last map ?

----------


## Mysterious Mapmaker XXIII

Hey, Azelor! It's been a while.

How are things going with this tutorial? What are you working on now, and what do you plan to do next?

----------


## ascanius

Hey, so I've been going through the tut for my continent/world to get a nice and detailed map for the 100th time, that's my plan at least.  I get very lost at the temp part, I'm not sure what I'm supposed to be doing.  I assume it's more or less like it is for pixies tut with different values and variables, right?  It's not very easy to follow at that part.

----------


## Azélor

> Btw, what are the red areas in your last map ?


These are the high pressure systems. For simplicity, it's the same thing as the withe areas. 




> How are things going with this tutorial? What are you working on now, and what do you plan to do next?


I haven't worked on it a lot recently. I'm finishing the temperature that I might post today.
I've also done some work of the precipitations. That's the next and  hopefully, the penultimate step. 




> Hey, so I've been going through the tut for my continent/world to get a  nice and detailed map for the 100th time, that's my plan at least.  I  get very lost at the temp part, I'm not sure what I'm supposed to be  doing.  I assume it's more or less like it is for pixies tut with  different values and variables, right?  It's not very easy to follow at  that part.



Are you referring to this post? http://www.cartographersguild.com/sh...l=1#post281530

It should be numbered 5.2 but wasn't because it is not finished. I'M working on it.

----------


## Azélor

*Step 5 part 2:** temperature placement
*
*Color key reminder


*

Tips: 

It might not be a good idea to paint the temperatures and precipitations too precisely. The combination of the temperature and precipitation maps can give odd overlapping climates at some places. With the select tool, never select anti-alias.With the continental influences, the color bands tend to be pulled toward the equator in winter but toward the pole in summer because the land is more affected by temperature changes.The mix of hot and cold waters at mid latitudes: You need to bend the temperatures bands. Increase in winter and decrease in summer their temperature slightly compared to the surrounding. See Eastern Canada or Northern Japan.  
See graphic, it's easier to follow:



*Details on extreme temperatures:*


*Winter temperature placement:* 

*Take the second value for a very large continent


Turquoise:  If there is an exchange of water north-south near the coast, its turquoise instead of blueBlue: appear on large landmasses above 50-55 but not on east/west coasts until 70. With a hot current the maximum could be 80-85. The coasts of a sea of ice or a closed sea are going to be blue above 50.Purple: it is usually in the center of a large blue area.  


*Summer temperature placement:* 


Dark orange: not over 40 near ocean, unless it’s a close one. Red:  normally between 20 and 35 but can go below 10 with dry conditions.  It’s never close to the seas above 30, unless it’s a closed sea.Not near cold or mild waters. Dark red: surrounded by a large red area. Low elevation.  


*Antarctica* seems slightly colder than the model.



*Instructions

*
 Ideally, do only one season and one hemisphere at a time to avoid confusion.Select 1 zone/influence at a time and paint using the graphic above.
Do dark red, red, blue and purple (extreme temperatures) after the others because they have specific guidelines. Ignore the altitude for now, but consider that the mountains limit heat exchange. Altitude layers: 1 for each 1000m. See below. Some minor corrections.   


*Adjustments
*

Fill the gaps if any.Make sure that you don’t skip a temperature layer when it was necessary to add one. Plains have smooth transition. Temperature and altitude are never a perfect match because we use 1000m chuncks.  


*Attitude: 
*


The picture shows the different temperature categories at different altitude. Ideally, this is just a guideline. 
Later you will need to make adjustment in order to have a transition between zones. 


Example:  Mexico




Here we have the default map without the elevation factored in (The area selected)The second map shows the modification at 1000m for the dark orange areaThe third map adds the modification for the orange area north of the first 
The fourth map shows how to try to make things more harmonious, by expanding the cold temperature south. Ideally, we should mostly focus on the center of the area because that where we should expect the highest altitude. 


*Final results:*

January


July

----------


## ascanius

So I went through and did a temp map of summer, I need to redo it.  I made a few mistakes, one very large mistake too.  I have an area the size of texas that is >35 C which makes no sense, don't know what I was thinking.  One thing you may want to go back and include the temp instead of/or with the color designation instead of just orange dark orange red dark red.  The biggest problem I had was the dark orange, red, dark red etc.  It started to get confusing which color you were talking about.

----------


## Dagann

I like this new step Azelor, however, i agree with ascanius.
Colors for temperatures are great, but adding figures woul help a lot.

Btw, i don't know if you know this site, it's really amazing and could help understanding climate and making a realistic climate map.
To access the options (temperature, winds, humidity,etc... all per altitude), just press "earth".
http://earth.nullschool.net/#2015/12...l/orthographic

----------


## Azélor

> So I went through and did a temp map of summer, I need to redo it.  I made a few mistakes, one very large mistake too.  I have an area the size of texas that is >35 C which makes no sense, don't know what I was thinking.  One thing you may want to go back and include the temp instead of/or with the color designation instead of just orange dark orange red dark red.  The biggest problem I had was the dark orange, red, dark red etc.  It started to get confusing which color you were talking about.


You mean I should say, very cold, cold, hot... instead of using the colours? I thought it would be less confusing to use the latter.
Or that I should indicate the names of each colours to avoid confusion?

----------


## ascanius

> You mean I should say, very cold, cold, hot... instead of using the colours? I thought it would be less confusing to use the latter.
> Or that I should indicate the names of each colours to avoid confusion?


Me personally I think just using the temp range would be better, like over 35C, 28 to 35C etc.  You could do severely hot, very hot and the others too and it would work just as well, using numbers are just my personal taste. Don't get me wrong I like the use of the color key you supplied but with the way it is now I cannot tell which dark orange your referring too.  By the way you also have 10 colors for the elevation key but only 8, unless I counted wrong, for the other first key.

----------


## Pixie

I shall be trying this out in a near future (that's when I find the time...), but it looks great and easy to follow. 

Just wanted to give my thumbs up for your effort, Azelor!

----------


## Mysterious Mapmaker XXIII

Hey, Azelor. It's been a while.

So, now that the temperatures are up, what's next? And how soon can we expect it? Thanks.

----------


## ascanius

Hey Azelor.  OK so I got my Temp maps finished.  I think they are fairly accurate, I hope.  Are you still working on this?

----------


## Azélor

> Hey, Azelor. It's been a while.
> 
> So, now that the temperatures are up, what's next? And how soon can we expect it? Thanks.


Precipitations




> Hey Azelor.  OK so I got my Temp maps finished.  I think they are fairly accurate, I hope.  Are you still working on this?


Yes, but I got lazy. Can you share the results?

I think most of it is not too complicated but I still got to sort out the effect of altitude on precipitation. It's sounds complicated but it's not, I guess, just a lot of data to check. Problem: air cools off linearly but the rain categories are exponential: the upper limit of the categories are defined by 12,5*2^t   where t is the number of the precipitation category starting with 0. (the last category doesn't have an upper limit) So, it means that the orographic lift will be stronger where it's dry at the sea level than where it's already wet. The orographic lift was almost invisible in British Colombia for instance. But then we mix 2 different things. The coast of BC and California is really wet because of : 1- the orographic lift or 2- because the mountains prevent the humidity from crossing over to the other side.

----------


## ascanius

here are the results.

Jan


July


I'm still a little uncertain about a few areas, mostly where the islands are, though the western one could almost be it's own continent, It is roughly the size of the eastern US or half of Australia.  Thats been my biggest area of trouble is trying to figure out how much of an influence those seas and islands have on everything.  For the pressure centers I simply pretended that they were part of overall land mass, which is somewhat seen with the July temps.  I also figured that the mountain ranges are going to act as a barrier for the pressure centers and thus temp which is seen with the January temp and how it bends around the area of mountains with the lakes.  With the high pressure center in Jan it's actually two, one to the north and one large one to the south of the mountain range.  One thing to keep in mind though is that this entire continent is roughly the size of Europe and Asia shifted north.

----------


## Azélor

I checked it and it's mostly good except some point. Yet, I could be wrong. 



  Color code:
Blue is colder
  Dark blue is 2 categories colder
  Red is hotter, same principle

  Summer changes:

Made the south west coast hotter (like California)North West, large landmass can be quite hot, but less so on the coastThe same is true for the east, where the coast is cooler but interior hotterHotter central sea because semi closed bodies of water accumulate more heatEastern tip, influence of the cold current is more limited 

  Winter
Made the south west hotter but the center colder since large landmasses are cooling more than the oceansNorth West is colderInterior seas are colder, at this latitude, the water receive less energy and these seas are covered with ice. And most of these seas have little heat exchange with the oceansAbout the pole, the pole is a dot in reality, yet here it is represented as a line. It needs to be of the same color (namely blue). It does look odd considering the area the south of the pole is actually colder but thats a trick from the projection. Okhotsk sea, even covered with ice the temperature is moderated by the flow of water from the Pacific.   About the pole, the pole is a dot in reality, yet here it is represented as a line. It needs to be of the same color (namely blue).  It does look odd considering the area the south of the pole is actually colder but thats a trick from the projection.

Other than that, my precipitation model is nearly finished. I just need to sort out 2 or 3 things. 

The most problematic is my little understanding behind the Eastern Asia winter monsoon. I know there is a large anticyclone (Siberian High) rotating clockwise, pushing the cold air to southern China and possibly beyond. 

So there is a sharp gradient of temperature north /south. I got this but it's not the only place there is one. 
There is apparently a semi-stationary front over central china in the winter months. This cold front brings cloudy and rainy conditions. This is caused by the mixing of cold dry air from the north and the hot moist air of the Pacific. 
It is close to the tropic, so the westerlies are probably weaker. 

That is how I understood it. Am I missing something? 
One thing that I'm not sure I understand is how the air from the Pacific is able to travel that far inland.

----------


## ascanius

Thanks Azelor, So far I'm thinking that this guide is working well.  You may want to make the suggestion of going through the guide twice one for practice and one version to keep and use.




> [*]About the pole, the pole is a dot in reality, yet here it is represented as a line. It needs to be of the same color (namely blue). It does look odd considering the area the south of the pole is actually colder but thats a trick from the projection. Okhotsk sea, even covered with ice the temperature is moderated by the flow of water from the Pacific.   About the pole, the pole is a dot in reality, yet here it is represented as a line. It needs to be of the same color (namely blue).  It does look odd considering the area the south of the pole is actually colder but thats a trick from the projection.[/LIST]


Doh!  Yeah, I feel stupid.  I forgot the pole is a single point.  The only thing I don't understand is why you made the north west peninsulas warmer in winter?  Other than that I understand all the other fixes you did, they make sense once I look at them.




> Other than that, my precipitation model is nearly finished. I just need to sort out 2 or 3 things. 
> 
> The most problematic is my little understanding behind the Eastern Asia winter monsoon. I know there is a large anticyclone (Siberian High) rotating clockwise, pushing the cold air to southern China and possibly beyond. 
> 
> So there is a sharp gradient of temperature north /south. I got this but it's not the only place there is one. 
> There is apparently a semi-stationary front over central china in the winter months. This cold front brings cloudy and rainy conditions. This is caused by the mixing of cold dry air from the north and the hot moist air of the Pacific. 
> It is close to the tropic, so the westerlies are probably weaker. 
> 
> That is how I understood it. Am I missing something? 
> One thing that I'm not sure I understand is how the air from the Pacific is able to travel that far inland.


I spent about two hours looking at the eastern Asia monsoon. I don't really think the winds have much to do with it.  I'm almost willing to bet it is due to the difference in temp and the humidity level and their change throughout the year, It makes more sense in my mind.  Sorry I cannot be more help.

----------


## Azélor

You, I should recommend reading the whole guide before starting?

The north east is warmer... I was going to say that you confused east with west but I did it too in my previous message. The pink means it's 1 category warmer. 
I think the blue is too cold to be on the coast, maybe on a closed sea it would be cold enough.

----------


## Deadshade

> The most problematic is my little understanding behind the Eastern Asia winter monsoon. I know there is a large anticyclone (Siberian High) rotating clockwise, pushing the cold air to southern China and possibly beyond. 
> 
> So there is a sharp gradient of temperature north /south. I got this but it's not the only place there is one. 
> There is apparently a semi-stationary front over central china in the winter months. This cold front brings cloudy and rainy conditions. This is caused by the mixing of cold dry air from the north and the hot moist air of the Pacific. 
> It is close to the tropic, so the westerlies are probably weaker. 
> 
> That is how I understood it. Am I missing something? 
> One thing that I'm not sure I understand is how the air from the Pacific is able to travel that far inland.


All large scale circulation phenomena are better understood in the vertical plane than in the horizontal plane.
Also temperatures say generally more and are more causal for large scale features than pressures.  This has a reason - energy exchages are better described by temperatures than pressures.
I have always seen that people who start modelling climates by trying to locate high and low pressure zones generally struggle to get Something consistent because the causalities are confusing.
The monsoon engine in the vertical plane is quite simple, one needs :
- a large land body (at rather low latitude, say around tropics)
- a large ocean body

As the ocean body is large, it is not far from being isothermal throughout the year.
The land body on the other hand strongly oscillates (the larger the amplitude, the stronger the effect) so that half of the year its temperature is above ocean and half of the year below.
Assuming N hemisphere, in summer the air above land is hot so it rises (corollary is that we have a low pressure but this is irrelevant)
Then in high altitude it must go somewhere. If it goes towards the ocean (here is the difficulty because it is not easy to say where the high altitude air will go) then it will sink above the ocean (corollary is that we have high pressure there).
Then because of mass conservation, the loop must be closed and the wet cold air goes again towards land on ground.
The result is that when this wet air rises again above the heated land, it expands and précipitations occur.

In winter it is opposite. It is the same vertical loop but it rotates in the opposite direction.

All this are necessary conditions but afaik the sufficient conditions for a monsoon regime to occur are not clearly known. Apparently the shape of the land/continent and oceanic currents (or their absence) play a role so that this becomes quite complicated. 
For mapping purposes I have always adviced that if you have a large land mass beside an ocean (preferably eastwards) around the tropics and want a monsoon, just put it there. 
You will find nobody who would argue that there should be none because nobody knows  :Smile:

----------


## ascanius

> You, I should recommend reading the whole guide before starting?
> 
> The north east is warmer... I was going to say that you confused east with west but I did it too in my previous message. The pink means it's 1 category warmer. 
> I think the blue is too cold to be on the coast, maybe on a closed sea it would be cold enough.



Yeah sorry, I forgot to proofread.  Honestly I don't remember what I was trying to say.   Anyway keep up the good work.

----------


## Azélor

> All large scale circulation phenomena are better understood in the vertical plane than in the horizontal plane.
> Also temperatures say generally more and are more causal for large scale features than pressures.  This has a reason - energy exchages are better described by temperatures than pressures.
> I have always seen that people who start modelling climates by trying to locate high and low pressure zones generally struggle to get Something consistent because the causalities are confusing.
> The monsoon engine in the vertical plane is quite simple, one needs :
> - a large land body (at rather low latitude, say around tropics)
> - a large ocean body
> 
> As the ocean body is large, it is not far from being isothermal throughout the year.
> The land body on the other hand strongly oscillates (the larger the amplitude, the stronger the effect) so that half of the year its temperature is above ocean and half of the year below.
> ...


I got this but the point I don't understand and as you said yourself: 



> In winter it is opposite. It is the same vertical loop but it rotates in the opposite direction.


the winter monsoon should be dry cold air but it is not and I want to know if my explanation above make sense.

Example:
https://en.wikipedia.org/wiki/Wuhan#Climate
https://en.wikipedia.org/wiki/Changsha#Climate
https://en.wikipedia.org/wiki/Nancha...hy_and_climate
https://en.wikipedia.org/wiki/Fuzhou
https://en.wikipedia.org/wiki/Hangzh...hy_and_climate

I could possibly ignore the winter monsoon of China, after all, a city like Wuhan receive possibly over 1200mm of rain during the 6 hottest months. It's enough to keep it humid. 
But I would rather have it.

----------


## Azélor

*Step 6, precipitations:*

*General explanations*:

What cause rain? : When the air gets colder. Colder air masses have a smaller moisture retention capacity than the hotter ones, and it fall to the ground. 

·         Rising air in low pressure areas: the Intertropical convergence zone (ITCZ) and the polar front. 
·         Polar front, when hot air masses encounter cold air masses. This is called a cold front.
·         Orographic lift: Air rising above the mountains. Side facing the dominant winds is rainy. 


*General tips
*
·         Precipitation tends to decrease when moving away from the source of water.
·         In summer, some islands and peninsulas are drier than inland locations but wetter in winter. Ex: Nova Scotia. 
·         If the winds are blowing directly from sea to land, there are more precipitations. 
·         Inland precipitation follows the directions of the winds. See the Asian monsoon in Mongolia. 
·         Precipitation categories spread a lot over flatlands. 
·         Size and shape of wet systems are influenced by dry systems. It bends and is push it away. Ex: Africa ITCZ. 
·         Higher latitudes tend to be drier than those close to the equator. 



Transition map, it's not mandatory but it's meant to help figuring out where it rains. 
*Temporary green/wet and white/dry map*

I recommend making maps like the ones below. For the sake of simplification, we can classify the different areas on the planet, according to whether it’s a dry air mass or a wet one mostly by using just the air pressure and wind directions. Here are the different zones. Green is for rainy areas, white is dry. 

January and July

 

1,2,3,4 are roughly the same thing.  They are dry.
5 and 6 are pretty similar but  have a different cause. They are wet. 
7 It’s just to represent the fact that the cold polar air is drier but not completely dry either.

1·         High pressure systems (H, for short) are dry and the areas receiving dry air are dry as well. 
2·         The equatorward east side of the oversea H is usually really dry. 
3·         Equatorward west of H is dry 
4·         Equatorward west side tend to be dry in winter. Dry but can have some rain at right angle from the sea. It never rain with cold currents. Orographic rain possible. In summer, they make the weather drier as in Brazil. 
5·         The west and poleward sides of H are wet. This is where the cold air from the poles and the hot air from the tropics collide. Strom formations are more common. They don’t travel as far west in winter as in summer because the continents have a higher air pressure.  
6·         Low pressure systems (L) are wet if there is water available and if the winds are not blocked by mountains. Inland winds are dry even if they are located in a low pressure area as it’s the case in North Africa in January.
7·          In summer especially, the areas receiving the cold air from the pole are drier.


*Mapping the precipitation using the 6 categories
*
Now, time to map the precipitation. I would recommend using the color scheme provided to paint it if you intend to use the script later.

There are 6 categories for precipitations, from 0 to 5: 

Category 0: The first one is pink/magenta and is actually a penalty I added later to get a more precise results. It can have a huge impact on colder areas. 
Category 1: The second category is transparent.
The other colors are as indicated in the document, 6 (whitish blue) being the wettest. 



*''Painting''* 

*1·        * *ITCZ* (number 6 on the white/green map). It can cover rather large area.  Cover the whole area until stopped by mountains. 
It has the highest precipitations (5), the size depend on the winds and the moisture they carry. More moisture=larger wet area. 
For example, Africa ITCZ is smaller than in South America presumably, because it receives half of its winds from the dry Sahara. 
Ideally the ITCZ is centered on the center of the low pressure, following the direction of the winds

Information: the Amazonian forest generates 50-80% of its own rain with its own transpiration. Less trees means less precipitation. 

January and July

 


*2·        * *High latitudes dominant Westerlies*: located mostly on west coasts. Number 6
Winter: Poleward of 30
Category 4 is on coasts at right angle mostly, on peninsulas and small islands.
Category 3 covers a distance of 10 to 15 degrees inland from the source of water. (1 degree is about 111,11km at the equator)
Category 2 is covering a large area 2000 or 3000 km from the sea.Summer: Poleward of 40-45
Category 4 is at right angle with the sea.
Category 3 is almost the default at mid latitudes as far as 2000-2500 km inland.
Category 2 is near the poles instead of 3.
Outside the poles, Category 2 is only a transition to the dry areas. 
January and July

 


*3·        * *Extratropical storm path*: Located in mid to high latitudes, west of oceanic high pressure centers. Number 5
Explanation: The High over the water pushes hot air toward the land and toward the pole, clashing with the colder air coming from the pole.  The clash is a cold front, a rapid cooling of the air, generating the precipitations.  Area is smaller in winter due to the H overland.
When the Westerlies become dominant, this influence gets weaker.Winter: poleward of 20-25
Category 4 is on coasts if direct winds, can go 10 degrees inland near tropics, otherwise it’s marginal.
Peninsulas and direct onshore winds are wetter are also category 4.
Category 3 covers a distance of 10 to 15 degrees inland from the source of water.
Category 2 is a much shorter transition: 5 degrees or less.Summer: poleward of 25 more or less 
Category 4 can go10-15 degrees inland but the distance gets considerable shorter by getting close to the polar circle (60-66).  
Past 40-45 of latitude: Category 4 becomes less common on coast but still possible inland
Category 3 is the default category at mid latitudes.
Category 2 is near the poles instead of 3.
Outside the poles, Category 2 is only a transition to the dry areas. 
January and July

 


*4·        * *Winter Monsoon*: near the sea in winter, on the east coasts. Represented by 1,3,4
Need winds from ocean, strength of precipitation depend on the angle between the sea and the land . High at right angle but low or null if parallel (see graphic).
Precipitation decrease quickly when moving away from the coast.

Edit About China: there is a semi permanent low pressure system over the southern part of the country in winter possibly similar to what happens over North America = instability. It would be more appropriate in the category above. 

January and july

 


Poles summer: hotter water means more precipitations, with the eastern side usually drier than the west because of the dominant wind direction and warmer waters.


*Orographic lift effect
*
Precipitations fall because the cooling of the air saturates it with water. The water only falls if the air is saturated. 
*
Notes*
·         Precipitations are centred on the flanks not on the ridges. (Actually, in Iraq, it's centered on the plains) 
·         Large areas have a larger effect and a rapid increase in elevation has also a bigger effect.
·         High pressure zones with cold currents do not generate an Orographic lift: example Atacama Desert.
-Polar winds do not have orographic lift either. 
*
Tip:* *when painting the precipitation related to the orographic lift, I've found it was easy to do it as follow. Select one level of precipitation and look at the elevation graphic to see at which altitude the precipitations should increase and use the paint tool on that color. This way, you will cover all the map in 1 click. Do this for the different altitude level if necessary. 
*
*Color altitudes* for reference (see graphic)

Green: 0-1 km (the different colors don't have an impact below 1000, they could but it's just too complicated)



*Explanation*: Take each category at a time and increasing the precipitation at these places only if the altitude is high enough.
When the default precipitation category at sea level is x (0-1 km), increase the precipitation.

Every ridge provoking an Orographic rain effect lowers the % moisture and makes the remaining air drier. It also means that in order to re-saturate the air of water, colder temperatures are needed. 

I'm not sure what the mountain on the second line is for. Ignore it. I will try to find what it means. 



*Magenta, category 0
*
·         The last part is to add the magenta penalty for the really dry places. This category is useful for moderate/cold areas that have low temperature but also low precipitations.
  Without this some steppes appeared as lush forests.
·         Should be put only where no other colors are present. 
·         Less likely at higher altitudes.
·         Spread out on plains but close with rugged terrain.

*Final results*:

January

July

----------


## Deadshade

> I got this but the point I don't understand and as you said yourself: 
> 
> 
> the winter monsoon should be dry cold air but it is not and I want to know if my explanation above make sense.
> 
> Example:
> https://en.wikipedia.org/wiki/Wuhan#Climate
> https://en.wikipedia.org/wiki/Changsha#Climate
> https://en.wikipedia.org/wiki/Nancha...hy_and_climate
> ...


It is not clear to me why you think that there is Something special to be explained.
There is no "winter monsoon" in this location - there is just a classical gaussian precipitation monsoon profile with a maximum around may-june. Precipitation in winter is low as expected.
The only phenomenon Worth to be noticed is that the temperature time derivative is strongest when passing from spring to summer. That means that the rate of temperature increase is at its maximum a bit before the summer when also the precipitation maximum is located.
If you want to understand what happens in that region (which is quite large going from Japan to East China) then there is a stationnary front (caused by the inverted monsoon like loop) where the wet air from pacific meets the very cold continental air.
This happens when the derivative is strongest (see above)  and after that the front moves northwards so that the summer/fall are drier.
That shows that this is Something completely different from a monsoon - the cause of monsoon précipitations is adiabatic expansion while the cause of the east china-japan precipitation maximum is a stationary front.
Why the front is stationnary and regular, I don't know but it may be studied somewhere if I had the patience to Google "Meiyu front".

----------


## Azélor

Meiyu, or plum rain start in late spring and continue during early summer: http://glossary.ametsoc.org/wiki/Mei-yu_front

Monsoon just mean an inversion on the winds pattern. It doesn't mean that it's going to be rainy. In this case roughly
summer: from sea to land
winter: from land to sea


My research so far had given me no satisfying answer. That is why I'm asking here, in case someone knew about it. 


But if it's a stationary front, I just wanted to be sure.

----------


## Deadshade

> My research so far had given me no satisfying answer. That is why I'm asking here, in case someone knew about it. 
> 
> 
> But if it's a stationary front, I just wanted to be sure.


Yes it is definitely a stationnary periodical front.

----------


## Azélor

I would advise to wait before doing the precipitation step because I rushed it a bit. I still need to revise it just in case. Still, comments are welcome.

So, now for the last step..

My first try was a mess and the second one was too complicated. 
This one is much simpler but unlike the previous one, it's not 100% automatic. The 2nd one generated all the temporary layers. While technically possible, it would be really complicated to do so now. 
So, my best option is to upload a .psd file. The file would include all the dummy/temporary layers set at the right place, in the right order and with already written names. One will just have to fuse his layers with the ones already present. 
This should guarantee that everything will work as intended.

Except that the size of the file might be too small or too big compared to the map, so people will need to adjust that. I made the temporary layers to be in the top left corner, and they are as small as possible (about 80 by 80 pixels).

----------


## mbartelsm

Hey Azelor, would it be possible for you to still explain how to do the last step manually? for those who don't use photoshop

----------


## Azélor

Well, I would have hoped that it could still work on Gimp. I'm not an expert, but I saw there could be a plugin maybe? 

Anyway, it's possible to do it manually since that's what I need to do to record the script. 


Basically, to find the resulting climates, we need to combine the temperature and precipitations layers, the 4 of them. 
The main problem is that it result in around 3600 different combinations. It depends on the magic wand to select each combination and depending on the colors used, your likely to push the magic beyond capacity.  Eliminating so of the by simplifying the table get the tola to 640. But the color problem is still there. 

It was not easy to find colors where 2 combo are sure to be unique. Obviously, the basic colors are nice but really useful since they cover the whole color spectrum, I had to convert them. I tried different things and although there might be other options, this on is working well. I can't simply change the colors in the tutorial because it would really not be intuitive, as explained below. 

Here what I did, a summary:

January temperature: from hottest to coldest, I replaced the colors with saturated blues, from bright to dark
July temperature: from hottest to coldest, I replaced the colors with saturated reds, from bright to dark
Combine the 2, and it give purple-ish colors. 100 combos

Here, we can isolate the combinations for tundra and eternal ice since the precipitations are not important to determine these. They are the 25 combos in the bottom right.total-tundra/ice= 100-25 = 75
We can simplify the table because now the temperatures are reversible: a cold January/ hot July is the same as a hot January/ cold July. Which further limit the possible combos. (75-5)/2=40
So we have 40 combinations for the temperatures. Some are extremely unlikely but still possible, especially on an alternate-earth.

Winter precipitations: blue, from bright to dark
Winter is in January in the north but in July in the south. So the precipitation map for January should have the top blue and bottom red. The opposite for July (Top:red bottom:blue)
Summer precipitations:red from bright to dark

Now, I realize that this part is not necessary because, the resulting combos can be simplified. Form 25 to 16. It's based on some mathematics: example 2 temperate cities will have the same climate even if they receive 1000mm and 20000mm of rain annually. After a certain pint the extra rain does not affect the climate (not based on the categories we are using). I decided to convert the remaining combinations in green. So I end up with the 3 primary  additive colors: red, blue, green.

Then, we need to combine the temp and precipitation layers to get the final combination. And by using the right color key, we can identify all the specific climates. 


I will provide more images later.

----------


## Mysterious Mapmaker XXIII

Nice to hear we're finally getting to determining the actual climate.

So, are you still doing the "automation" step or not? I'd like to know.

----------


## Charerg

The tutorial is shaping up nicely. So far I've managed to follow it pretty easily, though I guess it might help that I went through Pixie's earlier tutorial previously, so I'm somewhat familiar with the subject.

I thought I'd post my "steps" that are part of the tutorial here, since that way it's a bit easier to get feedback. There's also a WIP thread that includes some of the tutorial-related maps (currents and atmospheric pressures), but I'll throw them into the attachments so anyone reading this won't have to dig through the WIP thread to find the relevant maps.

So far I've completed the steps of the tutorial for January temperatures and precipitations. There's some room for improvement in the details, but I think it will be easier to just fine-tune the eventual climates if something seems a bit off, rather than trying to perfect the temperature and precipitation maps. Anyway, criticisms and suggestions are welcome.

January temperatures:


January precipitation:

----------


## Azélor

> Nice to hear we're finally getting to determining the actual climate.
> 
> So, are you still doing the "automation" step or not? I'd like to know.


Yes, It's almost working. It's getting it right til the 3/4 and messes everything else.



*@Charerg I will look at your world as soon as I can make the script work.*

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## Charerg

I went and finished the process manually, and here is the resulting climate map (including the climatologically relevant oceanic currents, for easy reference):



For purposes of comparison, here is a prior version of the climate map I did back during the summer:



Overall, I think it's a pretty big improvement over the previous map, which was partly done based on Pixie's tutorial and mostly done by just looking at Earth's Köppen climates on similar latitudes, or with making "educated guesses".

Although it has to be said that the final climate map shown here has been heavily "corrected" manually at places. It turned out that I screwed up with the precipitations, and Mediterranean climates were practically nonexistant, so I had to put them in manually. Also, I narrowed down the temperature combinations into 20 (from the maximum 100 possibilities) after merging the temperature maps, removing any weird "fringe areas". I didn't bother with refining the merged precipitation map, since I essentially did this manually by first selecting a temperature category (from the merged temperature map) and then going through the different precipitation areas within said temperature category. Theoretically, this would still result in 720 possible combinations, though not nearly all actually turned up. 

Still, it took a long time. In hindsight, it might've been better to reduce the temperature categories still more, theoretically you only need 10 to do the different climates (I think). Merging the precipitation maps also resulted in a lot of "fringe areas" with weird combos, especially in mountainous areas. If I were to do this manually again, I'd consider maybe refining the merged precipitation map a little before defining the climates. In any case, what this tells to anyone doing this process via script: don't expect a perfect result, there will probably be a lot of weird fringe areas that will require manual corrections. Also, you need to be pretty careful when doing the precipitation maps, since at least I found it's pretty easy to rush them, and the accuracy of the climate map suffered as a result.

What I also found out in the process is that for purposes of fantasy mapping it might actually be worth it to expand the Köppen classification to include dry polar climates (in my map, I added ES and EW, steppe tundra and polar desert). Present day Earth doesn't really have these climates (as far as I know), but during the Ice Ages they would have been pretty widespread. I think the reason why dry polar climates are marginal nowadays is because all glaciated landmasses are practically islands, with no major non-glaciated areas. This means that tundra climates only occur in maritime areas, without extending far inland.

That said, it's common in fantasy maps to include landmasses that are only partially glaciated (essentially similar circumstances to Ice Age Eurasia). In these kinds of circumstances, you'd probably have an Ice Cap climate (EF) surrounded by polar desert (EW), transitioning into steppe tundra (ES).

EDIT:
It's also worth mentioning that I didn't quite realize until comparing with Earth's Köppen climates that the influence of the Winter Westerlies extends all the way to the Hindu Kush and Pamir mountains, resulting in "dry summer" climate types. It might be worth it to add a footnote about this into the precipitation section. At least I seriously underestimated how far inland the effect of the Winter Westerlies extends.

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## Azélor

I took your maps and used them to run the script. It should be functional now. The result is at the bottom.  

The Mediterranean climates do exist in my version of the map, they cover maybe a quarter of what you have on your modified map (in number of pixels not in km2).

Yea, your right, some combination exist but they are really unlikely.




> Still, it took a long time. In hindsight, it might've been better to  reduce the temperature categories still more, theoretically you only  need 10 to do the different climates (I think). Merging the  precipitation maps also resulted in a lot of "fringe areas" with weird  combos, especially in mountainous areas. If I were to do this manually  again, I'd consider maybe refining the merged precipitation map a little  before defining the climates. In any case, what this tells to anyone  doing this process via script: don't expect a perfect result, there will  probably be a lot of weird fringe areas that will require manual  corrections. Also, you need to be pretty careful when doing the  precipitation maps, since at least I found it's pretty easy to rush  them, and the accuracy of the climate map suffered as a result.


It might be a good idea to reduce it further but it make the distinction between the arid, semi-arid and humid areas even less accurate. There are 10 different climate base solely on temperature: A, Ca, Cb. Cc, Da, Db, Dc, Dd, tundra, Ice. (without getting too much into details, we can't really identify Cc fro Cb with only 2 temperature maps)

 The arid climates are defined by the aridity level, which depend on the evaporation (based on temperatures) and the precipitations. This means that although I have 16 temperature combinations for A,each combo will have a different aridity level and some will be steppes or deserts.  

Fringes areas where pretty limited in y test. One thing I will mention (I mentioned it already but it was several months ago) is that the transition zone between the deserts and the humid climates is often missing. This happens often when the aridity changes rapidly due to a combination of a higher temperature and lower precipitations, and the fact that there are 4 layers with more or less random borders. You might end up with artefacts, or small odds colors. 

Weird climates are almost to be expected in mountainous areas because, as mentioned earlier, the temperature and precipitations can change a lot over a small area. Like in the Andes. 




> What I also found out in the process is that for purposes of fantasy  mapping it might actually be worth it to expand the Köppen  classification to include dry polar climates (in my map, I added ES and  EW, steppe tundra and polar desert). Present day Earth doesn't really  have these climates (as far as I know), but during the Ice Ages they  would have been pretty widespread. I think the reason why dry polar  climates are marginal nowadays is because all glaciated landmasses are  practically islands, with no major non-glaciated areas. This means that  tundra climates only occur in maritime areas, without extending far  inland.


By following the default classification, tundra and ice caps are only defined using the temperature.  It's so cold and the evaporation rate so low that they can't be dry, or barely even if they receive a small amount of precipitations. I think, but the formula I'm using for the aridity  tend to give weird results at low temperatures. 

The dry tundra might be interesting though. Even if, form a demographic point of view, the population density on the tundra is really low and would be even lower with an arid climate.




> tundra climates only occur in maritime areas, without extending far inland.


And in mountainous ones, especially in Tibet. But the reason they occur mostly on the coasts is because the temperature gradient is smaller than inland. Large landmass get hotter especially in summer making it too hot for a polar climate and even to hot for a tundra as in Yakutsk for example. 




> It's also worth mentioning that I didn't quite realize until comparing  with Earth's Köppen climates that the influence of the Winter Westerlies  extends all the way to the Hindu Kush and Pamir mountains, resulting in  "dry summer" climate types. It might be worth it to add a footnote  about this into the precipitation section. At least I seriously  underestimated how far inland the effect of the Winter Westerlies  extends.


Western disturbances as Indian climatologists calls it. I decided to include it with the orographic lift effect since it only rain at higher altitude (for this area and others at similar latitudes).






The original I took on the forum were a bit blurry so there's a lot of unwanted small dots here and there, but we can have the general picture. I haven't made any transition areas on this, it's just as it came out after running the script. That partly explain why the desert are about twice as big. 

Other things worth mentioning:

No Da: Da requires cold and below in winter and hot and above in summer. The only places with these temperature are too dry (either steppes or deserts)
No Dd: requires at least mild in summer and not warmer than deadly cold in winter. 

Other than that, it looks plausible but I haven't looked at the temp and precipitation maps to see if they contain errors.

----------


## Azélor

*Step 7, the script/the climates* (manual version at the bottom)

Instruction in order to use the script. Explanations to do it manually will come later. 


MAKE SURE YOUR NOT USING THE ORIGINAL OF YOUR LAYERS, KEEP THEM IN ANOTHER FILE BECAUSE THE SCRIPT WILL MODIFY THEM.ALSO, DON'T PUT OTHER LAYERS THAN THE 4 MENTIONED ABOVE.Make sure your layers were made in an RGB file (it should be the default color mode if you never touched it). If your not sure, you can check the colors to see if they match. 

Possible problems while running the script:
Some areas are not selected and there are no climates: make sure your colors matches those I used in the tutorial. And make sure that you are only using these colors on the layers.
*INSTRUCTIONS*
Download the zip fileOpen climates.psdExpand the Canvas size in order to have the same size as your map. Expand it toward the right and bottom to make sure the default layers stays at the top left: the position of these layers is important.Place your 4 layers (2 for the temperatures and 2 for the precipitations) in the file. Position them so your not missing a part.Fuse each of these layer with the corresponding already named layer. Make sure to keep the default names and the same order (for the layers).Fill the missing Background part with a pure blackMake sure all the layers are made visibleLoad the script: go to window, script, click on the symbols with a triangle with three lines on the right side on the window, select load a scriptclick on play to activate the script 
*


Improving the climate map:*


Cfc climates are not really accurate. The script cannot differentiate them properly from Cfb. Therefore, most of the Cfc are actually Cfb. Cfb is much more common than Cfc. Normally the poleward climate progression should be either Cfb,Dfb,Dfc... if the climate is more continental or  Cfb,Cfc,Tundra is the climate is more oceanic. Inland we could have Dfb climates right next to Cfc but never north of it.Some steppes are missing. When you see a desert and a humid climate without a steppe in between, it needs to be added manually. Expand the steppe inside the desert. Expand a lot if it's a plain and just a thin line if there is an elevation. 

Azelor climates.zip
For the manual version:

I hoped it's not too badly written, English is my second language and PS doesn't have a spellcheck. 



Also, these are the files I used for the script at their real size. I would recommend using these instead on the ones in the example above since they are smaller. And the larger one might contain some errors.

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## Charerg

Great work getting the script working!

Unfortunately I'm using GIMP, and I don't think there's a way to make *.atn files work with it (as far as I know). Anyway, if it's not too much trouble, would you mind running a 2nd "test run" of the script for me? 

I refined the precipitation maps considerably, and they should now be much more accurate than the previous admittedly somewhat rushed versions. Also, I put these maps in *.png format and left the graticule out, so that should take care of the random dots showing up. The temperature maps are unchanged for now, since I think they're acceptable, and it should be interesting to see how much the results differ with just better precipitation data. There won't be any Dd climates without changing the temp maps, but since Dd is very rare in any case, I don't think it's too much of a flaw if it's missing.

Regarding dry tundra: I suppose the sort of Ice Age-era "steppe tundra" might be represented with just "cold steppe" using the default classification, it's after all more or less the same thing. I'm not sure if anyone has attempted to use Köppen classification to re-construct Ice Age climate, but as far as I know the steppe tundra is thought to have supported a surprisingly rich fauna, inlcuding large herds of herbivores like horses, saiga antelopes, bison, mammoths and so forth. All in all, in terms of human population, it may have been a better environment than modern "wet tundra" because of this. Indeed, perhaps even better than the taiga, at least for a hunter-gathering culture. The dry climate would have also been an advantage in winter, leading to less thick snow.

----------


## Azélor

I did the climate for you world by myself to see if i was getting the same results, in case the tutorial could be improved. It took a long time even if I rushed it a bit, hopefully it will be worth it.I'm going to start with the temperature maps since there is less stuff to talk about.

*I have to warn you that I might make some small changes to the temperature section , more details about it later. Also, I change the explanations in the section on the effects of altitude and added a new picture but it's still the same thing as before. 

To set the table for the temperature maps, i did 2 influence maps. 

The color mean:
Red; hot current
Green: mild current
blue: cold current
yellow: continental
no color normal 
and continental plus is not there, the climate is not extreme enough

January



July




January: Before reading this, i need to say that I'm 100% sure that i skipped some place regarding the temperature changes due to elevation. But most of it should be on the maps.

I did not notice it as first but there is a problem with the temperature at higher elevation. I don't know if you used the table I provided but you seemed to take inconsideration that the temperature scale was linear. It's actually pretty erratic. It's true that you need to lower the temperature of 1 category for each 1000m for the hottest one but other (especially the coldest) cover a wider range of temperatures. So, for example, the north eastern continent summits would be much milder. If the base temperature at sea level is yellow: it's between 0 and 10, or 5 degree on average to make it simpler. Since the elevation is brown, it's 4000m high. The time lapse (cooling of the temperature with a rising altitude) is somewhere between 6 and 9 for 1000m depending on humidity. I took 6,5 for simplicity. So, for each 1000m, you lower the temperature by 6,5 degree Celsius. 

*You can have a look at the temperature progression based on a linear scale to see the difference. Some bars are bigger than others. 

4km x 6,5 = 26 degrees
since the temperature was around 5 at the base: 5-26 = -21
which mean that the high central range it should be either turquoise (dark green if you prefer) if the base color was yellow or green if the base is warmer.

I made the image smaller but the originals were blurry. Anyway, your maps just need some tweaking. I assume that use haven't put everything on the same layer? (I might need to warn people about it)

 

I will cover the map point by point. A lot of places have slightly different temperature due to some randomness I suppose. 

1- I used a mild oceanic influence, which explain that the coast is cooler. But I think your is more accurate. Also, I believe that the interior should be hotter at that latitude. 
2- Same as above, the interior should be pretty hot.
3,5, and 6 : there is a maritime influence there and the temperatures should be more spread out. 
4- I think it should be warmer. it's a hot current.
7- we could compare the climate of this coast with eastern Greenland and Svalbard. The current is kinda mild and it should be warmer than Antarctica. That is true for most of the polar coast except in the middle where it should be colder because of the ice and the small heat exchange from the warmer seas. 
8- a bit warmer maybe. I used a hot current influence there.
9- mild influence and as above, the temperature tend to be more spread out. Think about Chile and Argentina.
10- The change of temperature due the the altitude is smaller. 

July:



1- it's winter and made it cooler because of the rather mild oceanic influence. 
2- The orange stretch to much to the south. On the other hand, maybe your right and I've put too much dark orange in the small sea. 
3- It is a very narrow band of land with a very strong maritime influence, I doupt it could be that hot, maybe in the valleys inland but not on the coast. 
About this continent , and in general where the winds are blowing from west to east, you should move the maximum temperature a bit more to the east maybe. In my version, i considered that the mountains in the west somewhat blocked the winds which limited the air flow from the sea, hence higher temperatures.
4-The ocean is actually kinda cool and since winds are blowing from north to south east (approximately) it makes the climate milder, especially on the coast.  
5- I'm not sure it should be that hot.
6- Personally, I made it milder, taking southern Asia as a reference. 
7 and 8 - I expect the sea to be trapped under a layer of ice with really cold temperature. The winds are blowing from the pole toward the continent which push the cold air inland. Maybe I pushed a bit too far since the continent is also affected by the westerlies, which should bring milder air from the western ocean.

That's it for the temperature maps. 


Precipitations are a bit more tricky, partly because my system is a bit rigger ?  (To  make  or  construct  something (in  haste)  or  in  a  makeshift  manner)

Anyway, maybe I can improve it.

Starting with the pressure maps:
January, the pressure looks alright. 



About the winds, there is a lot of guesses and most of them are not really changing the weather too much
1- I don't think it changes much but I think it's mostly under the westerlies, which means the wind have a tendency to flow to the east. If the did converge to the north, I don't think it change the precipitations much.
2- I actually have no idea
3- Exact direction of the winds seems irrelevant since it's dry
4- At first, when looking at the continent, I thought that it looked like North America, but it lacks a huge mountains range to separate the high pressure system in the west from the rest of the continent. Therefore, it makes the winds blow more from west to east. Winds coming from the south are deflected in a clockwise fashion.
5- I don't think this should have such a large impact on the winds.
6- I don't see a reason why the winds should blow toward the south.
7- More a general note than a specific one: from here, the winds tend to get deflected more toward the north by the high pressure systems of the interior of the continent. As it is the case in Russia during winter.


July:



1- the air should converge but I'm not sure how exactly but again, i think the westerlies should stronger. Maybe there could be some easterly winds from the eastern ocean in the southern areas. My guess it that it should look a bit like the American summers. But from my point of view, it's the continent that I have the most uncertainties. 
2- In most case, the bending direction of the arrow is not so important but the direction of the wind is. 
3- On my map, I made the winds from the north converge and then flow to the east and eventually they would be deflected to the north probably. 
4- This area reminds me a lot about the Indian subcontinent, same shape, same latitude. I made it a low pressure area. 
5- I almost forgot about this area... Just like in Ecuador, the air converge under the ITCZ. Since the south is colder, the ITCZ tend to stay more on the northern side.
6- The direction of the winds here is just really confusing, I'm just going to ignore that area and do it roughly.

I think the rest was pretty good.

I made the green and white maps, even if they are just temporary, they can help with the precipitation maps. Green is humid and white is dry.
Yea, I know, I'm more the visual type of person. I need the illustrations.

January



And July




I will stop there for today.
There are about 39 points I wish to talk about just for the precipitations so it's might be another long post.
I would like if you could give some feedback, if something in the tutorial was poorly explained in light of the point I mentioned in the post. Of course, there are things that depend mostly on guesses so I could be wrong on a couple of things but it's not easy to know.

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## Charerg

Wow, that's a lot of work you've done! My thanks for putting in the effort!

I'll post a more detailed response once I've gone through your post in detail, but for now I should mention that I updated the Atmosphere & Winds maps from the previous versions before making the new precipitation maps.

Jan:


July:


I haven't read through your comments about my previous Wind maps yet, but I think these ones are a bit more "readable" (and should be a lot more accurate).

Edit1: Ok, went through your comments Re: Winds. I agree that the previous maps had quite a few inaccuracies (as you pointed out). Comparing your comments to my updated wind maps, I think I already managed to correct the previous inaccuracies, but do point out if I missed something. 

Regarding the tutorial, I think it might be helpful to actually draw the ITCZ as part of the process of determining wind directions, since the winds tend to converge there. At least I found it useful. Secondly, it might be useful to draw a few tiers of pressure like I did in my new maps, since this can make it easier to visualize the effects the high pressure centers have on the winds. That said, this does complicate the tutorial, and I guess it depends on just how accurate the person going through the tutorial wants the results to be, so maybe it should be an "optional" step.

Edit2: Temp maps

I have to admit that my temps maps were pretty "rough" and I just ramped up the temperature one category for each 1km elevation or so regardless of latitude. In this case, it's more of a case of "not reading the instructions until the work is already finished" than a fault with the instructions themselves! With that in mind, your versions of the temp maps should be more accurate (generally speaking). It should be interesting to see how this will affect the climates.

Edit3:

The maps should be visible now.

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## Azélor

I can't see you files, they haven't uploaded. This tends to happen when  you take too long to write the message, if you get disconnected. 

But as I said in the other message the temperature at sea level should  be good but not the ones at higher elevation. I can't guarantee that I  haven't missed an altitude layer.



Now for the precipitation maps

January: I did the precipitation for the orographic effect, but they were made roughly.  From what I can see, they seems pretty similar to yours. 
It is possible that I have confused the several maps while I was writing the numbers on them. I will specify it when I wrote the number for nothing. 



5,6,11, never mid, it's pretty much the same. But 6 might have a lake effect like Northern Iran.

1 and 17 -One of the biggest mistake on the map. This area is supposed to be dry like the coast of South Africa because it's a cold current.
4- is exactly like South Africa (Cape Town)
 3- (sorry if I don't follow the order of the numbers) Close to the Equator is the ITCZ. Reminds me a lot of Ecuador.
12- The landmass becomes hot since it's close to the tropic. It draws the air fro the sea toward the south. It's borderline between the ITCZ and the polar front. 
2- That's the effect of the ITCZ. Mountains are not high enough to block all the precipitations since the water contain so much moisture and there ar e many wide valleys where the air flow is not interrupted.  The remains dies out somewhere over the continent. 
Also, the souther semi-closed sea is much more humid since it receives the winds from the ocean. Evaporation is the sea also help giving more precipitable water. But the precipitation don't go as far inland. 
7 and 14: these are likely Mediterranean climates. It's supposed to be their rainy season. The winds are blowing more or less following the latitude lines. 14 is actually not so bad, the exact position of t he dry are is not the same in America and Europe for example. 
8- With the westerlies blowing, the precipitation can go far inland.
10- I'm not sure about the precipitations on the east coast. I'm not sure if the area between 30 and 40 degree of latitude should be drier or wetter. Since the high pressure centerr in the west have a stronger impact of the winds, it might make more sense if the area was drier like in your version. I'm not sure. 
9- They got the winds in their face, it should be more humid. Norway and Chile are not really good example since they got mountains blowing the flow of air. 
15- the polar air is pretty dry. The continent on the center is protected by the mountain ranges in the north from some of the cold air. 
16- I'm not sure the range is big enough to block all the moisture. It's definitely drier but not so much. But I could be wrong and maybe it's like northern Venezuela. 
 18- it's really dry in winter but polar ocean do bring some moisture during the summer.



July: 



5,6,7,10,15(India),18, 19 and 21  seems alright, never mind.

1 and 16- Same as mentioned for the other map. Mountains should be drier in that kind of area.
2- The ITZC, we could debate on where to actually put it, but it doesn't seems so bad either way.
3 and 13- like in Europe, the climate is dry til around it reaches the 40 degree.
4- The interior of the continent is hot and a low pressure system. It gets some dry air from the west but the moisture roam freely in the north due to the Westerlies and it also come from the south east like it does in North America. 
11- I wasn't sure f it was supposed to be dry since it's close to a high pressure center.
8- Is affected by the polar front and should be more humid. But 9 is not.
20- Like in South Africa, the precipitation are limited to the coast here. 
12, since the winds are blowing mostly from west to east, they carry more moisture. But it's still drier than on the south pole summer because it's colder and there is less water available for evaporation. 
14- I made the precipitation follow the flow of the wind. In my model, he continent is smaller than Asia, meaning the westerlies are stronger at this latitude. Meaning is not as humid as the south.
17- Not sure

I here are the precipitation layers in png

January



July




Btw I edited the step 7 post to include manual instructions. But once you layers are improved, I don't mind running the script if you send them to me or put them here as PNG. PNG is a good format if your using plain colors (the opposite of pixilated textures) and you won't need to compress them.

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## Charerg

Ok, I finished updating the temp maps. They're essentially more or less copied from your versions, with a few minor differences here and there, and possibly a few corrections with a couple of mountain ranges. Nothing major, in any case. At this point, I think it would be ideal if you could run the climate script with both your version of the precipitations and mine (posted in *.png in post #105), since it would be nice to see what the results look like before I return to the precipitation maps (in any case, I think a slight break is in order before I revisit the precipitations).

Also, another reason why I'd like to see the results is that while I agree with a lot of your suggestions, like area 2 (in your january map), I'm a bit on the fence with a few areas, namely 1, 4, 12 and 17 (again, january map). With area 12, the large peninsula (called Akanrias) forming the southern portion of the large western continent (Eocidar) has a pretty mountainous coastline creating a rain shadow (even if the mountains are not that high for the most part), and I think the Westerlies are blowing too much "along the coast" to fully cover the interior. Hence, I think it should be (relatively) dry, probably something like Patagonia, at least in the interior. That said, I confess it's possible I overestimate the effect of the mountains, after all the Alps don't seem to create much of a rain shadow.

The climate patterns of Southern Eocidar are also complicated by a monsoon cycle, since the landmass in question is very large, and at least I postulated that the ITCZ would reach almost 30 S latitudes during Southern Summer, causing a strong monsoon affecting the coastal areas north of the 30 S latitude. The Western coast is also very mountainous, so there would certainly be a lot of orographic lift. All in all, I think the height of the mountains alone would likely prevent "extremely dry" climates along the coast, except a narrow strip of coastal desert. That said, it's possible I made the "rainy area" reach too far south, since the region is (as you mentioned) influenced by a cold current.

Although I don't think the said cold current would be as cold as the Humboldt current, since the really "Arctic" water would be deflected into the large channel between Eocidar and Nomune (the southernmost continent), most of the water flowing along the southwestern coast of Eocidar would be 30-50 latitude water. Also, the southern waters of the "Outer Ocean" (Agalhain) might not be as cold  as the Pacific, since the "Antarctica" of this world is located north rather than south. Still, I might have made it too wet, it definitely shouldn't be wet enough for a rainforest climate, but I still think your version is too dry.

Re: area 17. Again, I think here the orographic lift should prevent dry climates, except in the "rain shadow" of the mountans and the very tip of the peninsula (called Menduine).

Anyway, it should be interesting to see what sort of climates turn up. The revised temp maps are in the attachments. Oh, and many thanks for putting a lot of effort into this, it's always a lot easier to see potential flaws when an alternate opinion is available!

EDIT:  In case you haven't run the script yet, I did actually update the precipitation maps slightly. Some your suggestions are implemented in the new versions, namely southwestern Eocidar is drier (though not as dry as in your version), and a few other adjustments too. The new maps are in the attachments.

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## Azélor

While you might have a point in that the Drake passage is much narrower, yet it doesn't have a very large impact.  The passage south of Africa is much wider and it result in a very similar climate for Namibia, vs Chile. Same thing for California and the Mediteranea. All these place have very similar climates. 

If we compare them more in details, we see a small difference between the north and south hemisphere. The coldest currents do seems to have an effect of precipitations (or it could be something else) but it's not an huge difference. 



That is pretty much what I followed but I don't think this model can be applied on the central eastern continent very well. 
In the other areas, although we could play with the parameters, it would still be mostly dry.


I wasn't entirely sure how to treat 12, my guess was that it would draw moisture from the north AND the south, since it's pretty hot and close to the ITCZ.

17, Was a bit harsh I agree but it does remind me of Chile (similar coastline and climate). 

About the mountains: as far as I can tell, there is very little effect on precipitation near the oversea high pressure centers. I'm not exactly sure why. The is nowhere in these areas where the mountains have higher precipitations, not even in the Atlas where the winds are blowing directly from the Mediterranean sea. Yes there is a major effect in Iran (for example) but it's only in winter when the high pressure system moves to the south, away from the Mediterranean sea. So the precipitation from the orographic lift should more or less follow the above image as well. It's clear that I painted every mountains without even thinking if it was supposed to be rainy or not. The orographic lift effect is completely wrong at these places.

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## Charerg

You're probably right about the cold currents. In the most recent version the "rainy area" reaches approximately 15 S latitude, which, if you look at Africa, is about where the Namib desert ends. All in all, I envision southwestern Eocidar as more or less comparable with Western Africa in terms of climate (in other words, not a desert, but not very rainy either for the most part). Considering that the African monsoon reaches all the way to the Sahel during the rainy season, I think the recent version is reasonable, with possibly the area west of Eocidar's "inner sea" a bit too wet in january, but it's hard to judge whether the precipitations are too high without seeing the climates themselves, since the equatorial latitudes mean a lot of evaporation even the areas with light rain are probably going to end up with a dry climate type.

Also, the Atlas mountains do receive rain during winter if I'm not mistaken? At least historically Northern Africa was "the bread basket" of the Roman Empire so it's not really desert (if not very rainy, necessarily). All in all, I think it's rare to find mountains with a true desert climate, except in the heart of the Sahara perhaps. So, I think a modest degree of rain along the mountain ranges during the "wet season" is justified. Although again, a bit hard to judge based on just the precipitation maps, I may have exaggerated the effect or made the rainy area too large.

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## Azélor

Western Africa ? which country exactly? Lagos got a very season precipitation pattern, dry winter wet summer. https://en.wikipedia.org/wiki/Lagos

The bread basket of the Empire was Egypt if I remember correctly, and although it is one of the most fertile land is the world, it barely rains. 

It would make sense to have dry mountains if the surrounding air is also dry, the water needs to come from somewhere.I edited the precipitation layer to include the orographic lift with the westerns mid latitudes drier, like I mentioned in the other post.

here are the results

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## Charerg

Egypt was another breadbasket, but Northern Algeria (to an extent) and especially Tunisia were also very productive (the former Carthaginian heartlands). 

Anyway looking at the climate maps, I think the temperatures are probably fine now. Af climate looks overall a bit too widespread compared to Earth. That, and deserts are overall too widespread compared to the steppes. Also, you're right that southwestern Eocidar could be maybe a bit drier at places, though I think your version is a bit exaggerated since it's essentially nothing but desert (and the occasional tundra) almost all the way to the Equator. Also, the southeastern continent (Anapar) is too dry in both our maps, the deserts there should be largely confined to the interior since most of the continent isn't really in desert latitudes. I think the peninsula of Akanrias (the large southern peninsula in southern Eocidar) should be "somewhere in the middle", it's too dry in my version and too wet in yours. There also seems to be a few weird high-latitude steppes in my version that should really be D climates (guess I need to increase precipitation there further).

That said, I'm pretty satisfied how most of the map turned out.  I think it will maybe take one round of further fine-tuning the precipitations and then the climate map should be "good enough" that it can be finished with some manual adjustments here and there. Btw, do you think Dc is a bit too narrow strip between the tundra and Db climates? It seems slightly thicker on Earth, although maybe it's just the differences in latitude.

By the reference to Western Africa I mean the area in general, there's a lot more BSh, Cwa and Aw than there are BWh, Am or Af. Although in my case the interior of the "supercontinent" will be inevitably largely desert, I think the intermediate steppe climates still cover way too little area compared to similarly dry areas on Earth.

In any case, my thanks for running the script, it spares me a lot of work not having to do all that manually!

EDIT:
I also think that the interior of the northernmost non-polar continent (called Neraduhr) should probably be "winter dry" because of the presence of vast glaciers to the north. It looks like some Dwb turned up in my map, which is good, but I still need to adjust the precipitations so the Dc climates in the interior are Dwc rather than Dfc. Do you think I'm right about this, or should it just be Df climate?

EDIT2:
Actually, I guess I do need the touch the temperature maps somewhat, since Cfc covers rather large areas in the northern half of the map (when they should be either Dfb or Cfb). Another question, do you think the island chain south of Neraduhr (the northernmost non-polar continent) should be Cfc, as it is now?

EDIT3:
Ok, here are the updated versions. I made the winters slightly colder in general (especially in the north, where I feel the Arctic glaciers would lead to more extreme winters), to make continental climates a bit more widespread. In places, I also ramped up the summer temperature a tier in order to eliminate Cfc climates (although I left some islands and mountains as Cfc).

In terms of precipitation, in general I tried to make steppe climates more widespread, and also to make the tropics less of "rainforest or desert". Southwestern Eocidar should be a bit drier now, with the desert more widespread, while the large equatorial area on the eastern side of the supercontinent (called Magatel) should have more BS and Aw (I hope). But we'll see how the results turn out. I think this will be the last "run of the script" unless there are some glaring problems, and the climate map can be finished with manual adjustments from here on out. The relevant maps are in the attachments.

Once again, many thanks for running the scipts for me (as well as otherwise spending a lot of time on improving my world)!

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## mbartelsm

Azelor, one question. When saving the monsoon climates, why do you choose only those two? wouldn't climates with more extreme variations also be considered monsoon?

EDIT: Would you also be interested in someone turning this into a PDF? I'm a graphic designer by trade and would be more than willing to turn this into a properly designed document (it's kind of hard to navigate it on the forum)

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## Azélor

Some steppes needs to be added manually between the humid climates and the deserts. A large band when it's a plain and a narrow one if there is an elevation. In some places, it makes the whole deserts dissapear. 

The tundra at the equator is not wrong if you look at the temperature maps, which are both between 0 and 10. This is something to be expected at this altitude http://koeppen-geiger.vu-wien.ac.at/...al_2006_A4.pdf

Anapar, it make sense if there is really a high pressure center right next to it, but I'm not entirely sure of that. 

Akanrias: my version but a bit drier sounds ok.


Dc: I think it's because you lack landmasses in the appropriate latitudes. In some places, the Db would turn Dc if it was not tempered by the ocean. 

Dwc, increasing the precipitations in summer should be enough (summer 4, winter no color). Decreasing it further in winter, I'm not sure it's a good idea. 
Btw, the w climates require to have their driest mouth to receive less than 10% of the wettest month. In summer, you would need one the two wettest category and in winter, you need one of the tree driest. So you need both extremes.

Of the Cc, only Cwc is included (because it's alwals located at higher altitudes, so it's rather easy to place). 
Csc barely even exist in the real world.
Cfc is not on the map. Because the differnece between Cb And Cc is the number of months with a certain temperature and we don't know the actual temp since we are using average. Still, there is an unknown color it the north and I will investigate... I could be Cfc.

The north is mostly covered by tundra, Dfc and Dfb. And some Dfa, which is nice.

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## Charerg

Cfc: I could be wrong, but I think the climate that shows up in the north is actually Cfc. At least I don't see what else it could be. Anyway, for clarity I marked the climates on your version of the map:



Anapar: I think the high pressure center probably is located west of the continent only during southern winter (July), and it would retreat into the channel between Akanrias and Nomune during the southern summer (January). Of course, there's always some guesswork involved, but I think this model is reasonable. That said, the recent versions aren't too far off, it just needs the BS areas added in.

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## Azélor

Yes it make sense. But then, most of this Cfc is actually Cfb.

And I have to admit that I am surprised by the dryness of some areas: the inland sea and the western northern continent (on the eastern side). 

The inland sea: these are mountains which mean more precipitation and lower temperatures. Yet, it is really dry.

The other continent, even if most of it turns into a steppe, I would have expected that it would be more humid.


anyway, the results: I added the steppes this time.

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## mbartelsm

I spotted a couple of bugs (or miss-uses) of the script. The first is that during the "climate 1" script it tries to select a couple of copied layers, the thing is that you Ps seems to be in french so the layers are named with the word "copie" while my Ps generates them with the word "copy".

The other problem is that the script doesn't seem to be selecting my level 1 precipitations properly, I tried leaving them transparent and with pure black, it doesn't work. In the end those areas are just left without processing and end up being transparent (no climates outputted)

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## Azélor

I fixed the script and it should fix both problems. I was aware of the language issue and I think that naming a layer as soon as it is duplicated should solver the problem. If not well yes, copie is copy, just select it and continue the script. It's because I managed to record the script without noticing that this layer was not properly named. 

And sure, you could do a .pdf once it's finished, which should be soon. I edited the first message to include links to all the steps.


Now on the biomes and the equivalent climates: a simplified version of this http://www.cartographersguild.com/at...1&d=1423197614
Right now, there are 13 different biomes but I feel that some of redundant. 

Alpine is a meta climate and can include all of them. It's used for practical reason since it's not possible to indicate all the climates in some mountainous areas. 

Ice: EF
Tundra: ET
Boreal forest (mostly pine trees) : Dd, Dc
Temperate: Cfb, Cfc, Dwa, Dfa, Dfb, Dwb, Cwc?
Subtropical forest: Cfa, Cwa, Cwb  (have a winter but temperature usually stick above 0)
Mediterranean: Csa, Csb, Csc?, Dsa, Dsb (dry summer)
Tropical rainforest: Af, Am  (tropical have a small temperature variation over the year)
Tropical dry forest: As, Aw

Savanna: hot steppes (BSh) (the dry season is normally in winter but I'm not sure if it's really different form the Mediterranean biome, both should have a forest with open canopies) 
Grassland: BSk and BSh as a transition between the Savanna and the desert climate. 
Scrubland: a desert with some vegetation like cacti.

Desert: a desert devoid of vegetation.

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## Charerg

There's a slight typo in the biome reference file, you refer to "Af" climate in Tropical dry forest and Monsoonal forest sections, I assume the reference should be Aw?

Btw, when you ran the script last time, did you use the updated temperature maps? Because it looks like Cfc still shows up, and the northern climates seem largely unchanged in general. And I'm pretty sure I "rigged" the temperature maps in such a way that the Cfc climates should have disappeared (the tundra border should be slightly different too).

In any case, if you don't mind running the script another time, I suppose it might be worth modifying the precipitation maps a bit further to see if I can make those Aw climates turn up. I guess there still needs to be more contrast between July and January precipitations. Also, I guess the Neraduhrian summer rains need to be ramped up in order for Dwc to appear.

Actually, now that I compare the precipitation maps, it appears that "Winter 1 (transparent) + Summer 3" combo results in Dwb climate, but "Winter 0 (pink) + Summer 3" combo means Cfc instead of Cwc, even though the contrast is higher in the latter case! Looking at the precipitation maps, I can see why you did this (so Earth's climates turn up more accurately, since even Eastern Siberia is Dfc), but it's still a bit weird (to my logic, anyway).

Edit: Ok, here are the precipitation maps. Hopefully, this time they're truly "final"! The main difference is that the tropical regions now have a *much* more pronounced dry season than previously, so Af should be confined to a more realistic area. Also, remember to use the updated temperature maps (in post #113).

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## Azélor

Yes, I fused the two biomes. Monsoon is also a dry forest because it's mostly the same thing. 

No I forgot to use it and as I already explained, you can't get rid of the Cfc. You'll need to edit it manually if you want to change the Cfb,Cfc,Dfb transition. 


I see that there is a problem here. The w climates should be : 0-5, 1-5, 2-5, 0-4, 1-4 and 0-3 , but not 1-3

About Siberia, we will see what the results will be but they could become Dw something. Normally, it should be Df something but since my rain model is simplistic, southern Siberia is drier than what it's supposed to be. Maybe I will change the instruction regarding the placement of the pink layer.

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## Charerg

Actually, the Cfc should be possible to remove via temperature map editing. The regions that turned into Cfc climates had "Cool" winter (Yellow) and "Mild" Summer (Peach). If one ensures that this temperature combination doesn't turn up, then Cfc climates should not appear. Anyway, what I did should replace Cfc partly with Dfb, partly with Cfb (although I left several islands as Cfc). Of course this could be done manually, but it's also possible to eliminate Cfc via temperature map changes. Also, I did change a few other areas too that I felt were in the wrong temperature category. For example, in the extreme south there is a Tundra->Dfb transition, I edited the temperature maps so there should now be a narrow band of Dfc between the tundra and the Dfb.

w climates: Ok, so the Dwb was a bit of an anomaly then. Although I guess it should still appear since I increased the summer precipitations in Neraduhr (there's now a large area of lvl 4).

Edit: Fixed my typo with the southern D climates (originally wrote "C" instead of D).

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## Azélor

Mild and cool is the only combo for Cc and even then, some Cfb also fit in it. For example, London and Reykjavik are in the same combination, one is at the lowest values possible and the other is closer to the maximum values.
 For now, I prefer to let keep Cfc and add more information about it. 

Ok, i did patch one color that was miscategorized but for the rest, there isn't much i can do. Else it will turn a lot of forests in America and Russia to Dwb/Dwc. 


While I don't see any Cfb->tundra transition, there is some Dfb->tundra and it would be better with a Dfc in between.

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## Charerg

Yeah, I guess the dicciculty is in classification. In some areas (like the extreme north) level 3 rain would realistically probably be pretty much the maximum value outside of islands and coasts. So the scale runs effectively from 0-3 in those latitudes rather than from 0-5 like in the tropical latitudes. Close to the tundra region even 0-2 could potentially be a "w" climate, but probably not in tropical areas. 

Yet the same classification needs to cover both areas. And the tropical areas are probably more significant, since Df and Dw have (broadly speaking), similar vegetation and animal life (since winter is never a growing season in the north, regardless of whether it rains or not). As such, for fantasy mapping it's perhaps a bit unnecessary to classify D climates that accurately, although I find it interesting personally.

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## Azélor

The thing is that in real life, the category 0 can have precipitations as low as 0. Literally meaning that if the precipitations in summer are greater than 0, it's going to be w something. But by using the average of 5 instead, we eliminate a bunch of them and put them in f instead. 

Yes, the impact of f/s/w on D climates is somewhat less critical than with hotter climates: especially with Dc and Dd climates. Anyway, the population of the Dd in pre-industrial times would be very close to 0. The whole of Siberia had a ridiculously low population density, with most people packed on the southernmost part.


EDIT: And here is my latest version of the biomes: 


I'm still undecided as to whether the scrubland should be a specific biome or not.Btw, have you finished with the modifications you talked about?

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## Charerg

I thought I was finished, but I did spot a few areas that could be improved a little. Namely the eastern portion of the northeastern continent (Rheada), which transitions straight from the dry climates into Df or Cw climates, even though this area roughly resembles Manchuria, so there should be a narrow fringe of Dw climates between the steppes and the coastal Df climates. I think the summer temperatures in the area were a bit too high in general. I tried to increase the summer rains in the eastern portion of the continent to reduce the dry areas somewhat, some Dwa should turn up now.

I guess the area could be tuned manually, but in case you haven't run the script yet, these are the (hopefully) final temperature and precipitation maps.

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## Azélor

If this is not the last test, for the next time, could you change the purple for deadly cold? The color is a bit off.

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## Charerg

Looking at the climate map, I believe the map has now reached a level of accuracy that it can be considered "final", apart from minor adjustments that are best done manually. Once again, many thanks, without your assistance it wouldn't have been possible to make this many tests with different temperature and precipitation maps! 

Overall, the climates seem pretty "balanced" now, the amount of Af looks realistic (apart from a few weird instances that will have to be fixed manually). One minor criticism is that Am seems to be pretty rare, although I guess it's not that common on Earth either. But the Am transition zone can be manually added between Af and Aw now that those two are ok. Btw, did you adjust the steppes manually, or are the ones appearing on the map script-generated?

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## Azélor

Am is about as common as they are in real life since it' a transition area. 

I adjusted the steppes manually and it should be ok.

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## mbartelsm

Azelor, what scale did you use for precipitations? I want to try my hand at defining biomes  with the maps I've already made, but I don't think guessing the scale you used will do me any good

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## Azélor

I think this is what you are looking for. http://www.cartographersguild.com/sh...l=1#post277812

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## mbartelsm

Must've missed it, thanks!

I made this biome placement tutorial as a follow up of this guide. To help people interested in defining their biomes as well as their climates.

I will now begin working on formatting this guide into a pdf and will post again when finished.

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## Mysterious Mapmaker XXIII

Hey there! Been a while!

So, now that the actual climate step is done, what's next?

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## Azélor

> Hey there! Been a while!
> 
> So, now that the actual climate step is done, what's next?


There is nothing else planned except maybe polishing the existing tutorial. 
And there was also the biomes right after the climates. 

Other things I considered implementing but were bad ideas: 
type of soil (strongly correlated to climates but I don't see why someone would bother with this)
population density (the correlation between climates and density is really weak)

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## Mysterious Mapmaker XXIII

I assume that "polishing" includes compiling the whole tutorial into one document? Or at least a more easy-to-look up format?

Anyways, I love your work on this tutorial, and look forward to using it for my own world.

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## Mysterious Mapmaker XXIII

Okay, normally I'd never even consider double posting, but this thing's dropped to nearly the bottom of the second page of this forum.

In any case, anything new to report with this? Thanks.

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## antillies

Hi Azelor,

Thank you for such a wonderful tutorial.  I'm doing my best to follow it, though I admit I'm sure it will take quite a few tries before I can get something precise (since I'm so new to this).

I'm running into a problem with the final (climate) step and the script.  When I load it up in Photoshop, it loads as a folder with various sub-actions, like this:



I'm not sure if that's how it's supposed to be loaded (I'm using CS6 if that makes a difference) but when I run the individual actions step-by-step, it doesn't seem to be behaving properly.  I end up with an outline of my land in an icy blue color and a green square in the upper left hand side.

I'm sure I must be doing something wrong.  If you might have any insight into what that might be I would greatly appreciate it.  Thanks!

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## Azélor

Yes it is supposed to be like that for the moment. I planned to merge the different sections after people tested it. In the meantime, I keep it that way as it makes it easier to see if one step is not working properly. 

It's not easy to identify the problem. If you could upload on image taken at the end of each step, I might be able to figure it out.

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## antillies

Thanks so much for the response, Azelor.  And that makes sense about keeping the actions separate to make debugging easier.

After playing around with it some more, I believe the quirkiness I was experiencing was due to user error on my part and not the script.  I've been able to get usable results with my last few passes and the most recent one worked flawlessly.  Sorry to have sounded the alarm.

Edit: 

Actually, I did have one question.  The script is producing a color that doesn't match any on the legend.  I think it may be Dwb but it's not an exact RGB match.  I've attached a snippet of the map with regions of Dfc, Dfb, and the mystery color (which is [35,67,33]).  Any idea which one it's supposed to be?

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## Azélor

It does look like it's the Dwb. Where does it appear on the map/ do you have other areas with this color?

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## Charerg

I guess it could be Cfc as well, doesn't look brown enough to be Dwb, at least not to my eyes. Any chance you'd share your map with us Antillles? It's always a joy to take a look at how the climate turned out after all that work of going through this tutorial.

Anyway, good to see that the thread is still alive and people are using the tutorial!

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## antillies

> Any chance you'd share your map with us Antillles? It's always a joy to take a look at how the climate turned out after all that work of going through this tutorial.


You're sweet to encourage me, Charerg.  I'll share but I still have a long way to go in terms of producing something's that accurate. 

I'm attaching the latest output below.  It's only a single continent at the moment and it's more of a proof of concept to see how the climates would fall with regards to latitude and general land shape.  I do not have an elevation map at the moment and, for this pass, just assumed everything was relatively the same height.  I apologize for its rather unformed appearance.

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## Azélor

I find it strange that the Db climates are located north of the Dc ones unless they are
1- located very far apart (for example Scandinavia is warmer than Siberia)
2- located at higher altitude

Also, the transition should be more like Db Dc or Cc and then tundra

So the color we were wondering about is clearly Cfb. But, as I explained a couple of pages ago, it's also Cfc. The difference between the two is not the monthly temperatures of July/January but the length of either winter or summer, I don't remember. To differentiate them, we would need to make more temperature maps and it would be a waste of time IMHO since Cc climates are uncommon. Actually, there is one temperature combination unique to Cc climates IIRC : 10-18 degrees all year long. But since the Cc climates tend to be located at high latitudes on Earth the difference of temperature between the seasons rarely allow this combination to occur. 

To continue, there should be a transition between Cb and Dc. It's usually Db. 

I am not sure that Dsa should cover that a large area, unless they are mostly located at higher altitude. It's located at the same latitude as the other Mediterranean climates but they have cooler winters, this is usually because of the altitude. but maybe it could be because of the continentality, I'm not sure. 

W and S climates should not tough each other but they may be close if there is a mountains range. That could explain the difference in precipitations.

Your Dwc climate look way off the track. Are there mountains around?

Lastly, you need a transition. There is an area that turn from desert to Cfb but you need to put a steppe there. All the way to the Csa, Csb

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## Charerg

Ok, I'll leave the more detailed feedback to Azelor, but here's a theoretical "box continent" that may be helpful:



It shows the most common climate types (as well as approximately where they usually appear), as well as what the transitions from one climate type to the next generally look like. Basically, if your continent is just a "flat slab" and heights are not taken into consideration, the climates should fit the "box model" (approximately). 

@Azelor:
Btw, I did the "box continent" fairly quickly, so if there are any obvious errors (in terms of latitude where a climate type appears), feel free to point those out.


Edit:

@Antilles:
Regarding your climate map, I agree with Azelor: if the center of the continent (the brown area) is Dsb and Dsa, there's definitely something wrong, as those climate types generally only occur on a few mountains on Earth. Basically almost all "D climates" should be Df, except in the eastern coast of large Asia-like continents, where Dw would appear (in the interior, the coasts should be Df). 

Ds climates should only appear in mountainous areas, on the western coasts (on Earth, Ds appears in the Rocky Mountains, Caucasus, Pamir and the Hindu Kush).

----------


## antillies

Thank you guys for your feedback!  Like I said, it was just a first try concept.  I'm going to reapproach the temperature and precipitation maps and see if I can get something better.

@Azelor: As I told Charerg, I didn't include any height considerations this time around because I was just curious how the climates would fall with no mountains.  But I'm glad to know it is Cfb.  Now that I have a general idea of how the process works, I'm going to try again and see if I can get something that is not so strange, haha.  Thanks.

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## Azélor

@Chareng: The climate transition would vary depending whether it's a large or a small continent. 

A... (something), hot steppe, hot desert, possibly some more desert, then steppe (usually the cold one depending on the latitude) , the Csa and so forth. 

A clarification on Dsa and Dsb: If you want to make them more common, you would only have to lower the average temperature of the planet by a few degrees. Making so would lower the temperatures of Southern Europe during winter just enough so the average are below zero in winter. The the Csa would become Dsa or Dsb, I suppose unless the change of temperature brings other changes to precipitations.

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## Charerg

@Azelor:
It's intended to be a "mid size" continent, with all those steppes and desert appearing in the interior. Essentially bit of a guideline that will hopefully be useful.


Re: Ds climates

Yeah, if you vary the climate just a little from "present day Earth" the changes in climate types can be pretty massive. Think about the Sahara, for example: the place was largely savannah no more than just 7000 years ago (see Neolithic Subpluvial)! So in that sense it's entirely possible that your world could have a different "climate pattern" than Earth, but keep in mind that those kind of planetwide temperature changes and such have global effects.

For example, a colder planet is also a drier planet, so if you lower the global temperatures, you'd have to take that into account (deserts and steppes would be more extensive).

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## acrosome

I think it would be very interesting to see this method applied to a terraformed Mars.  I had always sort of wondered how climate would shake out with one large northern ocean and the immense southern highlands.  With, say, a sea level just high enough to flood Marineris.  I'm sort of guessing an enormous southern BWk running directly into tundra.

I'm still slowly working on my terraformed Venus, which is a bit more of a challenge in some ways but then again more Earth-like than Mars in others...

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## Charerg

> I think it would be very interesting to see this method applied to a terraformed Mars.  I had always sort of wondered how climate would shake out with one large northern ocean and the immense southern highlands.  With, say, a sea level just high enough to flood Marineris.  I'm sort of guessing an enormous southern BWk running directly into tundra.
> 
> I'm still slowly working on my terraformed Venus, which is a bit more of a challenge in some ways but then again more Earth-like than Mars in others...


I agree it could be interesting, but I'm not sure if this method *can* be applied to Mars...because it's a much smaller planet, and I think it would have (has?) a different number of cells in it's atmospheric circulation.

If this site is accurate, Mars would have just one atmospheric cell (transporting air from the equator towards the pole). So, basically the equator and the area between the equator and the ocean would receive rain.

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## acrosome

Ah, but here's the really neat thing: no one really knows what determines the number of Hadley cells, so you can make up anything you like.  Certainly rotational period matters... and probably size... but also maybe atmospheric density, so once it's terraformed it might have more Earthlike cells.  Venus OTOH has a very dense atmosphere but barely rotates at all, so it also only has one Hadley cell per hemisphere.

I'm a bit of a Mars fanboi...

Honestly though, yes, you're probably correct that even a thicker atmosphere wouldn't give tiny Mars more cells.  So this might be a nice one-shot project for someone who knows what drives climate and could puzzle it all out.  *cough-Azelor*   :Smile:   At least figuring out currents in The Ocean wouldn't be comparatively complex.

Hell, maybe someday I'll turn my hand to it.  Someone would have to port Azelor's Photoshop script to GIMP first, though.  I looked into getting Photoshop but they've gone to a _subscription_ cloud-based model, which I find abhorrent.  I mean, $20/month?!?  Seriously?

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## Charerg

> Ah, but here's the really neat thing: no one really knows what determines the number of Hadley cells, so you can make up anything you like.  Certainly rotational period matters... and probably size... but also maybe atmospheric density, so once it's terraformed it might have more Earthlike cells.  Venus OTOH has a very dense atmosphere but barely rotates at all, so it also only has one Hadley cell per hemisphere.
> 
> I'm a bit of a Mars fanboi...
> 
> Honestly though, yes, you're probably correct that even a thicker atmosphere wouldn't give tiny Mars more cells.  So this might be a nice one-shot project for someone who knows what drives climate and could puzzle it all out.  *cough-Azelor*    At least figuring out currents in The Ocean wouldn't be comparatively complex.
> 
> Hell, maybe someday I'll turn my hand to it.  Someone would have to port Azelor's Photoshop script to GIMP first, though.  I looked into getting Photoshop but they've gone to a _subscription_ cloud-based model, which I find abhorrent.  I mean, $20/month?!?  Seriously?


It's also possible to do the final stage (determining climates) manually, though it's very time-consuming. Alternatively you can probably just put your maps in this thread and ask someone with Photoshop to run the script for you (like Azelor did for me, previously).

----------


## Azélor

I believe that the climate cookbook said that a faster rotation meant more cells while slower rotation is less. While other factors need to be considered I don't know exactly. Mars rotate at the same speed as Earth but Venus is MUCH slower. 

A denser atmosphere would make one air cell for each pole, if the pressure is high enough. That is likely what a terraformed Venus would look like. You need a lot a water vapor to do that. The difference between the equator and the Antarctic would be something like 15 degrees Celsius.

If the script doesn't work, you could still do it manually. That's how I recorded the script in the first place. 

Here are the instructions if you want to try.

----------


## Charerg

As a quick side project, I decided to turn one of my hand-drawn world maps into a digital format. Also, for old times sake, I decided to go through Azelor's tutorial to figure out the climate (again), after all, it wouldn't make sense to do it the easy way!

Anyway, this should be somewhat interesting for the climate-enthusiasts around here and help keep the thread alive. This world (called Neuril) won't be super-detailed in terms of topography (nor climate, necessarily) since it's intended as a quick project.

Here's what I have so far:

Elevations:


I forgot to include an elevation key, but it is the same one as in Azelor's Earthly example:

Dark Green: 0 to 250m
Green: 250 to 500m
Light Green: 500 to 1000m
Yellow: 1000 to 2000m
Orange: 2000 to 3000m
Brown: 3000 to 4000m
Dark Brown: 4000 to 5000m
Grey: 5000m or more

Currents:


January Atmospheric Circulation:


I made the July circulation as well, but I later realized that I had screwed up both my circulations by misplacing the oceanic high pressure centers  :Neutral: . Fortunately that shouldn't effect the temperatures (much), but I'll probably have to re-do the July circulation before drawing the July precipitation map (I already fixed the January version).

January Temperatures:


July Temperatures:


January Precipitations:


So, basically the July Precipitation is all that is missing before I can move on to the climate definition stage. As usual, if anyone has any suggestions/corrections to offer, those would be welcome. Oh, and btw, any volunteers for running the script for Neuril (I could process the maps manually as well, but it is quite time-consuming)?


Edit:

Actually, there's one place where I could use some opinions, it's this one:



At present, I made the whole area dry (apart from the mountain chain), but I'm a bit uncertain whether this was an accurate solution. Tbh, I'm not sure how this area should turn out. It's in the right place for a desert, but the continent arrangement is a bit unusual compared to Earth. For reference, the 30 S latitude line runs about through the center of the picture.

Also, here are the names of the various areas, for reference:

----------


## Charerg

Ok, I seem to have outpaced any feedback  :Razz: . I think I'll resort to double-posting considering that my previous post was pretty huge. 

Basically I finished the precipitations, although there were several changes to the January version. I realized that in my "dilemma" area there should indeed be a desert which was kind of missing from the previous precipitation map. Also, I had largely forgot to take the "extratropical storm paths" into account, and I added those as well. Anyway, here are the maps:

January:


July:


So yeah, it's on to the climate definition stage, I guess. In case that some generous volunteer wants to run the script for Neuril, I'll throw the Temp maps in the attachments as well. They're basically the same as previously, though I think I fixed one very minor flaw (one mountain range didn't have the 3000m+ elevation taken into account).

----------


## Azélor

For the temperature it look good. The only improvement I see would be to move the January blue region more to the east of the northern continent. I believe the east would be colder like Siberia mainly due to the direction of the winds and temperature of the western sea being warmer than the north and the eastern one. 

About the precipitations. 

January, the south eastern part of Urmil could be more rainy as a Mediterranean climate. 

I could run the script today unless you have other modifications to do.

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## Charerg

Actually, being my impatient self, I already did most of the work manually  :Wink: . Though I think it wasn't actually a bad idea to sort of "test" the manual instructions. I'm at the stage where all the maps have been combined and all that is left is to pick out the colours corresponding to various climates.

One important tip I did figure out is that in GIMP you need to use the "Addition" layer mode when the (PhotoShop-based) instructions say "Screen" in order to get the exact same colours as in the tutorial (Example: January temperatures layer in "Addition" over July temps in "Normal"). Other than that, I think you should post a larger picture of the "colour blocks", I had to scale them up quite a bit (making the blocks a bit blurry). Otherwise, the "manual tutorial" is pretty good.

Here's how my map looks before the final stage of defining the climates:



I suppose it might be worthwhile to still run the script, just to check if the two methods produce the same result (and to ensure I didn't do any mistakes in my manual implementation).

Edit:

Ok, here is the output:



Not too bad, I have to say. After adding in the steppes, most areas should be ok. Although one area where I was expecting a drier climate was eastern Urmil. I guess I didn't make the summers warm enough so it all turned into Dfc without any deserts appearing. Also, Da climates are basically completely absent. Again, probably because I made the summer temperatures overly mild in mid-latitude areas.

----------


## Charerg

I went ahead and modified the precipitation and temperature maps (namely temperature). As you (Azelor) suggested, the eastern part of Urmil should indeed be much colder, akin to Siberia. Also, I made the summers warmer. Here are the new temperature maps:

January:


July:


The precipitation maps also received some changes here and there, but as they were pretty minor I think I'll skip posting them this time around. I already processed the new maps manually (it's surprisingly fast once you have some practice doing it), and this is how the climates turned out:



Looking at the polar view of the North Pole, I think it looks ok, although now that I think of it, perhaps the climate is extreme enough that there should even be some Dd climates?



The South Pole looks ok now as well, although the ice cap should probably be a bit more extensive (as in, more circular in shape). Btw, the gradient is in 15 degree intervals in these "polar pictures".



Anyway, at this point I think the temperature maps are (largely) ok, but perhaps the precipitations could do with some extra work.

----------


## Azélor

> Actually, being my impatient self, I already did most of the work manually .  Though I think it wasn't actually a bad idea to sort of "test" the  manual instructions. I'm at the stage where all the maps have been  combined and all that is left is to pick out the colours corresponding  to various climates.


It's okay, I understand. When I have a new project, I can't get it out of my head and I can't stop working on it.




> Not too bad, I have to say. After adding in the steppes, most areas  should be ok. Although one area where I was expecting a drier climate  was eastern Urmil. I guess I didn't make the summers warm enough so it  all turned into Dfc without any deserts appearing.


Well, as you said, you still need to add the steppes, which can become quite large if the area is flat. (If I remember correctly)

----------


## Charerg

Yup, I was "on the roll" with the project so decided to go through with it.

Anyway, if you're up for it, I guess a 3rd round of modifications might be worth it. Though I don't think there are any major flaws, maybe you can pick out some areas in the precipitation maps that could be improved?

I ended up changing the peninsula of Orsamarka a little, since it looked too much like a "fat bulb" in the Ortographic projection (see the South Pole map in my prior post). So, this is how the temperatures and precipitations look like now:

Elevations (largely unchanged):


Jan Temp:


July Temp:


Jan Precipitation:


July Precipitation:

----------


## Charerg

Ok, I've now finished the climates. I can post the relevant temperature and precipitation maps if there's interest, but for now I think I'll just post the results:

Climate map before modifications (should be equivalent to a script-generated map):


And after manual modifications:


Things I did:

- Replaced all Cfc with Cfb and all Csc with Csb (as the Cc climates can't be accurately identified with this model, and they're really marginal in any case).
- Replaced As with Aw (again, really marginal and misidentified because areas close to the equator can get assigned into the wrong category).

Aside from that, I manually added some Dw climates in areas where I felt they were appropriate. And ofc the steppes were added in.

----------


## Azélor

Yes I did look at the precipitation maps and they looked acceptable but it wasn't a throughout analysis. 

The final map look fine.

----------


## arch-fiend

hey, sorry that im bringing another sleeping thread back from the dead but azelor i applied your guide to the example images you put at the end of your guides for temperature and precipitation and i made a full koppen-geiger climate map from it. used the classic koppen-geiger color scheme so it could be conspired to other koppen maps of the world. i would like you to look at it and compare it to the koppen-geiger map on the following website and tell me what you think. if your still interested in this guide project you've made i hope this helps.



http://hanschen.org/koppen/img/koppen_all_1901-2010.png

----------


## Charerg

> hey, sorry that im bringing another sleeping thread back from the dead but azelor i applied your guide to the example images you put at the end of your guides for temperature and precipitation and i made a full koppen-geiger climate map from it. used the classic koppen-geiger color scheme so it could be conspired to other koppen maps of the world. i would like you to look at it and compare it to the koppen-geiger map on the following website and tell me what you think. if your still interested in this guide project you've made i hope this helps.
> 
> 
> 
> http://hanschen.org/koppen/img/koppen_all_1901-2010.png


I think you made some mistakes in the implementation. Northern India, for example, definitely wouldn't have a Cs (dry summer, rainy winter) climate if using Azelor's precipitation maps (it's basically all category 0 in winter)! Or there may be some flaw in the script (perhaps some combinations are accidentally miscategorized?), if that's what you used.

Edit:
Actually, I'm pretty sure the implementation here is somewhat messed up. In your map most of eastern Asia is also "Summer dry", even though they're all category 0 in winter (the driest possible) in Azelor's precipitation maps. It seems that all "winter dry" categories outside the tropics have been miscategorized as "summer dry".

----------


## arch-fiend

> I think you made some mistakes in the implementation. Northern India, for example, definitely wouldn't have a Cs (dry summer, rainy winter) climate if using Azelor's precipitation maps (it's basically all category 0 in winter)! Or there may be some flaw in the script (perhaps some combinations are accidentally miscategorized?), if that's what you used.
> 
> Edit:
> Actually, I'm pretty sure the implementation here is somewhat messed up. In your map most of eastern Asia is also "Summer dry", even though they're all category 0 in winter (the driest possible) in Azelor's precipitation maps. It seems that all "winter dry" categories outside the tropics have been miscategorized as "summer dry".


woops, i figured out what happened. i had accidentally painted my summer and winter precipitations backwards. making summer blue and winter red. when i have the fixed map done ill edit it into this post.

edit:
yeah this looks a lot better. it looks like most other koppen maps now though i do see some issue areas like northern australia, the hot steppes of southern central india, south africa, south russia and mongolia, and north east europe at a glance.

----------


## Azélor

It look pretty good that way. One thing I need to mention is that I based myself with the Earth map on the first place so it's supposed to look almost identical. You just did it backward.

----------


## Charerg

> woops, i figured out what happened. i had accidentally painted my summer and winter precipitations backwards. making summer blue and winter red. when i have the fixed map done ill edit it into this post.
> 
> edit:
> yeah this looks a lot better. it looks like most other koppen maps now though i do see some issue areas like northern australia, the hot steppes of southern central india, south africa, south russia and mongolia, and north east europe at a glance.


Yeah, the steppes need to be added in manually, pretty much. It's probably a result of using just 6 levels of precipitation in the "base data" used to generate the climates. If you wanted to increase the accuracy, it could be an option to examine the possibility of using 10 precipitation layers, for example, and see if more accurate results can be achieved that way. Of course that would also ramp up the possible combinations from the present (3600 if I'm doing my maths right) by quite a lot (to 10 000 if you ramp the precipitation layers up to 10).

----------


## Shaetano

Hey,

I tried to apply your guide to my first map as well, but I think I am not very good with anything about wind and pressure systems. Would one of you (your work looks really awesome) maybe have a look at my different maps to tell me, where I'd have to adjust them?

Thanks in advance and kind regards

----------


## Charerg

> Hey,
> 
> I tried to apply your guide to my first map as well, but I think I am not very good with anything about wind and pressure systems. Would one of you (your work looks really awesome) maybe have a look at my different maps to tell me, where I'd have to adjust them?
> 
> Thanks in advance and kind regards


Well, we can certainly take a look at them, but you'd have to post them first (either in this thread, or in your own WIP thread)!

----------


## Shaetano

Hey! Thats really nice of you. I'll just add what I have to this post. Maybe I'll open a own thread once I am a bit more satisfied with my work..

My planet should be pretty earthlike, maybe somewhat bigger, but I don't want to change the stuff around climates too much. I have a hard time understanding everything like it is in guides for our world, let alone planets with sizez affecting it much more.. ^^

All pictures are a robinson projection. I know it isn't the best, but I had it before really started to reading the guides and just kept it for now.

Just a rough Heightmap with a draft of the currents.


Pressuresystems & winds January


Pressuresystems & winds July


Climate for January


Climate for July

----------


## Charerg

Well, you're right that the Robinson projection probably isn't ideal. Tbh, I'd recommend you to make an Equirectangular projection, because you're probably going to need it (it's easy to change an Equirectangular projection into another projection, but not vice versa). Also, the resolution is very low (1000x500 is very low res for a world map, you should ramp it up to 4000x2000 or something).

Anyway, here is the basic oceanic circulation:



For the most part it's similar to your version. The major addition is the Equatorial Countercurrents. Also, the southern pole shouldn't have a westerly flowing current (the winds there are the polar easterlies, resulting in easterly flowing currents). One area which is somewhat subjective is the narrow north-south oriented sea in the northeast corner of the map. I chose to essentially implement the circulation there as two separate gyres, but this is a little subjective. Areas like this are always very tricky, since no similar situation exists on Earth.

Next up, the location of the oceanic low and high pressure centers (semi-permanent, the exact location does fluctuate a little seasonally):



This should demonstrate the relationship between the oceanic and atmospheric circulations. It should be noted that I've omitted the ITCZ here.

Then, the pressures by themselves, and pressures with the dominant winds:





Once again, the major caveat is that the ITCZ has been left out (it's simply assumed to be a continuous low pressure belt at the equator, in reality the location varies seasonally), and there is some seasonal fluctuation in the exact placement of the pressure centers. One major phenomenon to point out is that the location of the high pressure centers on the eastern margins of the oceans means that the the opposite shore of the oceans (the eastern coasts of continents) are hit by cyclones, and in general receive much more rain than the western coasts in tropical latitudes.

Also, I should note that I didn't include any continental pressure centers. Imo they're not strictly necessary, and it's often easier to get a good picture of what the global circulation looks like by leaving the continental centers out. Outside of the ITCZ, the only continental pressure centers that have a major effect occur if you have a situation like Asia, and you have a strong Monsoon effect. It's up to you if you want to include that, but imo it's a perfectly valid option to just roll with the basic circulation.

----------


## Shaetano

Hi and thanks for the reply. Wow.. I must say, that is way more than I expected. Thank you for your help.

Most of your points helf me very much, I even started to redo the map as an Equirectangular projection. It isn't THAT much work anyway.. 

There are just one thing I want to add and two questions about your work. First - the big blue blob in the north-east which you identified as a part of the ocean is in fact just a blue part of the cold climate of the climate map. But I don't think that would affect the currents too much, so I'd just let them travel around the bigger landmass.

One thing is a bit unclear for me. The pressure/wind maps you posted - do they follow the guide for one of the seasons, or did you just but them together ignoring the current month as a little "overview". The tip about not including the continental pressure systems is great. They just add to my confusion and if their influence isn't that big, I think I won't inclue them either.

The 2nd question - how the hell do you draw these nice arrows?  :Very Happy:

----------


## Charerg

> Hi and thanks for the reply. Wow.. I must say, that is way more than I expected. Thank you for your help.
> 
> Most of your points helf me very much, I even started to redo the map as an Equirectangular projection. It isn't THAT much work anyway.. 
> 
> There are just one thing I want to add and two questions about your work. First - the big blue blob in the north-east which you identified as a part of the ocean is in fact just a blue part of the cold climate of the climate map. But I don't think that would affect the currents too much, so I'd just let them travel around the bigger landmass.
> 
> One thing is a bit unclear for me. The pressure/wind maps you posted - do they follow the guide for one of the seasons, or did you just but them together ignoring the current month as a little "overview". The tip about not including the continental pressure systems is great. They just add to my confusion and if their influence isn't that big, I think I won't inclue them either.
> 
> The 2nd question - how the hell do you draw these nice arrows?


The pressures and winds are just an overview, they're basically intended to demonstrate what the basic pattern should look like (like I mentioned, it does vary a bit between the seasons). As to the arrows, they're just hand drawn. In GIMP, you can use the "Smooth stroke" option and adjust the settings to draw nice and smooth lines.

----------


## Shaetano

Hey!

Like I said I started from the beginning again. With the help of your pictures I went through the guide again and hope I did a little bit better this time.

What I am unsure about are parts of the pressure systems. I followed the guide more or less, but don't know if it would be better / more realistic to change the size/position of the systems from season to season more than I did.

Here are my maps from step to step. The last one, after the climate script, isn't edited, as it takes really much time and I want to see if I have to change anything before I start with that.


General Map with heights & currents:


Winds & pressure January:


Winds & pressure July:


Temperatures January:


Temperatures July:


Rain January:


Rain July:


Climates:

----------


## Charerg

A bit of news to revive this thread (btw, shouldn't this be in the tutorial section now that it's finished?):

I made a bit of a modification for Azelor's tutorial, ramping up the precipitation categories to 8 (from 6). Additionally, I changed the scheme for categorizing the climate types (precipitation-wise, the temperatures remain unchanged). As most who have used the tutorial are probably aware, the current version tends to have a few problems (namely relative lack of BS areas in some cases, and a tendency for Af->BW or Aw->BW transitions), although it is still by far the best and most advanced climate-generation tutorial available. I think I've managed to identify and largely eliminate these problems, although this solution does complicate the tutorial slightly due to a greater amount of precipitation categories.

But before I get into the modified version, let's take a look at the changes I made to the climate categorization scheme:

*A) Defining the precipitation patterns of A climates separately from C and D*
A climates use different criteria when determining their type (f, m, w or s) than C/D. For example, it's entirely possible for a climate that would otherwise be classified as 'Cf' to be classified as Aw or As. Meanwhile, only the very rainiest climates (no month with less than 60 mm rainfall) qualify as Af.

I used the following scheme to classify the climate types, differentiating Af, Am, Aw and As from C/D f, s and w:



The table displays the different average precipitation values I used for the different precipitation categories, and the Mean Annual Precipitation (Pann) calculated for each combo (this is used to determine whether a climate is steppe or desert). Note that 's' or 'w' climates would qualify as Aw/As (but as you can see, some 'f' climates also qualify as Aw/As!). 

*B) Determining the aridity threshold*
Köppen uses different aridity thresholds depending on Mean Annual Temperature (MAT), and whether 2/3 or more of the precipitation occur in winter or in summer. These classes are quite different from the climate categories s, w and f. So, I used a different table to determine which aridity threshold should be used:



Here, areas below the purple line can never be arid (they are too rainy), areas below the yellow line can never be desert (they can still be steppe), and areas above the red line are always desert.

Finally, here is the table I used to determine the MAT value of the various temperature combinations (Tann=mean annual temperature), as well as the associated aridity thresholds (areas with Mean Annual Precipitation (MAP) below the threshold are classified as steppe, and areas with precipitation less than half of the threshold are desert):



The red lines denote the boundaries of different temperature-based climate types, the yellow line denotes the boundary between Da and Da2 (an extreme version of Da, this climate doesn't appear on Earth). Note that although I used 'f', 's' and 'w' here to signify the various aridity thresholds, these refer to the *aridity category*, not the actual climate category!
*
C) The Results*
With that out of the way, let's get into the testing of the modded version. First of all, I used data from WorldClim to generate temperature and precipitation maps for Earth (these should be almost identical to those used by Azelor in his earlier post). The only major difference is that these are in the Equirectangular Projection, and the precipitation categories are different:

January Temp:


July Temp:


January Prec:


July Prec:



And then, the final result. This is in the more commonly used "wikipedia colour scheme". Note that as no data was available for Antarctica, it has just all been painted as EF (I think some tundra exists there as well):



This is completely "script-generated" (actually manually done as I use GIMP), although I did shuffle some categories around to make it match actual Köppen maps of Earth as closely as possible. In general, it matches extremely closely, though there are three major divergencies:

1): Equatorial Africa is a bit messed up. This is caused by the "data gap" from having data only from January and July. As a result, there are weird instances of BS and As popping up, and Af and Am do not cover quite as much area as they should.

2): I classified any B climate with Cool (0-10) winters or colder as BSk/BWk. As a result, some areas like Northern Arabia have Bk climates instead of Bh. However, as drawing the Bh/Bk boundary at Cold (-10 to 0) would classify large areas of Central Asia and the North American prairies as Bh, I decided that this was the lesser of two evils.

3): The third difference is of course the distribution of the Cc climates. An old issue, and it can't really be fixed with the temperature categories we have withouth eliminating Cc climates completely (as pointed out by Azelor previously).

That said, I have yet to test this on a fictional world. The good news is that while this probably does sound rather complicated, it doesn't actually make the tutorial much harder to implement (you just have to use those 8 precipitation categories instead of the original 6). The bad news is that as I don't have PhotoShop, there is no possibility of scripting this, so this will be strictly manual-implementation-only. I'll try to put a "supplement for Azelor's instructions" together detailing how to implement this, in case someone is interested in trying this out (though I'm mostly making this for my own use  :Razz: ). I should note that this is perhaps intended more for the "advanced user" who is already familiar with the tutorial (and has preferably already tried the manual implementation before), since otherwise the extra complication will probably be of little benefit.

----------


## Azélor

I'm not sure if I misunderstood but A climates are different of C because of the temperatures, not the precipitations. 
I'm also unsure why you said that the precipitation categories should be different for A, C and D. What do you mean by that?

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## Charerg

> I'm not sure if I misunderstood but A climates are different of C because of the temperatures, not the precipitations. 
> I'm also unsure why you said that the precipitation categories should be different for A, C and D. What do you mean by that?


I mean that Af has specific criteria (no month with less than 60 mm) that are different from Cf/Df, as do Am (annual precipitation more than 25*(100-Pmin), where Pmin is the precipitation of the driest month). Likewise Aw and As are determined based on whether the month with less than 60 mm occurs in summer or in winter.

These criteria are much looser (in the case of Aw and As) than the criteria used to define Cs/Ds (3 times as much rain in the rainiest winter month than the driest summer month) and Cw/Dw (10 times as much rain in the rainiest summer month than the driest winter month). This is particularly true in our case perhaps, since we only have data from two months (so, effectively 3* the rain in winter->_s_ and 10* the rain in summer ->_w_). I'm not sure if you took these into account, but this does result in the possibility that some climates with the same precipitation pattern could be classified as either Aw/As or Cf/Df depending on which temperature group they belong to. 

The "Prec Combos" table I posted demonstrates this quite well. You can see that some precipitation combinations are classified as "f OR Aw" or "f OR As". One fringe case is the W4+S4 (avg. 50mm + avg. 50mm), which results in an even precipitation pattern, but is nevertheless too dry to be classified as either Af or Am. In most cases, this would actually fall into BS (because of the relatively low overall precipitation), but not in all. I chose to classify this as Aw, since it's more common than As, but there isn't really a category within A that represents this particular climate well.

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## Azélor

I took these numbers in consideration but since you used more categories, you had to redo the process anyway.

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## Charerg

> I took these numbers in consideration but since you used more categories, you had to redo the process anyway.


Yup, although I noticed there are actually some cases which should be Aw but are classified as Af in the standard version. Here's an example from Shaetano's recent map:

January rain:


July rain:


Climate zones:


Here the area outlined in red has category 2 rain (25-50mm) in January. Since this falls below the "no month with less than 60mm" threshold these areas should actually be classified as Aw and Am (rather than Af). Since I think Shaetano just used the script to generate the climates, this is probably a bit of a flaw in the "data processing" of the script. I assumed that you had lumped all the 'f climates' together into one category (since these would be classified as f climates if they were C or D), but I guess some combinations might be misplaced instead if you took the differences with the classification of the A climates into account?

----------


## Charerg

*cue Jurassic Park theme song*

As a bit of a pseudo-scientific intellectual curiosity, I've been thinking of attempting to do a map of the Köppen climates for Pangaea using the tutorial (should also be a fairly good test of the modified version). Since fantasy maps feature huge continents fairly regularly, this might also be somewhat informative for fantasy cartography in general. I chose to use 179 Mya (Toarcian) as the date, since at this date the majority of the Pangaean landmass was sitting straight on the Equator (an arrangement that is roughly comparable to the continent of Eocidar in my own con-world of Aduhr). Another reason for the choice of exact date is that there's good quality data available for this date from the PALEOMAP Project (CR Scotese has uploaded a pdf portfolio that has all sorts of maps for this date, you can check that out if you have an academia.edu account (actually, I think you can just use a google account for that)).

So, first off, the elevations:


This is my very rough adaptation based on the original map from the PALEOMAP project. You can find the original in the pdf I linked, or alternatively you can check out Mr. Scotese's Youtube channel (this vid at 180 mya shows about the same map in Mollweide projection). I forgot to include the elevation key here, but it's the usual one:

Dark Green: 0-250m
Green: 250-500m
Light Green: 500-1000m
Yellow: 1000-2000m
Orange: 2000-3000m
Brown: 3000-4000m
Dark Brown: 4000-5000m
Grey: 5000m-6000m

Note that there's a lot of guesswork involved in the elevations (like I mentioned, I did this fairly quickly, so it's meant to be merely "good enough", not necessarily accurate when it comes to the minor details). Still, I believe the elevations should be more-or-less accurate (like I mentioned, I based this on the PALEOMAP reconstruction), at least when it comes to the distribution of highlands/lowlands and their relative elevations. It's of course extremely debatable how tall the Appalachians might have been, for example, during the early Jurassic. However, we can make a decent guess based on modern geological features that the highest average elevations probably occurred in the Altai-Sayan region, with the then-recent collision between the Cathaysian-Amurian and Eurasian plates.

Regarding the climates, the main climate indicators we have that can help to reconstruct the Pangaean climates are lithological indicators (coal deposits, bauxites, evaporites and so forth) and floral indicators (leaf shapes of fossilized plants). The portfolio of PALEOMAP includes a basic map of the broad climate zones based on lithological indicators (again, Mr. Scotese also has a youtube vid that includes this). For the floral indicators, there's also data available, along with model-generated climate zones on the website of the Paleogeographic Atlas Project. Those will come in handy when this progresses into the stage where temperatures and precipitations are determined.

And finally, before I get started with the actual climate-related stuff, I should note that I'm assuming that Earth is in the same phase of the Milankovitch cycle as today (same axial tilt and so forth). With the intro out of the way, lets start with the currents:



Here, I've lazily drawn both neural and warm currents as red, and cold currents as blue (these represent relative rather than absolute temperatures, btw). I've put the approximate locations of the semi-permanent oceanic pressure centers on the map along with the currents (the actual locations will vary seasonally, this is just for visualizing the general pattern). I should note that although the portfolio does include a map of simulated oceanic currents, I've relied on my own intuition here (although it agrees with the simulated stuff for the most part). It should be noted that Mesozoic climate simulations tend to be highly inaccurate, and hence I think we're better off relying on the supercomputer of the human brain here  :Wink:  (not that this is a super-serious reconstruction, more like an intellectual experiment).

So, that's it for the preview. I'll try to get the atmospheric pressure systems/dominant winds maps together next (this is when stuff gets complicated).

----------


## Charerg

And the Pangaean "climate study" continues:

In the past I've often advocated largely ignoring the continental pressure centers, and concentrating on the oceanic pressure centers. In general, I think this is good advice because the oceanic pressure centers are largely anchored in place by the oceanic currents, and hence their position is much easier to determine. When it comes to continental pressure centers, they vary much more seasonally (and based on the topography), and also there is a relatively complicated interplay between continental and oceanic pressure centers, that can be hard to figure out (and not just *can be*, it *is* hard to figure out).

Anyway, this time, since this is an actual map of Earth (albeit 180 million years ago), I've decided to attempt to model the whole circulation. I used the general locations of the pressure centers in the currents map (see my previous post) as a starting point, with seasonal fluctuation taken into account. Since the Pangaean climate is believed to have experienced highly monsoonal behaviour, I've generally used modern northern hemisphere as a basis for both hemispheres (see this site for info about the Indian Monsoon on present-day Earth).

That said, the development of the Asian Monsoon is believed to be connected to the uplift of the Tibetan Plateau. So, I've generally assumed the Pangaean Monsoon to be somewhat less extreme particularly in the high latitudes, due to the absence of a similar formation.

So, the pressure and wind maps:

January:


July:


As I mentioned, I generally used the behaviour of oceanic pressure centers in the N. Pacific and N. Atlantic as a basis for both hemispheres. So, during northern summer, we see the N. Panthalassic high pressure center intensifying in response to the formation of a major low pressure center over Pangaea, and vice versa in the southern hemisphere. Similarly the Polar High Pressure Centers generally form over land in winter (cooling up faster than the ocean), and they expand equatorwards when high-elevation areas are present (Antarctic and Siberian highlands). During the summer, the Polar HPCs disappear, while the Subpolar Low Pressure Centers weaken accordingly and retreat to the continents (land warming up faster than the ocean), but during the winter they move back over the oceans and intensify significantly in response to the formation of the Polar HPCs over the continents.

So, in essence:
Winter-> the Polar cell intensifies, the Ferrel and Hadley cells retreat south
Summer->the Ferrel and Hadley cells intensify and expand north, the Polar cell becomes very weak

This is behaviour comparable to the northern hemisphere today, and as the closest present-day equivalent, I believe should be about right. Another thing worth pointing out is the direction reversal of the cross-equatorial trade winds. This is an important phenomenon when it comes to Tropical climates (if trade winds originating in the N. or S. hemisphere cross the equator, the Coriolis force inverts their direction from easterly to westerly flowing).

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## Pixie

I'm still behind in my project (which is bound to take years), but I follow this thread with renewed interest each time you guys post something this rich into the discussion.

Just wanted to say, Charerg and Azelor, that I am a passionate lurker of your work  :Wink:  Keep it up.

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## Charerg

Thanks for the comment, Pixie, it's nice to know that there's an audience reading this stuff  :Wink: .

But to continue my "Jurassic World" project, I've finished with the temperature maps. So, first off, the Zone of Temperature maps:

January:


July:


And then the temperature maps themselves:

January:


July:


I have to say that I had a much harder time making these than I expected. Mostly because it's really difficult to tell which climate each location should have, and multiple interpretations tend to be possible from the evidence. This would be much easier for Early Cretaceous era and beyond, since that's when flowering plants turn up. For the Jurassic, it's mostly an extinct ecosystem, even in terms of plants, with only some living fossils around. That said, I generally used the appearance of umbrella ferns (Dipteridaceae) as an indicator for a subtropical-to-tropical climate (since the modern species of the family don't grow outside of subtropical/tropical areas). Likewise, I've regarded the appearance of Ginkgo trees as indicators of a temperate climate (the tree is a living fossil, nowadays it only grows in China).

That said, this is all very speculative, owing to the limited evidence, and the difficulty in the interpretation. However, I think this is "close enough" that it should produce results that may be considered acceptable (if not necessarily accurate).

One thing worth noting about the Jurassic "greenhouse climate" is the low temperature gradient from the equator to the poles. Generally speaking, it seems the polar 70-90 latitudes had climates comparable to maybe 40-50 latitude temperate climates of today. New Zealand, for example, while situated very close to the South Pole (at least probably, several interpretations exist about this as well), had a climate that appears very similar to the climate New Zealand has today!

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## Charerg

So, I finished the precipitation maps. Before I get into the maps themselves, some notes:

*1. ITCZ Fluctuation*

In my earlier atmospheric features maps, I had envisioned the ITCZ fluctuating wildly from 30 N to 30 S in mainland Pangaea. When making the precipitation maps and comparing them to lithological and floral climate indicators, it soon became apparent that the ITCZ could not have fluctuated so wildly. The Kalahari area, the regions around the Gulf of Mexico and the Parana basin all have Jurassic deposits of aeolian sandstones, indicating the presence of vast dunes. It seems that the ITCZ fluctuation was comparable to that of modern Africa (approx. 20 N to 20 S), which in hindsight is perhaps obvious. In any case, that's an important note to take home. It seems that extreme ITCZ fluctuation as seen in modern Asia only occur if an equatorial ocean is present (the Indian Ocean on present-day Earth), with a major subtropical landmass. So, in the Jurassic the ITCZ may have reached 30 N and 30 S latitudes in the regions arround the Tethys Ocean (in Australia and Asia, in other words), but probably not in mainland Pangaea.

*2. The Amazon Basin*

On the Paleogeographic Atlas Project page I linked previously, Reese et all. (2000) reconstruct the Amazon Basin as having a tropical summerwet climate (savannah or hot steppe in Köppen). However, this conclusion was apparently made without data from the Amazon Basin itself, instead deposits from regions as far from each other as Equador and Morocco were taken, and an assumption made that the area in between had a comparable climate. I haven't studied the issue in detail, but on a cursory search it seems that more modern studies seem to disprove this idea, and instead suggest a very arid desert environment for Jurassic Brazil (see this web article from 2016, for example).

I suspect the "desert hypothesis" is more accurate, since the following factors were present:

*a)* The Amazon Basin wass surrounded by mountains (Guiana Highlands to the north, the Cordillera to the west and the African-Brasiliano mountains to the east), blocking oceanic influence.
*b)* There were vast desert both north and south of it.
*c)* It was far from all major bodies of water except the Panthalassic Ocean, but I think Panthalassic influence would have been reduced by factor *a*, and also by the general easterly direction of the dominant trade winds, which would have brought dry desert air into the Amazon region from the surrounding deserts.

So, I've decided to accept the desert hypothesis as the more accurate. Although given that the likelihood of equatorial deserts forming within supercontinental landmasses was one of the main questions I wanted to answer with this particular "climate study", I might do a bit more research on the Jurassic of Brazil in the future.

*3. Tien Shan and the Tarim Basin*

The northern Tarim basin has very rich coal deposits from early-to-mid Jurassic, suggesting vast marshlands and a generally humid climate. Furthermore, paleocurrent indicators suggest that no significant rainshadow existed (although currents seem to flow away from the Tien Shan mountains, indicating that it was a geologic feature already). From the late Jurassic onwards the Tarim becomes increasingly arid, and the Tien Shan develops a clear "wet flank" and "dry flank". Some of this humid->arid transition in the Tarim may be explained by the southward movement of Eurasia (bringing the Tarim region into the influence of the Tethys High). However, it is also likely that my elevation reconstruction is wrong, and that the Tien Shan was quite low during the period, only reaching greater elevations with the collision of the Lhasa Terrane with Eurasia.

As I'm too lazy to redo the elevation map (which depicts the Tien Shan close to its present-day elevation), I've given the range the significant rainshadow it probably would have if it was as high as in my reconstruction, but it's worth noting that it was likely much lower in reality (and lacked a major rain shadow). Likewise, the Tarim basin probably had a more humid climate in reality than what it will have in the eventual climate map.

*4. Antarctica*

It's worth noting that the precipitations in Antarctica are largely guesswork. Because of it's glacier-covered nature, Antarctica represents a large data gap in the knowledge of Gondwanan climates. Although there are Jurassic deposits from the Antarctic peninsula (which suggest a subtropical climate), next to no data exists for continental Antarctica.

*5. Eastern North America and Europe*

The Early Jurassic in eastern NAmerica and southern Europe appears surprisingly dry. Evaporite deposits exist in Nova Scotia, Portugal and Southern England, suggesting that a relatively arid climate was the norm. Considering their location on the eastern edge of Pangaea, this is unexpected (I would have presumed a relatively humid climate, like modern-day Mesoamerica). I can only presume that the area was heavily affected by arid winds from the interior of Pangaea, perhaps something like the island of Socotra today. It seems that in general, southern Europe had a fairly arid climate, though the rainfall was spread more-or-less evenly throughout the year (the absence of clear growth rings in fossilized tree stumps suggests the lack of a clear dry season).


And with the disclaimers out of the way, here are the the precipitation maps themselves:

January:


July:


So, next up are the climates themselves. I'll post the unmodified map, since the idea with my modifications to the tutorial is to eliminate the need to do major corrections to the climate map (presuming that the temp and precip maps have been sufficiently carefully done). It will be interesting to compare that to other reconstructions of early Jurassic climates, and see how the results look like.

----------


## Charerg

Ok, here are the climate maps:

First off, the unmodified version:


Regarding the goals of my modifications to the tutorial, the results seem good: there is virtually always a band of BS between BW and more humid climates (although it's a bit narrow in some cases like southern Africa, but that's because I didn't make the different precipitation belts sufficiently broad). Likewise, A climates have a nice Af->Am->Aw/As transition. That said, the equatorial region in Africa is a bit messed up (this happens often with the tutorial), and in general there is clear need to do some cleanup, if only to make the map more readable. So, all in all, it might be said that my modifications have improved the situation (particularly the distribution of the A climates and BS), but in practice it's largely impossible to create sufficiently high quality temp and precip maps to completely eliminate the need for manual modifications on the climate map.

In the map itself, I seem to have made a few regions a bit too dry, and the BS belt should be much broader in tropical Pangaea, but other than that it looks fairly plausible for the most part. It's worth noting that there is an As->Cs transition in the western areas of the continents, which looks a bit weird because the As climate looks like Am in this colour scheme (the Köppen colour scheme wasn't exactly designed with Jurassic climates in mind).

Here's the "cleaned up" version:


In the cleaned version, I've modified the equatorial climates so they make sense, and also adjusted the extent of BS so the result looks more plausible. I also got rid of some weird high-latitude steppes and deserts. A general overview of the climates:

*Overview:*

As expected, D and E categories are almost entirely absent. This is consistent with known evidence: it appears that very few locations on Earth experienced sub-zero temperatures during the Jurassic. The only substantial D climate to make an appearance is Db, which occurs in the Altai-Sayan region due to the high elevations of the area. Some ET and EF also appear around the very highest mountains, but in general both D and E are extremely marginal.

*Low latitude climates (0-30):*

Tropical Pangaea is dominated by an overall arid climate, with savannah (Aw), steppe (BS) and desert (BW) being the most common categories. This is consistent with the data: it appears that equatorial rainforests were almost nonexistent during the Jurassic. In fact Reese et all. (2000) reconstructed them as entirely absent on the Paleogeographic Atlas Project page I have linked previously. That said, climate models predict some rainforest turning up in the Indonesian regions and eastern Africa. As the available data is limited, there is no way to verify this, but my interpretation does agree with the climate models in that it seems plausible that some Af and Am would turn up in these locations.

The absence of equatorial rainforest seems to be due to a number of factors: the first is the overall arrangement of the continents, which does not favour the formation of rainforests near the equator. The megathermal climate of the Jurassic was probably also a factor: the tropical latitudes would have experienced extreme temperatures, and evaporation would have been consequently very high.

*Mid latitude climates (30-60):*

A characteristic feature of the Jurassic were the "Paratropical" climates that occurred in mid-latitude areas. In general, evidence indicates that the greatest biodiversity in both flora and fauna during the Jurassic occurred in the mid-latitude regions. Essentially today's "Temperate Zone" was tropical rainforest during the Jurassic for the most part. In my climate map, I've generally placed the A/C boundary around 45, but tropical conditions may have extended even further polewards (and subtropical conditions probably all the way to around 70 latitudes).

Again, this seems due to a number of factors: first off, falling under the influence of the westerlies (though some areas may have experienced a monsoonal climate, like Asia today), mid latitude areas would have received rainfall on a fairly regular basis. In addition, the megathermal climate would have ment warmer air carrying more moisture, resulting in a more humid climate in these areas than experienced today. At the same time, they weren't quite as hot as tropical regions, reducing the evaporation rate, which probably also favoured the formation of vast forests.

*High latitude climates (60-90):*

During the Jurassic, high latitude areas had a climate comparable to today's Temperate Zone. In my climate maps, I've retained the Cc climates that the tutorial generates, but the vast majority of Cc should actually be Cb. Evidence indicates that broad-leaved, apparently deciduous conifers dominated high latitude areas, suggesting that these regions likely did experience a mild winter, but even so truly cold conditions probably did not occur, even in areas extremely close to the poles.

So, that's it for this "climate study". Although the overall pattern of the climates should be a fairly good approximation of how the Early Jurassic Köppen climates would have looked like, keep in mind that this isn't necessarily accurate when it comes to the details (for example, the Tarim basin was probably more humid, like I mentioned in my previous post). It has certainly been an interesting and hopefully educative experiment!

Edit:
I updated the "cleaned version" of the climate map. The colour for As has been changed, so it's now much easier to identify. I also made a few minor changes.

Edit2:
I was inspired to refine the climate map a bit further. The main difference is that Asia is much more humid, which reflects the distribution of Jurassic coal deposits much better. My prior version would be more representative of Late Jurassic onwards, when Asia started to become increasingly arid. I also removed the high-latitude deserts from Antarctica. Although no evidence exists from continental Antarctica, it seems unlikely that high latitude deserts existed there, given that none appeared in Siberia. In addition, I replaced all Cc with Cb. Some Cc probably would have appeared in mountainous areas, but as I was a bit lazy, I chose to replace it all with Cb, since Cc climates tend to be fairly marginal. So, here's the final map, this time in the "Azelor colour scheme":

----------


## Charerg

Here's another "bonus step" to the tutorial for those interested, this time intended to refine the classification of Cb/Cc and Db/Dc climates.


*Definition of the boundary between temperate (a/b) and subpolar (c/d) climates in Köppen:*

As those familiar with the Köppen system know, the boundary between the _a/b_ and _c/d_ sub-classes is defined based on the length of the growing season (this is proxied by the number of months with mean temperature above 10 C). Both _a_ and _b_ sub-classes require at least 4 months with mean temperature above 10 C (approximating a growing season of at least 4 months), whereas the _c_ and _d_ climates have 1-3 months.

Since in the tutorial, we only generate the average temperatures of two months (January and July), we can't determine based on the existing maps how long the growing season is in each area. So, you need to create a new map, the number of months with mean temperature above 10 C. Here's one for Earth, generated from the WorldClim data I used earlier:



*Other uses of the map:*

Once you have a similar map of your world, you can straight up define the boundary (just pick the 1-3 months categories, and you have your _c/d_ climates). Btw, you should already have the "no months above 10 C" category covered by default (since that is the definition of E climate group, so it's synonymous with tundra and ice cap). Also, all A climates qualify as "12 months above 10 C", although the "12 months" category also extends to Ca, and even Cb in some cases.

In addition to allowing accurate definition of Cb/Cc and Db/Dc climates, a map showing the length of the thermal growing season can be useful information in other ways. For example, it can give you an idea about how productive the different areas might be in terms of farming (though precipitation isn't taken into account here, but your climate map should cover that).

Also, you can use this map to split the _Ca_ category into "true subtropical" climates with a minimal cold season, and more temperate climates with a longer cold season. As Köppen is often criticized (particularly by Americans who don't think New York should be classified as subtropical  :Wink: ) for having an overly broad Ca category, this may be desirable. Here's an example based on the previous map of Earth:



In the Trewartha classification, subtropical climates are defined as having at least 8 months above 10 C. I used a bit more strict criteria here, and drew the boundary at 9 months minimum. Since the definition of "subtropical" is very subjective, to say the least, you can choose whatever criteria you consider most accurate.

Edit:
I should mention that my "subtropical and tropical" category does include _b_ as well, because _a_ requires a hot summer (over 22 C average in hottest month). So, a tropical highland climate where the mean monthly temperature remains always above 10 and below 22 would be classified as _Cb_, even though it's quite different from, say, England which has a clear cold season. That's one of the advantages of doing a map like this in addition to the main tutorial: you can differentiate between climates that are seemingly similar in Köppen, but in actuality totally different.

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## Pixie

The point that Geoff's and Azelor's techinques only care about the extreme months is indeed a limitation. And you might be into something to break out of that, Charerg, but you would need to come up with a way to predict that kind of map you produced for Earth, for any given arrangement of land/ocean (which we like to call conworld)... Any thoughts?

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## Azélor

I did it with the mean temperatures on a spreadsheet (Excel) assuming the changes of temperatures were constant all year long. 
Then counter the number of months the temperature is under a specific threshold. 
Following that logic, 1 combination is supposed to be Cc but it's also possible to have a Cb with the same combination if the variation of temperature is different.

The map with the mean temperature above 10 C is indeed interesting but can it be done by using data we already have, such as temperatures?
Using more temperature categories would work but require a lot more thinking when mapping it.

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## Charerg

> The point that Geoff's and Azelor's techinques only care about the extreme months is indeed a limitation. And you might be into something to break out of that, Charerg, but you would need to come up with a way to predict that kind of map you produced for Earth, for any given arrangement of land/ocean (which we like to call conworld)... Any thoughts?


As the "months above 10" depends on largely the same factors that influence temperature, it should be possible to figure out approximate instructions for creating a map like this based on latitude, altitude and influences (continental, cold current, hot current). Those pieces of information we already have from the tutorial, only the effects of each need to be determined (like Azelor did in the temperature section).

Like Azelor mentioned, mean temperature could be used to approximate this, if we had more categories, but I think there would still be a lot of anomalies, and ultimately it's probably better to generate the "length of growing season map" separately as a "bonus map", so that the main tutorial remains as it is (since it's probably already complicated enough for most users).

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## PaGaN

Hi Azelor.
Just wanted to stop by and say Hi and compliment you on the excellent work done in this thread.
I will be calling on your hard work here when I (hopefully soon) get to the climate design for the world I'm working on. She's a WIP called Aerlaan over in the Regional/World Mapping forum.
Your feedback and opinions would be very much appreciated.
Thanks.
PaGaN

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## acrosome

I came back here after a couple of years hiatus and, wow, this is awesome.  (I've had climate discussions with Azelor and pixie in the past.)  I was inspired enough by this method to read a bit about making GIMP plugins since I don't have Photoshop but, well, I never considered myself a luddite but maybe I am because that stuff is clearly beyond me.

OTOH, since I'll really only have to do this once maybe I can do it by hand...

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## Charerg

So, I recently got myself QGIS and fiddled a bit with WorldClim's 1970-2000 dataset for average monthly temperatures and precipitations. Previously, I used the 1960-1990 dataset (and I believe Azelor used 1960-1990 as well).

I ran across some interesting stuff when looking at the distribution of "Deadly Cold" (below-38 C average) and "Severely Cold" (-25 C to -15 C). Take a look at this map of Eastern Siberia with the elevations overlayed over the January temperature zones:


The areas surrounded with red represent a "Chill" (-3 C to 0 C) category that I added in for Cc climates. The random dots around the Siberian coast are probably some kind of flaw in the dataset (given the extreme temperature differences with surrounding areas).

However, the thing I wanted to talk about is how the "Deadly Cold" seems to appear in the valleys and the Lena river basin, but not really in the highlands (the Verkhoyansk range shows clearly warmer temperatures relative to the surrounding lowlands). Similarly, in the Altai-Sayan region, "Severely Cold" occurs mostly in the lowlands.

And a similar thing seems to be going on in Alaska:


Again, the "Severely Cold" seems to concentrate in the lowlands: the Brooks range is clearly warmer than the Yukon river basin. Again, we see some weird dots of warmer-than-plausible areas along the coast. In addition, the highest peaks of the Alaska range are shown with progressively warmer temperatures the higher up you go. Some of them even show summer temperatures, which is clearly a flaw in the dataset.


I have to admit that I am thoroughly confused by this. Does anyone have a clue if the *lowlands are actually colder*  during the Siberian and Alaskan winter than the highlands, or is the WorldClim 1970-2000 dataset simply messed up regarding these areas?

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## Azélor

Yes the red part are clearly artifacts, bad overlapping data or lack of consistent data. The sea is frozen in winter there. 

For Siberia, I considered a lack of data. 

But it's an inversion of temperature. It's apparently very common in polar regions in winter. I did not know about that.
I don't understand it very well but it's when the air mass at ground level can't rise. 
This video gives a good explanation : https://www.youtube.com/watch?v=T_U3TXHBt-0

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## Charerg

> But it's an inversion of temperature. It's apparently very common in polar regions in winter. I did not know about that.
> I don't understand it very well but it's when the air mass at ground level can't rise. 
> This video gives a good explanation : https://www.youtube.com/watch?v=T_U3TXHBt-0


I did a bit of google-fu about this, and it seems there is indeed a genuine temperature inversion associated with cold-cored continental highs (namely the Siberian High). I googled up a chapter in Robin Mcllveen's "Fundamentals of Weather and Climate", this is what the book has to say about it:




> In early winter, the cooling of a land surface is greatly accelerated by the first snow falls, which practically eliminate further solar warming but maintain the net loss by terrestrial radiation. The surface and the overlying air cools progressively day by day, as in a polar night. The whole mass of overlying air sinks on top of the shrinking layer of cooling air near the surface, and the air aloft converges faster than the air near the surface diverges, so that the surface pressure rises. After setting up, the high surface pressure is maintained by a dynamic equilibrium between convergence and divergence which is common to all types of anticyclone, though not easily explained. Despite persistent subsidence, the absence of an underlying convecting boundary layer prevents the appearance of anything like the elevated subsidence inversions of other anticyclones; instead a relatively smooth inversion can extend several kilometres from the surface.


It then goes on to show a graph from Yakutsk (Jan 28, 1958 ) with the 1000 hPa temperature at -50 C, rising in a more-or-less linear fashion to approximately -25 C at 850 hPa, and then continuing to rise until 700 hPa (maybe about -17 C, the graph is unclear). Then the temperature starts dropping again. Looking at a basic pressure-to-altitude converter, 850 hPa is approximately 1500 metres, and 700 hPa about 3000 metres. I guess the presence of the high pressure area would push these altitudes a bit lower. Either way, it does seem like there's a pretty substantial temperature increase with altitude, especially from 0->1500 metres.

Looking at the temperature zones of Alaska, I'd guess a similar phenomenon (though probably less extreme) must be occurring with the Canadian High as well.

Edit:
This seems to happen in Mongolia too (reading this site). Apparently the coldest temperatures occur in the valleys between the mountain ranges. I guess that would be those "Severely Cold" areas appearing in the Altai-Sayan region.

Edit2:
Found some further info, which is a bit more applicable. From "Encyclopedia of Climate and Weather" (Dr. Stephen H. Schneider):




> Mean surface temperatures during the winter range from about -20 C near the Mongolian-Siberian border to about -45 C in the northeastern corner of Asia. The high density of the cold air and the overland cooling produce a persistent temperature inversion, such that the warmest air, which can be up to 20 C warmer than air near the surface, is typically found at an altitude of 600 to 1000 meters. Such inversions are thickest in the Yakutsk area.


With that in mind I guess we can conclude that the "climatically pedantic users" of the tutorial should probably raise the temperature category by one at 1000 metres, if they have the "Siberian High case" and Deadly Cold or Severely Cold occurring.

Edit3:

I did some further research, and the Alaskan and Mongolian cases seem about the same, although there the temperature rises about 10-15 C, again with the warmest air at about 1000-1500 metres above surface. At least this seems to be the case in the Alaskan interior and Northern Mongolia, the temperature inversion is much weaker in Southern Mongolia. I guess the big effect is that you essentially have to reach approx. 3 km altitudes before you're "about equal" with surface-level temperatures, so the mountains in most cases don't really have colder winter temperatures than the lowlands if an area is influenced by a cold-cored continental high.

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## Charerg

I made a few plots of what the temp inversion profiles look like. Since I think a 4th edit would be a bit overkill, I'll make this a separate post. Note that these are fairly rough, intended for the purpose of making rough estimates. I used the following sources:

*Fairbanks, Alaska:*
_A Climate Perspective of Observed and Modeled Surface-based Temperature Inversions in Alaska_ (Stefanie M. Bourne, link to paper)

The above thesis contains actual elevation-to-temperature profiles for various months averaged from 1957 to 2008. So, the simplified January profile I made based on this should be quite accurate. The data caps at 4 km though, so the dashed line from 4 km to 5 km in my profile is a guess.

*Mongolia, Ulaangom and Arvaikheer:*
_Air temperature distribution over Mongolia using dynamical downscaling and statistical correction_ (B. Gerelchuluun, link to paper)

The above contains pressure-to-temperature profiles averaged over 1981 to 2010 for Ulaangom and Arvaikheer. I was able to somewhat accurately correlate pressure to elevation by comparing the start point of the profiles to the elevation of the aforementioned places (Ulaangom 931 m, Arvaikheer 1817 m). Still, I can't claim the profiles I made are terribly accurate, but they should be okayish. Notably Ulaangom experiences a very strong temperature inversion (being located in a deep valley near the centre of the Siberian High), whereas the temperature inversion is very weak in Arvaikheer (probably due to the high elevation). Ulaangom apparently has the strongest temperature inversion in N. Mongolia, while Arvaikheer has the weakest.
*
Yakutsk:*
I wasn't able to find anything super-reliable for Yakutsk. I ended up making an estimate based on the Fairbanks profile and the literary sources I quoted in my prior post. Mcllveen's "Fundamentals of Weather and Climate" has that extremely deep inversion profile, but that's only for Jan. 28, 1958, so probably not representative of an average.


Anyway, here are the profiles:



What is notable from this is that apparently the inversion depth is about the same in Ulaangom (931 m) and Fairbanks (136 m). So, it seems that the inversion is relative to the "base level" of the surface. Although it may also be the case that the temperature inversion is just stronger in the Siberian High. If my estimate is anywhere near accurate, Yakutsk has an even deeper inversion profile, and you'd probably have to get near 6 km altitude before "breaking even" with the surface-level temperature.

*Edit:*
I found out that the Avg. Jan. Temp is about -41 C in Yakutsk (source). Also, found an extra source comparing the temperature inversion between Oymyakon (~700 m) and the Suntar-Khayata Glacier (~2000 m). Apparently, the Jan temp at the glacier is indeed ~20 C warmer than Oymyakon (link to paper). I somewhat smoothed this out since my estimate is intended to depict a long-term average. So, the Yakutsk estimate has been updated, and it's a bit less extreme now.

Also, apparently the temperature inversion happens in polar areas too. I found out one table from January 19, 1949, that provided temperatures compared to altitude in the Eureka Sound. At sea level, the temperature was -45 C, rising to a maximum of -21.8 C at 2.44 km (8000 ft) and then dropping to the "start point" at about 6.1 km (20 000 ft) at -44.7 C. So, the magnitude of the inversion seems relative to surface temperature: the colder the surface, the more strong the temperature inversion is.

With that in mind, I guess the following "Counterlapse-lapse Zones" (Cl-L Zone) could be summed up (the elevation needed to rise to max temp and then drop back to starting point):

Fairbanks: ~3 km Cl-L Zone, dT ~8 C
Ulaangom: ~4 km Cl-L Zone, dT ~13 C (I probably underestimated the Cl-L Zone in the temp profile slightly)
Yakutsk: ~5 km Cl-L Zone, dT ~15 C (rough estimate)


*Edit2:*
Here's a similar altitude-to-temperature graph to the one Azelor made previously, taking the temperature inversion into account. Note that this is only for *winter* in arctic or subarctic areas that experience continental conditions. For the summer temperatures, the usual graph should be followed.

----------


## Charerg

Okay, I spent a good chunk of time yesterday cleaning up all those artefacts from the temperature maps. I'm 100% certain I missed some tiny island in the Pacific somewhere, but overall the temperature maps should be in serviceable condition. Equivalent maps of the temperature zones using WorldClim's 1960-1990 dataset can be found in my prior post, or in Azelor's real world data.

_Note about temperatures in the Alaska Range:_
Originally, the Alaska Range had some serious temperature anomalies in the Jan temp map (the 6 km tall Denali with 10+ C temperature in January!). I fixed those up using the Jan temperatures of the 1960-1990 dataset. I suspect the anomalies were a genuine flaw in the 1970-2000 dataset rather than just artifacts like those random dots along the coastline.

*January temperature zones:*


*Major differences between 1970-2000 and 1960-1990:*

1. Winter Temperature Inversion in Continental Subarctic Regions
- The old 1960-1990 dataset had (incorrectly) basically all of the Altai-Sayan region and Alaska blanketed as "Severely Cold" (-38 C to -25 C), and most of E. Siberia as "Deadly Cold" (below -38 C). As previously discussed, these regions actually experience a temperature inversion, and the highland areas are noticeably warmer than this.

2. Deadly Cold in Central Greenland
- I suspect the 1960-1990 dataset had more limited data for Central Greenland. The 1970-2000 has C. Greenland as "Deadly Cold" in January.

3. Data for Antarctica
- Comparing the data to Azelor's sample maps (which have E. Antarctica as "Very Cold" in January), it seems the central regions of E. Antarctica maintain "Severely Cold" temperatures even during January.

*July temperature zones:*


*Major differences between 1970-2000 and 1960-1990:*

4. Largely unchanged
- The July temperatures show only minor differences. The only notable one is that C. Greenland is again depicted as colder. Apparently it maintains "Very Cold" (-25 C to -10 C) temperature even in July. In Antarctica, the Antarctic Peninsula is a bit colder than was the case in Azelor's sample map.

I did the precipitation maps too, but they seem basically the same in both datasets (only minor differences).

*
Edit:*
Oh, and I split the "Cold" category (originally -10 C to 0 C) into "Cold" and "Chill". The tutorial uses the 0 C isotherm as the boundary between C and D climates. However, most Köppen maps tend to use -3 C isotherm as the C/D boundary instead. The idea here is to basically save the -3 isotherms for Jan and July in order to later redraw the C/D boundary after the final climates have been defined. During the climate definition stage, the two categories are recombined into the old "Cold" category. 

This basically makes Cc and Cb a bit more widespread, and eliminates most of the "weird" D climates in places like the Southern Andes, Iceland and coastal Norway and Canada. It's optional since it's up to personal preference whether the 0 C or -3 C isotherm works better as the C/D boundary.

----------


## Azélor

Yea, the numbers look ok.
I noticed that the temperature lapse above 4000m seems constant based on your sample and close to the average I used to make the initial model. Around 7 / 7,5 C by 1000m.

----------


## Charerg

> Yea, the numbers look ok.
> I noticed that the temperature lapse above 4000m seems constant based on your sample and close to the average I used to make the initial model. Around 7 / 7,5 C by 1000m.


My profiles are intentionally somewhat simplified. I think it also depends a lot on the case. But yeah, I guess it does sort of go back to normal adiabatic lapse rate past a certain point, so it's probably ok to assume a linear lapse rate after passing "the hump".

Here's a sort of theoretical profile for those who want to have that "crazily high mountain at the top of the world". If you had a 10 km tall Nunatak jutting out of an approx. 2 km thick glacier, this is how the temperatures might look like (in winter):



The tropopause is about 10 km high over the poles, once you reach that point, the temperature no longer really drops (or drops much more slowly). Although the temperature at the tropopause could be a bit warmer than -60 C. According to wikipedia's "temperatures in the troposphere", the polar tropopause is only -45 C. Here's the quote from wikipedia:




> At middle latitudes, tropospheric temperatures decrease from an average of 15 °C at sea level to about −55 °C at the tropopause. At the poles, tropospheric temperature only decreases from an average of 0 °C at sea level to about −45 °C at the tropopause. At the equator, tropospheric temperatures decrease from an average of 20 °C at sea level to about −70 to −75 °C at the tropopause. The troposphere is thinner at the poles and thicker at the equator. The average thickness of the tropical tropopause is roughly 7 kilometers greater than the average tropopause thickness at the poles.

----------


## Charerg

Some time ago I made a post about generating an extra map that depicts summer length (aka length of the growing season), defined as the number of months with mean temperature above 10 C. The primary purpose for creating an extra map like this is to fix the distribution of a/b and c/d climates. The present tutorial tends to generate a lot of nonsensical transitions (like lowland Cc-> highland Db, to give an example), so there's definitely room for improvement in this regard. 

For those who don't know, Köppen defines c/d climates as having 1-3 months above 10 C mean temp, while a/b have a minimum of 4 months above 10 C. So the primary difference between Cb and Cc (or Db and Dc) is the summer length, not the the maximum or minimum temperatures per se.

Previously I didn't have anything in the way of instructions to offer about how to create such a map for a fictional world though, as Pixie noted:




> The point that Geoff's and Azelor's techinques only care about the extreme months is indeed a limitation. And you might be into something to break out of that, Charerg, but you would need to come up with a way to predict that kind of map you produced for Earth, for any given arrangement of land/ocean (which we like to call conworld)... Any thoughts?



So, here is an attempt of sorts to provide something of a guideline. First off, I've split the summer length into five categories: 

Eternal summer (12-10 Months above 10 C)Long summer (9-7 Months above 10 C)Mid-length summer (6-4 Months above 10 C)Short summer (3-1 Months above 10 C)Arctic summer (0 Months above 10 C)

Here's the above presented as a colour key:




*A. Creating a base map of summer length:*

The most important factors that control summer length are *latitude* and *elevation*. I've created a graphic that can be used as a guideline to create a base map of summer length:



Note that if you've already created a climate map (or just the temperature maps), then your "Arctic summer" category should already exist (the E climates are defined as "No month above 10 C"). You'll note that in tropical latitudes the transition from "Eternal summer" to "Arctic summer" is extremely abrupt. This is because the temperature varies very little during the year. So, either the elevation is enough to push the monthly mean temperature of *all months* below 10 C, or it is not. In actuality, there is a very narrow transition zone, but it's too marginal to be depicted in a world map of climates.

Here's a sample map of Earth I created using the above instructions:


Note that the above map is intentionally very rough, as I've "followed my instructions to the letter". For an improved map, I suggest utilising the techniques described by Azelor in the "temperature placement" section of the tutorial.


*B. Creating a map about local influences:*

However, the above doesn't take into account local influences. For the summer length map, you need to consider the "annual net influence" of various factors. In some cases, continental influence can cause a longer summer, sometimes shorter. The areas adjacent to oceanic high pressure centres tend to have a "hot continental" effect because of the clear and sunny weather they cause. Also, western Eurasia is significantly warmer than expected due to the combined effect of the Gulf Stream reducing arctic influence, and also the warm desert regions causing a strong "hot continental" effect. Eastern Eurasia, on the other hand, is colder than expected, with a noticeable "cold continental" effect due to the development of the Siberian High and the freezing of the Arctic Sea in winter. Northern Canada is similarly open to arctic influences since the sea freezes in winter and there is no moderating effect in play. 

Here's an example map I made for Earth:


Note that you can largely ignore tropical areas when creating the influence map, those areas are always going to be "eternal summer". When adjusting the summer length map with the influences, maritime influences should generally only effect 0-1000 m areas, as higher elevations tend to effectively block oceanic influence. A rule of thumb is to move the "summer length zone" about 5 degrees equatorwards if a cold influence is in play, or 5 degrees polewards if a warm influence is at play. It's not an exact science though, and in practice you need to make judgement calls. For example, "cold continental" doesn't usually reduce summer length to 0 months (unless the "cold continental" effect is caused by glaciers), but "cold maritime" could, as in the coasts of the Labrador peninsula or the southern tip of South America.


*C. Final map of summer length:*

Here's my Earth map after adjustments based on influence:


Note that my map isn't super-good or anything, as I did this fairly quickly and sloppily. Nevertheless, even a basic map like this could be used to make a *major* improvement with the distribution of a/b and c/d climates and eliminate all those nonsensical c->b transitions that the tutorial generates.

Finally, here's the equivalent "summer length" map generated from WorldClim's 1970-2000 dataset. It's a good reference, and you can check to see the difference between my sloppier version and the actual data:

* *










That's it for this bonus map. I realise my instructions are a bit short, but since the factors that affect summer length are largely the same that affect temperature in general, I figured there was no need to repeat instructions already covered by Azelor in the "temperature placement" section. Any comments, questions and suggestions are welcome and I hope you found this interesting and/or useful!

----------


## Pixie

> Previously I didn't have anything in the way of instructions to offer about how to create such a map for a fictional world though, as Pixie noted:


You certainly have now. I will have to find the time to try this but, at first sight, this is an awesome add-on to our effort. 
Charerg, I said this before, I think, and I'll say it again: I like the way you think  :Wink:

----------


## Azélor

Chareng, are you interested in having the original files? I have documents made with Photoshop, Illustrator and Excel. But some of them are really messy. 

The last post was pretty good.

----------


## Charerg

> Chareng, are you interested in having the original files? I have documents made with Photoshop, Illustrator and Excel. But some of them are really messy. 
> 
> The last post was pretty good.


I thought you posted the Excel files in the thread somewhere? But yeah you can send them, I'll try to have a look through them at some point. Though I doubt there's anything too major I could offer in the way of suggestions, your original "temperature placement" instructions are way more detailed than the stuff I put together for that summer length map  :Very Happy: . The only thing that comes to mind is that an alternative elevation-temperature graphic could be made for those who have access to a more detailed elevation map. For example, the "tundra line" (as defined by Köppen) in tropical areas is generally about 3500 m rather than 4000 m.

----------


## Charerg

Talking about that altitude graphic, in between working on Aduhr's height map, I've been trying to piece together something of a graph about altitude-to-temperature.

Unfortunately it's kind of tricky, because apparently the lapse rate varies a lot regionally. As an example, here's the approximate altitude of the tundra line as defined by Köppen (warmest month's mean temperature below 10 °C):

Tibetan Plateau (~35° N): 3850 m (more accurate)
Ethiopian Plateau (~10° N): 3500 m (approx.)
Venezuelan Andes (~5° N): 3250 m (more accurate)
Peruvian Andes (~15 °S): 3600 m (more accurate)

Note that the above are approximate altitudes, not exact. It still gives an idea about how much the lapse rate varies. The Tibetan Plateau is a *lot* warmer in summer than what one would expect based on just latitude and elevation. And on the other hand, the Venezuelan Andes seem chillier than one would expect. I guess these differences are caused by Venezuela having a very maritime, humid climate, with frequent cloud cover lowering the temperatures, whereas the Tibetan Plateau is extremely continental, with much hotter summer temperatures than might be expected. 

Still, if we go by the tutorial's temperature placement guide, the sea level temperature in all these areas falls into the same category (_Hot: 22 to 28 °C_). Although I guess Tibet is at least partially _Very Hot: 28 to 35 °C_, which does alleviate the problem somewhat. Still, it's pretty tricky to come up with a system that assumes equal lapse rates everywhere, but still delivers acceptably accurate results.

Edit:
Actually, I did a bit of mistake with the low tundra line of the Venezuelan Andes (comes with using relatively low res data and elevation maps split into zones). I checked with a DEM-generated elevation map split into 25 metre intervals, and actually the tundra line is closer to 3500 metres, even in Venezuela (about 3250 metres). Well, spotting that mistake definitely raises my hopes that something of a "reasonably accurate universal guideline" might actually be possible to create  :Very Happy: .

Edit2:
Updated more accurate altitudes for the Tibetan Plateau, and added data for the Peruvian Andes as well. It does seem that the tundra line is indeed considerably lower in the northern Andes.

----------


## Azélor

Problem number one: using categories makes things less precis. 
The 22 to 28 degrees is a pretty big range of temperatures considering an average lapse of 6 or 7 per 1000m increase in altitude.

I mean, if the base temperature is 22 at the base it reaches 0 at 3142m, assuming a lapse of 7.
At 28 at the base, it reaches 0 at 4000m, also assuming a lapse of 7. 

Thus all you examples are valid. 

Problem number two, 7 is just an average number for general purposes. If my memory is good, it usually range from 5 to 9, or maybe more if it is really dry.

----------


## Charerg

> Problem number one: using categories makes things less precis. 
> The 22 to 28 degrees is a pretty big range of temperatures considering an average lapse of 6 or 7 per 1000m increase in altitude.
> 
> I mean, if the base temperature is 22 at the base it reaches 0 at 3142m, assuming a lapse of 7.
> At 28 at the base, it reaches 0 at 4000m, also assuming a lapse of 7. 
> 
> Thus all you examples are valid. 
> 
> Problem number two, 7 is just an average number for general purposes. If my memory is good, it usually range from 5 to 9, or maybe more if it is really dry.


Note that in my examples the "tundra line" is actually 10 °C (since a climate with maximum mean temp below 10 °C is classified as ET in Köppen), not 0 °C (that would be the "glacier line", or EF).

From what I've calculated, when it comes to the effect of altitude on *monthly mean temperature*, the ratio definitely tends to be below 7 degrees. To follow up on my example about the Tibetan plateau, it hits the "glacial line" (mean temp below 0 °C) at roughly 5350m. If we use only the "known intervals", the _Hot (28-22 °C)_-_Warm (22-18 °C)_ line is about 1700 metres in the Tarim basin (so, 22 °C July mean temp at 1700m).

So, the monthly mean temp drops by 22 degrees in 3650m (5350m - 1700m). Assuming a linear lapse rate, that's about 166 metres per 1 °C, or about 6 °C per 1 km. And often the temperature drops even more slowly, especially in lower altitudes. The Brazilian Highlands in January have about 200 metres per 1 °C, so 5 °C per 1 km. There seems to be an overall tendency for the lapse rate to increase gradually with greater altitude (due to increasing dryness of air perhaps?).


*Edit:*
As another example, in the Venezuelan Andes in January (which is slightly warmer than July), the altitudes between the different zones are as follows:
_Hot-Warm boundary (22 °C)_: 1100m
_Warm-Mild boundary (18 °C)_: 1750m

This gives us an approx. 1°C per 163 m, or ~6 °C per 1 km lapse rate between 1,1 and 1,75 kilometres.

_Mild-Cool boundary aka "ET line" (10 °C)_: 3250m

This gives us 1°C per 188 m, which is about 5.3 °C per 1 km between 1,75 and 3,25 kilometres. Strangely enough in this case the lapse rate was seemingly faster at the lower altitude. That said, these are somewhat approximate numbers. 

Note that since the surface level is _Hot (28-22 °C)_, we have *at least* 183 m per 1 °C (~5.45 °C per 1 km) from 0 to 1,1 km (and could be 1 °C per 275 m [~3.6 °C per 1 km], if the sea level temp is 26 °C, as an example).

----------


## Gridello_Aspart

i need help that tutorial make me so confuse i cant figure out where its dry or where its not pls HELP  :Crying or Very sad:  i m not even sure i did other parts right i really need help i read other tutorial i understand it well but it always crated some blanks so i tried that one but it makes me confuse

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## Charerg

I ended up a learning a bit of Script-Fu in GIMP in order to automate the climate classification process (saving myself and others a lot of work in the future, I hope). For now, I've used the 8-step precipitation system introduced in my prior modification to the tutorial. Although I might also a create a version of the script for the original 6-step precipitation system at some point.

Here are the instructions for using the script:

Installation:
Place the script in the appropriate folder (usually /User/gimp-2.8/scripts). If uncertain, you can check Edit->Preferences->Folders->Scripts to see where the scripts are stored. Once the script is in the right folder, the script should be availabe (you can use Filters->Script-Fu->Refresh Scripts so you don't have to restart GIMP). Oh, and remember to extract it from the .zip file before use (but you knew that, right?  :Wink: ). You should now have the script available under the Image tab:



Restrictions for using the script:
- This has been written for and tested in GIMP 2.8, it might work with other versions as well, but I can't guarantee that
- The image needs to be RGBA (RGB with an Alpha channel)

Layer naming restrictions:
The temperature/precipitation layers need to have exactly the following names (the script searches for them by name and duplicates them in order to work out the climates):

JanTemp
JulTemp

JanPrec
JulPrec

Layer colouring restrictions:
The temperature and precipitation categories need to have exactly the following colours:

Temperature zones:

* *





*Temp Category*
*R*
*G*
*B*

Severely Hot
160
0
65

Very Hot
210
60
80

Hot
245
110
65

Warm
250
175
95

Mild
255
225
140

Cool
230
245
150

Cold
170
220
165

Very Cold
100
195
165

Severely Cold
50
135
190

Deadly Cold
95
80
160



The temperature zones in a slider:




Precipitation zones:

* *





*Prec Category*
*R*
*G*
*B*

200+ mm
135
0
180

140-200 mm
130
60
200

70-140 mm
105
70
200

40-70 mm
60
60
180

20-40 mm
70
95
150

10-20 mm
55
85
100

5-10 mm
35
50
50

0-5 mm
20
20
20



The precipitation zones in a slider:




Processing time:
I've somewhat simplified the climate generation process by merging many of the temperature combinations (all combos that result in Cb, for example), in order to speed up the script and also reduce unnecessary complexity. Still, I've only tested this on approx. 4000x2000 and it does take maybe 30-60 sec to process that. So it may take a while for GIMP to go through all the operations if you have a very high resolution map.

Sample map:
Here are some sample maps I used to test the script. These are generated from WorldClim's 1970-2000 dataset, although I haven't cleared any artifacts from them.

Prec samples:

* *










Temp samples:

* *










Generated climates:


Right, that's it for this post. The script itself can be found in the attachments. Any feedback about using the script or the generated climate zones is welcome of course.

Edit:
There's an updated version of the script available in this post.

----------


## Azélor

I have attached the files I was talking about earlier if you want to have a look.
It's strange that Psd files become much smaller if you turn the layers off. 

Chareng.zip


I have to say that I'm impressed by the compression ratio. Normally it's in the range of 5%, here it's around 75%. 


Also, Unless I missed something, you added more precipitation categories but haven't explained the difference this creates when placing them. 
I'm not sure that the cutting halfway technique give very good results. The progression from one category to the other is not clear, even in my tutorial. 
I find it not too difficulty to know where it rain and where it does not, the extremes. Yet everything in between is unclear. 

The problem comes from using odd numbered categories. The categories are not bad per see but they are not helpful to understand the precipitation spread. 
We should do another map for reference using 5 or 10 ml categories. It will be easier to see the progression.

----------


## Charerg

> I have attached the files I was talking about earlier if you want to have a look.
> It's strange that Psd files become much smaller if you turn the layers off. 
> 
> Chareng.zip
> 
> 
> I have to say that I'm impressed by the compression ratio. Normally it's in the range of 5%, here it's around 75%. 
> 
> 
> ...


That is a very good idea. Actually I kind of think both your original (extremely detailed, certainly more so than my calculations which used purely averages) and my precipitation threshold calculations are to some extent meaningless because the precipitation categories we use are so broad. For example, one temp combo has a threshold of 800 mm and another has 700 mm, and they both have the same precipitation pattern. Does the difference matter if the annual precipitation could be anything between 500 to 1000 within that same precipitation pattern?

This is actually a big reason why I decided to merge many of the temperature combos (within the same climate class) in the scripted version for GIMP (besides making it faster to write and easier to modify): I felt there wasn't any advantage to treating them separately as opposed to lumping them together and just using an approximate average threshold to determine whether it's an arid climate or not.

As to placing the precipitation categories, I admit that I don't have a really great way to place them myself. What I personally do is a combination of guesswork and looking at the example maps of Earth's precipitations for reference. As well as taking into account the general precipitation patterns covered in your instructions. At the end of the day it's an approximation, if only because we're working with precipitation data from only two months. Although I'd say that using more precipitation categories does have one distinct advantage: the sheer volume of combinations gives you more room to create a transitional BS stage between BW climates and more humid ones.

But making an example precipitation map using linear intervals for reference is indeed a great idea. And should be easy to generate too with a gradient map. I'll try to post some during the weekend.

----------


## Azélor

Well I already started  :Smile: 

It's been a while I haven't used Qgis. 
Here's a basic map for July showing the area with precipitations above 500ml.



I was going to subdivided the precipitations in 5ml categories but the highest value is 2381 ml. 
Yep almost 2,4 m of rain in a single month. Almost 4 times what London receive in a year. 
The maximum in January is barely over 900 ml. July is much more rainy world wide.

I'm trying to find a threshold where I can stop adding new categories. Beyond a point where increase in precipitation don't have any impact. Stopping at 300 ml still give 62 categories. That might still be too much unless I increase the resolution.

----------


## Azélor

I made a custom gradient in Qgis for each 5 ml
Yes, the pure black area receives exactly 0 ml of rain. 

It's fine until I reach 200 ml. Then I ran out of colours. 200 ml is the saturated pink-red and the colour of the other category following it is salmon.  
Past 200ml I made the categories 20 ml each. Til 500 when I ran out of desaturated colors to use. Everything above 500 ml is radioactive green. 
I'm not sure how it look (psychedelic?). I hope we can easily differentiate the categories. Any tips for improvements are welcome.

The problem with the in built gradient is that they appear to follow a linear shift from one colour to the other. But the human eye is less sensitive to some colour, therefore I could only count a third of the colour used by the program. 

January. It look like you will might need to download the map to see it properly. I'm using Firefox and by default the zoom is not good enough. I also have a zoom extension that allow to zoom further but he makes everything blurry. 


July


These were made with the 5 minutes resolution maps. 


But here's a comparison of Korea at the 3 different resolutions

10m


5m


2,5m



and I'm downloading the 978 mb file for the 30s resolution. Just to see how much more detailed it is.

----------


## Azélor

Adding black lines to separate the categories is not a good idea. Unless I make the map larger.



Or having a line for every 5 categories. I'm not convinced.

----------


## Charerg

Those maps are really good! Personally I planned to cap the precipitation at something like 250 mm (also, I use the 2,5m resolution datasets). Although I think using 10 mm intervals past 20 mm is probably sufficient. But I have to say that the detail in the 30sec is pretty impressive.

----------


## Charerg

As a minor update, I made a "version 2" of the script which makes the BS areas in warm tropical and subtropical regions a bit more extensive. Works pretty well for Australia, India and the Kalahari region, perhaps not so well in other areas. It's a relatively minor change, but probably does make the generated climates a bit closer to reality, here's the output:



I also considered reducing the area covered by w climates, but overall decided against it because shuffling one "winter dry" category over to f (humid) would create bad transitions in several places. For a really satisfactory solution, even more precipitation categories would need to be used, but I think the present version is a decent compromise even if it does make "winter dry" a bit too widespread.

I also realised I forgot to include the colour key for the climates in my previous post, here it is (the script generates the climates in the "max contrast colour scheme"):


The updated version of the script can be found in the attachments.

----------


## Charerg

> i need help that tutorial make me so confuse i cant figure out where its dry or where its not pls HELP  i m not even sure i did other parts right i really need help i read other tutorial i understand it well but it always crated some blanks so i tried that one but it makes me confuse


Just bumping this up since it showed up only now (your first posts need to be approved by a community leader before showing up on the forums). See the original post for the maps.

At first glance I admit I'm totally confused myself  :Very Happy: . Maybe start a bit slower and ask for feedback step-by-step? That way you usually end up with more feedback. Like start with the ocean currents for example. It might also be an idea to start a separate thread for your world, since I'm not sure how actively people check the tutorial thread.

----------


## Azélor

I fixed the attachments.
Using the 2.5 m resolution map instead is easy, since I keep the same colour scheme, I just need to change the base map.





> Just bumping this up since it showed up only now  (your first posts need to be approved by a community leader before  showing up on the forums). See the original post for the maps.
> 
> At first glance I admit I'm totally confused myself .  Maybe start a bit slower and ask for feedback step-by-step? That way  you usually end up with more feedback. Like start with the ocean  currents for example. It might also be an idea to start a separate  thread for your world, since I'm not sure how actively people check the  tutorial thread.



Woa, this just appeared out of nowhere. 
I'd also recommend opening a new thread to avoid confusion.

----------


## Azélor

New version. 2,5 minutes resolution.

It uses 5 ml categories until 200ml and then 10ml categories until 400+ ml where the colour is peach. The ^previous version used 20 ml  increases after 200. 




I can't find how to crop the map properly. The map should be 8640 x 4320 but I have no idea how to crop it properly, how to get rid of the white border before exporting the image in a png. 
The only solution I've found was to manually find the right scaling but there must be something better.

It's not that a big problem but the map look distorted a little and it would look better without these distortions.

----------


## Charerg

> New version. 2,5 minutes resolution.
> 
> It uses 5 ml categories until 200ml and then 10ml categories until 400+ ml where the colour is peach. The ^previous version used 20 ml  increases after 200. 
> 
> 
> 
> 
> I can't find how to crop the map properly. The map should be 8640 x 4320 but I have no idea how to crop it properly, how to get rid of the white border before exporting the image in a png. 
> The only solution I've found was to manually find the right scaling but there must be something better.
> ...


I personally crop them by having the ETOPO1 elevation data in the same file, then I export the elevation map in the same size as the precipitation/temp data. Since the elevation data has values for seafloor, you can see where the map ends, so to speak, and crop out the frames. Though I'd guess there should be some method in Qgis as well, but since I'm a total Qgis noob, I have no idea what that might be.

----------


## Azélor

It helps a little but you still have the same problem of having to zoom/change the scale carefully. 
It's odd that the program use a print screen instated of the actual map. 

On another note, I intend to rework the Excel spreadsheet to automate it. 
I manage to make an RGB to hexadecimal converter. I'm not sure I had to make a converter but with it, I can generate a colour table for the climates.

----------


## Azélor

It works! 

I did not know that but hexadecimal colours have one different value for each of the red, green and blue. So it's super easy to make additions. 
I did this using the same colours from the tutorial. 
The green chosen in this example is the green from the top left, the wettest. The colours seems to match with the colour key. 
I'm using a macro that I've found to fill the cells with the hexadecimal code from the cells. 
Using hexadecimal seems more complicated that using RGB, but with RGB I would need three different tables, one for each colour. With hexa, I just need one. 



Ok that could be useful but it's just a distraction.

----------


## Azélor

Ok so the spreadsheet looks like this.

I start with the temperature combinations. 



I have the min/max for each but only the average is relevant actually. 
The on the right, I find the average annual temperature. It is an average of the maximum and minimum temperature. 
My numbers are slightly different because I did not round them up this time. I guess I must have done that to save time. This time I can be more precise since Excel is going to calculate it.
Aridity index.




The grey area represent redundant combinations. They are also present on the two other graph but I haven't greyed them.
I usually left them there, maybe to avoid having holes in the graph. 

It is explained if the cells but these graph tells us what is the minimum threshold required to have a wet climate, steppe or desert. 
Depending when the dry season is, or if there is even one to begin with, the formula for aridity is slightly different. https://en.wikipedia.org/wiki/Aridity_index

----------


## AzureWings

Regarding the hexadecimal RGB color representations: Having already gotten the conversion working you probably already got all this, but in case it helps, the typical hexadecimal representation for colors (at least when using RGB which is commonplace) is really closely related to the delimited R,G,B - as you said, the hex number is split into three parts for the red green and blue, each two digits in base-16 meaning each part is 0-255 like often seen in common delimited/separated R,G,B representations.

So for example, that very reddish magenta shade in the corner of your table above (#FA0046) is Red FA, Green 00, Blue 46 which written in base-10 is Red 250, Green 0, Blue 70. 
So the hex representations in general are just #RRGGBB, where the R's are digits of the red value (FA in the above example), G's digits of the green (00 in the above example), B's the blue (46 in the above example). Updating or changing one of red, green, or blue for a given color just means changing the value in the two digits that represent that one of red, green or blue.

Sorry if you're already familiar with all of that and I'm being terribly presumptuous - but I'm used enough to the representation of RGB in hex that I don't usually think of it offhand as a separate representation, so thought I'd offer an explanation of my own on the off chance it can help your understanding. I guess what I'm getting at is it hopefully shouldn't be too much more complicated to use than separated RGB, because the conversion itself is pretty much a direct mapping - the only parts that get more complicated are trying to operate on the values, in which case you just need to make sure to treat the separate parts of the whole hexadecimal value appropriately as separate sections for red, green and blue.

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## Azélor

Prior to doing this, I never actually noticed that hexadecimal colours are exactly as you described. 
I was thinking that different colours had totally different code but it is actually pretty simple. 
Addition is easy since I'm using blue and red for winter and summer temperature respectively.
Green is for precipitation and I can manage to have only 1 variable by grouping them. Instead of having 1 for each season.

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## Charerg

> Ok so the spreadsheet looks like this.
> 
> I start with the temperature combinations. 
> 
> * *
> 
> 
> 
> 
> ...


Very nice work! I should note that personally I used slightly different averages:

* *




*Temp Class*
*Avg. Temp (°C)*

S. Hot
38

V. Hot
32

Hot
26

Warm
20

Mild
14

Cool
6

Cold
-4

V. Cold
-16

S. Cold
-30

D. Cold
-44





I made most of the averages to have linear 6 °C intervals since that doesn't change them too much from the actual values, and simplifies the calculations because you can merge a lot of the categories. For example, _S. Hot_ (38 avg) + _Warm_  (20 avg) would have a mean annual temperature of 29 °C (taking the average between the two months). But _V. Hot (32)_ + _Hot (26)_ would also have the same annual mean (29 °C) if the jumps between the categories are linear, so you can merge these combinations (since they have the same climate class (A) and the same mean annual temperature, and therefore the same aridity threshold).

Btw, how are you planning to differentiate between which threshold to use (S, W, F)? I used the 2/3 scheme so almost everything is considered either S or W for the purposes of determining the aridity, and only the prec combos along the central diagonal (for the most part) use F. 

However, this can result in some weird jumps around the equator potentially, when the threshold suddenly drops/rises a lot as you cross the equator because the difference between S and W thresholds is pretty huge. Especially apparent if the ITCZ has a strong bias towards either hemisphere (like on Earth, so you see some As turning up in Colombia and the Kongo basin).

Edit:
I guess I should also add that in the scripted version for GIMP I merged the temperature categories even further for the purposes of determining the aridity. I used the following overall scheme (Tann=annual mean temp):

_Hot_ temp combos (hotter A climates): Tann 38-29
- I used Tan 33 as an average for this group (so thresholds F: 800, W: 940, S: 660)

_Warm_ temp combos (milder A climates and most Ca): Tann 28-19
- Tann 23 for this group (thresholds F: 600, W: 740, S: 460)

_Temperate_ temp combos (cool Ca, Cb, warm Da): Tann 18-11
- Tann 13 (thresholds F: 400, W: 540, S: 260)

_Cold_ temp combos (Cc, Db, cooler Da): Tann 10-0
- Tann 5 (thresholds F: 240, W: 380, S: 100)

_Very Cold_ temp combos (Dc, Dd, very cold Da): Tann 0 to -15 (coldest non-E temp combo)
- Tann -6 (thresholds F: 20, W: 160, S: -)

It's a simplification, but since the precipitation categories are so broad, I don't think you lose much overall by doing this. 

To give an example, a change of 1 °C in mean annual temperature equals a 20 mm difference in precipitation threshold. As you probably noted, the avg. Tann values I used are roughly 10 °C apart (=200 mm). Let's say we have 70-140 mm in winter and 20-40 mm in summer. Using the min. and max. precipitations from that combination, the annual precipitation (Pann) would be 540-1080 mm. The next combo in that column (40-70 mm in winter, 20-40 mm summer) would have Pann 360-660 mm. For the purposes of determining the aridity we'd use the averages of course (810 mm and 510 mm), but you can see why I don't think it's necessary to use a separate aridity threshold for every temperature combination: the precipitation varies so much within a single prec combo that it doesn't result in increased accuracy. And also the gaps in avg. precipitation between the prec classes are so broad as well, that only differences on the scale of ~200 mm in the aridity threshold can actually be represented in any case.

----------


## Azélor

> I made most of the averages to have linear 6 °C intervals since that  doesn't change them too much from the actual values, and simplifies the  calculations because you can merge a lot of the categories. For example,  _S. Hot_ (38 avg) + _Warm_  (20 avg) would have a mean annual temperature of 29 °C (taking the average between the two months). But _V. Hot (32)_ + _Hot (26)_  would also have the same annual mean (29 °C) if the jumps between the  categories are linear, so you can merge these combinations (since they  have the same climate class (A) and the same mean annual temperature,  and therefore the same aridity threshold).



That is not something I have considered before. I did a quick test and since everything is linked, it's updated automatically. 
There are 40 combinations instead of 55? If I counted correctly.




> Btw, how are you planning to differentiate between which threshold to  use (S, W, F)? I used the 2/3 scheme so almost everything is considered  either S or W for the purposes of determining the aridity, and only the  prec combos along the central diagonal (for the most part) use F.


Coming soon.

Edit:

I use different and less categories but I believe the way we estimate yearly precipitations is the same. So I get the value for each combination. 
I gave them some names but they are not necessarily representative. 
6*(Summer precipitation + winter precipitations)

To determine if S, F or W
The parameter are different with C/D versus A

C/D : a certain % of  the yearly precipitations has to fall during the dry season, I used the value of summer everywhere to make things less confusing.
Also, a certain quantity of precipitation in millimetres has to fall during summer. 
The result I get is slightly different than the previous one, or is it?
I tweaked the previous model to make it fit with reality. Since my numbers are supposed to be identical, I will probably come to the same conclusion again. The values are usually pretty close anyway. 
Because we are using categories, the maximum and minimum of each is overlapping the others. Having more precipitations categories doesn't solve this problem but it makes it less bad. 
But before I start using more categories for precipitation, we need a better method for placing them on the map. 



A climates are different. this is where I might have made a big mistake in the older version apparently.
According to the spreadsheet:

Af is easy, must be above 60 all the time.
As and Aw need a very dry season, less than 4% of precipitations falling during the driest month and no more than 60 ml.
In theory Am includes everything left. It has a dry season but it is not very strong. it has 7 combo but 3 of them are on the central axis which should not even have a dry season in theory but are too dry to be Af.
Well they are often too dry to be Am anyway and fall into the steppes or deserts climates. 

Again there is a comparison on the left between the old and new model. 
It's very different but take in consideration that the old model made no effort to differentiate As from Aw. 
As is very rare and the difference between the two is marginal. 



Edit 2:

On the temperature sheet I added a table to indicate if an arid climate is hot or cold. The yearly average temperature must be above 18 in order to be hot. 
I used a condition to do it. 



and here is the modified version of the temperatures using your averages.
Same combinations share a colour, those that are unique don't have one. I added them manually. 
i see little reason no to use your numbers since it make the thing easier and it doesn't seems to have a negative impact of the precision of the simulation. Anyway, the differences would be marginal I suppose. 



Lastly, I haven't set any maximum/minimum temperatures for the extremes. I thin it's 43 and -68.

----------


## Azélor

Next will be creating the master table for the aridity index (and maybe other things will be included in the table as well)
the idea is to have a table that will automatically tells us if a climate is humid, a steppe or a desert. 
But will also tell us if it is S, F or W according to the position in the table. 

Basically, it's a huge table containing 36 aridity index tables (6x6). one for each of the rain combinations.
I will use different aridity tables depending if the rain combo is S,F or W. So the numbers won't be identical, there will be 3 different sets.
Then I compare the aridity threshold with the actual precipitations. for example, it mean the threshold first block in the top left (the wettest) will be compared to the precipitation of that block (2400).
 If precipitations are higher than the threshold, it will return a certain output, not sure what yet. Another output if it is less than the threshold but more than threshold/2. And a last output if it's something else. 

Maybe it will be on different table or just in one. I think it could work all in one but requires more planning maybe.

----------


## Charerg

I think you already know this, but just to make sure, be careful not to confuse the f, w and s climate categories with the precipitation thresholds. For the climate categories, the definition would be as follows:

C/D climates:
At least 3/4 precipitation occurs in winter-> s (dry summer)At least 10/11 precipitation occurs in summer-> w (dry winter)Otherwise f (no dry season)

Of course, those aren't the official definitions, but they apply in our case since we only use data from two months. They come from the requirements that the rainiest winter month must have at least 3x the precipitation as the driest summer month for an s climate, and for w climates you need 10x the rain in the rainiest summer month than the driest winter month.

Whereas to determine which threshold to use for the aridity calculations (I'll use capital letters F, W and S to refer to the aridity categories to avoid confusion), the following seems to be the usual formula (though I understand some authors have used a different one):

At least 2/3 precipitation occurs in winter -> S (use summer dry threshold)At leat 2/3 precipitation occurs in summer-> W (use winter dry threshold)Otherwise use F (evenly spread precipitation threshold)

Btw, you definitely have the _Am_ categories a bit off. It's a transitional category between _Af_ and the more dry climates, and ideally _Am_ should surround _Af_ . But even _Am_ requires *a lot* of rainfall. As a reminder, the formula for _Am_ is as follows:

Pann= mean annual precipitation
Pmin= precipitation of the driest month

Pann >= 25 (100-Pmin)

Meaning that if Pmin (precipitation of driest month) is 0 mm, you need at least 2500 mm annual prec for a climate to qualify as _Am_. And if Pmin is 60 mm (which is the maximum, since _Am_ needs to have at least one month with less than 60 mm), then only 1000 mm would be required. Which means that the *absolute minimum* annual precipitation for an _Am_ climate is 1000 mm.

The Bh/Bk boundary in your table is the exact one I used in the GIMP script as well. Although originally I classified anything with a _Cool (0-10 °C)_ season or colder as Bk, I switched to the mean temperature-based classification in the scripted version.

As a sidenote, here are the equivalent tables I've used in the most recent version of the GIMP script:

*A precipitation class table:*

*
C/D precipitation class table:*


Anything coloured in white is always considered arid (either BS or BW).

----------


## Azélor

Ok, I think we are talking about the same thing but I tend to use minus and capital letters interchangeably. I don't find it confusing. 

I know Am is a transition. but
The blue in the graphic are excluded because the driest months are over 60ml.
The orange and purple are too dry compared to the rest of the year. under 60ml. 
Only the area in the middle remains. 

Also, as I said in the previous message, some of these combination are considered A... but are actually too dry and fall into B climates.

----------


## Charerg

Yes, probably all categories marked as _Am_ in the new table would be considered arid. Btw, I forgot to mention that you need to be a bit careful when merging temperature combos with the same mean annual temp, because you can't merge them in cases when they occur in two different climate categories. As an example, _Hot+Hot_ has the same average (26 °C) as S. Hot+Mild, but the former would be classified as _A_ and the latter as _Ca_, because the temperature falls below 18 °C. So they would have to be still coloured separately.

Although in the scripted version I merged many of the categories with a similar _Tann_ value (not just those that have the same exact value) so I ended up with ~15 temp combos in total. Like I mentioned, I don't think it had a huge effect on the accuracy of the overall results because the precipitation categories are too broad for minor differences in annual mean temperatures to have a meaningful effect.

----------


## Azélor

Just as I thought. 



So there are 7 combinations in the top left blue area.

Do you know many functions in Excel? 
I had a crazy idea.
I think it would be possible to enter certain parameters for the corresponding climates and Excel would tell you what climate it is. 
Not only that, but he would also return an hexadecimal code to create a color key.
But there could be more. Imagine if we could export a map to Excel in a spreadsheet. 
Everything would be done by Excel and there would be no need for scripts that are complicated to make and hard to monitor if there are errors in it.

----------


## Charerg

> Just as I thought. 
> 
> 
> 
> So there are 7 combinations in the top left blue area.
> 
> Do you know many functions in Excel? 
> I had a crazy idea.
> I think it would be possible to enter certain parameters for the corresponding climates and Excel would tell you what climate it is. 
> ...


That definitely sounds like an idea worth exploring. Unfortunately I can't claim to be much of an Excel guru, but at least I own the program and have used it to some extent (that's something, right  :Very Happy: ). And it never hurts to learn some new stuff like scripting in GIMP and what-not. Anyway, here's the spreadsheet about which temperature combos I merged in the GIMP script. Apparently I narrowed them down to 14 overall (though many of these like Da (Very Cold), Dc, Dd, ET and EF were already merged in the original).



But yeah, you're right that there definitely should be some method to generate the climates other than scripting a long series of colour-picking and replacing operations. I mean, the professionals who create Köppen maps of Earth definitely don't use that method (well, actually they use GQIS for the most part, I believe).

Edit:
Btw, I think you're using a too low average value for the highest precipitation category (200 mm+). Because you use only 200 mm as the average, all _Am_ climates are eliminated. And the precipitation in the highest category could definitely go much higher than 200 mm.

If we consider the precipitation of the driest month in a precipitation combo bordering Af to be 25 mm, then the "Am threshold" would be 1875 mm. So, you'd need to use at least 285 mm as the average ((285+25)*6=1860 mm for the annual precipitation), in order for Am climates to appear.

----------


## Azélor

I'm trying to figure how to simplify the temperature groups down to 36 in Excel. It can be done I assume, but it's far easier to do in PS/Gimp. I can't figure out how. 
People would have to change the colours. it should not be too complicated. 
One thing I'm wondering then that would make the process easier would be to directly use the RGB colours instead of using this : https://www.cartographersguild.com/a...1&d=1453783031
Should we dump them? They look pretty but don't have any particular usefulness. 

I could increase the average precipitation for the highest category since 200ml is just the minimum. it would not be too complicated.
Assuming I just continue the progression, the last category would be between 200 and 400, with an average of 300 ml. Do you think that would be good?

here is the modified version for the A climates



i filled the gaps and Aw received more since it is more common. It's likely to be a arid though.
For Am, there are 2 combo that are the most likely. the light one is the rainiest. The dark one is just above 2000 ml per year. Do you think I should include 1 or both?

----------


## Charerg

> I'm trying to figure how to simplify the temperature groups down to 36 in Excel. It can be done I assume, but it's far easier to do in PS/Gimp. I can't figure out how. 
> People would have to change the colours. it should not be too complicated. 
> One thing I'm wondering then that would make the process easier would be to directly use the RGB colours instead of using this : https://www.cartographersguild.com/a...1&d=1453783031
> Should we dump them? They look pretty but don't have any particular usefulness. 
> 
> I could increase the average precipitation for the highest category since 200ml is just the minimum. it would not be too complicated.
> Assuming I just continue the progression, the last category would be between 200 and 400, with an average of 300 ml. Do you think that would be good?
> 
> here is the modified version for the A climates
> ...


Yeah I think that works better. Probably including both combos as _Am_ will work best, I think, though that's something that will just have to be tested out (maybe the light one should still be _Af_, not sure). Though one thing that I realised from those more detailed precipitation maps you posted is that actually _Am_ doesn't always have to be a transitional climate. Sometimes it can be just a rainier version of _Aw_. For example, the _Am_ areas in India (the Western Ghats) have as long a dry season as the rest of the subcontinent, they just receive over 2500 mm rain during the monsoon season, so they're still classified as _Am_ even if the driest month has close to 0 mm. But with our precipitation categories we're going to totally miss these cases (mainly occurring in SE Asia), unless we introduce an "above 500 mm" category for extreme monsoon rains or something.

The classification of the A climates is a bit weird in the sense that both Aw and As use the criteria that they are classified based on which season the "below 60 mm" month belongs to. But what if it's below 60 mm in *both* January and July? If it's 50+50 for example, the annual precipitation would be 600 mm. If we consider that an A climate has at least 18 °C annual mean (because the coolest month must be at least 18 °C), the min. F threshold would be 500 mm (=20*Tann+140). So that would technically not be dry enough to be considered BS, but neither does it receive more than 60 mm in either season. There just doesn't seem to be a climate category for this climate. Personally I did the same thing as you and just classified it as _Aw_ because it's more common. But I'm not sure if this is an ideal solution.


*Edit:*
One solution that comes to mind is to modify the criteria for _B_ in the case of _A_ climates. I'm not even sure if the usual F, W, S thresholds make much sense for tropical climates to begin with, because the temperature just doesn't vary that much between the seasons in tropical areas. In some cases winter can even be the hotter season in the tropics because it's the dry season (so no clouds to reflect sunlight back to space).

Maybe use the following:

*If coldest month > 18 °C*:
At least 2/3 of rain occurs in winter: Pth=20*Tann+140 (this is normally the F threshold)otherwise: Pth=20*Tann+280 (the normal W threshold)

That way the precipitation thresholds for Tann 18 °C would be 500 mm (if at least 2x rain in winter compared to summer) and 640 mm (all other cases). This should ensure that all areas that don't receive more than 60 mm in at least one season are always classified as arid.

*Edit2:*
Although I don't think this comes into effect using your categories, since all combos that don't have 50-100mm or above in at least one season seem to fall below the threshold in any case. Here's the "A sheet" with those categories that will always be considered arid (since the minimum Tann is 18 °C with A climates) marked out:

----------


## Azélor

I was wondering what your 14 climates are since I get 10 only. 
For example, what is located at Hot/cool?

Yea about the script. Excel uses marco.
Once the map is imported (still need to figure out how to export hexadecimal code)
The users will convert the file using a macro.
It is similar to using a script in PS or Gimp. 
It will look into the hexadecimal code and replace the values with new ones, 1 colour representing each climate. Using the Ctrl+F search and replace
Creating a map that looks like this but in hexadecimal code : https://en.wikipedia.org/wiki/K%C3%B...h_authors).svg
Or maybe with a different colour scheme. 
After that, run another script that will use your new values to fill the cells with the appropriate colors. Creating some sort of pixels if the cells are square. 
Then delete the hexadecimal code to have the finished product. 

The only problem with this maybe, is that the scale of this new created map could be different form the original map.

----------


## Azélor

> Yeah I think that works better. Probably including both combos as _Am_ will work best, I think, though that's something that will just have to be tested out (maybe the light one should still be _Af_, not sure). Though one thing that I realised from those more detailed precipitation maps you posted is that actually _Am_ doesn't always have to be a transitional climate. Sometimes it can be just a rainier version of _Aw_. For example, the _Am_ areas in India (the Western Ghats)  have as long a dry season as the rest of the subcontinent, they just  receive over 2500 mm rain during the monsoon season, so they're still  classified as _Am_ even if the driest month has close to 0 mm. But  with our precipitation categories we're going to totally miss these  cases (mainly occurring in SE Asia), unless we introduce an "above 500  mm" category for extreme monsoon rains or something.


By looking and the older version, the dark one was Am and the light one was Af.
I think I will put the 2 of them and we will see how it look. 




> The classification of the A climates is a bit weird in the sense that  both Aw and As use the criteria that they are classified based on which  season the "below 60 mm" month belongs to. But what if it's below 60 mm  in *both* January and July? If it's 50+50 for example, the annual  precipitation would be 600 mm. If we consider that an A climate has at  least 18 °C annual mean (because the coolest month must be at least 18  °C), the min. F threshold would be 500 mm (=20*Tann+140). So that would  technically not be dry enough to be considered BS, but neither does it  receive more than 60 mm in either season. There just doesn't seem to be a  climate category for this climate. Personally I did the same thing as  you and just classified it as _Aw_ because it's more common. But I'm not sure if this is an ideal solution.


I don't know, i'd just leave it that way. Last time you used it to generate a map, was it a problem?




> In some cases winter can even be the hotter season in the tropics  because it's the dry season (so no clouds to reflect sunlight back to  space).


That is very unlikely to happen in the model because we are using categories and averages. Another thing that happens in real life is that the maximum temperature in not always July. For example, it is in May in southern India and I recall an oceanic climate were it was September or October. But I don't think this is relevant. There ain't much we can do about it anyway.




> *If coldest month > 18 °C*:
> 
> At least 2/3 of rain occurs in winter: Pth=20*Tann+140 (this is normally the F threshold)otherwise: Pth=20*Tann+280 (the normal W threshold)


I don't know if it is a good idea. I'm compiling some sort of master aridity table with all the 3600 combinations. Actually, almost 2/3 are redundant but they are included anyway. 
It look like this so far:


The red is for desert, green is humid, the rest is a steppe. If you change the temperature, precipitation, or aridity threshold in the original tables, the results here will update. 
We will be able to see how everything is placed on the table.

----------


## Charerg

> I was wondering what your 14 climates are since I get 10 only. 
> For example, what is located at Hot/cool?


There are 10 climates of course, but I merged the temp combinations down to 14. For example, Hot/Cool is _Ca_, but it's split from the other _Ca_ climates because the annual mean temp is below 18 °C (so it has _Bk_ instead of _Bh_). Likewise I merged the warm _Da_ climates into a separate category (Da - Temperate) and the cooler _Da_ climates into two separate categories (Da - Cold and Da - Very Cold, the latter being already merged in the original tutorial). I painted the areas I merged with the same colour in the prior post. In terms of actual climates it's the exact same as your climate table, no changes there. I've just merged temp comboes with similar values that share the same climate class in order to simplify things.





> I don't know, i'd just leave it that way. Last time you used it to generate a map, was it a problem?


I'm not sure, I'd have to check how commonly it actually occurs.

*Edit:*
Ok, checked it out, the areas painted in purple are the _A_ climates that have the 40-70 mm category in both seasons (I used the 50 mm avg. for this category, so _Pann_ 600 mm):



Since I merged all the cooler A climates into a single temperature group and used 23 °C as an avg. annual temp for all of them, the F threshold is exactly 600 mm, as well. So these areas are sitting directly at "borderlne BS". They occur primarily in E. Brazil, equatorial Africa, and as a small strip in Sri Lanka. In Africa, they are caused by having limited data I think (only two months). The regions in Brazil might work better as BS though. But overall I guess it isn't a huge problem to classify these areas as _Aw_.

----------


## Azélor

I edit the s f w table to include less combo in the f range. I've found several Mediterranean climates areas that were categorized f. Like Bishkek that barely has a dry season but overall low precipitation. The change in the w is to take in consideration the colder climates like in Eastern Asia.

----------


## Charerg

> I edit the s f w table to include less combo in the f range. I've found several Mediterranean climates areas that were categorized f. Like Bishkek that barely has a dry season but overall low precipitation. The change in the w is to take in consideration the colder climates like in Eastern Asia.


So the darkened categories are shuffled over to f, right?

Looks ok to me, since the standard criteria for _s_ would translate to max. 25% rain in summer (whereas _w_ would be min. ~91%), though I understand that some liberties may have to be taken since the precipitation categories are so broad that it's hard to follow the classification exactly.

Edit:
Oh, now I understand it, they are shuffled over from f to s/w. Might make _s_ and _w_ a bit more widespread, but that's probably the case in my version as well, since I chose to err on the side of making them too widespread too, rather than having s/w cover less area than they should.

----------


## Azélor

> Oh, now I understand it, they are shuffled over from f to s/w. Might make _s_ and _w_  a bit more widespread, but that's probably the case in my version as  well, since I chose to err on the side of making them too widespread  too, rather than having s/w cover less area than they should.


That's it. I'm not sure what the impact will be yet.

Also, I've finished the aridity table. it is similar to what I had done previously. 
There is one strange thing occurring in that one S area , despite receiving less precipitations, is more humid than another F area on top of it. This is caused by the difference in threshold. 
I could change it manually but since both follow the model, neither are actually wrong. 

Attachment 103898

There are the different rain combinations for the moment : 21

Attachment 103899
Red lines are used to separate s, f and w
Am climate are split. half of them are in the f.
1 of the remaining is in the s. 
The last is in the w. 
It is a little annoying that the Am climate force me to have these 2 distinct categories. Otherwise they would be with S1 and W1.

F1 and W1 both have a steppe counted as humid because having a constant yearly mean temperature over 35 seems very unlikely to happen.

F1 is fractured. The area Aw/s is there because that climate does not fit in the F1 area, it's too dry. The other A/w are scattered quite a bit. I will not make a difference between As and Aw since on Earth, As is only found in some area like on mountains that generate local rain shadow effect. Anyway, it is extremely unlikely to have a As cover a large area.

----------


## Azélor

Moving forward, I have 34 possible temperatures combos once I exclude the tundra and ice caps. 
 With 22 precipitation possibilities this means 748 possibilities. How many do you have using you method?
If you have ideas to make it simpler, I'll be glad to hear.

The previous version had presumably 16 combinations for the precipitation but I had to do an extra manipulation in the script to sort the A climate.

edit: this mega sheet display all the 3600 possibilities



edit : actually, I can get rid of the redundant data by leaving the redundant temperatures cells empty.



I can get rid of them all by using a simple CTRL+F 
search **00
 replace with nothing


in green the temperature
in blue the precipitation, the value is the same inside each block
in yellow the results of the combination of the two

In order to do the precipitations, I gave a code to each combo.
it looks like that after the simplification: 



Again the beauty of Excel is that it updated the whole table after I realize I had entered the wrong value in one of the green squares.

I quickly achieved this: 



After getting rid of the redundant data and unifying all the tundra and ice caps, I have 748+2 combinations. exactly as expected.

After that, i will probably juxtapose the color for each temp groups (A,Ca,Bc ...) over each section.
Then, around the table, I will copy paste the sections and regroup them by their corresponding climate.

----------


## Charerg

> Moving forward, I have 34 possible temperatures combos once I exclude the tundra and ice caps. 
>  With 22 precipitation possibilities this means 748 possibilities. How many do you have using you method?
> If you have ideas to make it simpler, I'll be glad to hear.


I didn't really simplify the precipitations much beyound the obvious ones (like merging the f, s and w categories that are too rainy to be ever considered arid). Like you, I also had to include two separate categories for Am (Am/f and Am/s in my case). Furthermore, the Aw/f and As/f had to be split from the Aw/w and As/s groups, because while they would have been Aw/As if tropical, they would otherwise be considered f. Overall, I ended up with 34 precipitation categories (down from 8x8=64). With only 12 temperature groups though (+2 for ET and EF), I end up with just 408+2 possibilities, which wasn't too hard to manage since I used only 5 overall temp/prec tables (Hot, Warm, Temperate, Cold and Very Cold) to determine the extent of BW and BS. I listed in a prior post which temperature groups belong to each overall category.

Tbh, I don't think the precipitations can really be simplified a lot without risking some severe inaccuracies, especially with just 6 categories. I think you're much better off merging the temperature groups further if you want to simplify things, since many combinations have mean annual temps only a few degrees apart (and a 1 °C difference in _Tann_ only changes the precipitation threshold by 20 mm, which is pretty minor).

Personally I chose to keep _As_ as a category, because I don't think it adds too much extra complexity, and it could possibly be more widespread in some cases (like during the Jurassic, when the temperatures were hot enough to push today's _Cs_ regions into the _A_ category). Also, some Köppen maps show quite a bit of _As_ in E. Brazil (this one, for example). That Brazilian _As_ region is actually bit of a mystery, I haven't quite been able to figure out what causes it. Though that map probably exaggerates the extent somewhat, since it has some _As_->_Cw_ transitions, which seem rather unlikely to be real.

----------


## Azélor

I searched a bit and find some example like here here : https://en.wikipedia.org/wiki/Natal,...rande_do_Norte
The pattern I see is that the rainy season comes late in the summer and even later in some place than others. 
here, it's right in the middle of winter but is still Am : https://en.wikipedia.org/wiki/Jo%C3%A3o_Pessoa,_Para%C3%ADba
I just noticed that most climate maps don't even show As, it doesn't appear in most legend. 

I might change them later. Just keep in mind that the winter/summer precipitation close to the equator tends to be a bit strange at some places.

edit, I've finished sorting out the things : still 750

----------


## Charerg

> I searched a bit and find some example like here here : https://en.wikipedia.org/wiki/Natal,...rande_do_Norte
> The pattern I see is that the rainy season comes late in the summer and even later in some place than others. 
> here, it's right in the middle of winter but is still Am : https://en.wikipedia.org/wiki/Jo%C3%A3o_Pessoa,_Para%C3%ADba
> I just noticed that most climate maps don't even show As, it doesn't appear in most legend. 
> 
> I might change them later. Just keep in mind that the winter/summer precipitation close to the equator tends to be a bit strange at some places.
> 
> edit, I've finished sorting out the things : still 750


Great work!

I guess I could write the GIMP version of the original script based on this (unless you want to test it first), although I'd first have to check if GIMP's colour finding functions accept hexadecimal as an input.

About the Brazilian _As_, it definitely has a genuine dry season in summer, check out how the rainfall in Natal is divided between the seasons (numbers from that wikipedia link):

Summer half-season (October-March): 390.3 mm (and more than 1/2 of this falls in March)
Winter half-season (April-September): 1075.17 mm

Also, the _As_ area is about 5-10 °S: it's not sitting directly at the equator, so I don't think that can be the cause either. Originally I thought the ITCZ moved south of the area during summer (Oct-Mar), which would have caused a wind reversal (due to the direction reversal of the cross-equatorial trade winds), but I've realised that the ITCZ actually stays north of it for the whole year, so that can't be the case. 

It's an especially weird case since this is the east coast of South America, which you'd expect to be directly hit by the trade winds, but instead it's dry while the areas to the west and south of it are wet during the southern summer. Right now I'm thinking it has to be somehow connected to the movements of the South Atlantic High, as well as the continent shapes.

Though it seems that the _As_ probably occurs mostly along the coast, the inland regions are actually dry enough to be BS for the most part (caatinga). With that in mind, maybe I actually should shift my 50mm+50mm prec combo to BS in the tropical areas (although that has the unfortunate side effect of further messing up equatorial Africa).
*
Edit:*
Ok, I tested it out and GIMP does accept hexadecimals as well as RGB as input in the functions (hooray!). I'll start scripting the temperature merging parts. Do you have a colourised table about the final temperature combos (after everything redundant has been merged)? Like the table you posted in this post showing the colours (and hexadecimal codes) after Jan and Jul temps have been merged.

----------


## Azélor

> although I'd first have to check if GIMP's colour finding functions accept hexadecimal as an input.


It is possible to do something like that? Were would you enter that input? Can you post a picture, I wonder if there is something similar in Photoshop. 

That is not what I had in mind. 
Since I have the hex code, I can generate the colors corresponding to the code in the cells, all in Excel. After that I get rid of the text so there is just the colour left.
You end up with a color key similar to what we have in the old version. Export that as an image and use the key for the scripot to select each color, like before 

What I really want to do (at least I think it's an improvement) is to export a table to Excel containing the data, and have a script or something executed in Excel.

----------


## Charerg

> It is possible to do something like that? Were would you enter that input? Can you post a picture, I wonder if there is something similar in Photoshop. 
> 
> That is not what I had in mind. 
> Since I have the hex code, I can generate the colors corresponding to the code in the cells, all in Excel. After that I get rid of the text so there is just the colour left.
> You end up with a color key similar to what we have in the old version. Export that as an image and use the key for the scripot to select each color, like before 
> 
> What I really want to do (at least I think it's an improvement) is to export a table to Excel containing the data, and have a script or something executed in Excel.


I mean that when writing a script for GIMP, you can either write the following (I'll use an example function that sets the foreground colour):

(gimp-context-set-foreground "#000046")

or

(gimp-context-set-foreground '(0 0 70))

It's the same colour in both cases, but the function accepts both the RGB values (as a list) and the hexadecimal code (as a string) as input.

----------


## Azélor

Ok I think I understand. There are 2 kinds of scripts, in Photoshop at least. 
I don't know what kind of background you have, but my programming skills are pretty limited (some Java and HTML a few years ago) so this is more or less uncharted territory to me. 

The first and simpler is actually the same as a macro in Microsoft Office. The program records the actions and the macro will reproduce them if activated. Old method, long, prone to different kinds of errors...
The other one is like coding. Photoshop uses .jsx file to store scripts, like MS use VBA (visual basics?). It is done using a text editor like Notepad.

So all the script needs to do would be:

Open the document (a copy ideally)
Search for certain values (colors) and replace them with new ones. 
That's about it. 

Is that what you had in mind?


Off topic now but you should have a look at this: http://www.think-maths.co.uk/spreadsheet
Convert an image to an Excel spreadsheet, using RBG values and cells. It looks like an old tv screen from very close. 
Using these values, we could have recreated the image in hex code and processed the climates with the colour key.

----------


## Charerg

> Ok I think I understand. There are 2 kinds of scripts, in Photoshop at least. 
> I don't know what kind of background you have, but my programming skills are pretty limited (some Java and HTML a few years ago) so this is more or less uncharted territory to me. 
> 
> The first and simpler is actually the same as a macro in Microsoft Office. The program records the actions and the macro will reproduce them if activated. Old method, long, prone to different kinds of errors...
> The other one is like coding. Photoshop uses .jsx file to store scripts, like MS use VBA (visual basics?). It is done using a text editor like Notepad.
> 
> So all the script needs to do would be:
> 
> Open the document (a copy ideally)
> ...


Yeah the GIMP script is an actual script written in Scheme (the programming language used with most GIMP scripts). And yes, it basically searches for the appropriate layers by name, duplicates them in a separate layer group and runs through the long list of operations about finding and replacing colours. In the end, it generates the actual climate map, the "master map" that includes all the final colour combinations used to generate the climates, the map of merged precipitations (in green), the map of merged temperatures (with the redundant categories re-coloured), as well as the re-coloured temp and prec maps.

Here's the precipitation section's output as an example, since I already wrote that part based on that colour table you provided in this post. 

Input maps:

* *











Merged and re-coloured:


As far as I know, there's no method to create a macro in GIMP (and the script is probably more practical to write anyway since you can write it a part at a time and test each section separately).

----------


## Azélor

I will send you the spreadsheet, it will save you a lot of time.

----------


## Azélor

Sheets (exact order subject to change)

Rain: 
calculate the quantity of rain for each combinations
% of yearly precipitation falling in summer
% of yearly precipitations falling during the dry season

Temp:
calculate average temperatures for every combinations, even impossible ones
Calculate separately the aridity threshold fro s, f and w

Aridity
This table compares the precipitations and the thresholds and tell if the cell is arid, semi-arid or humid, using conditions. 
 //last time I did all that manually

Hex temp
Finding the cold of each temperature combination. Some tables are redundant
Temperature groups with the red added to indicate hot/cold deserts and steppes. 
Right of that is the same table but with the hex code.
Under that table, is a simplified version. I copy pasted the values. I thought I would need to keep a full table and that having hols would be a problem. Otherwise I would just have done the last one.
Last one is trimmed down with redundant combo.

The 3 tables on the right are the actual color combination as seen in Gimp/Ps and the instructions for the user : select this color and replace with this. I guess it could be automated too. 


hex rain:
hex codes for the precipitations. Lot of messing around.
Tells how to simplify the original table to have less combinations. Also include the conversion from blue/red to green. 
It would be possible to take the original value and convert them directly to green. 

Hex temp code : just contains the hex for the temperature

Hex climate :
Combine the hex of Red__Blue with __Green__ . Results in the yellow section. 
Redundant data is easy to spot since it only has 4 character instead of 6. And always ends with 00.

Climate groups:
Originally the sheet did not look like this. I regrouped the colours together. 
Tundra and Ice caps are special. The two characters in the middle don't matter.

You can see that everywhere I'm keeping the hex value with and without a #. This is because I don't know what to do with them next so i prefer to have both. The macro only recognize hex with # and won't convert the others.


color koppen
I entered 2 colour keys: yours and mine

Climates.zip

----------


## Azélor

> Yeah the GIMP script is an actual script written in Scheme (the programming language used with most GIMP scripts). And yes, it basically searches for the appropriate layers by name, duplicates them in a separate layer group and runs through the long list of operations about finding and replacing colours. In the end, it generates the actual climate map, the "master map" that includes all the final colour combinations used to generate the climates, the map of merged precipitations (in green), the map of merged temperatures (with the redundant categories re-coloured), as well as the re-coloured temp and prec maps.
> 
> Here's the precipitation section's output as an example, since I already wrote that part based on that colour table you provided in this post. 
> 
> Input maps:
> 
> * *
> 
> 
> ...


Can you share the code?

----------


## Charerg

> Can you share the code?


The code can be directly read from that script I posted earlier (just unzip and read the .scm file with Notepad++). It's essentially just a text file that GIMP reads. Although that's written for the 8-step precipitation system. I'll post the 6-step script when I've finished and tested it, too, of course.

But here's the precipitation part of the new code:

* *





; Set the selection settings
		(gimp-context-set-antialias 0)
		(gimp-context-set-feather 0)
		(gimp-context-set-sample-merged 0)
		(gimp-context-set-sample-transparent 0)
		(gimp-context-set-sample-criterion 0)
		(gimp-context-set-sample-threshold-int 0)

		;
		; Precipitation processing begins
		;

		; Create a new channel (screen) to cover the northern half of the globe
		(set! screen-ch (car(gimp-channel-new img width height "genScreen" 0 '(0 0 0))) )
		(gimp-image-insert-channel img screen-ch 0 0)
		(gimp-image-select-rectangle img 2 0 0 width (/ height 2))
		(gimp-context-set-foreground '(255 255 255))
		(gimp-edit-bucket-fill screen-ch 0 0 100 0 0 0 0)

		; Re-color January Precip Zones (n. hemisphere)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jan-prec '(210 200 250))
		(gimp-context-set-foreground '(0 0 250))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jan-prec '(190 170 240))
		(gimp-context-set-foreground '(0 0 225))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jan-prec '(150 130 220))
		(gimp-context-set-foreground '(0 0 200))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jan-prec '(90 80 160))
		(gimp-context-set-foreground '(0 0 175))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jan-prec '(240 235 160))
		(gimp-context-set-foreground '(0 0 150))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jan-prec '(235 0 140))
		(gimp-context-set-foreground '(0 0 125))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)

		; Re-color January Precip Zones (s. hemisphere)
		(gimp-image-select-color img 2 jan-prec '(210 200 250))
		(gimp-context-set-foreground '(250 0 0))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jan-prec '(190 170 240))
		(gimp-context-set-foreground '(225 0 0))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jan-prec '(150 130 220))
		(gimp-context-set-foreground '(200 0 0))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jan-prec '(90 80 160))
		(gimp-context-set-foreground '(175 0 0))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jan-prec '(240 235 160))
		(gimp-context-set-foreground '(150 0 0))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jan-prec '(235 0 140))
		(gimp-context-set-foreground '(125 0 0))
		(gimp-edit-bucket-fill jan-prec 0 0 100 0 0 0 0)

		; Re-color July Precip Zones (n. hemisphere)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jul-prec '(210 200 250))
		(gimp-context-set-foreground '(250 0 0))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jul-prec '(190 170 240))
		(gimp-context-set-foreground '(225 0 0))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jul-prec '(150 130 220))
		(gimp-context-set-foreground '(200 0 0))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jul-prec '(90 80 160))
		(gimp-context-set-foreground '(175 0 0))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jul-prec '(240 235 160))
		(gimp-context-set-foreground '(150 0 0))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-item img 2 screen-ch)
		(gimp-image-select-color img 3 jul-prec '(235 0 140))
		(gimp-context-set-foreground '(125 0 0))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)

		; Re-color July Precip Zones (s. hemisphere)
		(gimp-image-select-color img 2 jul-prec '(210 200 250))
		(gimp-context-set-foreground '(0 0 250))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jul-prec '(190 170 240))
		(gimp-context-set-foreground '(0 0 225))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jul-prec '(150 130 220))
		(gimp-context-set-foreground '(0 0 200))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jul-prec '(90 80 160))
		(gimp-context-set-foreground '(0 0 175))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jul-prec '(240 235 160))
		(gimp-context-set-foreground '(0 0 150))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 jul-prec '(235 0 140))
		(gimp-context-set-foreground '(0 0 125))
		(gimp-edit-bucket-fill jul-prec 0 0 100 0 0 0 0)

		(gimp-selection-none img)
		(gimp-image-remove-channel img screen-ch)

		; Merge the precipitation layers
		(set! jan-prec (car(gimp-layer-copy jan-prec 0)) )
		(set! jul-prec (car(gimp-layer-copy jul-prec 0)) )
		(gimp-image-insert-layer img jul-prec clim-group  0)
		(gimp-image-insert-layer img jan-prec clim-group  0)

		(gimp-layer-set-mode jan-prec 7)
		(gimp-layer-set-mode jul-prec 0)
		(gimp-layer-set-visible jan-prec 1)
		(gimp-layer-set-visible jul-prec 1)
		(set! prec-merg (car(gimp-image-merge-down img jan-prec 1)) )
		(gimp-item-set-name prec-merg "genPrecM")

		; Re-color the merged precipitation layer
		; Select and paint Af/f prec pattern
		(gimp-image-select-color img 2 prec-merg '(250 0 250))
		(gimp-image-select-color img 0 prec-merg '(250 0 225))
		(gimp-image-select-color img 0 prec-merg '(225 0 250))
		(gimp-image-select-color img 0 prec-merg '(225 0 225))
		(gimp-image-select-color img 0 prec-merg '(225 0 200))
		(gimp-image-select-color img 0 prec-merg '(200 0 225))
		(gimp-image-select-color img 0 prec-merg '(200 0 200))
		(gimp-context-set-foreground "#00FF00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint Am/f prec pattern
		(gimp-image-select-color img 2 prec-merg '(250 0 200))
		(gimp-image-select-color img 0 prec-merg '(200 0 250))
		(gimp-context-set-foreground "#00F500")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint Am/w prec pattern
		(gimp-image-select-color img 2 prec-merg '(250 0 175))
		(gimp-context-set-foreground "#00E100")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint Am/s prec pattern
		(gimp-image-select-color img 2 prec-merg '(175 0 250))
		(gimp-context-set-foreground "#00EB00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint As/s prec pattern
		(gimp-image-select-color img 2 prec-merg '(175 0 225))
		(gimp-image-select-color img 0 prec-merg '(150 0 250))
		(gimp-image-select-color img 0 prec-merg '(150 0 225))
		(gimp-image-select-color img 0 prec-merg '(125 0 250))
		(gimp-image-select-color img 0 prec-merg '(125 0 225))
		(gimp-context-set-foreground "#00D700")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint Aw/f prec pattern
		(gimp-image-select-color img 2 prec-merg '(225 0 175))
		(gimp-context-set-foreground "#00AB00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint Aw/w prec pattern
		(gimp-image-select-color img 2 prec-merg '(250 0 150))
		(gimp-image-select-color img 0 prec-merg '(250 0 125))
		(gimp-image-select-color img 0 prec-merg '(225 0 150))
		(gimp-context-set-foreground "#008700")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint BS/Aw/w prec pattern
		(gimp-image-select-color img 2 prec-merg '(225 0 125))
		(gimp-context-set-foreground "#005500")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Re-color the remaining prec patterns
		; 1st row
		(gimp-image-select-color img 2 prec-merg '(200 0 175))
		(gimp-context-set-foreground "#00A500")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 prec-merg '(200 0 150))
		(gimp-context-set-foreground "#007D00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 prec-merg '(200 0 125))
		(gimp-context-set-foreground "#004B00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; 2nd row
		(gimp-image-select-color img 2 prec-merg '(175 0 200))
		(gimp-context-set-foreground "#00CD00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 prec-merg '(175 0 175))
		(gimp-context-set-foreground "#009B00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 prec-merg '(175 0 150))
		(gimp-context-set-foreground "#007300")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 prec-merg '(175 0 125))
		(gimp-context-set-foreground "#004100")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; 3rd row
		(gimp-image-select-color img 2 prec-merg '(150 0 200))
		(gimp-context-set-foreground "#00C300")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 prec-merg '(150 0 175))
		(gimp-context-set-foreground "#009100")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 prec-merg '(150 0 150))
		(gimp-context-set-foreground "#006900")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; 4th row
		(gimp-image-select-color img 2 prec-merg '(125 0 200))
		(gimp-context-set-foreground "#00B900")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)
		(gimp-image-select-color img 2 prec-merg '(125 0 175))
		(gimp-context-set-foreground "#002D00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint BW/BS
		(gimp-image-select-color img 2 prec-merg '(150 0 125))
		(gimp-image-select-color img 0 prec-merg '(125 0 150))
		(gimp-context-set-foreground "#005F00")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		; Select and paint BW
		(gimp-image-select-color img 2 prec-merg '(125 0 125))
		(gimp-context-set-foreground "#003700")
		(gimp-edit-bucket-fill prec-merg 0 0 100 0 0 0 0)

		(gimp-selection-none img)
		;
		; Precipitations processed
		;;;



Keep in mind that this is written for GIMP using the functions and the programming language that GIMP uses. So I'm not sure how difficult or easy it would be to port the code to work with PhotoShop.

----------


## Azélor

That is pretty impressive! You have experience in programming?

I know it's unlikely to work. I just wanted to see it since I have no idea where to begin.

----------


## Charerg

> That is pretty impressive! You have experience in programming?
> 
> I know it's unlikely to work. I just wanted to see it since I have no idea where to begin.


Yeah I did study programming as a minor subject. So I do have a grasp of the basics (not a whole lot of experience though). Although I didn't have any experience with Scheme or scripting in GIMP, but fortunately GIMP is pretty user friendly in the sense that it includes a well-documented library of the available functions and there were some nice tutorials around the web too. It can be a bit difficult to get started but it's pretty easy once you get through the basic stuff.

Basically, just start simple, first figure out how to code in the duplication operations. Learning the basics like how to store some value in a variable (like a reference to a layer) is the biggest hurdle, once you manage that the colour picking and replacing is largely a lot of copy-paste (and testing so you don't accidentally write the wrong colour somewhere). If there are some sample scripts intended for beginners or scripting tutorials available for PhotoShop it shouldn't be too hard (it just takes some time and patience to learn the basics).

My climate script probably isn't the best learning material since it hasn't been written as a tutorial, and in any case it only applies to GIMP functions and their particular syntax.

----------


## Azélor

Photoshop accepts javascript, vbscript and applescript but I think the last one only works on mac.  
There are pdf guide for coding for each of the language. 
Applescript is close to written English.
I'll have a look at them and I'll try to code maybe.
I'll probably just end up using your script. 

It's not so bad if it doesn't work in PS. Anyone can get Gimp and Install it. Unlike PS which cost money. 

________________________________________________

Btw, I've found a ways to simplify the temperature. 

let's begin with those that have no impact because they are always identical. 

The funky colours on the right side. They are always wet. Green, orange and yellow. 
 Super Hot + hot and Super hot + warm always have the same climate, so we can keep only one of them. in yellow
Super hot+cool and very hot+cool are identical. in yellow too
Hot+very cold and super hot + super cold are identical. In red

changes




8 temp combos down
574 possibilities

Updated table



__________________________________________

The following have an impact but is small. It changes a steppe/humid/desert but only 1 at a time. Not 2 were humid become a desert fro example. 
Also the changes only impact 1 of the 36 precipitation blocks. 
They ussually impact more than one cell because the climate shift follow a diagonal. 

Secound table

Super hot + super cold is almost identical with very hot+super cold. dark red 
mild + very cold and warm+super cold are almost identical, therefore we could unite all the dc climates. green 
mild+cold and warm+very cold are almost identical. In purple
Super hot+cool and Super hot+mild are identical exept in one place. Desert vs steppe. magenta
Hot+cold and very hot + cold , almost identical. in orange. 

Very hot+warm and Hot+warm are identical except in one of the 36 rain combinations. one is a desert and the other one a steppe. 
Hot +warm is also almost identical to Warm+warm exept is one area where one is a steppe and the other is humid. 
We could fuse the 3 together using the one in the middle. Or maybe just fuse 2 of the 3 to minimize divergence. They appear red in the table. 

propositions:



if we take all these suggestions, the number of temperatuyre combinations drop at 21. 
With a total possibilities of 464, but the model loses some accuracy.

----------


## Azélor

made another version of the precipitation map for July but using only 10 ml categories all the way to 600 ml



I've managed to find out 

Change the foreground color
Select a particular color in the image
Fill a selection (don't know how to avoid filling an empty selection, but I guess it's not a problem if I import a color key)

The code is pretty long to select a color.

Of course I have the help of a listener or so that's how it's called. It can help sometime but it is clumsy and uses lab colors in his code. Selecting a color take a dozen lines of code at least, for each color.
Hopefully I can find a more efficient way to do it.

----------


## Charerg

> * *
> 
> 
> 
> 
> Photoshop accepts javascript, vbscript and applescript but I think the last one only works on mac.  
> There are pdf guide for coding for each of the language. 
> Applescript is close to written English.
> I'll have a look at them and I'll try to code maybe.
> ...


I think I'll use this system for the script. I don't think 574 is too many since we already have a list about which colour belongs to which climate group, so it's just a matter of a lot copy-paste basically to put that list into the code.

I should perhaps note that merging _Hot+Super Cold_ with the "never arid" categories will create a minor inaccuracy in Mongolia though. The aforementioned Ulaangom and surrounding regions actually have this temperature pattern, but they are classified as _BSk_. In our case those areas will become _Da_.

*Edit:*
Ok, I finished up the temperature merging part. Can you check out if the output is fine? This time I included "test boxes" in the maps that show all the combinations so it's easier to test for errors. I calculated 26+2 temp combinations left after merging, which should be correct. Likewise, I calculated 22 precip combos after re-colouring (so, 26*22 + 2= 574 total possibilities).

Precs after merging and re-colouring:


Temps after merging:


Btw, I've just re-coloured all the ET and EF as black since they've already been painted into the climate map at this stage (so there's no need to keep the temperature info for them).

----------


## Azélor

> I should perhaps note that merging _Hot+Super Cold_ with the "never arid" categories will create a minor inaccuracy in Mongolia though. The aforementioned Ulaangom and surrounding regions actually have this temperature pattern, but they are classified as _BSk_. In our case those areas will become _Da_.


No it does not change that area. The precipitation category affected is the one in the lower right only, the driest of the whole table. The area covers the central part of the Tarim bassin and northern Tibet, in that region. 
Mongolia is wetter because it receive some precipitation during the monsoon. 

I think the maps look ok. I just need to find what temperature you merged with what in terms of color. 


Also, I was wondering, what are your precipitation categories. I can't find the min/max, only the average.

----------


## Charerg

> No it does not change that area. The precipitation category affected is the one in the lower right only, the driest of the whole table. The area covers the central part of the Tarim bassin and northern Tibet, in that region. 
> Mongolia is wetter because it receive some precipitation during the monsoon. 
> 
> I think the maps look ok. I just need to find what temperature you merged with what in terms of color. 
> 
> 
> Also, I was wondering, what are your precipitation categories. I can't find the min/max, only the average.


That is in N. Mongolia, it's basically located in the valleys between the Altai and Sayan mountains (The Tarim is part of China, actually). It's actually the only area on Earth that has that temperature pattern (Hot+S. Cold), I think. Although it would probably be misclassified as _Da_ in any case, because our way of calculating the mean annual temperature (taking the extreme temperatures) makes it seem colder than what it actually is annually.

As to the maps, that precipitation table (and the categories) are exactly the same as in your prior post (since this version is intended to be a direct port). Likewise, I've followed the temperature tables exactly, so if you check the hexadecimal values of the colours from the table I included in the map, they should match with those detailed in the 1st proposition of your post. Unless you mean what categories I used in the 8-step version?

----------


## Azélor

Ok but if this area is in Northern Mongolia, it receive too much rain to be affected, so it should not be a problem. 




> Likewise, I've followed the temperature tables exactly,


Yes, I sorted that out. Just wanted to be sure the Excel table had the right values for the simplified table. 

Yes I wanted to know the 8 categories version. I'd be interested in making a new table by extending the one I already have. Just to see and maybe for future use.  
You did say that it improved the precision of the map?
If we can find a good way to implement the precipitation, having 2 more categories could be worth it. 
Otherwise, it might be better to stick with 6. 

I'm not sure if you know that but the initial method made by Pixie included only 5 instead of 6. I added the 6th to help identify the cold and dry climates. 
The possibilities jumped from 25 to 36 and now maybe 64.

----------


## Charerg

> Ok but if this area is in Northern Mongolia, it receive too much rain to be affected, so it should not be a problem. 
> 
> 
> 
> Yes, I sorted that out. Just wanted to be sure the Excel table had the right values for the simplified table. 
> 
> Yes I wanted to know the 8 categories version. I'd be interested in making a new table by extending the one I already have. Just to see and maybe for future use.  
> You did say that it improved the precision of the map?
> If we can find a good way to implement the precipitation, having 2 more categories could be worth it. 
> ...


The categories I used were:
0-5 mm
5-10 mm
10-20 mm
20-40 mm
40-70 mm
70-140 mm
140-200 mm
200+ mm

Though my averages were slightly odd at times (for example, I used 50 mm avg. for the 40-70 mm category, mostly cuz I like round numbers  :Very Happy: ). But yes, I do believe that having more categories particularly in the 40-140 mm region would probably improve the accuracy especially when it comes to the extent of the B climates.

Though I'm not sure if these should be followed exactly. The 70-140 mm in particular is a bit too broad. Also, it would probably be a very good idea to have either a 30-60 mm or 40-60 mm category, because 60 mm is the "boundary" between Af and the rest of the A climates.

One possibility would be to script the thing so it reads through the map pixel-by-pixel and just calculates the climates that way without doing all the colour picking. That way you could essentially use an arbitrary amount of categories, because the actual math would be done by the script itself. However, it would be more complicated to script and require some testing about the idea first. Also, the processing time could get fairly long if the script went through a whole high res map on a pixel-by-pixel basis.

Though that is just from the "data processing" point of view, I guess you meant the difficulty in creating good instructions about where to actually put the boundaries between different precipitation zones?


*Edit: Finished the script*

So, I coded in the climate definition stage. Here's the output:

Source maps:

* *














Climates:


Since I don't see any holes in the climate map, the script probably works as intended (given that I just copy-pasted all those hexadecimal codes from your Excel file into the various climate zones).

Some first glance remarks:

- Overall not too different from the 8-step version's output (which is expected given that both versions have the same temperature system (just simplified even further in the 8-step script))
- The extent of _w_ climates looks pretty good
- On the other hand, that shuffling of those categories that receive more than 30% rain in summer from _f_ to _s_ did not work out: since 25-50 mm in summer and 50-100 mm in winter counts as "summer dry", a big chunk of southern England and France gets classified as _Cs_
- Not sure about _Am_, seems to have had very limited effect in Africa, whereas in South America _Am_ seems a bit too widespread (or rather, some of it should be _Af_ instead)

I didn't post the script quite yet since this is just the first test run (and also in case you want to make some changes).

----------


## Azélor

I don't know about the usefulness of having 

0-5 and 5-10 m. I think 0-10 does a good job. When I look at the summer map, it's all desert except maybe in Israel and lower California, which are humid/steppe but they receive significantly more precipitations in winter. 

Something like that maybe?


0-1010-2020-4040-6060-100100-150150-200200+

And yes, I was talking about the instructions.
I had an idea. A new map to visualize precipitation distribution. The idea is that the world is divided in 3 zones. Rainy, dry and transition. Each zone (except the transition) has some sort of centre from which precipitations increase/decrease. It work a bit like a gradient. 
of course, it's possible that this model doesn't work everywhere. In the northern latitudes, the polar front distributes precipitations differently.

----------


## Charerg

> I don't know about the usefulness of having 
> 
> 0-5 and 5-10 m. I think 0-10 does a good job. When I look at the summer map, it's all desert except maybe in Israel and lower California, which are humid/steppe but they receive significantly more precipitations in winter. 
> 
> * *
> 
> 
> 
> 
> ...


I think I originally included the 0-5 mm category to help in identifying those ~0 mm rain in winter-cases of w climates, particularly in very high latitudes that have relatively low summer precipitations. But I agree, I don't think that was a necessary inclusion. Your suggested categories look very good. Though maybe the 2nd highest could be a bit rainier, would 160-240 and 240+ work better? That should help in identifying _Am_, I think (though maybe at the cost of making 100-160 mm too broad, not sure).

Also, the precipitation instruction does sound fairly interesting. Though tbh I'm kind of thinking that maybe the instructions for oceanic currents and atmospheric circulation should be more detailed, since people seem to struggle with those parts as well, and if you mess the currents and pressures up, there's no way the precipitations are going to be accurate.

----------


## Azélor

> Since I don't see any holes in the climate map, the script probably  works as intended (given that I just copy-pasted all those hexadecimal  codes from your Excel file into the various climate zones).


Ok but since we lowered the number of temperature categories, almost 200 combinations don't exist anymore. 

Yes, S climates are definitely too widespread. Blame Bishkek. Maybe W a little bit too but not as bad. 
But actually, these area have a summer low precipitations. Some areas are close to the Mediterranean climate even if the difference is smaller. If I remember correctly, in 10-20 years from now, some area in Brittany will have a Csb climate, as well as some areas in Southern England. 
The problem comes from when I start to doubt the numbers and go check city climate charts. Just looking at the lowest value or the Jan/Jul values is not a good indicator. You need to check the 3-4 months average, or just check the other mouth to avoid abberant data that is completely off the chart but just for one month.  
We should go back to the old model then, regarding S and W. 

About Brazil: the climate distribution is different. IRL the coast is drier, the west and south west and wetter and the hole is a bit drier. 
But in general, it's a shift between Am and Af on the edges. You can try a different configuration and compare the results but I don't think it's a big concern, less than the other one

----------


## Azélor

Is there something special with 240 ml?

----------


## Charerg

> Ok but since we lowered the number of temperature categories, almost 200 combinations don't exist anymore.


I did use the search function in Excel to eliminate the redundant stuff from the climate list.




> Yes, S climates are definitely too widespread. Blame Bishkek. Maybe W a little bit too but not as bad. 
> But actually, these area have a summer low precipitations. Some areas are close to the Mediterranean climate even if the difference is smaller. If I remember correctly, in 10-20 years from now, some area in Brittany will have a Csb climate, as well as some areas in Southern England. 
> The problem comes from when I start to doubt the numbers and go check city climate charts. Just looking at the lowest value or the Jan/Jul values is not a good indicator. You need to check the 3-4 months average, or just check the other mouth to avoid abberant data that is completely off the chart but just for one month.  
> We should go back to the old model then, regarding S and W.


I don't think the w climates are a problem, their distribution looked pretty good too me. But yeah, I'll shift those two over 30% rain in summer categories back to _f_. It makes more sense since those regions don't really have a dry season in summer, even though they receive more precipitation in winter.




> About Brazil: the climate distribution is different. IRL the coast is drier, the west and south west and wetter and the hole is a bit drier. 
> But in general, it's a shift between Am and Af on the edges. You can try a different configuration and compare the results but I don't think it's a big concern, less than the other one


Yeah I guess it's ok. I guess I'll check how the results would look with the Am/f categories shifted to Af/f (since I'll just have to shift a single precipitation combo to test that), but it's a pretty minor detail.




> Is there something special with 240 ml?


No, but it would push the average precipitation for the 2nd highest category up to 200 mm. That should improve the resolution in regards to _Am_ distribution, since you'd get more combos that produce an _Am_ climate.

For example, if 2nd highest (150-200 mm) has 175mm avg. and the bordering sub-60mm categories (40-60 mm and 20-40 mm) have an average of 50 and 30 mm, the numbers would look as follows:

Pann:
(175+50)*6=1350mm
(175+30)*6=1230mm

With 200 mm avg:

(200+50)*6=1500mm
(200+30)*6=1380mm

Am thresholds:
25*(100-50)=1250 mm
25*(100-30)=1750 mm

So I guess it doesn't make a difference regarding _Am_ distribution after all (unless using a higher average for 240+ than 200+). Never mind then.

----------


## Azélor

for future purposes:

I cleaned the spreadsheets and simplified them where I could.

To categorize what climate goes where. 
When the hex code for climates is generated, it is reported to another sheet in a single column were it need to be trimmed down to 572 possibilities.
Then create a new sheet for each climate. paste the column.
Filter the column with the climate criteria, like in this example for Am which is simple




and Af which is more complicated with 18 possibilities. Aw has 72. It is simple, just a lot of copy pasting. 




Damn I forgot about aridity! It was a bad idea then. If the model changes, the time it will take to update this is probably more than the time it takes to manually move the climates from one column to the other.


Trying the 8 categories in a new document :
There is now plenty of room for Am climates. 
I don't always use exact values, should I?. 
For example, it should only be possible to have Am climate if the dry month is over 4% of the yearly precipitations but I included places were it was 3,7%.

----------


## Charerg

I think you have the _Am_ climates off again. If we go by the official definition [_Pann_ > 25*(100-_Pmin_)], there is no requirement about not having a dry season (in fact, the _Am_ climates in India and other regions in SE Asia can have close to 0 mm rain in January). It just requires a high amount of annual precipitation (absolute minimum 1000 mm), and at least one month with less than 60 mm. Actually I'd guess that some _Am_ climates receive more precipitation annually than the more dry _Af_ climates.

As in my previous post, in this case the _Am_ thresholds would be:

Driest month 50 mm: 25*(100-50): 1250 mm
Driest month 30 mm: 25*(100-30): 1750 mm
Driest month 15 mm: 25*(100-15): 2125 mm

And in the case that the driest month was 0mm, this would be the threshold:
Driest month 0 mm: 25*(100-0): 2500 mm

So, any A climate that receives more than 2500 mm annually (and has a month below 60 mm) is classified as _Am_. This is the case with the _Am_ areas of India, like I mentioned. So, the following combos would be classified as _Am_:

Drowned (300 avg)+Moist (50 avg): 2100 mm (above 1250 mm threshold)
Drowned (300 avg)+Dry(30 avg): 1980 mm (above 1750 mm threshold)
Soaked (175 avg)+Moist (50 avg): 1350 mm (above 1250 mm threshold)

I'd suggest sticking to the official definition in order to avoid numerous weird Af->Aw and Am->BS transitions.

Also, _s_ climates have the following requirement (if we go by the Kottek et al. definitions of the climates, Köppen himself used 30 mm according to wikipedia):Driest summer month must be below 40 mm
So those _s_ climates that have a moist (40-60 mm) summer or rainier should actually be _f_.

*Edit: the Climate Map*

I finished shuffling those anomalous _s_ combos over to _f_. I also tested moving the Am/f prec combo (200+ and 100-50) over to _Af_, since I think that's closer to _Af_ than _Am_ (though it's easy enough to change it back if you want).



Overall I think it's pretty good now. Brittany still stays as _Cs_, since the source maps (in this post) have the region receiving 100-200 mm in winter and 25-50 mm in summer, but that's probably as accurate as we're going to get using 6 precipitation categories.

----------


## Azélor

Are you referring to this paper?

http://koeppen-geiger.vu-wien.ac.at/pdf/Paper_2006.pdf

Hum, I'm mostly using the Wikiepedia pages. I'm not sure if they are invalid or just using other references.
Well, it comes from a book : _Physical Geography: A Landscape Appreciation
_but I'm not sure it is related to Koppen.

If we assume that the Wikipedia pages are not good, does it mean I have other data that needs to be changed?

----------


## Azélor

> and at least one month with less than 60 mm.


In the table, the moist category is 40-60ml. It need at least a moth is this category. In our model, it conveniently fall either in July or January. Anything with a dry season above moist cannot be classified Am. 

I redid the table;
It should be fine now

----------


## Charerg

> Are you referring to this paper?
> 
> http://koeppen-geiger.vu-wien.ac.at/pdf/Paper_2006.pdf
> 
> Hum, I'm mostly using the Wikiepedia pages. I'm not sure if they are invalid or just using other references.
> Well, it comes from a book : _Physical Geography: A Landscape Appreciation
> _but I'm not sure it is related to Koppen.
> 
> If we assume that the Wikipedia pages are not good, does it mean I have other data that needs to be changed?


Well, most of the wiki pages have references to that paper or other similar ones, so they should be ok for the most part (I think, though I haven't checked). But yes, I've been using that paper as the reference for the climate definitions. Although ofc we use 0 °C Isotherm (instead of -3 °C) as the C/D boundary since we don't have a -3 to 0 °C temp category.

And yeah, the _Am_ distribution looks good now.

Edit:
Btw, is the Af/Am distribution better in the latest test of the script, or should I change it back to what it was?

----------


## Azélor

> Well, most of the wiki pages have references to that paper or other similar ones, so they should be ok for the most part (I think, though I haven't checked). But yes, I've been using that paper as the reference for the climate definitions. Although ofc we use 0 °C Isotherm (instead of -3 °C) as the C/D boundary since we don't have a -3 to 0 °C temp category.
> 
> And yeah, the _Am_ distribution looks good now.


As far as I am aware, the 0 vs -3 is not something climatologist agree on. I picked 0 because (Pixie probably) it makes more sense since it's the freezing point.

Not a big thing so I flipped them over to f. 



Oh Ignore the cell in the top. It is still purple.

----------


## Charerg

> As far as I am aware, the 0 vs -3 is not something climatologist agree on. I picked 0 because (Pixie probably) it makes more sense since it's the freezing point.


I'd say -3 is probably the better one since it pushes a lot of maritime and montane climates from Dc into Cc (Iceland, the Southern Alps, much of the southern Andes). But you still need the 0 °C in any case because it's the boundary between EF and ET, so using -3 °C would require adding a new temperature category (and a very narrow one too, being only from -3 to 0 °C). In that sense, using 0 °C is probably more practical, unless using some other method to create the temperature map (like creating a gradual greyscale map and then converting it into temperature zones instead of painting the zones directly by hand).

Though apparently the current model puts the Icelandic coast and the Southern Alps as Db (they should be either Cc or Dc depending on the isotherm used). I guess one way to fix that would be to split Mild (10-18 °C) into two categories, so the Cc/Dc could be more accurately separated from Cb/Db, but again that would require an extra temperature category (and again, probably quite narrow, maybe 10-14 °C would work).

----------


## Azélor

I we decide we want that (I'm not convinced if it will greatly improve accuracy) we just need to change the boundaries between the 2 categories. 
And redo the map with Qgis with -3. 

Apparently, if we dfo that, a big chunk of Europe goes fro Dfb to Cfb. Poland, Baltic states, Romania, Bulgary, which are usually classified as Dfb.

----------


## Charerg

> I we decide we want that (I'm not convinced if it will greatly improve accuracy) we just need to change the boundaries between the 2 categories. 
> And redo the map with Qgis with -3. 
> 
> Apparently, if we dfo that, a big chunk of Europe goes fro Dfb to Cfb. Poland, Baltic states, Romania, Bulgary, which are usually classified as Dfb.


Like I mentioned, you can't really get rid of the 0 °C boundary (it's necessary for EF).

And yes, to some degree it's a matter of taste whether to use -3 or 0 °C. The worldwide Köppen map posted in wikipedia uses 0 °C, though the -3 °C seems more common in publications overall.




> Oh Ignore the cell in the top. It is still purple.
> 
> Attachment 104056


The "cell in the top" probably should be _f_, I think. It has 50 mm in winter, so would require at least 500 mm in summer in order to be considered _w_. And in any case, 40-60 mm in winter isn't really a dry winter. Though I guess some places might still qualify as _w_ even with that precipitation pattern, if the rain exceeds 500 mm during the summer.

Kind of the same thing with Soaked+Dry, that shouldn't qualify as _w_.

Edit: 
Also, I think you missed my ninja edit, but I basically asked if the climate script is ok now, or if I should revert the Am distribution back to what it was in the 1st test.




> Btw, is the Af/Am distribution better in the latest test of the script, or should I change it back to what it was?

----------


## Azélor

Yes, I think this new one is better. It does include areas in Asia that were left out. 

You are right. Well of course the values are under the threshold of 90-91%. On the other model, we can't really change the categories much but here we can have more precision. 
Islamabad is very close to drowned + moist
And Pyeongchang is pretty much it according to Wikipedia, but because it's in altitude, it's probably uncommon. 

Special mention for Seymchan   
https://en.wikipedia.org/wiki/Seymch...ement)#Climate

----------


## Charerg

I decided to post the script since it's basically finished. In any case it's easy to enough to update this post if there are some minor changes.

First off, a repeat of the instructions (with the precipitation part changed) with a few additions:

Installation:
Place the script in the appropriate folder (usually /User/gimp-2.8/scripts). If uncertain, you can check Edit->Preferences->Folders->Scripts to see where the scripts are stored. Once the script is in the right folder, the script should be availabe (you can use Filters->Script-Fu->Refresh Scripts so you don't have to restart GIMP). Oh, and remember to extract it from the .zip file before use. You should now have the script available under the Image tab:



Restrictions for using the script:
- This has been written for and tested in GIMP 2.8, it might work with other versions as well, but I can't guarantee that
- The image needs to be RGBA (RGB with an Alpha channel)

Layer naming restrictions:
The temperature/precipitation layers need to have exactly the following names (the script searches for them by name and duplicates them in order to work out the climates):

JanTemp
JulTemp

JanPrec
JulPrec

Layer colouring restrictions:
The temperature and precipitation categories need to have exactly the following colours:

Temperature zones:

* *





*Temp Category*
*R*
*G*
*B*

Severely Hot
160
0
65

Very Hot
210
60
80

Hot
245
110
65

Warm
250
175
95

Mild
255
225
140

Cool
230
245
150

Cold
170
220
165

Very Cold
100
195
165

Severely Cold
50
135
190

Deadly Cold
95
80
160



The temperature zones in a slider:




Precipitation zones:

* *





*Prec Category*
*R*
*G*
*B*

200+ mm
210
200
250

100-200 mm
190
170
240

50-100 mm
150
130
220

25-50 mm
90
80
160

10-25 mm
240
235
160

0-10 mm
235
0
140



The precipitation zones in a slider:





And finally, there is a slight bug in the script. Here's how to avoid it:
The script uses bucket fill to fill a selectionIf the selection is empty, it has been set to bucket fill the *x 0 y 0* positionSince the bucket fill has been set to not fill transparent, as long as the pixel in x 0 y 0 is transparent, it does nothing

However, *if* the pixel in the (0,0) position is non-transparent, it (and adjacent areas in the same colour) will be filled with more-or-less random colours. So, *make sure that the pixel in the (0, 0) position is transparent in all the source maps* (see picture)!




Sample Map:
Here is a sample climate map using source maps generated from WorldClim's 1970-2000 dataset:

Source maps:

* *














Generated climates:


As usual, the script can be found in the attachments, and feel free to provide feedback if you have trouble using it.

UPDATE:
This version of the script doesn't strictly follow Köppen when it come to defining the arid climates. Hence, I recommend using the updated version available in this post.

----------


## Charerg

> Yes, I think this new one is better. It does include areas in Asia that were left out. 
> 
> You are right. Well of course the values are under the threshold of 90-91%. On the other model, we can't really change the categories much but here we can have more precision. 
> Islamabad is very close to drowned + moist
> And Pyeongchang is pretty much it according to Wikipedia, but because it's in altitude, it's probably uncommon. 
> 
> Special mention for Seymchan   
> https://en.wikipedia.org/wiki/Seymch...ement)#Climate


The wikipedia page for Seymchan keeps changing climate fairly frequently it seems. Last year when I checked it was listed as _Dsd_, and now it seems to have become _Dwd_ instead  :Very Happy: . Well, actually it would probably be classified as _Dsc_ with the numbers on the wiki page. The dry April (8.8mm) pushes it into "summer dry" just like many places in Alaska which receive more rain in summer than in winter but are still classified as _Ds_ (since the criteria for assiging "summer dry" in Köppen don't care about max. summer precipitation, only the driest month matters).

----------


## Azélor

Climate is changing very fast in Siberia.

----------


## Charerg

> Climate is changing very fast in Siberia.


Btw, Islamabad is _Cwa_ because it has a dry November (17.8 mm). So it would be Drowned+Very Dry.

----------


## Azélor

Assuming wee don't change anything about the precipitations, this model would have 34 combinations instead of 22 fro the previous one. 
Considering we have 64 possibilities instead of 36, it mean we get to keep 53% of them, compared to 61% previously. 

Some one them have the exact same desert/steppe/humid distribution but are in different zones, so I can't combine them. 



Also, I improved this table to automatically classify cells if the are hot/cold desert or hot/cold steppe. I tried to extend it to other climates but the formula just got very complicated at a point were it's hard to manage in Excel.

----------


## Azélor

Temperature combinations left after merging similar ones: still 28 but the distribution is different. ( tundra and ice caps included)
I less D and one more C. 



26*34+2=886

----------


## Charerg

> Temperature combinations left after merging similar ones: still 28 but the distribution is different. ( tundra and ice caps included)
> I less D and one more C. 
> 
> Attachment 104078
> 
> 26*34+2=886


That's not too bad.

One thing I experimented with was splitting the Mild (18-10 °C) category in two in order to fix the Cc distribution. I found that making the "sub-Mild" category 10-14 °C still made Cc too extensive, but 10-13 °C seems to fairly closely match the distribution of Cc climates. Including that could be a viable alternative to making a separate map to sort out the b/c boundary. That way you'd only need the temp maps to get the Cc distribution right. It would however ramp up the temp possibilites from 100 to 121.

Here's the July map with the 10-13 °C sub-Mild category (in light blue). It also has -3 to 0 °C "Chill" in grey but that's not a necessary addition. If you compare the extent of the sub-Mild category to Cc/Dc distribution in British Columbia and the British Isles, I think it's a fairly close approximation. Seems to work pretty well for Norway, too. Though some areas become Dc instead if using the 0 °C isotherm (those maps use -3 °C).

Here's the map:


Also, I think maybe the Db/Dc distribution could potentially be improved by changing the Cold/V. Cold boundary from -10 °C. At least in Europe Dc is somewhat less extensive than what it should be in the present model. I haven't experimented with that yet, though.

*Edit:*

I experimented a bit with -8 °C and -6 °C Cold/VCold boundary. Tbh the results are much less promising here than with the Cc distribution. The Dc distribution in Scandinavia would be more-or-less correct with -6 °C, but then you end up with northwest Russia and Eastern Canada having too extensive Dc areas. I ended up with -8 °C as a compromise. The results might be slightly better than the original -10 °C boundary, but I'm not sure. With -8 °C the Dc areas in mountainous regions become even more extensive than previously, although in reality the Hindu Kush and other similar cases are mostly Db.

Climate zones with sub-Mild (13 to 10 °C) and -8 °C Cold/VCold boundary:


In the end it might still be easier to create a separate map about the length of the growing season, since it doesn't look like the Db/Dc boundary can really be sorted with info from just two months.

----------


## Azélor

Your proposition is that we should add a new category 10-13?
It does increase the average temperature of the Mild climates.
It could look like that?



Gave it the color yellow and named the other category below as Chilly. That looks good?
Are there any other changes I should make for the temperature?
You think we are good with 6 precipitation categories? 
It would be easier to add new temperatures or changing them than adding new precipitation categories. So if the precipitations look right with 6 categories, we could add one temperature category. 

I think the most problematic Db/Dc area is in Siberia and your changes did not improve anything in that region. 
Also, I don't know what map you use as reference, hopefully the one Kottek because the one at Wikipedia seems pretty flawed. For instance, it shows a Dfb climate around the Bay James between Ontario and Quebec but that is clearly a Dfc area.

----------


## Charerg

> Your proposition is that we should add a new category 10-13?
> It does increase the average temperature of the Mild climates.
> It could look like that?
> 
> 
> 
> Gave it the color yellow and named the other category below as Chilly. That looks good?
> Are there any other changes I should make for the temperature?
> You think we are good with 6 precipitation categories? 
> ...


For regional maps, I tend to use Adam Peterson's Köppen maps (they use Kottek's criteria to classify the climates though).

But yes, the Cc distribution is probably the biggest issue right now. Even with the 6-step precipitation system the climate distribution is fairly good, though using 8 would probably improve the results I don't know if it's a necessity as such. Although there are some cases like the Af-Am transitions where the 8-step system tends to work better.

However, I'm not sure if the Cc distribution is best handled with the extra category, because that still leaves Db/Dc boundaries off, so maybe it's still better to just create that extra map about summer length and use that to fix the Cb/Cc and Db/Dc boundaries.

Also, the difficulty with adding more temperature categories or modifying the boundaries between them is that then we'd have to alter the instructions for placing the temperature zones.


*Edit:*
Although I guess one possibility would be to split the tutorial into two: the "basic tutorial" using the present temperature and precipitation systems, and an "advanced tutorial" that uses a larger number of categories to provide more accurate results. But in the advanced version it would be more difficult to create the source maps, since you need to be a bit more precise with a greater number of relatively narrow categories. Though maybe that complicates things unnecessarily. I don't know if the "average user" would have any interest in an extra complicated version.

*Edit2:*
I guess we could try both 11 temp categories and 8 prec categories? I think it could still be handled if the total possibilities would end up in the 800-1200 range.

----------


## Azélor

How will you generate the summer length map?
It will solve most of our problems?

Adding the 10-13 category doesn't look too complicated. One would only have to reduce the old 10-18 by 30-40%.




> Although I guess one possibility would be to split the tutorial into  two: the "basic tutorial" using the present temperature and  precipitation systems, and an "advanced tutorial" that uses a larger  number of categories to provide more accurate results. But in the  advanced version it would be more difficult to create the source maps,  since you need to be a bit more precise with a greater number of  relatively narrow categories. Though maybe that complicates things  unnecessarily. I don't know if the "average user" would have any  interest in an extra complicated version.


I just keep thinking about Mars and if the results of any of the 2 methods would make any sense. Precision would not matter at all since we would have no way to verify it.
Also, the geography of the planet is so different that all of our references are lost.


Assuming this is the version generated with the latest script: https://www.cartographersguild.com/a...9&d=1517397591


Areas at the tropic are too wet: Mexico (Northern half), Gujarat, Australia, South Africa
The Rockies in North America are also slightly too rainy. 
These are things that could be improved but at the same time, changes might affect other ares and make them inaccurate instead.

----------


## Charerg

> How will you generate the summer length map?
> It will solve most of our problems?
> 
> Adding the 10-13 category doesn't look too complicated. One would only have to reduce the old 10-18 by 30-40%.
> 
> I just keep thinking about Mars and if the results of any of the 2 methods would make any sense. Precision would not matter at all since we would have no way to verify it.
> Also, the geography of the planet is so different that all of our references are lost.


For the summer length map, there is that post I wrote (admittedly the instructions are fairly rough). It won't provide too precise results probably, but at least they will be consistent.

And you're right, precision is not a major concern with fictional worlds. I think the biggest problem is having "clean transitions" between different climate zones, and I do feel the 8-step system is a bit better in that regard. Since the jumps between the precision zones aren't so huge with 8, you end up with better transitions, generally speaking. So in that sense I'd definitely be interested in updating the 8-step script to work with the revised precipitation zones (for my own use if nothing else), since they did look fairly promising, at least in Excel.




> Assuming this is the version generated with the latest script: https://www.cartographersguild.com/a...9&d=1517397591


That is the one, yes.





> Areas at the tropic are too wet: Mexico (Northern half), Gujarat, Australia, South Africa
> The Rockies in North America are also slightly too rainy. 
> These are things that could be improved but at the same time, changes might affect other ares and make them inaccurate instead.


Well, if the change we are speaking is going from 6 to 8 prec categories, I don't think it risks inaccuracy, since it just means a bit better resolution, so to speak. Also, adding that 10-13 °C category might also help since it would push the avg. temperature of "Mild" climates up, making some regions more prone to being arid. Since in general, cold steppes should extend further north in North America and Central Asia than they do in the model.



*Edit:* About that summer length map

One technique I have been considering is just creating a gradual surface-level map going from 0 to 12 months above 10 °C. Ideally it would be completely gradual, so 0->0.1->0.2.... and so on. I think that could be achieved if elevation could be ignored. Then the elevation could possibly be taken into account with a script that calculates the effect.

There would basically be three factors:

Tavg=Average Annual Temperature
dT=Temperature variance within the year
Tele=Temperature drop due to elevation (this is subtracted from Tavg)

Normally, the start point for Tavg would be 10+dT (meaning 0 months below 10 °C). That is because this is intended to create an "adjustment layer" that is applied to that pre-existent surface-level map. So, for example, if the "base level" is 6Ma10 and the adjustment layer has 1Mb10 at 1 km, then the final number would be 5 Ma10 at that location. Although starting from tropical latitudes, there would also need to be a "buffer temperature" applied to push the below 12 Ma10 transition higher in altitude.

However, the big problem comes with dT. Based on latitude, we could assume that if Latitude= 90°, then dT=40 (just a random number in this case), and if Latitude=0°, then dT=2. And then assume a linear transition between the two extremes. That would describe the temperature variance between the seasons growing greater as you get closer to the pole. The problem comes because this ignores maritime and continental influences (which have a major effect on the temperature variance between the seasons), so I'm unsure if the results would still be acceptable.


*Edit2:*
Although with all that rambling said, I should add that I do think that the most "practical solution" right now is to simply accept that the Db/Dc distribution is going to be a bit off, and just fix what can be easily fixed (the Cb/Cc distribution). The summer length map should probably be considered a bonus map for those who have an interest in it in any case.

----------


## Azélor

Ok, I will finish updating the table later today and will send it to you when it's done.

----------


## Azélor

I haven't worked on the file yet but I have this!

----------


## Charerg

Does the "image-to-Excel-sheet" always produce the same shape? Since in our case you'd have to convert 4 maps from .png to Excel sheets. Either way, nice work.

----------


## Azélor

I haven't succeeded in converting a PNG. 
I'm exporting the the geo tif from Qgis to Excel using an asc file. 
It works so far but it's slow.

That was Iceland by the way.

----------


## Azélor

I've been busy doing a few thing, starting in no particular order.

Here, I've managed to put all the results from Hex climate (the 3600 combinations) in a single column
and then determining the climate, somehow

first it extract the temp and prec from the hex code
then search these values in 2 different tables to get the corresponding temperature, precipitation regime (s,f or w) and another one just for the A, and the threshold matching each temperature
Then we can calculate if the precipitation is greater than the threshold ans see if it is humid, steppe or desert
with the temp, we can figure out the 2 first letters and the aridity for the B climates
The two next colomn are just for A and B and use the data of other columns to figure out the missing letters, same for C and D on the last colomn
the climate columns puts everything together. it gives something like Ca__f but we can still understand 
final colour reads the letters we just found and look into the colour table (classic koppen colours scheme) to give a single colour for each climate

So by reading the first and last colomn, it tells you : pick this colour and replace it with this





This is the table assembled with links to the main tables for the precipitations:
It contain all the data used above and also a id colomn. 
Excel need the data to be in a specific order to work properly. I included numbers in case we need to put back the data in the original order since it makes it easier to modify the table as the Hex code is gibberish.
You can notice that several hex have the same value. This is because we simplified the precipitations and temperatures. I think Excel always picks the first one, but it doesn't matter if the numbers would be different since all result in the same climates in the end. Also, including this redundant data means that if we change the categories, we won't have values missing from the tables. 
But if we add new categories, we will have to expand the tables manually and adding the new links if necessary. 



here is for the temperature, same logic



the last table looks like this:
You just have the relevant data left : input code, corresponding climate, and the final colours
ideally, we should copy the result in another layer for modification : ordering the results by climate and deleting the many missing values. 
It is normal to have that many missing, they exist here only, not on the generated map. 
we could also add the # to convert the hex to a colour, delete the text, and we have a colour key
the other use of this is to convert the data all using Excel and in that case, no colour key is needed.

----------


## Charerg

Looks pretty good. There is just one potential issue that pops up regarding the classification into arid climates: the calculation of the aridity threshold.

Remember that the aridity threshold uses different criteria for choosing which threshold to use than the _f/w/s_ used in the climates, at least if we follow Kottek. As a reminder, climate group _w_ needs 10x the rain in summer compared to winter, but for the "aridity group W", just 2x is enough (or 2/3 of the annual rainfall, to put it differently)! So, you need to also specify which "aridity group" each precipitation combo belongs to. Of course you could use the climate groups instead if you want to simplify things, but then you might end up with many BS climates being pushed into Cf or Df because they use the lower "aridity group F" threshold. And I do recall several areas being a bit too humid in the latest script, so that is definitely a potential issue.

----------


## Azélor

About the excel spreadsheet of the world. Here's the explanation of the process starting with the precipitations:

Convert the main data from January and July to an hex code
using a table were each value in mm is tied to a hex value.
Each hex value represent a category

Example:

Attachment 104225

Then, we combine the values, one is red and the other is blue.
Convert these to a green value, one for each category. So far this is almost identical to the already existing tutorial. 
Precipitations done for the moment.


Temperature

Same as with precipitations, we take the base data and give them hex values. 
Combine both months.
Then convert temperatures to reduce the number of possibilities.
That's it for the temperatures.

Now we need to combine precipitation and temperatures, with an addition.
When that is done, we have the 500 something combinations.
Whit what I've explained in the previous message, we know what output give what climate, we just have to replace the values with the ones in the table and we get something like this once we format it properly
https://en.wikipedia.org/wiki/K%C3%B...h_authors).svg


It is still unfinished but so far it's seems to work. It is slow and I'm using the data at 10m resolution. It would be unthinkable for me to increase the resolution. We most of the time if only gets sluggish if there is missing data in the table as Excel tries to find something that does not exist, in 1 million of cells at the same time. Having good table is crucial. right now, each file has it's own table. I find it easier to work that way but I will have to link them to a master table so if I change the master, all the others will change to. 

I haven't done extended test but it looks like changing the data in the categories, such as changing the temperature boundaries, works fine. and it's not too difficult as long as the tables include the new values.
It's harder to add new categories as the links between tables need to be added, but I haven't tried it. 

For the moment, I will concentrate on finishing the generation of the climate world map based on the criteria we have settled for and we shall see the results.
One of my goal is to allow the modification and testing of different parameters without having to select a bunch of colours in Gimp/Ps

So this is just a first step.
Later, I will create another version that should be more flexible by including all the possibilities. It's an complete/unsimplified version if we can call it that way. Yet, it fuses the redundant temperature combinations such as Hot/cold and Cold/Hot since they are actually the same thing. Also simplify the tundra and polar climates so we only keep one of each. Lastly, it will only keep on Da and one Dd from the deadly cold winter column since they are either impossible or always wet unless then precipitations gets absurdly low. Even if we divided the precipitations by 2, they would still be humid. I also consider fusing together the tow Dc from the severely cold column but it might be affected if we decide to lower the precipitations. 

It uses the same principle but because it doesn't uses precipitations categories (actually, I still need to convert the value to green, but they are all unique) , it is easier to use for the sake of testing and seeing the results (I assume).
The only drawback maybe is that is results in a lot more combinations in the end. Not a problem for Excel as long as all possible results are covered in the tables, but a problem if we want to use that in the tutorial. 
With the actual number of categories, I get around 1298 combinations?

----------


## Azélor

> Looks pretty good. There is just one potential issue that pops up regarding the classification into arid climates: the calculation of the aridity threshold.
> 
> Remember that the aridity threshold uses different criteria for choosing which threshold to use than the _f/w/s_ used in the climates, at least if we follow Kottek. As a reminder, climate group _w_ needs 10x the rain in summer compared to winter, but for the "aridity group W", just 2x is enough (or 2/3 of the annual rainfall, to put it differently)! So, you need to also specify which "aridity group" each precipitation combo belongs to. Of course you could use the climate groups instead if you want to simplify things, but then you might end up with many BS climates being pushed into Cf or Df because they use the lower "aridity group F" threshold. And I do recall several areas being a bit too humid in the latest script, so that is definitely a potential issue.


I thought about it. I have 3 different columns, one for each. 
As to how one knows if the climate is dry in the summer, in the winter or neither: there is no formula (yet?)
You need to change these tables manually, that is where the data from the "cd climates" come from:



the issue I have with the s,f,w table is that because I'm using 6 categories maybe, following the strict formula doesn't give as a good climate distribution (based on experimentation in the past).
of course, I don't have that issue much with the A climates and could probably automate that part since I already have the formula (and it works), I just need to change the output.

I take back what I said about the A climates

----------


## Charerg

> I thought about it. I have 3 different columns, one for each. 
> As to how one knows if the climate is dry in the summer, in the winter or neither: there is no formula (yet?)
> You need to change these tables manually:
> 
> 
> 
> the issue I have with the s,f,w table is that because I'm using 6 categories maybe, the formula doesn't give as a good climate distribution (for experimentation in the past).
> of course, i don't have that issue much with the A climates and could probably automate that part since I already have the formula (and it works), I just need to change the output.


Good to see that you have it handled! I'm not sure about what you meant with the formula, but if we follow Kottek's definitions, it would be the following for determining which aridity threshold belongs to each prec combo:

W threshold: at least 2/3 of annual prec in summer (summer ~67% or more)S threshold: at least 2/3 in winter (summer ~33% or less)F threshold: other cases (summer rainfall 33-67%)

----------


## Azélor

S: the precipitations in winter must be at least 3 times the precipitations in summer
W: the precipitations in summer must be at least 10 times the precipitations in winter 
F : whaterver's left

results, the new of the left and the old on the right:


We already know that expanding the s is a bad idea. I don't know about the changes to w. The distribution looked ok last time.
The 2 w missing are 1-2% lower than the target. They need 91%, one is at 89% and the other at 90%. Considering we are using the average, in reality, about half of them would be classified w and the other f.

----------


## Azélor

I got the results. I have to admit that is was really cool to see the map unfold. 
But there are several problems as you can see:



The most obvious ones would be:
many w climates are categorized as s in Brazil for example
Af and Am climate look way off

Actually, I think the problem is in the method we use. The north and south end up having different colours for the winter/summer precipitations. They have the same her and so are not categorized correctly.
I guess that is the problem since most of these areas are in the south. 

Also:

there are As climates between the desert and the steppe
Steppes are too common in Europe and in Labrador. 
in Asia, there are w and s climates right next to each other
A desert in Vietnam

These are the most obvious.
I know there is missing data in a few places. This might explain some weird things like the steppe in Labrador.
I noticed that in Qgis while looking at a 0 in Alaska. That was the temperature and it really popped out since it was surrounded by -18, -19.
lastly, the map uses the 10m resolution, which is the lowest


I just noticed a few places in Europe with steppes on your map. Mine is worst but I was not expecting this. 
Vietnam is also shown to have steppes, strange.

----------


## Azélor

Fixing the southern hemisphere problem is easy. I just need to add a formula that will pick the red and blue code seperatly and reconstruct the code by switching these values.

Fixing the other cases will need more thoughts.

----------


## Charerg

> I just noticed a few places in Europe with steppes on your map. Mine is worst but I was not expecting this. 
> Vietnam is also shown to have steppes, strange.


Probably just a result of having data from 2 months (as well as a discrete number of categories). Those areas in Vietnam are in rain shadow during the peak of the monsoon season. Also, the steppe region in Myanmar is probably genuine since they appear in the Hans Chen Köppen map too (though you have to zoom in to notice it). For the rest, difficult to say, since there aren't any high-res Köppen maps of SE Asia around the web. On a quick search I was able to find this one, which includes the steppes in Myanmar too, but nowhere else. So, I'm inclined to think that the steppes in Vietnam probably appear due to the large data gap from using data of 2 months only.

Similarly, the BS areas In Europe aren't too far off the mark, there are BS climates in Spain, Greece, and even a bit around the Black Sea. Areas like northern Bulgaria are probably "borderline BS" too, since that was a favoured migration route for steppe nomads in the past. Although the BS is a bit too extensive in our case, but again this is probably caused due to data limitations (discrete categories and only two months). It's worth noting that the BS distribution is better with the 8-step map in Europe, so in this case I suspect the discrete number of categories to be the issue.

One problem that also turns up in your map is those anomalous northern _s_ climates, they definitely seem out of place.


*Edit:*
Oh, and a bit more ranting about the difference between the climate and aridity categories:




> S: the precipitations in winter must be at least 3 times the precipitations in summer
> W: the precipitations in summer must be at least 10 times the precipitations in winter 
> F : whaterver's left


Those are the definitions of the *climate categories* (s, w, f). But the *aridity categories* (S, W, F) use a different definition! So, a climate that is in the "climate group f" could belong to any of the aridity categories, because the criteria are not the same.

Here's an example:

Summer rainfall   100 mm
Winter rainfall      30 mm

This would be classified as an _f climate_ (since ~77% of the annual prec falls in summer), *however* it would still use aridity threshold for the "aridity group W", because more than 2/3 of the rain fall in summer!

So, the threshold would be calculated as follows:

Annual mean temp   15 °C
Prec threshold         20*Tann+280 = 580 mm


On the other hand, if the precipitation distribution was different:

Summer rainfall  70 mm
Winter rainfall     60 mm
Mean temp         15 °C

Again, an _f_ climate, but this time the rainfall is spread more evenly between the seasons, and it belongs to the "aridity group F":

Prec threshold    20*Tann+140 = 440 mm


And as a final example, the following:

Summer rainfall  40 mm
Winter rainfall     90 mm
Mean temp         15 °C

Another _f_ climate, but now over 2/3 fall in the winter, so this would belong to the "aridity group S":

Prec threshold   20*Tann = 300 mm


Note that all of the above examples were classified as _f_ climates, but the prec thresholds used were very different.

----------


## Azélor

I'm just using the numbers from the paper you keep talking about. I don't know where you see 2 sets of different numbers.

Take Csa for example: 

for the precipitations, winter must be have at least 3 times the precipitations of the summer
If the difference is smaller, then it's not a Cs climate. 




> (since ~77% of the annual prec falls in summer)


That would clearly fall in the w climates, like Cwa, according to the definition.  There is a rainy season and a dry one. If it was Cfa, it would be spread more evenly. 
You talk like I'm missing something really obvious. 

Maybe it's this? https://en.wikipedia.org/wiki/Aridity_index



> if rainfall occurs mainly in the hot season.


What does mainly means to you? I associated that with the w climates. 

So if: the precipitations in summer are at least 10 times the precipitations in winter.
the climate is rated w because rainfall occurs mainly in the hot season, that's how I defined it.

----------


## Charerg

> I'm just using the numbers from the paper you keep talking about. I don't know where you see 2 sets of different numbers.
> 
> That would clearly fall in the w climates, like Cwa, according to the definition.  There is a rainy season and a dry one. If it was Cfa, it would be spread more evenly. 
> You talk like I'm missing something really obvious.
> 
> Maybe it's this? https://en.wikipedia.org/wiki/Aridity_index
> 
> What does mainly means to you? I associated that with the w climates. 
> 
> ...


That's the thing, the aridity threshold is not synonymous with _w_ or _s_ climates. And the Kottek et al. paper does indeed define when to use which threshold, check page 4 of the pdf:




> The annual mean near-surface (2 m) temperature is denoted by Tann and the monthly mean temperatures of the warmest and coldest months by Tmax and Tmin, respectively. Pann is the accumulated annual precipitation and Pmin is the precipitation of the driest month. Additionally Psmin, Psmax, Pwmin and Pwmax are defined as the lowest and highest monthly precipitation values for the summer and winter half-years on the hemisphere considered. All temperatures are given in °C, monthly precipitations in mm/month and Pann in mm/year. 
> 
> In  addition  to  these  temperature  and  precipitation values a dryness threshold Pth in mm is introduced for the arid climates (B), which depends on {Tann}, the absolute measure of the annual mean temperature in °C, and on the annual cycle of precipitation:
> 
> Pth=
> 2*{Tann} if at least 2/3 of the annual precipitation occurs in winter,
> 2*{Tann}+28 if at least 2/3 of the annual precipitation occurs in summer, 
> 2*{Tann}+14 otherwise.


Note that since BS is defined as Pann<10*Pth and BW as Pann<5*Pth, you should multiply the above with 10 to arrive with the final threshold. So you end up with 20*Tann+x formula.

Btw, the wikipedia page for the aridity index has the exact same formula (although in cm instead of mm, so it's already been multiplied by 10). Although you're right in that wikipedia does not define exactly what the "mainly in summer/winter" means. Although the B climate class page in wikipedia does provide that information (in percentages).

----------


## Azélor

> So you end up with 20*Tann+x formula.


I end up with the same result but it's calculated differently.

----------


## Charerg

> I end up with the same result but it's calculated differently.


I think the formula itself is probably correct, if you end up with 20*Tann+280 for the "W category", and so forth.

The point I was making was that I don't recommend doing the following simplification:




> Hey, I got some numbers!
> 
> Replacing this :
> 
> •	If less than 30% of annual precipitation occurs in the summer : Annual precipitation (mm) < 20 × average annual temperature (°C)
> •	If more than 70 % of annual precipitation occurs in the summer: Annual precipitation (mm) < 20 × average annual temperature + 280 
> •	Else : Annual precipitation (mm) < 20 × average annual temperature + 140
> o	If annual precipitation is  < 50 % of the threshold = BW: desert climate
> o	If annual precipitation is between 50 and 100 % = BS: steppe climate
> ...


Because if you replace the "aridity categories" with the climate categories, you're inevitably going to end up with results differing from real köppen maps, because you're essentially using a different definition for the B climates. And in any case I don't think it's a necessary simplification. It does require an additional table though, in addition to the climate tables for A and C/D climate groups.

Although if you're aware of this then I've been essentially ranting about nothing, but just making sure so you don't end up overlooking this, since it's a pretty significant detail.

----------


## Azélor

> Although you're right in that wikipedia does not define exactly what the "mainly in summer/winter" means. Although the B climate class page in wikipedia does provide that information (in percentages).


I don't get your point at all.




> f you replace the "aridity categories" with the climate categories,


aridity = s,f,w ?
climate = A,B,Ca,Cb,Cc,Da,Db,Dc,Dd,E?

I think I understand what you mean now?
B and A climates use the aridity threshold of s,f,w depending where they are in the table, They use the same thresholds as the C and D climates, 
for example, despite being As, some will still use the f threshold

----------


## Charerg

> I don't get your point at all.
> 
> 
> 
> aridity = s,f,w ?
> climate = A,B,Ca,Cb,Cc,Da,Db,Dc,Dd,E?
> 
> I think I understand what you mean now?
> B and A climates use the aridity threshold of s,f,w depending where they are in the table, They use the same thresholds as the C and D climates, 
> for example, despite being As, some will still use the f threshold


No, let me try and rephrase that.

By "climate category" I refer to _s_, _f_ and _w_ as they are defined per the climates. So, the following familiar definition:

At least 10/11 (or ~91%) of rain occurs in summer (_w_ climate, winter dry)Between 1/4 to 10/11 (26-90%) of rain in summer (_f_ climate, no dry season)Less than 1/4 (25%) of rain falls in summer (_s_ climate, summer dry)

Of course, those are different for the A climates, but let us ignore those for now (also, I'm aware in reality it's a comparison between rainiest and driest months, but in our case the above applies since there is only data from two).


With the "aridity category/group", I refer to the categories that are used to define which aridity threshold to use:

At least 2/3 (or ~67%) of rain occurs in summer (_W_ aridity group, use threshold Tann*20+280)Between 1/3 to 2/3 (34-66%) of rain in summer (_F_ aridity group, use threshold Tann*20+140)Less than 1/3 (~33%) of rain falls in summer (_S_ aridity group, use threshold Tann*20)

I hope the definitions are clear now. The point I was making is that there is a pretty big difference between the two definitions. So, if you use the same criteria to define both the climate group (f, w, s) *and* the aridity category (which defines which threshold to use), the aridity groups would look as follows:

At least 10/11 (or ~91%) of rain occurs in summer (_W_ aridity group, use threshold Tann*20+280)Between 1/4 to 10/11 (26-90%) of rain in summer (_F_ aridity group, use threshold Tann*20+140)Less than 1/4 (25%) of rain falls in summer (_S_ aridity group, use threshold Tann*20)

I'm not sure if you've done the latter, but if that is the case, then this will cause a different distribution of B climates compared to official Köppen maps, since the definition of which aridity threshold is applied is drastically different from the one usually used with the Köppen system. Again, just making sure that you're aware that there is a pretty big gap between the definitions. Of course, you might already be on top of things.

----------


## Azélor

I wrote this before reading your last reply.

Back on the spreadsheet:
I've looked at some of the wrong climates to find out that Excel is right and did not miscalculate but I did mistakes. In the latest version of the script, 2 precipitations cells have the s threshold instead of the f they should have. Therefore, the aridity for them is wrong in some cases. To put things differently, Europe should be less arid.

That is what I've found so far.

Which brings me to the other point: the actual threshold system is bad. Some places are too dry other too wet and if you move from s to f, the aridity increase abruptly, faster than the actual increase in precipitations. Using a 3 thresholds is not enough, we need more in order to have something more gradual. 

We could assing a maximum and minimum value. They would be located in top right and bottom left. 
Everything in between gets a value depending on the % precipitations falling in winter for example.

Since the highest value is 1080 (or something, its just an example) if 100% ( or maybe a slightly lower % to fit or categories) of the precipitations fall in summer. The lowest is 760 if 0% or so fall in summer. 

It would make s climates more likely to be arid. F on the bottom left might be more wet if I get it right, resulting in a smoother transition. Less steppes in Europe ?

And f climates on the top right would be more arid.

At least I hope I get this right. I know it is a good idea but I often get the resoning behind the threshold wrong.

----------


## Charerg

> I wrote this before reading your last reply.
> 
> Back on the spreadsheet:
> I've looked at some of the wrong climates to find out that Excel is right and did not miscalculate but I did mistakes. In the latest version of the script, 2 precipitations cells have the s threshold instead of the f they should have. Therefore, the aridity for them is wrong in some cases. To put things differently, Europe should be less arid.


Haven't read your whole post yet but just a quick comment: remember the S threshold is actually the lowest (defined as 20*Tann). So, if moving from S to F, then the threshold would become higher (F being defined as 20*Tann+140), and the climate therefore more likely to be considered arid. Sorry if I'm repeating myself a bit here.

Maybe replacing the threshold with a more gradual "x factor" (20*Tann+x) that increases based on the percentage that falls in summer would work, but it could be difficult to determine the maximum value. Should the "x factor" be 280 mm at 100% falls in summer? In that case most areas would be less prone to aridity than is normally the case with köppen maps. And if it should be higher, then what would be the correct value?

All in all, it's perhaps easier to stick to the standard definition. That way, at least it's easy to make comparisons between generated maps and actual Köppen maps of Earth. Things are never going to be 100% accurate when working with data from just two months and a discrete number of categories that use average values instead of exact values.

----------


## Azélor

Basically you are saying that for instance, f climates don't use just 1 threshold but up to three different ones depending on the precipitation distribution during the year? I assuming it makes things smoother. But is it smooth enough.

I'm not sure it solves the jumping aridity problem.

----------


## Azélor

> Haven't read your whole post yet but just a quick comment: remember the S threshold is actually the lowest (defined as 20*Tann). So, if moving from S to F, then the threshold would become higher (F being defined as 20*Tann+140), and the climate therefore more likely to be considered arid. Sorry if I'm repeating myself a bit here.


Good I got it right this time.

----------


## Charerg

> Basically you are saying that for instance, f climates don't use just 1 threshold but up to three different ones depending on the precipitation distribution during the year? I assuming it makes things smoother. But is it smooth enough.
> 
> I'm not sure it solves the jumping aridity problem.


Yes, that is it exactly. I don't know if it solves any problems per se, but in any case that is exactly how it works in the real-world Köppen maps we're using as references. It could have an effect in southern Europe for example, though: many of the _f_ climates that are borderline _s_ would undoubtedly use the lowest aridity threshold (S) instead of the higher F threshold.

----------


## Azélor

Ok now I understand we you said the letters could be confusing. Indeed using the same letters for


At least 2/3 (or ~67%) of rain occurs in summer (_W_ aridity group, use threshold Tann*20+280)Between 1/3 to 2/3 (34-66%) of rain in summer (_F_ aridity group, use threshold Tann*20+140)Less than 1/3 (~33%) of rain falls in summer (_S_ aridity group, use threshold Tann*20) 

as s,f,and w climates, doesn't make much sense since they are 2 different things. 
I guess I'm just repeating you.  :Very Happy: 

I personally don't really care how Koppen name them, if it's too confusing we should change the naming, assuming we keep them.

Arid
Average 
Wet

West coast
Something (Faoofa?)
East coast (the dry summer is more prominent on east coasts, or the eastern sides of mountains in the case of Central Asia). it is the opposite for w pattern.
Now I remember I used to say precipitation pattern. For example, Brest in Brittany has a Cfb climate with a s pattern because the summer is drier than winter but not dry enough to qualify as a s climate.

----------


## Charerg

> Ok now I understand we you said the letters could be confusing. Indeed using the same letters for
> 
> 
> At least 2/3 (or ~67%) of rain occurs in summer (_W_ aridity group, use threshold Tann*20+280)Between 1/3 to 2/3 (34-66%) of rain in summer (_F_ aridity group, use threshold Tann*20+140)Less than 1/3 (~33%) of rain falls in summer (_S_ aridity group, use threshold Tann*20) 
> 
> as s,f,and w climates, doesn't make much sense since they are 2 different things. 
> I guess I'm just repeating you. 
> 
> I personally don't really care how Koppen name them, if it's too confusing we should change the naming, assuming we keep them.
> ...


I don't think they have been named as such, I just adopted the W/F/S terminology since it works for me (and kind of assumed everyone was "on the same page" as myself, I guess). 

But essentially it's the percentage of total rainfall that falls in summer from 0-100% divided into even thirds. I guess you could call them the "winterwet third", "middle third" and "summerwet third", though tbh that could be even more confusing since "winterwet" is more or less the same same as "summer dry". But precipitation pattern sounds good, as long as you use capital letters when talking about the pattern, and lowercase when referring to the climate class, then at least I'll understand (not sure about anyone else though  :Very Happy: ).

----------


## Azélor

This if for the climates. the area in light colour belong to s and w respectively but should be f if we follow the strict criteria of the formula. Do you want to try the new ones or keep the old distribution?


This is for the threshold
the dark ones actually falls into the green zone but barely. I know when at look at the exact numbers, those are rounded. 



This is the actual aridity table before I make the changes:

----------


## Azélor

It results generally in a increase in aridity 



Sorry It's supposed to be like this instead. the 22% is in s climates but we considered iit was a bad idea since it made large areas in Europe with Cs climates.

----------


## Charerg

> This is for the threshold
> the dark ones actually falls into the green zone but barely. I know when at look at the exact numbers, those are rounded.


Overall looks good. Just one minor quibble: since the precipitation pattern is divided into even thirds, it should look like a "mirror image" on both sides of the central diagonal. Yet in this case the "S pattern" has an extra combo (32%) compared to "W pattern". A minor matter, but technically the green 68% should be a "W pattern", that way you'd have the expected mirror image.

Edit:
Oh, and a question about the Winter 75+Summer 37,5 combo (referring to the avg. precipitations).

From the threshold table we see that this combo is in the "green zone". So, assuming that it had the F threshold previously (or "middle third" threshold, if you prefer), it shouldn't change in terms of aridity, yet apparently it did become more arid. Why is that the case?

----------


## Azélor

Alright : 



That cell is also going to become more arid.

----------


## Charerg

> Sorry It's supposed to be like this instead. the 22% is in s climates but we considered iit was a bad idea since it made large areas in Europe with Cs climates.


Actually that was an _f_ climate to begin with (assuming this was the previous table). The categories I shifted from _s_ back to _f_ for the script were 33% and 32%, so those weren't "technically s" to begin with. Also, I doubt the 22% would have an effect in Europe: it would be either BS or BW with the temperatures that occur in southern Europe. It might push some areas in Siberia or Canada from _f_ to _s_, though. In any case, we haven't tested how the 22% would work as an _s_ climate.

Edit:
Looking at the table for the 22% prec combo, it looks like you probably need it to be either ET/EF or at least a Cold winter for it to be something other than arid. I think we could include it as _s_ (might be wrong about that, maybe half of Siberia will become Ds or something  :Very Happy: ).

Btw, maybe 90% should be a _w_ climate? At 150 vs 17,5 it doesn't quite fulfill the 10x criterion, but it's not far off either.

----------


## Azélor

89% is not much farther. 

Yea, 22% was a f climate.

----------


## Azélor

Supposing we decide to use more thresholds.

We use the % of precipitations falling during the summer.
W has the highest value, and s the lowest. 
The difference between the maximum and the minimum is 280 mm at any given temperature.
so if
280 = 100% 
and 
0 = 0%
it means
2,8 = 1%

Every 1% increase in the share of precipitation for summer, increase the threshold by 2,8 ml.

We take the s threshold table as our base value 

Base threshold + 2,8 (% of yearly precipitations falling in summer)
So if the precipitation is split evenly at 50%, I get the exact same value as the actual f threshold. 

The table could look like this but I need to add the base threshold


There are 25 different thresholds instead of 3. Yet I have no idea what kind of impact to expect.

----------


## Azélor

The result 

 vs posted earlier

The version posted earlier still had that problem were increased precipitations resulted in increased aridity. The new version does not but it has 25 thresholds instead of 3. 
Now I just need to find how to make this work without creating a bunch of complicated things.

----------


## Azélor

Using this new model, I get 22 precipitation categories (like old model) and 26 temperature categories (less than previous

22x26= 528 +2 total combinations
down from 574 if I recall correctly



Also, I inverted the January and July precipitation for the southern hemisphere. The next version will not mix s and w climates there.

Now the actual methodology to find the thershold in

Adding the base threshold which depend on the temperature (it's based on the old s thershold)
to another threshold that is based on the precipitations
so the the s climate in the bottom left doesn't increase 
it increase gradually going to the top right, reaching the exact same value w had previously. 
f that are exactly on the diagonal, will have the same value as before.

So to find the actual threshold, all I have to do is to addition the 2 values.

----------


## Charerg

> The result 
> 
> Attachment 104269 vsAttachment 104270 posted earlier
> 
> The version posted earlier still had that problem were increased precipitations resulted in increased aridity. The new version does not but it has 25 thresholds instead of 3. 
> Now I just need to find how to make this work without creating a bunch of complicated things.


I'm not sure how good an idea it is to use the gradual threshold. Sure, the threshold will be more gradual, but is that necessarily a good thing? A lot of areas will become "more humid" as a result of this, since like 80% of the planet receives more than 2/3 of the total rainfall in summer. And I seem to recall we had the opposite problem of many such regions being classified as too humid to begin with?

Personally, I'm ok with the standard criteria used with the Climate Classification, after all we're not seeking to create a new or updated version of Köppen, so I'm reluctant to change the criteria (especially since it makes generated maps difficult to compare to real ones).


Also, in your latest temperature sheet, you shouldn't merge the lower right corner of Ca with the rest (because it has less than 18 °C annual mean, so Bk instead of Bh).

----------


## Azélor

Yes I changed it after but did not update the last picture.

So the final count is 552 combinations. 

Some areas become more arid, other more wet. As I said, the logic sounds right to me but I don't know if it will make the map better.
Don't forget that we use categories and Koppen used continuous data. We need to consider that.

result : 

I also just tried the improved 3 thresholds method but I must have done something wrong. It looks horrible.
I tried to fix the errors but ended up creating new ones. There is now a large desert in Siberia. I really have no idea what I have done wrong.
Also, there is a steppe in Alaska and I don't think it is an error. After all, the maps you linked showed steppes in Alaska.

I think I've found what was causing the problem.

----------


## Azélor

now the results:



Do you want the files?
I can send you the multiples threshold version, the 3 threshold version or both.
They are 150mb each uncompressed.

----------


## Azélor

I also did a quick test (updated the file mostly)
to include a 8 categories precipitation model. Right now, it has 1022 combinations. 

Having more categories makes it harder to combine stuff together. As the model is supposed to be more accurate, simplification would go into the other direction.

----------


## Charerg

> now the results:
> 
> 
> 
> Do you want the files?
> I can send you the multiples threshold version, the 3 threshold version or both.
> They are 150mb each uncompressed.


Yeah I'd like to update the script to use the 3 threshold version since that's the "standard criteria". And it does seem to overall work better, apart from that somewhat anomalous Dsa in Kazakhstan.




> I also did a quick test (updated the file mostly)
> to include a 8 categories precipitation model. Right now, it has 1022 combinations. 
> 
> Having more categories makes it harder to combine stuff together. As the model is supposed to be more accurate, simplification would go into the other direction.


I think 1022 is still manageable, provided that the final climate table can be compiled in Excel, it's possible to copy-paste the final hex codes into the appropriate climates in the script without too much trouble.

----------


## Azélor

Yea, that area is odd. Depending on the exact precipitation, it could be that while they are almost identical, they use different thresholds. 

That is a strange thing, the resulting code of these Dsa is not in the database. I must have made a mistake while converting the temperature values (simplification). But the temperature should have been converted to another value, and that value is a Dsa climate too. After verification, the data looks right. They are supposed to be humid but barely. The precipitation is 135mm, threshold 100mm. Seems fine just by looking at the numbers. 
I admit that I haven't checked the surroundings.
The exact data is for the cell BDG256:
Temp: Jan -12 / Jul: 26 (Da)
Prec: Jan: 12mm / Jul: 7 mm

Reducing precipitations in any way result in a steppe.
Increasing them in summer and it also become a steppe because of the changing threshold.
In creasing precipitations in winter makes it wetter. 

It is possible to have all the hex codes, the corresponding climates leters and resulting colour scheme on a single page. I called it final simple (I know I'm not good at finding names).
Paste the result right next to it. Deleted all the N/A having codes with only 4 characters. Delete the remaining duplicates. Put them in alphabetical order and your done. The lettering is akward sometimes but it should be hard to figure it out. 
Ex: "Ca  s" instead of Csa

----------


## Charerg

> Yea, that area is odd. Depending on the exact precipitation, it could be that while they are almost identical, they use different thresholds. 
> 
> That is a strange thing, the resulting code of these Dsa is not in the database. I must have made a mistake while converting the temperature values (simplification). But the temperature should have been converted to another value, and that value is a Dsa climate too. After verification, the data looks right. They are supposed to be humid but barely. The precipitation is 135mm, threshold 100mm. Seems fine just by looking at the numbers. 
> I admit that I haven't checked the surroundings.
> The exact data is for the cell BDG256:
> Temp: Jan -12 / Jul: 26 (Da)
> Prec: Jan: 12mm / Jul: 7 mm
> 
> Reducing precipitations in any way result in a steppe.
> ...


Shouldn't the threshold be 140 mm?

If you calculate the avg. between Jan -12 and Jul 26 you end up with Tann 7 °C, so the threshold should be 20*Tann=140 mm.

----------


## Azélor

In theory yes, and it would then be a steppe but we use categories. 

Therefore the average temperatures are -16 and 26: so 5 for the year.
So the result is 100ml or actually 80mm because the two categories are put together.

----------


## Charerg

> In theory yes, and it would then be a steppe but we use categories. 
> 
> Therefore the average temperatures are -16 and 26: so 5 for the year.
> So the result is 100ml or actually 80mm because the two categories are put together.


So I guess the problem is too broad prec categories (and perhaps the winter temp averages are too low?). Since (12+7)*6 should result in 114 mm annual prec. But instead we end up with (17.5+5)*6 = 135 mm. Okay, that didn't make a difference. So probably we should use higher averages for the winter temperatures. We're using -4 for Cold, right? That's probably ok, but maybe Very Cold should use -12 average to prevent it from dropping the annual mean temperatures into unrealistically low values?

----------


## Azélor

If it increase the aridity there, it will also increase it somewhere else. 
-12 will be too low for some places.

Edit: I mean too high. Since -12 is higher than -16 for example. Oups

----------


## Charerg

> If it increase the aridity there, it will also increase it somewhere else. 
> -12 will be too low for some places.


It wouldn't be such a bad thing if the steppes extended further north in Canada, since I think that is the case in real world Köppen maps. I think the error comes because we only use data from extreme months: the continental climates tend to have too low Tann values because of this.

----------


## Azélor

> It wouldn't be such a bad thing if the steppes extended further north in Canada, since I think that is the case in real world Köppen maps. I think the error comes because we only use data from extreme months: the continental climates tend to have too low Tann values because of this.


Maybe a little more arid yea.

I don't think using the extreme month has a big impact. The temperature follow a bell curve. Temperatures changes more quickly in spring and fall but more slowly in summer and winter. Overall, I don't think the yearly average would be very different.

----------


## Charerg

> Maybe a little more arid yea.
> 
> I don't think using the extreme month has a big impact. The temperature follow a bell curve. Temperatures changes more quickly in spring and fall but more slowly in summer and winter. Overall, I don't think the yearly average would be very different.


I'd say there definitely is some difference, because if there wasn't we should be able to sort out the Db/Dc boundary. It would be interesting to compile an "annual mean temperature" map using the categories we use and compare it with an actual map of annual mean temps.

----------


## Charerg

Ok, I've now updated the script.

Here's the output:

*
Note:*
I made the slight modification of including the [200+ / 100-50] precipitation combo with _Af_ instead of _Am_, since that was the case in the prior version as well (and overall the combo is closer to _Af_ prec pattern, I think).

Overall it does match real-world Köppen maps better than the previous version: Australia is more arid, and the cold steppes extend further north in North America and Central Asia. The updated script is in the attachments, as usual.

*
Installation:*
Overwrite the older version of the script if you're using it. Otherwise, follow the instructions in this post.

----------


## Azélor

Yes I think this is an improvement from the previous version.

Next?

----------


## Azélor

Trying to figure out a simple way to map precipitations (July). 


So ideally, each colour represent if an area is wet or dry and the reason why. Sometimes a darker colour is used to represent a wet yet drier area. 
I'm not sure where this is going.

----------


## Charerg

> Yes I think this is an improvement from the previous version.
> 
> Next?


I guess we could work on adding the 10-13 °C temp category. That should eliminate the awkward Cc-Db transitions that are very common right now and largely sort out the Cc distribution. I feel after that we could consider the main tutorial largely finished, unless you want to develop the 8-step precipitation version further?

Although I guess the development focus could be shifted towards refining the instructions, since I think we're in a pretty good spot with the climate generation even with just the 6 precipitation levels, now that the aridity thresholds have been sorted out (that really did fix a lot of the most problematic regions like Australia).

----------


## Azélor

> I guess we could work on adding the 10-13 °C temp category. That should eliminate the awkward Cc-Db transitions that are very common right now and largely sort out the Cc distribution. I feel after that we could consider the main tutorial largely finished, unless you want to develop the 8-step precipitation version further?


Maybe.
No, we don't really need 8 categories. 




> Although I guess the development focus could be shifted towards refining  the instructions, since I think we're in a pretty good spot with the  climate generation even with just the 6 precipitation levels, now that  the aridity thresholds have been sorted out (that really did fix a lot  of the most problematic regions like Australia).


Improving the instructions for the precipitations is a good idea.

----------


## Charerg

> Maybe.


I think it's definitely worth pursuing that improvement, since the _Cc_ distribution is the biggest flaw with the climate generation. Here's the 10x10 temp table where I've highlighted the "bad transition" from _Cc_ to _Db_:



And the 11x11 table, where the transition is now from _Cc_ to _Dc_ (as it should be):

----------


## Azélor

How do you put it on the map?
Select about 30% of the area?

----------


## Charerg

> How do you put it on the map?
> Select about 30% of the area?


I suppose that works, though in July the "Cool category" (if we follow the terminology in your prior post) tends to stick quite closely to the coast.

Here are the sample maps that include the new category, following your suggested colour scheme:

Jan:


Jul:





Tbh I haven't thought too much about the placement instructions yet. Though personally I plan to use an alternative method to create the temperature maps anyway. I made a little test continent and created a basic greyscale height map for it:



The scale used is the following:

*RGB*
*Elevation*

0
-150 m

1
-125 m

2
-100 m

6
0 m

255
6225 m



And converted into the standard elevation map:


Then as a test I've created a map of surface-level temperatures in July. Again, in greyscale, ranging from -50 to 50 °C (200 to 0 RGB, darker=warmer). Here are the different temperature bands with the continent shown on top, gradient mapped at 10 °C intervals:



*Colour*
*Temp (°C)*

Red
30+

Orange
20 to 30

Yellow
10 to 20

Green
0 to 10

Light Blue
-10 to 0

Blue
-20 to -10



Then I've blurred the temperature bands, creating a gradual transition. The elevation map can be directly translated into an "elevation adjustment layer" if the temperature is assumed to drop in a linear fashion with incresed elevation, and then applied as an overlay over the surface-level temp map. The final map (btw, the blurring is not shown here in the background layer, those are the original temp bands):



If you use this method you can easily include as many temperature layers as you like, since the elevation part is essentially automated, though it's a bit more work to create the gradual map of sfc-level temperatures. And of course you need a fairly detailed height map in greyscale for this method to work.

----------


## Azélor

It's actually 37,5 % following the logic:

Cool Max-Cool Min/Old Cool Max
13-10/18= 37,5%

The proportion is probably different in reality but anywhere between 30 and 40% is a good guess.

The temperatures around the Great Lakes look different from my memory. Is it normal for the boundary to follow coastline like that?

----------


## Charerg

> It's actually 37,5 % following the logic:
> 
> Cool Max-Cool Min/Old Cool Max
> 13-10/18= 37,5%
> 
> The proportion is probably different in reality but anywhere between 30 and 40% is a good guess.
> 
> The temperatures around the Great Lakes look different from my memory. Is it normal for the boundary to follow coastline like that?


The temperature certainly follows the coastline in the WorldClim maps. Whether that is normal, hard to say, but if the lakes don't completely freeze then the temperature probably is a bit warmer over the lake than overland in winter.

----------


## Azélor

I thought about the gradients too. The main problem to overcome is the irregularity between different regions at the same latitude.

----------


## Charerg

> I thought about the gradients too. The main problem to overcome is the irregularity between different regions at the same latitude.


Yes, but since you only need to worry about the surface-level temperatures, I don't think the whole process is necessarily more time consuming. Of course, if my test map was a serious map, I'd have made the temperatures in the interior hotter, cooler around the coast, and so forth (instead of having the temp bands roughly follow latitude lines). But in essence, it's not a different process to the usual one, you just have to make sure to include enough "temperature bands" that the final result can be blurred together.

Basically, first draw the "usual bands" like the 28, 22, 18, 10 °C and so forth, just like when following the usual tutorial, then start adding extra bands halfway in between until there's a sufficient density that everything can be blurred into a gradually transitioning map.

EDIT:
Btw, I think you're right that the Very Cold category is a bit more extensive in my latest maps. Apparently I forgot to reset the boundary back to -10 °C (I used -8 °C as the VCold/Cold boundary in one of those prior tests).

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## AzureWings

So I've been following your work here with great interest, although I'm still working my way through some of the distinctions you've been discussing in the Köppen-Geiger classifications themselves. I've been putting together a Python script to do the classification via the methods you've put forth here on input temperature/precipitation map images, and I'm a little unsure how you've been drawing the distinctions between the temperature types (specifically Ca/Cb/Cc/Da/Db/Dc/Dd) where the definition seems to depend on 'hottest 3/4 months' while the input data has only (approx) hottest and coldest. Are you basing it off of an annual overall average compared against that or some other threshold (for requirements like 'no more than 3 months average >10 °C')?

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## Azélor

We use the data we have. 
More specifically, we have the temperatures for the extreme month: January and July. We assume that these months are always the hottest and coldest of the year, even if it's not exactly the case in reality. 
We could have more months but it would require even more work. So we stick to 2 month to keep things from getting out of control.
Having only two months means we don't know what's between these extremes. 

For example, the difference between Cb and Cc is the number of months the temperature is above 10 C. 
Cb has 4+ months, Cc has between 1 and 3 months above 10 C (0 would be a tundra).
By default, in our model, only the coolest possible combination of the C climates has been assigned to Cc (Mild+Cool). It is the only one suitable for Cc but it is also a Cb climate. 
In reality, only about a third of these places are really Cc, the rest is Cb. That is because we use only data from 2 months. Also because we use categories and only keep the average temperatures. 
So, the temperature could be anything between 18 and 10 in July and anything between 0 and 10 in January.

What we have agreed is that Cc climate usually have an average temperature 13 and below in July. Making sure Ireland stays Cb but barely. 

We try to stick to the official criteria of Koppen. The same is true for precipitations. They can be found here : http://koeppen-geiger.vu-wien.ac.at/pdf/Paper_2006.pdf
But we use the 0 C isotherm instead of 3. There is no consensus about this. Also, the distinction between ice caps and tundra is the 0 isotherm, so we will be keeping it.

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## Charerg

> So I've been following your work here with great interest, although I'm still working my way through some of the distinctions you've been discussing in the Köppen-Geiger classifications themselves. I've been putting together a Python script to do the classification via the methods you've put forth here on input temperature/precipitation map images, and I'm a little unsure how you've been drawing the distinctions between the temperature types (specifically Ca/Cb/Cc/Da/Db/Dc/Dd) where the definition seems to depend on 'hottest 3/4 months' while the input data has only (approx) hottest and coldest. Are you basing it off of an annual overall average compared against that or some other threshold (for requirements like 'no more than 3 months average >10 °C')?


The _a_ class has the requirement that the hottest month must be above 22 °C, so the distribution is relatively easy to determine. Although in reality it also demands 4 months min. with avg temp above 10 °C as you noted, but it's very rare that any climate would be that hot in summer and still have less than 4 months below 10 °C.

In the system used in the tutorial, any climate that has the hottest month's temperature above 22 °C is classified as _a_ for this reason (as long as it doesn't qualify as _A_).

The _d_ class is similar in the sense that it also has the requirement that the coldest month is -38 °C or below. And if it's that cold, it can be safely assumed that it has max. 3 months above 10 °C (as long as it has a cooler summer than 22 °C in summer).

All in all, I'd say the distribution of _a_ and _d_ climates matches reality almost exactly, even when using these more limited criteria to determine the distribution. Unfortunately, this doesn't work so well for determining the boundary between _b_ and _c_ climates, since that is based solely on the number of months above 10 °C. If you've read all the walls of text posted lately, you might have noted that I suggested the addition of a new temperature category to try and improve the situation, especially regarding the distribution of _Cc_ climates.

At present, a climate that has a _Mild (10-18 °C)_ hot season and _Cool_ (0-10 °C) cold season will be classified as _Cc_. With the suggested extra temp category it would be _Cool_ (10-13 °C) hot season and _Chill_ (0-10 °C) cold season, which is a better approximation, but it's still pretty rough. So you could say that it's based on the annual mean temp, but it's a specific combination of the limited temperature categories we're working with, not an exact numerical threshold.

I also experimented a bit if the _Dc_ distribution could be similarly improved by either adding extra categories or changing the borders of existing ones, but unfortunately the experiments had limited success. Although the _Dc_ distribution does roughly follow an annual mean temperature isotherm (it's close to 2 °C or below mean annual temp, in this map), I wasn't able to find a combination that works.

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## AzureWings

Thank you! I've been trying to replicate your output from the script-fu version back on page 21, since you so kindly provided input data easily in reach along with the corresponding output, although I realize now the result there isn't totally current to the better representations you've been aiming for over the course of later discussion. The b/c distinction was exactly one of the major trouble spots I've been having - with a lot of the temperature bound conditions I was trying to use Cc/Dc would eat all the Cb/Db or vice-versa (Cfb still seems to be eating all of Cfc, actually, but western Europe at least seems to be more accurate with the suggested 13 °C threshold than without).

My current output is looking like this:


I've been using the R = 2 * ((10 * (Tann - 10)) + (3 * P)), P = % hot months precipitation / total annual precipitation, to determine the threshold for aridity, although I'm finding that many places seem to be coming out a bit drier than they should (that's my other biggest problem, along with other more localized issues like the Dwd and steppe climates showing up in the Nunavut area). That might be because I should be using the defined aridity categories to get thresholds instead; I'll give that a try in a bit.

Striking the balance between getting as accurate as possible and still having the input data be feasible to generate for speculative worlds does seem like a bit of a conundrum. Writing the script to check multiple months' data would be (relatively) straightforward, but brewing up twelve temperature maps of a given fictional world seems like it'd get a bit maddening. I almost think I prefer the idea of the extra  'length of growing season' map you considered earlier if it can sort Cb/Cc and Db/Dc out cleanly; while it's still extra data to make it doesn't seem quite so onerous.

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## Charerg

If you used the maps from page 21 as the input data, those don't have the Mild (10-18 °C) category split into Mild (13-18 °C) and Cool (10-13 °C), so it's no surprise that _Cc_ doesn't appear. Btw, are you using the 6 or 8 precipitation category maps as the source map?

Also, you're better off looking at actual köppen maps as references, since the "generated maps" will inevitably contain inaccuracies due to the various simplifications and limited categories used.

I wouldn't worry too much about the occasional _Dw_ and _BS_ in Alaska and Canada. It's basically a result from using data from just two months. And if you check out a high-res köppen map of Alaska, there actually is some BS there, so I'd guess the region is quite dry overall.
*
Edit:*
I should probably mention that the newest version of the script (using the 6 precipitation categories) is available on page 34.

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## AzureWings

I've been using the 8-category precipitation map. I've internally been trying to use temperature and precipitation values rather than categories whenever possible - the input data is quantized but I've been thinking I'll have the script use configurable color profiles for temperature and precipitation (so you could swap between different input color sets and categorizations without having to alter the actual script itself), so I want the core classification logic to be agnostic of the input data categories. As it stands now my script was set up for the 8-category precipitation data input.

Tried the piecewise aridity threshold function from the Kottek et al. paper Azélor linked and everything's super humid now (ex. Australia with no B climates at all), so I'm going back over my math again.

Edit: Ah, was missing the factor of 10 applied to the threshold. New output looks a bit better on the aridity all around - still seems a shade dry maybe but I think its an improvement over the continuous-threshold version:

----------


## Charerg

> I've been using the 8-category precipitation map. I've internally been trying to use temperature and precipitation values rather than categories whenever possible - the input data is quantized but I've been thinking I'll have the script use configurable color profiles for temperature and precipitation (so you could swap between different input color sets and categorizations without having to alter the actual script itself), so I want the core classification logic to be agnostic of the input data categories. As it stands now my script was set up for the 8-category precipitation data input.


That's definitely the best approach. My script is much more simplistic in the sense that it just goes through a long series of colour-picking operations the old fashioned way, and the actual math was done separately in Excel by Azelor. But it's a much better approach from a data processing point of view to just put the criteria for the climate groups within the script itself. So I guess your script goes through the map pixel-by-pixel, checking the value from each source map and calculating the climate type based on that?

Although I'm a bit surprised that _Am_ is so limited if you used the 8-step maps as a data source. How did you set up the criteria for _Am_ climates?

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## Azélor

There are steppes between the tropics in strange places.

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## Charerg

> There are steppes between the tropics in strange places.


In SE Asia and Africa, yes. But those are pretty much certainly due to having data from just two months. Also, the BS in Guatemala & Honduras is far too extensive (but again, probably due to the 2-month-only nature of the source data). Mexico looks about right.

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## AzureWings

Many of the steppe areas in the tropical zones (particularly in central Africa) do seem to be an artifact; the script organizes the two temperature inputs in order of how hot to distinguish summer/winter (so that the input order or hemisphere don't matter as much), but flipping the default order for regions that have the same input temperature category makes most of those areas at least somewhat smaller except for in Eastern Brazil (even though that change to the script shouldn't really change anything). The reason for that in turn is probably the calculation for the aridity threshold, which makes me wonder what the canonical way to treat the tropical zones with low seasonal temperature variation is, since which threshold is used depends on whether the high(er)-precipitation season is considered summer or winter. For reference, changing just that order gives this result:


For the _Am_ climates, looking at pixels that had both input temperatures > 18 °C (so all the _A_ pixels), after ruling out _Af_ by checking whether both precipitation inputs are > 60mm, I then check whether the lower precipitation >= (100 - (Pann / 25)), where I compute an estimate for Pann via (6 * P1) + (6 * P2). P1 and P2 are the mean of the min and max values for the precipitation categories of the two inputs (e.g. if input 1 is of the 100-200mm category, P1 = 150mm and so on). A possible issue in high-precipitation cases is what I set the highest precipitation category to; I kind of eyeballed it since all I had to go off of was the category being 200mm+ and so the estimate there is actually 250mm. It's possible that Pann is thus being misestimated and feeding _Am_ to _Aw_/_As_, but I don't know what to base the reference precipitation for that category on instead since it lacks a maximum (and if anything I'd have thought I was overestimating the highest precipitation category and that would've made _Am_ more common rather than less).

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## Charerg

> Many of the steppe areas in the tropical zones (particularly in central Africa) do seem to be an artifact; the script organizes the two temperature inputs in order of how hot to distinguish summer/winter (so that the input order or hemisphere don't matter as much), but flipping the default order for regions that have the same input temperature category makes most of those areas at least somewhat smaller except for in Eastern Brazil (even though that change to the script shouldn't really change anything). The reason for that in turn is probably the calculation for the aridity threshold, which makes me wonder what the canonical way to treat the tropical zones with low seasonal temperature variation is, since which threshold is used depends on whether the high(er)-precipitation season is considered summer or winter. For reference, changing just that order gives this result:
> 
> 
> For the _Am_ climates, looking at pixels that had both input temperatures > 18 °C (so all the _A_ pixels), after ruling out _Af_ by checking whether both precipitation inputs are > 60mm, I then check whether the lower precipitation >= (100 - (Pann / 25)), where I compute an estimate for Pann via (6 * P1) + (6 * P2). P1 and P2 are the mean of the min and max values for the precipitation categories of the two inputs (e.g. if input 1 is of the 100-200mm category, P1 = 150mm and so on). A possible issue in high-precipitation cases is what I set the highest precipitation category to; I kind of eyeballed it since all I had to go off of was the category being 200mm+ and so the estimate there is actually 250mm. It's possible that is raising Pann too much and feeding _Am_ to _Aw_/_As_, but I don't know what to base the reference precipitation for that category on instead since it lacks a maximum.


Usually "summer" is defined as Apr-Sept for northern hemisphere summer, and vice-versa for the southern hemisphere. If you define the seasons based on when the max. temperature occurs, you end up with a map that can provide misleading information. Consider the Ethiopian Plateau in your map: it's depicted as summer dry because it's hotter during northern hemisphere winter (which is the dry season), so the seasons are reversed relative to other areas nearby like India, which can be extremely confusing.

Regarding _Am_, I suspect you have the highest prec category set with a too low precipitation value. The higher the value is, the more _Am_ will appear. As an example, if you set it up as 420 mm, any area that has the highest category of rain (and the other season is less than 60mm) will be classified as _Am_ (since the condition will then be Pmin>=0).

Btw, if you still use the 8-step precipitations, those have a 140-200 mm category as 2nd highest, so the average there should be 170 mm. I think I used 280 mm as an average for the highest category (Azelor used 300 mm, which provides similar results).

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## AzureWings

Updated the script to distinguish summer/winter more specifically, with inputs identified to be northern-hemisphere-winter and -summer respectively for temperature and precipitation; should be more technically correct although it brings back a bunch of the _BS_ zones in the middle of tropical areas. Also raised the 'pseudo-max' used to compute the average for the top precipitation category quite a bit, which brought in a lot of _Am_ that hadn't been present before. 420mm seemed like a huge jump over the next highest category, but then I thought about it a bit and realized I've had more than that in one month where I live.

Regarding the 8-step categories, 140-200mm is what I used in the script for the second-highest category; I just hadn't checked back with it before describing the example in my post (I just included that to try to disambiguate what I meant by P1 and P2.

This is what I'm looking at now:


I'm still a little unsure about the tropical zones with low seasonal temperature variation and the aridity threshold. Wouldn't the piecewise formula introduce a discontinuity at the equator, since there's a strong bias towards one season in terms of precipitation despite similar temperatures between seasons (so flipping when is considered summer/winter at the equator would add that +28 term to the aridity threshold right along the line)? Or is it just that in reality it doesn't matter since the precipitation is still so high anyways in those regions if the full year is taken into account (so it's just a difficult-to-avoid consequence of using these constraints for input data)?

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## Azélor

I don't know where you live, but I believe the maximum is 2768 mm of rain for the month of July east of Mangalore, India. That is the highest value for the month.




> I'm still a little unsure about the tropical zones with low seasonal  temperature variation and the aridity threshold. Wouldn't the piecewise  formula introduce a discontinuity at the equator, since there's a strong  bias towards one season in terms of precipitation despite similar  temperatures between seasons (so flipping when is considered  summer/winter at the equator would add that +28 term to the aridity  threshold right along the line)? Or is it just that in reality it  doesn't matter since the precipitation is still so high anyways in those  regions if the full year is taken into account (so it's just a  difficult-to-avoid consequence of using these constraints for input  data)?


Yea, I always thought that the winter/summer distinction did not make much sense close to the equator and messes things up. 
Normally, we have already discussed long time ago that the timing of the dry season matters. 
Climates with dry winter need more precipitation to be considered wet than the always humid climates and the summer dry climates.
But here the winter/summer refers to temperatures only, not the precipitations.
Since the temperatures are the same, there is not winter or summer distinction.
So while there can be a dry season, it doesn't make much sense to apply the s or w threshold.
No matter when the precipitations fall, the impact will be the same. 

So, I think maybe the area around the equator should use the f threshold, instead of the w or s thresholds?
Does that make sense?

It might be a good idea to try to extend this rule to all the places with little temperature variations too.

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## Charerg

> Updated the script to distinguish summer/winter more specifically, with inputs identified to be northern-hemisphere-winter and -summer respectively for temperature and precipitation; should be more technically correct although it brings back a bunch of the _BS_ zones in the middle of tropical areas. Also raised the 'pseudo-max' used to compute the average for the top precipitation category quite a bit, which brought in a lot of _Am_ that hadn't been present before. 420mm seemed like a huge jump over the next highest category, but then I thought about it a bit and realized I've had more than that in one month where I live.
> 
> Regarding the 8-step categories, 140-200mm is what I used in the script for the second-highest category; I just hadn't checked back with it before describing the example in my post (I just included that to try to disambiguate what I meant by P1 and P2.


420 mm was just an example I used to demonstrate that if you use that high an average *all* areas covered by the highest prec category should become _Am_ (unless they're _Af_). I don't actually recommend using that value  :Very Happy: . Using an average value of 280-300 mm seems to work well.

If we wanted to catch the areas that have an _Am_ climate due to receiving more than 2500 mm annual precipitation we'd need to add a new precipitation category for areas that receive above ~450 mm during the rainy season (that would push the annual prec over the 2500 mm threshold, even if the dry season has 0 mm). An example would be Conakry, it has 5 months with close to 0 mm, but the rainfall is so extreme during the rainy season that the annual total is well above the 2500 mm, and hence it's an "automatic _Am_ climate", despite having a very pronounced dry season.


Re: the thresholds
I think it's good to keep in mind that the primary reason that equatorial Africa is a mess is the limited data: since we have data from just Jan and Jul, only the extreme positions of the ITCZ over Africa are depicted, leaving a big data gap over the equator. Fiddling with the thresholds to remove the steppes could make other areas too humid, and ultimately the overall climate distribution would still be off in equatorial Africa.

Also, I suspect that if we're going to use just one aridity category for equatorial climates (which does make sense), it's probably better to use W (so 280+Tann*20). Otherwise the aridity threshold may be set too low. In general, I think the S (Tann*20) threshold is set deliberately "too low", since the _Cs_ areas can include some rather arid regions. Köppen based his classification on vegetation, and "Mediterranean vegetation" roughly represented by _Csa_  typically contains plants adapted to quite dry conditions. Some tropical _BS_ areas are probably more verdant than the drier _Csa_ climates in actuality.

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## Azélor

> (which does make sense), it's probably better to use W (so 280+Tann*20).


But then it will be even drier. 
I know the problem mostly comes from using 2 months only. 
For example Accra, Ghana : the wettest month is in June but we use the data for July (less than a third of the precipitations of June). Therefore it appears a lot drier that it is in reality. 

But it is not really a problem since when mapping the precipitations, Accra should be located right under the ITCZ. 
Maybe we should<nt bother too much about it. 
The map generated with the script will always be "wrong" as long as we use real world data.  
That is ok as long as the tutorial fixes these problems by slightly altering the precipitations.

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## AzureWings

That's certainly true - while nose deep in working on this it's easy to forget that the goal is for the script to provide a good model for speculative worlds - and so the input data for such applications will probably be a bit 'cleaner' in the sense that it'll probably be closer to having the two extremes for everywhere, rather than a time snapshot that catches temperature/precipitation extremes for many places but not actually all.

Using the F aridity category for all pixels that have the same temperature in both input datasets gives the following result (after I toned down the top precipitation estimate to 300mm):


Or, using the W category as alternatively suggested in those same cases:


In practice the metric of 'same temperature in both seasons' will probably only hit equatorial regions, polar regions, and maybe a few other anomalous spots, and all the _E_ climates are exempted from aridity checks already in my script - but it does still seem a kinda iffy way to check. I could restrict the assessment only to climates that would fall into _A_ barring potential aridity and have the same temperature in both seasons, if that seems like it would be a better representation of equatorial climates where precipitation in either part of the year would have relatively equivalent impact on vegetation growth (although as Azélor mentioned this concern might apply to other regions where temperature has very little seasonal variation, where they exist besides equatorial zones).

Once I've set my script up to use configurable input color profiles it shouldn't be too hard to give an extra precipitation category a shot for very-dry-dry-season _Am_ detection and so on.

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## Azélor

It looks like an improvement.

The driest category would be 0-5 instead of 0-10 ml?

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## AzureWings

The added category would be a new very wet category (break 200mm+ into I think 200mm-400mm and 400mm+ categories - the former would have a mean of 300mm and the latter would use a stand-in 'assumed mean' of 450mm) like Charerg mentioned, since _Am_ regions with a very dry dry season can end up _Am_ by virtue of having sufficiently high precipitation in the rainy season, but a 200mm+ category at the top isn't rainy enough to do it:



> If we wanted to catch the areas that have an _Am_ climate due to receiving more than 2500 mm annual precipitation we'd need to add a new precipitation category for areas that receive above ~450 mm during the rainy season (that would push the annual prec over the 2500 mm threshold, even if the dry season has 0 mm). An example would be Conakry, it has 5 months with close to 0 mm, but the rainfall is so extreme during the rainy season that the annual total is well above the 2500 mm, and hence it's an "automatic _Am_ climate", despite having a very pronounced dry season.

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## Charerg

> Maybe we should<nt bother too much about it. 
> The map generated with the script will always be "wrong" as long as we use real world data.  
> That is ok as long as the tutorial fixes these problems by slightly altering the precipitations.


Yes, I agree it's fairly safe to just use the standard criteria everywhere, even for equatorial areas. Since the "baseline threshold" is already 500-700 mm in equatorial areas with high temperatures, it doesn't make as drastic a difference whether the threshold has +140 or +280 extra applied to it as it does with the cooler climates. And of course those numbers are somewhat arbitrary to begin with.

Also, I agree that in practice when creating precipitation maps for a fictional world the equatorial areas are very likely to get messed up a bit because the abrupt season reversal. In any case, it's almost inevitable that some degree of manual cleanup work will be needed with the final climate maps.

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## Era

Have I understood right that you folks are working on a program to model climate in speculative worlds? That would be very cool, and if so I'd be curious to know how far it's come along.

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## Charerg

> Have I understood right that you folks are working on a program to model climate in speculative worlds? That would be very cool, and if so I'd be curious to know how far it's come along.


I'm afraid it's not a program. We've created a script that takes the precipitation and temperature maps for January and July as input and outputs the climate map. There's the original version of the script made for PhotoShop by Azelor, and the more recent GIMP script myself and Azelor created in collaboration (and which is slightly updated from the original, I guess). There's also an unpublished version by AzureWings (which is written in Python, I believe, and should be compatible with GIMP, not sure about PhotoShop).

Running the script saves you the trouble of manually creating the climate map, but you still need to create the aforementioned precipitation and temperature maps.

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## Azélor

> Have I understood right that you folks are working on a program to model climate in speculative worlds? That would be very cool, and if so I'd be curious to know how far it's come along.


That is way more ambitious that want we could accomplish. Depending on the level of precision we would need a full team of climatologist/programmer and a bunch of very powerful computers.

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## Kalium

That at least sounds easier than doing the whole climate map by hand like I did. So many spreadsheets... You have my full respect for this thread.

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## Charerg

> That is way more ambitious that want we could accomplish. Depending on the level of precision we would need a full team of climatologist/programmer and a bunch of very powerful computers.


Btw, I think it might be a good idea to put a link to the latest version of the GIMP script in the OP, so at least people know that there's one available, since the thread has grown so long that it would be a pretty heroic undertaking for a potential tutorial-user to read through all 37 of the pages  :Very Happy: .

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## Dagann

> That is way more ambitious that want we could accomplish. Depending on the level of precision we would need a full team of climatologist/programmer and a bunch of very powerful computers.


Have you thought about Universe Sandbox ?
Of course it's not a NASA soft and the climate aspect still seems to be underdeveloped.
But they have hired this year an "Astrophysicist, Climate & Simulation Developer" and their incoming Planet Grid could improve the climate simulation.
Mays be i'm too optimistic, but is there nothing you could do with this soft ? (i do not own it btw)

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## Azélor

> Btw, I think it might be a good idea to put a link to the latest version of the GIMP script in the OP, so at least people know that there's one available, since the thread has grown so long that it would be a pretty heroic undertaking for a potential tutorial-user to read through all 37 of the pages .


Will do





> Have you thought about Universe Sandbox ?
> Of course it's not a NASA soft and the climate aspect still seems to be underdeveloped.
> But they have hired this year an "Astrophysicist, Climate &  Simulation Developer" and their incoming Planet Grid could improve the  climate simulation.
> Mays be i'm too optimistic, but is there nothing you could do with this soft ? (i do not own it btw)


I just have the old version of the software, I was not aware that they added climate in the newest version.

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## Pixie

I'm neither a software developer or a climatologist, but as a science teacher/communicator specialized in physics, I would love to contribute to the development of an automated tool.

You know, I think you are really on to something. I helped with the kickstart but as of now, I have less time and you guys have just sprinted away from me. I can follow your discussions but not pitch in anything useful. Still, if/when it comes to rewrite those instructions for precipitation and temperature maps (or something like that) - count me in.

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## AzureWings

Speaking of said script, I've made the Python script available on Github: https://github.com/PHarvey7/speculative-koppen. It's not a script for GIMP or Photoshop directly; it's a command-line tool that just takes image files as input (all my testing has been on .pngs) and outputs a new one (possibly less convenient than an in-editor script, but also potentially more flexible). The directions and some of the sample files describe a couple simple formats for raw text files you can make and pass as additional arguments to specify custom input and output color profiles - e.g. what colors the script outputs for each climate category, and what colors your input data images use and what those colors mean.

Right now the input images must be RGB with no alpha channel, though my next steps with the script at some point are to make it more flexible in that regard and be able to take RGBA, etc. Currently the input color profile should include a color for ocean that the script will treat as such, in lieu of having the ocean on an alpha channel. Running the script also requires Python 3 and Pillow installed (for Python image file operations).

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## Kalium

OOoh... I'm tempted to give that script a go. Will need to redraw my rain/temp maps but I'd like to see how it stands up against my hand done climates. If I get any interesting results I'll be sure to let you know.

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## AzureWings

So I was running through the whole process for a world I threw together just for this (actually just to make source data for something else I was going to try, so it's done a bit hastily and shoddily, admittedly). I was finding the first step of assigning temperature categories (the "paint by isotherm" stage, as one might call it) a bit tedious, and decided to throw together another script similar to the other Python command-line one for it. It takes the result of the previous phases (elevation map and the temperature/current influence zone maps) and outputs July/January temperature maps. The resulting output obviously needs a fair bit of manual work still to make better transitions (witness the amazing sheer horizontal isotherm lines!), but that's kind of a step you perform anyways. The idea is this script does a lot of otherwise-busywork to give you a further-ahead starting point to work from for assigning temperatures.

With the current testing, as I didn't have Earth data in a convenient format immediately on hand, I was tossing these in:

Elevation input


January temperature influence zones


July temperature influence zones


And was generating these as output:

January temperature map


July temperature map


Obviously there's still a long ways to go on the temperature maps themselves - but does the "suggested temperatures"/"prefill" functionality this tends towards seem like something that could be worthwhile or is it probably better to just start from scratch than to work by hand from this as a starting point? If it does seem like it'd be useful to people I'll toss this script up in the same Github repository after some more tweaks and cleanup.

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## Charerg

I guess it could be useful for those users too lazy to create those maps manually  :Very Happy: . Personally I'd suggest just starting from scratch, but it doesn't hurt to have options available (and who knows, maybe it could become really solid with enough tinkering).

But without the latitude lines visible it's hard to judge how closely the generated temp maps follow the guidelines in the temperature section.

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## Azélor

It might be useful, although mapping the temperatures is probably my favourite step.

I'd just like to point out about the temperature inversion Chareng and I talked about a while back. If Antarctica was a sea level, temperatures could be warmer.

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## AzureWings

All the script does at present is do the basic isotherm-per-influence-type placement and then apply elevation adjustments, although I do have a few ideas on how it could be improved further. It doesn't place any of the extreme temperatures (very/severely hot or deadly cold outside of elevation adjustments) currently. I probably should remember to specify that while for the climate script the only thing that matters for projection is that the inputs all use the same one the temperature script needs an equirectangular one, since it directly pulls latitude from the pixel location in the image.

With a quick lat/long overlay those outputs I mentioned before are here (the small lat lines are 5° increments, although they're a couple pixels off in some places).

'January'


'July'


One thing I've already tried out as an improvement is internally giving the 'base' temperatures as interpolated between the edges of the category based on the latitude (e.g. a location with no special current influences at 50°N in summer being warmer than a similar location at 65°N in summer, even though the guide puts them both at peach) instead of applying a flat value to all the temperatures in that zone. The result produced doesn't really seem better (compared to the output I've posted) in terms of the discontinuities, although intuitively it seems like it should be; elevated regions do seem to get more continuity within themselves that way, although it can result in 1000m elevated areas being more than one temperature category removed from adjacent lower-elevation zones. One advantage using a script can give is using a continuous space for temperatures internally. I might try and see how the temperatures turn out if after applying the base isotherm and elevation changes I run a small localized average across all the local temperature values as a little bit of a 'smoothing' effect. The horizontal isotherm lines are probably going to be here to stay, but places with multi-category deltas between adjacent areas should benefit.

How continuous/linear does the decrease of temperature with increasing elevation tend to be? Since the script can work on a continuous space more easily than working by hand I could have it take smaller elevation categories (500m and such) into account if they're present into the input data.

Regarding the temperature inversion, I recall reading that discussion. It's definitely something I could look into adding, although again it'd be done in a relatively formulaic fashion.

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## Azélor

> How continuous/linear does the decrease of temperature with increasing elevation tend to be? Since the script can work on a continuous space more easily than working by hand I could have it take smaller elevation categories (500m and such) into account if they're present into the input data.


To my understanding, moisture is one of the most important factor influencing the lapse rate (how fast temperatures cool down with altitude). It's higher when it's dry.
Also, under certain conditions like with very cold temperatures, the lapse rate is inverted: meaning the lowland is colder than the surrounding mountains.

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## Charerg

As Azelor noted, the lapse rate varies a lot. The drier the air, the greater the lapse rate. With relatively moist air and relatively low elevation (0 to 3 km), the lapse is maybe 4-5 °C/km typically, but it can go up to 6-7 °C/km if the air is dry. There is also a general tendency for the lapse rate to increase as elevation increases (presumably because the air becomes drier). 

The average lapse (used by Azelor in the temp guideline) is 7.5 6.5 °C/km, but that applies to the whole troposphere from 0 to 11 km. In the lower levels of the troposphere (say, 0 to 5 km) the lapse rate is typically below the average.

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## antillies

Azure, can I ask what your method was for placing the temperature influence zones as it appears in the code?  Ever since you automated the temperature placement, I’ve been mulling over how one could create a standalone program/script for the entire process, but conceptually I’ve been having problems figuring out how such a thing would recognize the placement of pixels relative to their placement on their continent (coast, interior, etc.), becauze that would be crucial for placing/generating pressure zones.

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## Azélor

> As Azelor noted, the lapse rate varies a lot. The drier the air, the greater the lapse rate. With relatively moist air and relatively low elevation (0 to 3 km), the lapse is maybe 4-5 °C/km typically, but it can go up to 6-7 °C/km if the air is dry. There is also a general tendency for the lapse rate to increase as elevation increases (presumably because the air becomes drier). 
> 
> The average lapse (used by Azelor in the temp guideline) is 7.5 6.5 °C/km, but that applies to the whole troposphere from 0 to 11 km. In the lower levels of the troposphere (say, 0 to 5 km) the lapse rate is typically below the average.


I think the driest lapse rate is close to 10°C/km.

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## AzureWings

As of right now the temperature influence zones are part of the input - that is, those maps are produced by hand in accordance with step 5-1 of Azélor's tutorial steps linked in the first post of this thread. The script I'm working on for placing temperatures takes the elevation map and two influence maps as input (one for each seasonal extreme). I am making it configurable so you could define more types of temperature influence zones with their own isotherm tables if you wanted, but placing those zones in the input data is still left up to the user in the current formation of the script.

That said, I did a little thinking about how one might implement other steps, although building a good model would be a lot more complicated for some of them (like generating wind patterns). If I were to try automating the temperature influence zones multiple steps end up tied together as what I'd want for input regarding wind directions (a pixel-by-pixel vector field) would be rather hard to produce by hand itself.

That said, the specific case of recognizing a pixel's distance from the coast wouldn't be all that hard to implement in principle, although I'm not sure how the running time would end up working out since the simplest approach I can think of is a graph traversal of all the land pixels in the image. At the base level you could just build a shortest-path tree (like with common versions of Dijkstra's algorithm) after framing the problem a certain way - do one pass through all the pixels to collect a list of pixels directly adjacent to ocean pixels, and then just build the shortest-path tree from the initial set of these (treat all the immediate coastal pixels as a single starting graph node) and mark each pixel with its 'shortest-path cost' (i.e. distance to nearest coastline). You'd want to have nonuniform edge weights between pixels to handle projection distortions in the input image - if you were using an equirectangular input for instance north/south pixel boundaries would all have the same edge weights but east/west ones would vary with latitude (and there'd be an edge case regarding the fact that the east/west edge weight becomes 0 at 90°, though really that just makes all the 90° pixels into a single node like the starting one was). Implementation details aside this would feasibly give you a 'how far from coast' value for every pixel though.

If you wanted to find, other 'relative locations on a landmass', say, pixels on a continent's west coast, you could use similar approaches - an initial pass to collect pixels immediately to the east of oceanic pixels followed by a depth-limited graph traversal to collect connected land pixels within a certain distance of those. If you wanted to handle things on the scale of metrics applied to individual landmasses - say, find pixels on a landmass only above a certain size - you could initially go through and do a union-find to produce a set of pixel values with each pixel given an identifier to say what distinct landmass it is a part of, possibly also with a reverse index from landmass ID to its component pixels (and the latter would make it really easy to see how big different landmasses are). You could combine this with the 'how far from coast' value mentioned above to filter out long, thin landmasses and find only those landmasses with continental-scale impact, then perform further checks on that (might be useful if trying to figure out what landmasses would block currents more strongly as opposed to say island chains).

Ultimately there a lot of ways to construct metrics of useful information that isn't immediately available to a program just from the raw input image - but the question becomes how long you're willing to have the program take to run. As is the temperature script right now is just basically a set of lookup tables and it only makes one pass over the input image (which is probably going to change soon incidentally), and while I haven't done much work to optimize its performance it already takes 5-10 seconds on an about 2000x1000 input image, which isn't all that big.

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## Charerg

> I think the driest lapse rate is close to 10°C/km.


If the air is absolutely dry it's ~10 °C/km, but that is pretty much never the case close to sea level. Maybe above 4-5 km the lapse rate could drop that low, but overall I think it's very rare for the lapse to be that drastic in the lower troposphere.

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## antillies

Azure, I'm incredibly impressed not only with how well-thought out your approach to this is but how much our minds are in agreement.  A pixel-by-pixel vector map is exactly what I had thought of for mapping winds.  Then, you would only need to add a "natural wind" vector map with one representing the pressure zones to produce a map of the resultant winds.  It would be then fairly simple to model what moisture is carried via the winds, perhaps having a pixel object that holds various values for wind direction, wind strength, amount of moisture carried, etc., to generate the (rough) precipitation map.   

Your suggestion to use a shortest-path tree is far better and more efficient than what I had been thinking of (finding the distances to all coasts for each pixel) to determine what pixels lie within the interior.   As you say, using those values combined with the number of pixels per land mass would make it very easy to filter out landmasses of certain sizes and distinguish island chains from actual continents.  The only remaining issue I can see with that is how to approach a North America/South America (or even Africa/Asia) situation where two continents are connected via a thin land bridge?

Personally, I'm not too concerned with run time.  The initial sweeps of "data collection" from the input sources to build the overarching data structures would take the longest I would imagine, depending on the image size.  To generate a full albeit basic climate model I'd be willing to wait even a few minutes, but I think that would be a very upper limit.  With optimization, I'm sure it would take at most a few dozen seconds, or at least that would be my goal.  I'm going to start fiddling around with this to see what I can get.

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## Azélor

What is a   A pixel-by-pixel vector map ? each direction would have a different colour? 




> a pixel object that holds various values for wind direction, wind  strength, amount of moisture carried, etc., to generate the (rough)  precipitation map.


That is doable, but the maximum value would be 255.  With 2 degrees per pixel instead of one, it's still precise enough.

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## antillies

> What is a   A pixel-by-pixel vector map ?.


My conception of it is not that the vector map itself would be represented by pixels but that the map (which could be any array- or table-like data structure, or even a collection of objects) would have a vector _associated_  with each pixel.  A vector in this case is a X and Y value between 1 and -1 that represents a specific direction in relation to a Cartesian plane (i.e. +Y is upwards on a graph, -Y is downward, +X is to the right, and -X is to the left).  (0,1) would then be a vector pointing directly "north"/in the +Y direction.  This is extremely useful for representing wind direction since vectors give us a full 360 degrees to work with.  The best thing about vectors is that they can be combined to give resultant vectors (i.e. Vector A + Vector B = Vector C), so applying a force (i.e. pressure) to a given vector representing a wind would be a simple calculation.

In regards to "pixel objects" (and here my idea for implementation may differ from Azure's), I meant more that when the data from the input images would be digested, every pixel would have a Python object associated with it that would contain relevant information for that specific place on the map.  That could include elevation, wind direction, wind strength, moisture level, its XY coordinate location on the map, if it falls on the coast or the interior, how far away from the coast is it, etc..  This may not be the most efficient means to store that information but it would be a decent place to start to keep things organized, at least for me.  That way, when the program begins to make the various other maps (wind, current, temperature zone, rainfall, etc.), it can call upon any information stored within that object when it is looking at any given pixel.

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## Azélor

So you would need to store the information outside of the image. Unless you use a bunch of layers.

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## antillies

Correct.  I know Azure is using Python for his script and coincidentally Python would also be the language I would use.  I think full-automation of the process might be too complex with Photoshop, or at least I don't know enough of the ins and outs of it to say.

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## Azélor

I think Photoshop can handle python (apparently) but it's not a native language. 

http://techarttiki.blogspot.ca/2008/...th-python.html

One thing I was wondering is how do you take the wind direction input and convert it to a vector. Well I guess I should start by asking what the input is in the first place. I assume it is something fancier than the arrows made by hand https://www.cartographersguild.com/a...4&d=1442594602

Maybe the winds are generated using the air pressure maps? After all, most of the time, winds are influenced by pressure differential between two zones and the Coriolis force, and the general atmospheric circulation pattern and mountains. I'm not sure how hard it would be to incorporate all these variables.

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## AzureWings

Indeed - there'd be more information associated with each pixel than could be stored in a color value. I've mainly been approaching the problem from the point of view of the scripts I've been writing, which aren't integrated with any image editors like PS or GIMP, so defining a place to store extra data isn't the tricky part. That said, you probably could full-automate the process in them, but working in an environment integrated with a much more heavyweight system like that has a good chance of incurring significant costs in efficiency (especially if you need to use image components like extra layers in large numbers as your data allocation). Not all of that extra information would ever be output to any one image, but would be used to construct them. By 'pixel-by-pixel vector field' I meant a data structure with a vector for every pixel in the source image - antillies' mention of "pixel objects" is pretty much right on to what ultimately would be needed, although the precise layout of that data might differ (not sure at the moment, but it is possible there would be relevant tradeoffs to storing a set of objects-per-pixel or having instead a set of parallel arrays that each hold one data value). For the moment I've been doing more of the latter since I've been working on single steps, but 'pixel objects' is a natural progression in constructing a more generalized approach to a broader/multistep formulation of the problem.

The reason efficiency is a concern is that these images can be fairly large, so whatever operations you do per pixel have a huge constant factor. I'll admit my perception is a bit colored by the fact that testing the code involves running it quite a bit  :Wink: , whereas in general use you're right that a command-line process in this style that could be launched in the background can reasonably take a bit of time to run. The space efficiency can be somewhat of a concern, since that doesn't make a big difference up to a certain point and then it is enormously important - and with the input sizes of the maps it can become an issue. For say a 4000x2000 image which isn't really all that big every byte of information stored per pixel is ~8MB of memory which, while not all that huge in and of itself, can add up, especially if one starts looking at higher-res images for source data (or say, storing a vector that in Python is probably two doubles, so 16 bytes per pixel -> 128MB for the 4000x2000 example - again not a ton by itself, but starting to look more concerning in the presence of multiple other similar data values). And if you move up to a significantly larger resolution like say 16000x8000 - then it's ~128MB per byte per pixel, and as soon as that adds up enough that the OS starts swapping memory to disk (at best, when you approach the amount of RAM on the system) the performance will become much, much worse.

The shortest-path tree idea comes from how a lot of flood-fill algorithms work - they treat pixels as nodes and do a graph traversal. Here we'd just do a weighted graph traversal and annotate each pixel/node with the distance we found from the 'supernode' (which represents all the oceanic pixels together). Practically we might need to separate closed seas from ocean first in constructing that set (e.g. like the Caspian Sea on Earth) but that can just be union-find again (and that can be implemented in linear time in this context; it just takes one pass over the image since we only care about adjacency and not distance for 'what pixels are part of which separated bodies of water?').

As for separating landmasses with a land-bridge, you could do union-find for major landmasses on only pixels meeting a threshold of distance-from-ocean (not just in pixel count, but distance - this both helps with projection distortions and keeps the algorithm's output agnostic to the image resolution). Then you could make these 'major landmass' pixel collections into supernodes to use as roots for a new graph traversal - labeling each pixel with the supernode they have the closest land distance to and using that to allocate pixels to discrete landmasses. Obviously this couldn't be flawless - it would depend significantly on what the threshold for considering something a narrower land-bridge is (i.e. how close to the ocean a pixel is considered coastal as opposed to central to a major landmass), but with the right tuning I'd expect this approach could reliably identify as separate North America vs South America or Eurasia vs Africa.

Regarding generating winds - I hadn't thought of that in terms of overlaying a base 'natural winds' framework the likes of which I think I recall fairly straightforward diagrams of before with another for the pressure systems - which could have systematically defined behavior based on an input of a human-readable color-by-pressure-system map like what is already in the tutorial.

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## Azélor

To me, the landbridge is the same as the coast from any continent. The area is just the same but smaller. Yet I not expert in python and might be missing something.

AzureWings: you need to store more data per pixel if the map is larger? Or maybe  I misunderstood ( 128mb per byte???).

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## AzureWings

Ah, no - sorry, the point I was making was that, from a perspective of storing extra data for every pixel, storing a small number of bytes per pixel can get costly if the map is big enough because it adds up rather quickly. My example was that for a 4000x2000 map, storing a vector of two floating-point values (double-precision, in Python, most likely, so 8 bytes each) per pixel is 16 * 4000 * 2000 bytes for the whole map, which is on the order of 128MB. That's for the whole map, so as I said it isn't too bad, but if you store multiple various chunks of extra data it can add up. There doesn't need to be more data per pixel if the map is larger - it's just that even a small amount of data per pixel can add up to a problematic size if the map is big enough.

The issue with the landbridges is if we wanted a script to be able to distinguish between separate continental landmasses - i.e. be able to tell the difference between one large connected continent and two continents connected by a narrow land bridge that is entirely 'coastal' in terms of its ocean proximity. It's easy to tell the land bridge is coast; the trickier part is being able to tell the continents apart. Although it isn't something explicitly used in the tutorial currently, for a script trying to find automated ways to make nicer results being able to distinguish between separate continental landmasses might be useful, and the landbridges add a complication to programmatically trying to tell which landmass a given pixel is a part of.

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## Azélor

Would'nt it be easier to make the delimitations manually? If you don't mind using a graphic software for that.

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## AzureWings

That is certainly an option - although if the delineations were drawn lines, a union-find would still be needed for the program to be able to separate pixels into their respective landmasses. A map with a distinct color per landmass would be easiest, but that would require an entirely separate input map (while it is conceivable delineating lines could be placed on an input image containing other miscellaneous data). It would simplify things in code and probably save running time, though the cost would be requiring additional sets of input data, and there's already quite a bit of input to get set up for it. It's kind of a trade-off, in other words, between how tricky it is to code the script and how much work needs to be put in by a user of the script; when possible I like to err towards the former and leave the script easier to use when possible. With the right thresholds for what marks a pixel as landlocked or coastal, automated major landmass detection seems like something that could largely operate completely automatically and still err on the correct side in how it divides pixels into distinct continents.

I'm getting a bit ahead of myself in this planning/speculation, though - I'm still setting things up for the temperature script to attempt more nuanced approaches from its basic form.

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## Vareck Bostrom

> To me, the landbridge is the same as the coast from any continent. The area is just the same but smaller. Yet I not expert in python and might be missing something.
> 
> AzureWings: you need to store more data per pixel if the map is larger? Or maybe  I misunderstood ( 128mb per byte???).


I scanned this thread so I might not be grasping the nature of the problem, but you can store any amount of data per pixel that you want. You can even use already defined file formats like planar TIFF or PNG. I presume you're going to want all pixel associated data in planar format anyway if you're going to be processing via CUDA or OpenCL, or am I missing something?

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## Vareck Bostrom

> The issue with the landbridges is if we wanted a script to be able to distinguish between separate continental landmasses - i.e. be able to tell the difference between one large connected continent and two continents connected by a narrow land bridge that is entirely 'coastal' in terms of its ocean proximity. It's easy to tell the land bridge is coast; the trickier part is being able to tell the continents apart. Although it isn't something explicitly used in the tutorial currently, for a script trying to find automated ways to make nicer results being able to distinguish between separate continental landmasses might be useful, and the landbridges add a complication to programmatically trying to tell which landmass a given pixel is a part of.


And once again I'm probably jumping in before I should, but what's wrong with doing an erode pass or two to get rid of narrow land bridges? Most software libraries (that I know of, anyway) that offer morphological componentization will offer some kind of tolerances around that. 

The problem is that "a pixel" has different meanings depending on your location on the map, and a method like this would have to take care with border or wraparound continents. You could also raise the sea level until you have the appropriate number of morphological components, lower it again, and define anything not identified as an individual component as a "land bridge".

I have to read the tutorial, but why do individual continents need to be identified at all? I would think that a somewhat robust climate module would take input boundary conditions and sort of sort all that out for itself?

At any rate, I make use of arrays that are sometimes interpreted as images and are easily imported and exported to common image formats, and I'd recommend this approach in general as it keeps the data in one simple package to move around. 

Here, for example, the world I have been working on: 


The first image is the daily heat map from solar insolation balanced by thermal energy bleeding into the lower layer of the atmosphere, which of course has a strong altitude component. The second image is the morphological breakdown of the individual land areas - but that's wrong and not used. I have a single continent and a couple large and medium islands.

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## AzureWings

Nothing's wrong with some number of erode passes (though we haven't had any discussion on ways to model erosion in this thread as far as I recall); that also seems a reasonable enough approach to distinguishing separate major landmasses. The by-hand tutorial as is doesn't make any direct use of distinctly identified landmasses, but it's not inconceivable that that information could be of use somewhere - being able to identify coastal regions as bordering a particular landlocked area might be of use (distinguish, for example, a coastal area not within the ITCZ's low-pressure zone in one season but bordering an inland region that is, from another coastal area also not within the ITCZ, bordering a different inland region that also isn't in the low-pressure zone). The continents aren't to be limited to a set number of distinct components - rather, whether they'd be distinct would ideally be a matter of relative connectedness, for which it is likely that existing solutions are out there (though personally I'm often of a bent to construct algorithms myself).

You're correct that simply using pixel adjacency neglects projection issues (or wraparound, though that's generally not too bad of an edge case to handle); I've been using pixel-based representational terminology a fair bit because the simplest beginning portion of the temperature-generation script (and the preceding temperature+precipitation -> Köppen-Geiger classification scripts) operated on individual pixels in isolation across images - the operations done up until the recent more complex discussion on each pixel have been completely separable (i.e. did not depend on the east/west position of a pixel nor its spatial relationship to surrounding ones) and so the varying meaning of each pixel in terms of geographic area wasn't relevant to those implementations. The more current discussion regarding how to better model various aspects of building the necessary data indeed needs to take projection distortion, adjacency across east/west boundary, and the +/- 90° latitude edge case into account.

Thus far (and we've only entered this discussion on automating steps other than the final, rather straightforward mapping from temperature+precipitation to climate class in the last week or thereabouts, I think) we've been more focused on the functionality of the climate modeling part, rather than optimization, so the significant potential boost via parallelism (on a GPU or otherwise) has not really been on the radar yet, re: CUDA/OpenCL/etc. Input for now has been in the realm of hand-generatable images, in line with the tutorial, which means that some conversion on data read is generally necessary - if partly just because it's nice to have the input be more human-visually-intelligible. I agree with storing the data in arrays overall; and at the higher end of efficiency optimizations parallel arrays of data would probably win out over a single array of multivalued objects containing multiple data components, though again at the current point we haven't been especially concerned with optimization. Storing the data internally in an image format with a lot of extra channels to accomplish that wasn't something that had occurred to me; that's a rather neat idea if not quite suitable for the base input format.

Modelling insolation is rather neat, by the way, although it wasn't directly a part of the method used in the existing tutorial here for coming up with temperatures.

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## Vareck Bostrom

> Nothing's wrong with some number of erode passes (though we haven't had any discussion on ways to model erosion in this thread as far as I recall); that also seems a reasonable enough approach to distinguishing separate major landmasses.


I meant binary morphological erosion - the opposite of morphological dilation.  https://en.m.wikipedia.org/wiki/Erosion_(morphology), not terrain erosion from weathering processes.

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## Azélor

> although if the delineations were drawn lines, a union-find would still  be needed for the program to be able to separate pixels into their  respective landmasses.


Not just lines but fill the whole landmass. I know it's another step but it should not take long to do. Whether it's worth it or not depend on the complexity of the algorithm, which a have absolutely no idea. 




> I presume you're going to want all pixel associated data in planar  format anyway if you're going to be processing via CUDA or OpenCL, or am  I missing something?


To be honest, I don't know what CUDA or OpenCL are.

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## AzureWings

> I meant binary morphological erosion - the opposite of morphological dilation.  https://en.m.wikipedia.org/wiki/Erosion_(morphology), not terrain erosion from weathering processes.


Thanks for clarifying! The result of applying morphological erosion to landmasses in this case though seems like it would be fairly similar to a simple distance-from-ocean threshold (if somewhat smoother), and obtaining distances from nearest ocean is something that would already be worthwhile in and of itself.




> Not just lines but fill the whole landmass. I know it's another step but it should not take long to do. Whether it's worth it or not depend on the complexity of the algorithm, which a have absolutely no idea.


It would save on running time, though the amount it would save by, while not insignificant, would still be marginal in that similar steps in terms of computational expense would still be necessary to construct other data (like distance from nearest ocean). It is an option though.




> To be honest, I don't know what CUDA or OpenCL are.


They're frameworks for parallel programming on GPUs (they're a bit broader than that but that's what I take to be the suggestion for their use here). Since there are a lot of individual data units (e.g. pixels) with similar operations being performed on each, if and when we aim to make any automated system for climate generation more efficient it's potentially a very good candidate for such parallelism.

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## Azélor

It takes about 5-10 minutes to do the continent step with Earth. Could have taken less time but I was concerned about what to do with the islands like Japan, Newfoundland, Madagascar, South east Asia...
It probably does not matter which continent they are part (or if they are part of a continent to begin with) since they are likely to be considered coastal.   
How would the algorithm manage archipelagos like the Canadian Arctic or Patagonia?
In winter, the Arctic would be considered like a single large landmass because of the ice sheet. At least that was the assumption in the tutorial. (although I did put Greenland as a continent)

Have you considered automating the precipitation part as well? I just want to know if you thought about it.

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## AzureWings

I'd figure you could have a configurable threshold for what latitude to approximate the start of an ice sheet in each season, and that could be taken into account by an algorithm for painting the temperature influence zones (i.e tend to paint even coastal land north of that line with continental zones unless they're under a hot/mild-current influence). If going into winds/pressure systems/other earlier data you might actually have the algorithm construct a 'mock landmass' out of the ice extent and treat it as a landmass.

If temperature generation is being automated, I figure it only makes sense to give precipitation a shot too, but I hadn't really looked at the task in any detail yet.

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## Azélor

I made a script for Photoshop to indicate the distance from the sea. I'm not sure if it's better than the default tool included (interior shadows, I think) since it has all these strange lines, but maybe it's supposed to be like that?
It does not take in account the map projection, and Antarctica is wrong since the script thinks there is a ocean south of the south pole.

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## AzureWings

While it looks visually a bit strange, I think there's a bit of visual trickery/optical illusion going on with how we see the darkness of the lines as compared to their surroundings, since the surroundings are varying too - all of the lines no matter how faint should essentially be 'ridges' where multiple coastal points are tied for nearest ocean pixel (moving off the line gets you closer to at least one of those points so it gets brighter, which is what forms the lines). So I think they're expected, just visually non-intuitive (especially since distinguishing relative brightness is something that is very sensitive to the surrounding visual context, such as with the checker shadow illusion).

One test you might give it is a perfect circle - outside of issues caused by aliasing artifacts there shouldn't be any striations there, since aside from the center of the circle any point within it should have only one nearest point on its perimeter.

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## Azélor

I guess it's normal.
The circle eventually becomes an octagon, though very gradually. It can't be a perfect circle since PS is a raster program. 

While it might be useful to know the distance from the sea, it does not help figuring the distance between points. So, I guess using PS for this is not a really good idea. 

I was also considering using Qgis to help with the precipitation step.

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## AzureWings

Unfortunately an averaging pass or two over the temperatures is not offering all that satisfactory of results overall (and causes the program to take several times longer to run). I'm taking a bit of a break from automation attempts to run through the hand process again with a bit more care on a new map - had the thought about previous comments regarding pack ice causing surrounded land and islands to act like continental zones in terms of temperature influence. Looking at where it's marked on that large, detailed current map you posted in the currents step, and on another one I found from the 1930's, permanent pack ice extent (at least, pre modern-era climate) comes down to about 70-75° latitude from the poles, while winter extent gets to about 60°, and from those baselines veering away from wherever warm/mild currents are present. Does that sound about right?

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## Azélor

> Unfortunately an averaging pass or two over the temperatures is not offering all that satisfactory of results overall (and causes the program to take several times longer to run). I'm taking a bit of a break from automation attempts to run through the hand process again with a bit more care on a new map - had the thought about previous comments regarding pack ice causing surrounded land and islands to act like continental zones in terms of temperature influence. Looking at where it's marked on that large, detailed current map you posted in the currents step, and on another one I found from the 1930's, permanent pack ice extent (at least, pre modern-era climate) comes down to about 70-75° latitude from the poles, while winter extent gets to about 60°, and from those baselines veering away from wherever warm/mild currents are present. Does that sound about right?


Yes, the presence of an ice sheet is highly dependent on the water temperature.

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## martinmike2

Hey,

Im giving this tutorial a try and have just finished my heightmap.  What do you guys think?

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## AzureWings

The high areas seem a bit... uniformly centered on the continents? If you're going for a height map that reflects some sort of tectonic basis, I might expect the mountains to reflect that in some way - right now they seem a little too much just 'located at the middle' everywhere. 

A point I think Charerg mentioned in another thread is that (if you're going for a relatively Earthlike configuration) >4km elevations are actually rather uncommon, and most continents on Earth don't have much of it (and not all continents reach the same sort of elevations - most of them don't have the Andes or Himalayas, and there are quite a few mountainous regions on Earth that don't hit 4km at all); moreover >6km is extremely rare and confined to the very highest peaks. It may be a consequence of just defining general areas but unless your intent is for your mountains to in general reach higher extremes than Earth's your white areas look a bit large. Likewise, you mostly just have the lowest elevation category along the coastlines; Earth has some large regions that are mostly comprised of the lowest elevations. Again, this only is a big deal if you're going for a very Earthlike configuration, although it will affect climates you end up with.

Also, you've got a few of what look like awkward artifacts around the north-central island chain - some lines that look like unfinished island outlines. That does raise another thing about the islands - while having them located in chains is good the average island size seems a little bit high (or put another way, the island chains seem to be really dense in terms of the amount of land in a given area). This seems like a 'sense of scale' issue that's pretty easy to run into (I do it a lot too) but in terms of relative size on a map of Earth many islands are just a couple of pixels (even at a resolution like 40000x20000). That's not to say islands the size of yours don't exist - obviously they do - but they tend to (often though not quite always) be accompanied by a number of smaller islands too. Granted, putting those smaller islands there can get tedious (and is often a bit visually unsatisfying), so it's a question of how precise you want to go.

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## martinmike2

I could definitely see those points as valid.  I might go back and reduce the height on some of the mountains.  For informative purposes, I've added the plate tectonics, the ocean currents, and the wind patterns

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## NadirtheFox

Hi!
I decided to give this tutorial a try. So far I have got oceanic currents, january and july wind maps, but before I go any further I just want to make sure that I got this right:



I think I messed up the wind maps somewhere but can't tell where :/
(Also I am sorry for my bad english)

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## Charerg

Overall looks really good. I don't see anything too far off the mark, and the elevations are very detailed.

As a minor nitpick, you forgot one of the oceanic high pressure centres in the July map, the one located here:



Remember that each time you have a "full loop" of oceanic currents, there will be a corresponding pressure centre.

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## NadirtheFox

Ok, I added this high pressure center as you suggested:

I also made some minor changes to the both wind maps to (hopefully) make them a bit more realistic:

I guess it is time for influence maps?

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## NadirtheFox

This is my best try at influence maps. I have to say that after three attempts I got pretty fed up with it, because this step turned out to be the hardest so far for me. I get the principle but judging where certain influence zone should end can sometimes be tricky especialy if you can't find any references appropriate for some areas  :Neutral: 
 (january influence)
 (july influence)

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## Charerg

> This is my best try at influence maps. I have to say that after three attempts I got pretty fed up with it, because this step turned out to be the hardest so far for me. I get the principle but judging where certain influence zone should end can sometimes be tricky especialy if you can't find any references appropriate for some areas 
>  (january influence)
>  (july influence)


The influence maps are just a useful aid that will help you weight the various influences affecting an area. Remember that their only purpose is to help you in creating the temperature maps, that's all they will be used for. So don't worry too much about them, just go with your best guess.

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## NadirtheFox

> The influence maps are just a useful aid that will help you weight the various influences affecting an area. Remember that their only purpose is to help you in creating the temperature maps, that's all they will be used for. So don't worry too much about them, just go with your best guess.


Good to know, Charerg XD I will keep that in mind next time. (But yeah - it turned out to not be such a big deal after all...)
And the temperature maps are now done:
 january
 july
I highlightet area that I'm concerned about the most. Is this sort of separated cold region even possible? It looks odd but kinda makes sense if you consider wind patterns of this region :/
Aside from that I hope I haven't messed up the rest too much.

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## Charerg

The cold region does indeed look "out of place". What is the idea behind it, shouldn't that area be under hot current (or at least maritime) influence, like Europe? 

Though I guess the areas north of that major mountain range would be fairly arctic.

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## NadirtheFox

The cold area is under continental influence (there is high pressure centre in the winter) while the region north of that mountain range is influenced by winds blowing from the sea (I treated it as mild maritime influence but maybe it should be cold?) Then again, it is possible that I overestimated the impact that these influences have on local temperatures...

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## Charerg

> The cold area is under continental influence (there is high pressure centre in the winter) while the region north of that mountain range is influenced by winds blowing from the sea (I treated it as mild maritime influence but maybe it should be cold?) Then again, it is possible that I overestimated the impact that these influences have on local temperatures...


I think it's unlikely that the area to the south of those moutains would develop a Siberian High-style high pressure center. It sounds a lot more plausible to me that the moutains would basically form a barrier between arctic and temperate air masses, and the areas to the south of the mountains should have a relatively temperate winter like Europe. At any rate the areas to the north should be colder than adjacent areas to to the south (assuming similar elevation).

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## NadirtheFox

Ok, I modified the area a little (temperatures are now a bit less extreme)

I am still hesitant when it comes to lowering the temperatures north of this mountain range - the winds should be blowing from the sea after all. (I'm keeping the winter high because the region feels wrong without it) And while we're at it: if the winds are blowing from land through that southern sea will they bring rainfall to the equatorial contnent? The closest comparison is probably the red sea, so the answer would be "no", but it isn't perfect match (I kinda need it to rain there)

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## Charerg

> Ok, I modified the area a little (temperatures are now a bit less extreme)
> 
> I am still hesitant when it comes to lowering the temperatures north of this mountain range - the winds should be blowing from the sea after all. (I'm keeping the winter high because the region feels wrong without it) And while we're at it: if the winds are blowing from land through that southern sea will they bring rainfall to the equatorial contnent? The closest comparison is probably the red sea, so the answer would be "no", but it isn't perfect match (I kinda need it to rain there)


Even if the wind does blow from the sea, those are arctic waters that could even be frozen for the most part, since the area isn't affected by a hot current in your currents map. That said, the current version is fairly good, although I'd suggest ramping up the temperatures of the islands and the coast along the southwestern margin, at least below the 60° latitude line, since those areas actually are affected by a warm current (like the coasts of Norway or Alaska).

Islands in general shouldn't experience extreme temperatures unless the sea around them actually freezes over (for example, check the winter temp in Iceland from this post, though keep in mind those maps have an extra "_Chill_" category from -3 to 0 °C largely covering the Icelandic coast, so don't get confused by that).


EDIT (About the rainfall question):

Let me see, by "equatorial continent" I assume you refer to the large warm landmass, and the southern sea is the small Mediterranean-esque seaway between the northern landmass and the equatorial landmass. I'd say this body of water could supply some degree of water to the regions around it, but at the end of the day the area is in desert latitudes and woul probably be arid overall. I suppose there could be a summer monsoon, and the eastern coast would likely receive fairly regular rainfall from that large equatorial ocean (which btw, should probably have a greater warming effect on the coasts of your northern continent, that's basically a relatively closed body of tropical water).

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## NadirtheFox

> Even if the wind does blow from the sea, those are arctic waters that could even be frozen for the most part, since the area isn't affected by a hot current in your currents map (...) I'd suggest ramping up the temperatures of the islands and the coast along the southwestern margin, at least below the 60° latitude line, since those areas actually are affected by a warm current


I can see your point Charerg. I made some more changes to the map according to it:

(I'll fix mountains later)
When it comes to the coasts of the equtorial/inner ocean tho... I suppose I can raise temperatres there by a category but I'm not 100% sold on that. This area is still quite far north and the winds are blowing mostly parallel to the coast so I'm not sure if it will have such a big impct on temperatures (Although I can be wrong about it) :/ 




> Let me see, by "equatorial continent" I assume you refer to the large warm landmass, and the southern sea is the small Mediterranean-esque seaway between the northern landmass and the equatorial landmass. I'd say this body of water could supply some degree of water to the regions around it, but at the end of the day the area is in desert latitudes and woul probably be arid overall. I suppose there could be a summer monsoon, and the eastern coast would likely receive fairly regular rainfall from that large equatorial ocean


That's the one. I count on that summer monsoon here as the continent is suposed to be covered by "tropical forest" at lest for its the larger part. But can I make land east of that inland range category "2" on rainfall map (so it's not completely dry)? (As a side note: I suppose I can make some "wip" names for main regions of this world/give them numbers to avoid confusion. I just can't use official names for copyright reasons)

Oh, and one more thing: Should I change something in july temperatures or can I leave them as they are?

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## Charerg

I guess the temperatures around the inner ocean might be fine as they are. As you say, the area is pretty far north. I didn't analyse the July map in detail, but it looked OK at a glance, so I'd say just move on to precipitations and modify the temperatures later if something looks weird (after all, the climate generation itself takes only a moment if you're using one of the available scripts).


*Edit @Azelor:*
Btw, speaking of scripts, it might be an idea to add a link to AzureWings' version as well to the OP. It does offer a lot of extra flexibility compared to the clunkier one I wrote, since you can set up as many temperature/precipitation categories as you think are necessary, and the actual math is then done by the script.

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## AzureWings

I've updated my version of the climate classifier script to be more accurate to the category definitions in the Kottek et al. paper (thanks again Charerg for pointing that out!). I've also added support for RGBA images or images with other extra channels besides R, G, and B (which the script now deals with by ignoring all those other channels, instead of just failing), and also added a short message printed to the command line on successful completion that includes the time that it took to run (based on just clock time, so if you put your computer to sleep for a while while the script is running and then start it up again the time will look extremely long  :Razz: ). The latter message can be optionally silenced if you'd rather not have it. I've pushed all the changes to Github so the same link from my earlier post will go to the latest version of the script.

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## NadirtheFox

Well, since the temperatures turned out to be a bit of an issue I decided to install Clima-Sim and check them this way  :Smile:  Here are the modified maps:
 It seems that I simply underestimated the effects some influences have on the northern landmass (well, sometimes by a lot apparently :/ ) Other changes are completely cosmetic.
Besides that I made (rough) temporary dry/wet maps:
 january   july
I can't tell if this is right or wrong :/ I followed the instructions but in some places I had to just guess and my intuition then was like ¯\_(ツ)_/¯  
What do you think? Is it "good enough" or something needs to be changed?

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## Charerg

The "rough" dry/wet maps are (like the influence maps) more of a guideline. I suspect some things may be off, but it's hard to say based on the rough maps, because it's difficult to compare these guideline maps to precipitation maps of Earth. With that in mind, you should probably move on to creating the actual precipitation maps, then you can actually generate the climates and start looking at places that seem to be off the mark.

Here are some precipitation maps of Earth you can compare yours to (from Climate prediction center):

January:


July:

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## TheSquareRootOf2

Hey! I also gave your wonderful tutorial a try, or at least started to. Here's what I've done so far.

Currents


January


July



I'm pretty certain that despite my efforts there are still many mistakes lurking. Especially with the winds. Anyway, I would love to hear feedbacks!
Thanks in advance.

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## NadirtheFox

Just a quick update: I finished precipitation map for january and before I start working on july map I just want to make sure that I am getting this thing right  (I intentionally ignored orographic lift because it's an additional work that doesn't seem to change all that much and I really need to speed the work up...)

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## Charerg

> Hey! I also gave your wonderful tutorial a try, or at least started to. Here's what I've done so far.
> 
> I'm pretty certain that despite my efforts there are still many mistakes lurking. Especially with the winds. Anyway, I would love to hear feedbacks!
> Thanks in advance.


A good start! The currents look to be ok on the whole. Not so sure about the winds, but it's probably easier to just move onwards to temperatures and precipitation, you can always change things later on as you learn more about climate and perhaps discover that your first interpretation has some flaws.




> Just a quick update: I finished precipitation map for january and before I start working on july map I just want to make sure that I am getting this thing right
>  
> (I intentionally ignored orographic lift because it's an additional work that doesn't seem to change all that much and I really need to speed the work up...)


Looks plausible on the whole, with some possibly problematic areas. Tbh, I kind of think that neither myself or Azelor really has the time for detailed feedback regarding precipitations, which is somewhat unfortunate. That said, now that there's plenty of scripts available, it's very easy to actually generate the climates and make changes to temperature and precipitation afterwards. So, my advice is to just go with your instinct for now and create the final precipitation map, later on I can hopefully provide some more detailed feedback about the climates themselves.

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## Azélor

I wanted to participate in the actual monthly challenge but ended up doing something else and life caught in the way. So I will post something else here. It is related by maybe I will repost in a new thread. I have been wanting to do this for some time. There are maps made to show the climates of the future but it's not really easy to understand what the changes are exactly, and where they occur. I did a couple of maps to try to better display the magnitude of the changes.

Summary: compare the climates from 2000 to 2100

First I took the data from here: http://koeppen-geiger.vu-wien.ac.at/shifts.htm
I modified the image and used the basic maps mostly.

Secondly, I chose the A1fi scenario because it seems to be the most plausible (but also a pretty pessimistic one).
This scenario predicts and increase of temperature of 4 degrees by the end of the century. Only the most optimistic ecologists believe we can still avoid passing over the 2 degrees mark, and that can only work if we adopt aggressive measures to curb down gas emission. But we are barely doing anything. 

Thirdly I started this:

Only to find out that I did not really have sufficient data to make the 2100 map. I mean, I needed to know what area remained the same and which one would need to be changed.

I redid the maps in photoshop, maybe not the best way to do it but whatever


Right now, it's still just a bunch of weird colours.

This map shows the change of climate according to the categories of the Koppen classification following this table where each arrow represent a change of one category:

This is very useful for my project but probably not so much for most people. A map showing the change in Celsius would be more useful but that is not what I needed.
It also show where the tundra will disappear and where the glaciers will melt (although the process can take decades if not centuries).
The grey areas represent deserts, steppes and places where the aridity is changing. I cannot include it because everything that has to do with arid climates uses different temperatures thresholds compared to the other climates categories. Anyway the relevant data is better left for another map because there would be too many colours. 


Here I looked at the changes in aridity. It only show the changes if it changes the category of the climate. So for instance, most of Southern Europe will become drier but not enough to become a steppe. Therefore the change is not displayed on this map because it is too small.
Also I need to remind that aridity is define as either an increase of temperature, reduction of precipitation or both at the same time resulting in an increased aridity. 

I made it so there is one colour for the deserts and one colour for the steppes.
The outlook is bad overall.
Most of the red areas are tundra in the Andes turning into deserts which might not be that dramatic considering the area is already one of the driest on the planet. With this classification, tundra can be dry or humid, only the temperatures matter. 

Rain pattern or seasonality: 
I included the usual three patterns plus a moderately dry winter season for the Am climate.



Changes between the 2 periods:

The dark green means that precipitation tend to be more evenly spread out during the year and so they now lack a proper dry season. That doesn't mean an increase of precipitation, just that they are spread differently. 
London will be able to grow palm trees with a climate similar to today's Marseilles. 

Lastly, the evolution of ice shelves at their maximum and minimum using different sources.
Minimum:

Maximum:

The changes during the hot season is huge. Only the coldest bays around some parts of Antarctica will be cold enough to sustain the shelve in the south. 
In the north, there is a small patch of ice survining just north of Greenland but the ice will be very thin, so I chose not to include it. 
The ice cover is still significant in winter but is made of young ice (1-2 years old max) and is relatively thin.

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## nwisth

Impressive work, Azélor! It is very sobering to see a climate change scenario presented graphically like that, but what really made it easy to picture was your mention of the palm trees of the British Riviera.  :Surprised: 

In the A1fi scenario, would sea levels remain more or less the same?

-Niels

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## Azélor

They have started planting palm trees in Halifax. I would be depressed if they survived the winter.

About sea level, I've found that it could be between 0,3 to 1m. 
It is an increase but not large enough to justify redrawing the coastlines. 
At less than 1m I think the impact is mostly marginal as long as the winds are calm.
I assume most people living near the ocean did anticipate storms and built accordingly. 
For them, a 1m increase is manageable but with a stormy weather the waves reach even higher. 

By the horizon of 2200, the rise will be more dramatic as the melting of Greenland accelerate. 3-4m increase, maybe more.
4m and half of Florida is permanently flooded. 

But on the short term, the biggest issue is not really the rising oceans.
It's human activities.
Urbanization, deforestation of the coastlines and subsidence https://en.wikipedia.org/wiki/Subsidence
Many cities like Miami are built right next to the sea on a spit https://en.wikipedia.org/wiki/Spit_(landform)
They have no mangrove to protect them from the winds/waves and concrete does not absorb water well well. 

Subsidence is when the ground is sinking, usually because the people are pumping underground water.

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## Charerg

A bit off topic, but there actually are already palms growing in Cornwall, for example these ones at St. Mary's Church, Penzance (see Flora of Cornwall for more info):



I guess it's not common knowledge, but some palm trees are reasonably resistant to cold, as long as the temperature does not fall below zero for extended periods. Some areas where palms grow naturally receive snowfall every now and then, like the mountains of South Africa or Australia (the species in the above image grows in New Zealand, you could say that it's more of a palm tree of temperate climates rather than tropical ones).

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## NadirtheFox

I have to admit that I actually find your work quite usefull Azélor  :Smile:  And kinda disturbing at the same time  :Neutral: 
Aaand I've finished july precipitation map:  I also changed january a little bit:  But when I tried to install the script I didn't get the option to use it. I double checked if I installed it correctly, I tried to install the other one... Still nothing. I looked for help in the internet and apparently it's because I'm using "elements version" and scripts are simply not available in it... But there is option to use them, I just can't click on it so maybe I'm just doing something wrong... Oh well. 
After manual climate generation and quick cleaning:  I got some weird holes in mountainous areas and in the far north where I didn't get any climates other than tundra and ice. I've managed to patch up the mountains (well... most of them) and I think that the far north should be mostly Dfc with some Dfd climate inland but I wanted to make sure in case I messed something up XD

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## Azélor

You could use the Gimp.

Most of the climates missing are Dc.
One of the mountains is a tundra, in blue.
 Some in the est are Db close to the steppes, assuming they are humid because I did not check.

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## Charerg

Overall the climates look pretty good, though there are a few things that might be a touch off. I'd draw a few demonstrative maps if I had the time (maybe next weekend), but here's some quick feedback.

1. The first thing that pops up in the July precipitation map is that the northern hemisphere subtropical ridge (the high pressure zone between the Hadley and Ferrel cells) seems to completely disappear during july.

2. At first glance, the vast equatorial deserts you have in both of your equatorial landmasses seem a bit suspect. While I understand that those areas fall under rain shadow at least to some degree the deserts still seem a bit too extensive. Especially the western equatorial continent seemingly has the rain shadow on the wrong side of the coastal mountains (the coast west of the mountains should be dry, as with the Atacama and Namib deserts).

That's it for now, I'll try to post something more detailed during the weekend.

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## Charerg

Ok, so I started to think about the more detailed feedback (and how to represent any feedback in a semi-coherent and understandable fashion) and kind of ended up doing my own take on Twierdza (well, sort of, I'll try and stick with the basic stuff).

First off, the oceanic currents:


For the most part, they're similar to your original, though there are a few areas of deviancy:

1) The major one is probably in the inner ocean. I gave the counter-equatorial current a strong bias towards the northern hemisphere and also made it a "single hemisphere loop" like the Guinea current. I suppose this hasn't really been covered by the tutorial, but there is a possibility that the counter-equatorial loop only exists as a single loop (instead of the usual "double loop") if you have a case like the Atlantic Circulation:



In this case, I chose to pull this trick off because it felt the most natural solution to the issues with the positioning of the subtropical high below your eastern equatorial continent. The original current map has a cold current skipping equatorwards across open water, but this is an unlikely scenario: typically the waters would just be carried almost directly towards the east (or even slightly polewards) by the westerly winds, unless there exists a landmass to divert their course.

With the biased counter-equatorial current, the southern hemisphere "Mid-Ocean Loop" can be pushed further towards the equator, allowing for a coast-following cold current that would explain the existence of a subtropical high. That said, this is just one possibility. Alternatively this land arrangement simply might not generate a subtropical high in this area.

2) Secondly I have a warm current extending further east in the northwest corner of the map than was the case in the original. Seeing that there is a lot of open water there without any major obstacle, I think it's likely that there would be a current from the "Big Ocean" into the "Cold Sea", similar to the Gulf Current that reaches quite far into northern waters (as can be seen in the Atlantic Circulation map).


With that out of the way, I did a sort of combined pressure/potential rainfall map for January:



Basically it maps out the major high and low pressure belts. One thing worth mentioning is that the subtropical high pressure belt tends to be much more continuous during the cold season, and more broken during the warm season, as can be seen here (as well as by checking the sample maps of Earth). I haven't quite compared this yet to your January rainfall map, but at a quick glance there aren't a huge amount of differences. That said, there were a couple of areas that I noticed were unexpectedly dry, especially the "temperate region" in the northwest portion of the map. But more about that later, once I've actually compared the maps in detail.

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## hunter714

Hi guys,

I am amazed by the work behind this tutorial. You have my utmost admiration for doing this.

I decided to try to follow it. I must say before starting this, I didnt have a clue about how climate works and what affects it and it took me some time to go through this (I need some time for things to settle in and well, its a complex subject too).

I have a bit rushed and didn't go too much in details for some things as I wasn't so sure about how to do them. I understood some things a bit later in the process and I think Ill redo a pass on everything now that I got a clearer idea of how it works.

I stared with a randomly generated planet I could see as believable.

Then I started adding tectonic plates that would reflect what was already my continents and islands. 
With it I tweaked a bit the continents, added some islands and mapped the elevation :



After this I followed the tutorial :

 - Surface current :



 - Air pressure and winds which I understand are not accurate enough for latter steps and my centers of pressure are way to big and it impeded me later : 

January :


July :


 - Zone of temperature

January : 
 

July :


 - Temperatures. I think most transition area are to straight and need to be redone appropriately to be more coherent :

January :


July :



 - Precipitations. I ignored most of the Orographic lift effect as I am not really sure how to implement it without having all my mountains under great precipitation and how category 0 would interact with this :

January :


July : 


And thanks to Charerg I generated a climate map with his gimp script :


I havent done the climates legend but if I understand correctly its the same colors used on the Köppen climate classification Wikipedia page.

Anyway thanks again to anyone who contributed on this. I had some fun doing this and it's quite interesting. I'll try to rework every step of the tuto to get a better result.

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## Charerg

The "category 0" rainfall has the wrong colour in the precipitation maps, which is likely the reason for the pink and white areas (those shouldn't appear).

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## hunter714

Indeed, I forgot about the opacity when merging the layers. thanks, it makes a lot more sense now.

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## Eldresh

I've started on my currents so I could figure out climates, but I'm really unsure about some of them, especially my polar currents (and of those especially my northern polar currents) and some of what's going on in some of the smaller gaps near the equator. Does anything here seem odd to anyone else? I feel like something wrong but I can't put my finger on it. I feel like the northern polar current should be going the opposite direction, but I can't figure out how the loops would close if I did that.

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## hunter714

I find it usefull to project the map on a globe in order to see how the poles are represented in 3d. For me it's a bit hard to take into account (with good precision) how the poles deformations in a square projection would realy look like on a globe.

I use https://www.maptoglobe.com/ wich is simple enough and you can draw on the map you imported and see it in 3d.

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## Charerg

Ok, here's the more detailed feedback about the January precipitations for Twierdza. 

As a sidenote it's recommended to start your own thread for your world, even if you're using the tutorial, since it helps to keep the tutorial thread focused on the tutorial itself and makes it a bit easier for people to follow discussions and provide feedback regarding particular conworlds.

But on to the feedback, here's my previous pressure/precipitation combined map overlaid over the January precipitations, where I've identified some regions which I disagree with. As always, the feedback is completely optional.



1. This area would be blanketed by the westerlies and should be wet, much like Europe (the extreme southern coast could remain dry).

2. This region closely resembles Peru or Namibia, the strip of land to the southwest of the Andean-like mountain chain should be a dry desert.

3. However, the basin area covered by the ITCZ would certainly not be dry, see Equador and Colombia for instance, or even the Amazon Basin.

4. This area, while in raind shadow, is still covered by the westerlies and wouldn't be completely dry, see Canada beyound the Rockies for comparison.

5. Assuming we go with the idea that there exists a subtropical high here, this area should be drier (see southwest Australia or Africa during January for comparison).

6. Being covered by the ITCZ, this region wouldn't be completely dry (at least, not likely). Even areas like the Tibetan Plateaua and Mongolia receive some rainfall during the warm season.


For reference here are the prec maps of Earth (which have been posted before in the thread):

January:


July:


That's it for the feedback regarding January! I'll try and post similar feedback about the July precipitations as well during the weekend.


EDIT:
Actually I spotted a few areas I didn't comment previously on the January map:



7. If the inner ocean does indeed develop a high pressure center, this region should be dry.

8. However, the high mountains facing the "outer ocean" should be wet, as this area would likely be hit by extratropical cyclones, and the orographic lift would contribute to the rainfall as well.

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## Eldresh

Yeah I tried looking at it on a globe but the problem I keep running into is most of these loops happen because a current hits land somewhere, but most of my land is either equatorial or polar, and it leaves me scratching my head a bit sometimes. I'll play around with it some more and see if I can get it to a place I like it.

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## Charerg

Right, here's the feedback for the July precipitations of Twierdza:



1. This high pressure area seems to disappear in July, despite being present in January: this area should be dry due to the presence of the oceanic high.

2. The ITCZ-influenced area of the equatorial continent should receive far more precipitation.

3. Similar to my feedback regarding January, the westerly influenced region is unlikely to be completely dry, it should receive some rainfall.

4. This region would be heavily  affected by the summer monsoon, and should receive at least some precipitation despite the coastal mountains.

5. Assuming that the inner ocean does indeed develop that subtropical high (I'm not 100% sure of that, the Indian Ocean doesn't have a subtropical high in the northern hemisphere, as an example), this region should be dry.

6. Similar to n. 3, the ITCZ-influenced area should receive more rainfall.


So that's it for my feedback about the precipitations. Next, I can take a quick look at the climates and see how the precipitation changes would affect them.

----------


## NadirtheFox

Azélor - Considering how many problems I had from my photoshop not being a "proper" version you are probably right and I should start using gimp XD And thanks for the advice on climates  :Smile:  I'll keep that in mind next time I get similar issue.
Charerg - Oh my...  :Neutral:  Now, that's some very impressively detailed feedback to be honest. Big thanks for that  :Wink:  I'll certainly update my maps based on it. That being said...
Area 1 in January: My reasoning behind making this region so dry was that it appears separate enough from other landmasses to develop it's own australia-like winter high (and I also went with "overland high pressure centre should be poleward of the oversea one") On the other hand this area being wet works for me better, sooo...
Areas 6 in January and July: I might be wrong about that but I think that the effect of a rain shadow is based more on relative height of the mountains rather than on their absolute height - If height difference is gradual enough not all of precipitated water will flow to the bottom and instead a significant portion of it will be absorbed by vegetation on the slopes and then released back to the air via transpiration and therefore have less vertical distance to cover  :Question:  So if the winds are blowing over high but gently sloped mountains some moisture can get through (I'm pretty sure that's the case with Mongolia) but if the rise is very sharp then there will form a rain shadow even if the mountains are relatively low (kinda like the Great Dividing Range) I meant the latter to be the case in this region (minus the "relatively low" part) but as I said: I might be wrong. (And as I look at it now I think it would still make sense to expand wet area in July)
But other than that I can't argue with anything else XD Massive thanks again  :Smile: 
(Oh, and in that case I will create separate thread for this world. I just didn't expect it to take this many posts XD)

----------


## Azélor

> I've started on my currents so I could figure out climates, but I'm really unsure about some of them, especially my polar currents (and of those especially my northern polar currents) and some of what's going on in some of the smaller gaps near the equator. Does anything here seem odd to anyone else? I feel like something wrong but I can't put my finger on it. I feel like the northern polar current should be going the opposite direction, but I can't figure out how the loops would close if I did that.


Overall, the currents are good except in the north pole. The blue current is close enough to the pole to flow toward the west.

----------


## Charerg

> Azélor - Considering how many problems I had from my photoshop not being a "proper" version you are probably right and I should start using gimp XD And thanks for the advice on climates  I'll keep that in mind next time I get similar issue.
> Charerg - Oh my...  Now, that's some very impressively detailed feedback to be honest. Big thanks for that  I'll certainly update my maps based on it. That being said...
> Area 1 in January: My reasoning behind making this region so dry was that it appears separate enough from other landmasses to develop it's own australia-like winter high (and I also went with "overland high pressure centre should be poleward of the oversea one") On the other hand this area being wet works for me better, sooo...
> Areas 6 in January and July: I might be wrong about that but I think that the effect of a rain shadow is based more on relative height of the mountains rather than on their absolute height - If height difference is gradual enough not all of precipitated water will flow to the bottom and instead a significant portion of it will be absorbed by vegetation on the slopes and then released back to the air via transpiration and therefore have less vertical distance to cover  So if the winds are blowing over high but gently sloped mountains some moisture can get through (I'm pretty sure that's the case with Mongolia) but if the rise is very sharp then there will form a rain shadow even if the mountains are relatively low (kinda like the Great Dividing Range) I meant the latter to be the case in this region (minus the "relatively low" part) but as I said: I might be wrong. (And as I look at it now I think it would still make sense to expand wet area in July)
> But other than that I can't argue with anything else XD Massive thanks again 
> (Oh, and in that case I will create separate thread for this world. I just didn't expect it to take this many posts XD)


About area 1 in January, I'm fairly confident it should be mostly wet. The continent stretches from about 35 to 60ish latitudes, too far north to fall under the subtropical ridge like Australia. In addition, the development of the massive high pressure zone over the large Asia-like continent would likely push the westerlies southward to an extent. At the same time, it's too far south (and too maritime-influenced) to really develop a Siberian High-style high pressure centre, since I think that requires permanent snow cover (the snow reflects most of sunlight back into space, contributing heavily to the extremely cold conditions).

About area 6 and rain shadow: I'm not an meteorologist, so take this with a grain of salt, but as far as I know, that is not the case. At least I've never read anything to suggest that "terrace jumping" rainclouds are actually a thing (ie. clouds first dropping their moisture on a gradual slope and then more-or-less re-forming on the spot). Tbh, I think there are several factors that argue against this, the big one being that most of the water would flow downslope, and also the generally low temperatures of mountains would mean relatively low evaporation. However, you are correct that a steep slope will be more rainy, as all (or most) of the moisture will be dropped there, whereas with a more gradual slope the effect of orographic lift is more, well, gradual.

As a sidenote, I doubt the Great Dividing Range contributes much to Australia's dryness. First of all, that's neither a continuous nor a steep mountain range, it's rather similar to say, the Brazilian Highlands: old, worn down and full of gaps. If you look at the rainfall maps of Earth, you can see that the boundaries of the "rain zones" don't follow the lines of the Dividing Range (except loosely, and then mostly in New South Wales where the Range is taller). As far as I can tell, Australia's dryness is pretty much 100% caused because of its geographic location (falling under the subtropical ridge). Indeed, Australia used to have a rainy, temperate climate back when it was located further south.

The "start a new thread" advice was mostly directed towards newcomers (since you're well on your way to completing the tutorial, I'm a bit late with that advice  :Wink: ), though I guess it doesn't hurt to start a fresh thread. Glad you found the feedback useful!

----------


## Eldresh

Thanks Azelor, that was the one I was most unsure about. For some reason I thought that you couldn't have a current going the opposite way from a current near it without them looping together somehow but then I realized I was being a little silly. Changed the northern current a bit. Thought about adding in a loop where that bit sticking further south is but I couldn't find a good place to close it so I decided to ignore it.

----------


## Eldresh

EDIT: ended up doing them manually, came out just fine that way so feel free to ignore the below post.

So I could use some help here figuring out where I went wrong trying to run the script, because I'm pretty sure the output isn't supposed to look like this.



I didn't use anti-aliasing, and as far as I can tell all of my colors are correct. I'm using Photoshop CS6, and I had to run the script through the actions panel because the included instructions didn't work as far as I could tell (Window > Script doesn't exist in any of my menu options). Should I try installing gimp and seeing if the gimp script works, or did I screw something up? If I knew the specific codes for the colors the script is asking for I could check them better.

----------


## nwisth

Yeah, that looks a lot like the first results I got as well. I finally got it to work after giving up using Photoshop, and installed GIMP ver 2.8 (not the latest 2.10, which didn't work) - which I used to run Charerg's 6-step script from further up the thread. I also had to redo all my precipitation maps because I had used brush tool instead of pencil tool - that removed the unsightly lines.

Good luck!  :Smile: 

-Niels

EDIT: I forgot about the updated 6-step script, which you want to use instead.

----------


## Eldresh

I ended up realizing I could just do them manually instead of with the script, and they came out just fine that way. Brush tool was probably my issue as well, but as part of doing stuff manually I went and adjusted some of my borders. 

Just needs some tweaking now. I don't know what exactly happened with the script, but I'm willing to chalk it up to my ability to somehow break anything and using brushes.

----------


## Daniel Gimenez

Hi everyone! 

I have been mesmerized by all the great brains I see here! Clever people indeed I am so impressed and I feel so thick and slow!
So I started to do my own Climate map, but when doing the rain I got soooo stuck, especially with the storm paths (if I did the other stuff correcly that is).

I am new here so if at least I could have a little bit of your help from you guys to figure out all this that would be great, to earn this at least I could exchange some of my exppertise, for I am a 3D artist, so I know a thing or two about 3D graphics and imagery that I could share.

Anyways, so here are the relevant images (They are square because I am using a 3D program, so textures have to be square, but I guess nobody will have any problems interpreting them):
OceanCurrents and Landmasses:


Pressure and Winds January and July



RainWIP January&July



And finally some bling!



If i have done something wrong I apologize I am new in any kind of forum...

Thanks.

----------


## AzureWings

The bling is very pretty!  :Smile: 

I have to confess I'm having a hard time trying to figure out the projection on your square map. That said, from what I can see the level of detail you have is rather impressive. Some of the 'cold' currents look to start just a little bit close to the equator, in my opinion - the ones that go from hot currents to mild/cold around 30-40 degrees latitude while near a coastline. Winds and precipitations are an area I tend to have some issues with myself so I'm not sure I have a lot to say there (and did I completely overlook storm paths being in the tutorial?  :Surprised: ).

-------------

This is very (very) belated, but I'd also like to mention that some earlier problems some folks were having in using my command-line script for outputting the climates were likely due to the fact that the script's default precipitation input colors are the purple-to-black shading Charerg used for some precipitation maps earlier in the thread, not the categories in Azélor's main tutorial. I've added an alternative input precipitation profile ("altPrecProfile") to the script's Github repository that should match the precipitation categories in the tutorial; to use it instead of the script's default run the script with the flag 
--precprof="altPrecProfile" (assuming you're in the same directory as the Python script itself).

----------


## Charerg

As a bit of an aside regarding the script and the formulas used to calculate the climates, there's apparently a sort of "official formula" that can be used for the aridity threshold if you want it to be gradual rather than using Köppen's original formulas. Using this paper as a source, a fellow named Patton originally published a modified formula in 1962 (using inches and fahrenheit as units). Converted into centimetres and degrees celsius (as used in some publications apparently), the formula looks like this:

_R(cm) = 2.3*T - 0.64*Pw + 41_

Where _R_ is the aridity threshold, _T_ is the mean annual temperature, and _Pw_ is the percentage of rain that falls in the winter half-year. Note that this gives the threshold in _cm_, so for our purposes we'd want it converted into _mm_:

_R(mm) = 23*T - 6.4*Pw + 410_



Let's quickly compare the output of the formula with those of the standard criteria (I'm assuming mean annual temperature _T_ is 20 °C in all examples):

*Scenario 1 (50 % of rainfall in winter):*
Standard Köppen: _R(mm)=20*T + 140_ = 540 mm
Modified Köppen: _R(mm)=23*T - 6.4*50 + 410 = 550 mm_

As can be noted, the formula has clearly been calibrated to produce essentially the exact same result as the original formulas in this situation.

*Scenario 2 (30 % of rainfall in winter):*
Standard Köppen: _R(mm)=20*T + 280_ = 680 mm
Modified Köppen: _R(mm)=23*T - 6.4*30 + 410 = 678 mm_

Again, the output close to the "1/3 boundary" is essentially the same. What about the extreme case then?

*Scenario 3 (0 % of rainfall in winter):*
Standard Köppen: _R(mm)=20*T + 280_ = 680 mm
Modified Köppen: _R(mm)=23*T - 6.4*0 + 410 = 870 mm_

Here we can see a notable difference: the modified formula produces a higher aridity threshold if all of the rainfall is concentrated in summer. What if most of the rain falls during winter?

*Scenario 4 (70 % of rainfall in winter):*
Standard Köppen: _R(mm)=20*T_ = 400 mm
Modified Köppen: _R(mm)=23*T - 6.4*70 + 410 = 422 mm_

Again, the output around the "2/3 boundary" is very close to the threshold produced by the original formula.

*Scenario 5 (100 % of rainfall in winter):*
Standard Köppen: _R(mm)=20*T_ = 400 mm
Modified Köppen: _R(mm)=23*T - 6.4*100 + 410 = 230 mm_

And the notable difference is again in the extreme case: as can be noted, the modified threshold produces a very low aridity threshold if all the rainfall comes in winter.


I'm a bit on the fence about this, on the one hand, using a more gradual threshold does make sense, but a lot of places do actually receive close to 100% of their annual rainfall during the summer half-year, so they would become a bit more arid with this formula, though I'm not sure whether that is a good thing or not. Also, I do feel the threshold probably plunges too low when it comes to climates that receive all their precipiation in the winter half-year. I'm pretty sure any place with an annual mean temp of 20 °C and only ~250 mm of annual precipitation would have extremely limited vegetation, yet it would not be classified as arid.


EDIT @ AzureWings:
I think the biggest problem potential users have with using the script are the prerequisites of installing Python and Pillow, as well as maybe lack of experience with using the command line prompt. Also, from the replies in the thread some might have missed that it has to be run through the command window in the first place  :Razz: . So I think adding detailed instructions about how to actually activate/use the script might help people to run it (I guess I might give those instructions as well, if you're a bit busy).

For example, the readme tells to use "python ./skcc.py ..." to run the script. But if you're using a more recent version of Python (3.3 or higher), that would actually be "py skcc.py ..." (assuming you're in the same directory as the script). Also, the source maps have to be in the same folder as the script itself, if I'm not mistaken (I don't think that was mentioned in the ReadMe).

EDIT2:
Ok, I tested out the modified aridity formula using the old source maps from this post. I modified the temp category averages slightly to match those used in my script. Here's the new output using the standard formulae (closely matches the one produced by the GIMP script, as expected):



And here's the output using the modified formula:



Needless to say, the results don't look exactly promising. Some places like Kazakhstan that receive more rain in winter end up being classified as humid, whereas tropical areas are classified as overly arid, especially in Africa.

EDIT3:
As an extra test, I also tested out the old 8-step precipitation maps in combination with the modified aridity threshold. In this case the 8-step maps produce a noticeably better result (though still not perfect):

----------


## AzureWings

> As a bit of an aside regarding the script and the formulas used to calculate the climates, there's apparently a sort of "official formula" that can be used for the aridity threshold if you want it to be gradual rather than using Köppen's original formulas. Using this paper as a source, a fellow named Patton originally published a modified formula in 1962 (using inches and fahrenheit as units). Converted into centimetres and degrees celsius (as used in some publications apparently), the formula looks like this:
> 
> _R(cm) = 2.3*T - 0.64*Pw + 41_
> 
> Where _R_ is the aridity threshold, _T_ is the mean annual temperature, and _Pw_ is the percentage of rain that falls in the winter half-year. Note that this gives the threshold in _cm_, so for our purposes we'd want it converted into _mm_:
> 
> _R(mm) = 23*T - 6.4*Pw + 410_


I like the idea of a more continuous-space model, but I agree that in practice it doesn't seem to work out quite so nicely. The tropical zones do seem to be make-or-break points for a lot of Köppen-Geiger climate modeling because they're high temperature and very high rainfall but often concentrate so much of that precipitation to one half of the year.

Although being published lends it some notable weight of ethos I'm almost tempted to try out this formula but scaled to match its extremes with the extremes of the Köppen thresholds. The discontinuities always seemed to be the thing that concerned me a bit about the original aridity thresholds more than the values themselves that those thresholds arrived at.




> I think the biggest problem potential users have with using the script are the prerequisites of installing Python and Pillow, as well as maybe lack of experience with using the command line prompt. Also, from the replies in the thread some might have missed that it has to be run through the command window in the first place . So I think adding detailed instructions about how to actually activate/use the script might help people to run it (I guess I might give those instructions as well, if you're a bit busy).
> 
> For example, the readme tells to use "python ./skcc.py ..." to run the script. But if you're using a more recent version of Python (3.3 or higher), that would actually be "py skcc.py ..." (assuming you're in the same directory as the script). Also, the source maps have to be in the same folder as the script itself, if I'm not mistaken (I don't think that was mentioned in the ReadMe).


The python invocation can depend somewhat on how your installation is set up - on mine for example I'm actually invoking with 'python3' because I have 2.x and 3.x versions of Python installed side-by-side and need to disambiguate, but I knew that wasn't going to be typical of most users. If the default is to invoke with 'py' now that just makes things more confusing and awkward, ugh.... The source maps don't need to be in the same folder as the script itself, but you need to provide the path to the source maps from the current working directory, not just the file names of the input maps (so if you're in the folder where the script is running and the input maps aren't, you need to provide either absolute paths or relative paths from the folder the script is in to the maps). This also means you can output to a folder other than where the script is, too; just provide a filepath to an output file location in some other folder.

I'll pop a few more points into the readme's FAQ about python invocation and file paths. Noting any of your experience in the installation process might be helpful; I did it in a relatively ad-hoc way that's probably not a very good place to explain from for a lot of people.

----------


## Azélor

The main issue with the equator belt is that there is no actual winter/summer seasons but the formula used these seasons.
Normally, the impact of precipitations on the aridity is different depending on when the rainy season is. 
But the seasonality of precipitations has no impact when temperatures are mostly constant. 

The current formula is not the best to find the aridity in these regions.

----------


## Charerg

> I like the idea of a more continuous-space model, but I agree that in practice it doesn't seem to work out quite so nicely. The tropical zones do seem to be make-or-break points for a lot of Köppen-Geiger climate modeling because they're high temperature and very high rainfall but often concentrate so much of that precipitation to one half of the year.
> 
> Although being published lends it some notable weight of ethos I'm almost tempted to try out this formula but scaled to match its extremes with the extremes of the Köppen thresholds. The discontinuities always seemed to be the thing that concerned me a bit about the original aridity thresholds more than the values themselves that those thresholds arrived at.


You probably have a point there: I think Köppen himself treated the values used in these formulas as convenient approximations, more than anything else. I tested out a few versions adapted to have less variance than the formula I cited previously (which had a variance of 640 mm in the threshold between 0% and 100% of precipitation in winter).


First, a "mid-variance version" (scaled to match the result of standard Köppen at 15% and 85%). R is the aridity threshold, T is the mean annual temperature, and Pw is the percentage of rain that falls in the winter half-year.
_R(mm) = 20*T - 4.0*Pw + 340_

Here is the resulting map (I used the colour scheme in Kottek et al. (2006) this time around):



Second, a "low-variance version" (scaled to have slightly less variance than standard Köppen, the "summer dries" are a bit drier).
_R(mm)= 20*T - 2.5*Pw + 300_

And the map:


Note that I used the 8-step precipitation maps in these tests (as I was a bit too lazy to switch back to the 6-step maps, though I'll test those too later).


For purposes of comparison, here's the "high variance" version I tested out previously (including the climate key):



Overall, I think the "mid-variance" performed best out of these, though maybe that would not be the case if the "low variance" version was adjusted to match the "summer dry" extreme of Köppen (right now, assuming T=20 °C and 100% of rain in winter, the standard formula would give 400 mm as the threshold whereas the "low variance" formula here would give a slightly higher 450 mm).


*EDIT:*
I tested out an adjusted version of the "low-variance" formula, scaled to closely match the extremes of standard Köppen. Perhaps unsurprisingly, this produced the results that matched most closely with actual Köppen maps.

_R(mm)= 20*T - 3.0*Pw + 300_

Here is the resulting map (using the 6-step precipitation maps):


The good thing is that this seems to get rid of that annoying patch of _Dsa_ in the middle of the steppe we had previously in Kazakhstan.

----------


## Charerg

> I'll pop a few more points into the readme's FAQ about python invocation and file paths. Noting any of your experience in the installation process might be helpful; I did it in a relatively ad-hoc way that's probably not a very good place to explain from for a lot of people.


Since quite a few users seem to have some problems with using the script, I guess it might be worthwhile to attempt to give a precise step-by-step guideline. Here's my attempt at providing some detailed instructions (feel free to copy anything you find useful):


*A. Prerequisites*

To use the script, you need to first install two things: _Python 3_ and _Pillow_. Python is the programming language that the script uses. Assuming you're using Windows, the latest version can be downloaded from here. 

Pillow is essentially an add-on that adds image handling functionality to Python. Since the script uses these functions, you need to install it as well (note that you need to _download and install Python_ *before* installing Pillow). Pillow can be dowloaded from here. You'll note that there are many different versions of Pillow available. You need to pick the version that matches your OS and the Python version you just installed. Assuming you're using Windows, the windows installers are located at the bottom of the list. In my case, since I installed Python 3.7, I picked _Pillow-5.4.1.win-amd64-py3.7.exe_ (the most recent version as of Jan 2019).


*B. Using the script*

The script itself is activated through the command prompt.  The simplest way to launch the command prompt is to press "Windows+R" to open the Run Window, then write "cmd" and press enter. Now that you have the command prompt open, you need to navigate into the folder where your script is located (I also recommend storing the source maps in the same folder to keep things simple). This page provides instructions on how to do that. As an example, here's my window after navigating into the right folder (this is just one way of doing this):



Now you can activate the script. There are several optional flags, such as telling the script to use an alternate colour profile for the climates or for the precipitation and/or temperature maps. All the relevant commands are detailed in the script itself (which you can easily read or modify with NotePad++), as well as in the ReadMe. As an example, here I've run the script without any optional commands:




Hope this was helpful to those who were struggling with Azure's script. Feel free to ask if something was left unclear.

----------


## rebelandarunner

> I'm afraid it's not a program. We've created a script that takes the precipitation and temperature maps for January and July as input and outputs the climate map. There's the original version of the script made for PhotoShop by Azelor, and the more recent GIMP script myself and Azelor created in collaboration (and which is slightly updated from the original, I guess). There's also an unpublished version by AzureWings (which is written in Python, I believe, and should be compatible with GIMP, not sure about PhotoShop).
> 
> Running the script saves you the trouble of manually creating the climate map, but you still need to create the aforementioned precipitation and temperature maps.


Sorry guys, lurker on the site and new to this thread.  Apologies in advance, but I sort of skipped 3 years of messages on the thread, however, as I've been going through the steps of building a world of my own, I'm fascinated by the script and the inputs required to run it.

Is there an FAQ somewhere that I can read through to figure out what I need?

Thanks!!

----------


## Charerg

> Sorry guys, lurker on the site and new to this thread.  Apologies in advance, but I sort of skipped 3 years of messages on the thread, however, as I've been going through the steps of building a world of my own, I'm fascinated by the script and the inputs required to run it.
> 
> Is there an FAQ somewhere that I can read through to figure out what I need?
> 
> Thanks!!


You need to create four maps: January Temperature, January Precipitation, July Temperature, July Precipitation. Ideally in .png format, and make sure they're all the same size, and only contain pixels in the appropriate colours (ie. the category-specific colours covering the landmass, and the ocean as either a separate colour or transparent). There are several optional colour codes, although you can also use a custom code if you want (Azure's script is easy to modify to use whatever colour profile you want, and also accepts alternate colour profiles as inputs).

Other than that, I posted a quick guide on how to use Azure's script (linked in the OP), and there's also a readme file included that provides further instructions.

----------


## rebelandarunner

Ah, Ok.  Thank you.  I'll have a go at this and see how I do.

----------


## Naima

> Since quite a few users seem to have some problems with using the script, I guess it might be worthwhile to attempt to give a precise step-by-step guideline. Here's my attempt at providing some detailed instructions (feel free to copy anything you find useful):
> 
> 
> *A. Prerequisites*
> 
> To use the script, you need to first install two things: _Python 3_ and _Pillow_. Python is the programming language that the script uses. Assuming you're using Windows, the latest version can be downloaded from here. 
> 
> Pillow is essentially an add-on that adds image handling functionality to Python. Since the script uses these functions, you need to install it as well (note that you need to _download and install Python_ *before* installing Pillow). Pillow can be dowloaded from here. You'll note that there are many different versions of Pillow available. You need to pick the version that matches your OS and the Python version you just installed. Assuming you're using Windows, the windows installers are located at the bottom of the list. In my case, since I installed Python 3.7, I picked _Pillow-5.4.1.win-amd64-py3.7.exe_ (the most recent version as of Jan 2019).
> 
> ...


What is this script for?

----------


## Charerg

> What is this script for?


It takes temperature and precipitation maps as input, and generates the Köppen climates.

----------


## Naima

doesn't consider mountains , winds and sea moisture?

----------


## Azélor

> doesn't consider mountains , winds and sea moisture?


These elements needs to be taken care of in the previous steps, before using the script.

----------


## Clemens

For some reason the GIMP script does not output a proper map. I have no idea why. If someone could figure it out for me I'd be much obliged. Attached a zipped up .xcf file. 

I found some transparent pixels in my precipitation maps and some wrong coloured bits from a mistake with an anti-aliasing bucket fill in the temperature maps, but now that I've done select by colour on each layer I have no idea anymore why it would put out this mostly white map. 

Climate.zip

EDIT:
Colours were slightly off, never mind. Issue got fixed.

----------


## acrosome

Hi, All, I'm back.

What is the functional difference between Charerg's GIMP script and AzureWings' Python script?  Are they interchangeable- just different implementations?  

EDIT-- It looks like Charerg's uses Azelor's original six precipitation levels whereas AzureWings' uses eight?  I don't see instructions anywhere for changing precipitation with altitude for eight categories.  Are there any?

Is one or the other now considered the "official" best solution by you three gurus?  I ask because I see that AzureWings' has a link in the OP whereas Charerg's does not.

----------


## Azélor

> Hi, All, I'm back.
> 
> Is one or the other now considered the "official" best solution by you three gurus?  I ask because I see that AzureWings' has a link in the OP whereas Charerg's does not.


I have no idea why I've chosen one over the other.
The only thing I know for sure is that the precipitation placement is completely messed up but that's the part before using the script. 
Ideally we would be using formulas to figure out the precipitations and temperatures.

----------


## acrosome

Yes, I'm finding precipitation placement to be a challenge.  I'm just never satisfied with it.  I try using the Earth as an example, but other worlds are not Earth, y'know?  Your tutorial gives great guidelines on a lot of things, but then when you all are modeling Earth to check your algorithms you are showing temperature and precipitation maps that are clearly contrary to the rules of thumb in the tutorials.  But, I guess that allows wiggle room to make a world whatever you want, within limits?   :Smile:

----------


## Charerg

> Hi, All, I'm back.
> 
> What is the functional difference between Charerg's GIMP script and AzureWings' Python script?  Are they interchangeable- just different implementations?  
> 
> EDIT-- It looks like Charerg's uses Azelor's original six precipitation levels whereas AzureWings' uses eight?  I don't see instructions anywhere for changing precipitation with altitude for eight categories.  Are there any?
> 
> Is one or the other now considered the "official" best solution by you three gurus?  I ask because I see that AzureWings' has a link in the OP whereas Charerg's does not.


AzureWings' script doesn't use any particular precipitation profile, you can configure it to use the 6-step precipitation levels (or however many you want, for that matter). The quickest way is probably directly modifying the script using Notepad++ (comment out the "pColorTableDefault" and replace it with this one, if you're using the same RGB values as in my GIMP script):




> pColorTableDefault = {(210, 200, 250):300.0, (190, 170, 240):150.0, (150, 130, 220):75.0, (90, 80, 160):37.5, (240, 235, 160):17.5, (235, 0, 140):5.0}


The above should be fairly self-explanatory, it contains the RGB value of a given precipitation level, and the corresponding avg. precipitation in millimetres for that level (if you're using different RGB values, you may need to modify the values to match). I also posted some instructions about using the script that might be helpful.


The output from both my earlier GIMP script and AzureWings' script should be about the same, but the latter is preferable since it's more flexible in the sense that you can set up custom temperature and/or precipitation profiles easily, and it isn't tied to any particular program or a version (for example, I use GIMP 2.10 nowadays, so I can't use my prior script because that was made for 2.8 ).


EDIT:



> Yes, I'm finding precipitation placement to be a challenge.  I'm just never satisfied with it.  I try using the Earth as an example, but other worlds are not Earth, y'know?  Your tutorial gives great guidelines on a lot of things, but then when you all are modeling Earth to check your algorithms you are showing temperature and precipitation maps that are clearly contrary to the rules of thumb in the tutorials.  But, I guess that allows wiggle room to make a world whatever you want, within limits?


There is always a lot of guesswork when creating a fictional world, though there are certainly more plausible and less plausible interpretations. Climates do have a fairly predictable overall pattern if you make the basic assumptions of a similar rotation rate and axial tilt (and therefore similar atmospheric circulation) to Earth. I might add that probably the majority of things in the Earth-based temp and precipitation maps that appear to contradict the rules of thumb given in the tutorial are only _seemingly_ contradictory. If you have any particular examples of areas that feel off or appear contradictory that would be great, because that would help in identifying some areas or phenomena that might need extra instructions. Hey, who knows, maybe we'll eventually manage to come up with something a bit more thorough when it comes to instructions for the precipitation maps  :Very Happy: .

Oh, and any thoughts of "making a world whatever I want" are clearly heretical, my errand child. Listen to the great Azélor: science and formulae are the way. Trust in the method, my child, and pray for enlightenment. After all, you wouldn't want the climate inquisition to get you, now would you?  :Wink:

----------


## acrosome

> Oh, and any thoughts of "making a world whatever I want" are clearly heretical, my errand child. Listen to the great Azélor: science and formulae are the way. Trust in the method, my child, and pray for enlightenment. After all, you wouldn't want the climate inquisition to get you, now would you?


Oh, High Priest Charerg, help me to overcome my error!  I abase myself before the altar of the Great God _Formulae_!  I shall henceforth live The Method truly, unburdened by heretical thought!   :Very Happy: 

More seriously, don't get me wrong- this all rocks, and is a very creative way to generate climates.  But Earth's climate has changed dramatically many, many times.  Limiting myself to only recent history off the top of my head I can list the Saharan Savannah and the Mammoth Steppe as well-known examples.  That's all I was talking about- e.g. that I could nudge things toward a large desert if I wanted one... or not... by where I place the rains.

Thanks for the info- I'll use the Python script.

I may shortly ask for input on my climate project, but I was unhappy with my recent maps and am now re-doing them more diligently.  I think I tried to rush through some foundational steps to get to the "good stuff" more quickly, and suffered for it, so I'm at it again.  On the positive side, I get faster at this every time I re-start...

----------


## acrosome

How do you merge the precipitation layers in Gimp?  Some precipitation overlaps, so they have to "add" somehow, right?

----------


## Charerg

> How do you merge the precipitation layers in Gimp?  Some precipitation overlaps, so they have to "add" somehow, right?


I don't think they can be easily merged if you made the various influences in separate layers. So you'll probably have to do that manually, pretty much. 

Although I guess it would be possible to set up the precipitation levels with a colour scheme that would make adding them on top of each other possible, but then we'd need many more levels and even intervals. Right now, the interval between levels varies from 10 mm for the lowest layer (0-10) to 100 mm for the second highest (100-200). Given that the interval varies this much, it's not really possible to easily merge the levels from two separate layers without messing things up.

----------


## acrosome

Ah, ok.  Thanks.

----------


## arch-fiend

azelor i have a bit of a problem with interpreting the rules you have in 5.1 zone of temperature. you state that hot currents have no impact in summer since the land is hotter than water and no impact between the tropics, however in the maps you drew you had hot currents drawn as far north as northern Scandinavia in the middle of summer and had hot currents consistently throughout the tropics. is it that you drew them anyway for demonstration purposes but later on in your system it becomes irreverent?

----------


## Azélor

> How do you merge the precipitation layers in Gimp?  Some precipitation overlaps, so they have to "add" somehow, right?


I think it work in photohop by using the multiply mode on the layers. Isn't there something similar in Gimp?





> azelor i have a bit of a problem with interpreting the rules you have in  5.1 zone of temperature. you state that hot currents have no impact in  summer since the land is hotter than water and no impact between the  tropics, however in the maps you drew you had hot currents drawn as far  north as northern Scandinavia in the middle of summer and had hot  currents consistently throughout the tropics. is it that you drew them  anyway for demonstration purposes but later on in your system it becomes  irreverent?


I drew the current mostly based on their actual temperatures and also somewhat compared to the surrounding temperature. 
We could say that I classified them according to what would make the model work best, even if that is cheating a bit.

----------


## arch-fiend

your rules for how hot current influences work are as follows




> Hot current (red) : areas affected by winds blowing from a hot current. Hot current have no impact in summer since the land is hotter than the water and it’s considered normal instead. 
> Also, they have no impact between the tropics either.


but you also made these 2 maps to demonstrat the effect

https://www.cartographersguild.com/a...075790&thumb=1

https://www.cartographersguild.com/a...075817&thumb=1

in these 2 maps you have drawn hot influences in both summer and in the tropics where you stated it has no effect, which is correct your maps or your rules? or are we suppose to draw the influences anyway and the guide you have created will simply make hot influences drawn in the summer and tropics have no effect later on in the guide?

*edit, i just read ahead on the guide and i can answer this question for myself, yes your guide does show how even if you draw the hot current influinces in summer and in the tropics it doesent actually effect the temperature map*

----------


## Majortopio

First off, I want to thank Azelor, Charerg and Azure for their work in this tutorial. So helpful! Can't believe how manageable this makes climate "generation" and just generally helped me understand how the whole system works together.

I'm posting because I'm having some trouble with the pressure systems. I just can't seem to wrap my head around the exact way that they interact with each other to create precipitation. I've tried a couple of times now, and I just feel like eventually I just end up grasping at straws, guessing how it ends up, not actually following the flow of things.

I've attached two very rudimentary combined pressure/temporary wet maps to illustrate my process. I can't really pinpoint any concrete problem areas, but they show how confused it all gets. There's no elevation yet, just to make it a bit easier. I guess my question is... Are there any places you guys can identify where I've completely screwed up? I'm kind of doubtful as to whether I've placed overland HP systems correctly, for example.

The "+" shaped continent in the center is kind of emblematic of the problem, it gets funky desert areas on the tips of each peninsula, where I kind of feel like it should be wet all year round pretty much.

Any help would be much appreciated!!

Continents 

January 

July

----------


## acrosome

I think I'm ready for some criticism on my climate work, now, if any of the gurus can chime in...

----------


## acrosome

I'm getting a puzzling error when I attempt to run AzureWings' Python script:




> dean@deans-thelio:~/speculative-koppen-master$ python ./skcc.py --tempns="./JulTemp.png" --tempnw="./JanTemp.png" --precns="./JulPrecip.png" --precnw="./JanPrecip.png" --outfile="output.png"
> Error: Invalid precipitation map color value: (107, 165, 210)


But 107,165,210 is the color defined as _ocean_ in the readme files.  Any idea what is up, here?  I can't just leave the ocean blank, either, since it says 0,0,0 is an invalid color too.

----------


## AzureWings

I could probably make that bit a hair more robust... but offhand the first thing I'd check is whether one or both of your precipitation maps has one or more pixels of ocean in pixels where your temperature maps have land (current script version assumes the land pixels will line up between temp and precipitation maps, and so checks to see if a pixel is ocean only by looking at the temp maps. It's an oversight - thanks for causing me to notice it, even if it turns out not to be the cause of your issue - I'll release a fix when I'm not in dire need of getting to sleep).

----------


## acrosome

Whoa.  Ok, I'll have to figure out how I can find such a problem pixel in Gimp.

I guess I can select all land on my land mask and erase everything else, on all four maps...

EDIT-- After a LOT of pixel-hunting, I got it to work!

----------


## AzureWings

I've released a bugfix for my script in the case where one or both precipitation maps have ocean in pixels where the temperature maps do not. The version of the script on the Github repository (see the link in the first post of the thread) should be fixed now; the former behavior of the script is that pixels where either temperature map had ocean were treated as ocean. Now the script behaves as intended in that regard; if any of the four input maps have ocean for a pixel, it is treated as ocean for all four (my intent here was to make it easier to use source maps that might have slightly different coastlines, such as might occur for high-detail maps where temperature and precipitation were generated separately and/or not by hand).

And for GIMP - try, for each precipitation map, selecting all land on your land mask and then with that selected try the "intersection" selection type (I think it's the last of the four selection types in the row of select types for the different select tools) with the select-by-color tool (with anti-aliasing, feather, etc. off and a threshold of 0) on the ocean color. If you're left with any pixels after doing so, they were ocean-colored pixels but were inside the land mask.

(Sorry if you're already plenty familiar with the selection types.)

----------


## acrosome

> And for GIMP - try, for each precipitation map, selecting all land on your land mask and then with that selected try the "intersection" selection type (I think it's the last of the four selection types in the row of select types for the different select tools) with the select-by-color tool (with anti-aliasing, feather, etc. off and a threshold of 0) on the ocean color. If you're left with any pixels after doing so, they were ocean-colored pixels but were inside the land mask.
> 
> (Sorry if you're already plenty familiar with the selection types.)


Yes, that's what I did.  It turns out that I had a lot of off-color pixels.  Somehow.  I'm guessing that at some point I used a brush instead of a pencil.  I also started painting a sample of the color listed in the error on the map, then selecting that color to find the other pixels.

----------


## Naima

> *Step 4, winds:* 
> 
> Use the pictures to figure out how the winds are blowing. *Figure A*
> Or use the main map at the bottom at the page. 
> All the figures are from the North Hemisphere except E. 
> 
> Attachment 76626
> 
> *
> ...


Due to polar cell and coriolis effect shouldn't the wind go from east to west around the polar region ?

----------


## Naima

Also another question I noticed that winds and sea currents do not always follow the same direction while building the world , is this normal?

----------


## antillies

Been a few months since someone has posted here so allow me to break the silence.

I'm taking a hard look at automating the entire process with as little input from the user as possible.  One question I have about currents though (and Charerg, I'd appreciate your input on this as well): for the warm current that flows north past the Philippines, why does it continue north/northeast?  Why doesn't it instead flow toward the Chinese coast?  Are the winds already beginning to affect it at that point?  Or is it a matter of where the continental shelf is?  The latter would confuse me further because (at least according to this map) there is a current, albeit a weaker one, that flows adjacent to the coast.

----------


## Charerg

> Been a few months since someone has posted here so allow me to break the silence.
> 
> I'm taking a hard look at automating the entire process with as little input from the user as possible.  One question I have about currents though (and Charerg, I'd appreciate your input on this as well): for the warm current that flows north past the Philippines, why does it continue north/northeast?  Why doesn't it instead flow toward the Chinese coast?  Are the winds already beginning to affect it at that point?  Or is it a matter of where the continental shelf is?  The latter would confuse me further because (at least according to this map) there is a current, albeit a weaker one, that flows adjacent to the coast.


The oceanic circulation always forms a loop, and since there is water "draining east" around the 45°, it follows that water must flow south->north to replace it. The winds would also be a factor. Also, due to that mass of water having been deflected northwards by the Philippines, it already has some momentum behind it in that direction. 

It's not dependant on the continental shelf, as these are surface currents (and indeed primarily tied to atmospheric circulation, ie. wind patterns).

Edit:
I guess the main takeaway when it comes to automating the process is identifying bodies of water that would contain a "closed loop", since it would be good to replicate the circular pattern of real oceanic currents. Most smaller bodies of water and inlets/bays could mostly be ignored as they wouldn't make that big of a difference when it comes to the global climate. I made a sort-of simplified example based on the 1943 map of the North Atlantic circulation a while back (might have posted this already), that might be helpful in identifying the patterns:

----------


## antillies

> The oceanic circulation always forms a loop, and since there is water "draining east" around the 45°, it follows that water must flow south->north to replace it. The winds would also be a factor. Also, due to that mass of water having been deflected northwards by the Philippines, it already has some momentum behind it in that direction. 
> 
> It's not dependant on the continental shelf, as these are surface currents (and indeed primarily tied to atmospheric circulation, ie. wind patterns).


I appreciate the reply Charerg, and that makes good sense.  The issue is, as you address later in your edit, that the above logic is reasonable given an established system but when trying to build it from scratch, it cant necessarily be relied upon to direct how things should be built.




> Edit:
> I guess the main takeaway when it comes to automating the process is identifying bodies of water that would contain a "closed loop", since it would be good to replicate the circular pattern of real oceanic currents. Most smaller bodies of water and inlets/bays could mostly be ignored as they wouldn't make that big of a difference when it comes to the global climate.


I thank you for saying this since my own approach has been slightly different.  I hadnt considered isolating the bodies of water themselves and using those edges to be the guide for currents.  I may try to incorporate this approach if my current piece-meal building of the currents becomes frustrated.  Ive been having the most trouble with the islands in southeast Asia but if I were to do it as you suggest I would ignore that area entirely since it would not qualify as a major body of water (whereas the Pacific and Indian oceans would).  




> Attachment 119884


I dont believe you have shared that but its beautifully made.  How did you make the current lines so smooth?

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## Charerg

> I dont believe you have shared that but its beautifully made.  How did you make the current lines so smooth?


They're drawn using the Paths tool in GIMP. It's a bit slow-going to draw the currents that way (as opposed to drawing them with a brush, for example), but it achieves a nice, clean look.

----------


## Uncle Twitchy

I've been gone from this forum for about ten years, but recently decided to rework my RPG/story world map after seeing Artifexian's tutorials on world building and map making... and then when I decided to tackle quasi-realistic climate zones I found this incredible thread and the work you fine folk have put into this.

Wow.

So... my work in progress. I'm needing some help if folks here would be willing to give it to an old grognard who hasn't been an active member of this incredible community (but would like to be).

Pressure zones and wind patterns are stymying me. I think I have the basics down, but would really appreciate some critique and input here.

Using Azélor's elevation colors (and I'm still working on refining the elevation map, but the basics are there enough to let me get the ocean currents, pressure zones, and wind currents), here's my elevation and rough, basic ocean current map:



Yes, it's rough, and there are likely areas that should have currents, but I understand the basic principles and know about closed loops and the directions and flows -- I'm unsure where the warm currents would continue to be warm coming back to close the loops, but anyway...

So this is what I came up with for my pressure zones -- have I done these correctly? This is where I start to get confused.

January


July


And even if I have these correct, I'm completely at a loss as to how the wind currents should go beyond the basic principles of the high pressure winds sending winds outward in a spiral (clockwise or counter depending on the hemisphere) and low pressure winds in an inward spiral going in the opposite direction. This was my attempt and doing the January winds before I threw up my hands and decided I didn't have a clue what I was doing:



Thanks in advance for any help you folks can give me!

----------


## Uncle Twitchy

Oh, did I post my request for help in a dead thread?

----------


## Charerg

The thread isn't completely dead (as I'm sure you've noted), though it can often take a long while before you get any really useful feedback. This stems from the reality that giving feedback in these matters tends to get rather time-consuming pretty quickly. However, as the tutorial has been around for a while there are actually a great many old threads where the climate has been tackled before, and those can be a great resource for someone looking for advice. For example, _davoush_ and _Tiluchi_ are two users worth checking out (though there are many others as well). 

Also, you can never go wrong by looking at the climatic/wind/pressure patterns as they exist on Earth and taking that as a basis. Basically, it can be a decent strategy to just go through the tutorial with what you consider to be the most plausible scenario. As your knowledge develops further and you notice things that are off, you can always revisit the wind patterns/climates etc. later on. It doesn't have to turn out perfect on the first try  :Wink: .

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## Uncle Twitchy

Thanks, Chareg! Believe it or not, I've read all of these threads, pretty thoroughly, though the whole placement of pressure zones and and wind patterns has been pretty daunting for me to wrap my head around. I'm just hoping for some feedback to see if I'm heading in the right direction.  :Smile: 

(It occurs to me that I may need to reassess some of my coastlines to show where the ocean currents are battering against them and when they're flowing past them. I'm getting there!)

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## arch-fiend

ok so its been bothering me for 13 months, since the last time i posted, every time i try to use this guide it bothers me, so im going to finally ask. i know azelor is using actual climate data for his guide and that climate data clearly shows a huge low pressure zone in australian summer, but WHY does that low pressure zone expand out over the pacific ocean? no other contanent seems to do this to any where near the same degree that australia does, the low pressure zone even begins slightly before australia in the indian ocean, whats going on here?

----------


## Azélor

Looking at it closely, the West Pacific pressure is lower all year long. 
I guess the temperatures tend to be higher in the west. 
The sea in the east is colder because it gets the currents coming from Antarctica and North America.
After than, the current merge and travel around the equator westward and accumulate heat in the process. 

https://en.wikipedia.org/wiki/Walker_circulation

It looks like I never paid enough attention to ocean's temperature.

----------


## arch-fiend

> Looking at it closely, the West Pacific pressure is lower all year long. 
> I guess the temperatures tend to be higher in the west. 
> The sea in the east is colder because it gets the currents coming from Antarctica and North America.
> After than, the current merge and travel around the equator westward and accumulate heat in the process. 
> 
> https://en.wikipedia.org/wiki/Walker_circulation
> 
> It looks like I never paid enough attention to ocean's temperature.


so would this happen on any large ocean at the equator? what happens if there are no continental shelves at the equator?

----------


## Azélor

Then I assume the temperature and pressure will be even along the equator.

----------


## arch-fiend

i think one mitigating circumstance that some maps will have is something that i have. the ocean currents that are arriving in indonesia are especially warm because both the cold currents from the north and south american coast are exceedingly far away from where those currents arrive at the australian and indonesian continental shelves. on a fantasy map im working on however while i have a long stretch of ocean between 2 southern continents i have a northern continent between them, this northern continent's cold current will mix with the equatorial current that eventually reaches the eastern coast of the western continent and will cool the current to a sufficient degree that i dont think it would cause this walker circulation to a degree noticeable enough that i need to track the pressure zone created there.

i think its a case where earth is actually performing stranger climatological phenomenon than fiction.

----------


## Azélor

Like Africa, it does get in the way of the equatorial current.




> i think its a case where earth is actually performing stranger climatological phenomenon than fiction.


Not necessarily. We know more about the climate of Earth than of a fictional world because we can observe it but there are things we do not understand.
 It's hard to tell how things will change if you change the variables.

----------


## Coggleton

For the temperatures in part 2 of section 5 (at https://www.cartographersguild.com/s...l=1#post285140), there's no indicator for Hot Currents in the summer or Continental+ in the Winter. Is there a reason for this, and what values should those zones/latitudes be associated with?

----------


## Azélor

> For the temperatures in part 2 of section 5 (at https://www.cartographersguild.com/s...l=1#post285140), there's no indicator for Hot Currents in the summer or Continental+ in the Winter. Is there a reason for this, and what values should those zones/latitudes be associated with?


It's just like the normal temperatures.

----------


## Coggleton

Ah, I see. With that, I've finished up a tentative sea-level temperature map for January and July using the same color guide suggested earlier, but I've noticed a couple issues:

- There are places where there are drastic jumps in temperature ranges, such as from blue to yellow or orange to peach. While I imagine these would need the missing temperatures in between them, how think should I expect these ranges to be? Would I connect them with similar temperature regions that occur closer to the poles?
- How thick should the temperature influence zones due to ocean currents be?
- Would these changes be best implement before or after I account for elevation?

Thank you.

January:


July:

----------


## Azélor

> - There are places where there are drastic jumps in temperature ranges, such as from blue to yellow or orange to peach. While I imagine these would need the missing temperatures in between them, how think should I expect these ranges to be? Would I connect them with similar temperature regions that occur closer to the poles?


Yes, you need a transition zone. If the gradient is strong the bands will be thinner.




> - How thick should the temperature influence zones due to ocean currents be?


It depends on the direction of the winds. If it's blowing directly inland, it could spread for several hundred of kilometres. 
If it blows in the opposite direction, then the effect is limited to coastal areas.

----------


## TheSquareRootOf2

Hey, is your temperature chart based on a mathematical model, or is it purely empirical data? I would be interested in automating the process of temperature determination to make it less tedious and hopefully more precise than what we're doing manually so far. If you know of any interesting research papers or models on that subject, that would be of great help! Alternatively, if anybody else has already tried that, I'd be happy to hear about it.

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## Azélor

It's empirical. But the relationship between precipitations and temperatures that gives the aridity uses maths. 
Azurewings might have a mathematical model but I don't know of any unless one where the planet is made with a uniform surface, with an uniform albedo.

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## AzureWings

> If you know of any interesting research papers or models on that subject, that would be of great help! Alternatively, if anybody else has already tried that, I'd be happy to hear about it.


I have thrown together a temperature generator, but instead of an expert systems/analytical approach I just built a neural net classifier for temperature bands that takes the elevation map as input and outputs January and July temperature band maps. I haven't put it out publicly on Github or anything yet because 1) it's pretty slow still (takes ~45 minutes to run on a 4000x2000 image) and 2) I wasn't sure under what conditions I'm allowed to distribute the training dataset I trained the classifier on (that's become more clear recently, but I'm somewhat novice at all the license details and so I've been reluctant to proceed).

There's also a software program called Clima-Sim, from Weather Graphics, which is aimed for simulating Earth but can apparently also be finagled to work for other arbitrary geographies and lets you edit a variety of relevant variables including axial tilt and so on. From what little I know about it it actually crunches a bunch of the equations to compute climatological predictions given those input items. That said, it's not free and I don't know all that much about it in detail, and it might be tricky to use in the sense of requiring a lot of climatological knowledge to get the best results. You can search it online if you're curious about it.

----------


## srm038

> Hey, is your temperature chart based on a mathematical model, or is it purely empirical data? I would be interested in automating the process of temperature determination to make it less tedious and hopefully more precise than what we're doing manually so far. If you know of any interesting research papers or models on that subject, that would be of great help! Alternatively, if anybody else has already tried that, I'd be happy to hear about it.


I have done some work on a mathematical model for temperature here. I took a lot of inspiration and knowledge from this thread but I'm not using GIMP or PS to do the actual work, so there would have to be some tweaks. But I've found this gives a reasonable baseline for temperature and a smoother, more precise transition between zones. Exact numbers can also help if for whatever reason you're interested in the temperatures at other times of the year and yearly precipitation/temperature values. I hope this helps somewhat!

----------


## TheSquareRootOf2

> I have thrown together a temperature generator, but instead of an expert systems/analytical approach I just built a neural net classifier for temperature bands that takes the elevation map as input and outputs January and July temperature band maps. I haven't put it out publicly on Github or anything yet because 1) it's pretty slow still (takes ~45 minutes to run on a 4000x2000 image) and 2) I wasn't sure under what conditions I'm allowed to distribute the training dataset I trained the classifier on (that's become more clear recently, but I'm somewhat novice at all the license details and so I've been reluctant to proceed).


That's an interesting approach! How did you manage to feed your neural network enough standardized temperature maps to make it effective? 




> There's also a software program called Clima-Sim, from Weather Graphics, which is aimed for simulating Earth but can apparently also be finagled to work for other arbitrary geographies and lets you edit a variety of relevant variables including axial tilt and so on. From what little I know about it it actually crunches a bunch of the equations to compute climatological predictions given those input items. That said, it's not free and I don't know all that much about it in detail, and it might be tricky to use in the sense of requiring a lot of climatological knowledge to get the best results. You can search it online if you're curious about it.


Yeah, I've heard of it and gave the demo version a try, but not being able to save your model is too much of a drawback for me. Essentially, this is a model like this -although simplified- I'd like to build. I have no deep climatological knowledge, but I've got some understanding of fluid mechanics and thermodynamics so I guess it's a start.




> I have done some work on a mathematical model for temperature here. I took a lot of inspiration and knowledge from this thread but I'm not using GIMP or PS to do the actual work, so there would have to be some tweaks. But I've found this gives a reasonable baseline for temperature and a smoother, more precise transition between zones. Exact numbers can also help if for whatever reason you're interested in the temperatures at other times of the year and yearly precipitation/temperature values. I hope this helps somewhat!


Wow, how come I did not find that blog earlier? The research paper linked in the first article is also particularly interesting, since it pretty much follows the approach I had in mind and goes much further in depth than I could on my own.

----------


## AzureWings

> That's an interesting approach! How did you manage to feed your neural network enough standardized temperature maps to make it effective?


Well, you're correct in noting that unlike a lot of machine leaning problems there's a very finite amount of training data available!  :Razz:  A lot of what I've done is to try to extract additional input parameters from the input Earth data (which is just the elevation + land-vs-ocean map). For example, before running the classifier I compute two different measurements of the shortest distance from each land pixel to ocean (one weighting distances in a manner incorporating elevation and one that's just raw distance) and feed those in as additional inputs to the model. This has a visible impact when you look at my output as isotherms do "bend" in the middle of large landmasses similarly to how the real ones do in many cases. My results have a ways to go yet (notably they miss things like current influences and high-latitude temperature inversions), but do make clear isotherms while being a bit more organic than direct application of hard cutoffs. They're worse than I'd like but better than I'd feared (with only the one training set I'd worried about overfitting or just not having enough data for learning to begin with, since I can't exactly get more).

For an example, the result of running my classifier on this elevation map:


Was the following:

Northern hemisphere's summer ("July"):


Northern hemisphere's winter ("January"):


I also tried a similar approach for precipitation, but the results were far less satisfactory with just the set of derived input parameters (latitude, elevation, distance from ocean, and distance from ocean weighting changes in elevation as increased distance) I had already. What I'd like to add to the input parameters (for both temperature and precipitation) is some sort of "density of ocean vector" element that represents both the "net" direction to and how much ocean is close to a point, but I've been having trouble figuring out how to compute it efficiently (since at base the problem is aggregating all the ocean pixels for every land pixel; that's pretty much a non-starter unless I can determine a way to reuse enough of the work to avoid being O(x^2 * y^2)).




> I have done some work on a mathematical model for temperature here.


Thanks for linking your blog - that's some _amazing_ work. I especially like the looks you take at demographics and trade - those are things I was interested in simulating too but I had no idea at all where to start.

----------


## srm038

> Thanks for linking your blog - that's some _amazing_ work. I especially like the looks you take at demographics and trade - those are things I was interested in simulating too but I had no idea at all where to start.


Thanks!

Hopefully it sparks the imaginations and labor of others.

----------


## Coggleton

For the transition maps you suggest for precipitation (https://www.cartographersguild.com/s...l=1#post287571), for parts 3 and 4 you mention "Equatorward west side" for both - what does the "equatorward west side" refer to for zone 4?

----------


## CTA

Planning to run through this process soon--how do you make a world topographic map? Specifically, how do you figure out which regions are supposed to be at which heights? I currently have a map of my world's plate tectonics, but will I need anything else?

----------


## Michi il Disperso

Goodevening everyone!
I followed this tutorial for my brand new world, i found it really entertaining, especially guessing temperature and precipitation zones!
Now i'm struggling with the script for GIMP; i managed to run it after some problem, but now i'm stuck to an error: unorderable types: str() > float ()
Anyone can unravel it for me?
I'm rather ignorant in software in general..

Thanks!

Michele

----------


## Charerg

> Goodevening everyone!
> I followed this tutorial for my brand new world, i found it really entertaining, especially guessing temperature and precipitation zones!
> Now i'm struggling with the script for GIMP; i managed to run it after some problem, but now i'm stuck to an error: unorderable types: str() > float ()
> Anyone can unravel it for me?
> I'm rather ignorant in software in general..
> 
> Thanks!
> 
> Michele


The GIMP script is made for GIMP 2.8, it probably won't work properly with a newer version of GIMP. You could either download the old version of GIMP to run the script, or use AzureWings' script (which doesn't require any program to be run, though it does involve installing Python).

----------


## Michi il Disperso

> The GIMP script is made for GIMP 2.8, it probably won't work properly with a newer version of GIMP. You could either download the old version of GIMP to run the script, or use AzureWings' script (which doesn't require any program to be run, though it does involve installing Python).


Well,  i said that badly, i actually used the AzureWing's one with Python

----------


## AzureWings

To clarify, are you trying to run the script via GIMP or on its own via Python from a command-line? The latter method's correct; the description of it in the first post as a "GIMP script" is a bit misleading.

----------


## Azélor

It's not a script made to run on Gimp?

----------


## AzureWings

No, it's made to be run from a command line, with the filepaths for input and output image files as arguments. Part of the idea there was you could use GIMP, Photoshop or whatever else you want to make the temperature/precipitation maps without needing to use a specific editor to be compatible with the script.

----------


## Michi il Disperso

> To clarify, are you trying to run the script via GIMP or on its own via Python from a command-line? The latter method's correct; the description of it in the first post as a "GIMP script" is a bit misleading.


Yep, with Python, 3.4 if im not wrong. I found infinite obstacle to install pillow on 3.0! 
Ive run it and said that some pixels are wrong colours, so i done some pixel hunting, the next time i tried he keep telling that error unorderable types: sum() > float(). I used altprecprofile, modified in the colors by me (i hope well) and defaulttemppprofile Also modified by me because ive used different colours. Have you hot some suggestion? Ive broke it? 😛

----------


## AzureWings

Go ahead and PM the modified altprecprofile to me (or just post it) and I can take a look and see if I can reproduce the error with it. Another thing that might be helpful is if you could pass on the stack trace Python outputs below "Traceback:" before the actual error message when it occurs.

----------


## Michi il Disperso

> Go ahead and PM the modified altprecprofile to me (or just post it) and I can take a look and see if I can reproduce the error with it. Another thing that might be helpful is if you could pass on the stack trace Python outputs below "Traceback:" before the actual error message when it occurs.


Well, it seems he didn't like the way i wrote (default) in the ocean line in the defaulttempprofile: i changed it with the actual ocean color and now it say: Error: Invalid color in input data (did not match input profile): (0, 0, 0)
i guess i'm in for more pixel hunting...

EDIT: Finally i manage to have the output!
I don't really know how.. i have arrived to a point were eveything i do the error was   Error: Invalid color in input data (did not match input profile): (107, 165, 210)  the color of the ocean, so after a lot of tries i assigned a random temperature value to ocean instead of O and.. it somehow worked... with oceans in right places and climate.. well.. i guess correct.. it's a bit of a puzzle but i think i can manage to make some adjustment!

----------


## AzureWings

Glad you got it working! The (Default) token to have a default input value for unrecognized colors is case-sensitive, for future reference. The ocean color showing up as an 'invalid' color can result from mismatches in which areas are land and ocean between different input maps (that's a bug I need to fix still).

----------


## Charerg

@Azelor:
I guess this might be a good opportunity to change the text in the opening post to something like "Python script for generating the climates" since the GIMP part really could be misleading to some users. I also made an ad-hoc guide on how to use Azure's script some time ago, it might be useful to link that in the opening post as well.

----------


## Michi il Disperso

> The (Default) token to have a default input value for unrecognized colors is case-sensitive


Yes, i tryed many ways, still don't know if that was the problem...




> The ocean color showing up as an 'invalid' color can result from mismatches in which areas are land and ocean between different input maps (that's a bug I need to fix still).


I found it reading the thread, that could be the source of the error, so i checked every coastline pixel, but i'm nearly sure it wasn't the case.. anyway, it worked! And still work, i'm doing some change in the temp map.




> @Azelor:
> I guess this might be a good opportunity to change the text in the opening post to something like "Python script for generating the climates" since the GIMP part really could be misleading to some users. I also made an ad-hoc guide on how to use Azure's script some time ago, it might be useful to link that in the opening post as well.


Thanks Charerg, i found and followed your tips; unfortunatly each version of Python (and every pc i guess) seems to have a particular way to write the input in the cmd.. or maybe it's only i'm a total newbie with programming and such...

About the Climate in general: i didn't found in the thread anything related to big lakes or internal sea (like Mediterranean) and how they can influence the climate nearby; if i'm not wrong they smooth a bit the temperature variations and rise a bit the humidity; is there some quantification of this i can look for?

----------


## Coyil

okay, so after Michi il Disperso lead me to this thread I am trying to make this climate-thingy work. Now I have some questions concerning my currents:



- are they "realistic"?
- should the southern or northern polar currents flow into the other direction?
- should there be an exchange of water between the southern equatorial and polar currents?
- Is there anything else wrong?

----------


## Michi il Disperso

Hi! 
I try to answer some, mind i'm not an expert, i just pass the tutorial as you're doing!
I have made some small correction in your currents, these are mine opinions, not scientific corrections, so wait for the answer of someone more expert XD
Just a thing: in your projection you cut some latitude? If not, mind that the north pole is actually a point, not a line, so the mountains should be line as well.
I think the excange of water is fine, it could occour with a deep ocean current that isn't interesting for the climate.

----------


## CTA

Speaking of invalid color values, I just ran through this tutorial and encountered the paintbrush-mixed-colors-problem. To solve this, I wrote a python script to correct all non-standard (standard as defined by the script's input) color pixels in an input image to the nearest standard color. This script worked pretty well for me, and I figure I'm not the only one who's encountered this problem; @AzureWings do you think it would be worthy of inclusion on the speculative-koppen GitHub?

----------


## CTA

Also, I ran the script! This is what I got:


There are some wonky climate zones there, which I figure I should smooth out manually.

I can't figure out which colors correspond to which climate zones. The chart Azelor put on the main post doesn't seem to correspond to what I have--could someone please let me know what I'm missing?

----------


## CTA

I'm hoping there aren't any deeper inconsistencies--my precipitation maps seemed a bit sparse to me and now my world is pretty desert-heavy. Does it look implausible?

----------


## MrBragg

Not an expert here by any stretch of the imagination, but those big regions of BWh over the equator seem a bit suspect, unless there's something quite different about this planet compared to earth.  That's typically where'd you'd expect rain forest, not desert.

----------


## CTA

Exactly! I think I screwed up my precipitation maps. (Btw, all orbital characteristics are the same as earth; no major differences there)

These are my ocean currents:


Jan winds & pressure zones (red = high pressure, green = low pressure):


Jan precipitation:


Jul winds & pressure zones:


Jul precipitation:


Maybe I overstated the effect of the July high-pressure zone on the big continent (Rarenarena)? I'd really appreciate any advice!

----------


## Coyil

@Michi il Disperso: Thanks, I will try to implement your changes and see what happens.

----------


## Michi il Disperso

Hi CTA, as i said before i'm not an expert; but i can say:
- I would have drawn currents a bit differently, but that's a minor
- high and low pressure zones are correctly placed, just a bit too big, in my opinion: i think your continents are rather small-ish to have big continental effects (they are thin, there is not a huge mass of ground far from sea)
- precipitation would be definitely higher near the equator, all year long, especially in the leftmost and center-right continents. I think you can add really a lot of rain there ^^ we expext a low pressure zone almost all year long 

Those are only opinions, i still struggling to balance my map, so let's ear someone else!

----------


## AzureWings

> There are some wonky climate zones there, which I figure I should smooth out manually.
> 
> I can't figure out which colors correspond to which climate zones. The chart Azelor put on the main post doesn't seem to correspond to what I have--could someone please let me know what I'm missing?


I agree with MrBragg and Michi il Disperso that the equatorial aridity is suspect, since land directly on the equator should be getting hit by the ITCZ for at least part of the year and thus get a lot of precipitation, barring some extreme mountains causing a powerful rain shadow (and even then the windward side of those mountains would still probably be very wet).

To find out what climates colors correspond to, you can peek at the table ("kColorTableDefault") in the script (or look at the "defaultOutputProfile" color profile, which replicates the climate category colors the script defaults to) for a mapping from RGB to Köppen category.

Regarding the pixel cleanup script, what sort of results are you getting from it? I experimented with a similar feature that I called a "fuzzy" mode for the script itself but kept getting "hot pixels" where a gradientized/antialiased border between two regions was resulting in isolated pixels getting assigned colors that were neither of the two bordering categories. Regarding adding the script to the repository, I've sent you a PM with a few questions pertaining to that.

----------


## CTA

I'm getting exactly the results I want--as far as I can tell, every erroneous pixel is changed to the value of its nearest neighbor. 
This is my jan temp map before running the script:

And this is my jan temp map after running the script:


(you have to zoom in on the borders to see the difference)
The only problem I encountered was that certain pixels were corrected to ocean in some maps and corrected to a prec/temp zone in others. To solve this, I wrote another short script that goes through all 4 images and converts those pixels to ocean.

----------


## CTA

@Michil il Disperso: Sounds good, I'll make those changes! Question about the ICTZ: should I expand it so it covers the equator or should I just widen its impact (winds, precipitation, etc)? I'm leaning towards the latter given the advice in Azelor's tutorial and the climate cookbook.

----------


## Michi il Disperso

I tend to go pretty wide and with very very intense rainfall in the ICTZ, lowering the intensity on the edge. I submit the one i do for my world. Again, i think there is much guess in this exercise ^^

----------


## AzureWings

> I'm getting exactly the results I want--as far as I can tell, every erroneous pixel is changed to the value of its nearest neighbor. 
> 
> [...]
> 
> The only problem I encountered was that certain pixels were corrected to ocean in some maps and corrected to a prec/temp zone in others. To solve this, I wrote another short script that goes through all 4 images and converts those pixels to ocean.


That's pretty good output! The ocean vs. land problem shouldn't need a separate step once I've fixed up the ocean detection bug (the intended behavior of the climate script is already to only treat as land pixels that are land in all four input maps).

----------


## CTA

Alright, how about these?
New jan precipitation map:

New july precipitation map:

New climate zones:


For this one, I made the assumption that winds blowing from hot current onto land would create precipitation, even if they were coming from a high-pressure zone, but idk if that's valid. I also expanded the effects of the ITCZ over the equator, though i didn't expand the zone much itself.

----------


## Michi il Disperso

Well, to me is far better! Obviously the output of the script need always a bit of managing, but i think this is a more realistic option.
I wonder why there is still a desert bit in the triangular rain area in the north of the center continent... i see rain there, it shuldn't be desert... however... good job ^^

----------


## CTA

@AzureWings: Sounds good, I'll send over the script today

----------


## CTA

Maybe it's because the rain is fairly light?

----------


## Michi il Disperso

> Maybe it's because the rain is fairly light?


Yes, i believe that the calculus that the script do count that rain really light

----------


## CTA

Alright, finalized my climate map! Here it is:


I smoothed out a few climate zones, added steppes, and corrected things that didn't really make sense (example: the antarctica-like continent at the top was mostly ET, not EF). Some climate zones might still be misplaced (based on their descriptions), but I'm choosing to believe that the script worked and everything is more or less fine.

----------


## AzureWings

The precipitation scale I used with Azélor's color profile increases by factors of 2 with each step up the color chart, so the darkest purple and colorless are both still pretty dry and there's the additional factor that the precipitation threshold to not be arid gets higher the warmer it is (as a result of higher evaporation rates) so such a relatively-equatorial region needs more rain to avoid being arid than somewhere colder would. It seems very frequent (including on Earth) for As/Aw climates (like the lighter tropical blues) to directly border steppe climates. By the numbers then that little northern bit of desert ringed by steppe is probably correct.

I'm not sure what's up with the ET vs. EF in the northern continent. Assuming you're using the default temperature profile did it have any of color (230, 245, 150)? That's the only category between 0 and 10 degrees C by default which makes it something of the deciding factor between ET and EF.

----------


## Michi il Disperso

Thanks for the explanation!

----------


## Coyil

I need a little help with the script. I managed to install python and pillow, but i cannot find the option to run scripts in Photoshop.
Also, is the climates.atn the script?
I am an absoulte n00b when it comes to programming and scripting beyond excel basic.

A step by step guide for dummies would be much appreciated.

*facepalm* I think i got it.

I got it now. C&C welcome:



Personally I think the coast ist too wet and I might have expanded the super dry zone too much. I have to chack with the currents again.

Precipitatio maps January & July


Temperature maps January & July


Currents:

----------


## Michi il Disperso

Glad you managed to get it work! I had some difficulties myself!
Maybe you could try to expand the intermediate rain zone a bit and reduce the heavy rain zone; and yes, i think too the super dry is just a bit too big. 
Anyway, good work!

----------


## Osellic

Hey everyone! Having a bit of trouble determining where to place my high and low pressure centers!
Also, any help on if my currents are correct would be wonderful. 

The vague white lines are my lattitude markers, and represent 60N at the top, 30N beneath it, 0 beneath it, and 30S at the bottom.

Thanks in advance!

Link to map: https://imgur.com/a/YbvUBXw

----------


## Michi il Disperso

Hi Osellic! It could be useful to have the full world map; are the other parts just ocean? Also the poles? If the top of the image is 90N, remember that that line is a point in reality, so you'll have to stretch the ice covering all the upper side.
The currents seems fine to me, i would have skipped the current in the inner sea, i think it's just too narrow to have a significant current; and the  first part of the downward current in the west coast should be a cold current (cold respect the surrounding ocean); Also, the upward hot current in the east coast should gently deviate eastward past 30N rather than turn abruptly, but that's a minor.

----------


## Coggleton

I had some questions about the precipitation transition zone regions that I was hoping someone could help with, specifically the bits down below:




> 3· Equatorward west of H is dry
> 4· Equatorward west side tend to be dry in winter...
> 5· The west and poleward sides of H are wet...


It seems like 3 and 5 are contradictory- or does it mean that dead west of the high-pressure zones is wet, while only the equator-west "diagonal" area is dry? And regarding number 4, what is that referring to? I thought it might mean continents, but on both the January and July maps it appears to the west of high pressure zones (#1), which contradicts 5.

----------


## Charerg

I ended up messing around with writing a GIMP plug-in in Python and updated my old climate script. The new version is largely adopted from Azure's script and works in a similar fashion: the plug-in processes the source maps on a pixel-by-pixel basis. Since this one uses many of GIMP's in-built functions, it's not as efficient as Azure's script, and the processing time is quite long. On the other hand, it now has a nice progress bar that shows how far along the script is from completing. While this is a GIMP plug-in, I'm not 100% sure that it works without installing Python. Anyway, here are the instructions:

Installation:
Place the plug-in in the appropriate folder (usually /Program Files/GIMP 2/lib/gimp/2.0/plug-ins). If uncertain, you can check Edit->Preferences->Folders->Plug-ins to see where the plug-ins are stored. Once in the right folder, the plug-in should be available (you can use Filters->Script-Fu->Refresh Scripts so you don't have to restart GIMP). You should now have the script available under the Image tab:



Restrictions for using the script:
- This has been written for and tested in GIMP 2.10 
- The image needs to be *RGBA* (RGB with an Alpha channel) with *8-bit Integer* precision

Layer naming restrictions:
The temperature/precipitation layers need to have exactly the following names (the script searches for them by name):

JanTemp
JulTemp
JanPrec
JulPrec

Layer colouring restrictions:
The temperature and precipitation categories need to have exactly the following colours (with the ocean tiles coloured in a separate colour, *not transparent* as in prior versions of the script):

Temperature zones:

* *





*Temp Category*
*R*
*G*
*B*

Severely Hot
160
0
65

Very Hot
210
60
80

Hot
245
110
65

Warm
250
175
95

Mild
255
225
140

Cool
230
245
150

Cold
170
220
165

Very Cold
100
195
165

Severely Cold
50
135
190

Deadly Cold
95
80
160



The temperature zones in a slider:




Precipitation zones:

* *





*Prec Category*
*R*
*G*
*B*

200+ mm
210
200
250

100-200 mm
190
170
240

50-100 mm
150
130
220

25-50 mm
90
80
160

10-25 mm
240
235
160

0-10 mm
235
0
140



The precipitation zones in a slider:




Ocean tiles:

* *





*R*
*G*
*B*

155
205
230






Sample Map:
Here is a sample climate map using source maps generated from WorldClim's 1970-2000 dataset:

Source maps:

* *














Generated climates:


The plug-in can be found in the attachments. Feel free to provide any feedback if you have trouble using it. Note that the new script has been set up not to generate any _Cc_ climates, and uses a gradual aridity threshold (unlike the previous versions, which used a stepped threshold as in most publications of Köppen maps).


*Update (28/10/20):*
- Now supports off-colour pixels (they are assumed to have 0 °C temperature and 1,0 mm precipitation)
- Fixed a bug where the script could paint the ocean in the wrong colour if opacity was set at less than 100%

Big thanks to Coggleton for reporting the issues with the script, this update should solve the problems related to off-colour pixels.

----------


## Coggleton

> Restrictions for using the script:
> - This has been written for and tested in GIMP 2.10 
> - The image needs to be *RGBA* (RGB with an Alpha channel) with *8-bit Integer* precision
> ---
> The temperature and precipitation categories need to have exactly the following colours (with the ocean tiles coloured in a separate colour, not transparent as in prior versions of the script):


Thanks - this looks like it will be quite helpful for me! One question- you mention the various colors need exact RGB values. What about the L,C, and h values? I'm not too familiar with GIMP and other color values besides RGB, so this might just be my ignorance showing.

----------


## Charerg

> Thanks - this looks like it will be quite helpful for me! One question- you mention the various colors need exact RGB values. What about the L,C, and h values? I'm not too familiar with GIMP and other color values besides RGB, so this might just be my ignorance showing.


LCH (and HSV) are only alternative ways of defining a colour. If you change the Hue or Saturation for example, the program adjusts RGB values accordingly.

----------


## Josh Foreman

> Feel free to provide any feedback if you have trouble using it. Note that the new script has been set up not to generate any _Cc_ climates, and uses a gradual aridity threshold (unlike the previous versions, which used a stepped threshold as in most publications of Köppen maps).


First of all, THANK YOU for making this!  This is exactly what I was hoping someone would develop.  Second of all, I'm well versed in Photoshop, but never used GIMP before, and I'm having problems with some of the basics of your instructions, which I feel like must have been a continuing and evolving conversation spread over these 50-something pages.  Is there a post I can search for that might help with the basics?  Such as: How to make sure my image is RGBA? (I added an Alpha Channel, but I'm not sure if that's what does it?) What kind of image should my source be?  Black continent with white water?  Land on a separate layer? When you say the layers have to be specific colors do you mean a layer that's completely filled with that RGB?  I was able to run the script, but it was looking for layers that weren't there.  I feel like I'm close, but missing something fundamental.  Thanks for any help you can provide!  :Smile:

----------


## Charerg

> First of all, THANK YOU for making this!  This is exactly what I was hoping someone would develop.  Second of all, I'm well versed in Photoshop, but never used GIMP before, and I'm having problems with some of the basics of your instructions, which I feel like must have been a continuing and evolving conversation spread over these 50-something pages.  Is there a post I can search for that might help with the basics?  Such as: How to make sure my image is RGBA? (I added an Alpha Channel, but I'm not sure if that's what does it?) What kind of image should my source be?  Black continent with white water?  Land on a separate layer? When you say the layers have to be specific colors do you mean a layer that's completely filled with that RGB?  I was able to run the script, but it was looking for layers that weren't there.  I feel like I'm close, but missing something fundamental.  Thanks for any help you can provide!


If all of the source layers (JanTemp, JanPrec, JulTemp and JulPrec) have an alpha channel, you should be good to go. You can see if a layer has an alpha channel by looking at the layers tab, layers without an alpha channel have their names bolded:



To see how the source maps should look like, take a look at the "sample map" section of the instructions in my prior post. You can also test the script using the source maps provided in that section: all you need are those four layers and you should be able to activate the script succesfully and produce the climate map.

----------


## Josh Foreman

> To see how the source maps should look like, take a look at the "sample map" section of the instructions in my prior post.


Ah, maybe I'm misunderstanding what this plugin is actually meant to be accomplishing.  I thought it would take a fictional world map I've made and determine Koppen climate zones.  But based on the WorldClim's 1970-2000 dataset inputs you've got there, I'm guessing the colors are driving the data for your plugin?  And I don't know how to generate those colors on my fictional planet except by vague, under-educated guesses. Do you know of any resources to help me with that part that must come before using your plugin?

----------


## Charerg

> Ah, maybe I'm misunderstanding what this plugin is actually meant to be accomplishing.  I thought it would take a fictional world map I've made and determine Koppen climate zones.  But based on the WorldClim's 1970-2000 dataset inputs you've got there, I'm guessing the colors are driving the data for your plugin?  And I don't know how to generate those colors on my fictional planet except by vague, under-educated guesses. Do you know of any resources to help me with that part that must come before using your plugin?


Yes, namely this tutorial. The first post on the first page has links to all the different sections (figuring out oceanic currents, wind patterns, temperatures and precipitations for a fictional world). Only the last part (generating the climates) is automated  :Smile: .

----------


## Josh Foreman

> Only the last part (generating the climates) is automated .


I see.  THanks for clarifying.  And if you're taking requests.... make a Python script that does all that other stuff too!   :Wink:

----------


## Charerg

> I see.  THanks for clarifying.  And if you're taking requests.... make a Python script that does all that other stuff too!


There have been some efforts along those lines, mostly from AzureWings. But that's a much greater undertaking than just taking in the temp and precip data and churning out the climates. Myself, I haven't made any serious attemps at creating a script that could arrive at a plausible temperature or precipitation map from just a land-ocean map as input (or even a DEM). But considering that you'd essentially have to create a simplified climate simulator, more-or-less, with maybe some user-fed parameters (like how warm the planet is relative to Earth), the processing time could become an issue pretty quickly (even my most recent script takes quite long to go through a map).

----------


## MrBragg

This tutorial has been super helpful, but after working through it I had a question about some of the example maps of earth that are presented.

For the southern summer, the example map shows the southern Andes preventing moisture from the westerlies from reaching central / eastern Patagonia, which seems all well and good.  For the northern summer, though, the corresponding map for North America shows the precipitation extending across all of the continent, despite similar latitudes and the coastal ranges / Rocky Mountains being just as high in altitude as the southern Andes.  I was wondering if there was some reasoning behind these differences in behavior, or if I'm missing something else?

----------


## Charerg

Actually I'd say the southern Andes having such a drastic effect is the abnormal case here. Normally you'd expect cyclonic rainfall to not be so heavily affected. It's maybe a slight error in perception to view the regions that are affected by the westerlies as being constantly swept with moisture-laden westerly winds. In reality they are a belt of localised low pressure centers that circle the globe on a west-to-east vector (more-or-less), so the winds tend to be predominantly westerly when viewed globally, but not necessarily locally.

I suspect Patagonia has an unusual local wind pattern which for some reason favours constant westerly winds, though admittedly this is just a guess on my part.

----------


## MrBragg

Picturing things like that makes a lot of sense and actually helps clarify some other questions I had.  Thanks!

----------


## rdanhenry

> Actually I'd say the southern Andes having such a drastic effect is the abnormal case here. Normally you'd expect cyclonic rainfall to not be so heavily affected. It's maybe a slight error in perception to view the regions that are affected by the westerlies as being constantly swept with moisture-laden westerly winds. In reality they are a belt of localised low pressure centers that circle the globe on a west-to-east vector (more-or-less), so the winds tend to be predominantly westerly when viewed globally, but not necessarily locally.
> 
> I suspect Patagonia has an unusual local wind pattern which for some reason favours constant westerly winds, though admittedly this is just a guess on my part.


South America is rather narrow at the southern end. That there is not much in the way of terrain features in the area may reduce some of the variation in wind patterns compared to wider continental areas with a variety of terrains over larger expanses? There is also very little land that far south globally. Air can pretty much just keep circling the globe without hitting much of anything until it reaches the Andes.

----------


## Coyil

Hey all

After further tinkering with my world to be, I once again request your criticism of my mapped currents.
I am most unsure about the ocean between the southern continents and the north-eastern coast of the north-eastern continent.
Thank you in advance.

----------


## acrosome

Why does Asia not get rains from the extratropical storm path?  Or Africa and Australia, for that matter?

----------


## Charerg

> Why does Asia not get rains from the extratropical storm path?  Or Africa and Australia, for that matter?


Erm, they do get rains? It's just that for some reason the guide puts the Australian extratropical cyclone rains under the westerlies, and the Asian and African ones under "winter monsoon" (and partially under the westerlies as well).

----------


## Coggleton

So, I went through the guide and did the initial precipitation map for January. However I'm unsure about some of the instructions with the transition map, as well as how far I should have certain zones go inland, so I haven't done any orographic lift effects or added in the penalty areas yet; would someone kindly be able to let me know if I'm roughly in the right direction? Much appreciated.

January Precipitation (Same color indicators as in guide/Charerg's legend):


January Pressure and Wind:

----------


## Coggleton

My apologies for doubleposting, but I think I might've run into an error with the new script; whenever I try to run it, it gives me the following error message:

"Plug-in 'Climate Generator' left image undo in inconsistent state, closing open undo groups."

Running it in the python console only gives the following error:
Traceback (most recent call last):
  File "<input>", line 1, in <module>
RuntimeError: execution error

Is there any way to figure out what line of code the script crashed on?

I've tried reinstalling GIMP and installing Python, but to no avail. I have, however, been able to successfully run an older version of the script that's on post 334 of the thread. It can be seen as follows, with an added legend:



Going off of intuition, it seems odd that the tundra would go so far south on the coast right continent's top "finger". I'm also guessing that any internal tundra zones are due to mountains.

----------


## Charerg

The forum seems to have eaten the attachment (Attachment 125632) from your previous post, as it is not visible. Just to clarify, which version of GIMP are you running, and which version of the script you have an error with, the one posted in post #555?

----------


## Coggleton

I replaced the attachment with a text description of the error.

My GIMP version is 2.10.22, and it's the script in post #555 that I'm having issues with.

----------


## Charerg

> I replaced the attachment with a text description of the error.
> 
> My GIMP version is 2.10.22, and it's the script in post #555 that I'm having issues with.


Can you post your source maps, so I can try to reproduce the error?

----------


## Coggleton

Of course; thanks in advance for taking a look at this.

JanTemp:


JulTemp:


JanPrec:


JulPrec:

----------


## Charerg

Well, the good news is that I had no problems with reproducing the error. The bad news is that it might take a while to figure this one out. Anyway, thanks for reporting the bug, I'll see if I can squash this one.

----------


## Coggleton

How does the script handle transparent pixels? While I thought I already filled them in, apparently I missed a couple. Then again, it still didn't fix the problem. New maps are pasted below.

JanTemp:


JulTemp:


JanPrec:


JulPrec:

----------


## Charerg

The current implementation doesn't tolerate any "off-colour" pixels at all (transparent or otherwise), which was the cause of the error. Though the matter is a bit more complicated, because apparently the script itself could paint the ocean in the wrong colour if you happened to have your opacity set at something other than 100% when running the script.

Anyway, I think I managed to figure this out now. I added a new line to the script that sets the opacity at 100% before painting the ocean and also new checks have been added so the script now tolerates off-colour pixels (it now assumes temperature to be 0° C and precipitation 1,0 mm if the colour is off). I'll post the updated version soon, once I've tested it a bit more with your source maps to make sure I didn't miss anything.

Edit:
Though I'd suggest making the maps a bit smaller than 22500x11250 for the new script, that could take a long while to process  :Wink: .

Edit2:
Ok, the script has now been updated (see post #555 for the updated version).

----------


## Coggleton

Heh, you have a point about the image size- unfortunately, scaling it down from the original size after i had made my elevation maps is what caused things to become janky (several pixels became various shades which I found unhelpful).

But the good news is, your code just ran and successfully executed.

EDIT: And after some color swapping, I'm proud to present the results below:


Besides the large amount of tundra up north, there's also considerably more desert than I expected. But in any case, I'm really grateful you threw the code together for this.

----------


## Skalimoi

Hello, I've been encountering a nasty error using the Python script and I don't know how to solve it:



It seems to be a code error rather than an user error, but I'm not sure. Any help would be greatly appreciated. Should I upload my input images just in case you need them? I'm running the script via Conda, should that be a problem.

----------


## Charerg

> Hello, I've been encountering a nasty error using the Python script and I don't know how to solve it:
> 
> 
> 
> It seems to be a code error rather than an user error, but I'm not sure. Any help would be greatly appreciated. Should I upload my input images just in case you need them? I'm running the script via Conda, should that be a problem.


You're running AzureWings' script linked in the OP, right? In any case you should probably post the entire command line you're attempting to run, this looks to be just a typing error, rather than an error in the code itself (as the error message doesn't point to a particular line of code). I'm not sure if running the script through Conda would be a problem or not. I'd suggest attempting to activate it through the command prompt to see if you get the same error?

----------


## Skalimoi

> You're running AzureWings' script linked in the OP, right? In any case you should probably post the entire command line you're attempting to run, this looks to be just a typing error, rather than an error in the code itself (as the error message doesn't point to a particular line of code). I'm not sure if running the script through Conda would be a problem or not. I'd suggest attempting to activate it through the command prompt to see if you get the same error?


Yes, I'm using AzureWings' script. I tried running it with the command prompt, but I got the same error. The command line I'm using is:


```
python ./skcc.py --tempns=TEMPJULY.png --tempnw=TEMPJANUARY.png --precns=PRECIPJULY.png --precnw=PRECIPJANUARY.png --outfile=OUTPUT.png --precprof=RAIN --tempprof=TEMPERATURE
```

I'll attach the custom color profiles I'm using, too. Maybe there's a problem with those:

TEMPERATURE.txt RAIN.txt

They're in .txt format, as the forum wouldn't let me upload them just as files. Hope they can help you.

----------


## Charerg

You need to put the file names in quotes, for example:

python ./skcc.py --tempns="TEMPJULY.png" --tempnw="TEMPJANUARY.png" --precns="PRECIPJULY.png" --precnw="PRECIPJANUARY.png" --outfile="OUTPUT.png" --precprof="RAIN.txt" --tempprof="TEMPERATURE.txt"

Aside from that, there was one instance of using tab instead of spaces in the custom color profiles (which Python will surely complain about).

----------


## Skalimoi

> You need to put the file names in quotes, for example:
> 
> python ./skcc.py --tempns="TEMPJULY.png" --tempnw="TEMPJANUARY.png" --precns="PRECIPJULY.png" --precnw="PRECIPJANUARY.png" --outfile="OUTPUT.png" --precprof="RAIN.txt" --tempprof="TEMPERATURE.txt"
> 
> Aside from that, there was one instance of using tab instead of spaces in the custom color profiles (which Python will surely complain about).


Okay, I changed the command line to that, I checked every color profile to delete any tab, and now I have another problem. The script doesn't detect my ocean color and keeps saying that it isn't specified in the profile: "Error: Invalid color in input data (did not match input profile): (255, 255, 255)". This was after I changed my ocean color to white because I had the same error with the blue color I had before. The color is indeed specified in the new profiles, which I'll attach again just in case.

TEMPERATURE.txt RAIN.txt

In order to correct this, I tried to use the correct_colors.py script with the following line:


```
python ./correct_colors.py input_img="TEMPJULY.png" output_img="TEMPJULY_CORRECTED.png" colors="TEMPERATURE.txt"
```

And it returns the following error:


The color profile IS in the directory. I haven't moved any file. I put the same arguments as in the README file. I'm sure the answer is plain and simple, but I'm afraid I can't wrap my head around it. 

I'll attach my input images too, in case you need them. Thank you
images.zip

----------


## Charerg

I'm actually not sure how Azure's script handles ocean colors when reading from an input profile. Personally I just tend to write any custom colors into the script itself. You could do that as well, open "skcc.py" and replace the original defaultOceanColor (line 56) with the new one.

----------


## Skalimoi

Finally solved it! For anyone who may have the same problem: you need to change the value of the Ocean color from "O" to any random number. It seems that the script doesn't recognise the default value properly.

----------


## acrosome

I'm working on precipitation, and I'm trying to interpret this graphic:



Am I interpreting it correctly that precipitation is 1 (or maybe 0?) at altitudes higher than 4000m?  Or, put another way, once altitude gets VERY high does precipitation start to drop rather than increase from orographic lift?

I have many very high altitude provinces in otherwise high precipitation areas, so this is important for my worldbuilding.

----------


## AzureWings

> Finally solved it! For anyone who may have the same problem: you need to change the value of the Ocean color from "O" to any random number. It seems that the script doesn't recognise the default value properly.


Sorry about this one; I've known about the "(ocean color) is invalid/not in input profile" bug since a very long time ago and due to a couple of obnoxious and awkward reasons hadn't fixed it despite the fix being rather simple. I just pushed a version of the Python script to Github that should put a stop to this particular bug once and for all.

For reference, it wasn't a problem with the input profile specifically, but rather typos in an internal check made to see if a pixel matches an ocean (or other "ignored") color. The problem arose specifically if there were pixels that were ocean in either or both of the nothern-hemisphere-winter temperature and the northern-hemisphere-summer precipitation input maps, but not ocean in the other two. With the new version you should be able to use an 'O' or an 'X' in lieu of a number to specify an ocean/ignored color in input profiles, even when the input maps conform to the above scenario, without experiencing this error (pixels that are ocean in some input maps but not others will be treated as ocean for the final output).




> Am I interpreting it correctly that precipitation is 1 (or maybe 0?) at altitudes higher than 4000m? Or, put another way, once altitude gets VERY high does precipitation start to drop rather than increase from orographic lift?


I'm not as knowledgeable on the subject as Azélor, Charerg and some of the other regulars, but at very high altitudes I would think the possibility does exist that enough precipitation occurs on the preceding lower slopes that the remaining precipitation on the higher levels is diminished. Apparently this can also depend on the temperature of the moist air mass: if it is warmer, it is likely to precipitate most heavily lower in elevation than a colder air mass would (but I'm unsure if this is enough to overcome the fact that the warmer air mass is likely to have a lot more precipitation to drop overall compared to the cold one).

However, at those elevations it might start to become less relevant anyways because the temperatures might push the climate category towards E climates where precipitation amounts don't matter, so if the provinces in question aren't warm to begin with it might end up being a moot point (for reference, going off of Azélor's chart for the temperature impact of elevations, at 4000m, if at sea level the temperature would have been any lower than 36 degrees C, the temperature will be less than 10 degrees C). If it doesn't get up to 10 degrees or higher in at least one of summer or winter it's an E climate and the precipitation amount doesn't affect the category.

----------


## Charerg

AzureWings is correct, the precipitation does eventually diminish as the elevation becomes very high. For example, both the Altiplano and the Tibetan Plateau are very dry landscapes. As pointed out, the effect is somewhat diminished by the cold conditions prevalent in these altitudes (hence, low evaporation), and anything higher than 4000 m in altitude typically has an E-type climate.

----------


## acrosome

Ah, as I had thought.  Though it does seem odd to have a stark border between a precip 5 area and a precip 0 one...

Thanks.

----------


## Coggleton

So I've started to look at using Azure's script for the holdridge biomes, and I'm wondering if there's an issue with it coloring in oceans correctly- for the below picture, oceans are colored as (107, 165, 210) while in the output file I passed it should have oceans as (155, 205, 230). Below are the command line, resulting map, and actualOutputProfile - what's interesting is that everything else besides the oceans used the correct color.

Function call:
> py ./skcc.py --tempns="suru jul temp.png" --tempnw="suru jan temp.png" --precns="suru jul prec.png" --precnw="suru jan prec.png" --tempprof="actualTempProfile" --precprof="actualPrecProfile" --outprof="actualOutputProfile"  --outfile="suruClimateAzure.png"

Output Profile (Pasted as a text file, I have a file on my computer without the .txt so it ran well):
actualOutputProfile.txt

Result:


I also ran it using the holdridge mode, and same issue: everything but the oceans conformed to the output profile.

Function call:
>py ./skcc.py --tempns="suru jul temp.png" --tempnw="suru jan temp.png" --precns="suru jul prec.png" --precnw="suru jan prec.png" --tempprof="actualTempProfile" --precprof="actualPrecProfile" --mode=holdridge  --outprof="actualHoldridgeOutputProfile"  --outfile="suruBiomeAzure.png"

Output Profile:
actualOutputProfile.txt

Result:


Once Charerg's climate script runs on my (actual scale) map, I'll plan on posting both outputs to see how they compare in case someone finds it useful/interesting.

----------


## AzureWings

You are correct, thanks for pointing that out! When I backported improved and reorganized I/O file handling code from my temperature project I overlooked the fact that I'd changed a few keywords. I've updated the output profile loading code to properly recognize the 'Ocean' keyword (it had been replaced with 'Ignored').

----------


## Coggleton

Glad I could help point that out! And as promised, here are the results for those interested(both scripts had the same color profile and map inputs; the only thing changed was the ocean color to help make some zones more visually distinct, as well as the legends being added):

Charerg's Climate Script:


Azure's Climate Script:


Azure's Biome Script:


It seems like Charerg's script tends to result in drier climates being preferred over others, such as in a couple of areas where As and BSh border each other (and a rare sighting of Dwa in Azure's output as a side note). Though a question for Azure: how much does your script differ procedurally from mbartelsm's biome guide? If not at all, then because mbartelsm's relies on the temperature/precipitation maps and only uses the climate maps to adjust tropical forests I would expect this to have minimal impact on biome placement.

----------


## AzureWings

So I don't know how the color categories in mbartelsm's guide work out in comparison since my script just crunches raw numbers under the hood (that way if you have input data with more/fewer/different category values you can use it as an input still via a custom color profile), but basically it takes the same temperature/precipitation input data, uses the temperature input to approximate a biotemperature by averaging the summer and winter temperatures interpolated across points on a hardcoded sine wave, and then uses the biotemperature approximation and the precipitation input to index into the Holdridge biome grid/triangle. Warm temperate and subtropical are arbitrarily split at a biotemperature of 17 degrees C. The default Holdridge output profile follows mbartelsm's output colors but you could give each individual biome hex in the triangle a distinct color if you wanted with a custom profile. The script doesn't do anything at all with the climate map for biomes (indeed it doesn't have any way to take one as an input; and it doesn't compute the Köppen category internally when doing biome classification either).

In terms of output differences on the biome end, they'd most likely be a result of the biotemperature approximation step.

With respect to Charerg's script and arid regions, we might be using slightly different calculations to determine the aridity threshold/evapotranspiration term  (I think we had some discussion in the thread about that a while back). I think I preferred the notion of a continuous model but for whatever reason I left my script doing the piecewise version.

----------


## Charerg

As Azure points out, the most recent iteration of my Gimp script uses a gradual aridity threshold, which causes a slight difference regarding which areas are considered arid, when compared to the traditional Köppen formula for determining the aridity threshold.

----------


## acrosome

Where can I find AzureWings' biome script?

----------


## AzureWings

It's the same script as my climate one, just add "--mode=holdridge" to the command-line invocation (and there are different valid categories for custom output profiles if you want to use those).

If you meant the script in general, you can download it from Github at the link Azélor put in the first post of the thread.

----------


## acrosome

Awesome, thanks.  I guess I have to download the new version.  Mine is quite old.

And while I'm at it, I'm sure that you have heard it before, but thank you for this excellent tool.

----------


## acrosome

EDIT-- FYI, I'm going to post this separately, now, since no one seems to be answering.  I guess you guys have developed lives outside of the guild or something...   :Wink: 

Howdy. I'm looking for advice on how to handle the rainshadows of large mountain ranges.







The issue tends to be the huge ranges on Ishtar and the shield volcanoes on Atlu.  Does this look right?  Bear in mind that much of eastern Ishtar is pretty dry to begin with.  Any pointers about any other problems you see with these is welcome, too, of course.  I'm much worse at precipitation than I am with temperature maps.  And regarding that, just in case you are interested:





And here is the climate output:

----------


## Xeviat

How is everyone doing? Frequent lurker, very rare poster. Been working on my own map, struggling a bit with second guessing myself on some of the preliminary steps like setting up the water currents and air pressure to even really get into the nitty gritty of this tutorial.

Thanks for the tutorial by the way!

Does it look like my water currents and air pressure/currents seem okay?

Water Currents


Air Current (Jan)


Air Current (July)


And why yes, this is Earth 50 million years in the future, based on Christopher Scotese's work ( https://www.researchgate.net/publica..._in_the_Future )

----------


## Coggleton

So, after redoing Temperature/precipitation maps for my planet because I didn't like how it turned out, I got an error when using the skcc.py code for Holdridge zones:

"Error: Invalid Holdridge category in output profile: Af"

Unfortunately, this type of error isn't listed in the ReadMe; could someone kindly tell me what that means?

The command code I used to run it is as follows:
py ./skcc.py --tempns="JulTemp.png" --tempnw="JanTemp.png" --precns="JulPrecip.png" --precnw="JanPrecip.png" --tempprof="actualTempProfile" --precprof="actualPrecProfile" --outprof="defaultOutputProfile" --mode=holdridge --outfile="output.png"

----------


## AzureWings

Holdridge mode has different output categories than Köppen mode (since it's a different classification system) - the repository should include an appropriate default output profile for that mode too, but it is a different file. Instead of --outprof="defaultOutputProfile" (which contains the Köppen output coloring categories like Af) use --outprof="holdridgeDefaultOutputProfile", and that should fix that problem.

----------


## davoush

Hi, I haven't posted for a long time, but I've recently been making another attempt at a semi-realistic climate map. I am following Azelor's tutorial "by hand" (no scripts), and the map here is just a rough draft for now as I fine tune it.

 I was hoping some of the more knowledgeable members here could help me with a few climate questions. I have marked the areas I have questions about on the map with numbers. I have also marked the Jan/July ITCZ and relevant currents (blue=cold current, red = warm current).



Some questions:

1) The eastern continent only has a small tropical landmass, surrounded by a large body of ocean in tropics, so I have assumed the summer winds don't converge quite as far north as they do in Asia. I could be wrong though? Perhaps the July ITCZ should remain more northern across the ocean as it leaves the central continent, then extending to ~30N in the eastern continent? This would give this continent a more monsoon-influenced climate.

2) I am quite unsure about the general climate of the "long narrow" landmass of the central continent. It is largely within the subtropical ridge (20-40N). It receives a warm current on its southern side, and a cold (polar) current on its northern side. During winter, the Westerlies' influence is quite strong reaching about 30N, giving a band of Mediterranean-like climate as the Westerlies retreat northwards in summer. However: I am unsure whether it should be this dry overall: I have no idea where the winds would be blowing on the landmass in most of this region. Below 20N to the equator, the trade winds would be quite stable as the ITCZ remains around the equator here year-round. I think the land wouldn't develop any strong high or low pressure zones either. Perhaps the southern warm-current and northern cold-current would have a strong effect I haven't accounted for? 

3) I have given the eastern half of "long-narrow" a fully-humid subtropical climate, as it receives poleward winds from the high-pressure zone to the east, similar to North America's east coast or China. It borders a patch of Mediterranean climate, as I think there would be a small high-pressure centre to the north in summer (due to the cold current meeting the hot land), but this might be unrealistic?

4) In the tropical centre of the continent, I expect that the northern half will be overall drier, as the winds mostly blow overland from the north to the ITCZ. The winds coming from the south might be wetter, as they cross more ocean. Around 10N to the west, there is a kind of "reverse-Somalia"  I thought the winds here blow parallel to the coast, coming overland through a desert, so there is little precipitation. Perhaps the warm winds would actually blow more onshore (eastwards) here, and warm current could make this area (10-20N) wetter? (I find that these little details are what make climate so interesting.)

5) In the South-Eastern part of the main continent, I have given it a large "winter dry" subtropical climate. In winter, I imagine most of the winds will be blowing offshore towards the ITCZ in the north. Perhaps the far-eastern coast should be "fully-humid" (like China), where the high-pressure meets wet air from the ocean even in winter? 

6) I'm unsure if I've properly factored in how the "triangle-shaped" body of water would affect climate here? I imagine there'd more or less be a semi-permanent high due to the cold current, so I've made both sides quite arid.

Any advice much appreciated. Thank you!

----------


## WoodytheClimateGuy

Hey Charerg! I am once again impressed by your work! Could you except my friend request? I want to ask you some questions because I want to do something similar with the greenhouse Earth climate, especially with the poles having rainforests. Also, I sent you a private message asking you some questions about this. I am new and I want to learn more about how to do this! Thanks!

----------


## Tiluchi

It's been a while since I worked on climates (although I'll be starting again as I re-work my world geography) so I'm quite rusty, but I'll do my best to answer some of your questions. Overall I think it would be helpful if you could post your input maps from topography onwards for us to review; otherwise it's hard to answer some of these questions.




> Hi, I haven't posted for a long time, but I've recently been making another attempt at a semi-realistic climate map. I am following Azelor's tutorial "by hand" (no scripts), and the map here is just a rough draft for now as I fine tune it.
> 
>  I was hoping some of the more knowledgeable members here could help me with a few climate questions. I have marked the areas I have questions about on the map with numbers. I have also marked the Jan/July ITCZ and relevant currents (blue=cold current, red = warm current).
> 
> Some questions:
> 
> 1) The eastern continent only has a small tropical landmass, surrounded by a large body of ocean in tropics, so I have assumed the summer winds don't converge quite as far north as they do in Asia. I could be wrong though? Perhaps the July ITCZ should remain more northern across the ocean as it leaves the central continent, then extending to ~30N in the eastern continent? This would give this continent a more monsoon-influenced climate.


I'd say that the July ICTZ should move further north there; note that on Earth the ICTZ is pulled northwards by the Mesoamerican peninsula, which is even narrower. Your instinct that the continent should be more monsoon-influenced seems correct to me.




> 2) I am quite unsure about the general climate of the "long narrow" landmass of the central continent. It is largely within the subtropical ridge (20-40N). It receives a warm current on its southern side, and a cold (polar) current on its northern side. During winter, the Westerlies' influence is quite strong reaching about 30N, giving a band of Mediterranean-like climate as the Westerlies retreat northwards in summer. However: I am unsure whether it should be this dry overall: I have no idea where the winds would be blowing on the landmass in most of this region. Below 20N to the equator, the trade winds would be quite stable as the ITCZ remains around the equator here year-round. I think the land wouldn't develop any strong high or low pressure zones either. Perhaps the southern warm-current and northern cold-current would have a strong effect I haven't accounted for?


I don't have a great answer for this; certainly there should be some desert there in the subtropical latitudes, but my instinct is that there's probably a little too much at the moment. I think the continent is large enough for there to be at least some small high and low pressure zones, and thus some monsoon rains to moisten up the coast at least. My sense is that the hot desert reaches a little too close to the equator in the south; by 15º or so N or S the equatorial rains usually create at least a steppe or savanna climate. It's also hard to tell if there's any topography affecting precipitation, blocking monsoon rains and/or creating orographic lift.




> 3) I have given the eastern half of "long-narrow" a fully-humid subtropical climate, as it receives poleward winds from the high-pressure zone to the east, similar to North America's east coast or China. It borders a patch of Mediterranean climate, as I think there would be a small high-pressure centre to the north in summer (due to the cold current meeting the hot land), but this might be unrealistic?


That seems more or less correct to me.




> 4) In the tropical centre of the continent, I expect that the northern half will be overall drier, as the winds mostly blow overland from the north to the ITCZ. The winds coming from the south might be wetter, as they cross more ocean. Around 10N to the west, there is a kind of "reverse-Somalia"  I thought the winds here blow parallel to the coast, coming overland through a desert, so there is little precipitation. Perhaps the warm winds would actually blow more onshore (eastwards) here, and warm current could make this area (10-20N) wetter? (I find that these little details are what make climate so interesting.)


This is why it would be useful to see your input maps; I can't tell which way the winds are blowing here so it's hard to answer this question. If they're coming from the west, then the area should be wet. If they're coming from the inland to the east then maybe it would be a bit dryer, but I think it should be predominately west winds at this latitude, which would give the coast a monsoon climate even if there's a desert to the northwest. Note that the reason Somalia is so dry is that the Sahel and the Ethiopian Highlands block all the monsoon winds coming from the west; if anything it might be dryer in the eastern part rather than the west.




> 5) In the South-Eastern part of the main continent, I have given it a large "winter dry" subtropical climate. In winter, I imagine most of the winds will be blowing offshore towards the ITCZ in the north. Perhaps the far-eastern coast should be "fully-humid" (like China), where the high-pressure meets wet air from the ocean even in winter?


My instinct is that this should be more humid, or at least some of that area, but again it's a little hard to see without seeing your maps of the winds and pressure zones. Also, if there are any mountains here topography would make somewhat of a difference.




> 6) I'm unsure if I've properly factored in how the "triangle-shaped" body of water would affect climate here? I imagine there'd more or less be a semi-permanent high due to the cold current, so I've made both sides quite arid.


That seems correct to me, though I'm not 100% sure; the current would be the main part of this that would have any influence.




> Any advice much appreciated. Thank you!


It looks great overall by the way! Love the land shapes. My only other minor correction is that the islands in the far east of the eastern continent should be tropical rainforest at that latitude.

----------


## WoodytheClimateGuy

> It's been a while since I worked on climates (although I'll be starting again as I re-work my world geography) so I'm quite rusty, but I'll do my best to answer some of your questions. Overall I think it would be helpful if you could post your input maps from topography onwards for us to review; otherwise it's hard to answer some of these questions.
> 
> 
> 
> I'd say that the July ICTZ should move further north there; note that on Earth the ICTZ is pulled northwards by the Mesoamerican peninsula, which is even narrower. Your instinct that the continent should be more monsoon-influenced seems correct to me.
> 
> 
> 
> I don't have a great answer for this; certainly there should be some desert there in the subtropical latitudes, but my instinct is that there's probably a little too much at the moment. I think the continent is large enough for there to be at least some small high and low pressure zones, and thus some monsoon rains to moisten up the coast at least. My sense is that the hot desert reaches a little too close to the equator in the south; by 15º or so N or S the equatorial rains usually create at least a steppe or savanna climate. It's also hard to tell if there's any topography affecting precipitation, blocking monsoon rains and/or creating orographic lift.
> ...


Hey man! Hope you get into climates again!

----------

