The Köppen–Geiger climate classification made simpler (I hope so)

[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:

Activating the script.png

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:
TempSlider.png

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:
PrecSliderAzel.png

And finally, there is a slight bug in the script. Here's how to avoid it:

• The script uses bucket fill to fill a selection
• If the selection is empty, it has been set to bucket fill the x 0 y 0 position
• Since 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)!

Corner Transparency.PNG


Sample Map:
Here is a sample climate map using source maps generated from WorldClim's 1970-2000 dataset:

Source maps:

 

JanPrec.png

JulPrec.png

JanTemp.png

JulTemp.png

Generated climates:
genClimates_test2.png

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 . 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.

sheet27.png

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.

sheet28.png

[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.

sheet30.png

26*34+2=886