A few things should be pointed out. First and foremost is:
- Further to point #1 above, the point of this kind of climate model is not to accurately predict the weather every single day for 87 years, even though that's what the model contains. The point is to experiment, and experimental science is built on prediction. Evaluating those predictions makes for better models down the road. I'm no climatologist, so I'll let the Oregon Climate Change Research Institute explain Why We Use Climate Models.
- In the map above, white is 100% snow coverage, and the white becomes more and more transparent at the fringes from 99% to 1% snow coverage, until the bare background is 0% snow coverage. The resolution of the climate model is only 0.44 degrees, so the fit isn't exact at the coastlines.
- The data is from the Canadian Centre for Climate Modeling and Analysis, hosted at Environment Canada. The exact model is the Fourth Generation Canadian Regional Climate Model (CanRCM4), RCP 8.5
- That global warming kinda sneaks up on you, doesn't it? It's gradual, but when it loops back down to 2014, it's pretty obvious. I imagine people in Grande Prairie, Alberta are looking forward to the end of the 21st century.
- Here is a post on my other, nerdier blog about how to make maps in Python based on the CCCma's NetCDF files. There are plenty of examples out there on plotting these files, but not with the format CCCma uses.
- There's also code on my GitHub, with links to nbviewer notebooks
- Tools used: Python with IPython, netCDF4, Matplotlib, Basemap and PIL; Photoshop; Gfycat.