WorldClim - Climate Change Data Analysis

Historical & future Precipitation/Temperature Climatologies (1970-2100) - Maps plotting and time series extraction

MAPS

• Data: https://www.worldclim.org/data/index.html
• author: Loïc Duffar https://github.com/loicduffar

This python notebook uses data from the wordclim web site to plot future climatologies maps on a given area. The outpus are stored as image files (png) and raster files (geotif).

How to PROCEED ?

The data files must be divided into different sub-folders:

• a folder containing the 12 tif files with historical monthly climatologies (1 file per month of the year)
• a folder with sub-folders for the various future periods (e.g. 1921-1940, 1961-1981, 1981-2100). Each subfolder contains 4 tif files for each model (and each file contains 12 bands for the the different months).
• Optionnaly, the global DEM file can be downloaded from WorldClim to plot the elevation map on the area

Run the first script, then one of the following ones:

1. Customization
2. Plot maps for one scenario, one period & one model (12 months and annual)
3. Generate a bunch of maps of all scenarios/periods/models (annual maps in png files)
4. Plot the elevation map

GRAPHS

4 SSP scenarios and 9 Global Climatic Models

• Data: https://www.worldclim.org/data/index.html
• author: Loïc Duffar https://github.com/loicduffar

This python notebook uses data from the wordclim web site to compute the evolution of future precipitation/temperature on a given watershed (average of the pixels inside the polygon). The ouputs are stored in an excel file.

How to PROCEED ?

The data files must be divided into different sub-folders:

• a folder containing the 12 tif files of historical monthly climatologies (1 file per month of the year)
• a folder with 4 sub-folders for the various future periods (1921-1940, 1961-1981, 1981-2100). Each folder contains 4 tif files for each model (and each file contains 12 bands for the the different months).

Then run cells in order:

1. Historical monthly climatologies 1970-2000
2. Future climatologies
3. Multi index by Model, Scenario and Period
4. Concatenate historical and future precipitation in a single dataframe
5. Plot annual evolution of annual precipitation in future, according to all SSP scenarios and all GCM
6. Plot future evolution for each month, according to 4 SSP scenarios and 9 GCMs
7. Store statistics in a dataframe
8. Plot montly projections (all SSP scenarios and all GCMs)
9. Store all outputs in an excel file