Climate change models representing global processes are the main source of data for understanding future conditions. Data generated by these General Circulation Models (GCMS) are available through the CMIP5 website for further analysis. However, for many applications there is a need for further modeling and analysis as the scale of the data is too coarse for regional or local studies.

Downscaling methods are used to take data available at large scales to make predictions at local scales. The two main approaches to downscaling climate information are dynamical and statistical.

Dynamical downscaling requires running high-resolution climate models on a regional sub-domain, using observational data or lower-resolution climate model output as a boundary condition.  These models use physical principles to reproduce local climates, but are computationally intensive.

Statistical downscaling is a two-step process consisting of i) the development of statistical relationships between local climate variables (e.g., surface air temperature and precipitation) and large-scale predictors (e.g., pressure fields), and ii) the application of such relationships to the output of global climate model experiments to simulate local climate characteristics in the future.

ICBA has undertaken both these forms of downscaling for the MENA region and training material are available under the following links

Dynamical downscaling

Exercises and data Exercise
Exercise 1 (English)

Exercises and data 
Exercise 1 (English) Exercise 1 (Arabic)

Scripts
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