Climate data interpolation with deep neural networks: a comprehensive dataset of historical and future climate for Africa

Databases of high-resolution interpolated climate data are essential for analyzing the impacts of past climate events and for developing climate change adaptation strategies for managed and natural ecosystems.  To enable such efforts, we contribute an accessible, comprehensive database of interpolated climate data for Africa that includes monthly, annual, decadal, and 30-year normal climate data for the last 120 years (1901 to present) as well as multi-model CMIP6 climate change projections for the 21st century. The database includes variables relevant for ecological research and infrastructure planning, and comprises more than 25,000 climate grids that can be queried with a provided ClimateAF software package. In addition, 30 arcsecond (~1km) resolution gridded data, generated by the software, are available for download (https://tinyurl.com/ClimateAF). The climate grids were developed with a three-step approach, using thin-plate spline interpolations of weather station data as a first approximation, subsequent fine-tuning with deep neural networks to capture medium-scale local weather patterns, and lastly dynamic lapse-rate based downscaling to a user-selected resolution, or to scale-free point estimates with the ClimateAF software package. The study contributes a novel deep learning approach to model orographic precipitation, rain shadows, lake and coastal effects, including the influences of wind direction and strength. The climate estimates were optimized and cross-validated with a checkerboard approach to ensure that training data was spatially distanced from validation data. We conclude with a discussion of applications and limitations of this database.