Climate Data Projections

The climate science community sources a suite of models to inform decision makers on future climate. Among the most widely used are GCMs (Global Climate Models or Earth System Models) that capture the non-linear complexity of the Earth to represent changes across the climate system for key processes and contexts. Future climate projections are presented in three main forms, multi-model ensemble, range of climate models, and deviation from historical baseline. Climate Change Knowledge Portal (CCKP) allows users to explore further climate indices derived from GCMs used in IPCC Fifth Assessment Report (AR5) report by different timeframes, statistics, emission scenarios, and models in map and charts.  Data can be presented per individual models or through the multi-model ensemble. CCKP prioritizes analysis using multi-model ensembles as they are more robust and have proven to be most successful in representing expected changes. A detailed metadata can be found here.

According to the IPCC AR5 and Turn Down the Heat report:

  • Under RCP8.5, by the end of the century warm nights are expected to occur about 95 percent of the time in tropical west and east Africa and about 85 percent of the time in southern Africa, with only limited inter-model spread.
  • By 2100, the multi-model mean of RCP8.5 projects that 75 percent of summer months would be hotter than 5 standard deviations and substantially higher than the global average.
  • Subtropical southern Africa could see a decrease of annual precipitation by up to 30 percent, contributing to an increase in aridity in this region.
  • The wetting of tropical East-Africa occurs predominantly during the austral summer (DJF), whereas the drying of southern Africa occurs predominantly during the austral winter (JJA). 
  • The overall area of hyper-arid and arid regions is projected to grow by 10 percent under RCP8.5 by 2080–2100 relative to 1986–2005.
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Use the menu above to visualize different climate projection layers and chart data. Click on the map to get location specific data.