Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models

Abstract

Global and regional climate change assessments rely heavily on the general circulation model (GCM) outputs such as provided by the Coupled Model Intercomparison Project phase 5 (CMIP5). Here we evaluate the ability of 25 CMIP5 GCMs to simulate historical precipitation and temperature over central Africa and assess their future projections in the context of historical performance and intermodel and future emission scenario uncertainties. We then apply a statistical bias correction technique to the monthly climate fields and develop monthly downscaled fields for the period of 1948–2099. The bias‐corrected and downscaled data set is constructed by combining a suite of global observation and reanalysis‐based data sets, with the monthly GCM outputs for the 20th century, and 21st century projections for the medium mitigation (representative concentration pathway (RCP)45) and high emission (RCP85) scenarios. Overall, the CMIP5 models simulate temperature better than precipitation, but substantial spatial heterogeneity exists. Many models show limited skill in simulating the seasonality, spatial patterns, and magnitude of precipitation. Temperature projections by the end of the 21st century (2070–2099) show a robust warming between 2 and 4°C across models, whereas precipitation projections vary across models in the sign and magnitude of change (−9% to 27%). Projected increase in precipitation for a subset of models (single model ensemble (SME)) identified based on performance metrics and causal mechanisms are slightly higher compared to the full multimodel ensemble (MME) mean; however, temperature projections are similar between the two ensemble means. For the near‐term (2021–2050), neither the historical performance nor choice of models is related to the precipitation projections, indicating that natural variability dominated any signal. With fewer models, the “blind” MME approach will have larger uncertainties in future precipitation projections compared to projections by the SME models. We propose the latter a better approach in regions that lack quality climate observations. Our analyses also show that the choice of model and emission scenario dominate the uncertainty in precipitation projections, whereas the emission scenario dominates the temperature projections. Although our analyses are done for central Africa, the final Bias‐Corrected Spatially Downscaled data set is available for global land areas. The framework for climate change assessment and the data will be useful for a variety of climate assessment, impact, and adaptation studies.

Evaluating Climate Models with an African Lens

“…The region is critically understudied, in part because of limited data availability (McCollum et al 2000;. Several studies have assessed climate model precipitation based on observations and reanalysis (Haensler et al 2013a,b;Aloysius et al 2016). However, given the lack of gauge data included in precipitation datasets for this region, particularly for recent decades (Fig.…”

Simulated hydrologic response to projected changes in precipitation and temperature in the Congo River basin

“…GCMs do not consistently capture observed rainfall seasonality and heavy rainfall in regions of the central CRB and in most cases do not show key features such as seasonality and heavy rainfall in regions of the central CRB (Aloysius et al, 2016;Washington et al, 2013). The biases in the GCM-simulated precipitation, particularly in the tropical regions, have been attributed to multiple factors including poorly resolved physical processes such as the mesoscale convection systems, inadequately resolved topography due to the coarse horizontal resolution and inadequate observations to constrain parameterization schemes.…”

“…Global climate models whose outputs are used in this study. Further details about comparison of model outputs and key references for GCMs are given in Aloysius et al (2016).…”

Remote land use impacts on river flows through atmospheric teleconnections

“…The most complex modeling approaches account for the highest number of feedback processes. However, the sign, magnitude, and location of impacts vary widely even among state-of-the-art climate models (Pitman et al, 2009;Aloysius et al, 2016). Key future improvements in climate models' ability to simulate ∆P 5 from LUC will contribute to the governability of TMR.…”