How well are daily intense rainfall events captured by current climate models over Africa?
The ability of state-of-the-art climate models to capture the mean spatial and temporal characteristics of daily intense rainfall events over Africa is evaluated by analyzing regional climate model (RCM) simulations at 90- and 30-km along with output from four atmospheric general circulation models (AGCMs) and coupled atmosphere–ocean general circulation models (AOGCMs) of the Climate Model Intercomparison Project 5. Daily intense rainfall events are extracted at grid point scale using a 95th percentile threshold approach applied to all rainy days (i.e., daily rainfall ≥1 mm day−1) over the 1998–2008 period for which two satellite-derived precipitation products are available. Both RCM simulations provide similar results. They accurately capture the spatial and temporal characteristics of intense events, while they tend to overestimate their number and underestimate their intensity. The skill of AGCMs and AOGCMs is generally similar over the African continent and similar to previous global climate model generations. The majority of the AGCMs and AOGCMs greatly overestimate the frequency of intense events, particularly in the tropics, generally fail at simulating the observed intensity, and systematically overestimate their spatial coverage. The RCM performs at least as well as the most accurate global climate model, demonstrating a clear added value to general circulation model simulations and the usefulness of regional modeling for investigating the physics leading to intense events and their change under global warming.
KeywordsAfrica CMIP5 AGCMs/AOGCMs Daily intense rainfall Regional climate model
Support from the U.S. Department of Energy Office of Science (award DE-FG02-10ER65092) is gratefully acknowledged. WRF was provided by the University Corporation for Atmospheric Research (http://www.mmm.ucar.edu/wrf/users/download/get_source.html). Simulations are performed on the high performance computing platform at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin. We also gratefully acknowledge the GCM modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and the World Climate Research Program’s Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP5 multi-model dataset. Support of this dataset is provided by the Office of Science, U. S. Department of Energy. We also thank two anonymous reviewers for their helpful comments.
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