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Title: Biases in CMIP5 Sea Surface Temperature and the Annual Cycle of East African Rainfall
In much of East Africa, climatological rainfall follows a bimodal distribution characterized by the long rains(March–May) and short rains (October–December). Most CMIP5 coupled models fail to properly simulatethis annual cycle, typically reversing the amplitudes of the short and long rains relative to observations. Thisstudy investigates how CMIP5 climatological sea surface temperature (SST) biases contribute to simulationerrors in the annual cycle of East African rainfall. Monthly biases in CMIP5 climatological SSTs (508S–508N)are first identified in historical runs (1979–2005) from 31 models and examined for consistency. An atmo-spheric general circulation model (AGCM) is then forced with observed SSTs (1979–2005) generating a set ofcontrol runs and observed SSTs plus the monthly, multimodel mean SST biases generating a set of ‘‘bias’’ runsfor the same period. The control runs generally capture the observed annual cycle of East African rainfallwhile the bias runs capture prominent CMIP5 annual cycle biases, including too little (much) precipitationduring the long rains (short rains) and a 1-month lag in the peak of the long rains relative to observations.Diagnostics reveal the annual cycle biases are associated with seasonally varying north–south- and east–west-oriented SST bias patterns in Indian Ocean and regional-scale atmospheric circulation and stability changes,the latter primarily associated with changes in low-level moist static energy. Overall, the results indicate thatCMIP5 climatological SST biases are the primary driver of the improper simulation of the annual cycle of EastAfrican rainfall. Some implications for climate change projections are discussed  more » « less
Award ID(s):
1650037
PAR ID:
10213754
Author(s) / Creator(s):
Date Published:
Journal Name:
Journal of climate
Volume:
33
ISSN:
0894-8755
Page Range / eLocation ID:
8209–8223
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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