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Inmuch of EastAfrica, 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 simulate this annual cycle, typically reversing the amplitudes of the short and long rains relative to observations. This study investigates how CMIP5 climatological sea surface temperature (SST) biases contribute to simulation errors 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 atmospheric general circulation model (AGCM) is then forced with observed SSTs (1979–2005) generating a set of control runs and observed SSTs plus themonthly, multimodelmean SST biases generating a set of ‘‘bias’’ runs for the same period. The control runs generally capture the observed annual cycle of East African rainfall while the bias runs capture prominent CMIP5 annual cycle biases, including too little (much) precipitation during 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–southand east–westoriented 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 that CMIP5 climatological SST biases are the primary driver of the improper simulation of the annual cycle of East African rainfall. Some implications for climate change projections are discussed.

Biases in CMIP5 Sea Surface Temperature and the Annual Cycle of East African Rainfall