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Projected changes in mean rainfall and temperature over East Africa based on CMIP5 models
Author(s) -
Ongoma Victor,
Chen Haishan,
Gao Chujie
Publication year - 2017
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5252
Subject(s) - coupled model intercomparison project , representative concentration pathways , climatology , environmental science , baseline (sea) , climate model , climate change , ensemble average , mean radiant temperature , geology , oceanography
ABSTRACT This study presents potential future variations of mean rainfall and temperature over East Africa (EA) based on five models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and representative concentration pathways (RCPs): 4.5 and 8.5. In this study, climate simulations of two timeframes, a baseline period (1961–1990) and projection period (2071–2100), are compared. The models reproduce EA's bimodal rainfall pattern but overestimate and underestimate seasonal rainfall of October–December (OND) and March–May (MAM), respectively. Rainfall is projected to increase under the two scenarios. Larger increases in rainfall will occur during the OND season than during the MAM season and in RCP8.5 than in RCP4.5. During the last half of the 21st century, EA is likely to warm by 1.7–2.8 and 2.2–5.4 °C under the RCP4.5 and RCP8.5 scenarios, respectively, relative to the baseline period. Scenario uncertainty is projected to exceed model uncertainty from the middle to the end of the 21st century. The central parts of Kenya and the Lake Victoria Basin will witness the highest increases in seasonal rainfall. The probability density functions (PDFs) of future seasonal rainfall show a positive shift and a statistically insignificant increase in variance relative to the baseline. Thus, EA is likely to experience an increase in extreme rainfall events. Understanding the future climate variability in EA is important for planning purposes but these results are based on relatively course resolution models prone to bias and therefore should be used with caution. There is a need for further research on climate projections over EA, including determining the causes of the poor performance of global models in reproducing rainfall climatology and trends over the region.