Open AccessPredicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices
Author(s) -
Chris Funk,
Andrew Hoell,
Shraddhanand Shukla,
Ileana Bladé,
Brant Liebmann,
J. Brent Roberts,
Franklin R. Robertson,
G. J. Husak
Publication year - 2014
Publication title -
hydrology and earth system sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.001
H-Index - 133
eISSN - 1607-7938
pISSN - 1027-5606
DOI - 10.5194/hess-18-4965-2014
Subject(s) - climatology , sea surface temperature , environmental science , climate change , boreal , spring (device) , walker circulation , pacific decadal oscillation , indian ocean , oceanography , geography , precipitation , geology , meteorology , mechanical engineering , archaeology , engineering
In eastern East Africa (the southern Ethiopia, eastern Kenya and southernSomalia region), poor boreal spring (long wet season) rains in 1999, 2000,2004, 2007, 2008, 2009, and 2011 contributed to severe food insecurity andhigh levels of malnutrition. Predicting rainfall deficits in this region onseasonal and decadal time frames can help decision makers implement disasterrisk reduction measures while guiding climate-smart adaptation andagricultural development. Building on recent research that links morefrequent East African droughts to a stronger Walker circulation, resultingfrom warming in the Indo–Pacific warm pool and an increased east-to-west seasurface temperature (SST) gradient in the western Pacific, we show that thetwo dominant modes of East African boreal spring rainfall variability aretied to SST fluctuations in the western central Pacific and central IndianOcean, respectively. Variations in these two rainfall modes can thus bepredicted using two SST indices – the western Pacific gradient (WPG) andcentral Indian Ocean index (CIO), with our statistical forecasts exhibitingreasonable cross-validated skill (rcv ≈ 0.6). In contrast, thecurrent generation of coupled forecast models show no skill during the longrains. Our SST indices also appear to capture most of the major recentdrought events such as 2000, 2009 and 2011. Predictions based on thesesimple indices can be used to support regional forecasting efforts and land surfacedata assimilations to help inform early warning and guide climate outlooks