HESS Hydrology and Earth System Sciences HESS Hydrol. Earth Syst. Sci. 1607-7938 Copernicus Publications Göttingen, Germany 10.5194/hess-18-4965-2014 Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices Funk C. https://orcid.org/0000-0002-9254-6718 1 Hoell A. 2 Shukla S. https://orcid.org/0000-0003-0077-6733 2 Bladé I. 3 Liebmann B. 4 Roberts J. B. 5 Robertson F. R. 5 Husak G. 2 US~Geological Survey and University of California Santa Barbara Geography, Santa Barbara, USA University of California Santa Barbara Geography, Santa Barbara, USA Universitat de Barcelona and Institut Català de Ciències del Clima, Barcelona, Spain University of Colorado and NOAA Earth Systems Research Laboratory, Boulder, USA NASA Marshall Space Flight Center, Huntsville, USA 10 12 2014 18 12 4965 4978 Copyright: © 2014 C. Funk et al. 2014 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://hess.copernicus.org/articles/18/4965/2014/hess-18-4965-2014.html The full text article is available as a PDF file from https://hess.copernicus.org/articles/18/4965/2014/hess-18-4965-2014.pdf

In eastern East Africa (the southern Ethiopia, eastern Kenya and southern Somalia region), poor boreal spring (long wet season) rains in 1999, 2000, 2004, 2007, 2008, 2009, and 2011 contributed to severe food insecurity and high levels of malnutrition. Predicting rainfall deficits in this region on seasonal and decadal time frames can help decision makers implement disaster risk reduction measures while guiding climate-smart adaptation and agricultural development. Building on recent research that links more frequent East African droughts to a stronger Walker circulation, resulting from warming in the Indo–Pacific warm pool and an increased east-to-west sea surface temperature (SST) gradient in the western Pacific, we show that the two dominant modes of East African boreal spring rainfall variability are tied to SST fluctuations in the western central Pacific and central Indian Ocean, respectively. Variations in these two rainfall modes can thus be predicted using two SST indices – the western Pacific gradient (WPG) and central Indian Ocean index (CIO), with our statistical forecasts exhibiting reasonable cross-validated skill (<i>r</i><sub>cv</sub> ≈ 0.6). In contrast, the current generation of coupled forecast models show no skill during the long rains. Our SST indices also appear to capture most of the major recent drought events such as 2000, 2009 and 2011. Predictions based on these simple indices can be used to support regional forecasting efforts and land surface data assimilations to help inform early warning and guide climate outlooks.

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