Abstract
Abstract. 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 (rcv ≈ 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.
Highlights
1.1 How understanding trends can lead to better drought predictionsSince 2003, scientists from the University of California, Santa Barbara’s Climate Hazards Group, the US Geological Survey, the Universitat de Barcelona, the National Ocean and Atmospheric Administration’s (NOAA) Earth Systems Research Laboratory, Physical Science Division, Climate Analysis Branch, and the National Aeronautics and Space Administration have been working to improve the US Agency for International Development’s Famine Early Warning System Network’s (FEWS NET) drought early warning capabilities for eastern Africa
This process can be repeated for the second component, which explains most of the remaining variance of the data, after the first principal component has been removed
The January sea surface temperature (SST) data were chosen as a predictor because the East African climate experts typically gather in mid-February at the Greater Horn of Africa Climate Outlook Forum (GHACOF) to produce a regional forecast for East Africa
Summary
Since 2003, scientists from the University of California, Santa Barbara’s Climate Hazards Group, the US Geological Survey, the Universitat de Barcelona, the National Ocean and Atmospheric Administration’s (NOAA) Earth Systems Research Laboratory, Physical Science Division, Climate Analysis Branch, and the National Aeronautics and Space Administration have been working to improve the US Agency for International Development’s Famine Early Warning System Network’s (FEWS NET) drought early warning capabilities for eastern Africa. In this introduction, we describe how our deepening understanding of boreal spring rainfall trends can lead to useful new SST indices that support better drought prediction. Further confirmation of this relationship was obtained from a simulation using the Community Atmospheric Model (CAM), which suggested that anomalous diabatic heating over the Indian Ocean decreased onshore moisture transports (Funk et al, 2008)
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