Probabilistic seasonal forecasting of African drought by dynamical models

As a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982-2007) seasonal hydrologic hindcasts run at 0.258, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soilmoisture percentile as indices. In terms ofBrier skill score (BSS), the system is more skillful than climatology out to 3-5 months, except for the forecast of soil moisture drought over centralAfrica. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soilmoisture than the SPI6. Drought forecasts based on SPI6 are generallymore skillful than for soilmoisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soilmoisture drought forecast can bemore skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode fromobservations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land-atmosphere coupling, is necessary.