A general circulation model sensitivity study was carried out to investigate the influence of global sea surface temperature (SST) on Sahel rainfall. This study was inspired by the impressive model simulations of Sahel rainfall reported by Folland et al. and Rowell et al. The model was integrated from June through September with three different atmospheric initial conditions and four years (1950, 1958, 1983, and 1984) of SST. In three out of four cases (1950, 1983, 1984), the area-averaged simulated rainfall anomaly was consistent with the observations. However, the model's internal variability was rather large. The simulated anomalies had relatively larger sensitivity to the initial conditions in this study than those in a desertification study performed previously by the authors. This model failed to simulate the rainfall anomaly for 1958. Additional model experiments are needed to establish the role of SST variation in determining the Sahel rainfall variation.

This is a general circulation model sensitivity study of the physical mechanisms of the effects of desertification on the Sahel drought. In the desertification experiment, the moisture flux convergence and rainfall were reduced in the test area and increased to the immediate south of this area. The simulated anomaly dipole pattern was similar to the observed African drought patterns in which the axis of the maximum rainfall shifts to the south. The circulation changes in the desertification experiment were consistent with those observed with those observed during sub-Saharan dry years. The tropical easterly jet was weaker and the African easterly jet was stronger than normal. Further, in agreement with the observations, the easterly wave disturbances were reduced in intensity but not in number. Descending motion dominated the desertification area. The surface energy budget and hydrological cycles were also changed substantially in the anomaly experiment -from Authors

Predicting the temporal and spatial patterns of South Asian monsoon rainfall within a season is of critical importance due to its impact on agriculture, water availability, and flooding. The monsoon intraseasonal oscillation (MISO) is a robust northward-propagating mode that determines the active and break phases of the monsoon and much of the regional distribution of rainfall. However, dynamical atmospheric forecast models predict this mode poorly. Data-driven methods for MISO prediction have shown more skill, but only predict the portion of the rainfall corresponding to MISO rather than the full rainfall signal. Here, we combine state-of-the-art ensemble precipitation forecasts from a high-resolution atmospheric model with data-driven forecasts of MISO. The ensemble members of the detailed atmospheric model are projected onto a lower-dimensional subspace corresponding to the MISO dynamics and are then weighted according to their distance from the data-driven MISO forecast in this subspace. We thereby achieve improvements in rainfall forecasts over India, as well as the broader monsoon region, at 10- to 30-d lead times, an interval that is generally considered to be a predictability gap. The temporal correlation of rainfall forecasts is improved by up to 0.28 in this time range. Our results demonstrate the potential of leveraging the predictability of intraseasonal oscillations to improve extended-range forecasts; more generally, they point toward a future of combining dynamical and data-driven forecasts for Earth system prediction.