Composites of SST, wind, rainfall, and humidity have been constructed for years of high rainfall during September, October, and November (SON) in equatorial and southern-central East Africa. These show that extreme East African short rains are associated with large-scale SST anomalies in the Indian Ocean that closely resemble those that develop during Indian Ocean dipole or zonal mode (IOZM) events. This is corroborated by the observation that strong IOZM events produce enhanced East African rainfall. However, it is also shown that the relationship between the IOZM and East African rainfall is nonlinear, with only IOZM events that reverse the zonal SST gradient for several months (extreme events) triggering high rainfall. Comparison of the wind anomalies that develop during extreme IOZM events with those that develop during weaker (moderate) events shows that strong easterly anomalies in the northern-central Indian Ocean are a persistent feature of extreme, but not of moderate, IOZM years. It is ... Abstract Composites of SST, wind, rainfall, and humidity have been constructed for years of high rainfall during September, October, and November (SON) in equatorial and southern-central East Africa. These show that extreme East African short rains are associated with large-scale SST anomalies in the Indian Ocean that closely resemble those that develop during Indian Ocean dipole or zonal mode (IOZM) events. This is corroborated by the observation that strong IOZM events produce enhanced East African rainfall. However, it is also shown that the relationship between the IOZM and East African rainfall is nonlinear, with only IOZM events that reverse the zonal SST gradient for several months (extreme events) triggering high rainfall. Comparison of the wind anomalies that develop during extreme IOZM events with those that develop during weaker (moderate) events shows that strong easterly anomalies in the northern-central Indian Ocean are a persistent feature of extreme, but not of moderate, IOZM years. It is ...

Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time series over land, but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols and because of the large climate variability presently limits confidence in attribution of observed changes.

The Middle East and southwest Asia are a region that is water stressed, societally vulnerable, and prone to severe droughts. Large-scale climate variability, particularly La Niña, appears to play an important role in regionwide droughts, including the two most severe of the last 50 years—1999–2001 and 2007/08—with implications for drought forecasting. Important dynamical factors include orography, thermodynamic influence on vertical motion, storm-track changes, and moisture transport. Vegetation in the region is strongly impacted by drought and may provide an important feedback mechanism. In future projections, drying of the eastern Mediterranean region is a robust feature, as are temperature increases throughout the region, which will affect evaporation and the timing and intensity of snowmelt. Vegetation feedbacks may become more important in a warming climate. There are a wide range of outstanding issues for understanding, monitoring, and predicting drought in the region, including dynamics of the regional storm track, the relative importance of the range of dynamical mechanisms related to drought, the regional coherence of drought, the relationship between synoptic-scale mechanisms and drought, the predictability of vegetation and crop yields, the stability of remote influences, data uncertainty, and the role of temperature. Development of a regional framework for cooperative work and dissemination of information and existing forecasts would speed understanding and make better use of available information.

Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) rainfall monitoring products have been extended to provide spatially contiguous rainfall estimates across Africa. This has been achieved through a new, climatology-based calibration, which varies in both space and time. As a result, cumulative estimates of rainfall are now issued at the end of each 10-day period (dekad) at 4-km spatial resolution with pan-African coverage. The utility of the products for decision making is improved by the routine provision of validation reports, for which the 10-day (dekadal) TAMSAT rainfall estimates are compared with independent gauge observations. This paper describes the methodology by which the TAMSAT method has been applied to generate the pan-African rainfall monitoring products. It is demonstrated through comparison with gauge measurements that the method provides skillful estimates, although with a systematic dry bias. This study illustrates TAMSAT’s value as a complementary method of estimating rainfall through examples of successful operational application.

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30 year (1983–2012), temporally consistent rainfall data set for Africa known as TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series) using archived Meteosat thermal infrared imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10 day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation data sets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit and Global Precipitation Climatology Centre gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm d⁻¹ (21%) compared to other data sets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.