This dataset was created to support the paper 'Frequent but Predictable Droughts in East Africa Driven By A Walker Circulation Intensification'. This analysis draws together data from six categories: 1. Observed gridded rainfall values 2. Observed sea surface temperatures (SST) 3. Climate change simulations of SST time series 4. Seasonal predictions of SST time series 5. ERA5 atmospheric reanalysis fields 6. MERRA2 atmospheric reanalysis fields Rainfall comes from the Climate Hazard Center Infrared Precipitation with Stations archive (CHIRPS, https://www.nature.com/articles/sdata201566) and Centennial Trends (https://www.nature.com/articles/sdata201550) archives. The observed SST data are from the NOAA Extended Reconstruction sea surface temperature data set (version 5). The seasonal SST forecasts from the North American Multi-Model Ensemble (NMME). The projected SST and precipitation simulation time-series are from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The reanalyses evaluated were the ERA5 and MERRA2. While all of these data are publicly available, we pull together here salient time series supporting the basic results of our paper. Our key points are: -- Human-induced warming in the western V area of the Pacific combined with La Niña, has produced frequent, predictable March-April-May droughts. -- Thermodynamic analyses link these droughts to a stronger Walker Ciruclation, driven by predictable warming in the Western V region. -- CMIP6 simulations indicate that western V warming is largely human-induced, this warming has enhanced and will enhance the Walker Circulation. The NMME seasonal climate forecasts are based on coupled ocean-atmosphere models, intialized monthly with observed conditions. The coupled ocean-atmosphere models in the CMIP6 archive, on the other hand, are initialized in the early 19th century, and then run into the future, constrained by changes in aerosols and greenhouse gasses. The NMME provide operational forecasts. The CMIP6 provides climate change simulations. For the rainfall and SST data, the only major processing has been seasonal and spatial averaging and the calculation of anomalies. For the ERA5 and MERRA2 reanalyses, seasonal 'diabatic heating' terms were calculated using existing precipitaiton, radiation and sensible heat flux fields. This was combined with vertically integrated heat convergence to obtain a total estimate of 'atmospheric heating' -- which we use to examine changes in the strength of the Walker Circulation.

The decline of the eastern East African (EA) March-April-May (MAM) rains poses a life-threatening ‘enigma’, an enigma linked to sequential droughts in the most food insecure region in the world. The MAM 2022 drought was the driest on record, preceded by three poor rainy seasons, and followed by widespread starvation. Connecting these droughts is an interaction between La Niña and climate change, an interaction that provides exciting opportunities for long lead prediction and proactive disaster risk management. Using observations, reanalyses, and climate change simulations, we show here, for the first time, that post-1997 OND La Niña events are robust precursors of: (1) strong MAM ‘Western V Gradients’ in the Pacific, which help produce (2) large increases in moisture convergence and atmospheric heating near Indonesia, which appear associated with (3) regional shifts in moisture transports and vertical velocities, which (4) help explain more frequent dry EA rainy seasons. Understanding this causal chain will help make long-lead forecasts more actionable. Increased Warm Pool atmospheric heating and moisture convergence sets the stage for dangerous sequential droughts in EA. At 20-yr time scales, we show that these Warm Pool heating increases are attributable to observed Western V warming, which is in turn largely attributable to climate change. As energy builds up in the oceans and atmosphere, we see stronger convergence patterns, which offer opportunities for prediction. Hence, linking EA drying to a stronger Walker Circulation can help explain the ‘enigma’ while underscoring the predictable risks associated with recent La Niña events.

By design, no special software or programming expertise is required to access the spreadsheet containing our results.