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Data for: Tailored forecasts can predict extreme climate informing proactive interventions in East Africa

Cite this dataset

Funk, Chris (2023). Data for: Tailored forecasts can predict extreme climate informing proactive interventions in East Africa [Dataset]. Dryad.


This perspective discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision-making. Following an unprecedented sequence of five droughts, in 2022, 23 million east Africans faced starvation, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution-based insights can be combined with the latest dynamic models to predict droughts at eight-month lead-times. We then discuss behavioral and social barriers to forecast use and review literature examining how EWS might (or might not) enhance agro-pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization (WMO) “Early Warning for All” plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African-led EWS, and building better links between EWS and agricultural development efforts can support long-term adaptation, reducing chronic needs for billions of dollars in reactive assistance.


This data set draws from four widely used sources: the Climate Hazard Center Infrared Precipitation with Stations archive (CHIRPS), the NOAA Extended Reconstruction sea surface temperature data set (version 5), seasonal SST forecasts from the North American Multi-Model Ensemble (NMME) and projected SST time-series from Phase 6 of the Climate Model Intercomparison Project (CMIP6). While all of these data are publicly available, we pull together here all the salient time series supporting the basic results of our paper. 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.

The data are organized in a spreadsheet with tabs corresponding to figure panels. 

The Figure 1B tab contains 1981–2022 March-April-May (MAM) and October-November-December (OND) CHIRPS rainfall totals averaged over the eastern Horn of Africa (Ethiopia, Kenya and Somalia east and south of 38E, 8N). This extremely food-insecure area suffers from sequential droughts. There has also been a well-documented decline in the MAM rains beginning around 1999. This tab also contains seasonal totals expressed as 'Standardized Precipitation Index' (SPI) values. These were calculated by fitting a Gamma distribution to the MAM and OND rainfall time-series and then translating the associated quantile values to a standard normal distribution. Seasons with SPI values of less than -0.44Z or greater than +0.44Z fall within the below-normal or above-normal terciles.

The Figure 1E tab contains observed standardized 'West Pacific Gradient' (WPG) and 'Western V Gradient' (WVG) time-series for, respectively, the OND and MAM seasons. These gradients measure the difference between standardized equatorial east Pacific (NINO3.4) and standardized west Pacific SST time series. The data are standardized because relatively small temperature increases in the very warm west Pacific can be dynamically important. The observed gradient values show that warming in the west Pacific, combined with a lack of warming in the NINO3.4 region, has led to large increases in Pacific SST gradients. This sets the stage for sequential droughts in the eastern Horn.

The Figure 1F tab contains Indo-Pacific SST time-series from 152 CMIP6 climate change simulations. These simulations are based on the moderate warming Shared Socio-economic Pathway 245 scenario (SSP245). Time-series are provided for the OND equatorial west Pacific, MAM Western V region, and OND western Indian Ocean region. Observed NOAA SST time series are also provided. The human-induced warming signal is pronounced in the CMIP6 simulations. During the 2016/17 and 2020/2022 La Niña sequences, climate change contributed to exceptionally warm equatorial west Pacific and Western V SST. During the positive Indian Ocean Dipole event in 2019, climate change contributed to exceptionally warm western Indian Ocean SST. The western Indian Ocean region corresponds with the western box used to calculate the Indian Ocean Dipole (IOD). The 2019 IOD event was associated with flooding and a desert locust outbreak. The 20202022 period was associated with five sequential droughts in East Africa.

The Figure 2A tab contains observed and predicted 19822022 MAM and OND Pacific gradient time series (WVG and WPG). The forecasts are based on six models from the North American Multi-model Ensemble (NMME). The OND forecasts are based on NMME predictions made in May. The MAM forecasts are based on NMME predictions from September. The data have been accessed via the IRI data library. Six individual standardized SST forecasts for the NINO3.4 and west Pacific regions are extracted for each model and then combined using a weighted average proportional to each model's skill (R2). The NINO3.4 and west Pacific SST are then used to calculate the WVG and WPG forecasts. Observed WVG and WPG values are based on NOAA Extended reconstruction version 5 SST.

The Figure 2B tab is very similar to 2A but contains the west Pacific OND and MAM time series. While SST observations and CMIP6 simulations indicate more frequent extremely warm SSTs (tabs 1E and 1F), these can be predicted surprisingly well, offering opportunities to anticipate associated climate extremes.

The Figure 3A tab contains the CMIP6 simulation data supporting panel 3A. The standardized WPG and WVG time series are provided for 152 CMIP6 SSP245 simulations, and the individual changes in event frequencies have been calculated for each simulation. These changes contrast WPG and WVG event frequencies in 20202030 versus 1920-1979.  An increase in event frequency is a very robust result, due to the very robust warming in the west Pacific. This latter warming can be verified via the data in the Figure 1F tab if desired. Note that a few CMIP6 models only had one simulation. Results for these models were not listed in the inset in Fig. 3A, due to space limitations. 

Usage notes

This spreadsheet should be accessible via Excel or Google sheets.


United States Agency for International Development

Bill & Melinda Gates Foundation

NASA Global Precipitation Monitoring Mission