• Energy Research

This commentary 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. The main messages of this commentary will be widely. Climate change is interacting with La Niña to produce extreme, but extremely predictable, Pacific sea surface temperature gradients. These gradients will affect the climate in many countries creating opportunities for prediction. Effective use of such predictions, however, will demand cross-silo collaboration.

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.

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 2020–2022 period was associated with five sequential droughts in East Africa. The Figure 2A tab contains observed and predicted 1982–2022 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 2020–2030 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.  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 spreadsheet should be accessible via Excel or Google sheets.

Abstract. In eastern Africa droughts can cause crop failure and lead to food insecurity. With increasing temperatures, there is an a priori assumption that droughts are becoming more severe, however, the link between droughts and climate change is not sufficiently understood. In the current study we focus on agricultural drought and the influence of high temperatures and precipitation deficits on this. Using a combination of models and observational datasets, we studied trends in six regions in eastern Africa in four drought-related annually averaged variables – soil moisture, precipitation, temperature and, as a measure of evaporative demand, potential evapotranspiration (PET). In standardized soil moisture data, we find no discernible trends. Precipitation was found to have a stronger influence on soil moisture variability than temperature or PET, especially in the drier, or water-limited, study regions. The error margins on precipitation-trend estimates are however large and no clear trend is evident. We find significant positive trends in local temperatures. However, the influence of these on soil moisture annual trends appears limited as evaporation is water limited. The trends in PET are predominantly positive, but we do not find strong relations between PET and soil moisture trends. Nevertheless, the PET-trend results can still be of interest for irrigation purposes as it is PET that determines the maximum evaporation rate. We conclude that, until now, the impact of increasing local temperatures on agricultural drought in eastern Africa is limited and recommend that any soil moisture analysis be supplemented by analysis of precipitation deficit.

# Dataset - Exploring and Anticipating Extreme East African Short Rains [https://doi.org/10.5061/dryad.f1vhhmh4z](https://doi.org/10.5061/dryad.f1vhhmh4z) This data set contains the time series supporting the main results presented in the upcoming manuscript 'Exploring and Anticipating Extreme East African Short Rains in a Warming World with the Indo-Pacific Heating Gradient'. This deposit contains a spreadsheet with the underlying data to reproduce figures and major results. This data set draws from five widely used sources: * The Climate Hazard Center Infrared Precipitation with Stations archive (CHIRPS) and the Centennial Trends Gridded Rainfall archive: Funk C., Peterson P., Landsfeld M., Pedreros D., Verdin J., Shukla S., Husak G., Rowland J., Hoell A. and Michaelsen J. (2015) The climate hazards group infrared precipitation with stations - a new environmental record for monitoring extremes, Scientific Data, 22, 150066. [http://www.nature.com/articles/sdata201566](http://www.nature.com/articles/sdata201566). doi: 10.1038/sdata.2015.66. Funk C., Nicholson S. E., Landsfeld M., Klotter D., Peterson P. and Harrison L. (2015) The Centennial Trends Greater Horn of Africa Precipitation Dataset, Scientific Data, 2, 150050. DOI: 10.1038/sdata.2015.50. doi:10.1038/sdata.2015.50. * The NOAA Extended Reconstruction sea surface temperature data set (version 5): Boyin Huang, Peter W. Thorne, Viva F. Banzon, Tim Boyer, Gennady Chepurin, Jay H. Lawrimore, Matthew J. Menne, Thomas M. Smith, Russell S. Vose, and Huai-Min Zhang (2017): NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5. NOAA National Centers for Environmental Information. DOI:[10.7289/V5T72FNM](10.7289/V5T72FNM). * ERA5 Reanalysis atmospheric heating values: Hersbach, H., et al. (2017): Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). DOI: [10.24381/cds.143582cf](https://doi.org/10.24381/cds.143582cf) * Seasonal SST forecasts from the North American Multi-Model Ensemble (NMME): Kirtman, B. P., et al. (2014). "The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction." Bulletin of the American Meteorological Society 95(4): 585-601. [https://doi.org/10.1175/BAMS-D-12-00050.1](https://doi.org/10.1175/BAMS-D-12-00050.1). [https://www.ncei.noaa.gov/products/weather-climate-models/north-american-multi-model](https://www.ncei.noaa.gov/products/weather-climate-models/north-american-multi-model). While all of these data are publicly available, we compiled this dataset of all the salient time series supporting the basic results in our paper. All data are for October-November-December (OND). **OND Precip Timeseries** Eastern East Africa CHIRPS and Centennial Trends Precipitation Eastern Horn = Kenya, Ethiopia and Somalia east and south of 38E,8N SPI = Standardized Precipitation Index *Eastern Horn Precipitation Time Series* The 'OND Precip Timeseries' tab contains a time series of OND precipitation totals (in mm) for the Eastern Horn of Africa region. Two data sets are provided - 1950-2014 values from the station-based Centennial Trends archive and 1981-2023 observations from the satellite-gauge 'Climate Hazards InfraRed Precipitation with Stations (CHIRPS) archive. The two time-series are very similar, and a regression is used to adjust the 1950-1980 Centennial Trends data with 1981-2023 CHIRPS values. A gamma distribution fit has been used to transform the totals into 'Standardized Precipitation Index' values. **ERA5 Timeseries** The Indo-Warm pool Heating Gradient (IWHG) is based on atmospheric heating over the western Indian Ocean and Indo-Pacific Warm Pool. These time series are provided here, along with total precipitable water. NOAA extended SST v5 and ERA5 OND Data Western Indian Ocean - 50-70E, 10S-10N Indo-Pacific Warm Pool - 90-150E, 15S-15N West IO Atmospheric Heating Anomalies [Wm-2] - Anomalies from 1991-2000 baseline. Warm Pool Atmospheric Heating Anomalies [Wm-2] -Anomalies from 1991-2000 baseline. **NMME Forecasts - SST** NMME sea surface temperature forecasts, for OND, from 7 different lead times, for four ocean regions, are provided here, along with the observed OND sea surface temperatures. * NMME Forecasts-SST(WIO): Western Indian Ocean, 50-70E, 10S-10N * NMME Forecasts-SST(EIO): Eastern Indian Ocean, 90-110E, 10S-0 * NMME Forecasts-SST(EqWestPac): Eq Western Pacific Ocean, 110-140E, 15S-15N * NMME Forecasts-SST(ONDNino34): Nino3.4, 170-120W, 5S-5N **NMME Forecasts - Precipitation** Data drawn from the NMME Precipitation Forecast Multi-Model Means eHorn NMME precipitation for OND, mm per day NMME precipitation forecasts, for the eastern Horn of Africa in OND, from 7 different lead times, are provided, along with the observed Eastern Horn of Africa SPI values. **NMME Forecasts - IWHG** Indo-warm pool Heating Gradient Estimates [Wm-2] Based on a 1982-2023 regression with observed OND IOD, West Pacific and NINO3.4 SST IWHGest = 12 + 323 IOD - 193 WP + 94 Nino3.4 **NMME Forecasts-IWHG(quantile):** Quantile Matched Indo-warm pool Heating Gradient Estimates [Wm-2] NMME-based forecasts OND IWHG values, based on a regression between observed IWHG values and SST in the Indian and Pacific Ocean. This regression translated SST forecasts from the 'NMME Forecasts - SST' tab into IWHG estimates. IHWHG estimates based on the observed OND SST. This correction process translates the estimated IWHG value into a 1982-2023 quanitile, which is then translated into an IWHG value using the observed 1982-2023 ERA5 values. Quantile-matched estimates uses the observed 1982-2023 CHIRPS SPI. During the 16 rainy seasons since October-November-December (OND) of 2016, the eastern Horn of Africa (eHorn) has experienced an exceptional sequence of extreme rainy seasons, with 8 dry seasons, 6 wet seasons, and just 2 normal rainy seasons (Figure 1). In 2016/17 and 2020/22 climate change-enhanced west Pacific sea surface temperatures (SST) amplified the influence of La Nina, leading to hazard two-season and five-season drought sequences that forced millions of people into starvation as crops failed and millions of livestock perished. Drought conditions during 2020-22 were exceptionally intense, persistent, extensive and hot, devastating livelihoods and producing repetitive, debilitating and cumulative shocks to herds, crops, water availability, and household incomes. More than eight million livestock died and millions of people faced the threat of starvation, and emergency humanitarian relief efforts required more than $2 billion USD. Extreme rains in March-April-May (MAM) of 2018, due to a Madden-Julien Oscillation brought flooding and displacement, while positive Indian Ocean Dipole (IOD) conditions in 2019 and 2023 contributed to excessive rains, flooding and displacement. These extremes provide potential opportunities for prediction, proactive risk management, and improved agricultural and water management outcomes. Here, focusing on OND rains, we explore the use of a new Indo-Pacific Heating Gradient indicator to understand and predict extreme eHorn rains. This data set draws from five widely used sources: the Climate Hazard Center Infrared Precipitation with Stations archive (CHIRPS), the Centennial Trends Gridded Rainfall archive, the NOAA Extended Reconstruction sea surface temperature data set (version 5), ERA5 Reanalysis atmospheric heating values, and seasonal SST forecasts from the North American Multi-Model Ensemble (NMME). While all of these data are publicly available, we pull together in this dataset all the salient time series supporting the basic results in our paper. All data are for October-November-December (OND).

Abstract. In eastern Africa droughts can cause crop failure and lead to food insecurity. With increasing temperatures, there is an a priori assumption that droughts are becoming more severe. However, the link between droughts and climate change is not sufficiently understood. Here we investigate trends in long-term agricultural drought and the influence of increasing temperatures and precipitation deficits. Using a combination of models and observational datasets, we studied trends, spanning the period from 1900 (to approximate pre-industrial conditions) to 2018, for six regions in eastern Africa in four drought-related annually averaged variables: soil moisture, precipitation, temperature, and evaporative demand (E0). In standardized soil moisture data, we found no discernible trends. The strongest influence on soil moisture variability was from precipitation, especially in the drier or water-limited study regions; temperature and E0 did not demonstrate strong relations to soil moisture. However, the error margins on precipitation trend estimates are large and no clear trend is evident, whereas significant positive trends were observed in local temperatures. The trends in E0 are predominantly positive, but we do not find strong relations between E0 and soil moisture trends. Nevertheless, the E0 trend results can still be of interest for irrigation purposes because it is E0 that determines the maximum evaporation rate. We conclude that until now the impact of increasing local temperatures on agricultural drought in eastern Africa is limited and we recommend that any soil moisture analysis be supplemented by an analysis of precipitation deficit.

Significance Increased extreme heat exposure from both climate change and the urban heat island effect threatens rapidly growing urban settlements worldwide. Yet, because we do not know where urban population growth and extreme heat intersect, we have limited capacity to reduce the impacts of urban extreme heat exposure. Here, we leverage fine-resolution temperature and population data to measure urban extreme heat exposure for 13,115 cities from 1983 to 2016. Globally, urban exposure increased nearly 200%, affecting 1.7 billion people. Total urban warming elevated exposure rates 52% above population growth alone. However, spatially heterogeneous exposure patterns highlight an urgent need for locally tailored adaptations and early warning systems to reduce harm from urban extreme heat exposure across the planet’s diverse urban settlements.