DOI: 10.25349/d9mc8z
title: Data for: Tailored Forecasts Can Predict Extreme Climate Informing Proactive Interventions in East Africa

description: 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 &gt;$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.
description: 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. 
description: This spreadsheet should be accessible via Excel or Google
 sheets.