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Mid-season assessment of maize growing conditions in Southern Africa (2018-2019) reveals reason for concern

Mid-season assessment of maize growing conditions in Southern Africa (2018-2019) reveals reason for concern

Authors: Will Turner, Laura Harrison, Greg Husak

Editor: Juliet Way-Henthorne


  • Season Precipitation Performance Probability shows a high likelihood of below-normal rainfall throughout Angola, western Mozambique, Namibia, Botswana, Zimbabwe, and central South Africa with little chance of recovery.
  • Northeastern South Africa’s maize triangle does show a likelihood of normal season precipitation totals due to DJF recovery.
  • Current vegetation conditions (based on NDVI) are quite poor in the aforementioned countries.
  • End-of-season vegetation conditions, based on WRSI, are projected to be below average in the same countries.


This blog communicates season-to-date rainfall along with modeled and observed crop impacts for the 2018-2019 maize growing season in southern Africa. It also employs some of the new operational monitoring products available from the Climate Hazards Center.  

The southern Africa agricultural season broadly extends through the October-April season. While all regions are more dependent on subsets of that interval, the main producer of the region, South Africa, is shown to be strongly responsive to the December-February (DJF) rainfall (Shukla, in review). To capture these regional conditions and, specifically, conditions for South Africa, we begin with estimated probabilities of rainfall being below normal, normal, and above normal for each of the two monitoring windows (October to April and DJF).

The Season Precipitation Performance Probability (SPPP) quantitatively evaluates the probability of a current season’s total precipitation to finish in a given tercile, corresponding to below-normal (< 33rd percentile), normal (33rd–66th percentile), and above-normal (> 66th percentile) conditions, with respect to the historical record. Below normal to-date rainfall has significantly reduced the probability of normal total season (October to February) rainfall for much of the Southern Africa (Figure 1, above). Figure 2 (below) includes the 2-pentad CHIRPS-GEFS forecast in order to provide additional certainty of season outcomes.

The DJF monitoring window (Figures 3 & 4) is largely in agreement with the full-season window for our areas of concern. In both the October to April and the DJF monitoring windows, we see a significant uptick in the likelihood of given tercile outcomes (darker hues). This supports Shukla’s findings regarding the strong response of total seasonal rainfall to the DJF subset. Notably,  the two outlooks differ in the positive change in potential DJF precipitation performance in northeastern South Africa (northern maize triangle area). This region shows an increased likelihood for above-normal DJF rainfall (recovery of rains; potential for groundwater recharge).

The DJF subset shows a similar ability to capture the impact of to-date rainfall on likely season total outcomes. Figure 3 (above) is based only on to-date rainfall. Figure 4 (below) includes the 2-pentad CHIRPS-GEFS forecast.

NDVI products, which capture the greenness of the vegetation, are useful for indicating how this to-date rainfall has impacted vegetation so far (Figure 5). Percent of average NDVI during mid-to-late January (Source: NDVI-MODIS GLAM) is more than 30% below normal for approximately 20% of South Africa’s major cropping area for this season. Some areas of northeastern South Africa and Eswatini show closer to normal or above normal NDVI; these correspond to areas that the CHIRPS monitoring products (above) showed recent recovery in rains.

Figure 5: Percent of normal NDVI during mid to late January (Source: NDVI-MODIS GLAM). Shades of green indicate areas of above normal vegetation greenness, while shades of brown indicate areas of below normal.

While useful for categorizing projected seasonal rainfall, the SPPP product’s unidimensional nature does limit its ability to wholly provide context to current/projected agricultural performance. Specifically, plant-water availability is not strictly defined by total precipitation; the other half of the equation, which reduces this availability, is evapotranspiration. With this in mind, it is prudent for us to use additional monitoring products that take this evaporative demand into account.

For this, the Famine Early Warning Systems Network (FEWS NET) commonly uses the Water Requirement Satisfaction Index (WRSI) to monitor growing seasons throughout Africa and Central America. The WRSI uses gridded precipitation (PPT) and reference evapotranspiration (RefET) inputs, along with crop phenological parameters, to calculate the ratio of plant-available water to crop-specific water demand at each stage of crop development. In so doing, the index puts specific interest on the timing of the rainfall (as opposed to just the total). Below, we show the results of WRSI model simulations that provide more details about current maize impacts.

Overall, lackluster rainfall early in the 2018-2019 season likely delayed the start of the crop growing season (Figure 7; shown in shades of red). Parts of central South Africa still have not received sufficient rainfall for germination (Figure 7; shades of brown). This late onset of rains could also be influencing the negative NDVI anomalies seen before, as vegetation may not be fully mature this year, while in previous years it is nearly mature by this time.

Figure 6: The dekad identified as the Start of Season (SOS) for a location (pixel) is defined as the first dekad with at least 25 mm of rainfall followed by a total of at least 20 mm in the next two consecutive dekads. Areas colored gray have yet to receive sufficient rainfall for a season start.

Figure 7: 2018-19 start of season anomaly with respect to the 1981-2017 mode SOS. Areas colored in shades of blue indicate regions where the current season started earlier than the historical mode, while areas colored in shades of red started late. 
The lightest beige color indicates areas that have not yet started but are not beyond the mode start date. Darker hues of brown indicate areas that are past the mode SOS for that location and still have yet to receive sufficient rainfall for a season start.

In similar fashion to the SPPP calculation, we can use precipitation and RefET historical records to run out the remainder of the season and provide a collection of scenarios for the end of season WRSI. The median of these scenario outcomes (WRSI Outlook) is then compared to the median historical WRSI. We find that while starts did eventually occur for most of the region, the delay was followed by continued below average rainfall, and thus the outlook WRSI anomaly (Figure 8) and percent of normal (Figure 9) are well-below average throughout the aforementioned dry areas in the SPPP plots.

Figure 8. 2018-19 simulated end of season WRSI anomaly with respect to the 1981-2017 median WRSI. Hues of red indicate below average WRSI, while shades of blue indicate above average. Gray indicates average WRSI.

Figure 9: 2018-19 WRSI as a percent of normal, with respect to the 1981-2017 median WRSI. Green hues indicate above average WRSI, whereas orange/purple hues indicate below average. Pink denotes areas that have yet to receive sufficient rainfall for a start, and are beyond the mode SOS.

Additionally, the poor rainfall outlook for the 3rd dekad of January results in significant worsening of the WRSI Outlook (Figures 10 & 11).

2018-19 simulated WRSI anomaly (Figure 10, above) and percent of normal (Figure 11, below) including CHIRPS-GEFS forecast data for the 3rd dekad of January.

In agreement with our SPPP product, we see similar areas of concern in Angola, western Mozambique, Namibia, Botswana, Zimbabwe, and South Africa. As with the DJF monitoring window, there remains a potential for recovery to average crop conditions in northeastern South Africa.

As previously stated, most of these areas of concern are predominantly driven by DJF rainfall, so confidence in these observations should continue to increase over the next few weeks. More to this point, as season progress continues (Figure 12) and crop phenological cycle advances, crop-water demand will increase, and rainfall performance will have an increasing impact on the overall crop-water satisfaction. This is evident in the dramatic increase in severity of the negative WRSI anomaly and percent of normal when including CHIRPS-GEFS forecast for the third dekad of January.

Figure 12: The 2018-19 Season Progress combines the current SOS and the spatially varying LGP map (from GeoWRSI) to calculate how far along the season is at the current time as a percentage of the LGP.

This collection of products allows for in-depth monitoring of (1) the current state of rainfall-dependent growing seasons and (2) the impact to-date conditions could have on end-of-season agricultural productivity. While not as accurate as post-harvest assessments, these precipitation estimates, NDVI, and crop-water models help to provide sources of information for early warning and timely action.

Product Directories:



A Concerning 2018-2019 Southern Africa Cropping Season

Laura Harrison, Chris Funk, Will Turner, and Juliet Way-Henthorne

Special thanks to Climate Hazards Center and NASA FAME members for their contributions

Main points:

  1. Below normal rainfall so far in many rainfed cropping areas
  2. Pessimistic forecast for early 2019 rainfall in some areas of concern based on multi-model forecast and expectation for El Niño
  3. Forecast hotter than normal temperatures poses additional hazard for crops

Poor growing conditions to start the 2018-2019 season

Thus far, the 2018-2019 cropping season has been lackluster in terms of rainfall performance across much of Southern Africa (Figure 1a). Rainfall accumulations from mid-October to late December are below the 1981-2017 average by 50 mm to 200 mm in areas south of northern Zambia and northern Mozambique. In southeast Angola, southern Zambia, and parts of Zimbabwe, Botswana, South Africa, and Madagascar, these anomalies are 1.5 to 2 standard deviations below average- and thus are very dry when compared to the same period for past 1981-2017 seasons. Based on December CHIRPS preliminary rainfall data and CHIRPS-GEFS short term forecasts, CHC Early Estimates indicate that more recent rains may be closer to expected climatology for the majority of this region. Timely improvement in South Africa would be a much-needed positive change, as the country is a major contributor to regional maize production. The December data indicate a swath of continued below normal rainfall stretching across southern Angola, much of Zimbabwe, and Madagascar.

Figure 1. Rainfall performance thus far in 2018-2019 season. (a) Climate Hazards Center Early Estimates show rainfall anomaly and standardized precipitation index (SPI) for recent ~2 month and 20-day accumulation periods. The last 10 days of each period is based on an unbiased GEFS forecast (CHIRPS-GEFS). (b) Probability of October to April 2018-2019 rainfall total being below normal, normal, or above normal, based on to-date accumulations and if historical mid-December to April rainfall were to complete this season.

Rainfall during the next two to four months will be an important determinant of production outcomes. Rainfall forecasts for this period are concerning (see next section) given expected El Niño conditions and the low rainfall seen thus far across the region. Time will tell the actual outcome. Impacts thus far include delayed planting, crop damage from heatwaves, and poor veld and livestock conditions, according to the Southern Africa Development Community (SADC)’s December 2018 Agromet Update.

What if rainfall for the rest of the season is the same as in previous years? This scenario can be helpful to gauge the importance of season-to-date rainfall for the seasonal total. Also, it incorporates to some extent historical tendencies for persistence or transience in sub seasonal rainfall, if such patterns exist. Based on current conditions and climatological outcomes, there appears to be a high probability (50-90%) that many critical maize growing areas in South Africa, and also in the Caprivi Strip and parts of Angola and Zambia, will experience low seasonal rainfall totals (Figure 1b). Seasonal rainfall forecasts are pessimistic for some for these areas, as discussed below.

Below normal rainfall in early 2019 is the most likely scenario

For a large part of southern Africa that has already seen rainfall deficits, climate model forecasts indicate a 40-50% chance of rainfall during January to March falling below normal. Below normal is defined here as falling into the driest 1/3 of years. This outlook is coming from a multi-model ensemble forecast from the North American Multi-Model Ensemble (NMME). Areas with forecast below-normal rainfall include South Africa, Zimbabwe, Botswana, southern Zambia, central and southern Mozambique, south and eastern Angola, and Namibia. (Figure 2a).

Figure 2. Current forecasts and El Niño assumptions. (a) NMME probabilistic rainfall forecast for January to March 2019 based on December 2018 initial conditions. (b) Root zone soil moisture forecasts from the NASA Forecasting for Africa and the Middle East (FAME) project. Maps accessed 19 Dec 2018. More information can be found at (c) Average October to March CHIRPS rainfall anomaly for previous El Niños of similar expected moderate strength. Selections based on Niño3.4 SST.

Most models agree with respect to below-normal rainfall as the most likely category that these areas will fall into. If one considers the rainfall forecast from one of these NMME models (for December onwards) and the soil moisture conditions that may have been present in November, elevated concerns for crop impacts appear justified. NASA FAME forecasts, based on the GEOS5 model, show root zone soil moisture (in the top 1 meter of soil) in low-percentile categories for January and February (Figure 2b) and through April.

These pessimistic forecasts and observed deficits align with the expectation for El Niño and outcomes of past El Niño seasons. Figure 2c shows the average rainfall anomaly for October to March across five previous moderate strength El Niños (1994-95, 2002-03, 2004-05, 2006-07, and 2014-15). According to the CPC/IRI December forecast, for January to March, there is a 90% chance of El Niño through the 2018-19 austral summer. You can check the current outlook here.

Another hot, dry season?

Heat stress and high evaporative demand due to above-normal temperatures also appear to be a hazard for this season. The current NMME forecast for January to March 2019 near-surface air temperatures is reminiscent of what the models forecast for the 2015-2016 season (Figure 3). In early 2016, hotter than normal days exacerbated longer-term effects of rainfall deficits and were a factor in reducing South Africa maize yields by 40% (Archer et al., 2017). Similar to 2015-2016, the 2018-2019 NMME forecast for January to March (December initial conditions) show +0.5°C to +1°C anomalies for most of southern Africa and a prominent epicenter with +1°C  to +2 °C anomalies. The forecast epicenter is smaller in 2018-2019 than in 2015-2016, which is consistent with the difference in expected 2018-2019 El Niño strength compared to the very strong 2015-2016 El Niño.

Figure 3. NMME 2-m air temperature anomalies forecast for upcoming January to March season based on (a) December 2015 (b) December 2018 initial conditions.

AGRO-CLIMATE ALERT: Very Poor Somali Deyr Harvest Highly Likely


Chris Funk, Gideon Galu, Greg Husak, Will Turner, Juliet Way-Henthorne


An analysis of mid-season rainfall, WRSI simulations, and crop statistics indicate a high probability of a very poor Somali Deyr 2018 growing season. Rainfall deficits in Kenya also indicate poor growing conditions.

FEWS NET food security outlooks rely on a series of information products. Before the growing season, an analysis of climate modes and climate forecasts provides an evidence-based foundation for seasonal rainfall outlooks. By mid-season, however, rainfall observations, crop and hydrology models, and other forms of monitoring provide more detailed and accurate sources of information. These information sources can be especially powerful in areas with short growing seasons (such as Somalia), where arid conditions create a short window for successful harvests. FEWS NET science partners have been steadily improving our monitoring resources, and here, we use some of these products to assess likely agroclimatic outcomes for the Somali 2018 Deyr growing season. Crop statistics, provided by the FEWS NET data warehouse, allow us to further leverage these resources to provide quantitative projections of Maize/Sorghum crop production values. While not as accurate as post-harvest assessments, these preliminary rainfall-based projections suggest that a poor cropping season is very likely. Poor pasture conditions also seem probable for some regions.

Poor seasonal rainfall performance

Dry conditions appear extremely likely over southern Somalia and most of Kenya. Note the large discrepancy between the ARC2 (Fig. 1-left) and CHIRPS (Fig 1-right) results over Eastern Ethiopia. CHIRPS incorporates additional station data provided by the Ethiopian Meteorological Agency and is much more representative of the current situation in eastern Somalia.  Rainfall estimates from CPC’s ARC2 archive and the USGS/CHC’s CHIRPS2 data sets converge on similar outlooks for a very dry October-December growing season for Somalia (Figure 1). The CPC provides Seasonal Rainfall Performance Probability (SPP) Analyses for October-December. These analyses use observed ARC2 data up to the present and then completes the remainder of the season with all previous historical ARC2 observations. This enables them to estimate the probable seasonal outcome (Figure 1, left).  Based on the SPP analysis through mid-November, most of Somalia (including the main crop growing areas) are very likely (>75% chance) to receive below-normal rainfall. The USGS/CHC has also recently started producing an “Early Estimate” product that combines observed CHIRPS data with CHIRPS-compatible 10-day GEFS rainfall forecasts. The right panel of Figure 1 shows Early Estimate rainfall anomalies for October 1 through November 25th. Dry conditions appear extremely likely over southern Somalia and most of Kenya.

Figure 1. Left – CPC Seasonal Precipitation Performance predictions for October-December 2018. Right – CHC Early Estimate seasonal rainfall anomalies.

Somalia Deyr Rainfall and Crop Production Projections

Next, we examine in detail rainfall averaged across crop-growing zones in Bay, lower, and middle Shabelle districts (Figure 2). These rainfall estimates combine October and the 1st two dekads of November, with CHIRPS-GEFS forecasts used to fill in dekad 2 of November. For 2018, these seasonal totals look very low, almost identical with previous signature drought years: 1998, 1999, 2003, 2005, 2010, and 2016.

Figure 2. October 1-November 20 CHIRPS observations for the main Somali crop growing regions.

Using historical sorghum and sorghum+maize crop production data from the FEWS NET data warehouse, we can use logistic regression to relate these seasonal rainfall totals to 1995-2016 Deyr seasonal production estimates (Figure 3). We treat sorghum separately because we found that it had a very strong relationship with rainfall (R2~0.7). We also provide maize+sorghum total production projections to support food security analyses. It should be noted that these projections are not intended to replace careful post-harvest crop assessments, but, rather, as an advanced outlook on the general pattern such assessments are likely to indicate. We have excluded 1997 and 2011 from our estimation procedure, because 1997 was an exceptionally wet year with modest production (presumably due to flooding), while 2011 was a normal rainfall year with low crop production (presumably due to conflict).

One of the most notable features of Figure 3 is the non-linear relationship between rainfall and crop production. Below 100 mm of rainfall, crop production drops very quickly. A 2018 rainfall total of ~60 mm indicates poor crop production. Using take-one-away cross-validation and regression, we have estimated the 2018 Deyr Sorghum and Sorghum+Maize production totals as 45 and 81 thousand metrics tons. These estimates correspond to ~50% and 60% of the 2012-2016 average production (i.e. production about half of the recent average production). Eighty percent confidence intervals, based on the cross-validation results, indicate ranges of 43-46 and 77 to 83 thousand metric tons for Deyr Sorghum and Sorghum+Maize production.  Note that the late November rainfall estimates are based on weather model forecasts, and there is still room for the season to improve; however, the overall outlook is very pessimistic.

Figure 3. Scatterplots of Rainfall and Somali Deyr Crop Production

Per Capita Sorghum+Maize Production

According to United Nation estimates, Somalia’s population has doubled since 1995, while Deyr crop production totals have not increased. Figure 4 shows per capita Deyr Maize+Sorghum production estimates (i.e. crop production per person). In the last 10-12 years, approximately half of the Deyr seasons were associated with very low per capita crop production outcomes. 2018 appears likely to be another such season, with per capita maize+sorghum production of around 5.4 kg per person.

Figure 4. Deyr Per Capita Sorghum+Maize Production

Concerns About Pasture Conditions

Next, we turn to an assessment of pastoral conditions, focusing on the same three regions (Bay, Lower Shabelle and Middle Shabelle). This analysis is not meant to be an exhaustive assessment of rangeland conditions for the entire country. Furthermore, rangeland conditions may be more variable than crop outcomes, since grasses can respond quickly to late-season rains. However, through the first dekad of November (Figure 5) vegetation conditions (as represented by the USGS eMODIS NDVI) appear quite low. Vegetation greenness in these regions typically peaks in the last dekad of November.

Vegetation conditions exhibit persistence, and robust lagged relationships with prior rainfall totals. Hence, the end-of-November values can be predicted very accurately based on 1st dekad of November values and October 1-November 20 rainfall estimates (R2 values of ~0.8). Times series of the observed and predicted regional NDVI are shown in Figure 6. According to these estimates, peak NDVI in these regions appears substantially below normal and similar to previous severe drought years. Note also the sequence of repeated low Deyr NDVI values. The 2016, 2017, and 2018 values are all quite low – indicating repetitive shocks. The 2017 season March-May Gu season was poor, while the 2018 Gu season was normal-to-above normal. So, four out of the last five seasons seem to have been associated with poor growing conditions: Deyr 2016, Gu 2017, Deyr 2017, and Deyr 2018.

Figure 5. Regional eMODIS NDVI time series. The red lines depict the median values for the 2003-2017 period. The blue line shows 2018 conditions.


Figure 6. Time series of November dekad 3 eMODIS NDVI, along with predictions based on dekad 1 NDVI and October-November rainfall

Conclusions and Context

The most effective drought early warnings systems rely on multiple indicators, transitioning from climate forecasts and analogs before the onset of a rainy season, to mid-season projections of likely food system disruptions. Here, we have used FEWS NET monitoring tools to focus on mid-season projections for Somalia. While these projections should not be mistaken for assessments, our analysis has shown that for Somalia, a high level of predictability can be obtained in mid-November. This skill rests on the short duration of the Deyr growing season, the general aridity of the region, and the persistent nature of vegetation, soil moisture, and crop conditions. Unfortunately, the results presented also suggest another poor crop-growing season for Somalia. For the Bay and Shabelle regions analyzed, poor vegetation conditions also appear likely – conditions which could quite likely persist until the March-May Gu rains of 2019. While not examined in great detail here, precipitation totals and WRSI (Figure 1 and 2) also appear quite poor over Kenya, and poor Kenyan short rainy season harvest could be problematic, ultimately contributing to regional food and price stresses.  Please note that it is probable that the marginal agricultural areas of the southeastern lowlands are likely to be more adversely affected by the current delayed onset, the quality of the seasonal rains, and the substantially reduced growing period.

The results concerning the Deyr harvest expressed here should be placed in the context of a good 2018 Gu harvest. Figure 7 below, taken from the 2018 post-Gu FSNAU-FEWS NET joint assessment (here), shows that the 2018 Sorghum+Millet Gu harvest was estimated to be about 147 thousand metric tons, the best harvest since 2010. On average, the Gu and Deyr harvests similar in magnitude, about 90 thousand metric tons, so the good Gu harvest will certainly help partially offset the poor 2018 cropping season. Current maize and sorghum prices are much lower than their peak in 2017, but historically prices also typically increase between December and April-May. It should also be noted, however, that the 2016 and 2017 Gu harvests were below normal, as were the 2016 and 2017 Deyr season harvests.

Key messages:

  • Rainfall-based early assessments of the 2018 Deyr harvest indicate substantial (40-50%) deficits with little chance of recovery.
  • Vegetation conditions in some areas are also quite poor, and predicted to remain so unless unseasonal rains arrive.
  • While the 2018 Gu season was very good, Somalia also faces the stress of repetitive shocks in four of the past five rainy seasons, compounded by increasing population stress.


Figure 7. Gu Season Cereal Production – figure taken from the 2018 post-Gu FSNAU-FEWS NET joint assessment

Concern for the “short rains” 2018 season in eastern Horn of Africa

Contributors: Laura Harrison, Chris Funk, Martin Landsfeld, Will Turner, Greg Husak, Juliet Way-Henthorne

As East Africa’s October to December “short” rainy season approaches its midpoint in many areas, parts of Kenya, southeastern Ethiopia, and much of southern Somalia have seen substantial rainfall deficits. Factors include a late start to the onset of seasonal rains, fewer than normal rain days, and long dry spells. Based on the Climate Hazards Center Early Estimate, a monitoring data resource that provides early indications of sub-seasonal to seasonal rainfall performance, central Kenya and Somalia’s cropping zones may end up with 50-100mm deficits for the October 1st to November 10th, 2018 period (Figure 1).

Figure 1: Climate Hazards Center Early Estimate for the 2018 short rains status made on November 5th, 2018. The CHC Early Estimate approach combines CHIRPS final and preliminary rainfall estimates (30-day or 60-day) with a compatible, unbiased version of the 10-day GEFS ensemble mean forecast (see Figure 3). Figure 1 is a rainfall anomaly composite of preliminary CHIRPS October 2018 rainfall and CHIRPS-GEFS, released November 1st for Nov. 1st-10th. Note that October data is preliminary and subject to change in the final version of CHIRPS, which will be available mid-November. In CHIRPS final, Somalia data includes FAO SWALIM reports and Ethiopia data includes Ethiopia National Meteorological Agency reports.

While some areas may receive relief in November, poor crop outcomes should be a concern in short growing season areas, such as Somalia. Concern for the performance of the short rains in the eastern Horn and Deyr crop production, in particular, is based on convergent evidence from independent data sets and a pessimistic outlook from the current rainfall forecast.

  1. Convergent evidenceUSGS’s expedited MODIS NDVI anomalies show vegetation productivity in a degraded state at the end of October. Like CHIRPS, NOAA CPC’s ARC2 data shows expansive rainfall deficits across the eastern Horn and similar patterns with respect to rainfall anomalies (Figure 2). These came during the typically wettest month of the short cropping season in Somalia’s main cropping zones.

Figure 2. Left: USGS/EROS eMODIS NDVI anomaly for last 10 days on October 2018. Figure from USGS FEWS NET Data Portal Right: NOAA CPC ARC2 October to December 2018 rainfall anomaly.  Figure from CPC Africa Desk.

2. Rainfall outlooks

Based on GEFS forecasts, relief is not expected during the first two weeks of November for most of Kenya and for at least two of Somalia’s main cropping zones (Figure 3). National Multi-Model Ensemble (NMME) forecasts for November rainfall vary widely. Outlooks from NOAA CPC, based on historical November and December rainfall, indicate a higher than 60% chance of below normal OND totals in southern Somalia and southeastern Ethiopia.

Figure 3. CHIRPS-GEFS rainfall anomaly forecast for the 10-day period beginning November 4th, 2018. More can be found about this data product and how to access it a blog on the UCSB Climate Hazards Center website

Blending CHIRPS Data and GEFS Forecasts for an Enhanced Rainfall Forecast Product

Contributors: Martin Landsfeld, Laura Harrison, Chris Funk, Juliet Way-Henthorne

Background – CHIRPS    

The Climate Hazards Group Infrared Precipitation with Stations data set (CHIRPS) is a land-based, quasi-global (latitude 50°N-50°S), 0.05 degree resolution precipitation data set. It has a relatively long-term period of record (1981 – near present). CHIRPS is based on a well-developed climatology and perturbed with infrared satellite measurements and in-situ observations to estimate gridded precipitation in near-real time. A final monthly precipitation product is created about 2 weeks after the end of the month when all the station data (from over 10 different sources) has been collected and blended with the satellite estimates. A preliminary product, CHIRPS Prelim, is available on the dekadal time scale and available 2-3 days after each dekad and month. It is blended with only 2 sources of station data— WMO GTS and CONAGUA.

You can find more information about CHIRPS at:

Figure 1.  CHIRPS rainfall estimates product.


Background – GEFS

NCEP’s Global Ensemble Forecast System (GEFS) is a weather forecast system that provides daily forecasts out to 16 days at 1 X 1 degree resolution at 6-hour intervals.

Figure 2. GEFS rainfall estimates product.

This forecast product can be very useful to early warning famine and hydrological monitoring efforts, so the Climate Hazards Group (CHG) creates forecast precipitation fields at the dekadal (10 day) time scale and makes those available to researchers and decision makers in the EWX data viewer at:

The data is also available for download in GeoTIFF format through our FTP service at:

Precipitation is accumulated to 10-day intervals, every day. The precip_mean, anom_mean, and zscore_mean directories contain the means of the ensemble runs, and the file names are comprised of the dates of the first day of the forecast, followed by the last day of the forecast.


Blending GEFS Forecasts with CHIRPS

To make GEFS forecasts interoperable with CHIRPS, we bias-correct and downscale GEFS with respect to CHIRPS. The method that is followed is similar to the standard cumulative density function matching process of bias-correction. For a given target 10 day period’s GEFS forecast, its rank with respect to its climatology (1985 – present) is identified, and then a value of the same rank from the CHIRPS climatology is used to replace GEFS forecasts for each ~5km pixel. A comparison is shown below.

Figure 3. Comparison of GEFS 1 degree resolution forecast (left) with the blended CHIRPS-GEFS 0.05 degree forecast (right). 


Accuracy assessment:

GEFS and CHIRPS-GEFS 10-day accumulations were compared to historical Ethiopian station measurements. Ethiopia maintains many stations through the time period since 1985 and has a varied topographic relief. Significant improvements were made, as seen below (Figure 4).


The correlation, mean bias, and root mean squared error are shown in Table 1. Significant improvements in the correlation to station values were improved, and the mean bias and RMSE were reduced, demonstrating the increased accuracy when the GEFS forecasts are blended with the CHIRPS climatology and long-term time series.

                             Corr. Bias RMSE

               GEFS: 0.51 1.45     63.54

CHIRPS-GEFS:   0.68 1.05 37.26

Table 1. Comparison coefficients of GEFS and CHIRPS-GEFS estimates with rain gauge stations.

Spatial correlations where calculated between CHIRPS-GEFS forecasts and CHIRPS Final estimates for dekadal time periods over the entire time series. High correlations exist over significant portions of Africa for the first dekad of April (Figure 5). Other correlations, biases, and mean-biased errors for other time periods can be found at: 

Figure 5. Correlation between CHIRPS-GEFS and CHIRPS Final for the 1st dekad of April (1985-2016). 



Real-time precipitation forecasts are critical for predicting flooding events and protecting property and lives. When torrential rains hit Kenya in mid to late April 2018, major flooding occurred, overwhelming drainage systems and collapsing dams. The Kenya Meteorological Department wrote:

Heavy rain has been affecting the central, the south-west and south-east areas of the country, including the capital Nairobi, since the beginning of the month, causing floods, flash floods, and casualties. According to media, as of 20 March, the death toll has reached at least 15 people in the provinces of Central, Nyanza, and Eastern. They also reported that around 1000 people were evacuated in the counties of Makueni (Eastern province), Kilifi and Tana (Coast province). Over the next 24 hours, more heavy rain with local thunderstorms is forecast for the affected areas. 

The impacts of this flooding can be seen below (Figures 6 and 7).

Figure 6. A passenger is rescued from his submerging vehicle following heavy downpour in Nairobi on April 15, 2018.

Figure 7. Submerged vehicle in Nairobi, April 24th, 2018. 


The CHIRPS-GEFS 10-day forecast, made on April 10th, 2018, could have been used to help predict these events and warn residents of impending flooding. As an example, consider the left image in Figure 8 below. This map, obtained from the CHG experimental Early Warning Explorer ( using the CHIRPS GEFS latest under Global Datasets, shows a downscaled GEFS forecast for the 10th – 20th of April. Below is a comparison of  the anomalies of the CHIRPS-GEFS forecast with the subsequent CHIRPS Final for the same time period. The right image shows the observed CHIRPS data for that same time period. While the agreement is not perfect, the CHIRPS GEF forecasts did a very good job of capturing the potential high rainfall amounts. Figure 9 shows forecasts and observations for the next 10 days April 21-30.

As these maps illustrate, the CHIRPS GEFS forecasts can be very useful tools for flood prediction. Current efforts at the CHG are setting up daily updates of these predictions. These results will be available via ftp at,, and within the EWX.

CHIRPS-GEFS Forecast, 2018/4, dekad 1       CHIRPS Estimates, 2018/4, dekad 1

Figure 8. CHIRPS-GEFS forecast for dekad 2 of April, 2018 (left) and the CHIRPS Final estimates (right) for the same time period. 


The period of the 21st -30th of April, 2018 shows a similarly good comparison between the prediction and the CHIRPS estimate.


Figure 9. CHIRPS-GEFS forecast for dekad 3 of April, 2018 (left) and the CHIRPS Final estimates (right) for the same time period. 

Announcing the Climate Hazards Center

The Climate Hazards Group is both pleased and honored to announce that we are transitioning to an official center, The Climate Hazards Center, which will continue to operate within the UC Santa Barbara Geography department. Since 2003, the Climate Hazards researchers – a cooperative of multidisciplinary scientists and food security analysts from UC Santa Barbara, Africa, and Central America have worked closely with the US Geological Survey and the USAID Famine Early Warning Systems Network (FEWS NET) to utilize climate and crop models, satellite-based earth observations, and socio-economic data sets to predict and monitor droughts and food shortages among the world’s most vulnerable populations. The Climate Hazards team continues to support critical planning and timely humanitarian assistance that ultimately saves lives and livelihoods.

Now, as the Climate Hazards Center (led by Dr. Chris Funk as initial director), we are an officially recognized entity within the University of California. While this is only the beginning of this journey, we are already benefiting from a new capability to pursue Memorandums of Understanding with influential entities like the World Meteorological Organization. Further, with the added visibility that the officially recognized Center affords, we seek to increase the circulation of critical knowledge. While the accumulation of potentially life-saving information has always been a primary focus of the Climate Hazards Group, we hope to better organize and communicate this knowledge by expanding the reach of our research. As this exciting adventure unfolds, the CHC will, in close partnership with USGS and USAID, strive to constantly evolve as a center based upon the principles of scientific integrity and excellence in early warning and climate risk management and adaptation. It is in this spirit of ethical responsibility that we aim to create an institution and lasting legacy that inspires people to continue pursuing humanitarian-focused science with the shared vision of helping thousands of people for many years to come.

The Climate Hazards Center wishes to extend our appreciation to our Geography Chair Stuart Sweeney, our Executive Officer Mo Lovegreen, and our long-time faculty advisor Joel Michaelsen. We are also deeply grateful to Jim Verdin and Jim Rowland, our long-standing partners from USAID and USGS, who have helped guide our efforts from the very beginning. Thank you for making this dream a reality. 


Senior Research Geographer, Researcher, Director CHC (Chris Funk, Ph.D.)

Principal Investigator, Researcher, Co-Director CHC (Greg Husak, Ph.D.)

Writer, Editor (Juliet Way-Henthorne)

Update on 2018 Blue Nile forecast

Update to the guest blog by the Ad hoc Blue Nile Forecast Group (listed alphabetically): Sarah Alexander (1), Paul Block (1), Annalise Blum (2), Shraddhanand Shukla (3), Shu Wu (1), Temesgen Yimane (2), Ben Zaitchik (2)*, and Ying Zhang (2).

  1. University of Wisconsin-Madison, Madison, WI, USA

2. Johns Hopkins University, Baltimore, MD USA

3. University of California Santa Barbara, Santa Barbara, CA, USA

*Correspondence can be addressed to

The end of July represents the midpoint of the Kiremt rainy season in the Blue Nile basin. On average, just over half of the June-September rainfall total is realized by the end of July. This is also true for the annual total rainfall, January-December (Figure 1; shown spatially in Figure S1). This makes the end of July an opportune time to provide an update on the seasonal forecasts of 2018 Blue Nile rainfall and river flow that we posted at the outset of the rainy season.

Figure 1: Cumulative Blue Nile basin rainfall, January-December, according to CHIRPSv2 1981-2017 climatology. On average, 54% of annual rainfall occurs by the end of July (month 7).
At the time of our first post, there were already reports that rains had come early to portions of the Blue Nile basin, and we found near unanimity across statistical and dynamically-based seasonal forecasts that the June-September Kiremt rainfall would be average to above average. Forecasts of Blue Nile flow showed the same tendency. Consistent with these forecasts, rainfall in the basin through the first dekad of July was well above average (Figure 2). Positive rainfall anomalies for early season rainfall exceeded 50 mm over the majority of the basin, with significant areas showing anomalies greater than 100 mm. For context: according to CHIRPS estimates, average June rainfall for the basin is 195 mm, and average June-September total rainfall is 942 mm. So anomalies on the order of 50-100 mm through only the first third of the rainy season are quite meaningful.
Figure 2: CHIRPS-prelim cumulative seasonal rainfall anomalies for June 1 – July 15, 2018. Information on the CHIRPS-prelim product is available at

Interestingly, however, the North American Multi-Model Ensemble (NMME) forecasts launched in July show a more mixed outlook than the same ensemble had offered in May. At the time of the May forecast, every NMME model had a mean prediction of average to above average rainfall for June-September (Figure 10 in our original post). Looking at forecast July-September rainfall in the July initialized NMME simulations, we see that there are now several models that predict below average rainfall, albeit with only a modest negative anomaly (Figure 3). This breaks the consensus that existed in May. This shift to drier forecasts might reflect the influence of a shifting El Niño outlook, as the predicted probability of an El Niño forming by the end of the season is higher now than it was in May (Figure 4; compare to Figure 9 in the original post).

Figure 3: Forecast of July-September rainfall anomaly for the Blue Nile basin (mm) in July initialized forecasts of NMME models. Mean is shown by (x), circles are individual members, and boxplots show quartiles. The “Average” boxplot consists of the average forecast of each participating model.
Figure 4: Oceanic Niño Index forecast, generated by NOAA CPC and the International Research Institute (IRI) and issued on July 23, 2018.

Notwithstanding this somewhat drier outlook from NMME, the strong rains observed in June and early July got the basin off to a wet start. This, combined with an NMME forecast that still, across all models, points to average rainfall conditions for the remainder of the season, suggests that the Kiremt rains will be average to above average overall. The probability of a dry year is low, though some NMME models now suggest that late season rains might be lower than average.

Recent Extreme Temperatures Enhanced by Climate Change

Chris Funk

As noted by a recent report by the World Meteorological Society (and many other news articles), July of 2018 has brought exceptionally warm air temperatures to many parts of the globe. Fires rage in Sweden and Greece, Japan experienced deadly torrential rains (1,800 mm at Shikoku) followed by temperatures reaching 106°F. In Algeria, Ouargla reported a maximum temperature of 124.3°F and Morocco set a new record at Bourfa at 110°F.  In Canada, as many as 70 people may have died due to an extreme heat wave in Quebec. Closer to home, the WMO report identifies an extreme of 125.6°F in Death Valley, while Chino, Burbank and Van Nuys set records at 120°F, 114°F and 117°F.

This blog posting is not intended to provide a formal climate attribution analysis, such as those provided by the Bulletin of American Meteorological Society. The goal here is just to look at the data, which is quite compelling on its own. We begin by looking at annual (July to June) air temperatures for Coastal Southern California (Figure 1). This data was obtained from the Earth Systems Research Laboratory. What is very concerning about this time series is how every year since 2014 has been very warm. I’ve circled these values. This type of persistent warmth can dry out vegetation and provide great background conditions for fires.

Annual (July-June) average tempetures for south coastal California.
Figure. 1. Annual (July-June) average temperatures for south coastal California.
Figure 2. Five year average air temperatures for south coastal California.
Figure 2. Five year average air temperatures for south coastal California.





We can present the same data as five-year averages to highlight the recent transition to warmer conditions (Figure 2). Now the past five-year average clearly stands out as way warmer than any value on record before 2013. The current five-year average temperature (~52.8°F) is about two degrees Fahrenheit warmer than the average from just a few years ago. This is a large change in a short period of time. Beginning with the last El Niño in 2014/16, we may have transitioned to a much warmer climate regime, and climate model projections indicate that this warming will continue. I have also plotted similar results for Central California where the Ferguson fire rages (Figure 3). We have seen an historically unprecedented and very rapid increase in air temperatures. Currently, about 20 fires are burning in California, and 2017 was clearly the most destructive fire year on record. While warm temperatures are just part of the recipe for fire disasters, this part of the puzzle has clearly been expanding rapidly.

Figure 3. Five year average temperatures for central California.
Figure 3. Five year average temperatures for central California.



If we produce a similar plot of GLOBAL five-year average air temperature anomalies, based on NASA estimates of land surface temperatures (Figure 4), we see that global temperatures have also jumped upwards over the past five years, reaching unprecedented heights. The magnitude of the jump in coastal (Figure 2) and central (Figure 3) California has been substantially greater in magnitude, however. Also shown in Figure 4 are completely independent predictions of global land air temperature anomalies based on the current state of the science collection of climate change simulations. The fit to the observations is extremely good (R2=0.97). Climate change has caused the recent increase in global temperatures.

Figure 4. Global NASA GISS five-year average air temperature anomalies for land areas.

The rise after 2018 is based on a pessimistic but realistic ‘business as usual’ climate change scenario in which the climate modelers have assumed a continued rapid increase in greenhouse gasses. I have annotated this time series with 10 year steps to emphasize what we may likely experience between 2019 and 2048. Between 2009 and 2018, we have already seen a problematic increase in global and California air temperatures related to numerous climatic hazards. Without dramatic efforts to reduce our greenhouse gas emissions, the models (which have been extremely accurate so far) tell us that we are likely to experience three more similar increases between now and mid-century. For poor people living in very warm regions (like India), such warming may lead to severe health impacts as described by Somini Sengupta. For California, a further intensification of droughts and fire risk seems likely as  temperatures continue to increase rapidly.

To visualize US temperature changes, we can use the cool ‘Climate Explorer’ website provided as part of the U.S. Climate Resilience toolkit. If you click HERE you should get a map of the continental U.S. that shows the number of days in a year with maximum air temperatures of greater than 95°F. The map is divided with a vertical bar identified with left-right arrows (<>). On the left hand side of the bar is a map of recent counts based on 1961-1990 observations. On the right is a map of estimates for 2090 based on a continued higher emissions trajectory. Grab the central bar and slide it back and forth, and you can see the predicted change – big increases in the frequency of very warm days.

We can also use the Climate Explorer to examine likely changes in a given location – like Santa Barbara county (HERE). This time series contrasts likely outcomes given a continuation of our current high emission pathway (shown in pink and red) and likely outcomes if we act to curb emissions (blue). As you can see, there is substantial uncertainty, but we see a substantial difference between the scenario averages (red and blue lines) by mid-century. If we do not curb emissions soon, by 2100 the models suggest that we could very well see annual average maximum temperatures increase by ~+7.3°F, according to the ensemble average.


Climate Change Likely to Contribute to Another East African drought in 2018

by Chris Funk


I needed to come in to the office today (Martin Luther King Day) to work on another blog because I am worried about the strong probability of yet another East African drought in March-May of 2018. I wanted to come in to work today because it is Martin Luther King Junior’s birthday, and this is one way of honoring his life. Dr. King has always been a personal hero, teaching us to care deeply for each other, fighting for our shared humanity in a non-violent way.

It is not our genes that make us human, but rather our highest aspirations expressed in coherent action.

I would like to dedicate this post to all the first responders in Africa and California, who have helped so many face the ravages of drought, fire, and flood.

The main objective of this post is to summarize some of our climate change research, and link it to a pessimistic outlook for the March-May East African rains. But I can’t not mention that since the Whittier Fire broke out near my house in the woods on July 8th of 2017, it seems like disasters in Santa Barbara have been rampant. The Whittier fire burned through July, August, and September. Then October, November, and December were exceptionally dry (Figure 1) and hot (Figure 2).  Many regions of the southwest have received less than half the amount of rain typically received by this point in winter. Even more striking, however, is the exceptional warmth, shown in Figure 2 averaged over the South Coast Drainage near Santa Barbara. Recent average air temperatures have been about 57° Fahrenheit. The 2017 air temperatures were about 61.5°F. This very warm weather and an almost total lack of rainfall resulted in very dry vegetation conditions. Everywhere I hiked this winter branches were brittle-dry, like matchsticks.

Figure 1
Figure 1. October 1 2017-January 14th precipitation, expressed as a percent anomaly.
Figure 2
Figure 2. October 1 2017-January 14th precipitation, expressed as a percent anomaly.

When fall transitions to winter, the Santa Barbara area starts to get ‘sundowner’ and ‘Santa Ana’ weather conditions, which blow rapidly down from the mountains towards to the sea. Typically, these occur after it rains and pose little risk. This year, however, these winds were extremely rapid, up to 70 miles per hour, and raced across tinder-like branches. When a fire broke out behind the city of Ventura on December 4th, these intense winds combined with abundant super-dry vegetation and a spark, producing the massive Thomas Fire. While aperiodic, the spread of the fire over the remainder of the month was at times insane, advancing an acre a second, leaping up the coast towards Carpentaria and then Montecito and Santa Barbara.

Then on Tuesday January 9th, a rainstorm struck the coastal mountains behind Montecito. Under normal conditions, such a storm would have had little impact. But the fire has been so recent the hills were bare, and the soil crusty; literally baked by the intense flames of the Thomas Fire.

Today the Santa Barbara/Montecito community feels crushed by the weight of the resulting mudflow which tore down through Montecito in the very early morning at speeds of up to 20 miles per hour. The Thomas Fire had been horrible. It caused many of us to evacuate, and all of us to breathe ash-filled air for weeks. More than a thousand structures had burned in what had quickly become the largest wildfire in recorded history. But the fire response had been massive and very successful. More than 8,000 courageous firefighters, 100 fire engines, and dozens of helicopters, sent from all across the western US had come together to defeat the Thomas Fire. On January 9th we lost at least twenty lives in a terrible flood that ripped all the way down to the coast. The Thomas Fire was not even 100% contained until January 12th.

The Walker Circulation links us with East Africa

While I am not an expert on California climate, I do think that there is an important climate feature that links California with East Africa: the Walker Circulation. Understanding this climate pattern helps inform successful predictions of drought, such as our group’s successful predictions of the 2016 October-December, 2017 March-May, and 2017 October-December droughts in East Africa. A recent assessment by the World Food Programme highlights the extent and some of the impacts of these sequential droughts: “This relentless sequence of climatic shocks together with insecurity and high food prices is having a profound negative impact on vulnerable populations”. Assessments by the Famine Early Warning Systems Network (FEWS NET) identify very large food insecure populations, especially in Somalia and Eastern Ethiopia.

To understand how fragile some of these populations are it helps to understand how deep poverty impacts how people eat. World Bank data for Kenya and Ethiopia suggest that the poorest 20% of households live on $330 and $251 dollars per year per person. In very poor households, most of this money (~60%) will typically be spent on food. If you live among a very vulnerable pastoral community, making your living off the itinerant grazing of yours flocks, you have to depend heavily on food purchases. Droughts can inflict a triple threat – food prices go up, purchasing power and savings erode as livestock die, and weak and dying herds also stop producing milk and meat, important sources of calories.

Figure 3
Figure 3. Schematic drawing showing the over-turning cells of the Walker Circulation.

While there are a lot of complexities involved in East African climate, the predictable components on climatic time scales tend to be associated with a large circulation feature called the ‘Walker Circulation’ (named after another personal hero of mine, Sir Gilbert Walker). Figure 3 shows a schematic depiction of the Walker Circulation. The 2D drawing goes from the Earth’s surface to the top of the troposphere. Moving from left to right we start in Uganda and end up in the eastern Pacific. The Walker Circulation is centered on the equator, though across the east Pacific the sub-tropical high pressure cells extend poleward, keeping California sunny and dry. Near Indonesia (~130°E), where the Indian and Western Pacific oceans meet, we find extremely warm ocean waters. Above these warm waters we find lots of rainfall and ascending air. In the lower atmosphere, winds blow into this region from both the equatorial Pacific and Indian Ocean, helping to produce moisture convergence and ascending air motions. In the upper atmosphere (near 15 km) the atmosphere becomes very stable, and the ascending air parcels are shunted to the east or west. Most of the energy carried by these parcels ends up reinforcing the east Pacific sub-tropical highs. These high pressure cells, in turn, drive the Pacific trade winds that blow west towards Indonesia, reinforcing the Walker Circulation. It’s these cells that help make it dry and sunny in California.

Over the Indian Ocean, there is also an Indian Ocean branch of the Walker Circulation. This circulation is associated with dry subsiding air over eastern East Africa, and dry hot surface conditions. To a large extent, eastern East Africa is dry because of the moisture and energy convergence over Indonesia. This convergence drives rising air, some of which sinks over East Africa.

Climate variations like El Niños, La Niñas, and warming trends in the oceans can modulate the strength of the Walker Circulation, increasing or reducing the probability of East African droughts. Research by the Climate Hazards Group and FEWS NET (discussed below) has suggested that climate change is increasing the strength of the Walker Circulation, reducing rainfall during the March-May long rainy season, and increasing the impact of La Niña events.

 Why climate change is increasing the frequency of East African droughts
The Climate Hazards Group Perspective

Declines in East African rainfall were first identified as part of our efforts to improve drought early data sets for the FEWS NET. Our early climate analyses correctly isolated the immediate cause of the drying (an intensification of the Indian Ocean branch of the Walker Circulation), but probably over-emphasized the role played by the Indian Ocean. In the early 2010s, we doubled down on our efforts to collect and analyze the best possible rainfall data, and published a series of reports for the US Agency for International Development. Our Kenya report, for example, showed the strong correspondence between a measure of low frequency warming, the Indo-Pacific Area Index and declines in Kenya rainfall. Most of this work has focused on the March-May rainy season. We have refined our ideas over time, going through the classic science progression of ‘description’, ‘explanation’, and ‘prediction’. Here I just recap some of the description, explanation, and prediction efforts, with the caveat that many have contributed to these papers and many other good studies have also been done (this is not an exhaustive literature review). A long list of publications is shown at the end of this post.


With support from the FEWS NET, we have been able to invest heavily in building the best possible international data sets for East Africa (see A and B). While there is essentially no debate about the recent declines of the East African March-May ‘long rains’ these data sets let us say with confidence that there has been a large persistent increase in the frequency of poor East African seasons. Figure 4 shows a long time series of standardized rainfall averaged over eastern/central Kenya, southeastern Ethiopia, and Somalia. This image is from a recent paper, currently in final review.

Figure 4
Figure 4. A long time series of rainfall for the region outlined in the left panel show a very large increase in the frequency of dry seasons. Since 1998, only 2010 and 2013 exhibited healthy long rains. Based on CHIRPS and Centennial Trends rainfall as described here:

Since 1998, only 2010 and 2013 exhibited above normal rainfall performance. Paleoclimate analyses by Jessica Tierney and co-authors also indicate a long term decline and a negative relationship to global warming. It should be noted that the October-December ‘short’ rains have not exhibited similar declines, probably due to warming in the eastern Indian Oceans. Nonetheless, the dramatic increase in the frequency of East African March-May droughts threatens the economic stability of poor households. More frequent climate shocks make it very hard to build up reserves and escape from a cycle of poverty.


Our ability to explain the mechanisms behind the recent declines has been advancing quickly. This work has tended to emphasize the impact of the ‘West Pacific Warming Mode’ – a low frequency warming trend mode found in both the observed sea surface temperature record and climate change simulations. The warming mode pattern is associated with a characteristic ‘Western V’ sea surface temperature pattern over the Pacific. This pattern is also quite similar to the ocean temperature anomalies found during recent East African March-May droughts.

Figure 5
Figure 5. Standardized March-May sea surface temperature composites based on signature East African drought years (1984, 2000, 2004, 2009, 2011).

We can characterize the sea surface temperature patterns associated with these droughts by averaging conditions during recent severe dry seasons (Figure 5). This figure shows an area where warming is associated with less rainfall over East Africa (primarily the Western North Pacific, outlined with a yellow box), and an area in the eastern equatorial Pacific where warm (El Niño-like) sea surface temperatures are associated with more rainfall over East Africa. The area over the eastern equatorial Pacific is strongly influenced by El Niños and La Niñas – the major form of inter-annual climate variability. El Niños warm the east Pacific and slow the Walker Circulation. La Niñas do the opposite. At decadal time scales, sea surface temperatures in the east Pacific have not warmed substantially, and this is a major difference between the observed climate and predictions made by climate change models.

The western Pacific, on the other hand, has tracked very closely with climate change predictions. This is an area where sea surface temperatures are strongly controlled by downwelling radiation. La Niñas also tend to produce warming as well. The combination of these two influences are shown in Figure 6 (details here). Western North Pacific Ocean temperatures have increased dramatically, in line with climate change predictions. It is important to note, however, that this region warms during La Niña events as well. The combination of long term warming and the responses to La Niña events creates a strong gradient between the western and central Pacific amplifying the impact of La Niña events.

Figure 6
Figure 6. The red line shows west Pacific sea surface temperatures from the yellow box in the previous figure. The blue line shows the same from a large set of climate change simulations. The green line shows estimates based on a combination of climate change averages and the influence of La Ninas and El Ninos.

We can now explain fairly well why a ‘Western V’-like pattern amplifies the Walker Circulation, linking increases in sea surface temperatures (Figure 6) with declines in the East African long rains (Figure 4). Near the surface, equatorial sea level pressure values track closely with ocean temperatures. As shown by many recent studies, warm west Pacific Ocean conditions are associated with lower pressures and convergent low level winds that blow from the Indian and Pacific into the area around Indonesia, enhancing the Walker Circulation. Figure 7 quantifies the effects of the warming west Pacific by contrasting new (1981-2016) west Pacific warm events with old (1921-1980) warm events. This figure depicts rainfall and low level circulation changes based on a large set of atmospheric model simulations. The details of the drying signal over East Africa are not reproduced too well, but note the strong modulation of the moisture bearing winds over the Indian Ocean (blowing away from Africa). These simulations identify large (more than one and a half standardized anomaly) increases in precipitation over Indonesia. This indicates a large increase in the strength of the Walker Circulation (Figure 3), which tends to increase subsidence over East Africa, increasing the frequency of droughts.

Figure 7
Figure 7. Composites of a large (20 member) ensemble of CAM5 atmospheric model simulations to diagnose changes in precipitation and low level (850 hPa) wind anomalies by contrasting 1981-2016 and 1921-1980 warm WNP events. Image produced by Laura Harrison.
Figure 8
Figure 8. Here we present estimates based on ensemble climate change averages (blue line) and estimates based on climate change averages and the influence of ENSO, based on regressions with NINO3.4 sea surface temperatures.

Looking further up in the atmosphere, things become even more interesting. Examining similar atmospheric change composites (Figure 8) we can see the full implications of a ‘Western V’ warming pattern. This map shows changes in upper level geopotential heights (i.e. the maps of high and low pressure systems we see in weather reports) along with changes in upper level winds. Over the northern and southern mid-latitudes, we see very large (~+50 m) increases in heights. These high pressure cells sit right underneath the sub-tropical westerly jet. Smaller, but equally important, are the increases in upper level heights found near Indonesia. Taken together, this pattern of high pressure sends upper level winds first to the west across the northern and southern Pacific near 30°N/S, and then turns those winds towards the central Pacific near the equator. These anomalous flows from the north and south converge east of 150°E, producing subsidence, and enhancing the Walker Circulation, creating a La Niña-like response. Over the western Indian Ocean we see an enhancement of the Indian Ocean branch of the Walker Circulation. When warm Western V conditions combine with cool La Niña conditions in the East Pacific, we are set to have an elevated chance of East African drought.

Attribution Studies

Since 2012, we have been active participants in the special climate attribution issue of the Bulletin of the American Meteorological Society’s annual issue on extreme event attribution. This special issue examines each year’s extreme events, and formally assesses questions of climate attribution. Articles in these special issues ask: can extreme events from each year be attributed to anthropogenic influences?

Our analysis of the 2014 East African March-May drought found: “Anthropogenic warming contributed to the 2014 East African drought by increasing East African and west Pacific temperatures, and increasing the gradient between standardized western and central Pacific SST causing reduced rainfall, evapotranspiration, and soil moisture”. Our analysis of the 2015 Ethiopian drought found: “Anthropogenic warming contributed to the 2015 Ethiopian and southern African droughts by increasing El Niño SSTs and local air temperatures, causing reduced rainfall and runoff, and contributing to severe food insecurity”. In general, our work suggests that both El Niño and La Niña-like climate disruptions may be made more intense by climate change, leading to opportunities for prediction.


Our ability to predict long rains droughts has moved through three stages. During Stage 1 (~2004-2009) an increased drought frequency had been identified and related to the Walker Circulation. At this point we had little predictive skill. During Stage 2 (2010-13) we were identifying the link between Indo-Pacific warming and an increase in La Niña impacts on the Greater Horn of Africa. This helped support a successful prediction of the severe famine-producing 2010-11 drought. During Stage 3, we then started making the link between the west Pacific gradient and using statistical models to predict East African droughts. These latter efforts are quite similar to the successful Climate Outlook Forum process used by East African scientists. This work is important, because dynamic forecast models still continue to miss major drought events, such as the March-May and October-December droughts from 2017 (Figure 9 and 10). Partnering with many agencies we were able to use ‘Stage 3’ type forecasts to provide an effective early alert before the poor March-May 2017 season. In the early spring of 2017, timely humanitarian response helped prevent a repeat of the catastrophic runaway food prices seen in 2011. Unfortunately, we may be looking at yet another poor rainy season. The rest of this post describes a statistical forecast for the March-May 2018 season.

Figure 9
Figure 9. International Research Institute rainfall forecast for March-May 2017.
Figure 10
Figure 10. International Research Institute rainfall forecast for October-December 2017.

We begin by looking at the correlation between observed 1998-2017 CHIRPS rainfall over central/Eastern Kenya, all of Somalia, and central/southeastern Ethiopia (see Figure 3 from here for the precise region) and observed December NOAA Extended Reconstruction sea surface temperatures (Figure 11). We find a fairly strong negative correlation with the ‘Western V’ and a fairly strong positive correlation with the equatorial eastern Pacific. This relationship holds up reasonably well back to about 1993, but then seems to disappear, leading some climatologists to claim that the long rains are weakly linked to Indo-Pacific sea surface temperatures and relatively unpredictable. Our view on this subject is quite different.

Figure 11
Figure 11. 1998-2017 correlations between observed March-May East African rainfall and December sea surface temperatures.

Following the very large 1997/98 El Niño, sea surface temperatures in the west Pacific warmed substantially (Figure 6). La Niña-like March-May seasons have been associated with much larger Walker Circulation intensifications (Figure 7), that draw near surface winds over the Indian Ocean away from Eastern Africa (Figure 8). Looking at the upper-troposphere (Figure 8), we see large increases in the westward winds over East Africa, characteristic of an enhancement of the Indian Ocean branch of the Walker Circulation (Figure 3). This enhancement can help explain the emergent correlation structure plotted in Figure 11 and the fact that ‘new’ La Niñas appear to have a stronger negative impact on the East African long rains.

Figure 8 also helps understand the correlation pattern shown in Figure 11. ‘Western V’ warming in the blue areas of Figure 11 tends to increase upper level heights with the ‘U’ shaped pattern shown in Figure 8. Cooling in the red/orange areas of Figure 11 tends to reduce upper level heights within the two central Pacific cyclones shown in Figure 8.

So, dynamically, the Western V warming and central Pacific cooling patterns tend to fit ‘hand-in-glove’. The upper-level cyclones produced by cool central Pacific conditions slot neatly within the surrounding height increases associated with warmer Western V sea surface temperatures, produce intense convergent wind patterns and subsidence across the eastern equatorial Pacific. This enhances the Walker Circulation and increases the frequency of droughts over East Africa.

Figure 12
Figure 12. December 2017 NOAA Extended Reconstruction Version 4 sea surface temperature anomalies. Based on a 1981-2010 baseline.

Conditions in December of 2017 (Figure 12) appear conducive to below-normal March-May 2018 rains. We see generally warm conditions across the ‘Western V’ region and La Niña sea surface temperatures across the eastern equatorial Pacific. We use the magenta and cyan regions from Figure 12 to develop predictive relationships.

Figure 13
Figure 13. Scatterplot between December Western V sea surface temperatures and March-May East African precipitation.
Figure 14
Figure 14. Scatterplot between December East Pacific sea surface temperatures and March-May East African precipitation.

Figures 13 and 14 show, respectively, scatterplots between 1998-2017 December Western V and eastern Pacific sea surface temperatures and standardized March-May East African rainfall. Both predictive relationships are similar in magnitude (R=-0.70 and +0.68). Red dots in Figure 13 and 14 show estimated outcomes for March-May 2018, based on each individual predictor. The results are quite similar, with forecasts of about a -1 standard deviation rainfall deficit.

Figure 15
Figure 15. Scatterplot of 1998-2017 Western V and East Pacific sea surface temperature values

Figure 15 shows the 1998-2017 Western V and East Pacific sea surface temperature values. Note that these time series are also inversely correlated (R=-0.70). This year appears typical for a recent moderate La Niña/East Africa drought season. Some Decembers looked worse (1998, 1999, 2010 and 2011), but many (13) Decembers looked better, so we might expect to find ourselves in the below normal rainfall tercile. The 2007, 2000, and 2016 seasons might be close analogs. The 2017 conditions appear much more characteristic of new drought years than 2016. That year was atypical, in that we had very warm Western V sea surface temperatures and fairly neutral East Pacific conditions.

Figure 16 (not included)
Figure 16. Scatterplot of predicted and observed East African precipitation values. Green circles show estimates based on take-one-away cross validation. The red circle shows the 2018 forecast, along with 80 percent confidence intervals.

We can use cross-validation to produce 1998-2017 forecasts for March-May 2018 (Figure 15). This forecast indicates a high probability of below normal rains. Note that this scatterplots suggests that we can identify most (eight out of nine) poor rainy seasons with a very low false alarm rate. We also see this in our Western V and East Pacific scatterplots (Figures 13 and 14); when Western V or East Pacific sea surface temperatures a particularly warm or cold (respectively) we do not see above normal Eastern East African March-May rains. Our below normal forecast appears broadly consistent with the most recent National Multi-Model Ensemble climate model forecasts. These forecasts also call for an end to La Niña conditions by mid-spring. This could alter the seasonal outlook substantially. This transition might suggest a more pessimistic outlook for the early part of the rainy season. Such transitional conditions tend to translate into more certainty for farmers in marginal areas. In semi-arid crop growing regions of central-eastern Kenya, Southern Somalia and Belg-dependent regions of Ethiopia, typical growing seasons are already short, so poor rains during the first half of the rainy season can be very disruptive. A poor Somali Gu harvest outlook may be one of the impacts of these predicted dry conditions, since this growing season is so short. Unfortunately, many of the La Niña high risk regions correspond to regions that received very poor rainfall totals over the prior three seasons: October-December 2016, March-May 2017, and October-December 2017 (Figure 17). Some regions of eastern Kenya and southern Somalia also experienced poor March-May 2016 conditions, and may be looking at five poor rainy seasons in a row.

Figure 17
Figure 17. Total CHIRPS rainfall for October-December 2016, March-May 2017 and October-December 2017 expressed as an anomaly from the 1981-2016 average.


Relevant Recent Research

Williams P. and Funk C. (2010) A Westward Extension of the Tropical Pacific Warm Pool Leads to March through June Drying in Kenya and Ethiopia, USGS Openfile Report 1199,

Funk, C., Eilerts, G., Davenport, F., and Michaelsen, J., (2010) A Climate Trend Analysis of Kenya-August 2010, USGS Fact Sheet 2010-3074: 

Funk, C. (2011) We thought trouble was coming, Nature Worldview, 476.7.

Funk C, J. Michaelsen and M. Marshall (2012) Mapping recent decadal climate variations in precipitation and temperature across Eastern Africa and the Sahel, Chapter 14 in “Remote Sensing of Drought: Innovative Monitoring Approaches”, edited by B. Wardlow, M. Anderson and J. Verdin, Taylor and Francis, 25 pages.

Galu, G., J. Kere, C. Funk, and G. Husak (2011) Case study on understanding food security trends and development of decision-support tools and their impact on a vulnerable livelihood in East Africa. Global Assessment Report on Disaster Risk Reduction 2011, United Nations, International Strategy for Disaster Risk Reduction, Geneva, Switzerland.

Liebmann, B., Bladé I., Kiladis G. N. , Carvalho L. M. V., Senay, G., Allured D., Leroux S., Funk C (2011) African Precipitation Seasonality Based on Daily Satellite Data from 1996-2009, J. of Climate, Journal of Climate 25, no. 12 (2012): 4304-4322.

Funk, C., (2012) Exceptional warming in the western Pacific-Indian Ocean Warm Pool has contributed to more frequent droughts in Eastern Africa, Bull. Amer. Met. Society,v7(93) p. 1049-1051.

Hoell, A. and C. Funk (2013): The ENSO-related West Pacific Sea Surface Temperature Gradient, Journal of Climate. J. Climate, 26, 9545–9562.      doi:

Funk C., G. Husak, J. Michaelsen, S. Shukla, A. Hoell, B. Lyon, M. P. Hoerling, B. Liebmann, T. Zhang, J. Verdin, G. Galu, G. Eilerts, and J. Rowland, 2013: Attribution of 2012 and 2003-12 rainfall deficits in eastern Kenya and southern Somalia [in “Explaining Extreme Events of 2012 from a Climate Perspective”]. Bull. Amer. Meteor. Soc., 94, S45-S48.

Hoell A. and C. Funk (2013) Indo-Pacific Sea Surface Temperature Influences on Failed Consecutive Rainy Seasons over Eastern Africa, Climate Dynamics, DOI: 10.1007/s00382-013-1991-6. p1-16.

Hoell, A., Funk, C., & Barlow, M. (2013). The regional forcing of Northern hemisphere drought during recent warm tropical west Pacific Ocean La Niña events. Climate Dynamics, 1-23.

Liebmann, B., Hoerling M., Funk C., Dole, R. M., Allured A., Pegion, P., Blade I., Eischeid, J.K, (2014) Understanding Eastern Africa Rainfall Variability and Change, Journal of Climate.

Funk, C., Hoell, A., Shukla, S., Bladé, I., Liebmann, B., Roberts, J. B., and Husak, G. (2014). Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices. Hydrology and Earth System Sciences Discussions, 11(3), 3111-3136.

Shukla, S., McNally, A., Husak, G., & Funk, C. (2014). A seasonal agricultural drought forecast system for food-insecure regions of East Africa. Hydrology and Earth System Sciences Discussions, 11(3), 3049-3081.

Shukla S., Funk C. and Hoell A. (2014) Using constructed analogs to improve the skill of March-April-May precipitation forecasts in equatorial East Africa , Env. Res. Letters, Environmental Research Letters 9.9 (2014): 094009.

Funk, C.,  Hoell A., Husak G., Shukla S. and Michaelsen J. (2015) The East African monsoon system: seasonal climatologies and recent variations. Chapter for “The Monsoons and Climate Change”, Leila M. V. Carvalho and Charles Jones (Eds.)

Funk, C. and Hoell A. (2015) The leading mode of observed and CMIP5 ENSO-residual sea surface temperatures and associated changes in Indo-Pacific climate, J. Climate, 28, 4309-4329,

Shukla S., Safeeq M., Aghakouchak A., Guan K. and Funk C. (2015) Role of Temperature in the Water Year 2014 Drought in California, Geophysical Research Letters, 42(11), 4384-4393. DOI: 10.1002/2015GL063666

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.

Funk, C., Shukla S., Hoell A. and Livneh B. (2015) Assessing the contributions of East African and west Pacific warming to the 2014 boreal spring East African drought, BAMS Climate Attribution Issue. 96.12 (2015): S77-S82.

Shukla S., Roberts, J., Hoell A., Funk C., Robertson F. and Kirtmann, B. (2016) Assessing North American Multimodel Ensemble (NMME) Seasonal Forecasts, Climate Dynamics. 1-17.

Funk, C., Harrison L., Shukla S., Hoell A., Korecha D., Magadzire T., Husak G., and Galu G., 2016, Assessing the contributions of local and east Pacific warming to the 2015 droughts in Ethiopia and Southern Africa, Bulletin of the American Meteorological Society, December 2016, S75-S77.

Funk, C and Hoell A. (2017) Recent Climate Extremes Associated with the West Pacific Warming Mode, AGU Monograph-Climate Extremes: Patterns and Mechanisms. Ed. by Simon Wang,  Jin-Ho Yoon, Chris Funk and Robert Gillies, published by Wiley Press (2017): 165. ISBN: 978-1-119-06784-9.

Liebmann B., Bladé I., Funk C., Allured D., Hoerling, M., Hoell, A., Peterson, P., Thiaw, M.T. (2017) Climatology and Interannual Variability of Boreal Spring Wet Season  Precipitation in the Eastern Horn of Africa and Implications for its Recent Decline, J. Climate, Journal of Climate 30.10 (2017): 3867-3886,

Brown M.E., Funk, C., Pedreros, D., Korecha D., Lemma M., Rowland J., Williams E. and Verdin, J. (2017) Climate Trend Analysis of Ethiopia-Examining Subseasonal effects on Crops and Pasture Conditions, Climatic Change, DOI 10.1007/s10584-017-1948-6.

Funk C, Davenport F, Harrison L, Magadzire T, Galu G, Artan G, Shukla S, Korecha D, Indeje M, Pomposi C, Macharia D and Husak G (2017) Anthropogenic enhancement of moderate-to-strong El Niños likely contributed to drought and poor harvests in Southern Africa during 2016, Bulletin of the American Meteorological Society, 37.S1-S3, DOI. 10.1175/BAMS-D-17-0112.2.

Philip S, Kew S.F., Jan van Oldenborgh G, Otto F, O’Keefe S, Haustein K, King A,  Zegeye A, Eshetu Z, Hailemariam K, Singh R, Jjemba E, Funk C,  Cullen H, Attribution analysis of the Ethiopian drought of 2015, J. Climate,

Funk C, McCormick S, Galu G, Massawa, E., McCormick S, Omondi P, Sebina E, Shitote S, White L (2017), Climate Change Vulnerability Impact Assessments and Adaptation in East Africa, Summary for Policy Makers, USAID PREPARED Project.

Nicholson, SE, Funk, C. and Fink A (2017) Rainfall over the African continent from the 19th through the 21st century, J Global and Planetary Change, In Press.

Funk C, Harrison L, Shukla S, Pomposi C, Galu G, Korecha D, Husak G, Magadzire T, Davenport F, Hillbruner C, Eilerts G, Zaitchik B and Verdin J (2017) Examining the role of unusually warm Indo-Pacific sea surface temperatures in recent African droughts, Q. J of the R. Meteorological Society. In Review, minor revisions.

How understanding climate change contributed to successful prediction of the 2016 and 2017 East African droughts

Effective drought prediction can be enhanced by a clear understanding of the drivers of drought. How we conceptualize climate change influences our ability to identify the fingerprints of change. Together with Simon Wang, Jin-Ho Yoon, and Robert R. Gillies, I have helped edit a new AGU book on ‘Climate Extremes: Patterns and Mechanisms’, examining how climate change may be bringing more extreme events. Recognizing these influences, can improve our ability to anticipate climate extremes. In the chapter I wrote for this book, I discuss how increases in the intensity of both the El Niño-Southern Oscillation (ENSO) and the West Pacific Warming Mode may be making both El Niño-like and La Niña-like extremes more severe. In 2015 and 2016, an anthropogenic enhancement of the 2014-16 El Niño event may have exacerbated the severe droughts that struck northern Ethiopia and Southern Africa. CHG scientists have a BAMS article examining this topic. In 2016 and 2017, extreme West Pacific warming likely contributed to the  severe East African droughts that struck in October-November-December (2016) and March-April-May (2017), resulting  in current near-famine conditions in Somalia (Figure 1) and eastern Ethiopia (Figure 2). Recent Famine Early Warning Systems Network (FEWS NET) reporting finds that in Somalia 2016 and 2017 harvests were very poor (~25% and ~50% of normal, respectively). In many areas of Eastern Ethiopia and Central Somalia, the twelve month June 2016-May 2017 rainfall was the lowest on record (since 1981). Many Pastoralists have lost more than 60% of their herds – a huge loss in livelihood and accumulated wealth. Both Ethiopia and Somalia suffer from outbreaks of cholera, with Somalia experiencing more than 50,000 cases since January 2017.

FEWS NET June-September Food Security Outlook for Somalia
Figure 1. FEWS NET June-September Food Security Outlook for Somalia

“Improved humanitarian access in Somalia, and urgent, sustained assistance in Somalia and southeastern Ethiopia, is needed to mitigate very high levels of acute malnutrition and the threat of loss of life.” (FEWS NET Alert)

FEWS NET June-September Food Security Outlook for Ethiopia
Figure 2. FEWS NET June-September Food Security Outlook for Ethiopia

The situation is dire, but would likely have been worse without humanitarian assistance. In Somalia, US humanitarian assistance doubled between December and January (from assistance for 0.5 to 1 million people), and then doubled again between January and February, reaching 2.4 million in June. While the distribution and quantity of aid could be increased, humanitarian relief is providing life-saving assistance to millions of people; current UN estimates indicate that food aid is reaching about 2.5 million people out of a targeted 3.3 million. An important corollary of this assistance is the stabilization of cereal prices. Between October of 2010 and May of 2011, the prices of red sorghum in the Somali city of Baidoa climbed by 300%. These price increases made it extremely difficult for poor households to purchase food, contributing to ~205,000 drought-related deaths between January and June of 2011 (link). In 2017, by comparison, sorghum prices in Bay increased by 70%, and mortality rates have not increased by the large amounts seen in 2011. Ethiopia and Somalia continue to face very dangerous near-famine levels of insecurity. Water supplies and rangeland conditions are likely to deteriorate as we enter the dry season (July-September). Levels of international assistance remain below the required levels. Nonetheless, timely assistance in 2017, guided by effective early warning, has helped millions of people. The region, however, will almost certainly continue to face severe water and fodder shortages, since the next likely chance of rain will not come until October.

 Effective predictions of the 2016 and 2017 East African Droughts

Here at the Climate Hazards Group, we believe that climate change is making sea surface temperatures more extreme, with hotter Eastern Pacific conditions during El Niños, and warmer Western Pacific conditions during La Niña-like time periods. We also believe that such extreme sea surface temperatures can provide opportunities for prediction. This approach led to our successful prediction of both the 2016 and 2017 East African droughts (Figure 3), as reported here on this blog. In our first posting, from October 9th, we noted that very warm Western Pacific and moderately cool Eastern Pacific sea surface temperatures would likely result in below normal October-November-December rains. This was expressed as a statistical forecast for dry (-1 standardized anomaly) conditions. We also noted that ‘we should be concerned about the possibility of two poor rainy seasons in the spring and fall of 2016’ in Eastern Kenya and Southern Somalia. In the next blog, on November 9th, we included October rainfall in our predictions, noting that Eastern Kenya and Southern Somalia October rains are very highly correlated (r=0.91) with October-November-December rainfall totals.

List of CHG 2016/2017 blog results.
Figure 3. List of CHG 2016/2017 blog results.

In December of 2016 we turned our attention to the 2017 March-April-May season. Our concern was that we might see yet another drought, driven by a combination of persistently warm Western/Northern Pacific sea surface temperatures and cool La Niña-like Eastern Pacific conditions. A statistical model based on observed sea surface conditions performed well, predicting six out of seven of the most recent droughts using that model. We predicted a substantial (-1 standardized anomaly) East African drought. In December of 2016, East Pacific sea surface temperatures were near neutral, while Western/Northern Pacific sea surface temperatures were exceptionally hot. These conditions were quite different than in 2010 (the last severe drought) when both the West Pacific and East Pacific were cooler. In January and February we updated our forecasts, while also engaging in many discussions with our fellow early warning counterparts in the US, Europe and Africa. In January FEWS NET issued an alert suggesting that severe drought, rising prices, limited access and dry forecasts might produce famine in Somalia in 2017. The drought monitoring and climate predictions produced by the East African IGAD Climate Prediction and Applications Centre (ICPAC) during this time period were excellent and accurate; rapidly identifying the severe October-November-December dryness while also predicting below normal 2017 spring rains based on statistically recalibrated global climate model forecasts. In February of 2017, a joint alert was issued by FEWS NET, the World Food Programme, the European Commission, and the UN Food and Agriculture Organization identifying the elevated risk of Somali drought (based in part on a CHG forecast) and calling for ‘urgent and substantial’ provision of food aid and ‘resource mobilization to address the impact of an extended post-2016 lean season’.

In late April of 2017 we analyzed empirical relationships between March-April rainfall and Somali ‘Gu’ sorghum harvests, suggesting that the data indicated that April was by far the most important month for grain filling, and predicting that 2017 ‘Gu’ harvests were going to be very poor (about 50% of normal) based on poor March-April 2017 rainfall. The 2016 and 2017 forecasts have verified. The 2016 and 2017 rainy seasons were poor. Vegetation/pasture were very heavily degraded, as predicted, and the 2017 Gu harvests were low, as we estimated using March-April rainfall observations.

Can we resolve the East African Climate Change paradox?
March-June rainfall anomalies in the eastern ‘Longcycle’ crop growing region of Ethiopia.
Figure 4. March-June rainfall anomalies in the eastern ‘Longcycle’ crop growing region of Ethiopia.

FEWS NET climate change research began in 2003 when in the course of routine analysis we came across severe declines in annual precipitation in agriculturally productive and heavily populated regions of eastern Ethiopia. Figure 4 shows an updated time series of March-June rainfall for this region, through 2017. We see a severe decline in rainfall in a densely populated food insecure area; in the 20 years since 1998; only 5 years have been above normal, based on a 1900-2017 baseline. Our recent papers have also documented increased crop water stress, reduced soil moisture and stream runoff, and declines in vegetation. This drying is part of a wide-spread drying tendency associated with a strong Walker Circulation (abcde), which we believe is related to anthropogenic warming in the Indo-Pacific.  The Walker Circulation is the world’s largest atmospheric circulation feature, and is made up of contrasting cells of ascending air and heavy rainfall near Indonesia and dry descending air over the Eastern Pacific and East Africa/Western Indian Ocean. Steve Baragona’s Voice of America story on the current East Africa drought (here) provides a great animation showing how the Walker circulation contributes to drying over East Africa. Pete Peterson has also produced a nice animation showing how increased rainfall near Indonesia is associated with declining East Africa precipitation (here).

The relationship between climate change and the March-May East African ‘long’ rains has been a topic of considerable debate, largely because climate change models predict that East Africa should already be getting wetter, while observations show that it this region is drying, resulting in the ‘East African Climate Paradox’. This has engendered two basic explanations for the East African drought. According to the first explanation the climate models are wrong, and East African March-May drying is due to low frequency (anthropogenic) warming in the Western Pacific and Indian Ocean, probably exacerbated by natural La Niña-like climate tendencies. According to the second explanation, the models are right, and East African drying is primarily due to an extreme expression of natural decadal variability. Studies focused on observed rainfall (1, 2, 3, 4) and paleo-climate indicators (5, 6) tend to support hypothesis 1. These studies note that the CMIP climate change models fail to represent well the March-May rains (6) while also over-estimating El Niño-related sea surface temperature increases in the Eastern Pacific (7).

We think that climate models are great, but not perfect. They have trouble representing (‘parameterizing’) the exceptionally complex processes associated with tropical precipitation, cloud formation and coupled ocean-atmosphere phenomena like the El Niño-Southern Oscillation. The models tend to overemphasize ENSO-related warming in the Eastern Pacific, leading, we believe to a spurious weakening of the Walker Circulation and increased precipitation over Eastern Africa during March-April-May. Here, we present a data-driven analysis based from a paper (link) that we have just submitted to the Quarterly Journal of the Royal Meteorological Society for a special issue focusing on the research of the International Precipitation Working Group.

Composites of standardized March-May NOAA Extended Reconstruction sea surface temperature observations for East African drought years: 2011, 1984, 2000, 2009, 1999 and 2004. Anomalies based on a 1981-2010 baseline. Values screened for significance at p=0.1.
Figure 5. Composites of standardized March-May NOAA Extended Reconstruction sea surface temperature observations for East African drought years: 2011, 1984, 2000, 2009, 1999 and 2004. Anomalies based on a 1981-2010 baseline. Values screened for significance at p=0.1.

We start by simply plotting global standardized March-May sea surface temperature anomalies during the six driest (1981-2016) eastern East African rainy seasons: 2011, 1984, 2000, 2009, 1999 and 2004. When eastern East Africa is dry, this region of the Western North Pacific tends to be very warm, and these warm sea surface temperature conditions are associated with circulation patterns that intensify the Walker Circulation (Figure 5E,F in link), increasing the Pacific trade winds, increasing rainfall near Indonesia, and bringing dry air down over Eastern Africa.

Scatterplot of rainfall estimates based on West Pacific sea surface temperatures
Figure 6. Scatterplot of rainfall estimates based on West Pacific sea surface temperatures

In 2016 and 2017, we used the negative relationship between Western and Northern Pacific sea surface temperatures and East African rainfall to produce our successful forecasts. Figure 6 shows the relationship between standardized 1998-2017 East African March-May rainfall and rainfall estimates based on sea surface temperatures in the yellow box in Figure 5. While not a perfect predictor, this is a strong teleconnection that correctly predicts all the recent droughts. 2017 is shown with a red dot. Since Nino 3.4 sea surface temperature conditions were actually slightly positive (El Niño-like) in March-May of 2017 (usually associated with wetter than average conditions), the very warm Western North Pacific ocean conditions seem largely responsible for the 2017 East African drought. We refer to this region as a ‘longcycle’ crop growing area because it focuses on high cool areas of eastern Ethiopian highlands, where crop have a long growing cycle, but can produce much higher yields than quicker maturing ‘shortcycle’ varieties.

Observed standardized March-May sea surface temperatures in the Western North Pacific (red) along with estimates of sea surface temperatures changes from a multi-model climate change ensemble (blue).
Figure 7. Observed standardized March-May sea surface temperatures in the Western North Pacific (red) along with estimates of sea surface temperatures changes from a multi-model climate change ensemble (blue).

Figure 7 shows a long time series of standardized March-May Western Pacific sea surface temperatures, along with the corresponding ensemble average standardized sea surface temperatures from a large (53 member) set of climate change simulations from the climate explorer. There is a strong relationship between climate change and sea surface temperatures that explains 40% of the season-to-season variance. The time series has a large (>+1 standardized anomaly) climate change influence, as well as a step-like increase after the 1997/98 El Niño, when East Africa transitioned to drier condi tions.

Interestingly, we can see the cooling influence of the Agung, El Chichon, and Mount Pinatubo volcanic eruptions in 1965, 1982 and 1991-92. These dips indicate that radiation plays an important role in determining Western North Pacific Ocean temperatures. There is also an interannual El Niño influence, with Western North Pacific sea surface temperatures being cooler and warmer during El Niño and La Niña years. We often see recent El Niño events followed La Niña-like climate conditions and increases in West Pacific sea surface temperatures. The 1997/98, 2002/03, 2009/10, 2006/07 and 2015/16 El Niño events have all been followed by warm West Pacific sea surface temperature conditions. We then experienced East African droughts in 1999, 2000, 2001, 2004, 2008, 2009, 2011 and 2017. While more research on this is needed, it seems that El Niño events release energy from the lower ocean that ends up warming the Western Pacific, creating opportunities for prediction.

The substantial post-1997 warming of the Western North Pacific (Figure 7) has been associated with a concomitant decline in East African March-May rainfall. Figure 8 shows 15-year averages of standardized eastern East African March-May rainfall (blue line) along the with regression estimates of eastern East African March-May rainfall based on 15-year averages of observed Western North Pacific sea surface temperatures (blue line) and Western North Pacific sea surface temperatures from a climate change ensemble (purple line) .  Low frequency (15 year average) variations in March-May East African rainfall time series track closely (r=0.7) with estimates based on sea surface temperatures. As the Western North Pacific has warmed, the Walker Circulation has intensified and East African rainfall has declined substantially. Neither East African March-May rainfall nor Western North Pacific sea surface temperatures track closely with the Pacific Decadal Oscillation or smoothed El Niño (Eastern Pacific) sea surface temperatures (Fig. 4 in link). While natural decadal variability probably helped enhance East African precipitation in the 1980s and 1900s, the current substantial decline and low 2017 rainfall outcome appears largely due to anthropogenic warming of the Western North Pacific.

15-year averages of East Africa March-May rainfall (blue), and estimated East Africa rainfall based on observed 15-year Western North Pacific sea surface temperatures (red), and climate change simulations of Western North Pacific sea surface temperatures (purple).
Figure 8. 15-year averages of East Africa March-May rainfall (blue), and estimated East Africa rainfall based on observed 15-year Western North Pacific sea surface temperatures (red), and climate change simulations of Western North Pacific sea surface temperatures (purple).

To resolve the East African Climate Paradox, I would suggest that we can explain most large scale sea surface temperature changes in the Pacific as arising from two patterns of climate variability – the El Niño-Southern Oscillation (ENSO) pattern and the ‘West Pacific Warming Mode’ pattern (JCLIM paper; Chapter in AGU Extremes Book). Both modes of variability are associated with warming, but in different places. ENSO-related warming appears in equatorial Eastern Pacific, associated with strong El Niño events. We have recently argued that anthropogenic warming enhanced the extreme 2015/16 El Niño event, increasing the severity of the 2015 and 2015/16 Ethiopian and Southern African droughts (here). Following El Niño events, we then tend to see large increases in Western Pacific sea surface temperatures, contributing to the 1999, 2000, 2001, 2004, 2008, 2009, 2011 and 2017 East African droughts.

When we focus on how mean sea surface temperatures and precipitation averages are changing in the models, we find substantial discord. The models are predicting a shift towards an El Niño-like climate and increases in East African precipitation.

When we instead focus on how sea surface temperature extremes, and associated precipitation anomalies, are changing in the climate change models, we find substantial consilience. Ironically, ensemble averages of climate change simulations may actually be more prone to biases. Small problems, like the tendency to overestimate the strength of El Niños (cf. Figure 3b JCLIM paper), may strongly influence the ensemble average. Focusing how the models represent extreme events may be more informative, especially in the context of drought early warning.

Differences in Community Earth System Model March-May standardized precipitation for very warm versus warm Western North Pacific sea surface temperatures.
Figure 9. Differences in Community Earth System Model March-May standardized precipitation for very warm versus warm Western North Pacific sea surface temperatures.

The climate change models are predicting that we will experience both more extreme El Niño events and more extreme Western North Pacific events. We explore this in depth in our new paper. In this study, we examine a large (40 member) ensemble of climate change simulations from the Community Earth System Model, and explore the change in precipitation responses associated with very warm versus just warm Western North Pacific sea surface temperatures (Figure 9).  The climate change models predict that we will experience more frequent very warm Western North Pacific sea surface temperature conditions (Figure 7), and that when these conditions arise we will see a stronger Walker circulation (more rainfall near Indonesia), and less rainfall over Eastern Africa and the southwestern Arabian peninsula (Figure 9).

Summing Up

In conclusion, it seems likely that the recent increased frequency of East African March-May droughts are related to warmer Western Pacific sea surface temperatures (Figures 5, 6, 8 and 9), which have warmed substantially due to a combination of anthropogenic climate change and ENSO influences (Figure 7). These droughts appear to be largely predictable, and associated with severe human impacts which can be partially mitigated though humanitarian assistance.

In Somalia in 2010/11, drought, political instability, violent conflict, and global food price volatility resulted in 258,000 deaths, with 133,000 of these deaths being children under five years old (link). In 2017, Somalia and eastern Ethiopia again face one of the most severe droughts on record (here).

In Eastern Ethiopia millions of people of people face severe hunger. Herd sizes have been dramatically reduced; “households have few, if any, livestock to sell and … milk availability will remain very low in 2017”. Some 1.7 million people are estimated to be facing severe food shortages, associated with 20-50% caloric deficiencies. Unless food aid allocations are increased, caloric deficits may exceed 50%, and “poor households in the worst-affected pastoral areas will begin to move into Catastrophe (IPC Phase 5) and acute malnutrition and mortality may rise further” (FEWS NET alert, July 19th). In Somalia, the most recent assessments by the Food Security and Nutrition Analysis Unit – Somalia (FSNAU report) identify more than 50,000 cases of Acute Watery Diarrhea/Cholera, and very high levels of physical wasting among children in camps holding internally displaced persons (IDPs). July 2017 FSNAU estimates indicate that some 3.24 million people face crisis or emergency levels of food insecurity. Since February, emergency food aid assistance has been scaled up from aid for 1 million people to aid for 2.4 million in June of 2017. This assistance has helped, but more aid is needed, and access to humanitarian assistance in many areas in central and southern Somalia remains a challenge.

Time series of March-May rainfall from eastern East Africa.
Figure 10. Time series of March-May rainfall from eastern East Africa.

While the food security crises in eastern Ethiopia and Somalia have been caused by many factors in addition to drought, the long term decline of the March-May East African long rains (Figure 10) has certainly contributed to food insecurity in this region. The 1900-2017 time series shown in Figure 10 has been produced by combining 1900-1980 Centennial Trends precipitation data with the 1981­­-2017 CHIRPS archive. Both of these data sets benefit from a high quality collection of rainfall gauge observations, and there is a high level of agreement between these observations during their period of overlap (1981-2014), with a correlation of 0.94. Figure 10 shows standardized March-May rainfall anomalies, with a value of -1 indicating a poor season. Of the nineteen years since 1999, eight seasons have been poor (1999, 2000, 2001, 2004, 2008, 2009, 2011 and 2017), and fourteen seasons have been below normal, based on a 1900-2017 baseline. These eight dry seasons have been associated with very warm Western North Pacific sea surface temperatures (Figure 6). Anthropogenic climate change has helped produce these warm sea surface temperature conditions (Figure 7). Estimating the influence of Western North Pacific warming on rainfall (Figure 9) indicates a strong negative (~-0.7 standardized anomalies) rainfall response, similar in magnitude to results from a previous experiment using the CAM5 atmospheric model (here). Anthropogenic climate change has likely contributed to the current March-May drought and the increased frequency of poor March-May rainfall outcomes. Many of these droughts, however, appear to be predictable.