Category Archives: Forecasting

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 https://earlywarning.usgs.gov/fews. Right: NOAA CPC ARC2 October to December 2018 rainfall anomaly.  Figure from CPC Africa Desk. http://www.cpc.ncep.noaa.gov/products/international/africa/africa.shtml

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 http://blog.chg.ucsb.edu/?p=443

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:

http://chg.geog.ucsb.edu/data/chirps/

http://www.nature.com/articles/sdata201566

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:

http://chg-ewxtest.chg.ucsb.edu

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

ftp://ftp.chg.ucsb.edu/pub/org/chg/products/EWX/data/forecasts/CHIRPS-GEFS_precip/10day.

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:

http://chg.ucsb.edu/forecasts/gefs-chirps/corr.html 

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

 

Using CHIRPS-GEFS

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 (http://chg-ewxtest.chg.ucsb.edu/) 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,  ftp://ftp.chg.ucsb.edu/pub/org/chg/products/EWX/data/forecasts/CHIRPS-GEFS_precip/10day, 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. 

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 zaitchik@jhu.edu

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 https://earlywarning.usgs.gov/fews/product/597.

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.

Average to above average Blue Nile River flow expected in 2018

A 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 zaitchik@jhu.edu

 

Ethiopia will soon begin filling the reservoir of the largest hydropower dam in Africa, the Grand Ethiopian Renaissance Dam (GERD). Construction of the GERD has been highly controversial because it sits on the Blue Nile River (Figure 1), which provides about 60% of total natural Nile flow into Egypt. Egypt relies almost exclusively on the Nile River for its renewable freshwater supply. When complete, the GERD will rise 155 meters tall, have an installed generating capacity of over 6,000 megawatts, and create a 74 cubic kilometer reservoir. This dam will be the first major infrastructure project in Ethiopia on the main stem of the Blue Nile.

Figure 1: The Nile Basin (yellow), including the GERD site (orange star) and GERD catchment (green)

The history, politics, and current diplomatic impasse regarding the construction and operation of the GERD have been covered extensively and sometimes breathlessly in media outlets around the world (for example: here, there and everywhere). We won’t get into those issues here, but suffice it to say that there is reason for concern. This is true both because of the actual hydrological impacts that filling of the GERD reservoir might have on downstream countries and because the dam has become a focal point for broader geopolitical tensions. In this context, we ask a simple forecast question: what is the status of seasonal forecasts of Blue Nile River flow? Few would disagree that filling the reservoir during wet years would lead to fewer acute impacts on downstream countries. While it is not clear whether Ethiopia will pursue a forecast-based filling policy (valuable as that might be!), we believe it would be useful for interested Parties to share a similar understanding of how the rainy season is likely to unfold in each year of the filling period if and when they engage in discussions of filling plans and responses.

With this as motivation, this post will: (1) offer a brief review of the drivers of rainfall variability in the Ethiopian Blue Nile basin; (2) describe existing statistical and dynamically-based forecast systems; and (3) offer an informal ensemble prediction for 2018 flows. The objective of this prediction is not to supplant existing forecast systems in the region. High quality meteorological seasonal forecasts are available from the Greater Horn of Africa Climate Outlook Forum (GHACOF) as well as from the Ethiopian National Meteorological Agency and other nations’ forecast centers. Rather, we aim to present a broad range of predictions generated using different methods, and generated in the absence of any perception of subjective input.

Punchline

For those in a hurry: our multi-method seasonal forecast ensemble shows a strong likelihood of average to above-average Blue Nile basin rainfall and associated Blue Nile River flows at the GERD site in 2018 (Figure 2). The result holds true across statistical forecasts and dynamically-based forecasts (Figure 3). This is consistent with regional seasonal outlooks issued by several independent modeling centers. If the Ethiopian government does begin to fill the reservoir this year, then, these forecasts suggest that there is a low probability that reasonable filling activities would lead to catastrophically low flow in Nile River downstream. This seasonal outlook does carry substantial uncertainties, as we describe below.

Figure 2: Percentage of forecasts in this study predicting below normal, near normal, and above normal June-September rainfall (left) and June-December streamflow at the GERD site (right) for 2018, including eight NMME models (ensemble mean for each model) and eight statistical models. NMME forecasts are adjusted for mean biases. No variance adjustment was applied to any of the model forecasts.

Figure 3: (A) Boxplots of June-September historical rainfall, 1982-2017, according to CHIRPS rainfall estimates (grey), and of the 2018 forecasts for dynamically-based models participating in the North American Multimodel Ensemble (NMME; green) and for statistically-based models applied in this study (blue). Dashed lines show the upper and lower terciles of historical rainfall totals. (B) As in (A), but for the June-December forecast Blue Nile flow at the GERD site.

Drivers of rainfall variability in the Blue Nile basin

The Ethiopian Blue Nile (or Abay River, to Ethiopians) is located in the western portion of the country, in a region that gets the vast majority of its rainfall in the summertime kiremt season, between June and September. The flow of the Nile at the GERD site is highly seasonal, following the rains. Peak flows are most commonly seen in October (Figure 4A). Precipitation in the Blue Nile is variable from year to year (Figure 4B), and this rainfall variability is associated with strong interannual variability in Blue Nile flows (Figure 4C). The coefficient of variation for annual rainfall in the basin is on the order of 6%, while the coefficient of variation in annual flow near the GERD is close to 20%.

Figure 4: (A) climatological monthly average rainfall for the GERD catchment area (CenTrends data) and climatological monthly average Blue Nile River flow at El Diem, near the GERD site; (B) density function of annual total CenTrends rainfall for the GERD catchment; (C) as in (B), but for El Diem flows. For all plots rainfall data are for the period 1965-2009, constrained by our access to flow data

 

The effort to characterize, explain, and predict this variability dates back thousands of years, to the Nilometers of Ancient Egypt and the stories of the Bible. It has also been an active focus of modern climate research, and we won’t attempt a review here; see Berhane et al. (2013) or Nicholson (2017), among others, for more comprehensive treatment. Some prediction-relevant highlights from the literature on drivers of Blue Nile variability are:

  1. The identification of a tropical Indian Ocean influence on East Africa summer rains, in which low pressure in the Indian Ocean enhances the flow of moist near-surface westerly winds across the Congo Basin and into Eastern Ethiopia.
  2. Complementary work on the westerly wind influence that identifies potential predictors in surface or near-surface pressure anomalies in the tropical Atlantic and the Arabian Peninsula, or the gradient between the two.
  3. Evidence that the southern hemisphere subtropical high pressure centers—the Mascarene in the Indian Ocean and the St. Helena in the Atlantic Ocean—influence the inflow of water vapor to the region.
  4. Studies showing that the strength and location of the Tropical Easterly Jet (TEJ) and, to some extent, the African Easterly Jet (AEJ) modify convection in the region. Links between the TEJ and the Quasi Biennial Oscillation (QBO) have raised the possibility of QBO as a predictor of Blue Nile flow.
  5. Perhaps most prominently, extensive study of the influence that the El Niño Southern Oscillation (ENSO) has on summertime Ethiopian rainfall, both synchronously and with a time lag. The ENSO influence is preeminent in operational statistically-based prediction systems, and its impact is clear in almost any analysis of rainfall variability (e.g., Figure 5). During the summer rains, El Niño events are associated with below average rainfall; this is the opposite of the well-known influence ENSO has on the eastern Horn of Africa and Equatorial East Africa during the October-December short rains.  The mechanism of the summertime ENSO influence, however, is still debated, and could be multifaceted. Influences of ENSO on subtropical highs, on water vapor advection, and on the TEJ and AEJ have all been proposed.

Figure 5: annual rainfall anomaly (mm) associated with El Niño relative to non-El Niño years. Precipitation data are from CHIRPS for the period 1981-2014. El Niño years are defined in terms of Niño 3.4 anomaly.

This partial list of drivers of variability points to the potential for teleconnection-based prediction in the region. But it also highlights the complexity of interacting climate dynamics that affect the Blue Nile basin (Figure 6). These influences also appear to vary over the course of the season. Berhane et al. (2013) find that the influence of ENSO and of Indian Ocean predictors tends to be strongest late in the rainy season and non-significant early in the season. Further, the influences are non-stationary in time. The ENSO teleconnection, for example, which is central to many prediction systems, was stronger prior to 1976 than afterwards, and recent warming in the western Pacific and Indian Oceans has had a substantial impact on rainfall across East Africa.

Figure 6: schematic map of some of the leading proposed influences on the Ethiopian summer rains. ENSO = El Niño Southern Oscillation, AEJ = African Easterly Jet, TAH = Tropical Atlantic High, SHH = St. Helena High, green arrow = Mediterranean Sea water vapor import, AL = Arabian Low, SAM = South Asian Monsoon, TEJ = Tropical Easterly Jet, IOSST = Indian Ocean SST, MH = Mascarene High.

Existing seasonal forecast models

The importance of rainfall in East Africa has inspired many efforts at seasonal prediction, including a number that are directly relevant to Blue Nile flow forecast. The majority of these efforts were undertaken for research and are not currently operational, while a smaller number are available as operational forecast systems. We divide these efforts into statistically-based forecast models, which are based on observed relationships between rainfall (or river flow) and time lagged large scale predictors in the climate system, and dynamically-based forecast systems, which employ global climate models to forecast atmospheric conditions and surface meteorology at lead times of weeks to months.

Most of the published statistical models of Ethiopian summertime rainfall–the season that matters most for Blue Nile flows–derive their skill all or in part from associations with ENSO and/or Indian Ocean surface conditions (Table 1). Gissila et al. (2004) leveraged these teleconnections to generate a nine predictor linear regression model in which summertime Ethiopian rainfall is positively associated with western Indian Ocean SST and negatively associated with SST in the tropical Pacific Ocean (Niño 3.4 region). This is consistent with empirical relationships and proposed mechanisms described above.  Korecha & Barnston (2007) take a slightly different approach, using the evolution of Niño 3.4 SST and tropical Atlantic Ocean SSTs in the months leading up to the rainy season as predictors, alongside the late spring Niño 3.4 SST. Their predictive regression model indicates that springtime cooling in the Niño 3.4 region is associated with more precipitation, perhaps because this metric captures a trend away from El Niño (or a trend into La Niña) conditions in the months leading up to the onset of rains.

Nicholson (2014) included atmospheric fields in her identification of predictors. This led to a three variable linear regression model in which summertime rains in East Africa are a function of zonal wind strength in the TEJ exit region, SST gradients in the tropical and subtropical Pacific Ocean, and tropical Indian Ocean SLP. Segele et al. (2015) cast a wide net for predictors, generating an ensemble of multivariate regression models from a family of potential atmospheric and SST predictor fields. The Ethiopian National Meteorological Agency use a statistical analog approach based on ENSO indices. Historical analog years are selected based on similarity in a family of ENSO indicators diagnosed both from observed and forecast ENSO conditions for the coming rainy season. These analog years are then applied to forecast seasonal total rainfall terciles for homogenous regions across Ethiopia and also to forecast second order properties of the rainy season such as onset and cessation of rains and probability of extreme events (Korecha & Sorteberg, 2013).

A number of global dynamically-based seasonal forecast systems are also available for application to Blue Nile forecast. The raw output of some of these dynamically-based systems are publicly available (notably all models participating in the North American Multi-Model Ensemble (NMME); Kirtman et al. 2014), and all centers generate products that can be incorporated into value-added analyses by operational forecasting organizations. Analysts with the Famine Early Warning System (FEWS), for example, generate outlooks that take into account dynamically-based predictions from NMME, the European Centre for Medium-range Weather Forecasts (ECMWF), and the United Kingdom Meteorological Service (Met UK), along with ancillary climate data and expert opinion. The Greater Horn of Africa Climate Outlook Forum (GHACOF) generates probabilistic consensus forecasts by merging multiple dynamically-based and statistical forecasts, based heavily on input from participating national meteorological agencies and the IGAD Climate Prediction and Application Center (ICPAC). The International Research Institute for Climate and Society (IRI) produces a widely used forecast based on bias corrected and weighted ensembles of dynamically-based forecast systems.

Here we examine as many of these statistically and dynamically-based forecasts as we had information and time to pursue, both to see how the models have performed in recent years and to provide an outlook for the 2018 rainy season. For statistical models that are not operational we adopted the predictors and model structure from the literature but refit all coefficients based on a consistent suite of datasets, updated to as recently as the data allowed. For operational systems that have issued their own 2018 predictions we simply report the publicly issued forecasts.

Models used in this study

Precipitation

The statistical models used in our analysis are listed in Table 1. In all cases we used June-September total precipitation from CHIRPS as the predictand. Models were fit using a combination of predictors from NCEP/NCAR reanalysis product and NMME lag-0 forecast products (forecasts for May, made in May). The NMME lag-0 fields were used for any May predictors required for 2018 June-September prediction, since latency in the reanalysis product makes it impossible to use May reanalysis fields for an operational prediction that is issued by the beginning of the rainy season. Leave-one-out cross-validation was used when fitting these models to optimize for out-of-sample predictive accuracy.  The period of retrospective analysis was 1982-2017, the longest record for which we could obtain output from both statistical models and dynamically-based prediction systems. Recognizing that teleconnections might have shifted over this 36 year period, we also fit one model using the shorter 2001-2017 record in order to see how changes post-2000 might influence model skill (Blum-2 in Table 1). Our analysis of dynamically-based forecast systems is limited to models in the NMME ensemble, as those were the only models for which we could easily obtain both historical and operational forecasts. The years 2011 and 2017 were not available for all NMME models included in the analysis, so the evaluation period was restricted to 1982-2010 plus 2012-2016. Precipitation from NMME models was bias corrected to CHIRPS prior to evaluation.

Table 1: Statistical models used in this study. P_JJAS=Sum of precipitation for June, July, August and September; SST=Sea Surface Temperature; All variables (predictors and predictand) were standardized (subtract mean and divide by standard deviation 1982-2017). Note that in applying previously published models we retain their structure and list of candidate predictors, but coefficients and, in some cases, selected variables are different from the originally published versions.

Model/Paper Model Structure Predictor variables
Gissila et al. (2004) Multivariate linear regression March, April, May SST for tropical western Indian Ocean, 10ºS-10ºN, 50-70ºE, tropical eastern Indian Ocean, 10ºS-0,90º-110ºE and Nino3.4
5ºS-5ºN, 170ºW-120ºW
Korecha & Barnston (2007) Multivariate linear regression The difference of May minus February–March SSTs over the south Atlantic, 30°S–40°S, 30°W–15°W
The difference of May minus the February–March Niño-3.4 SST, 5°N–5°S, 170°W–120°W
May Niño-3.4 SST, 5°N–5°S, 170°–120°W
Nicholson (2014) Multivariate linear regression May u 200 hPa, 30ºE-50ºE and 0º -10ºN
May difference in SST:

(170º -265ºE, 5ºS – 5ºN) – (137ºE-160ºE, 18ºN – 28ºN)

May sea level pressure (SLP) for 80º -90ºE,  5º 15ºN
Segele et al. (2015) Mean of 11

Multivariate linear regression models (Fig.5 in Segele et al. 2015)

20 predictors from Table 1 in Segele et al .(2015)
Wu – this study Linear Inverse Model May, June, July, Aug and Sep EOFs of Local Precipitation, Tropical Pacific SST, Tropical Atlantic SST, Indian Ocean SST, North Pacific SST, North Atlantic SST,

Southern Ocean SST, Global 850 hPa and 200 hPa Geopotential height

Alexander – this study Principal Component Regression   First two principal components of May Tropical Pacific Ocean SST, March-Feb Central Indian Ocean SST, April Tropical Atlantic SST, and Indian Monsoon SLP Index
Blum-1 – this study Multivariate linear regression May SST for tropical western Indian Ocean, 10ºS-10ºN, 50ºE-70ºE

May sea level pressure (SLP) for 80ºE – 90ºE, and 5ºN – 15ºN

May difference in SST: (170ºE – 265ºE, 5ºS – 5ºN) – (137ºE-  160ºE, 18ºN – 28ºN)

Southern Europe air temperature at 100hPa, 10ºE-15ºE, 45ºN-50ºN

North Arabian Sea March meridional temperature flux at 925 hPa, 60ºE-65ºE, 20ºN-25ºN

Blum-2 – this study Multivariate linear regression (fit using 2001-2017 data) Eastern Indian Ocean zonal geopotential height flux at 300 hPa, 80º-85ºE, 5ºS-0º

Arabian Sea meridional moisture flux at 925 hPa, 60º-65ºE,10º-15ºN

North tropical Pacific SST, 171ºE-175ºE, 16ºN-18ºN

Southeastern Europe zonal temperature flux at 100hPa, 25ºE-30ºE, 40ºN-45ºN

Historical model performance for prediction of June-September precipitation is summarized in Table 2, corresponding to the hindcasts plotted in Figure 7. This summary is intended to give a general perspective on performance rather than a detailed or exhaustive assessment of model behavior. We consider the anomaly correlation coefficient (ACC) as a measure of linear relationships between predictions and observations independent of mean and variance, root mean square error (RMSE) of the anomalies to quantify errors in variance, and bias. We also quantify the Hit Score for predictions, which is calculated by dividing the observational record into three terciles (below normal, near normal, and above normal) and calculating the fraction of predictions that fell into the correct category over the period of analysis. To focus on extremes, we include both a standard Hit Score and a Hit Score for Extremes, which considers only the hit rate for top and bottom tercile events. All scores are for cross-validation out-of-sample predictions, rather than for model fit.

Figure 7: 1982-2017 hindcasts for June-September Blue Nile basin precipitation. Grey lines are statistical models, with the solid black line showing the ensemble average of statistical models applied in this project. Blue lines are NMME forecasts initialized in May of each year–each light blue line is the ensemble average of a single model and the dark blue line is the full NMME ensemble average for all models that had complete data records. Dashed black line is CHIRPS June-September precipitation anomaly averaged across the Blue Nile basin.

Table 2: Evaluation of statistical and dynamically-based hindcasts for the period 1982-2017 (for NMME models only 1982-2010 and 2012-2016 were available for all models).

Models ACC RMSE (mm) BIAS (mm) HitScore HitScore_Extremes
Gissila2004 0.36 72.7 -2.1 0.50 0.52
Korecha2007 0.52 62.3 -0.1 0.44 0.52
Ncholson2014 0.56 60.1 0.4 0.47 0.52
Segele2015 0.47 64.8 1.0 0.47 0.52
Alexander – this study 0.70 52.2 -3.5 0.61 0.70
Blum-1 – this study 0.70 51.7 1.0 0.58 0.65
Blum-2 – this study 0.43 67.1 1.1 0.39 0.48
Wu – this study 0.66 55.7 -1.4 0.64 0.70
Mean Statistical Model 0.66 56.7 -2.8 0.58 0.65
CMC1-CanCM3 0.23 89.5 485.8 0.35 0.45
CMC2-CanCM4 0.58 95.3 547.8 0.59 0.64
COLA-RSMAS-CCSM4 -0.08 80.3 -284.8 0.29 0.27
GFDL-CM2p1-aer04 0.29 73.5 -113.5 0.38 0.41
GFDL-CM2p5-FLOR-A06 0.19 72.3 98.3 0.47 0.50
GFDL-CM2p5-FLOR-B01 0.41 65.6 84.3 0.56 0.59
NASA-GEOSS2S 0.49 105.7 -281.1 0.44 0.55
NCEP-CFSv2 0.33 84.4 103.6 0.44 0.50
Mean NMME 0.55 57.9 80.1 0.62 0.68

 

Table 3: Evaluation of statistical and dynamically-based hindcasts for the period 2001-2017 (for NMME models only 2001-2010 and 2012-2016 were available for all models).

Models ACC RMSE (mm) BIAS (mm) HitScore HitScore_Extremes
Gissila2004 0.12 77.3 -12.9 0.39 0.36
Korecha2007 0.37 62.9 -18.1 0.28 0.36
Ncholson2014 0.4 61.3 -12.5 0.33 0.36
Segele2015 0.14 67.0 -18.0 0.44 0.45
Alexander – this study 0.77 42.3 -15.2 0.67 0.73
Blum-1 – this study 0.49 58.7 1.3 0.39 0.36
Blum-2 – this study 0.45 60.5 -9.1 0.33 0.36
Wu – this study 0.69 48.5 6.1 0.61 0.64
Mean Statistical Model 0.72 51.3 13.0 0.44 0.55
CMC1-CanCM3 0.57 71.2 461.8 0.38 0.5
CMC2-CanCM4 0.54 85.3 536.2 0.62 0.7
COLA-RSMAS-CCSM4 -0.4 81.4 -306.9 0.31 0.2
GFDL-CM2p1-aer04 -0.05 82.1 -133.3 0.62 0.6
GFDL-CM2p5-FLOR-A06 -0.2 73.5 82.5 0.38 0.3
GFDL-CM2p5-FLOR-B01 0.32 63.8 69.9 0.44 0.5
NASA-GEOSS2S 0.31 95.3 -245.9 0.5 0.5
NCEP-CFSv2 0.32 87.9 68.2 0.31 0.4
Mean NMME 0.54 56.3 66.6 0.63 0.7

 

Our evaluation of NMME members confirms previous work with ensembles of dynamically-based prediction systems, in that performance is quite mixed across different models. The NMME ensemble average prediction, however, shows predictive skill that is comparable to many of the statistical models, and the NMME hit rate is as good as the top performing statistical models. Table 2 shows large bias values for some NMME models prior to bias correction, but this does not affect the application of the models to study anomalies. We note that these are direct NMME forecasts of precipitation in the GERD watershed. We did not consider “hybrid” approaches here, in which dynamically-based forecast systems are used to predict the predictors for a statistical model (e.g., Shukla et al. 2014, Gleixner et al. 2017), and which represent another powerful way to make use of dynamically-based models.

For the statistical models, we find that skillful predictions can be obtained using a number of different model structures and sets of predictors. All models rely to some extent on associations with surface conditions in the Tropical Pacific and/or Indian Ocean, reflecting the known importance of lagged ENSO connections and Indian Ocean conditions to the region. Skillful models also generally include some combination of predictors that capture the influence of Tropical Atlantic conditions or of atmospheric predictors that capture synoptic dynamics affecting East Africa, though the combination of these predictors varies by model. This is consistent with the recent work of Nicholson (2014) and Segele et al. (2015) who showed that diversifying predictors beyond the traditional ENSO and Indian Monsoon zones can enhance predictive skill. The fact that skill can be obtained using different combinations of these predictors makes it difficult to apply these prediction-oriented models to advance fundamental understanding of the drivers of summertime rainfall variability. These mechanisms, including the relationship between correlated predictors in these models, is the subject of ongoing research in the climate community. The models included here show some range in statistical performance. In general, the models that were customized for Blue Nile basin rainfall and developed using more recent data records show slightly higher skill for this application. A prediction based on the mean forecast of all statistical models fares well in both evaluation time periods.

While the 1982-2017 baseline has the advantage of offering a 36 year record for model evaluation, we do not necessarily expect that model skill is stationary over this full period. Indeed, the strength of various teleconnections to East Africa is known to vary as a function of climate variability, and over the past 37 years it is also possible that global warming has altered climate dynamics relevant to prediction. For this reason we also evaluate all models over the shorter 2001-2017 period (Table 3). Evaluation for this shorter record is not as statistically robust, but it offers a view of how well models are doing under more current conditions. All of the previously published statistical models show poorer performance for 2001-2017 than they do for the whole record. This is not surprising, as these models were developed without using the most recent years that are now available; we recalculated the coefficients using these recent years, but we did not revisit model structure. The magnitude of the drop-off is rather large, however, which is consistent with the observation that correlation with previously reliable predictors in the Pacific Ocean has been inconsistent since 2000. Several of the models we developed specifically for this study maintain their level of skill when tested against this more recent period. NMME models show some changes in skill for the 2001-2017 period, but these changes show no evidence of being systematic, at least within the limits of this cursory analysis. In sum: statistical approaches provide meaningful skill for seasonal forecast of Blue Nile basin rainfall, though there is considerable unexplained variance for all modeling approaches. The NMME models vary widely in their performance, but the NMME ensemble average offers respectable performance relative to many statistical approaches.

Blue Nile flow

Both the statistically and dynamically-based models described above predict rainfall. To convert a rainfall forecast to a Blue Nile streamflow forecast we adopted a variation of the Water Balance model (WatBal; Yates 1996). It takes monthly precipitation (P), and climatological monthly mean temperature (T) and diurnal temperature range (DTR) to produce monthly streamflow within a river basin. CRU TS data are used for climatological T and DTR. CenTrends and CHIRPS data are used for precipitation. Note that in order to take the predicted JJAS total precipitation as the updated inputs to produce the predicted streamflow, the model is calibrated using the disaggregated historical JJAS monthly P and the climatology of T and DTR. Climatological rainfall was used for Oct-Dec of 2018, as there is little rain in these months.

The calibrated WatBal model offers strong performance in the historical record. The monthly time series show a correlation of 0.96 (p-value <.0001) between the observation and calibration (Figure 6A). For interannual variability on  total flow the correlation is 0.77 (p-value <.0001). The scatter plot of monthly observation and calibration also aligns with the 45 degree line (Figure 6B). However, in general the calibrated streamflow shows an overestimation during early months of the calendar year while an underestimation during the later months (Figure 6C). Accumulatively, the calibrated streamflow is approximately 4% higher than the observed streamflow annually. Flow estimates generated by applying precipitation hindcasts to the hydrological model are shown in Figure 6D.

Figure 8: (A) comparison of observed streamflow at El Diem and simulated calibrated streamflow from WatBal, in units of million cubic meters (Mm3); (B) scatter plot of observed monthly streamflow at El Diem (x-axis) and calibrated streamflow from WatBal (y-axis); (C) monthly average streamflow based on observations at El Diem and calibrated streamflow from WatBal. Data for all plots is 1965-2009; (D) hindcasts of June-Dec total Blue Nile flow at the GERD site (Mm3) generated by applying statistical (grey lines) and dynamically-based (blue lines) precipitation hindcasts to the WatBal model. Dashed black line in (D) is the result of historical WatBal simulation driven with CHIRPS rainfall.

The outlook for 2018

The outlook for 2018 is highly informed by the fact that we are currently in the waning months of a La Niña event, which is expected to fade to neutral conditions that will persist through the summer rains (Figure 8). Since most droughts in the Blue Nile basin occur during El Niño events, current conditions and the majority of projections through the season suggest that drought is unlikely to occur. There is, however, the potential for El Niño conditions to emerge before the end of the season, which might affect late season rains in a manner that the statistical models considered here do not directly capture. Many factors play into climate variability in the Blue Nile basin, but the absence of El Niño provides a strong precondition towards average or above average Blue Nile flows.

Figure 9: Oceanic Niño Index forecast for 2018, generated by NOAA CPC and the International Research Institute (IRI) and issued on May 14, 2018.

As shown in Figure 2, both statistical and dynamical models suggest that there is a strong likelihood of average to above average rainfall in the Blue Nile basin in the 2018 summer rainy season. The predictions are not uniform, however. There is a distribution in statistical models that includes three models with a median prediction that is in the upper tercile of the historical record, four models that are in the middle tercile, and one that is in the lower tercile (Figure 10). For NMME, predictions for seven of the eight models we were able to obtain have a median forecast in the top tercile. There is, however, considerable spread in the absolute forecast of the NMME models.

Figure 10: Boxplots illustrating range of NMME ensemble predictions and statistical models with re-sampled error added back in. Dashed lines show below/above normal conditions. Statistical boxes represent range of 2018 forecast plus 100 resampled errors (from 1982-2017 leave-one-out predictions).

The spread shown in Figure 10 is a combined result of differences in rainfall variability between models–GEOS_S2S, for example has a large interannual standard deviation in precipitation, and Figure 10 shows bias corrected but not standardized forecasts–and of differences in the forecast relative to each model’s historical record. Standardized anomalies range from 0.5 to > 2.0 standard deviations above the historic mean. We chose not to standardize when presenting results because the majority of models have variability that is smaller than CHIRPS observed interannual variability. This means that for a wet forecast like 2018, presenting the absolute rather than variance-adjusted anomalies represents a conservative error when assessing the potential volume of excess flow predicted to be available. Ensemble spread across realizations of each NMME model is also substantial, with select ensemble members of several models yielding below average precipitation forecasts. The uncertainty in both statistical and dynamically-based models is clear in the smoothed histograms shown in Figure 11, which indicate that both families of forecasts include some members that allow for below average conditions to develop in 2018, even if it is a low probability.  Figure 12 shows the same smoothed distribution for streamflow estimates.

Figure 11: Smoothed histograms showing climatology (1982-2017) compared to statistical and NMME predictions for 2018 June-September rainfall. Dashed lines show below/above normal conditions. Note: climatology (n=37),NNME (n=8),statistical (n=8)

Figure 12: As in figure 11, but for June-December streamflow at the site of the GERD. Note: climatology (n=37),NMME (n=8),statistical (n=8)

These precipitation forecast results can be compared to those issued by operational forecasting institutions. The dynamically-derived IRI forecast of May 15, 2018, for example, shows somewhat elevated probability of high rainfall over much of the Blue Nile basin (Figure 13). The NOAA Climate Prediction Center (CPC) categorical forecast, calculated from the full, unweighted NMME ensemble, also shows a slightly elevated probability of above average rainfall in Ethiopia (Figure 14). ECMWF June-August precipitation forecasts are for normal rainfall conditions in the Blue Nile region. The GHACOF and Ethiopia NMA forecasts were not available at time of writing, but we will update to include them when they go online.

Figure 13: IRI July-September precipitation forecast, derived from weighted analysis of dynamically-based forecast systems. Red box indicates approximate location of the GERD watershed.

Figure 14: CPC categorical predictions for July-Sep precipitation in the May NMME ensemble.

Conclusion

While seasonal precipitation and hydrological forecasts of the Blue Nile River carry substantial uncertainties, the relative consistency of the 2018 prediction across models of different origin and structure provides some confidence that there is a high probability of average to above average flow in the coming season.  The sampling of models considered here suffer from inconsistent performance in recent years, sensitivity to calibration period (for statistical models) and to uncharacterized spatial biases (for dynamically-based systems), and the inherent limitations that come when forecasting an imperfectly understood system. Nevertheless, the consensus outlook is consistent with (and, indeed, driven in large part by) the understanding that drought in this region usually coincides with El Niño events, and we are currently experiencing a fading La Niña, with ENSO neutral conditions likely in the coming months. We do note that the ENSO teleconnection to the Blue Nile basin is not fully understood and appears to be inconsistent over time. This contributes to forecast uncertainty, and is an important area of ongoing research.

This exercise has been motivated in large part by the fact that Ethiopia will soon begin to fill the reservoir of the Grand Ethiopian Renaissance Dam. This is a major hydrological activity with considerable economic, social, and political implications. Decisions regarding the timing and rate of filling clearly depend on many factors that fall beyond the scope of seasonal forecast. Nevertheless, we feel that it it is valuable for Parties involved in filling decisions to have a common and realistic set of expectations for water availability in each year of the filling period. Consensus seasonal hydrological forecasts can inform those expectations on a year by year basis, perhaps easing one source of tension as riparian countries engage in the shared challenge of managing a transboundary waterway under rapid development.

References

Berhane, F., Zaitchik, B., & Dezfuli, A. (2014). Subseasonal analysis of precipitation variability in the Blue Nile River Basin. Journal of climate, 27(1), 325-344.

Gissila, T., Black, E., Grimes, D. I. F., & Slingo, J. M. (2004). Seasonal forecasting of the Ethiopian summer rains. International Journal of Climatology, 24(11), 1345-1358.

Gleixner, S., Keenlyside, N. S., Demissie, T. D., Counillon, F., Wang, Y., & Viste, E. (2017). Seasonal predictability of Kiremt rainfall in coupled general circulation models. Environmental Research Letters, 12(11), 114016.

Kirtman, B. P., Min, D., Infanti, J. M., Kinter III, J. L., Paolino, D. A., Zhang, Q., … & Peng, P. (2014). The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bulletin of the American Meteorological Society, 95(4), 585-601.

Korecha, D., & Barnston, A. G. (2007). predictability of June–September rainfall in Ethiopia. Monthly weather review, 135(2), 628-650.

Korecha, D., & Sorteberg, A. (2013). Validation of operational seasonal rainfall forecast in Ethiopia. Water Resources Research, 49(11), 7681-7697.

Nicholson, S. E. (2014). The predictability of rainfall over the Greater Horn of Africa. Part I: Prediction of seasonal rainfall. Journal of Hydrometeorology

Nicholson, S. E. (2017). Climate and climatic variability of rainfall over eastern Africa. Reviews of Geophysics.

Segele, Z. T., Richman, M. B., Leslie, L. M., & Lamb, P. J. (2015). Seasonal-to-interannual variability of ethiopia/horn of Africa monsoon. Part II: Statistical multimodel ensemble rainfall predictions. Journal of Climate, 28(9), 3511-3536.

Shukla, S., Funk, C., & Hoell, A. (2014). Using constructed analogs to improve the skill of National Multi-Model Ensemble March–April–May precipitation forecasts in equatorial East Africa. Environmental Research Letters, 9(9), 094009.

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

by Chris Funk

Introduction

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.

Describe

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: ftp://ftp.chg.ucsb.edu/pub/org/chg/people/chris/papers/Revised_Relating_Recent_Droughts_to_Extreme_SSTS_v6.pdf.

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.

Explain

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.

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, http://pubs.usgs.gov/of/2010/1199/pdf/ofr2010-1199.pdf

Funk, C., Eilerts, G., Davenport, F., and Michaelsen, J., (2010) A Climate Trend Analysis of Kenya-August 2010, USGS Fact Sheet 2010-3074:    http://pubs.usgs.gov/fs/2010/3074/pdf/fs2010-3074.pdf 

Funk, C. (2011) We thought trouble was coming, Nature Worldview, 476.7. http://www.nature.com/news/2011/110803/full/476007a.html

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. http://www.preventionweb.net/english/hyogo/gar/2011/en/bgdocs/Galu_Kere_Funk_&_Husak_2010.pdf

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. http://dx.doi.org/10.1175/BAMS-D-12-00021.1

Hoell, A. and C. Funk (2013): The ENSO-related West Pacific Sea Surface Temperature Gradient, Journal of Climate. J. Climate, 26, 9545–9562.      doi: http://dx.doi.org/10.1175/JCLI-D-12-00344.1

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.) https://doi.org/10.1007/978-3-319-21650-8_1.

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, http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-14-00334.1

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, https://doi.org/10.1175/JCLI-D-16-0452.1.

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, https://doi.org/10.1175/JCLI-D-17-0274.1.

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.

Reformulated NMME simulations indicate probable dryness for the 2017 and 2018 short and long rains in eastern East Africa

Chris Funk, Andy Hoell, Wassila Thiaw, Diriba Korecha, Gideon  Galu, Miliaritiana Robjhon, Endalkachew Bekele, Shraddhanand Shukla, Pete Peterson, and James Rowland

Introduction

This analysis has two objectives:  to i) provide a timely multi-agency assessment of the forthcoming short and long rainy seasons for eastern East Africa, while also ii) advancing towards a multi-agency paper describing a ‘FEWS NET Integrated Forecast System’ (FIFS). The main ideas behind FIFS are to improve the accuracy of seasonal precipitation forecasts by using statistical ‘Model Output Statistic’ (MOS) reformulations based on output from coupled ocean-atmosphere models, such as the North American Multi-Model Ensemble (NMME).  Note that while the work presented here is ‘experimental’, it is backed by years of FEWS NET research and successful forecasts based on similar approaches. The basic relationships used to drive these forecasts involve La Niña-like modifications of the Walker Circulation, which FEWS NET research, motivated by the 2011 famine in Somalia, has shown to be a key driver of droughts during both the October-November-December ‘short’ rains and the March-April-May ‘long’ rains.  Please see the list of papers at the end of this report.

The method explored here is called ‘adaptive regression’ (AdReg), which is an outgrowth of the modified constructed analog approach explored by Shukla and Funk.  AdReg is also similar to the process that an expert statistical climate forecaster might carry out, i.e. similar to the approaches used to derive outlooks for climate outlook fora.  AdReg has two steps: selection and estimation. AdReg employs three selection criteria: correlation, activity, and dynamic significance. In this application, ensemble average NMME precipitation fields will be used as predictors. We will use October-November-December (OND) NMME forecasts to predict OND eastern East African (EEA) rainfall and OND NMME forecasts to predict MAM EEA rainfall.

Background

This work has been motivated by the severe food crisis in EEA. As announced in a recent FEWS NET alert, the prospect of a fourth, and perhaps even a fifth, consecutive drought looms over East Africa due to the emerging La Niña-like sea surface temperature conditions. At present, the world faces an extremely high level of food insecurity, with an estimated 81 million people requiring urgent assistance. In East Africa, consecutive droughts have pushed millions to edge of starvation. In Somalia, food insecurity remains very high, with close to a million people (more than 800,000) facing near-famine (Emergency) conditions, another 2.3 million people in a severe food crisis, and another 3.1 million facing substantial food stress; an estimated 388,000 children under the age of five are acutely malnourished. In Kenya, persistent high food prices continue to drive high levels of food insecurity. In the Somali region of Eastern Ethiopia food security conditions are approaching famine conditions, and over all there are ~5-10 million Ethiopians in facing acute food insecurity.

Figure 1 shows the early October Integrated Phase Classification (IPC) food insecurity outlook map from www.fews.net. Orange and red areas in this map indicate areas experiencing a severe food crisis (orange) or emergency (red). These shades indicate severe food stress. For this study, we have selected the area outlined in black. This region was selected because of its high level of food insecurity, regional homogeneity, and fairly good data support.

Figure 1. FEWS NET Food Security Outlook from Early October
Figure 1. FEWS NET Food Security Outlook from Early October

Over the past thirty years, rainfall in this region during the OND season has remained fairly steady, probably due to the beneficial influence of warming western Indian Ocean SSTs. Rainfall during the MAM long rains, however, has declined substantially. Figure 2 shows a 1900-2017 March-April-May Standardized Precipitation Index (SPI) for this area, based the Centennial Trends (1900-1980) and CHIRPS (1981-2017) data sets, centered on a 1981-2010 baseline. The Centennial Trends and CHIRPS datasets are highly correlated (0.93) over the 1981-2014 time period.  What is striking about Figure 2 is large frequency of dry seasons since 1999. Only 2010 and 2013 experienced good rainfall performance. The onset of La Niña-like climate conditions combined with warm SSTs in the western Pacific could set the stage for another sequence of back-to-back droughts during the OND 2017 and MAM 2018 long rains.

Figure 2. Eastern East Africa MAM Standardized Precipitation Index time series.
Figure 2. Eastern East Africa MAM Standardized Precipitation Index time series.
Data

Here, we make AdReg forecasts based on the ensemble average of the latest NMME model simulations. Bias corrected precipitation simulations have been provided by Brent Roberts and the NASA SERVIR program. These forecasts have been bias corrected using Global Precipitation Climatology Center precipitation fields. In general, the NMME models predict moderate La Niña conditions (Figure 3) during the fall and winter of 2017/18, followed (in most models and simulations). In Figure 4 we see the NMME OND precipitation forecast. Consistent with a moderate La Niña forecast and moderately warm West Pacific SSTs, we see strong subsidence at the dateline near the equator. Such subsidence is typically a robust indicator of an enhanced La Niña-like amplification of the Walker Circulation. Over East Africa, we see a general tendency to slightly above normal rainfall.  An examination of historical correlation between the NMME and OND rainfall in our target region, will question and revise this assessment (below).

Figure 3. NMME NINO3.4 forecasts, from http://www.cpc.ncep.noaa.gov/products/NMME/current/images/nino34.rescaling.ENSMEAN.png
Figure 3. NMME NINO3.4 forecasts, from http://www.cpc.ncep.noaa.gov/products/NMME/current/images/nino34.rescaling.ENSMEAN.png
Figure 4.Standardized OND NMME ensemble forecasts of precipitation.
Figure 4.Standardized OND NMME ensemble forecasts of precipitation.
Methods

Here we explore the application of ‘adaptive regression’, a new type of statistical reformulation being developed by FEWS NET. AdReg filters potential global climate predictors by three criteria: correlation, activity, and dynamic significance. Correlation is based on the historical correlation between our target and each locations historical SST or precipitation forecast time series. Our main target is 1997-2016 OND rainfall in our Eastern East Africa study site (Figure 1). We begin by using a threshold of |0.5| as our criteria for correlation.

Figure 5 shows the correlation between OND EEA precipitation and the NMME forecast SST and Precipitation. Correlation values less than 0.5 and greater than -0.5 have been set to 0. For OND we see a positive-negative-positive relationship to SST in the Western Indian, Western Pacific and Eastern Pacific Ocean. The precipitation correlation pattern looks similar to that shown in diagnostic analyses of the short rains. Note, however, the details of the correlation structure over Eastern Africa and the Indian Ocean. During dry EEA OND years, the model produces strong subsidence and drying to the east of East Africa, primarily over the Indian Ocean. In the actual NMME forecast (Figure 5), conditions are fairly neutral in this area, and fairly active over the ENSO-related regions of the western and eastern Pacific.

To generate AdReg forecasts, we follow two steps: selection and estimation. AdReg employs three selection criteria: correlation, activity, and dynamic significance. Here we use correlation threshold of 0.5, a standardized anomaly threshold of 0.5Z, and a mean precipitation threshold of 30 mm for these criteria. NMME locations a historical correlation of less than -0.5 or greater than +0.5 are retained.

We next screen for activity. Areas that have a standardized anomaly, in this season, of less than -0.5Z or greater than +0.5Z are retained. We then screen for ‘dynamic significance’; areas with long term average NMME precipitation of less than 30 mm are not used. Figure 6 shows an example of masking, based on the 2017 OND forecast process.

Figure 5. Correlation between observed 1982-2016 EEA OND precipitation and 1982-2016 NMME Precipitation forecast made in early October.
Figure 5. Correlation between observed 1982-2016 EEA OND precipitation and 1982-2016 NMME Precipitation forecast made in early October.
Figure 6. Mask used to predict the OND 2017 rains. Orange areas are positively correlated with observed EEA OND rainfall. Blue areas are negatively correlated.
Figure 6. Mask used to predict the OND 2017 rains. Orange areas are positively correlated with observed EEA OND rainfall. Blue areas are negatively correlated.

The next step in the AdReg process calculates the first and second principal components (PCs) of the NMME forecasts. As an example, Figure 7 shows the correlation between the first AdReg principal component and the NMME precipitation predictions. This is an ENSO signal. The final step uses regression to translate the PCs into forecasts of the predicted target.

Take-one-away cross-validation is used to assess the out-of-sample skill of the AdReg procedure. For each year, that year’s data is withheld, the AdReg process calculated and then forecast accuracy is evaluated.

It should be noted that selection processes can create artificial skill, even when using cross-validation. The AdReg process attempts to minimize this effect by working with the low order principal components of the NMME forecasts. In the NMME models, this often corresponds with ENSO-like patterns of variability. The results presented here tend to focus on ENSO-like patterns similar to those studied in FEWS NET diagnostic analyses (see papers below).

Figure 7. Correlation between AdReg PC1 and the OND NMME precipitation forecasts.
Figure 7. Correlation between AdReg PC1 and the OND NMME precipitation forecasts.

 

Results 1. The OND Outlook

We begin by presenting (Figure 8) results for OND 2017 based on a model with masking based on a historical correlation of |0.5|, as standard anomaly of at least ±0.5Z and a mean precipitation rate of 30 mm. This model performs fairly well (cross-validated correlation of 0.68 with standard error of 0.8Z). While some droughts are missed, most are captured fairly well, including droughts in 2010 and 2016. A brief sensitivity analysis (Table 1) indicates fairly robust performance across our model criteria, except for when we set the correlation value to a high value (0.7). These strict criteria likely induces over-selection which performs poorly under cross-validation. Also shown in table 1 are cross-validated correlation and standard error values based on the raw NMME OND forecasts over the study area. While the models have some skill (r=0.45), AdReg skills are higher. A correlation of 0.45 implies explaining ~20% of the variance, AdReg correlations (~0.68%) imply explaining 46% of the variance. Raw NMME forecasts, regressed against the target, are shown in Figure 8 (triangles). These results are based on the bias corrected NMME forecasts, more experimentation with the underlying NMME precipitation fields will be examined in later analyses.

The outlook for the OND EEA rains based on AdReg is -0.8Z ± 1Z, based on 80% confidence intervals. Below normal appears to be the most likely outcome.

Figure 8. AdReg forecasts for EEA OND rains. Cross-validated results for 1982-2016, and the 2017 forecast. Bars denote 80% confidence intervals. Also shown are estimated based on regression with the raw EEA OND NMME (triangles).
Figure 8. AdReg forecasts for EEA OND rains. Cross-validated results for 1982-2016, and the 2017 forecast. Bars denote 80% confidence intervals. Also shown are estimated based on regression with the raw EEA OND NMME (triangles).
Correlation Activity Mean Rainfall CV R CV Standard Err
|0.5| >±0.5Z > 30 mm 0.68 0.8Z
|0.7| >±0.5Z > 30 mm -0.4 1.4Z
|0.3| >±0.5Z > 30 mm 0.63 0.8Z
|0.5| >±0.5Z > 0 mm 0.70 0.7Z
|0.5| >±0.5Z > 100 mm 0.70 0.7Z
|0.5| >±0.0Z > 30 mm 0.69 0.7Z
|0.5| >±0.8Z > 30 mm 0.67 0.8Z
Raw EEA OND NMME Precipitation 0.45 0.9Z

Table 1. Sensitivity Analysis for the OND Rains.

Results 2. The MAM Outlook

We next examine the relationship between the NMME OND precipitation forecasts and historical MAM EEA precipitation. We use the past 20 years (1998-2017), because FEWS NET research has shown a much stronger teleconnection with La Niña following the 1997/98 El Niño event, when the western Pacific SSTs increased substantially. Interestingly, we see quite a strong correlation between the OND NMME precipitation forecasts and the observed MAM EEA rainfall in the following spring (Figure 9). This ENSO-like pattern likely indicates the likelihood of a persistent overturning circulation that continues to disrupt rainfall across the tropics. The magnitude of these correlations is quite high (~±0.7). The cross-validated AdReg correlation is high (+0.7), with a fairly low standard error (0.69Z). The associated 2018 forecast would be -0.6Z ± 0.9Z; a below normal outlook with considerable uncertainty. Figure 10 shows the scatterplot of cross-validated forecast results, along with the 2017 and 2018 MAM forecasts. Figure 10 shows the corresponding NMME correlation pattern for the MAM AdReg forecast; again a strong ENSO-like pattern (the PC1 time series is dominated by strong ENSO-like inter-annual variations).

Interpretation

These results suggest, in line with numerous studies, that La Niña-like conditions are associated with dry conditions over the easternmost section of equatorial East Africa. The NMME can successfully anticipate such conditions, and provides a very useful guide to anticipating East African hydrological extremes. By focusing on the models’ large scale representation of ENSO, AdReg can be used to produce skillful automated forecasts, in early October, for both the short and long rains. For the short rains, our level of confidence might be quite high, since we have already begun the season, rainfall to date is slow to start, we can see evidence of a La Niña-like transition in the observed circulation, and near-term weather predictions tend to be pessimistic, at least over large parts of East Africa. If October is dry, the overall season will likely be poor (Figure 11). Our level of confidence in the MAM 2018 outlook is lower, given the large span of time, and the good possibility that La Niña-like conditions may fade. On the other hand, our forecast of -0.6Z is also quite similar to the ‘New Normal’ implied in the long term decline of the EEA long rains (Figure 2). Given the combination of current La Niña-like conditions, the pessimistic but skillful AdReg results shown here, and general tendency for EEA MAM rains to be below normal in the absence of strong forcing from El Niño or Indian Ocean Dipole-like conditions, assumptions of below normal rains for both the short and long rains may be warranted. The MAM outlook value would hover at the edge of the normal and below normal tercile boundary.

Figure 9. Correlation between observed EEA MAM rains and NMME OND precipitation from the preceding October.
Figure 9. Correlation between observed EEA MAM rains and NMME OND precipitation from the preceding October.
Figure 10. AdReg forecasts for EEA MAM rains. Cross-validated results for 1982-2016, and the 2017 forecast. Bars denote 80% confidence intervals.
Figure 10. AdReg forecasts for EEA MAM rains. Cross-validated results for 1982-2016, and the 2017 forecast. Bars denote 80% confidence intervals.
Figure 11. Correlation between NMME OND Precipitation and PC1 for MAM EEA forecast.
Figure 11. Correlation between NMME OND Precipitation and PC1 for MAM EEA forecast.
Figure 12. EEA October and OND rainfall, 1981-2016.
Figure 12. EEA October and OND rainfall, 1981-2016.
Prior research

While not detailed here, the reader should be aware of numerous studies supported by USAID, the USGS and NASA, to inform decision making in context like this. This work seeks to better understand and anticipate EEA droughts.*

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, http://pubs.usgs.gov/of/2010/1199/pdf/ofr2010-1199.pdf

Williams P. and C. Funk (2011) A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa, Climate Dynamics, V37.11-12, p. 2417-2435.  http://www.springerlink.com/content/u0352236x6n868n2/fulltext.pdf

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.

Hoell, A. and C. Funk (2013): The ENSO-related West Pacific Sea Surface Temperature Gradient, Journal of Climate. J. Climate, 26, 9545-9562. doi: http://dx.doi.org/10.1175/JCLI-D-12-00344.1

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. (2014). La Niña diversity and Northwest Indian Ocean Rim teleconnections. Climate Dynamics, 1-18.

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. 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, http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-14-00334.1

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 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 226 (2017): 165.

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.

Hoell A, Hoerling M, Eischeid J, Quan W-X, Liebmann B (2017) Reconciling theories for human and natural attribution of recent East Africa drying. J Clim 30:1939-1957

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.

A Late April Assessment Indicates Poor Long Rains and Low Gu Harvests for Somalia

Chris Funk, Pete Peterson, Peris Muchiri, Diego Pedreros, Greg Husak, Diriba Korecha, Gideon Galu, Laura Harrison, Will Turner, Marty Landsfeld and Shrad Shukla

This post examines conditions across East Africa at the close of April. As predicted by the  CHG, ICPAC, and a joint assessment by FEWS NET, WFP, FAO and JRC, exceptional warming in the West Pacific appears to have continued to produce subsidence and drying over East Africa.  At present (Figure 1), the FEWS NET food security outlook for Eastern Africa is very concerning, with a June-September outlook calling for IPC phase 3 (crisis) or 4 (emergency) across Kenya, South Sudan, southern Ethiopia and Somalia. The FEWS NET perspective seems largely congruent with the most recent seasonal assessment by the World Food Programme (here).

In Kenya, southern Ethiopia and Somalia June-September food security outcomes will be strongly influenced by rainfall in March and April, since most of the long rains tend to come in these months, and moist soils during this period support the establishment of healthy crops. As we will show below, poor March-April rainfall can be a good predictor of low crop production in Somalia.

FEWS NET Food Security Outlook for June-September 2017. Orange and red shades denote crisis and emergency conditions. Black shading in South Sudan indicates famine.
Figure 1. FEWS NET Food Security Outlook for June-September 2017. Orange and red shades denote crisis and emergency conditions. Black shading in South Sudan indicates famine.

We begin by looking at the observed March to late April rainfall performance using NOAA CPC ARC2 and CHIRPS rainfall fields enhanced with data provided by FAO SWALIM and the National Meteorological Agency of Ethiopia. All evidence indicates poor rainfall performance for much of the Greater Horn of Africa. We then examine the relationship between Somali Gu Sorghum crop production statistics and March-May rainfall. We find that March-April rains are by far the most important – and current March-April totals indicate very poor sorghum production totals for 2017. We conclude with a brief look at the current climate conditions and the performance of the NOAA GEFS weather forecasts.

March-April Rainfall Assessment

March-April 24th ARC2 anomalies (Figure 2) and March-April 20th ‘enhanced’ CHIRPS data, expressed as standardized precipitation index values (Figure 3) are in strong agreement that there has been wide-spread drought across almost all of Kenya, Somalia and Uganda as well as southern Ethiopia, eastern South Sudan, north-central Tanzania and western Yemen. Note that the units in these maps are different. It is useful to consider rainfall anomalies both in terms of absolute magnitude (Figure 2) and as standardized anomalies (Figure 3). In Figure 2 we note very large (100 mm) rainfall deficits across central Kenya, Uganda and in the SNNPR region of Ethiopia; these large deficits could be associated with large disruptions in key crop growing areas. In Figure 3 we see that the seasonal rainfall progress has been exceptionally dry, in a statistical sense (<-1 standard deviations) across most of the Horn.

NOAA CPC ARC2 precipitation totals from March 1st-April 24th 2017.
Figure 2. NOAA CPC ARC2 precipitation totals from March 1st-April 24th 2017.

In Somalia, where even normal rainfall totals are characteristically low, we find that our estimates indicate an exceptionally poor March-May season. While the results in Figure 2 do not indicate performance over the last dekad of April, CPC ARC2 totals for April 21, 22, 23 and 24 show almost no rainfall over Somalia.

CHIRPS Standardized Precipitation Index values from March 1st-April 20th 2017.
Figure 3. CHIRPS Standardized Precipitation Index values from March 1st-April 20th 2017.

For Somalia, it is important to realize that we have been able to incorporate a fairly dense network of gauge observations provided by FAO SWALIM. Figure 4 shows April 1st to April 20th enhanced CHIRPS rainfall totals. The numbers on this map show rainfall totals from either the SWALIM stations or WMO GTS observations. Across all of East Africa, very few regions appear to have received more than 60 mm of rain so far in April. From a crop perspective, this means that planting has been delayed across Kenya and Somalia, and crop growth is likely to be running substantially behind normal. For example, ARC2 data at Meru, in central Kenya, indicates a seasonal accumulation of ~120 mm, less than half of the normal 270 mm. Results in Baidoa (Bay Region Somalia), Dif in far eastern Kenya, and  Kibre Mengist in south-central Ethiopia are similar.

Enhanced CHIRPS precipitation for April 1 to April 20, 2017.
Figure 4. Enhanced CHIRPS precipitation for April 1 to April 20, 2017.

Seasonal Rainfall Ensembles

To examine likely outcomes for the total March-May season we have combined March 1 to April 20 CHIRPS rainfall totals and then examined the possible combinations of future rainfall by sequentially inserting one of the past 36 years (1981-2016) and then examining the associated distribution of seasonal rainfall totals. We begin by showing these results for the Bay Province of Somalia (Figure 5), which is currently facing food security crisis (i.e. just short of famine) conditions (see Figure 1). We start at a low seasonal total of 42 mm for Bay on April 20th – this low value and large deficit is primarily due to the low April rainfall totals, as shown in the SWALIM station data (Figure 4). To explore the remainder of the season, we sample the CHIRPS data using all prior seasons. Advancing one dekad by this approach gives us a seasonal total for the end of April of 90 mm, only 60% of long term average. As we will see below, this large March-April deficit will very likely be associated with large crop production deficits. Proceeding through the rest of May in this same fashion we arrive at a spread of possible outcomes ranging from near normal to very low, with an average outcome of 174 mm, 74% of the long term average. In the context of the past 20 years, this would be a 1-in-5 year drought (i.e. 20th percentile); 2011, 2001, 2008, and 1999 were a little drier.

Cumulative rainfall ensemble for Bay region of Somalia. Observed CHIRPS dekads are used from February through April 20th.
Figure 5. Cumulative rainfall ensemble for Bay region of Somalia. Observed CHIRPS dekads are used from February through April 20th.

Repeating this process for each pixel, we can assess the probability of March-June rainfall being less than 85% of the long term average (Figure 6) and less than 50% of the long term average (Figure 7). Figure 6 indicates that the regional as a whole is very likely at this point to end with below normal rainfall. The certainty of this outcome is much less in northern East Africa, although some Belg growing regions in the eastern highlands of Ethiopia and the northernmost parts of Somalia and Yemen are shown to have an 80% chance of below normal rains. Across southern Somalia, southern Ethiopia, all of Kenya and much of Tanzania a below normal outcome seems almost certain, given historical rainfall distributions.

Probability of March-May rainfall totals being below normal (less than 85% of average) based on historical rainfall distributions.
Figure 6. Probability of March-May rainfall totals being below normal (less than 85% of average) based on historical rainfall distributions.

Looking at areas likely to see catastrophic (<50% of normal) March-June outcomes, we see that such an outcome is very likely (>50% probability) across much of Kenya and near the Mandera triangle area at the intersection of Somalia, Ethiopia and Kenya. These are regions that have received low March-April 2017 rains (Figure 2 and 3) and have historically had short ‘long’ seasons – such that they now have low chances of anything but poor outcomes. We can see this in more detail by looking at cumulative rainfall totals for the Eastern (Figure 8) and Central (Figure 9) province of Kenya using the USGS Map Viewer. For Eastern province, seasonal rainfall totals have been extremely low (~140 mm), in line with 2010/11, and far below the typical seasonal total of ~310 mm. Historically, rainfall stops in this region at the end of April, hence we find a very high probability of very low rainfall (Figure 7).

Probability of March-May rainfall totals being extremely low (less than 50% of average) based on historical rainfall distributions.
Figure 7. Probability of March-May rainfall totals being extremely low (less than 50% of average) based on historical rainfall distributions.

For the densely populated, well observed Central Province of Kenya, we find that seasonal rainfall accumulations are the lowest in the 2001-2016 RFE2 period of record. The observed 257 mm is far below the average of 518 mm, and substantially lower than values in 2010-2011 at this time (330 mm).

Cumulative RFE2 rainfall totals for the Eastern province of Kenya. Data from https://earlywarning.usgs.gov/fews/mapviewer/index.php?region=af.
Figure 8. Cumulative RFE2 rainfall totals for the Eastern province of Kenya. Data from https://earlywarning.usgs.gov/fews/mapviewer/index.php?region=af.

In many of these arid land regions current assessments of water hole conditions indicate alert or near-dry conditions – at or near the end of the rainy seasons – it is very likely that conditions will soon get worse in these locations as evaporation takes its toll on surface water stores.

Cumulative RFE2 rainfall totals for the Eastern province of Kenya. Data from https://earlywarning.usgs.gov/fews/mapviewer/index.php?region=af
Figure 9. Cumulative RFE2 rainfall totals for the Eastern province of Kenya. Data from https://earlywarning.usgs.gov/fews/mapviewer/index.php?region=af

Assessing likely crop growing outcomes for Somalia’s Gu season

We next turn to Somalia’s Gu sorghum production outlook. This analysis is based on 1999-2016 Gu sorghum production for three key growing regions: Bay, Shabelle Dhexe and Shabelle Hoose. Our objective here is not to produce a precise crop production assessment for Somalia Gu production, but rather to highlight that the poor March-April rainfall totals, alone, are likely to produce serious reductions in crop production. Both the available production data and crop water requirement estimates from a simple crop model indicate that May rainfall will be unable to make up for the poor rainfall distribution in April. Both the crop production and CHIRPS rainfall data in Somalia are likely to be noisy. This analysis is intended to imply that a poor harvest is very likely – but not provide a precise quantitative Somali production forecast.

We began by totaling sorghum production from Bay, Shabelle Dhexe and Shabelle Hoose and related these totals to CHIRPS rainfall from March-May, March-April, and May.  We found an okay level of correspondence between crop production and March-May and March-April rainfall, with corresponding R2 values of 0.22 and 0.34. The correlation between sorghum production and May rainfall was actually weakly negative (-0.25), which helps explain why using March-April totals, rather than March-May totals, improved our predictive skill. The corresponding correlation between April rainfall and Gu sorghum production was fairly high (0.51). April is the key month for crops, according to the empirical data.

To generate a prediction of Gu production in Southern Somalia we regressed (Figure 10) March-April rains in Bay, Shabelle Dhexe and Shabelle Hoose against observed production anomalies (based on a 1999-2016 baseline). We then extracted the average April 1-20 rainfall total from our enhanced CHIRPS data set (27 mm) and assumed 25 mm for the last dekad of April. This latter value was a compromise between the April 21-25 observed ARC2 outcome (~0 mm) and the optimistic weather forecasts (discussed further in the next section). These assumptions and our regression indicate very low March-April rainfall totals and corresponding very poor level of crop performance (-50%), similar to previous recent drought years.

Figure 10. Scatterplot of southern Somalia sorghum production percent anomalies and March-April rainfall [mm].
Figure 10. Scatterplot of southern Somalia sorghum production percent anomalies and March-April rainfall [mm]. The circle marked in red is the production estimate for 2017, based on March-April rainfall.
To further corroborate these results we looked at the relationship between pixel-level Water Requirement Satisfaction Index (WRSI) end-of-season values and onset of rains dates for Bay (Figure 11). The WRSI is an index that shows crop water stress – a value of 100 means no water stress. Note that under normal conditions (with a start in the first or second dekad of April), Bay WRSI values are low (~50). This is a very marginal farming region. In the WRSI model the onset of rains triggers crop growth. It is calculated by identifying areas that receive at least 25 mm of rainfall in a dekad, followed by 20 more mm in the next 20 days. Since our enhanced CHIRPS data indicate average Bay rainfall totals of 12 and 15 mm for the first and second dekad of April, it seems unlikely that the region experienced onset conditions in those dekads. While this outcome was uncommon, we see a large decline in end-of-season WRSI. Our crop simulation results reinforce the critical nature of good early rains.  Crops require weeks of decent rainfall to emerge, put on green vegetation and then divert resources to build up grains. Even if southern Somalia receives torrential rain in the next several weeks it seems unlikely that conditions will be conducive to rainfed agriculture.

Distribution of end of season WRSI in Bay Somalia, stratified by onset date.
Figure 11. Distribution of end of season WRSI in Bay Somalia, stratified by onset date.

GEFS Forecasts, Current Climate Conditions and What We Know Now

GEFS 7 Day precipitation forecast [mm] April 25-May 2nd.
Figure 12. GEFS 7 Day precipitation forecast [mm] April 25-May 2nd.
We next briefly explore the skill of NOAA’s Global Ensemble Forecast System (GEFS) weather forecasts and discuss their current forecasts for the Horn. The issue we focus on here is the likelihood that current optimistic forecasts (Figure 12) for rainfall over Somalia will verify. These forecasts call for more than 80 mm of rain during the upcoming week. Such relief could definitely improve rangeland conditions and prospects for irrigated agriculture. CHG assessments of GEFS forecast skill (Figure 13) show some promising areas of high correlation, but not for the first half of May or the end of April. Thus while these forecast could prove accurate, the should probably be treated cautiously, since historically they have had fairly low correlations with observations over Somalia, and we have yet to see any rainfall totals approaching this magnitude appear across the region. On the other hand, it is certainly plausible that Somalia could see a few weeks of healthy rain before the season comes to a close. Such rains could improve rangeland conditions and water availability, but may not provide much relief to crop areas in Somalia. Based on the data analyzed here, reliable maps of observed rainfall (Figs. 2-4) provide a solid basis for predicting Gu agricultural outcomes, which look bleak for 2017. Central and Eastern Kenya and the Mandera triangle region also appear very likely to large precipitation deficits.

Correlation between GEFS 2-week forecasts and CHIRPS data for different initialization dates.
Figure 13. Correlation between GEFS 2-week forecasts and CHIRPS data for different initialization dates.

For Kenya, field reports indicate that the area planted with crops is less than 50% of normal in the southeastern lowlands. Central, eastern and coast Kenya has experienced a late onset of rains, only episodic precipitation, and a shortened growing season. At present, maize crops are only just emerging or are very young. Forecasts from the Kenya Met Department are for a normal ending time for the March-May season, suggesting that these crops are unlikely to have time to complete germination and grain filling. The poor March-May trends appear to be part of an ongoing drying trend (Figure 14) associated with warming in the Western Pacific. This trend has helped produce repetitive shocks, reducing household food security and resilience.

March-May rainfall trends based on CHIRPS data enhanced with a dense network of Kenya Met Department observations.
Figure 14. March-May rainfall trends based on CHIRPS data enhanced with a dense network of Kenya Met Department observations.

As an example, consider a time series of NDVI anomalies for the Coastal Province of Kenya (Figure 15). Since 2009, typical vegetation conditions have been below normal, with large drought events in 2009, 2010/11, 2012, and 2016/17. The current 2016/17 appears to be the worst event.

USGS eMODIS NDVI anomalies from the Coastal Province of Kenya.
Figure 15. USGS eMODIS NDVI anomalies from the Coastal Province of Kenya.

Real-time monitoring of current Belg season in Ethiopia

Diego Pedreros, Diriba Korecha, and Chris Funk

Background

Ethiopia experiences three climatic seasons, with high rainfall during two major rainy seasons. The country’s economy is largely agrarian, in which pure farming, mixed farming, and livestock herding (pastoralists) are common practice. Consistent increases in population, over-exploitation of natural resources such as natural forest and swampy lands for agriculture, and well as an alarming expansion of urbanization impose untenable burdens on Ethiopia’s social and economic strata. The agricultural sector in particular supports 85% of the population and thus is central to the livelihoods of the rural poor in Ethiopia (Conway et al. 2007; Deressa 2006). Current agricultural and herding practices in the country mainly rely on seasonal rainfall and water available in perennial rivers and dams; only a small fraction of Ethiopian agriculture is irrigated.  A significant decline in annual agricultural production has been observed during drought years (Lemi 2005).

It has been documented that food shortage and scarcity of water have led to local and nationwide famines, mainly due to complete or partial failures of short (Belg, February-May) and long (Kiremt, June-Sep) rainy seasons over various parts of Ethiopia (NMSA, 1996). The failure of seasonal rainfall is often caused by either misplacement or weakening of large-scale seasonal rain-producing systems. Stephanie et al. (2016) documented that droughts and famines, such as the socio-economic catastrophe of 2011, call attention to the need for reliable seasonal forecasts for rainfall in Ethiopia to allow for agricultural planning and drought preparations.

Drought-related famine is the result of several factors, where lack of rainfall is only the first (Webb et al. 1992). Famine, in itself, cannot be taken as evidence of drought, while it is also not possible to assess the role of societal conditions without knowledge of the extremeness of rainfall deficits (Viste et al, 2013). To address this quandary, some scholars (Funk et al. 2008; Williams and Funk 2011) have documented rainfall declines in southern and eastern Ethiopia, especially in the spring season.

Dry Belg seasons affect all of Ethiopia, causing the largest relative precipitation deficits in the south, where it is the main rainy season. The southern and southeastern lowlands have been drier than normal in every year from 1998 through 2010, with 2009 having the worst drought incidences. This description considers normal as being the average if rainfall over the years 1981-2010. For instance, Viste et al. (2013) noted that even though both the Belg and Kiremt seasons were dry in both 1984 and 2009, the large-scale patterns reflect the fact that in 1984 the Kiremt was one of the driest seasons, whereas the Belg was particularly dry in 2009. The core of the 2009 drought was located farther south, covering the Horn of Africa and the northern part of East Africa, where the February–May season is the main rainy season.

Belg as the main rainy season over south and southeast Ethiopia

While Kiremt is the main rainy season in many parts of Ethiopia, and Belg rains contribute about two-thirds of the annual rainfall for the southern and southeastern Ethiopia (Figure 1). For the Belg season, precipitation shows strong variability and is less reliable both from a temporal and spatial viewpoint, especially over the northern half of Ethiopia.

Percentage contribution of Belg rainfall for annual rainfall totals
Figure 1: Percentage contribution of Belg rainfall for annual rainfall totals

The Belg rains start falling over southern Ethiopian in February. During a wet year, rain usually starts around mid-January and continues without prolonged dry spells through February over Belg growing regions as well as Belg rain-benefiting regions of Ethiopia (Figure 2).

Spatial distribution of February mean rainfall climatology in mm
Figure 2: Spatial distribution of February mean rainfall climatology in mm

In March (Figure 3), Belg rains start to expand north and eastwards and cover the southwest to northeast regions of the Rift Valley. The western and eastern escarpment of the Rift Valley regions also receive rains. Much of the Southern Nations, Nationalities and People’s Region (SNNPR), the central and eastern half of Oromia, and the eastern Amhara regions usually receive more rainfall than other parts of the country.

Spatial distribution of March mean rainfall climatology in mm
Figure 3: Spatial distribution of March mean rainfall climatology in mm

Belg rains reach their peak in April (Figure 4), particularly over the regions where Belg is the main rainy season as well as secondary rainy season (over south-southeast, central, east and northeast Ethiopia). Aside from those regions, Belg rains extend eastward and covers the Somali region, where Belg is the main rainy season. In many cases, severe droughts happen when April rains fall short of their predicted climatological values.

Spatial distribution of April mean rainfall in mm
Figure 4: Spatial distribution of April mean rainfall in mm

May is the last month of Belg season (Figure 5), when rain starts to retreat/decline, slowing from eastern and southern sectors of Ethiopia. In contrast, Kiremt seasonal rains further expand west and northwards. Sometimes, Belg and Kiremt seasons merge during a fast transition from El Nino (Belg) to La Nina (Kiremt) or El Nino (Belg) to neutral (Kiremt) episodes.

Spatial distribution of May mean rainfall climatology in mm
Figure 5: Spatial distribution of May mean rainfall climatology in mm

Homogeneous regimes of Belg season

In Ethiopia, onset and cessation of seasonal rainfall vary considerably within a few kilometers distance due to altitudinal variations as well as orientation of mountain chains and their physical influence on atmospheric flow. In particular, diverse topography and strong seasonal variation over the country indicate the potential physical justifications to delineate rainfall patterns on various spatial scales. Based on existing evidence, rainfall seasonality, and above all by considering localized social and economic practices, we delineated the country into four homogeneous regimes. The characteristic of each homogeneous regime is mainly a reflection of their typical seasonal agro-climatic practices as well as the contribution and benefit of seasonal rainfall that prevails in each regime (Figure 6).

Homogeneous regimes of Belg season in Ethiopia
Figure 6: Homogeneous regimes of Belg season in Ethiopia

Social and economic description of homogeneous regimes

  • Purple area (Regime I): This regime receives 40-70% of the total annual rainfall during February, March, April, and May, with rainfall maxima occurring from late March to mid-May), a variety of grain crops (maize, sorghum, teff, barley), root crops (potatoes), pasture, and water storage are commonly practiced over various portions of the region. Most parts of this region are identified as pastoralist.
  • Green area (Regime II): Belg is the second rainy season and contributes 30-50% to annual rainfall totals. These regions usually receive less rainfall compared to southern Ethiopia despite the fact that they rely on Belg rains to produce short-cycled crops, root crops, land preparation for long-cycle crops, pasture, and water storage.
  • Blue area (Regime III): This regime receives up to 30% of their annual rainfall totals from Belg rainfall. Belg rains usually fall from March and continue without prolonged dry spells up to October/mid-November. Belg rains especially contribute to land preparation, planting/sowing of long-cycle (18 dekads) crops (e.g., maize, sorghum), and to contribute for extension of long rainy season that usually spans from March to November.
  • White area (Regime IV): Most parts of these regions receive less than 20% of annual rainfall totals from Belg rainfall because Kiremt is the major rainy season here, although it rains from mid-April/May to September/October.

 

The 2017 Belg Season up to the 2nd dekad of April

Up to the second dekad of April 2017 (to April 20th), the south eastern part of Ethiopia has received a low percentage of the expected rainfall.  See Figure 7.

Rainfall Percent anomalies (1st of February- 20th of April) for the 2017 season.
Figure 7: Rainfall Percent anomalies (1st of February- 20th of April) for the 2017 season.

On a regional level, we examined Regime 1 as shown on Figure 6.  At the end of dekad 2 of April 2017, this region overall had received below average rainfall. Figure 8 shows the seasonal rainfall accumulation for the entire Regime 1 region.  The black line shows how the overall rainfall for the region for the 2017 season deviates from the long term mean (thick red line). This season has received exceptionally low rainfall. The current total (~80 mm) is only 57% of the long term average (~140 mm). Only 3 out the 36 prior had lower seasonal totals, making this season a 10th percentile event – a one-in-ten-year drought if current conditions persist.

Seasonal cumulative rainfall for Regime 1 for every year since 1981 to 2017. Black line represents the development of the 2017 season, red thick line shows the long term mean.
Figure 8: Seasonal cumulative rainfall for Regime 1 for every year since 1981 to 2017. Black line represents the development of the 2017 season, red thick line shows the long term mean.

WRSI for grasslands

The low rainfall during the season in Regime 1 translates into low soil moisture availability for plants.  Figure 9 shows the WRSI for grasslands at the 2nd dekad of April.  By this dekad only a few areas have received enough rainfall for the WRSI model to start. We have been monitoring polygon 1 in southern Ethiopia as shown in Figure 9.  This polygon showed signs of stress, based on WRSI values, since the last dekad of March and exhibited no signs of recovery by the 2nd dekad of April.

The WRSI model show that just a few areas have received enough rainfall to support grasslands and some of these areas are already showing stress, as shown in Polygon 1.
Figure 9: The WRSI model show that just a few areas have received enough rainfall to support grasslands and some of these areas are already showing stress, as shown in Polygon 1.

Looking in more detail into Polygon 1, Figure 10 shows the seasonal cumulative rainfall for every year since 1981 using CHIRPS data. The black line represents the 2017 season, the thick red line shows the long term average, and the two black squares on the axis represent the 33rd and 67th percentiles. The plot shows that the season started as normal, but by the 1st dekad of March rainfall values started to decrease and have not recovered since.     The current total (~120 mm) is only 60% of the long term average (~200 mm), and we see that this season’s total to date is also associated with a one-in-ten-year drought, if the current dryness continues.

Spatially averaged seasonal cumulative rainfall for every year since 1981 for polygon 1. Black line depicts the development of the 2017 season to the 2nd dekad of April.
Figure 10: Spatially averaged seasonal cumulative rainfall for every year since 1981 for Polygon 1. Black line depicts the development of the 2017 season to the 2nd dekad of April.

Figure 11 shows the ensemble of potential outcomes for Polygon 1 based on the observed cumulative rainfall to the 2nd dekad of April, completing the season with historical CHIRPS data for each year. The red line shows the long term average, the black squares show the 33rd and 67th percentiles of the historical data while the red dot is the average of the ensemble. The green triangles represent 1 standard deviation (+) or (-) from the ensemble’s average. The outlook table on Figure 11 shows the probability at the end of the season.  This results indicates that there is a 94% probability that the seasonal accumulation for polygon1 is below normal.    Looking at the ±1 standard deviation range shown in Figure 11, the likely outcomes range from near normal to extremely dry. The most likely outcome, described by the mean of the ensemble, is a total of about 270 mm, or 77% of the long term average.

Potential outcomes based on the observed data to the 2nd dekad of April 2017 and completing the season with original data from each year. The side table show that there is a 94% chance that the seasonal total would be below normal.
Figure 11: Potential outcomes based on the observed data to the 2nd dekad of April 2017 and completing the season with original data from each year. The side table show that there is a 94% chance that the seasonal total would be below normal.

Conclusion

Low rainfall during the Belg 2017 season has been observed since the beginning of March.  These low values in rainfall have been affecting the availability of water for crops and grasslands primarily in southern and southeastern Ethiopia. This analysis focuses on a specific area in southern Ethiopia where the conditions are getting worse as the season progresses.  Even though there is more than a month to the end of the season, there is a 94% probability that the season ends below normal for this region. Substantial (75% of normal) rainfall deficits seem likely, and given the ensemble of historical outcomes, very low seasonal rainfall totals are quite possible.

References

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Deressa, T. T. (2006). Measuring the economic impact of climate change on Ethiopian agriculture: Ricardian approach. CEEPA Discussion Paper No. 21. Pretoria: University of Pretoria (http://econ.worldbank.org).

Lemi, A. (2005). Rainfall probability and agricultural yield in Ethiopia. Eastern Africa Social Science Research Review, 21(1): 57-96.

NMSA (1996), Climatic and agroclimatic resources of Ethiopia, Natl. Meteorol. Serv. Agency of Ethiopia, Meteorol. Res. Rep. Ser., 1(1), 1–137.

Webb P, Braun Jv, Yohannes Y (1992). Famine in Ethiopia: policy implications of coping failure at national and household levels. Research Reports, vol 92. International Food Policy Research Institute, Washington, D.C.

Funk C, Dettinger MD, Michaelsen JC, Verdin JP, Brown ME, Barlow M, Hoell A (2008). Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. PNAS 105:11081–11087

Stephanie Gleixner, Noel Keenlyside, Ellen Viste,  Diriba Korecha (2016):  The El Niño effect on Ethiopian summer rainfall. Clim Dyn, DOI 10.1007/s00382-016-3421

Viste, E, Korecha, D. and Sorteberg, A. (2013): Recent drought and precipitation tendencies in Ethiopia. Theoretical & Applied Climatology. V. 112, p535-551