Category Archives: Precipitation

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.
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.
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.

A 2016 Climate Review and Continued Concerns for the 2017 Long Rains

Chris Funk

2016 in review – Exceptional El Niño and La Niña-like climate conditions

This posting will briefly review some aspects of 2016 climate and update our outlook for the 2017 March-June long rains (we remain pessimistic, although the outlook has improved somewhat).

2016 appears very likely to be the hottest year on record. Figure 1 shows annual NASA GISS global air temperature anomalies. The last point (2016) is based on January-November anomalies, since December data are not yet available. It seems that the massive 2015/16 El Niño helped release heat from the sub-surface of the ocean, resulting in the 2016 global air temperature anomaly (~+1°C) being substantially warmer than the previous 2001-2010 average (~+0.6°C). Ed Hawkins provides a very compelling representation o this warming spiral here. NASA also has a compelling new animation of OCO-2 satellite-based CO2 observations.

Figure 1. NASA GISS global air temperature anomalies.

Will this warming continue as a step up to a new ~+1°C ‘normal’ or will we return to more modest conditions in 2017? We will have to wait and see. What we do know, however, is that warming is already influencing food security. Somini Sengupta’s New York Times article discusses how ‘heat, hunger and war are forcing African’s onto a road on fire’, helping to fuel a dangerous and difficult increase in migration in the Sahel. Grace et al.’s 2015 paper identifies strong direct links between warming air temperatures and increased frequencies of low birthweight babies in Africa; very warm air temperatures can have direct negative impacts on mothers and infants. Warming likely increases risks of civil war in Africa and has non-linear negative impacts on economic production.

Recent FEWS NET research, however, has also highlighted another important way that global warming affects food security – by increasing the warmth of local Indo-Pacific sea surface temperatures. ‘Local’ in this sense means that we are seeing global warming manifest as localized pockets of very warm sea surface temperatures. This has important implications for climate prediction and drought early warning.

Pockets of localized very warm sea surface temperatures can enhance both El Niño-like and La Niña-like climate patterns. To illustrate this, we have prepared the following animation based on standardized January-February-March sea surface temperature from a single climate change simulation produced using the Canadian Earth System Model version 2, obtained from the Climate Explorer. The animation shows 1980-2030 sea surface temperatures, expressed as standardized anomalies from the 1961-1990 mean, with exceptionally warm (p<2.5%) regions identified with contours. The stretch in the image coloring goes from -2.5 to +2.5 standardized deviations. The point here is that as we enter the 21st century, we see pockets of very warm sea surface temperatures. Some of these anomalies look like El Niños, with warm conditions in the eastern Pacific. Some of these anomalies look like the West Pacific Warming Mode, with enhanced sea surface temperatures in the western Pacific and eastern Indian Ocean. Some look like the first half of 2016 (El Nino). Some look like conditions in October-November-December of 2016 (La  Nina plus West Pacific Warming Mode).

Our animation of a single climate change simulation can be compared to a much less extreme animation based on the average of a large multi-model ensemble, also obtained from the Climate Explorer. The stretch and contouring here is the same as in the individual model. In this view of climate change, we tend to see a fairly even warming of the global oceans, and relatively few dramatic regions of warming until the 2020s. This animation provides a valuable assessment of how climate change may influence the oceans, on average, but this multi-model ‘average’ will not be what we will experience in any giver year. Rather, what we will experience will be sea surface temperature variations that resemble a single climate change simulation (i.e. like this animation) – which seems likely to produce stronger El Niño-like and La Niña-like climate conditions.

2015 and 2016 may,  unfortunately, have provided us with examples of such El Niño-like and La Niña-like variations. During 2015, as successfully predicted by global climate models and NOAA, we saw the onset of an exceptionally strong El Niño that helped produce severe droughts in Ethiopia, Southern Africa, and many other regions. Our article in the Bulletin of the American Meteorological Society’s (BAMS) annual climate attribution issue makes the case for anthropogenic enhancement of the 2016/16 El Niño to increased aridity and reduced runoff in Ethiopia and Southern Africa. In Southern Africa, current FEWS NET assessments indicate that the ‘worst affected areas of Zimbabwe and southern Somalia are expected to be in Emergency (IPC Phase 4) acute food insecurity during the lean season’, while ‘areas of Southern Madagascar are in Crisis (IPC Phase 3) during the lean season as households face larger gaps in their basic food needs’. Other chapters of the BAMS special issue find increases in heat, humidity, dryness, drought, cyclones, wildfires and tidal floods, and decreases in arctic sea ice and cold in the northeastern United States.

During the summer of 2016 we experienced a rapid transition from El Niño to La Niña-like conditions. The west Pacific and eastern Indian Ocean warmed dramatically while the the eastern equatorial Pacific cooled. These conditions formed the basis of our accurate forecast for below normal East African October-November-December rains. Recent FEWS NET assessments document these very poor rains in Somalia, Eastern Kenya and Southeastern Ethiopia and describe the related high levels of food insecurity – ‘Emergency (IPC Phase 4) is likely in some areas of Somalia, and among some households in Ethiopia, while Crisis is expected for other areas of Somalia, southeastern Ethiopia, and northeastern Kenya through May 2017.’ Current FEWS NET reports identify Crisis conditions in northern Somalia, and vegetation conditions there look very poor (Figure 2), with current conditions very similar to 2010. If East Africa experiences poor 20 17 long (March-June) rains, food security in parts of Somalia, Ethiopia and Eastern Kenya are likely to deteriorate further, potentially resulting in conditions similar to 2010/11, which saw severe food emergencies in Kenya and Ethiopia and famine conditions in Somalia. The rest of this blog posting provides an outlook for the 2017 March-May long rains.

Figure 2. eMODIS NDVI for northern Somalia. Image obtained from earlywarning.usgs.gov.

A below normal long rainy season seems likely, given current sea surface temperatures

This section of our blog updates the forecast we provided in December, using November-December and just December sea surface temperatures, as opposed to the October-November December sea surface temperature data we used last month. For brevity, we do not recreate all the analysis provided last month, but merely update our results.

Before presenting our statistical forecast, we briefly review current conditions, based on the anomalies for last 30 days from NCEP/NCAR reanalysis data, provided by NOAA’s Earth System Research Laboratory. Figure 3 shows the past 30 day near-surface wind anomalies. Figure 4 shows anomalies of precipitable water (the total amount of water vapor in the atmosphere). Over the Indian Ocean and central Pacific the wind anomalies (Figure 3) continue to show a strong equatorial pattern associated with increased moisture convergence over the Indo-Pacific Warm Pool and decreased precipitable water over Eastern Africa (Figure 4). Over the eastern Indian Ocean the wind anomalies are very large (~7 ms-1). Over the western Indian Ocean and eastern East Africa we also find large (~-7 kgm-2) reductions in total precipitable water. CHIRP and NASA TMPA precipitation data (not shown) also show this strong dipole between East Africa/Western Indian Ocean and the Warm Pool region.

Figure 3. Near surface wind anomalies for the past 30 days.
Figure 4. Total precipitable water anomalies for the past 30 days.

We next examine the sea surface temperatures associated with this anomalous circulation. We use both November-December and December surface temperatures to highlight possible changes in climate conditions, since the Climate Prediction Center anticipates a transition towards ENSO-neutral conditions by early February. Figure 5 shows both November-December and December NOAA Extended Reconstruction sea surface temperature anomalies, expressed as standardized deviations from a 1981-2010 baseline. Both November-December and December exhibit very warm conditions in the warm pool and extra-tropics, with a characteristic ‘western V’ pattern combined with modestly cool central Pacific anomalies. This pattern appears to be driving the anomalous winds and moisture conditions shown in Figures 3 and 4.

Figure 5. November-December and December sea surface temperature anomalies.

This sea surface temperature pattern is similar to the West Pacific Warming Mode. Figure 6 shows a time series of West Pacific Warming Mode index values, based on November-December data. What we see is that recent El Niño events are often followed by periods of high West Pacific Warming Mode index values, with current October-November West Pacific Warming Mode conditions similar to 2010.

Figure 6. Time series of West Pacific Warm Mode index values. Vertical black lines denote recent El Nino events.

As presented last month, we next examine (Figure 7) a scatterplot of the average November-December Warm Pool and North Pacific sea surface temperatures versus central Pacific sea surface temperatures. This analysis is shown for neutral and cool ENSO seasons. 2016 is shown with a red circle. The current conditions are pretty unique. What we see is a combination of very warm Warm Pool/North Pacific sea surface temperatures combined with just moderately cool east Pacific sea surface conditions. These unique conditions make it hard to identify analog seasons. The National Multi-Model Ensemble forecasts (Figure 8) for February to April predict a continuation of precipitation dipole between the East Africa/Indian Ocean and Warm Pool regions, similar to what we are seeing in the observations. The March-April-May ensemble climate forecast (not shown) has a similar dipole, but with a weaker intensity.

Figure 7. Scatterplot of November-December Warm Pool/North Pacific and Eastern equatorial Pacific sea surface temperature anomalies.
Figure 8. The National Multi-Model Ensemble February-March-April precipitation forecast.

We next repeat the cross-validated forecast procedure used last month, but based on both November-December and December sea surface temperature fields. Warm Pool, North Pacific and central Pacific sea surface temperatures are used as predictors. We also limit ourselves to making forecasts for just ENSO-neutral and cool seasons. It is very important to note that the March-June data used to train this forecast is just for part of East Africa – southeastern Ethiopia, southern Somalia and eastern Kenya. Hence this forecast only targets a limited region of the Horn, a region strongly influenced by equatorial Indo-Pacific climate anomalies and the Somali Jet.

Figure 9. Cross-validated forecasts of March-June East African rainfall.

The green dots in Figure 9 show our cross-validated forecasts based on November-December observations. The overall cross-validated skill is modest (R2=0.36), but the ability to distinguish between wet and dry seasons is good. The 2017 forecast based on November-December sea surface temperatures is -0.8Z±1.2Z (red star), where ‘Z’ denotes a standardized departure from normal. This suggests that a below normal season is likely but not all that certain. To capture some of the uncertainty associated with the current climate state, which has seen modest cooling over the last month in the warm Warm Pool and North Pacific regions, and modest warming over the cool Central Pacific region, we also present a forecast based on just December sea surface temperatures. This forecast is also below normal (-0.5Z,  blue star), but is less pessimistic than results obtained from November-December. For the -0.8Z forecast, the probability of rainfall being less than -0.5 is 60%. For the -0.5Z forecast, this probability would be 50%.

In summary, while current conditions are hard to interpret due to the unique conditions (Figure 7), we are currently seeing a strong overturning East Africa/Warm Pool circulation anomaly (Figures 3 and 4) that climate models predict lasting into February-March-April (Figure 8). Our statistical forecasts anticipate below normal long rains, and this outlook appears substantiated by current circulation anomalies. We will continue to update our analysis on a monthly basis.  As we transition out of La Niña-like conditions, we may see Warm Pool precipitation decrease, leading to a more favorable outlook for East Africa. For now, however, a below normal outlook seems warranted. One plausible scenario might be a poor start to the season, with normal rains later in the season.

 

Below normal forecast for the 2017 East African long rains

Chris Funk, Greg Husak, Diriba Korecha, Gideon Galu and Shraddhand Shukla

Here we present an empirical forecast of below normal rainfall for the East African 2017 March-June (MAMJ) long rains. This forecast is based on statistical relationships between March-June rainfall in eastern East Africa (38-50°E, 5°S-8°N) and October-November Sea Surface Temperature (SST) observations in the Indo-Pacific. FEWS NET research has shown that dynamic models in this region and season lack skill, we therefore advocate using an outlook based on FEWS NET’s numerous statistical and diagnostic analyses focused on understanding and predicting the long rains (a, b, c, d, e, f).

As predicted based on September SSTs and October Kenya-Somalia rainfall, much of eastern East Africa received very poor October-November 2016  short rains and in many regions vegetation conditions are extremely poor. For the southeastern highlands region of Ethiopia, current dry conditions follow four poor rainy seasons during the 2015 and 2016 Belg and Kiremt rainy  seasons. For southern Somalia and eastern Kenyan crop growing areas, poor short rain harvests in 2016 follow a poor 2016 March-June cropping season. Current vegetation conditions in our focus region are very degraded (Figure 1). A poor 2017 March-June long rains could lead to yet another poor harvest in eastern Kenya, south-eastern Ethiopia and southern Somalia while delaying any substantial respite for pastoralists.

Figure 1. eMODIS NDVI anomalies and focus region.
Figure 1. eMODIS NDVI anomalies and focus region.

Eastern East Africa has experienced more frequent MAMJ droughts since 1995 (Figure 2) due to warm conditions in the Indo-Pacific warm pool, cool La Nina-like conditions in the eastern Pacific, and Pacific Decadal Oscillation-like SST variations in the northwest Pacific. FEWS NET research has suggested that warming in the Eastern Indian and Western Pacific Ocean has led to stronger March-June droughts during La Nina-like seasons (a, b).

In this study we focus on recent years with cool or neutral October-November eastern equatorial Pacific SSTs. During these years strong predictive relationships allow us to anticipate below normal rains for the following March-June rainy season. During El Niño-like years, predictability for the long rains tends to be low. While El Niño can bring above normal rains, some El Niño events, like 1992, were associated with drought and famine conditions. Excluding these El Niño seasons produces more coherent prediction results.

Beginning in 1980, we identify 16 cool-neutral October-November seasons, based on Niño 4 SSTs: 1980, 1981, 1983, 1984, 1985, 1988, 1989, 1995, 1996, 1998, 1999, 2000, 2007, 2008, 2010, and 2011. However, as shown below (Table 1), the selection of years has relatively little impact on our prediction of below normal rains. Selecting cool seasons or using all the years after 1980/81 produce similar pessimistic (below normal) forecasts for 2017.

Figure 2. Eastern East Africa standardized rainfall anomalies [Z scores]. Based on a 1981-2014 baseline.
Figure 2. Eastern East Africa standardized rainfall anomalies [Z scores]. Based on a 1981-2014 baseline.
Figure 3 shows a map of the correlation between October-November SSTs during neutral-Nina years and March-June East African rainfall the following spring. This is a familiar pattern we have seen associated with recent long rains droughts. A curve of negative correlations (~-0.7) begins in the north-central Pacific, curves through the Indo-Pacific Warm Pool and continues into the southeastern Pacific. A region of positive correlation in the north- central Pacific provides a counterpoint, and the combination of these warm and cool SSTs creates a pattern of low and high pressure that intensifies moisture convergence and rainfall over the eastern Indian Ocean and the Western Pacific – an area called the ‘Warm Pool’ because of its high average ocean temperatures. Increased Warm Pool precipitation leads to below normal East African rainfall.

Figure 3. Correlation between EA long rains and October-November SSTs.
Figure 3. Correlation between EA long rains and October-November SSTs.

Motivated by a desire to understand, and better anticipate, back-to-back droughts such as those associated with the 2010/2011 East African food security crisis, FEWS NET scientists have published numerous papers describing how warm Warm Pool and cool central Pacific SSTs combine to produce dry long rain conditions (a, b, c, d, e, f), while other scientists have emphasized the important role played by the north Pacific (g, h). All three regions (Warm Pool, North Pacific, East Pacific) are currently warm or cool in ways that are conducive to poor East African long rains. In 2012 and 2014, FEWS NET scientists used these relationships to make successful forecasts of below normal rains based on February or March SSTs.  Here, we extend this work to examine levels of predictability based on October-November SSTs, finding reasonable levels of skill and a forecast for poor March-June 2017 rainfall.

Figure 4. January-November 2016 Standardized Precipitation Index values. Based on CHIRPS data.
Figure 4. January-November 2016 Standardized Precipitation Index values. Based on CHIRPS data.

This forecast activity has been prompted by two factors – the very poor performance of 2016 January-November rainfall (Figure 4), and the emergence of a very strong West Pacific SST gradient (WPG) (Figure 5). What we see in Figure 4 is an area of extreme sustained dryness in the eastern Horn that appears associated with both the current poor vegetation conditions (Figure 1 below) and also the sustained low Normalized Difference Vegetation Index values throughout the year. Figure 5 suggests that this dryness has been associated with the development of an extremely steep West Pacific Gradient, caused by a combination of very warm West Pacific SSTs and moderately cool La Niña SSTs. What is interesting about Figure 5 is that some (but not all) recent strong El Niños rapidly transitioned into strong West Pacific Gradient events: the 1987/88, 1997/98 and 2008/09 Niños were followed by strong gradient conditions when cool La Niña SSTs combined with warm conditions in the Western Pacific. Following the 1997/1998 and 2008/09 Niño events, strong gradient conditions also tended to persist for several years while long rains performance tended to be below normal (Figure 2).

Figure 5. Time series of standardized 6-month running average Nino 3.4 and West Pacific SSTs, and their difference (WPG = Nino3.4 minus West Pacific).
Figure 5. Time series of standardized 6-month running average Nino 3.4 and West Pacific SSTs, and their difference (WPG = Nino3.4 minus West Pacific).

Focusing more closely on the Warm Pool region shown in Figure 3, which we selected because of its high correlation with March-June long rains in East Africa, we see very warm conditions (Figure 6, top), suggestive of below normal March-June rains. Several previous post-El Niño years (1998, 2008, and 2010) exhibited similar levels of warmth. Focusing just on years with neutral or cool El Niño-Southern Oscillation conditions, we find a strong negative relationship between October-November Warm Pool SSTs and East African (EA) long rains in the following year (Figure 6, bottom), with a +1°C increase in Warm Pool SSTs associated with a ~-3 standardized anomaly decrease in EA precipitation. Note, however, that a +1°C anomaly is unprecedented in this region (Figure 6, top). The high slope coefficient for this warm pool region is likely to be related to the strong relationship between increases in Warm Pool SSTs and rainfall over the Warm Pool. This region is very warm, and a +0.5°C increase in SST can lead to cyclones and very heavy Warm Pool convection. In turn, this increased precipitation can drive an overturning circulation associated with subsidence and reduced rainfall over East Africa.  Atmospheric model simulations support this relationship, showing a strong (~-0.8) anti-correlation between ensemble average Warm Pool and East African precipitation. A simple bivariate regression with Warm Pool SSTs suggests that we might expect a -1.5Z standardized March-June rainfall anomaly given the current observed SST conditions (-1.5Z = +0.5°C * -3 Z/°C + 0.05Z).

Figure 6. Top - October-November 2016 Warm Pool SST anomalies, derived using a 1981-2010 baseline. Bottom – Scatterplot showing October-November Warm Pool SST anomalies and standardized East African March-June rainfall anomalies.
Figure 6. Top – October-November 2016 Warm Pool SST anomalies, derived using a 1981-2010 baseline. Bottom – Scatterplot showing October-November Warm Pool SST anomalies and standardized East African March-June rainfall anomalies.

Figure 7 shows similar results but for the North Pacific region from Figure 3. We identify a strong negative relationship between North Pacific SST and East African long rains.  October-November North Pacific SSTs are very warm (~+0.9°C), similar to 1998, 2007, 2010, and 2011, seasons preceding below normal long rains (Figure 2). A simple bivariate regression prediction based on North Pacific SSTs would be: -1.4Z = +0.9°C * -1.8Z/°C + 0.2Z.

Figure 7. Top - October-November 2016 North Pacific SST anomalies, derived using a 1981-2010 baseline. Bottom – Scatterplot showing October-November North Pacific SST anomalies and standardized East African March-June rainfall anomalies.
Figure 7. Top – October-November 2016 North Pacific SST anomalies, derived using a 1981-2010 baseline. Bottom – Scatterplot showing October-November North Pacific SST anomalies and standardized East African March-June rainfall anomalies.

Figure 8 shows a similar scatterplot and time series for the eastern Pacific region identified in Figure 3. Here we find a modest predictive relationship (R2=0.34). East Pacific SSTs do a fairly good job of stratifying dry events. Just using East Pacific SSTs to predict MAMJ rains we would arrive at below normal conditions -0.7Z = 1.8Z/°C * -0.5°C *+ 0.2Z.

Figure 8. Top - October-November 2016 East Pacific SST anomalies, derived using a 1981-2010 baseline. Bottom – Scatterplot showing October-November East Pacific SST anomalies and standardized East African March-June rainfall anomalies.
Figure 8. Top – October-November 2016 East Pacific SST anomalies, derived using a 1981-2010 baseline. Bottom – Scatterplot showing October-November East Pacific SST anomalies and standardized East African March-June rainfall anomalies.

A difficult question to answer, because we have so few samples, is whether a very warm Western and Northern Pacific, acting alone, can produce substantial East African drying. Figure 9 chracterizes the current situation and identifies two modestly cool East Pacific analogs (2000 and 2008). This scatterplot shows the unique conditions we face at present. The x-axis represents the average Warm Pool/North Pacific SSTs. The y-axis shows our East Pacific SST anomalies. The average of our Western and Northern Pacific time series indicate the warmest conditions on record. The East Pacific shows modest La Niña-like cooling. Because of the non-stationarity of the climate system it is hard to find a large number of analogs. The two closest seasons with ~-0.5°C eastern Pacific SST appears to be October-November of 2000 and 2008, although 2008 is much closer. The right panels of Figure 9 show the longs rains for the following years (2001, 2009). These results could indicate a range of outcomes from slightly below normal to the worst on record (see Figure 2). We do not have to have a strong La Nina to have a severe long rains drought.

Additional evidence supporting the important role of Western and Northern Pacific SSTs comes from our statistical analyses (Figures 6-7), published forecast models (references cited above) and the dynamic response of climate models to Western and Northern Pacific forcing (i), West Pacific forcing (c), and the West Pacific Gradient (d), which is driven by both the western and eastern Pacific, and is currently very strong (Figure 5).

Figure 9. Assessing risks during modestly cool East Pacific seasons. Left - scatterplot of the average WP/NP SST anomalies and East Pacific Anomalies. Right - CHIRPS MAMJ anomalies for modestly cool East Pacific analog seasons.
Figure 9. Assessing risks during modestly cool East Pacific seasons. Left – scatterplot of the average WP/NP SST anomalies and East Pacific Anomalies. Right – CHIRPS MAMJ anomalies for modestly cool East Pacific analog seasons.

We conclude by combining all three predictors using a standard statistical technique – take-one-away cross-validated regression – to evaluate the robustness of our prediction scheme. Cross-validation is carried out by sequentially removing a year’s data, recalculating regression coefficients without this data, and then comparing the forecasted values with the real observations. These values are shown in the scatterplot in Figure 10. The cross-validated R2 is modest (0.36), but the model appears to do a good job of capturing most of the dry events in 1999, 2000, 2008, 2009, 2011 and 2012 but not 1984. Two false alarms appear in 1989 and 2001, although 2001 rainfall performance was dry in some current crisis areas (Figure 9).

Figure 10. Forecasts of March-June Eastern East African rains based on October-November SSTs. Green circles show cross-validated hindcasts. Red star denotes the 2017 forecast (a -1 standardized anomaly ± 1.2Z).
Figure 10. Forecasts of March-June Eastern East African rains based on October-November SSTs. Green circles show cross-validated hindcasts. Red star denotes the 2017 forecast (a -1 standardized anomaly ± 1.2Z).

While our regression-based forecast for 2017 MAMJ rains is very low (-1.5Z), we advocate a more modest outlook this far ahead of the long rains. We assume that MAMJ 2017 will be similar to the other 8 years for which our forecast was below normal. These analog years correspond to the dots to the left of the y-axis in Fig. 10.

Table 1. Forecast statistics for various sets of model years. Years
Table 1. Forecast statistics for various sets of model years.

This approach seems robust to different selections of years (Table 1).  In all cases we find a prediction for below normal MAMJ rainfall. Please also note that the strong negative western and northern slope coefficients remain fairly robust across all the year selections. Knowing the true value of these coefficients is difficult, but the data converges on a pessimistic outlook.  Table 1 also suggests that during neutral-Nina seasons East Africa’s sensitivity to Warm Pool and North Pacific SSTs increases.

Assuming 1989, 1999, 2000, 2001, 2008, 2009, 2011 and 2012 as analog years leads to a forecast of -1Z±1.2Z, with the range depicting 80% confidence intervals. This model gives a 75% chance of below normal rains and has an historic hit rate of predicting 6 out of 7 recent droughts (86% success rate). The red star in Figure 10 shows our 2017 forecast. Different selections of years were tested (Table 1), and all selections produced similar results: below normal longs rains are the most likely outcome, primarily due to negative teleconnections with western and northern Pacific SST. Our forecast range (-1Z±1.2Z) also fits with the outcomes spanned by our two modest-Nina analogs (2000/2001, 2008/2009, Figure 9).

Note that the closest analog (2008/2009) was a very poor rainy season, and that our WPG time series (Figure 5) and regression estimates all suggest SST conditions similar to those preceding some very poor rainy seasons (1999, 2000 and 2011).  In Figures 11 and 12 we highlight this similarity by showing 2016 and 2010 October-November SST and rainfall anomalies. 2016 SSTs are warmer in the western Pacific and northern Pacific than in 2010, but the eastern Pacific is warmer as well. Milder La Niña conditions in 2016 are accompanied by exceptionally warm conditions in the western and northern Pacific. The corresponding ‘Western V’ structure contrasting the western/northern Pacific with cool eastern Pacific SSTs tends to produce dry conditions in the eastern Horn (Figure 3). The observed 2016 SSTs (Figure 11) look a lot like the inverse of our correlation map (Figure 3), indicating a good chance for below normal rains.

Figure 11. Standardized October-November SSTs. A 1981-2010 baseline was used to derive anomalies.
Figure 11. Standardized October-November SSTs. A 1981-2010 baseline was used to derive anomalies.

October-November rainfall anomalies (Figure 12) also suggest conditions similar to, but not identical with, 2010. During October-November of 2010 La Niña-like conditions were better developed, with deeper rainfall reductions near the dateline and more enhanced precipitation over the warm pool. On the other hand, in 2016, rainfall deficits over the Indian Ocean, indicative of the strength of the overurning circulation, are actually larger than those present in 2010. 2016 conditions over the Warm Pool and eastern Pacific appear La Niña-like, with a vigorous overturning circulation. This circulation, however, seems weaker than in 2010, consistent with weaker 2016 La Nina conditions. Going forward, monitoring maps like Figure 11 and 12 will help us assess whether the climate is maintaining a dangerous state or transitioning to a more normal conditions.

Figure 12. October-November CHIRP anomalies.
Figure 12. October-November CHIRP anomalies.

Increased confidence in our outlook is provided by building a MAMJ rainfall composite using atmospheric global circulation model simulations for our analog years. When our statistical model predicted below normal rains based on October-November SSTs, the climate model simulations, driven with observed MAMJ SSTs the following year, indicated a strong overturning circulation with drying over the Horn of Africa (Figure 13). These results suggest that the observed overturning circulation (Figure 12) may persist into 2017. These simulations provide convergent support for our statistical analyses.

Figure 13. Standardized March-June precipitation from ECHAM5 simulations for analog years.
Figure 13. Standardized March-June precipitation from ECHAM5 simulations for analog years.

We can produce a spatial map of the expected drying by using the composite function in the GeoCLIM tool (Figure 14). This map is based on 1989, 1999, 2000, 2001, 2008, 2009, 2011 and 2012. Spatially, these results are broadly similar to rainfall estimates produced by the ECHAM5 model (Figure 13), which also shows below normal rainfall across much of eastern Africa for our analog years. Southern Ethiopia, Somalia and Central and Eastern Kenya appear likely to receive below normal rains. While anticipated conditions in northern Somalia are normal, this could be due to deficiencies in our CHIRPS data – this region has very few stations, and satellite-based rainfall estimates can have trouble seeing low stratus-related rainfall over the easternmost areas of East Africa. Low confidence in our data in this region tranlates into low confidence in our forecast for northern Somalia. On the other hand, this region can also be influenced by extra-tropical disturbances (see below). For Ethiopia, Kenya and Southern Somalia, we have good station coverage as well as high confidence in our satellite estimates.

Figure 14. March-June CHIRPS rainfall anomalies for selected analog seasons - 1989, 1999, 2000, 2001, 2008, 2009, 2011 and 2012.
Figure 14. March-June CHIRPS rainfall anomalies for selected analog seasons – 1989, 1999, 2000, 2001, 2008, 2009, 2011 and 2012.

This analog analysis indicates that the belg growing season in the eastern highlands of Ethiopia may be below normal, resulting in the fifth poor season in parts of this region, which had below normal belg and kiremt rains in 2015 and 2016. Crop growing conditions in central/eastern Kenya and southern Somalia may also be below normal. For many Eastern Kenya and Southern Somalia maize growing regions this would mean a third poor season, since MAMJ and October-November 2016 rains were low. Pastoral areas in these areas may receive little respite.

Summary: Ocean conditions appear similar to 2010/11 and 2008/09, and for our target region below normal rains in MAMJ appears to be the most likely outcome given October-November SST conditions. We are certainly facing an elevated chance of very poor rainfall in at least parts of our target region, though we find normal years within our span of analogs, and SST conditions may change between now and 2017. Spatially, many of the areas expected to receive poor 2017 March-June rains are already very dry (Figures 1 and 4), and the hydrologic impact of these consecutive droughts could be similar in magnitude to 2010/11. We will be able to provide more information as the season progresses. We may see the ‘Western V’ SST pattern cool while eastern Pacific SSTs warm. If not, a continued below normal outlook seems warranted. Continued monitoring of large scale Indo-Pacific SST and rainfall anomalies will help us guage the persistence of the current overturning circulation that produced an exceptionally dry short rains season.

Spatial and Seasonal Forecast Limitations

In closing, we would like to emphasize the spatial and seasonal limitations of this forecast, which has been targeted on Eastern Equatorial East Africa (Figure 1), where prior FEWS NET research has noted strong negative links between increased precipitation over the eastern Indian and Western Pacific Ocean during the March-June season. Deep convection over the Indo-Pacific warm pool almost always produces subsidence and drying over at least part of this focus region. This response, however, tends to be strongest near the equator, and our outlook should not be considered as a forecast for all or even most of the Greater Horn of Africa. Many different climate drivers play different roles in different locations, creating a complex climatic tableau.

In October-May, in the northern parts of the Horn, we know that Ethiopia, Sudan, Eritrea, Djibouti, Yemen, Saudi Arabia and other middle east countries, and northern Somali (Somali Land) are all significantly affected by the southward penetration of north-south or back-hanged mid-latitude troughs. These troughs often originate from polar low pressure cells and penetrate into the tropical regions, where they are associated with short-lived (few days to a week) rainfall events across northern Africa, the Mediterranean, the Middle East and other countries bordering or confined within the Great East Africa Rift Valley (north of the Equator). These wet anomalies are very common between October and May, and current ECMWF forecasts for December indicate substantial trough activity (Figure 15). Whenever these troughs approach or lie along the Rift Valley, the Sahara and Arabian High pressure system disintegrates and is partly pushed over the Arabian Sea, which usually leads to the formation of easterly moisture flows from the Arabian Sea and north Indian Ocean.  These troughs are typically well-established in the mid-troposphere (500 hPa) but sometimes deepens up to 200 hPa, depending on the westerly frontal systems from which they emerge. Some years with anomalously wet October-February conditions were aligned with times when more westerly fronts and the associated trough systems penetrated in to the tropics. The wet anomaly over Ethiopia between 23-30 November 2016 appears to be related to such a trough. The number of troughs so far developed and that will be developing in the coming days seem likely to be more frequent (Figure 15). This anomaly is expected to enhance unseasonal rains over northern sectors of GHA and the Middle East in months of December 2016 as well as January 2017.

We would like to emphasize that in addition to large scale global teleconnection systems associated with the El Niño/Southern Oscillation, West/North Pacific and Indian Ocean Dipole there are a number of other key regional factors that are just as important as ENSO in predicting rains during the October-May semi-dry seasons over the northern sectors of the GHA and the Middle East. These include the pressure gradient and sea surface temperature gradient between the Indian Ocean and Arabian Sea (including the land surface), the oscillatory nature of the North Atlantic Oscillation, the west-east pressure/sea surface temperature gradient over the subtropical Atlantic Ocean, the number and frequency of circumpolar troughs, the position and intensity of the semi-permanent high/low pressure systems (the Azores, Siberia, Sahara, Arabia highs) and middle and upper-tropospheric dynamics and the Madden-Julien Oscillation. Here, we do not take these complexities into account, so our forecast is only appropriate for the March-June over eastern equatorial areas of East Africa.

Figure 15. ECMWF forecast of 850 hPa temperature and 500 hPa geopotential heights. Trough analysis by Diriba Korecha.
Figure 15. ECMWF forecast of 850 hPa temperature and 500 hPa geopotential heights. Trough analysis by Diriba Korecha.

Acknowledgements:  We would like to thank James Verdin and Andrew Hoell for constructive and insightful discussion and analysis involving the current climate situation. We would also like to thank FEWS NET and the USGS for supporting the research that supports this assessment.