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

by Chris Funk


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

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

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

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

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

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

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

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

The Walker Circulation links us with East Africa

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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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

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

Attribution Studies

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


Relevant Recent Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Funk C., Nicholson S. E., Landsfeld M., Klotter D., Peterson P. and Harrison L. (2015) The Centennial Trends Greater Horn of Africa Precipitation Dataset, Scientific Data, 2, 150050. DOI:  10.1038/sdata.2015.50. doi:10.1038/sdata.2015.50.

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

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

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

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

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

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

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

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

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

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

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


Average to mildly above average crop success anticipated for much of the major maize producing areas of Southern Africa, with pockets of moderately reduced crop success in subsistence farming and livestock grazing regions.

Will Turner and Greg Husak

The Water Requirement Satisfaction Index (WRSI) is a water balance model that incorporates crop parameters such as the length of growing season and phenological information, along with soil characteristics. This model is commonly used to monitor growing seasons throughout Africa and Central America by the Famine Early Warning Systems Network (FEWSNET). Specifically, the model uses gridded precipitation and reference evapotranspiration (RefET) inputs to calculate the ratio of plant-available water to crop-specific water demand at each stage of crop development. This post describes a real-time monitoring application to examine the progress of the Southern Africa 2017-2018 maize growing season, at approximately the halfway point of the season.

The analysis combines September, October, and November final CHIRPS rainfall estimate (reference link, data link), and December CHIRPS Preliminary data (data link) to identify the to-date 2017-18 Start of Season (Figure 1). The dekad identified as the Start of Season (SOS) for a location (pixel) is defined as the first dekad with at least 25 mm of rainfall followed by a total of at least 20 mm in the next two consecutive dekads. For the example shown here, which only has data through the end of December, dekads 2 and 3 of December only use the 25 mm threshold to identify a potential start. Whether or not the second threshold was met cannot be determined until January CHIRPS Prelim is released. All areas that have not met these requirements by the third dekad of December are labeled as ‘Yet to Start’ (see the areas of Figure 1 in light gray color).

Historical CHIRPS (1981-2016) was used to identify the SOS for each of the last 36 years, and calculate the median historical SOS (Figure 2).

Figure 1
Figure 1. 2017-18 start of season as of December 31, 2017. Areas colored gray have yet to receive sufficient rainfall for a season start.
Figure 2
Figure 2. Median start of season based on CHIRPS from 1981-2016. Areas colored pink consistently do not receive sufficient rainfall for a season start.

To examine the potential outcomes of the current season, 36 WRSI scenarios were run using the result from Figure 1 as the SOS. To get credible simulations of the season, hybrid composites of both precipitation and RefET¹ were created using season-to-date information and previous years to estimate one potential remainder to the season. The precipitation input of each scenario used 2017 CHIRPS final through November and 2017 CHIRPS Prelim for December. The RefET input used 2017 data through September. Because RefET is not available for October-December of 2017 due to the latency of the dataset, these 9 dekads were filled with average RefET. The 36 season scenarios were then run to completion using corresponding year-specific CHIRPS and RefET (i.e. Scenario #1 used 2017 data for Sep-Dec, and 1981 data for Jan-May; Scenario #2 used 2017 data for Sep-Dec, and 1982 data for Jan-May; etc.).

To build a library of historical WRSI output, the WRSI calculation was run using the 36 years of historical SOS from Part 1 of the examination and historical CHIRPS and RefET.

Investigating the difference of the median historical SOS (Figure 2) from this season’s SOS (Figure 1) reveals the timeliness of the current season (Figure 5). The median of the 36 2017-18 WRSI scenarios (Figure 3) was then compared to the WRSI median of the historical runs (Figure 4) to find the simulated WRSI anomaly (Figure 6).

WRSI Colormap
WRSI Colormap for Figures 3 & 4.
Figure 3
Figure 3. Median WRSI for the 2017-18 maize growing season, based on 36 scenarios. Areas colored cyan have not yet received sufficient precipitation for a season start.
Figure 4
Figure 4. Historical median WRSI for the Southern Africa summer maize growing season, based on 1981-2016 WRSI. Areas colored pink consistently did not receive sufficient rainfall for a season start.
Figure 5
Figure 5. 2017-18 start of season anomaly with respect to the 1981-2016 median SOS. Areas colored in shades of blue indicate regions where the current season started earlier than the historical median, while areas colored in shades of red started late. Areas colored orange indicate regions that are past the median SOS for that location and still have yet to receive sufficient rainfall for a season start. Areas colored beige have not yet started, but are not beyond the median start date.
Figure 6
Figure 6. 2017-18 simulated end of season WRSI anomaly with respect to the 1981-2016 median WRSI. Areas colored beige have yet to receive sufficient rainfall for a season start in 2017-18.

Comparing the graphics shown in Figure 5 and 6 we identify some interesting patterns, and can summarize how changes in SOS may impact this season’s crop production in Southern Africa.

  • Major production zones in the Maize Triangle of South Africa, Northern Zimbabwe, Central and Northern Mozambique and Malawi experienced slightly early to on-time SOS (Figure 5). As a result of this, these areas are all showing average to above-average WRSI (Figure 6) using this analysis.
  • More marginal production zones in Southern Zimbabwe and Southern Mozambique, where subsistence farmers are dependent on local production for their food security, experienced more extreme SOS anomalies (early in Zimbabwe, late in Mozambique). These anomalies are leading to below-average WRSI anomalies.
  • Throughout much of Botswana and West-central South Africa, there has been a lack of seasonal onsets. These delays are anticipated to have negative impacts on the crop growing season, even if rain begins in early January.

This analysis is currently experimental, but we think it should be insightful in identifying areas which are likely to experience crop production anomalies early in the season. We will continue to monitor throughout the growing season.

The next logical step would be to start including CHIRPS-GEFS forecast to get projected rainfall information, identify the chances of SOS in the upcoming dekad, and then use that to forecast rest-of-the-season progress.

¹Dataset presented at the 2017 FEWS NET Science Update Meeting. Hobbins MT (2017), A global reference evapotranspiration service for the FEWS NET Science Community: derivation, FEWS applications, next steps. FEWS NET Science Update Meeting: Agro-Climatology for Food Security Assessment, Washington, D.C., 6-8 September. (Oral)