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.

Very poor pasture conditions likely to persist through early spring

Chris Funk

This is yet another post about the low short/Deyr rains in East Africa, but today’s focus is on the very poor pastoral conditions in southeastern Ethiopia, northeastern Kenya, and Somalia. This blog is motivated by the extremely low NDVI values, discussed in a recent alert by FEWS NET as well as an email from my friend John McGrath at OXFAM who forwarded me an email from his colleague James Firebrace, who is reporting some severe outbreaks of acute malnutrition and water shortages in eastern Somaliland. After analyzing the available remote sensing data, I find such reports plausible, given the cumulative January-November NDVI record, and would suggest we pay very close attention to food security conditions in this area.

I was already planning to write this post when I arrived at work this morning, before I read John’s email. My concerns were motivated by the very large observed NDVI anomalies combined with the fact that the full impact of the October/November dryness may not be fully registered by the current vegetation.  There is a large degree of persistence and predictability in the NDVI record, and we wrote a paper on this back in 2006 that included a target application on exactly the region under stress now. NDVI tends to lag behind rainfall, so the current poor rainfall could lead to further drying. Even without more drying, it is unlikely (but possible) that the region will see much relief until spring 2017.

Figure 1 shows the most recent MODIS land surface temperature and NDVI anomalies. Many pastoral areas are very warm, which may indicate low levels of soil moisture, and satellite-observed vegetation levels are near or below historic lows in many areas.

 

Figure 1. MODIS Land Surface Temperature and NDVI anomalies for the 1st dekad of November 2016.
Figure 1. MODIS Land Surface Temperature and NDVI anomalies for the 1st dekad of November 2016.

These conditions appear related to long term persistent dryness in the region, as indicated by Standardized Precipitation Index values for the last dekad, month, 2 months, 3 months and 6 months (Figure 2).

Figure 2. Standardized Precipitation Index values calculated from the 1st dekad of November 2016. From earlywarning.usgs.gov.
Figure 2. Standardized Precipitation Index values calculated from the 1st dekad of November 2016. From earlywarning.usgs.gov.

Convergent evidence for water stress and poor water availability for cattle, goats and camels is provided by the FEWS NET waterpoint viewer. Figure 3 shows the current assessment – much of northeastern Kenya and southeastern Ethiopia is under alert or near-dry conditions.

Figure 3. USGS Waterpoint conditions as of early November 2016.

Figure 3. USGS Waterpoint conditions as of early November 2016.

We can look at stressed regions using time series of MODIS Normalized Difference Vegetation Index (NDVI) data. MODIS NDVI measures vegetative health at very high resolution (250m). Figure 4 shows time series of NDVI from far eastern Ethiopia. One year, 2003, was less green. Two recent very poor years 2009/2010 and 2010/2011 were actually greener. By early November, most years have already reached or passed the maximum NDVI value. Typical behavior is for the vegetation to senesce and dry from this point forward.

Figure 4. Somaliland NDVI time series.
Figure 4. Somaliland NDVI time series.

Figures 4, 3, 2 and 1 provide convergent evidence support John McGrath’s concern about very dry conditions in far eastern Ethiopia. This year looks way drier than most recent years.  In terms of long term moisture stress and poor pastoral conditions, it is very concerning that both January-to-early November NOAA CPC RFE2 rainfall totals and January-to-early November NDVI averages indicate very poor conditions (Figure 5). I am not an expert on pastoral early warning, but I believe this kind of repeated aridity can weaken herds and reduce their milk production.

Figure 5. Scatterplot showing 2003-2016 Jan-Nov RFE2 rainfall totals and average eMODIS NDVI for Somaliland.
Figure 5. Scatterplot showing 2003-2016 Jan-Nov RFE2 rainfall totals and average eMODIS NDVI for Somaliland.

Focusing on the same type of target – the anticipated average NDVI conditions between the second dekad of November and the last dekad of March, we find that simple regressions between the Jan-Nov1 NDVI (as shown in Figure 5) and Nov2-March NDVI have a reasonable R2 (~0.4). We can use this simple regression to project a plausible ‘what-if’ scenario for the rest of the dry season. This result is shown in Figure 6.

Figure 6. Somaliland NDVI projections.
Figure 6. Somaliland NDVI projections.

What this figure is telling us is that its quite likely that unless an unseasonal storm drops a lot of water in Somaliland, further browning of the vegetation is likely, as we saw in 2009/2010 and 2010/11. These seasons also appear to be the closest analogs, based on our projected Nov 2016-March 2017 ‘forecast’.

We next carry out a similar analysis for the Mandera region of NE Kenya (Figure 7). Once again, we see historically low levels of NDVI, drier than in 2010. Here, we do seem to be earlier in the season, and there seem to be more years that had a late December recovery.

Figure 7. NDVI time series for the Mandera region of NE Kenya.
Figure 7. NDVI time series for the Mandera region of NE Kenya.

Applying our same ‘forecast’ strategy, i.e. predicting Nov 2016-March 2017 NDVI based on January-November 2016 NDVI (R2=0.4), gives us the results shown in Figure 8. Here the forecast is more modest – a simple continuance of current historic low levels. The analog years based on this analysis might be 2005/2006 and 2010/2011. The USGS waterpoint analysis (Figure 3) provides a strong level of convergence on dryness in this area, and barring an unseasonal upturn in the rains, which has happened before, poor pastoral conditions seem likely to continue, based on the data analyzed here.

Figure 8. Projection of Nov 2016-March 2017 NDVI for Mandera.
Figure 8. Projection of Nov 2016-March 2017 NDVI for Mandera.

We conclude by presenting a new experimental Potential EvapoTranspiration (PET)  forecast, produced by Daniel McEvoy (DRI) and Shrad Shukla (CHG). This forecast (Figure 9) shows the median CFSv2 ensemble forecast over East Africa. The CFSv2 is a sophisticated coupled ocean-atmosphere model used for seasonal forecasts. The total number of ensemble members is 28. They were initialized at an interval of 5 days starting October 8th, 2016 through November 7, 2016.  According to this figure the PET outlook appears to be mostly within normal range (standardized anomaly of -0.44 to 0.44) over the drought regions (Kenya and Somalia).

Figure 9. PET forecasts for Eastern Africa. Kindly provided by Daniel McEvoy (DRI) and Shrad Shukla (CHG).
Figure 9. PET forecasts for Eastern Africa. Kindly provided by Daniel McEvoy (DRI) and Shrad Shukla (CHG).

Putting this all together, we can say that there is lots of evidence that pastoral areas in eastern Ethiopia, NE Kenya, and Somalia appear to have experienced very low rainfall, and that this has resulted in very low NDVI values, both at present and over the January-November time frame (Fig. 6, 8). The poor pasture conditions associated with these low NDVI values appears associated with very warm air temperatures (Figure 1) and water point estimates that appear to be almost dry in many places (Figure 3). The cumulative low NDVI values of the current Jan-Nov season appear to be very low, likely to persist, and similar to seasons like 2009/2010 and 2010/2011.  PET forecasts appear normal, but normal behavior for this region is associated with senescence and NDVI declines in most cases.

Setting aside the outlook for spring 2017 rains, we can look at FEWS NET assessments from January of 2010 and 2011 as possible indicators of where we might be in a few months (a, b, c). We at the CHG are not qualified to do food security assessments, but it does seem safe to say that the current pastoral conditions are very poor in many places, and unlikely to improve until spring. This may mean continued reductions in herd health and pastoral livelihoods. Close monitoring and assessment seems warranted.

 

 

A very poor East African short rains seems almost certain

Chris Funk

 

Last month on this blog we predicted a poor short rainy season for Eastern Kenya and Southern Somalia. Unfortunately that forecast verified. Strong cool-warm-cool sea surface temperature contrasts across the cool Western Indian Ocean, warm Eastern Indian/Western Pacific, and cool Eastern Indian oceans conspire to produce a very strong precipitation dipole (Figure 1) contrasting very heavy precipitation over the Indo-Pacific Warm Pool to the east and the Western Indian Ocean and East Africa to the west. USGS crop and rangeland models indicate ‘no start’ to the growing season almost everywhere.

Figure 1. October 2016 CHIRP rainfall anomalies
Figure 1. October 2016 CHIRP rainfall anomalies

This is very concerning given that the short rains typically end in November for most places in Eastern Kenya and Southern Somalia in November. There is also a great deal of persistence in the climate system between October and November-December. Thus the correlation between October and October-November-December (OND) E. Kenya/S. Somalia rains (please see last post for the specific region) is extremely high: 0.91. Despite that, the mean of the OND rains (224 mm) is much greater than the mean for October (64 mm) (Figure 2).

Figure 2. Scatterplot showing the strong relationship between October and OND E. Kenya/S. Somalia rains.
Figure 2. Scatterplot showing the strong relationship between October and OND E. Kenya/S. Somalia rains.

This is really a quite stunning predictive relationship. November-December E. Kenya/S. Somalia rains also have a strong correlation with October sea surface temperatures (Figure 3). These correlations are really high, with a negative relationship to the Indo-Pacific Warm Pool of -0.9, and positive relationships to the Western Indian and Eastern Pacific of +0.5 and +0.7.

Figure 3. Correlation between November-December E E Kenya/S Somalia rainfall and November-December sea surface temperatures.
Figure 3. Correlation between November-December E E Kenya/S Somalia rainfall and November-December sea surface temperatures. Based on data from 1996-2015.

Unfortunately, the current (October) Ocean looks very much like the opposite of Figure 3 – warm in the Indo-Pacific Warm Pool and cool in the Eastern Pacific (Figure 4). The warmth in the West Pacific is quite exceptional >+2.5 standardized anomalies, which is equivalent to a very warm 30.3 Celsius.

Figure 4. Observed October 2016 sea surface temperatures expressed as standardized anomalies. Based on a 1981-2010 baseline and NOAA Extended Reconstruction v4 data.
Figure 4. Observed October 2016 sea surface temperatures expressed as standardized anomalies. Based on a 1981-2010 baseline and NOAA Extended Reconstruction v4 data.

These strong SST gradients and warm Western Pacific sea surface temperatures contributed to the strong Precipitation dipole (Figure 1) and dry East African conditions in October. At this point, as shown in a recent Africa Hazards Briefing by Nick Novella at NOAA’s Climate Prediction Center (Figure 5), rainfall is very low and recovery is very unlikely. Nick’s awesome Seasonal Performance Probability Tool uses the long historical record of the ARC2 dataset to explore the potential future outcome of a season at a given time and place. It is not looking good for Eastern Africa.

Figure 5. Past rainfall performance and potential future rainfall performance at Baidoa, Somalia. Each grey line represents a projection based on a previous year of the ARC2 data set.
Figure 5. Past rainfall performance and potential future rainfall performance at Baidoa, Somalia. Each grey line represents a projection based on a previous year of the ARC2 data set.

We next explore this issue using two separate approaches: analog seasons and formal (but simple) cross-validated forecasts of OND rainfall. Our composites are based on similar dry Octobers (Figure 6)  1996, 1998, 2005, and 2010 exhibited similar very low October rains.

Figure 6. 1981-2016 CHIRPS E Kenya/S. Somalia rainfall and analog years.
Figure 6. 1981-2016 CHIRPS E Kenya/S. Somalia rainfall and analog years.

What is interesting about these years is that they also tended to exhibit a strong gradient between the Indo-Pacific Warm Pool and Eastern Pacific Ocean (Figure 7). The Western Indian Ocean and the associated Indian Ocean dipole likely played a role as well, but is not explored here, since recent correlations with E Kenya/S Somali rains seems quite low (Figure 3).

Fig. 7. Indo-Pacific warm pool and Eastern Pacific sea surface temperatures from the boxes identified in Figure 5. Dry October analogs are shown with filled black circles.
Figure 7. Indo-Pacific warm pool and Eastern Pacific sea surface temperatures from the boxes identified in Figure 5. Dry October analogs are shown with filled black circles.

Note that the selection of analog years was based on just dry Octobers, so finding our analogs clustered in Figure 7 helps reinforce the strong sea surface temperature forcing indicated in Figure 3, which in turn helps explain the strong Oct-OND relationship indicated in Figure 2. When I used the excellent GeoCLIM tool to plot rainfall anomalies for these analog seasons (Figure 8) I found that these analogs would indicate very poor rainfall performance in Kenya and Somalia, and a poor performance of the Deyr rains in Ethiopia. The 2016 March-April-May rains were very poor in Kenya and Ethiopia. Southeastern-Central Ethiopia is extremely food insecure, having experienced substantial droughts or dryness in the past two Belg and past two Kiremt rainy seasons.

Figure 8. Analog CHIRPS anomalies, expressed as percentages.
Figure 8. Analog CHIRPS anomalies, expressed as percentages.

The prospect of a poor E. Kenya/S. Somalia season can be quantified formally via a simple cross-validated regression using October CHIRPS observations (Figure 2) and Indo-Pacific and East Pacific sea surface temperatures (Figure 7). I first predicted November-December precipitation and then added in the observed October values. The forecast (Figure 8) predicted 1996-2015 OND rains very well (cross-validated R2 of 0.8) and projects an expected OND total of 83 mm, with a standard error of +/- 53 mm. So it seems very likely that the season will be very poor.

This poor season will follow a very poor 2016 MAM rainy season. I made a MAM+OND ‘forecast’ by combining the observed MAM and October E. Kenya/S. Somalia values with our November-December forecasts (Figure 9). The story that emerges is that this region may experience the worst MAM+OND rains in the past 20 years. Since we can be almost certain that short rains outcomes are going to be poor, especially for farmers in Eastern Kenya and Southern Somalia, it may be possible to engage in proactive response planning. While pasture conditions can change more rapidly if anomalous late season rains arrive, current  estimates of vegetation health look quite low.

Figure 9. Cross-validated forecasts of E Kenya/S Somalia MAM+OND 2016 rains.
Figure 9. Cross-validated forecasts of E Kenya/S Somalia MAM+OND 2016 rains.

FEWS NET continues to develop new tools for monitoring and modeling drought.  For example, support from NASA SERVIR is enabling us to provide CHIRPS-compatible Global Ensemble Forecast Systems (GEFS) forecasts, which can be viewed on our Early Warning eXplorer.  Collaboration with the Desert Research Institute has created the FEWS Engine which leverages the incredible processing power of the Google Earth Engine (GEE). We can use the GEE to produce NDVI composites for our analog years (Figure 10) reinforcing our concerns.

Figure 10. Google Earth Engine composites of OND MODIS NDVI for 2005 and 2010.
Figure 10. Google Earth Engine composites of OND MODIS NDVI for 2005 and 2010.

All indicators converge on a very high probability of a very poor short rains. Looming on the horizon is the potential of La Nina-like conditions in the spring. While current NOAA CPC assessments indicate that this is unlikely, if a La Nina did arrive, and the West Pacific continued to be very warm, we might see another poor season in the spring of 2017. If so, providing an adequate response to a 2016 short rains drought would help increase East African resilience in 2017.

Concerns about the Kenya/Somalia short rains

globalpopulation-2
Figure 1. Schematic representation of person years, based on human population from 8000 BC through 2050 AD.

By Chris Funk

Welcome to the first installment of the Climate Hazards Group blog, which we have started to discuss potential climate hazards, current climate extremes, and climate change. I am a federal scientist who has worked for many years with the US Geological Survey’s Center for Earth Resources Observations and Science and the US Agency for International Development’s Famine Early Warning Systems Network (FEWS NET) and collaborating FEWS NET scientists at the Climate Prediction Center, NASA’s Goddard Space Flight Center Hydrology Lab, and the Earth Science Research Laboratory.  I am also an affiliated research professor with the University of California, Santa Barbara Geography Department, and the Research Director for the Climate Hazards Group. We (the CHG) works closely with the NASA SERVIR project, using state-of-the-art satellite observations to improve food security decision making.  In this blog we will analyze and discuss potential climate extremes related to Indo-Pacific sea surface temperatures, focusing on time scales of one to three months and food insecure countries.   We will also leverage new scientific data sets and models to examine potential real world extremes, especially droughts.

Please note, however, that this blog only represents our personal insights and opinions, and not the USGS, FEWS NET, or any of our partner agencies.

The objective of this web space is to monitor climate and put it in historic context. We are living in a unique period of rapid human transition, but also have an unprecedented ability to observe our planet. Since I am a climate watcher, this blog will capture some of that watching and relate what we see to potential food security hazards. Since climate impacts millions of people, my hope is that these posts may be of interest to colleagues in the developing world, FEWS NET, SERVIR, partner agencies like the World Food Programme and other humanitarian relief agencies.

My first observation is that we are living in a time of tremendous change. Think of it this way (Fig. 1). Begin in 1966, and add up all the people living in that year with the number of people living in 1967. Continue adding through to 2055. That’s a measure of ‘human power’ – the total number of person-years people have available to laugh, cook, cry, work and do whatever. Calculating this number we get an estimated total of 631 billion person-years. This staggering amount of time is about 49 times the age of our universe (~13 billion years).

Given such an epic span even I could tell a joke that my teenagers found funny (though of course they wouldn’t laugh).

Now let’s start counting backwards from 1966. We get all the way back to 1000 AD before we accumulate a similar number of person-years. Between 1966 and 2055 as much will happen, in terms of human action, insight, suffering and achievement, as in the 960 years before 1966. So if you feel a little overwhelmed by the world, take heart, the world is probably a little overwhelmed by you as well. Medieval history, the Renaissance, most of the great Chinese empires, the rise of great and powerful Islamic societies, the age of reason, all the wars, the art, the discoveries, and the industrialization between 966 and 1965 – we are likely to achieve that much human activity in our uncertain age of consequence.

Going back another ~631 billion person-years takes us back another two and a half millennia, to around 1500 B.C. Then the next 631 billion years brings us 6,500 years back to about 8000 B.C., the age of the ‘Neolithic’ (new rock) revolution that saw the first continuous human settlements, the advent of farming, and the first domestication of animals. So our current era of the anthropocene will contain about one quarter of human history, expressed in person-years.

I think that we better be paying close attention in this age of compressed activity. Luckily, we have satellites, climate models and networked observations systems. We know that climate change will make our oceans much warmer, and in today’s blog we will examine how warming in the West Pacific and Eastern Indian ocean, combined with La Niña-like cool conditions in the central Pacific, may increase the chances of drought during the upcoming ‘short’ October-November-December (OND) rains in Eastern Kenya and Southern Somalia. More specifically, I am concerned about potential back-to-back droughts in this region in the spring and fall of 2016. We are facing a situation that combines very warm Eastern Indian Ocean/Western Pacific sea surface temperatures with La Niña-like cool  sea surface temperatures in the eastern Pacific: I do some a simple statistical forecast to argue that we need to be concerned about poor East African OND rains.

This part of the world has two rainy seasons each year, one in the spring and one in the fall, and repeated dry seasons can be very hard on the millions of food insecure farmers and pastoralists that live in this region. The spring 2016 March-May rains this year were very low (Fig. 2). This region/season has experienced a substantial decrease in rainfall due to warming in the West Pacific and increases in the West Pacific Warming Mode. A warm West Pacific can contribute to droughts in both the spring and fall.  In the spring we’ve seen a substantial increase in drought frequencies – note in Fig. 2 that ~10 out of the last 16 March-to-May seasons have been dry in comparison to the 1900-1950 mean.

timeserieskensom_mam_rain
Figure 2. Eastern Kenya/Southern Somalia standardized rainfall anomalies. Based on CHG CHIRPS and CenTrends data sets.

 

Here I present a very simple statistical forecast of OND Eastern Kenya/Southern Somalia rains based on observed September sea surface temperatures. We begin by taking a look at the correlation between OND rains and September sea surface temperatures (Fig. 3, left). What we see here is that there is a strong negative (<-0.7) correlation with sea surface temperatures over the west Pacific (boxed area with blue shading), and a similar positive (>+0.7) correlation with sea surface temperatures over the equatorial east Pacific (boxed area with red shading). What we see in this figure is that cool La Niña-like conditions are associated with low East African rains, as are warm west Pacific sea surface temperatures.

What concerns me, right now, is that the moderately cool  La Niña conditions will combine with the very warm conditions in the western Pacific to produce a very strong sea surface temperature gradient, which tends to produce east African droughts. The right panel in Fig. 2 shows observed September sea surface temperature anomalies, expressed as standardized deviations. What we see is modest cooling in the central Pacific, and some very warm (>+2 standard deviations) warm conditions in the west Pacific. Note that the Indian Ocean pattern in the right panel of Fig. 3 also resembles the Indian Ocean Dipole — which is also conducive to dry conditions over the Horn of Africa.

Figure 3. Top – correlation between Kenya/S. Somalia OND rainfall and September sea surface temperatures. Bottom – current September sea surface temperatures expressed as standardized anomalies.

We can place these west Pacific sea surface temperatures in historic context by plotting them as a time series (Fig. 4). We have pointed out the very strong climate change component in West Pacific sea surface temperatures in several papers (a,b,c,d,e), and it is very obvious from this figure. There is a very strong warming trend starting around 1970. It is also interesting to note that all of the past four Septembers have been pretty hot. Right now, West Pacific sea surface temperatures are very warm (third warmest on record). These conditions are likely to persist given the strong thermal inertia of the West Pacific and the latest coupled climate model forecasts (not shown).

timeserieswestpacificssts
Figure 4. West Pacific September sea surface temperature anomalies [deg C].

We next use the observed sea surface temperatures to predict OND East Africa rains using a simple cross-validated regression model. I predicted 1996-2015 Kenya-Somalia OND rains using time series of west Pacific and central Pacific sea surface temperatures. These results are shown in Fig. 5 as blue dots.  While not perfect, this model does a good job of discriminating between most wet and dry seasons. The associated regression coefficients suggest that the influence of the West Pacific is about twice as important as central Pacific. I then used these coefficients to predict 2016 OND Eastern Kenya-Southern Somalia rains (red dot). The very warm West Pacific sea surface temperatures combine with modestly cool central Pacific conditions to produce a forecast for low Eastern Kenya/Southern Somalia rainfall (~-1Z). Note however, that the cross-validated standard error was fairly high (0.8 of a standardized anomaly), so there is a lot of uncertainty in this projection. Below normal rainfall seems likely, however, and this may mean repeated shocks following the poor rainfall performance this spring (Fig. 2).

Figure 5. 1996-2015 cross-validated forecasts of OND Eastern Kenya/Southern Somalia rains (blue circles), together with the 2016 forecast (red circle).

Please bear in mind that this forecast only applies for Eastern Kenya and Southern Somalia. Other nearby regions, like south-eastern Ethiopia or northeastern Tanzania may also be dry, but I did not explicitly evaluate these areas. On the other hand, standardized rainfall maps from past seasons with similar sea surface temperatures showed wide-spread drying  (Figure 6). Note also that current food security situations in Eastern Kenya and Southern Somalia are not at crisis levels. We should be concerned, however, about the possibility of two poor rainy seasons in the spring and fall of 2016.

Figure 6. CHIRPS OND standardized precipitation index maps for similar (analog) seasons.
Figure 6. CHIRPS OND standardized precipitation index maps for similar (analog) seasons.

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