Forecast for below normal Somali Gu rains based on January sea surface temperatures

Chris Funk, Amy McNally, Greg Husak, Laura Harrison, Shraddhand Shukla, and Nicholas Novella

Figure 1. July-December Noah Soil Moisture Ranks for 2010 and 2016

 

 

Here we present yet another statistical forecast for below normal East African March-May rains. As predicted, October-December East African rainfall was below normal – in fact extreme dryness was experienced in Somalia, and the Somaliland area of Ethiopia (WFP, FEWS NET). At a global scale, the world faces unprecedented emergency food assistance needs, with four countries – Nigeria, South Sudan and Yemen facing a credible risk of famine. For Somalia, the Inter-Agency Working Group on Disaster Preparedness for East and Central Africa has released an urgent call for action, and the Somalia Food Security and Nutrition Analysis Unit warns of possible famine in Somalia. The FSNAU-FEWS NET countrywide seasonal assessment conducted in December 2016 and made public on February 2nd finds that 2.9 million people will likely face Crisis and Emergency (IPC Phases 3 and 4) through June 2017.

To help identify drought conditions associated with protracted stress on livestock, the FEWS NET science team has analyzed six-month (July-December) soil moisture estimates (Figure 1). These estimates were produced at NASA using the Noah land surface model, CHIRPS precipitation, and MERRA2 surface forcings (air temperature, etc.). The outputs suggest that conditions in parts of coastal Kenya, Somalia and the Somaliland region of Ethiopia are among the driest on record (over the 1982-2016 time period), and as bad or worse than 2010. The failed 2010 season was the beginning of famine that killed more than 250,000 Somalis; half of these were young children . Looking at soil moisture, as opposed to just rainfall, sparse rainfall (a, b, c).

Figure 2. Time series of standardized July-December Somalia soil moisture for the region highlighted in Figure 1.

Extracting and plotting (Figure 2) standardized soil moisture values from region identified with blue polygon in Figure 1 (basically Somalia plus far-eastern Ethiopia), we can see that for this large area, the past six months have been the driest since 1982, due to the combined influence of low rainfall above normal temperatures. We will focus on forecasts for this region.

We now turn to forecasting the March-May 2017 Gu rains, based on January 2017 NOAA optimum interpolation sea surface temperatures. This posting will be broadly similar to the last several outlooks. The process of predicting East African long rains based on observed January sea surface temperatures has been examined specifically by FEWS NET scientists to provide support for food security situations like we currently face in the Horn of Africa. The results presented here, however, focus on Gu rains in the Somalia region shown in Figure 1.  For continuity with our previous blog posts, we also focus on seasons with cool or neutral central equatorial Pacific sea surface temperatures (1984, 1986, 1989, 1990, 1996, 1997, 1999, 2000, 2008, 2009, 2011, and 2012). These were years identified in our December 5th post, based on Niño 4 sea surface temperatures. In this study we use these years to calculate correlations and build a simple regression-based forecast.

Figure 3. Correlation between Somalia Gu rainfall and January sea surface temperatures. Correlations screened for significance at p=0.1.

Figure 3 shows the correlation between March-May Gu rains in our study region (Somalia) and January sea surface temperatures for years with cool or neutral central equatorial Pacific sea surface temperatures. Pink boxes denote regions selected as predictors: the West Pacific, Central Pacific, and South Pacific. In our previous forecast we selected the North Pacific, as opposed to South Pacific as a predictor. That forecast was based on different region and time-period. Dark yellow lines also mark the characteristic ‘Western V’ structure often associated with recent East African long rains droughts. The Western and Southern Pacific regions appear to be the strongest controls of the Somalia Gu season during years with neutral or cold central Pacific temperatures. As discussed in the Appendix below warm ocean conditions in these boxes help drive subsidence over the central Pacific and Indian Ocean. As second negative teleconnection region appears to the southeast of Madagascar. Warm ocean conditions here may slow the northward progression of the long rains. This region was not selected as a predictor, because the influence of this region has not been investigated in depth.

Figure 4. Observed January NOAA Optimum Interpolation sea surface temperatures, expressed as standardized departures from a 1981-2010 baseline.

Figure 4 shows the actual observed January 2017 sea surface temperature conditions. Very warm (+1.5 standardized anomalies) sea surface temperatures are found in the Western V regions.  This  ‘Western V’ pattern associated with the West Pacific Warming Mode produces dry conditions over East Africa. Slightly cool conditions are found in the Central Pacific. Note that very warm temperatures are also found to the southeast of Madagascar. Figures 5-7 show scatterplots for each of the individual predictor regions for years with neutral or cool central equatorial Pacific sea surface temperatures. Based on the individual Western Pacific and Southern Pacific predictors, the upcoming Gu season might be very dry (<-1Z). Based on the Central Pacific alone, Gu conditions might be near normal.

Figure 5. Scatterplot of West Pacific January sea surface temperatures and March-May Somalia rainfall. Star indicates associated prediction for 2017.
Figure 6. Scatterplot of South Pacific January sea surface temperatures and March-May Somalia rainfall. Star indicates associated prediction for 2017.
Figure 7. Scatterplot of Central Pacific January sea surface temperatures and March-May Somalia rainfall. Star indicates associated prediction for 2017.

We next combine our predictors in a multivariate cross-validated regression (Figure 8). Given the small number of degrees of freedom two predictors were used: the average of the west and south Pacific boxes, and the east Pacific sea surface temperatures. The regression slope indicated a stronger forcing for the average of the west and south Pacific boxes: -2.2Z per °C versus +0.6Z per °C. Each green circle shows one cross-validated hindcast estimate. In general the model does a good job identifying most drought years. When the model predicted below normal rains, below normal rains typically occurred (5 out of 8 times). The direct regression forecast from our model was very low (-1.3Z) – and we feel it is hard to justify such a negative outlook given the anticipated transition out of La Niña-like conditions. A more cautious approach is taken, in which we assume that all the years with below normal forecasts are reasonable analogs. We then use the mean of the observed rainfall for these years as our forecast (-0.8Z) and the standard deviation of these below normal forecast years as our standard error estimate. Our forecast and 80% confidence intervals are shown with a red circle and cross-hatch in Figure 8. The distribution associated with this forecast would indicate an 80% chance of below normal (<0Z) rainfall and a 20% of above normal (>+0Z). There would a 50% chance of poor or very poor rains (<-0.8Z).

Figure 8. Cross-validated Somalia Gu hindcasts (green circles) based on January sea surface temperatures. Red star indicates 2017 Gu forecast and 80% confidence intervals (-0.8Z±1.1Z).

Figure 9 summarizes our results. Somalia Gu rains have experienced a substantial decline. During seasons with neutral and cool Central Pacific sea surface temperatures (marked with stars), ‘Western V’-like sea surface temperatures in January (Figure 3) tend to be followed by below normal Gu rains. Not every drought fits this pattern (1991, 2005), but the evidence analyzed suggests that below normal rains for 2017 are likely. Since 1999, 12 out the last 18 Gu seasons have been below normal (<0), our model suggests that current Pacific sea surface temperatures resemble the conditions preceding many of these below normal years.

Figure 9. Time series of standardized Somalia Gu rainfall (bars). Stars mark prior seasons with cool or neutral central Pacific sea surface temperatures. Black circles and lines indicate 2017 prediction (-0.8Z±1.1Z).

It should also be noted, however, that some dynamic forecast models predict a transition out of La Niña-like conditions before June of 2017. Sea surface temperature conditions could change dramatically over the next three months. A poor start to the rainy season, followed by improved conditions might be associated with a transition to warmer temperatures in the eastern Pacific. At present, (Fig. 10) January rainfall anomalies continue to indicate strong subsidence over East Africa and the Indian Ocean. This pattern seems likely to persist.

Fig. 10. January 2017 CHIRP rainfall anomalies (Source USGS/Climate Hazards Group/USGS).

 Appendix

This technical appendix looks briefly at current (past 30 day) climate conditions associated with current (January) sea surface temperatures. It is not intended for a general audience. On the other hand, understanding what is driving the current climate state provides support for our statistical projection. We are currently seeing a strong dipole in the amount of water vapor over East Africa and the Indian Ocean versus the Indo-Pacific Warm Pool. We also find low water vapor totals along the equator near the dateline. Our statistical model essentially predicts a continuation of this pattern for the Gu season. Associated with this dipole are strong equatorial westerly low level wind anomalies over the equatorial Indian Ocean – advecting atmospheric moisture away from East Africa and towards Indonesia. At the dateline we also see easterly zonal anomalies. This is characteristic of mild La Niña-like conditions, but the rest of the eastern equatorial Pacific looks non-La Niña-like. Presumably these  wind anomalies and water vapor conditions are associated with the observed January sea surface temperature conditions (Figure 4). The effect of warm sea surface temperatures in the equatorial West Pacific is well understood – warm tropical waters produce warm atmospheric conditions associated with low pressure, which can help draw in low level winds. More interesting might be the role played by warm south Pacific temperatures. Climatologically, upper level winds to the northeast of New Zealand move from west to east, curving north towards the equator to meet similar southerly flows from north of the equator. These converging flows produce upper level convergence and subsidence, reducing precipitation over the central Pacific. This helps to define the structure of the Walker Circulation. Over the past 30 days the upper level wind anomalies exhibit upper level ridging to the northeast of New Zealand and northwest of Hawaii that has enhanced north-south winds that converge near equator and dateline. We conjecture that this upper level riding is associated with a barotropic response to the very warm sea surface temperatures to the northeast of New Zealand and northwest of Hawaii. We can look at upper level velocity potential anomalies to see how this might associated with changes in the Walker Circulation. Purple areas in this figure are associated with upper divergence. Red regions are associated with upper level convergence. Upper level convergence will associated with subsidence and reduced precipitation, and this is what we see over Somalia and the central Pacific. We might interpret our statistical model to suggest that if temperatures in the West Pacific and South Pacific predictor regions remain substantially warmer than those over the central Pacific, atmospheric conditions will likely continue to exhibit the dry East Africa Indian Ocean/Wet Warm Pool/Dry Central Pacific pattern that has dominated the past four-to-five months.

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