Gideon Galu

Attribution of 2012 and 2003-2012 rainfall deficits in eastern Kenya and southern Somalia

Africa has experienced more frequent boreal spring dry events (Funk et al. 2008; Williams and Funk 2011; Lyon and DeWitt 2012; Funk 2012). In the spring of 2012, below-average March–May rains across parts of eastern Kenya and Southern Somalia (a region bounded by 4°S–4°N, 37°E–43°E, green polygon, Fig. E1A) once again contributed to crisis and emergency levels of food insecurity (FEWS NET 2012a). In some regions, rainfall deficits of more than 30% led to crop failures and poor pasture conditions, causing families in Kenya to move in search of work or take children out of school, and inhibiting Somalia’s recovery from the acute malnutrition and famine caused by the 2010–11 drought. While not particularly severe, the poor March–May 2012 rains added to climatic stresses associated with a series of March–May dry events occurring in 2007, 2008, 2009, and 2011. Figure E1b shows March–May (three month) Standardized Precipitation Index (SPI; McKee et al. 1993) values, based on 1981–2012 FEWS NET precipitation data (see Supplemental Material for a brief description). Dry events, defined as March–May seasons with SPI values of less than -0.5, are shown in orange. In fragile food economies, these repetitive dry events can lower resilience, disrupt development, and require large infusions of emergency assistance. It is not the climate alone that creates these outcomes, but rather the climate’s interaction with extreme poverty, high-endemic rates of malnutrition, limited or nonexistent governmental safety nets, and poor governance. In 2011, for example, the worst drought in 60 years combined with chronic food insecurity, high global food prices, and the actions of Somali terrorists produced an estimated 258 000 deaths in Somalia (FEWS NET, 2013). In this study, we examine the question of whether sea surface temperatures (SSTs) caused the poor 2012 March–May eastern East African rains and increased the frequency of dry events over the past decade (2003–12), using two new Global Forecast System E. ATTRIBUTION OF 2012 AND 2003–12 RAINFALL DEFICITS IN EASTERN KENYA AND SOUTHERN SOMALIAThe European summer of 2012 was marked by strongly contrasting rainfall anomalies, which led to flooding in northern Europe and droughts and wildfires in southern Europe. This season was not an isolated event, rather the latest in a string of summers characterized by a southward shifted Atlantic storm track as described by the negative phase of the SNAO. The degree of decadal variability in these features suggests a role for forcing from outside the dynamical atmosphere, and preliminary numerical experiments suggest that the global SST and low Arctic sea ice extent anomalies are likely to have played a role and that warm North Atlantic SSTs were a particular contributing factor. The direct effects of changes in radiative forcing from greenhouse gas and aerosol forcing are not included in these experiments, but both anthropogenic forcing and natural variability may have influenced the SST and sea ice changes................................................................................................................................................................... iv

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

[1] Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

Earlier famine warning possible using remote sensing and models

Remote sensing allows scientists to detect slowly evolving natural hazards such as agricultural drought. Famine early warning systems transform these data into actionable policy information, enabling humanitarian organizations to respond in a timely and appropriate manner. These life-saving responses are increasingly important: In 2006, one out of eight people did not have enough food to eat and 22 million more people became sufficiently undernourished to require intervention, prompting 22 countries to provide

Assessing the Contributions of Local and East Pacific Warming to the 2015 Droughts in Ethiopia and Southern Africa

6.5 billion in food aid. Since their inception in the mid-1980s, the combination of monitoring and mitigation systems has dramatically reduced the number of famines caused by biophysical hazards, such as floods, drought, and pests, that destroy food crops [Murphy and McAfee, 2005]. Yet despite this notable achievement, many countries, mostly in Africa, face chronic and increasing food insecurity.

Anthropogenic Enhancement of Moderate-to-Strong El Niño Events Likely Contributed to Drought and Poor Harvests in Southern Africa During 2016

Introduction. In northern Ethiopia (7°–14°N, 36.5°– 40.5°E, NE) during June–September (JJAS) of 2015 and in southern Africa (13.5°–27°S, 26.5°–36°E, SA) during December–February (DJF) of 2015/16, main growing seasons rains were extremely poor. In Ethiopia, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) (Funk et al. 2015c) and Centennial Trends (Funk et al. 2015b) data indicated one of the worst droughts in more than 50 years (FEWSNET 2015). More than ten million people currently require humanitarian relief (FEWSNET 2016a). SA rains were also extremely poor (FEWSNET 2016b); in Mozambique and Malawi, February maize prices were more than twice the five-year average, and in Zimbabwe the president has declared a national disaster in view of the El Niño–induced poor rains and the escalating food insecurity situation. NE has been experiencing long-term rainfall declines (Funk et al. 2008; Funk et al. 2005; Jury and Funk 2013; Viste et al. 2012; Williams et al. 2012). The eastern Ethiopian highlands have exhibited recurrent soil moisture and runoff deficits since the 1990s (Funk et al. 2015c). NE rains in 2015 were the driest on record, but station data density prior to 1950 is very sparse for Ethiopia (Funk et al. 2015b). SA rainfall does not exhibit a decline, but the 2015–16 drought was severe. The impact of ENSO on Ethiopian rainfall is well documented (Fig. S15.1; Camberlin 1997; Degefu 1987; Diro et al. 2011; Gissila et al. 2004; Korecha and Barnston 2007; Korecha and Sorteberg 2013; Segele and Lamb 2005): the warm phase of ENSO is associated with suppressed rains during the main wet season (JJAS) over north and central Ethiopia. There have also been numerous papers documenting a negative teleconnection between El Niño and SA rainfall (Supplemental Fig. S15.1; Hoell et al. 2015; Jury et al. 1994; Lindesay 1988; Misra 2003; Nicholson and Entekhabi 1986; Nicholson and Kim 1997; Reason et al. 2000; Rocha and Simmonds 1997).

How will East African maize yields respond to climate change and can agricultural development mitigate this response

Introduction. In December–February (DJF) of 2015/16, a strong El Niño (Niño‐3.4 SST >29°C) contributed to a severe drought over southern Africa (SA; Funk et al. 2016). A 9‐million ton cereal deficit resulted in 26 mil‐ lion people in need of humanitarian assistance (SADC 2016). While SA rainfall has a well‐documented nega‐ tive teleconnection with Niño‐3.4 SSTs (Hoell et al. 2015, 2017; Jury et al. 1994; Lindesay 1988; Misra 2003; Nicholson and Entekhabi 1987; Nicholson and Kim 1997; Reason et al. 2000; Rocha and Simmonds 1997), the link between climate change and El Niño remains unclear (Christensen et al. 2013) due to the large natural variability of ENSO SSTs (Wittenberg 2009), uncertainties surrounding measurements and trends (Solomon and Newman 2012), intermodel differences in ENSO representation and feedbacks (Guilyardi et al. 2012; Kim et al. 2014), and difficulties associated with quantifying ENSO strength (Cai et al. 2015). Figure 18.1a highlights observational uncertain‐ ties (Compo and Sardeshmukh 2010; Solomon and Newman 2012) using four datasets: ERSSTv4 (Huang et al. 2015), HadISST (Rayner et al. 2003), Kaplan SST (Kaplan et al. 1998), and Hurrell (Hurrell et al. 2008). These products differ substantially in their represen‐ tation of cool events and Niño‐3.4 variance. Two SST products indicate significant upward trends; two SST products do not. These data have been standardized to remove systematic differences in variance. Focusing just on the behavior of moderate–strong El Niño events (MSENEs), we can produce more ro‐ bust (first order) statistics by comparing the means of the top ten warmest Niño‐3.4 events between 1921–80 and the top six warmest events between 1981–2016. Rather than using a set SST threshold, MSENEs are defined as 1‐in‐6‐year warm events. This provides a simple nonparametric approach that takes advantage of the well understood quasi‐periodic nature of ENSO to identify MSENEs across multiple models and simulations. Modest changes in the number of events (say, 1‐in‐7 or 1‐in‐5) produced modest increases and decreases in El Niño temperatures, but did not sub‐ stantially change the results. We begin our analysis in 1921 (because ship data before 1921 is limited), and divide the remaining 96 years into two time periods with relatively weak and strong radiative forcing, respectively. Examining changes in MSENE means (horizontal lines in Fig. 18.1a), we find that all the observational datasets identify significant increases (Fig. ES18.1 examines ERSSTv4 errors). Note that we are not explicitly ex‐ amining changes in ENSO variance, ENSO means, or Niño‐3.4 SST trends, but only Niño‐3.4 magnitudes AFFILIATIONS: Funk—U.S. Geological Survey, Center for Earth Resources Observation and Science, and UC Santa Barbara Climate Hazards Group, Santa Barbara, California; Davenport, harrison, shukLa, pomposi, anD husak—UC Santa Barbara Climate Hazards Group, Santa Barbara, California; magaDzire, gaLu, anD koreCha—UC Santa Barbara Climate Hazards Group, Santa Barbara, California, and Famine Early Warning Systems Network; artan—Intergovernmental Authority on Development (IGAD) Climate Prediction & Applications Centre, Nairobi, Kenya; inDeje—IGAD USAID/Kenya and East Africa Planning for Resilience in East Africa Through Policy Adaptation, Research, and Economic Development, Nairobi, Kenya; maCharia—Regional Center for Mapping of Resources for Development, Nairobi, Kenya; nsaDisa—Director of the Southern African Development Community’s Climate Services Centre.

A climate trend analysis of Ethiopia

We analyze the response of Kenyan maize yields to near-term climate change and explore potential mitigation options. We model county level yields as a function of rainfall and temperature during a period of increased regional warming and drying (1989–2008). We then do a counter factual analysis by comparing existing maize yields from 2000 to 2008 to what yields might have been if observed warming and drying trends had not occurred. We also examine maize yields based on projected 2026–2040 climate trends. Without the observed warming and drying trends, Eastern Kenya would have had an 8% increase in maize yields, which in turn would have led to a net production increase of 500,000 metric tons. In Western Kenya, the magnitude of change is higher but the relative changes in predicted values are smaller. If warming and drying trends continue, we expect future maize yields to decline by 11% in Eastern Kenya (vs. 7% in Western Kenya). We also examine whether these future losses might be offset through agricultural development. For that analysis, we use a household panel dataset (2000, 2005) with measurements of individual farm plot yields, inputs, and outputs. We find that under a scenario of aggressive adoption of hybrid seeds and fertilizer usage coupled with warming and drying trends, yields in Western Kenya might increase by 6% while those in Eastern Kenya could increase by 14%. This increase in yields might be larger if there is a corresponding increase in usage of drought-tolerant hybrids. However, wide prediction intervals across models highlight the uncertainty in these outcomes and scenarios.