Laura Harrison

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

Modulation of the Southern Africa precipitation response to the El Niño Southern Oscillation by the subtropical Indian Ocean Dipole

The climate of Southern Africa, defined as the land area bound by the region 15°S–35°S; 12.5°E–42.5°E, during the December–March rainy season is driven by Indo-Pacific sea surface temperature (SST) anomalies associated with the El Niño Southern Oscillation (ENSO) and the Subtropical Indian Ocean Dipole (SIOD). The observed December–March 1979–2014 Southern Africa precipitation during the four ENSO and SIOD phase combinations suggests that the phase of the SIOD can disrupt or enhance the Southern Africa precipitation response to ENSO. Here, we use a large ensemble of model simulations driven by global SST and ENSO-only SST to test whether the SIOD modifies the relationship between Southern Africa precipitation and ENSO. Since ENSO-based precipitation forecasts are used extensively over Southern Africa, an improved understanding of how other modes of SST variability modulate the regional response to ENSO is important. ENSO, in the absence of the SIOD, forces an equivalent barotropic Rossby wave over Southern Africa that modifies the regional mid-tropospheric vertical motions and precipitation anomalies. El Niño (La Niña) is related with high (low) pressure over Southern Africa that produces anomalous mid-tropospheric descent (ascent) and decreases (increases) in precipitation relative to average. When the SIOD and ENSO are in opposite phases, the SIOD compliments the ENSO-related atmospheric response over Southern Africa by strengthening the regional equivalent barotropic Rossby wave, anomalous mid-tropospheric vertical motions and anomalous precipitation. By contrast, when the SIOD and ENSO are in the same phase, the SIOD disrupts the ENSO-related atmospheric response over Southern Africa by weakening the regional equivalent barotropic Rossby wave, anomalous mid-tropospheric vertical motions and anomalous precipitation.

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

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).

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

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.

Corrigendum: The Centennial Trends Greater Horn of Africa precipitation dataset

This corrects the article DOI: 10.1038/sdata.2015.50.