Attribution Analysis of the Ethiopian Drought of 2015

Abstract

In northern and central Ethiopia, 2015 was a very dry year. Rainfall was only from one-half to three-quarters of the usual amount, with both the “belg” (February–May) and “kiremt” rains (June–September) affected. The timing of the rains that did fall was also erratic. Many crops failed, causing food shortages for many millions of people. The role of climate change in the probability of a drought like this is investigated, focusing on the large-scale precipitation deficit in February–September 2015 in northern and central Ethiopia. Using a gridded analysis that combines station data with satellite observations, it is estimated that the return period of this drought was more than 60 years (lower bound 95% confidence interval), with a most likely value of several hundred years. No trend is detected in the observations, but the large natural variability and short time series means large trends could go undetected in the observations. Two out of three large climate model ensembles that simulated rainfall reasonably well show no trend while the third shows an increased probability of drought. Taking the model spread into account the drought still cannot be clearly attributed to anthropogenic climate change, with the 95% confidence interval ranging from a probability decrease between preindustrial and today of a factor of 0.3 and an increase of a factor of 5 for a drought like this one or worse. A soil moisture dataset also shows a nonsignificant drying trend. According to ENSO correlations in the observations, the strong 2015 El Niño did increase the severity of the drought.

Brief communication: Drought likelihood for East Africa

“…Our results are broadly consistent with the recent analysis of Ethiopian drought projections by Philip et al (2017), who also use observations to reduce the model uncertainty in GCM projections. They project future changes in drought by the use of only GCMs which can reproduce well the observed distribution of February to September climatological rainfall.…”

“…This is because there always remains a concern that a rejected model may hold important information about expected future changes, even if having strong biases in modelling the present day. Nev-ertheless, as a further sensitivity study, we also apply the same method as Philip et al (2017) for our study region. This is with the CHIRPS dataset, and we place our findings in Fig.…”

“…For East African rainfall predictions in particular, this needs to link to accurate forward projections of oceanic variability. Strong teleconnections are known to exist between El Niño-Southern Oscillation (ENSO) and East African rainfall (Segele et al, 2009;Gissila et al, 2004;Gleixner et al, 2017), and with longer-term fluctuations in Pacific sea surface temperatures, either increasing or decreasing rainfall (Funk et al, 2014;Liebmann et al, 2014;Gleixner et al, 2017). Larger ensembles of simulations by each model are also important, and especially when analysing the probability of extreme events.…”

Attributing high-impact extreme events across timescales—a case study of four different types of events

“…This approach has been used before, e.g. Philip et al (2017) for drought, Schaller et al (2014) and Van der Wiel et al (2017) for heavy precipitation and Uhe et al (2016) and van Oldenborgh et al (2015) for temperature.…”

“…Philip et al 2017). Unsurprisingly, impacts are highest in countries with unstable regimes and, thus, have very high vulnerability.…”