Fredrik Wetterhall, EGU2014 Slide 1 of 16 Forecasting droughts in East Africa Emmah Mwangi 1, Fredrik Wetterhall 2, Emanuel Dutra 2, Francesca Di Giuseppe 2, and Florian Pappenberger 2 1. Kenya Meteorological Agency 2. European Centre for Medium Range Weather Forecasts

Fredrik Wetterhall, EGU2014 Slide 2 of 16 Introduction – Climate of East Africa  East Africa: two rainy seasons (Mar- May & Oct-Dec)  Movement of ITCZ  IOD, ENSO, MJO, QBO  GDP - rainfed agriculture

Fredrik Wetterhall, EGU2014 Slide 3 of 16 Introduction – drought outlook  Increase in frequency and intensity of droughts: , Major economic and humanitarian impacts Accurate drought predictions with adequate lead time is essential  Existing seasonal forecasting system; GHACOF (Greater Horn of Africa Climate Outlook Forum)

Fredrik Wetterhall, EGU2014 Slide 4 of 16 Djibouti Ethiopia Eritrea Somalia Kenya Burundi Rwanda Uganda Tanzania Sudan  ICPAC ( IGAD Climate Prediction and Application Centre )  GHACOFs – 1998  GHACOFs – 3 times a year; March-May, July- August, October- December Great Horn of Africa region (GHACOF)

Fredrik Wetterhall, EGU2014 Slide 5 of 16 Regionalization of the countries using PCA into homogeneous climatological zones Correlation analysis with SSTs QBO, IOD, Ocean gradients Regression analysis Analogue technique: find years with similar climate drivers as the current year Dynamical models from several centres YearDJFJFMFMAMAMAMJMJJJJAJASASOSONONDNDJ

Fredrik Wetterhall, EGU2014 Slide 6 of 16 October-December 2013 Statement  Problems related to water scarcity are likely to occur in northwestern and northeastern Kenya ; monitoring and contingency measures are necessary in order to adequately cope with the situation.  Diseases associated with water scarcity  Food security is expected to deteriorate in the eastern sector

Fredrik Wetterhall, EGU2014 Slide 7 of 16 Research questions: Does ECMWF seasonal forecast of precipitation have skill over eastern Africa? If so, is this information useful for the decision makers?

Fredrik Wetterhall, EGU2014 Slide 8 of 16 Observational data and forecast Monthly rainfall for the 34 homogeneous zones over the period 1961–2009 Hindcasts of ECMWF System 4, 15 members from Skill assessment: Quantitative skill in of precipitation forecast (ACC, CRPSS, ROC) Qualitative evaluation mimicking the outlook forecast – Seasonal forecasts of precipitation anomalies – Seasonal forecasts of standardised precipitation index

Fredrik Wetterhall, EGU2014 Slide 9 of 16 Anomaly correlation coefficient (MAM)

Fredrik Wetterhall, EGU2014 Slide 10 of 16 Anomaly correlation coefficient (SON)

Fredrik Wetterhall, EGU2014 Slide 11 of 16  Prediction skill declines with increasing lead time  Skill is higher in the OND than in MAM  For both methods, there is higher skill in lead time 2 than lead time1 in the OND season  SYS-4’s negative drift in SSTs over the NINO 3.4 region which highly impacts precipitation over East Africa. Continuous Rank Probability Skill Scores (CRPSS)

Fredrik Wetterhall, EGU2014 Slide 12 of 16 SYS-4 September and October forecasts and the consensus forecast, then the outlook could have been adjusted for the Kenya coast, Ethiopia and Sudan. Use of system-4 in the consensus framework – OND 2000

Fredrik Wetterhall, EGU2014 Slide 13 of 16  If the consensus would have been updated in October using SYS- 4 forecast, then the wet conditions observed on the Eastern part could have been captured. Use of system-4 in the consensus framework – OND 2006

Fredrik Wetterhall, EGU2014 Slide 14 of 16 Combining the outlook and SYS-4’s March forecast would have helped adjust the wet forecast over Ethiopia and Sudan to dry. Use of system-4 in the consensus framework – MAM 2009

Fredrik Wetterhall, EGU2014 Slide 15 of 16 Conclusions SYS-4 has significant skill in forecasting precipitation over the study area with in predicting the short rains for October-December The subjective assessment showed that there is a potential added advantage using SYS4, especially in terms of a late update of the forecast – Needs to be further evaluated Use of SPI made the forecast more easy to interpret and showed the areas with anomalies in a more homogenous way

Fredrik Wetterhall, EGU2014 Slide 16 of 16 Thank you for you attention! Mwangi, E., Wetterhall, F., Dutra, E., Di Giuseppe F. and Pappenberger, F., (2014), Forecasting droughts in East Africa, Hydrology and Earth System Sciences, 18,