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Forecasting droughts in East Africa

Emmah Mwangi1, Fredrik Wetterhall2, Emanuel Dutra2, Francesca Di Giuseppe2, and Florian

Pappenberger2

1. Kenya Meteorological Agency

2. European Centre for Medium Range Weather Forecasts

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Introduction – Climate of East Africa

East Africa: two rainy seasons (Mar-May & Oct-Dec)

Movement of ITCZ IOD, ENSO, MJO,

QBO GDP - rainfed

agriculture

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Introduction – drought outlook

Increase in frequency and intensity of droughts: 2008-2009, 2010-2011

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)

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-10 0 10 20 30 40 50

-30

-20

-10

0

10

20

30

DjiboutiEthiopia

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)

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• 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

Year DJF JFM FMA MAM AMJ MJJ JJA JAS ASO SON OND NDJ

2001 -0.7 -0.6 -0.5 -0.4 -0.2 -0.1 0.0 0.0 -0.1 -0.2 -0.3 -0.32002 -0.2 0.0 0.1 0.3 0.5 0.7 0.8 0.8 0.9 1.2 1.3 1.3

2003 1.1 0.8 0.4 0.0 -0.2 -0.1 0.2 0.4 0.4 0.4 0.4 0.3

2004 0.3 0.2 0.1 0.1 0.2 0.3 0.5 0.7 0.8 0.7 0.7 0.7

2005 0.6 0.4 0.3 0.3 0.3 0.3 0.2 0.1 0.0 -0.2 -0.5 -0.8

2006 -0.9 -0.7 -0.5 -0.3 0.0 0.1 0.2 0.3 0.5 0.8 1.0 1.0

2007 0.7 0.3 -0.1 -0.2 -0.3 -0.3 -0.4 -0.6 -0.8 -1.1 -1.2 -1.4

2008 -1.5 -1.5 -1.2 -0.9 -0.7 -0.5 -0.3 -0.2 -0.1 -0.2 -0.5 -0.7

2009 -0.8 -0.7 -0.5 -0.2 0.2 0.4 0.5 0.6 0.8 1.1 1.4 1.6

2010 1.6 1.3 1.0 0.6 0.1 -0.4 -0.9 -1.2 -1.4 -1.5 -1.5 -1.5

2011 -1.4 -1.2 -0.9 -0.6 -0.3 -0.2 -0.2 -0.4 -0.6 -0.8 -1.0 -1.0

2012 -0.9 -0.6 -0.5 -0.3 -0.2 0.0 0.1 0.4 0.5 0.6 0.2 -0.3

2013 -0.6 -0.6 -0.4 -0.2 -0.2 -0.3 -0.3 -0.3 -0.3      

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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

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Research questions:

Does ECMWF seasonal forecast of precipitation have skill over eastern Africa?

If so, is this information useful for the decision makers?

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Observational data and forecast

Monthly rainfall for the 34 homogeneous zones over the period 1961–2009

Hindcasts of ECMWF System 4, 15 members from 1981-2010

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

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1

Anomaly correlation coefficient (MAM)

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1

Anomaly correlation coefficient (SON)

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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)

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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

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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

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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

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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

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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, 611-620