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Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa
Author(s) -
Ogutu Geoffrey E. O.,
Franssen Wietse H. P.,
Supit Iwan,
Omondi P.,
Hutjes Ronald W.A.
Publication year - 2017
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4876
Subject(s) - forecast skill , climatology , hindcast , probabilistic logic , environmental science , anomaly (physics) , predictability , ensemble average , precipitation , forcing (mathematics) , shortwave , statistics , meteorology , mathematics , geography , geology , radiative transfer , physics , quantum mechanics , condensed matter physics
This study evaluates the potential use of the ECMWF System‐4 seasonal forecasts ( S4 ) for impact analysis over East Africa. For use, these forecasts should have skill and small biases. We used the 15‐member ensemble of 7‐month forecasts initiated every month, and tested forecast skill of precipitation ( tp ), near‐surface air temperature ( tas ) and surface downwelling shortwave radiation ( rsds ). We validated the 30‐year (1981–2010) hindcast version of S4 against the WFDEI reanalysis ( WATCH Forcing Data ERA ‐Interim) and to independent relevant observational data sets. Probabilistic skill is assessed using anomaly correlation, ranked probability skill score ( RPSS ) and the relative operating curve skill score ( ROCSS ) at both grid cell and over six distinct homogeneous rainfall regions for the three growing seasons of East Africa (i.e. MAM , JJA and OND ). S4 exhibits a wet bias in OND , a dry bias in MAM and a mix of both in JJA . Temperature biases are similar in all seasons, constant with lead‐time and correlate with elevation. Biases in rsds correlate with cloud/rain patterns. Bias correction clears biases but does not affect probabilistic skills. Predictability of the three variables varies with season, location and lead‐time. The choice of validating dataset plays little role in the regional patterns and magnitudes of probabilistic skill scores. The OND tp forecasts show skill over a larger area up to 3 months lead‐time compared to MAM and JJA . Upper‐ and lower‐tercile tp forecasts are 20–80% better than climatology. Temperature forecasts are skillful for at least 3 months lead‐time and they are 40–100% better than climatology. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher in all lead months against the alternative datasets. The forecast system captures El‐Niño Southern Oscillation ( ENSO )‐related anomalous years with region‐dependent skill.