Performance of state-of-the-art C3S European seasonal climate forecast models for mean and extreme precipitation over Africa
Performance of state-of-the-art C3S European seasonal climate forecast models for mean and extreme precipitation over Africa
Seasonal hydrological forecasts at high spatial and temporal resolution can help manage water resources and mitigate impacts of extreme events but are dependent on skillful and operational seasonal forecasts from climate models. In this study, we evaluate precipitation forecasts from five operational climate models with a potential to drive hydrological forecasts: European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UK-Met), Météo France, Deutscher Wetterdienst, and Centro Euro-Mediterraneo sui Cambiamenti Climatici. The Multi-Source Weighted-Ensemble Precipitation is used as a reference data set to evaluate the model skill. The performance of individual models is evaluated on daily, weekly, monthly, seasonal, and climatological periods, and for selected target months, lead-times and drought events, and compared to unweighted and skill-weighted multi-model ensemble mean forecast. For all models, the lead 1-month forecast can replicate the climatological mean, monthly mean, and monthly anomaly precipitation, although much of this skill originates from the first week of the forecast. The skill drops rapidly for lead 2-month and longer and is highest in drier regions and seasons. The forecast skill of monthly meteorological drought events at lead 1-month is modest. All models represent the monthly variation in the length of wet and dry spell days at lead 1-month, but the skill is weak for heavy and very heavy precipitation days. ECMWF is found to be the most skillful model, followed by the UK-Met, although the multi-model weighted average provides the highest performance compared to individual models and the un-weighted multi-model mean.
Africa, ECMWF, European C3S, climate models, extreme precipitation, seasonal forecast
Gebrechorkos, Solomon
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Pan, Ming
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Beck, HE
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Sheffield, Justin
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4 March 2022
Gebrechorkos, Solomon
ff77f8a3-b6ef-4cfd-aebd-a003bf3947a5
Pan, Ming
10c372fa-0e0e-4eb5-b95b-06a8f9786fc8
Beck, HE
80c1bc63-432b-4f27-a1e3-a492320c1223
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Gebrechorkos, Solomon, Pan, Ming, Beck, HE and Sheffield, Justin
(2022)
Performance of state-of-the-art C3S European seasonal climate forecast models for mean and extreme precipitation over Africa.
Water Resources Research, 58 (3), [e2021WR031480].
(doi:10.1029/2021WR031480).
Abstract
Seasonal hydrological forecasts at high spatial and temporal resolution can help manage water resources and mitigate impacts of extreme events but are dependent on skillful and operational seasonal forecasts from climate models. In this study, we evaluate precipitation forecasts from five operational climate models with a potential to drive hydrological forecasts: European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UK-Met), Météo France, Deutscher Wetterdienst, and Centro Euro-Mediterraneo sui Cambiamenti Climatici. The Multi-Source Weighted-Ensemble Precipitation is used as a reference data set to evaluate the model skill. The performance of individual models is evaluated on daily, weekly, monthly, seasonal, and climatological periods, and for selected target months, lead-times and drought events, and compared to unweighted and skill-weighted multi-model ensemble mean forecast. For all models, the lead 1-month forecast can replicate the climatological mean, monthly mean, and monthly anomaly precipitation, although much of this skill originates from the first week of the forecast. The skill drops rapidly for lead 2-month and longer and is highest in drier regions and seasons. The forecast skill of monthly meteorological drought events at lead 1-month is modest. All models represent the monthly variation in the length of wet and dry spell days at lead 1-month, but the skill is weak for heavy and very heavy precipitation days. ECMWF is found to be the most skillful model, followed by the UK-Met, although the multi-model weighted average provides the highest performance compared to individual models and the un-weighted multi-model mean.
Text
Water Resources Research - 2022 - Gebrechorkos - Performance of State‐of‐the‐Art C3S European Seasonal Climate Forecast
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More information
Accepted/In Press date: 27 February 2022
e-pub ahead of print date: 4 March 2022
Published date: 4 March 2022
Additional Information:
Funding Information:
This work was supported by “FutureDams” (grant number ES/P011373/1) project and the “Building REsearch Capacity for sustainable water and food security In drylands of sub‐saharan Africa” (BRECcIA) (grant number NE/P021093/1) project, both of which are supported by UK Research and Innovation as part of the Global Challenges Research Fund.
Publisher Copyright:
© 2022. The Authors.
Keywords:
Africa, ECMWF, European C3S, climate models, extreme precipitation, seasonal forecast
Identifiers
Local EPrints ID: 455487
URI: http://eprints.soton.ac.uk/id/eprint/455487
ISSN: 0043-1397
PURE UUID: c3c94be9-4ea6-4616-b452-f8d7e471aeef
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Date deposited: 22 Mar 2022 18:10
Last modified: 17 Mar 2024 03:55
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Author:
Ming Pan
Author:
HE Beck
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