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Earth Syst. Dynam., 9, 313-338, 2018
https://doi.org/10.5194/esd-9-313-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
28 Mar 2018
A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies
Adjoua Moise Famien1,2, Serge Janicot2, Abe Delfin Ochou1, Mathieu Vrac3, Dimitri Defrance2,5, Benjamin Sultan2,5, and Thomas Noël4 1Université Félix Houphouët Boigny, LAPAMF-UFR SSMT, 22 BP 582, Abidjan 22, Côte d'Ivoire
2Sorbonne Université, IRD, CNRS, MNHN, Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques, LOCEAN, 75005 Paris, France
3LSCE-IPSL, CNRS/CEA/UVSQ, Centre d'Études de Saclay, Orme des Merisiers, Gif-sur-Yvette, France
4Climate Data Factory, Paris, France
5ESPACE-DEV, Université Montpellier, IRD, Université Guyane, Université Réunion, Université Antilles, Université Avignon, Montpellier, France
Abstract. The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950–2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders. It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century.
Citation: Famien, A. M., Janicot, S., Ochou, A. D., Vrac, M., Defrance, D., Sultan, B., and Noël, T.: A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies, Earth Syst. Dynam., 9, 313-338, https://doi.org/10.5194/esd-9-313-2018, 2018.
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Short summary
This study uses the cumulative distribution function transform (CDF-t) method to provide bias-corrected data over Africa using WFDEI as a reference dataset. It is shown that CDF-t is very effective in removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets, particularly for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields.
This study uses the cumulative distribution function transform (CDF-t) method to provide...
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