A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

Famien, Adjoua Moise; Janicot, Serge; Ochou, Abe Delfin; Vrac, Mathieu; Defrance, Dimitri; Sultan, Benjamin; Noël, Thomas

doi:https://doi.org/10.5194/esd-9-313-2018

Articles | Volume 9, issue 1

https://doi.org/10.5194/esd-9-313-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/esd-9-313-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 9, issue 1

Research article

|

28 Mar 2018

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 Famien, Serge Janicot, Abe Delfin Ochou, Mathieu Vrac, Dimitri Defrance, Benjamin Sultan, and Thomas Noël

Supplement

https://doi.org/10.5194/esd-9-313-2018-supplement

Data sets

Bias corrected GCM input data for ISIMIP Fast Track S. Hempel, K. Frieler, L. Warszawski, J. Schewe, and F. Piontek https://doi.org/10.5880/PIK.2016.001

EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI) S. Lange https://doi.org/10.5880/pik.2016.004

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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.