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

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Final revised paper (published on 28 Mar 2018)
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Preprint (discussion started on 04 Dec 2017)
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Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Referee Comment #1', Toshichika Iizumi, 04 Jan 2018
- AC1: 'A bias-corrected CMIP5 dataset for Africa using CDF-t method. A contribution to agricultural impact studies.', Adjoua Moise Famien, 25 Feb 2018
RC2: 'Interactive comment on "A bias-corrected CMIP5 dataset for Africa using CDF-t method. A contribution to agricultural impact studies" by Adjoua Moise Famien et al.', Anonymous Referee #2, 23 Jan 2018
- AC2: 'A bias-corrected CMIP5 dataset for Africa using CDF-t method. A contribution to agricultural impact studies.', Adjoua Moise Famien, 25 Feb 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish as is (25 Feb 2018) by Somnath Baidya Roy

AR by Adjoua Moise Famien on behalf of the Authors (05 Mar 2018) Author's response Manuscript

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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.