The climatic imprint of bimodal distributions in vegetation cover for western Africa

Yin, Zun; Dekker, Stefan C.; van den Hurk, Bart J. J. M.; Dijkstra, Henk A.

doi:https://doi.org/10.5194/bg-13-3343-2016

Articles | Volume 13, issue 11

https://doi.org/10.5194/bg-13-3343-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/bg-13-3343-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 13, issue 11

Research article

|

08 Jun 2016

Research article |

| 08 Jun 2016

The climatic imprint of bimodal distributions in vegetation cover for western Africa

Zun Yin, Stefan C. Dekker, Bart J. J. M. van den Hurk, and Henk A. Dijkstra

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Final revised paper (published on 08 Jun 2016)
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Preprint (discussion started on 13 Nov 2015)
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Interactive discussion

Status: closed

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

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RC C8423: 'RC to Yin et al.', Anonymous Referee #2, 11 Dec 2015
- AC C10479: 'Reply to Referee #2', Zun Yin, 13 Apr 2016
RC C9585: 'Interesting analysis', Anonymous Referee #4, 05 Feb 2016
- AC C10480: 'Reply to Referee #4', Zun Yin, 13 Apr 2016

Peer-review completion

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

ED: Publish subject to minor revisions (Editor review) (20 Apr 2016) by Florian Wittmann

AR by Zun Yin on behalf of the Authors (12 May 2016) Author's response Manuscript

ED: Publish as is (18 May 2016) by Florian Wittmann

AR by Zun Yin on behalf of the Authors (18 May 2016)

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Short summary

Bimodality is found in aboveground biomass and mean annual shortwave radiation in West Africa, which is a strong evidence of alternative stable states. The condition with low biomass and low radiation is demonstrated under which ecosystem state can shift between savanna and forest states. Moreover, climatic indicators have different prediction confidences to different land cover types. A new method is proposed to predict potential land cover change with a combination of climatic indicators.