Articles | Volume 18, issue 1
Biogeosciences, 18, 95–112, 2021
https://doi.org/10.5194/bg-18-95-2021
Biogeosciences, 18, 95–112, 2021
https://doi.org/10.5194/bg-18-95-2021

Research article 07 Jan 2021

Research article | 07 Jan 2021

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

Peter Horvath et al.

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (30 Sep 2020) by Akihiko Ito
AR by Peter Horvath on behalf of the Authors (01 Nov 2020)  Author's response    Manuscript
ED: Publish subject to technical corrections (18 Nov 2020) by Akihiko Ito
AR by Peter Horvath on behalf of the Authors (20 Nov 2020)  Author's response    Manuscript
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Short summary
We evaluated the performance of three methods for representing vegetation cover. Remote sensing provided the best match to a reference dataset, closely followed by distribution modelling (DM), whereas the dynamic global vegetation model (DGVM) in CLM4.5BGCDV deviated strongly from the reference. Sensitivity tests show that use of threshold values for predictors identified by DM may improve DGVM performance. The results highlight the potential of using DM in the development of DGVMs.
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