Articles | Volume 18, issue 1
https://doi.org/10.5194/bg-18-95-2021
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, Hui Tang, Rune Halvorsen, Frode Stordal, Lena Merete Tallaksen, Terje Koren Berntsen, and Anders Bryn

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

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 
Download
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.
Altmetrics
Final-revised paper
Preprint