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

Horvath, Peter; Tang, Hui; Halvorsen, Rune; Stordal, Frode; Tallaksen, Lena Merete; Berntsen, Terje Koren; Bryn, Anders

doi:https://doi.org/10.5194/bg-18-95-2021

Articles | Volume 18, issue 1

https://doi.org/10.5194/bg-18-95-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-18-95-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 1

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

Supplement

https://doi.org/10.5194/bg-18-95-2021-supplement

Data sets

High-resolution DM-based and RS-based PFT maps Peter Horvath, Hui Tang, Rune Halvorsen, Frode Stordal, Lena Merete Tallaksen, Terje Koren Berntsen, and Anders Bryn https://doi.org/10.5061/dryad.dfn2z34xn

Model code and software

geco-nhm/DGVM_RS_DM_Norway: First release Peter Horvath https://doi.org/10.5281/zenodo.4399235

Modification and scripts for running CLM4.5BGCDV and sensitivity experiments H. Tang https://doi.org/10.5281/zenodo.4415469

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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.