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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2020-149
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2020-149
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 12 Jun 2020

Submitted as: research article | 12 Jun 2020

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This preprint is currently under review for the journal BG.

Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models

Peter Horvath1,4, Hui Tang1,2,4, Rune Halvorsen1, Frode Stordal2,4, Lena Merete Tallaksen4,5, Terje Koren Berntsen2,4, and Anders Bryn1,3,4 Peter Horvath et al.
  • 1Geo-Ecology Research Group, Natural History Museum, University of Oslo, P.O. Box 1172, Blindern NO-0318 Oslo, Norway
  • 2Section of Meteorology and Oceanography, Department of Geosciences, University of Oslo, Norway
  • 3Division of Survey and Statistics, Norwegian Institute of Bioeconomy Research, P.O. Box 115, NO-1431 Ås, Norway
  • 4LATICE Research Group, Department of Geosciences, University of Oslo, Norway
  • 5Section of Physical geography and Hydrology, Department of Geosciences, University of Oslo, Norway

Abstract. Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterised strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVM) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products, but rarely by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DM), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV). PFT profiles obtained from an independently collected vegetation data set from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVM often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, we suggest implementation of three novel PFT-specific thresholds for establishment in the DGVM. We performed a series of sensitivity experiments to demonstrate that these thresholds improve the performance of the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of DGVMs for broader regions. Also, we emphasize the potential of establishing DM as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

Peter Horvath et al.

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Peter Horvath et al.

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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 development and benchmarking of DGVMs.
We evaluated the performance of three methods for representing vegetation cover. Remote sensing...
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