DOI: 10.5061/dryad.dfn2z34xn
title: Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

description: 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.
description: The dataset presented here
 consists of two raster maps displaying PFT coverage based on a Remote
 Sensing (RS) and a Distribution model (DM) product. The data is covering
 Norway at a resolution of 30m and 100m respectively. The first PFT map is
 derived from RS originally created by Johansen (2009) using aggregation
 based on a conversion table which is closely described in the accompanying
 article.  The second PFT map is created by first assembling vegetation
 type predictions from individual DMs carried out by Horvath et al. (2019),
 and then aggregating the types based on the same conversion table. The
 accompanying ".clr" file consists of RGB color codes used to
 display the PFT maps in a GIS software and was used to present the maps in
 the article.    Horvath, P., Halvorsen, R.,
 Stordal, F., Tallaksen, L. M., Tang, H., and Bryn, A.: Distribution
 modelling of vegetation types based on area frame survey data, Applied
 Vegetation Science, 22, 547-560, https://doi.org/10.1111/avsc.12451,
 2019. Johansen,
 B. E.: Satellittbasert vegetasjonskartlegging for Norge, Direktoratet for
 Naturforvaltning, Norsk Romsenter, 2009.