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. Vegetation is an important component in global ecosystems, affecting thephysical, hydrological and biogeochemical properties of the landsurface. Accordingly, the way vegetation is parameterized strongly influencespredictions of future climate by Earth system models. To capture futurespatial and temporal changes in vegetation cover and its feedbacks to theclimate system, dynamic global vegetation models (DGVMs) are included asimportant components of land surface models. Variation in the predictedvegetation cover from DGVMs therefore has large impacts on modelled radiativeand non-radiative properties, especially over high-latitude regions. DGVMs aremostly evaluated by remotely sensed products and less often by other vegetationproducts or by in situ field observations. In this study, we evaluate theperformance of three methods for spatial representation of present-dayvegetation cover with respect to prediction of plant functional type (PFT)profiles – one based upon distribution models (DMs), one that uses a remotesensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFTprofiles based on physiological and ecological processes, a DM relies onstatistical correlations between a set of predictors and the modelled target,and the RS dataset is based on classification of spectral reflectance patternsof satellite images. PFT profiles obtained from an independently collectedfield-based vegetation dataset from Norway were used for the evaluation. Wefound that RS-based PFT profiles matched the reference dataset best, closelyfollowed by DM, whereas predictions from DGVMs often deviated strongly from thereference. DGVM predictions overestimated the area covered by borealneedleleaf evergreen trees and bare ground at the expense of boreal broadleafdeciduous trees and shrubs. Based on environmental predictors identified by DMas important, three new environmental variables (e.g. minimum temperature inMay, snow water equivalent in October and precipitation seasonality) wereselected as the threshold for the establishment of these high-latitudePFTs. We performed a series of sensitivity experiments to investigate if thesethresholds improve the performance of the DGVM method. Based on our results, wesuggest implementation of one of these novel PFT-specific thresholds (i.e.precipitation seasonality) in the DGVM method. The results highlight the potential ofusing PFT-specific thresholds obtained by DM in development of DGVMs inbroader regions. Also, we emphasize the potential of establishing DMs as areliable method for providing PFT distributions for evaluation of DGVMsalongside RS.

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