This repository contains R script used to generate and compare three methods for describing vegetation in Norway. The data is acompanying the manuscript: "Improving the representation of high-latitude vegetation in Dynamic Global Vegetation Models" by authors: Peter Horvath, Hui Tang, Rune Halvorsen, Frode Stordal, Lena Merete Tallaksen, Terje Koren Berntsen, and Anders Bryn currently in review for in Biogeosciences available as a preprint.
Scripts used to run the DGVM are available at this GitHub repository
Spatial data are be available for download at the Dryad Digital Repository.
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 rarelyless often by other vegetation products or by in-situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day 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). While DGVMs are process-based models, DM relies on a statistical correlation between a set of predictors and the modelled target, and RS dataset is based on classification of spectral reflectance. PFT profiles obtained from an independently collected field-based 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, three new parameter thresholds were selected (e.g. parameters based on temperature, snow and precipitation). We performed a series of sensitivity experiments to investigate whether that these thresholds improve the performance of the DGVM. Based on our results, we suggest implementation of one of thesethree novel PFT-specific thresholds (precipitation seasonality) for establishment in the DGVM. The results highlight the potential of using PFT-specific thresholds obtained by DM in development and benchmarking of also other 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.