Comparison of simulations from a state-of-the-art dynamic global vegetation model (LPJ-GUESS ) driven by low- and high-resolution climate data

Abstract. Simulations of dynamic global vegetation models (DGVMs) are typically conducted at a spatial resolution of 0.5°, while higher-resolution simulations remain uncommon. This coarse resolution eliminates detailed orographic features and hence, associated climate variability, which are especially pronounced in mountainous regions. The impact of disregarding such variability on vegetation dynamics has not been thoroughly examined. In this study, we explore the differences in regional outcomes between the DGVM LPJ-GUESS simulations conducted at high and low spatial resolutions. Using the CHELSA algorithm, we create an elevation-informed high-resolution climate dataset for a domain encompassing the European Union and use it to perform simulations. Comparative analysis reveals significant systematic discrepancies between the two resolutions. Furthermore, we quantify the extent to which the underrepresentation of orographic climate variation affects regional predictions across the European Union.