AUTHORS: Rebecca S. Snell*, Ohio University; Ché Elkin, University of Northern British Columbia; Sven Kotlarski, Federal Office of Meteorology and Climatology MeteoSwiss; Harald Bugmann, ETH Zürich
ABSTRACT: Mountain forest landscapes provide a wide range of ecosystem goods and services (e.g., timber production, protection from natural hazards, maintaining biodiversity) and are especially sensitive to climate change. Dynamic vegetation models are commonly used to project climate change impacts on forests, but the sensitivity of process-based forest landscape models to uncertainties in climate input data has received little attention, especially regarding the response of ecosystem services. Using a dry inner-Alpine valley in Switzerland as a case study, we tested the sensitivity of a process-based forest landscape model (LandClim) to different sources of baseline climate data, lapse rates, and future climate change derived from different climate model combination chains and downscaling methods.Under the current climate, differences in baseline climate accounted for the majority of the variation at lower elevations, while differences in lapse rates caused large variability at higher elevations. Under climate change, differences between climate model chains were the greatest source of uncertainty. In general, the largest differences for species were found at their individual regional distribution limits, and the largest differences for ecosystem services were found at the highest elevations. Ecosystem services were generally less sensitive to the downscaling methodology. Thus, our results suggest that the greatest uncertainty when simulating how forest ecosystem service provisioning will respond to future climate change, is due to differences between climate model chains. Despite the high computation cost, we recommend using as many climate scenarios as possible when projecting future forest response to climate change. In addition, care should be taken when evaluating climate impacts at landscape locations that are known a priori to be sensitive to climate variation, such as high-elevation forests.