Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)

Abstract

Abstract. Understanding uncertainties and sensitivities of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyze sensitivities (change in model outputs per unit change in inputs) and uncertainties (changes in model outputs scaled to uncertainty in inputs) of vegetation dynamics under climate change projected by a state-of-the-art dynamic vegetation model (LPJ-GUESS 4.0) across European forests addressing the effect of both model parameters and environmental drivers. We find that projected forest carbon fluxes are most sensitive to photosynthesis-, water- and mortality-related parameters, while predictive uncertainties are dominantly induced by climatic drivers, and parameters related to water and mortality. The importance of climatic drivers for predictive uncertainty increases with increasing temperature and thus, from north to south across Europe, in line with the stress-gradient hypothesis, which proposes that environmental control dominates at the harsh end of an environmental gradient. In conclusion, our study highlights the importance of climatic drivers not only as contributors to predictive uncertainty in their own right, but also as modifiers of sensitivities and thus uncertainties in other ecosystem processes.

Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics

“…The Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) model is a process-based dynamic vegetation model which can simulate the global vegetation distribution with its associated carbon, nitrogen, and water cycles (Smith et al, 2001(Smith et al, , 2014Oberpriller et al, 2022). The model has three possible modes of representing vegetation.…”