Research infrastructures play a key role in launching a new generation of integrated long-term, geographically distributed observation programmes designed to monitor climate change, better understand its impacts on global ecosystems, and evaluate possible mitigation and adaptation strategies. The pan-European Integrated Carbon Observation System combines carbon and greenhouse gas (GHG; CO2, CH4, N2O, H2O) observations within the atmosphere, terrestrial ecosystems and oceans. High-precision measurements are obtained using standardised methodologies, are centrally processed and openly available in a traceable and verifiable fashion in combination with detailed metadata. The Integrated Carbon Observation System ecosystem station network aims to sample climate and land-cover variability across Europe. In addition to GHG flux measurements, a large set of complementary data (including management practices, vegetation and soil characteristics) is collected to support the interpretation, spatial upscaling and modelling of observed ecosystem carbon and GHG dynamics. The applied sampling design was developed and formulated in protocols by the scientific community, representing a trade-off between an ideal dataset and practical feasibility. The use of open-access, high-quality and multi-level data products by different user communities is crucial for the Integrated Carbon Observation System in order to achieve its scientific potential and societal value.

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.

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 v4.0) across European forests (the species Picea abies, Fagus sylvatica and Pinus sylvestris), considering uncertainties 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 environmental drivers and parameters related to water and mortality. The importance of environmental drivers for predictive uncertainty increases with increasing temperature. Moreover, most of the interactions of model inputs (environmental drivers and parameters) are between environmental drivers themselves or between parameters and environmental drivers. In conclusion, our study highlights the importance of environmental 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. Reducing uncertainty in mortality-related processes and accounting for environmental influence on processes should therefore be a focus in further model development.

<p>Model predictions about future states of ecosystems under environmental change are uncertain. Understanding which factors drive these uncertainties is of immense value for directing research, but also for their interpretations. Here, we analyse sensitivities and uncertainties of a state of the art dynamic vegetation model (LPJ-GUESS) across European forests. We found that predictions of carbon fluxes are most sensitive to structure-related and mortality-related parameters, but most uncertainty is induced by drivers, nitrogen-, water- and mortality-modules. The uncertainty induced by drivers increases with increasing temperature, decreasing precipitation and from north to south across Europe. Moreover, environmental conditions change the resulting uncertainties in other processes. In this context, we encounter that the stress-gradient hypothesis is implicitly displayed in the model processes. In conclusion, our study stresses the importance of  environmental drivers for ecosystem predictions not only due to their uncertainty contributions but also because they determine the uncertainties of other processes.  </p>