Comment on gmd-2021-287

doi:10.5194/gmd-2021-287-cc3

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.

Highlights

Regardless of the output variable, LPJ-GUESS was most sensitive to photosynthesis-related parameters, parameters controlling the wood turnover and tree allometry (k_rp), water-related parameters, mortality-related parameters and environmental drivers (Fig. 1)
We found that environmental drivers contributed most of all processes/drivers to the predictive uncertainty (Fig 2), regardless of the considered model output
For TSB, we found that increasing mean annual temperature increased the uncertainty contributions of environmental drivers, water- and establishment-parameters, while the uncertainty due to nitrogen- and tree structure- related parameters decreased (Fig. 4a)

Summary

Introduction

Terrestrial ecosystem models have emerged in the last three decades as a central tool for decision making and basic research on vegetation ecosystems (Cramer et al, 2001; Fisher et al, 2018; IPCC, 2014; Smith et al, 2001; Snell et al, 2014). Theoretical Ecology Lab, University of Regensburg, Universitätsstraße 31, 93053 Regensburg, Germany. Different models usually agree in their essential projections for a given ecosystem, they often differ in essential details, for example regarding the future carbon uptake of forest ecosystems (Huntzinger et al, 2017; Krause et al, 2019). When considering the impact of these uncertainties for directing research (Tomlin, 2013), and to interpret and understand projections (Dietze et al, 2018), it is of immense value to know which factors drive these uncertainties. The IPCC started in its Fifth Assessment Report to systematically analyze uncertainties and attribute them to model inputs (IPCC, 2014) similar to other predictive sciences (e.g. nuclear reactor safety (Chauliac et al, 2011), energy assessment for buildings (Tian et al, 2018) or policy analysis (Maxim and van der Sluijs, 2011))

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Results

Discussion

Conclusion