Modelled net carbon gain responses to climate change in boreal trees: Impacts of photosynthetic parameter selection and acclimation.

Boreal forests are crucial in regulating global vegetation-atmosphere feedbacks, but the impact of climate change on boreal tree carbon fluxes is still unclear. Given the sensitivity of global vegetation models to photosynthetic and respiration parameters, we determined how predictions of net carbon gain (C-gain) respond to variation in these parameters using a stand-level model (MAESTRA). We also modelled how thermal acclimation of photosynthetic and respiratory temperature sensitivity alters predicted net C-gain responses to climate change. We modelled net C-gain of seven common boreal tree species under eight climate scenarios across a latitudinal gradient to capture a range of seasonal temperature conditions. Physiological parameter values were taken from the literature together with different approaches for thermally acclimating photosynthesis and respiration. At high latitudes, net C-gain was stimulated up to 400% by elevated temperatures and CO in the autumn but suppressed at the lowest latitudes during midsummer under climate scenarios that included warming. Modelled net C-gain was more sensitive to photosynthetic capacity parameters (V , J , Arrhenius temperature response parameters, and the ratio of J to V ) than stomatal conductance or respiration parameters. The effect of photosynthetic thermal acclimation depended on the temperatures where it was applied: acclimation reduced net C-gain by 10%-15% within the temperature range where the equations were derived but decreased net C-gain by 175% at temperatures outside this range. Thermal acclimation of respiration had small, but positive, impacts on net C-gain. We show that model simulations are highly sensitive to variation in photosynthetic parameters and highlight the need to better understand the mechanisms and drivers underlying this variability (e.g., whether variability is environmentally and/or biologically driven) for further model improvement.