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

Abstract

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 CO2 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 (Vcmax, Jmax, Arrhenius temperature response parameters, and the ratio of Jmax to Vcmax) 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.

A derivation error that affects carbon balance models exists in the current implementation of the modified Arrhenius function

“…We sought to determine whether the missing term in M2002 had a meaningful impact on fitted temperature response parameters due to its prevalence in photosynthetic temperature response data and vegetation modelling (Kattge & Knorr, 2007; Duursma & Medlyn, 2012; Rogers et al ., 2017; Smith & Dukes, 2017; Stinziano et al ., 2018, 2019; Kumarathunge et al ., 2019). Our present analysis suggests that there is a large impact on E a for both V cmax and J max ; however, there were minimal impacts on k 25 , H d and Δ S (though note that H d, J max was underestimated) (Fig.…”

“…We sought to determine whether the missing term in M2002 had a meaningful impact on fitted temperature response parameters due to its prevalence in photosynthetic temperature response data and vegetation modelling (Kattge & Knorr, 2007;Duursma & Medlyn, 2012;Rogers et al, 2017;Smith & Dukes, 2017;Stinziano et al, 2018Stinziano et al, , 2019Kumarathunge et al, 2019).…”

The acclimation of leaf photosynthesis of wheat and rice to seasonal temperature changes in T‐FACE environments

“…Goodness of estimations was assessed using the r 2 of linear regression, as well as the relative root mean-squared error rRMSE (=RMSE/mean of the measured values), between estimated and measured values. Critical errors were defined if the difference in estimations between assuming seasonal temperature acclimation and assuming no acclimation is ≥10%, according to Archontoulis, Yin, Vos, Danalatos, and Struik (2011) and Stinziano, Bauerle, and Way (2019).…”

A derivation error that affects carbon balance models exists in the current implementation of the modified Arrhenius function

“…We sought to determine whether the missing term in M2002 had a meaningful impact on fitted temperature response parameters due to its prevalence in photosynthetic temperature response data and vegetation modelling (Kattge & Knorr, 2007;Duursma & Medlyn, 2012;Rogers et al, 2017;Smith & Dukes, 2017;Stinziano et al, 2018;Stinziano et al, 2019;Kumarathunge et al, 2019). Our present analysis suggests that there is a large impact on Ea for both Vcmax and Jmax, however there were minimal impacts on k25, Hd, and ΔS (though note that Hd,Jmax was underestimated) (Fig.…”