Abstract/Summary

Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long-term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C (Vcmax,25) and the leaf respiration rate at 25°C (R25)) following increasingly well-understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco-Evolutionary Optimality (EEO)-based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT-specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5-year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R25 and Vcmax,25 vary substantially both temporally and spatially within the same PFT (C.V. >20%). Our EEO-based scheme captures 62% of the temporal and 70% of the spatial variations in Vcmax,25 (73% and 54% of the variations in R25). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO-based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO2 uptake by terrestrial ecosystems. Our approach thus simulates climate-carbon coupling more realistically, with fewer parameters.