Peak growing season patterns and climate extremes-driven responses of gross primary production estimated by satellite and process based models over North America

Abstract

Representations of the seasonal peak uptake of CO2 and climate extremes effects have important implications for accurately estimating annual magnitude and inter-annual variations of terrestrial carbon fluxes, however the consistency of such representations among different satellite models and process-based (PB) models remain poorly known. Here we investigated these issues over North America based on a large ensemble of state-of-the-art gross primary production (GPP) models, including two solar-induced chlorophyll fluorescence (SIF)-based models (WECANN and GOPT), three remote sensing driven light-use efficiency (LUE) models, and 10 PB models. We found that the two SIF-based GPP estimates were bilaterally consistent in spatial patterns of peak growing season GPP (GPPPGS; with the largest uptake at the Corn-Belt area in the United States) and climate extremes-driven responses. The simulations from three LUE models showed relatively consistent spatial patterns of GPPPGS and climate extremes-driven responses, which agreed well with SIF-based estimates and satellite based metrics. Obviously differed from SIF and LUE based estimates, the simulations from PB models exhibited noticeable divergences and mostly failed to reasonably replicate the spatial pattern of GPPPGS. In addition, satellite models and PB models were comparably able to capture the effects of climate extremes on GPP, but showing obvious divergences in the magnitude of impacts among different models, and the former outperformed the latter in locating GPP changes caused by climate extremes. We discussed the possible origins of such discrepancies in state-of-the-art models with focus on PB models. Improving the parameterizations of critical variables (e.g. leaf area index) and better characterizing environmental stresses could lead to more robust estimates of large-scale terrestrial GPP with PB models, thus serving for accurately assessing global carbon budget and better understanding the impacts of climate change on the terrestrial carbon cycle. Our study offers a baseline for improving large-scale estimation of terrestrial GPP.