Stepwise Calibration of Age-Dependent Biomass in the Integrated Biosphere Simulator (IBIS) Model

Many land surface models (LSMs) assume a steady-state assumption (SS) for forest growth, leading to an overestimation of biomass in young forests. Parameters inversion under SS will potentially result in biased carbon fluxes and stocks in a transient simulation. Incorporating age-dependent biomass into LSMs can simulate real disequilibrium states, enabling the model to simulate forest growth from planting to its current age, and improving the biased post-calibration parameters. In this study, we developed a stepwise optimization framework that first calibrates "fast" light-controlled CO2 fluxes (gross primary productivity, GPP), then leaf area index (LAI), and finally "slow" growth-controlled biomass using the Global LAnd Surface Satellite (GLASS) GPP and LAI products, and age-dependent biomass curves for the 25 forests. To reduce the computation time, we used a machine learning-based model to surrogate the complex integrated biosphere simulator LSM during calibration. Our calibrated model led to an error reduction in GPP, LAI, and biomass by 28.5%, 35.3% and 74.6%, respectively. When compared with net biome productivity (NBP) using no-age-calibrated parameters, our age-calibrated parameters increased NBP by an average of 50 gC m-2 yr-1 across all forests, especially in the boreal needleleaf evergreen forests, the NBP increased by 118 gC m-2 yr-1 on average, increasing the estimate of the carbon sink in young forests. Our work highlights the importance of including forest age in LSMs, and provides a novel framework for better calibrating LSMs using constraints from multiple satellite products at a global scale. Physical and biological process-based models always overestimate the biomass of young forests, with an assumption that they usually hold maximum carbon stocks like old-growth stands. Such an assumption can lead to biased carbon fluxes and stocks in further simulation. Considering stand age in LSMs improves their ability to simulate real forest growth. In this study, we develop a stepwise method to account for stand age effects in model simulations by assimilating remotely sensed information on vegetation productivity, leaf area, biomass, and age. To reduce the computational cost of the complex original code, we use a substitute model constructed using a machine learning method for calculations. The improved model successfully reproduces the changes in ecosystem biomass and fluxes as forest age varies. Our research provides a novel approach to improving other land surface models for predicting age-dependent ecosystem properties. We presented a stepwise calibration framework for better integrating age-dependent biomass into the integrated biosphere simulator model We utilized a machine learning-based model as an alternative to the physical model, accelerating the calibration process The calibration noticeably improved the simulation of gross primary productivity, leaf area index, and biomass