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

Abstract

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.