Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methods

Abstract

A comprehensive understanding and accurate modelling of the terrestrial carbon cycle are of paramount importance to improve projections of the global carbon cycle and more accurately gauge its impact on global climate systems. Land surface models, which have become an important component of weather and climate applications, simulate key aspects of the terrestrial carbon cycle, such as photosynthesis and respiration. These models rely on parameterisations that require careful calibration. In this study we explore the assimilation of atmospheric CO₂ concentration data for parameter calibration of the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) land surface model using an EnVarDA method, an adjoint-free ensemble-variational data assimilation method. By circumventing the challenges associated with developing and maintaining tangent linear and adjoint models, the EnVarDA method offers a very promising alternative. Using synthetic observations generated through a twin experiment, we demonstrate the ability of EnVarDA to assimilate atmospheric CO₂ concentrations for model parameter calibration. We then compare the results to a VarDA method that uses finite differences to estimate tangent linear and adjoint models, which reveals that EnVarDA is superior in terms of computational efficiency, fit to the observations, and parameter recovery.