Ecosystem model optimization using in situ flux observations: benefit of Monte Carlo versus variational schemes and analyses of the year-to-year model performances

摘要

Terrestrial ecosystem models can provide major insights into the responses of Earth's ecosystems to environmental changes and rising levels of atmospheric CO2. To achieve this goal, biosphere models need mechanistic formulations of the processes that drive the ecosystem functioning from diurnal to decadal timescales. However, the subsequent complexity of model equations is associated with unknown or poorly calibrated parameters that limit the accuracy of long-term simulations of carbon or water fluxes and their interannual variations. In this study, we develop a data assimilation framework to constrain the parameters of a mechanistic land surface model (ORCHIDEE) with eddy-covariance observations of CO2 and latent heat fluxes made during the years 2001-2004 at the temperate beech forest site of Hesse, in eastern France.