Assimilating Multi-site Eddy-Covariance Data to Calibrate the CH₄ Wetland Emission Module in a Terrestrial Ecosystem Model

Abstract. In this study, we use a data assimilation framework based on the Adaptive Markov Chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS model using CH₄ eddy covariance flux observations from 14 different natural boreal, temperate and arctic wetlands. The objective is to derive a single set of calibrated parameter values. The calibrated parameter values are then used in the model to validate its CH₄ flux output against independent CH₄ flux observations from five different types of natural wetlands situated in different locations, assessing their generality for simulating CH₄ fluxes from boreal, temperate and arctic wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH₄ fluxes) and facilitated detailed characterisation of the posterior parameter distribution. A reduction of around 95 % in the cost function and approximately 50 % in RMSE against the observations were achieved. The results of validation experiment indicate that for four out of the five validation sites the RMSE was successfully reduced, demonstrating the effectiveness of the framework for estimating CH₄ emissions from wetlands not included in the assimilation experiment. For wetlands above 45° N, the total mean annual CH₄ emission estimation using the optimised model resulted in 28.16 Tg C y^-1, and for regions above 60° N, it resulted in 7.46 Tg C y^-1.