Assimilating multi-site eddy-covariance data to calibrate the wetland CH4 emission module in a terrestrial ecosystem model

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 50 % in RMSE was achieved, reflecting improved agreement with the observations. The results of the 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 yr−1 and for regions above 60 ° N it resulted in 7.46 Tg C yr−1.