Preprints
https://doi.org/10.5194/egusphere-2024-3305
https://doi.org/10.5194/egusphere-2024-3305
26 Nov 2024
 | 26 Nov 2024
Status: this preprint is open for discussion.

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

Jalisha Theanutti Kallingal, Marko Scholze, Paul Anthony Miller, Johan Lindström, Janne Rinne, Mika Aurela, Patrik Vestin, and Per Weslien

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.

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Jalisha Theanutti Kallingal, Marko Scholze, Paul Anthony Miller, Johan Lindström, Janne Rinne, Mika Aurela, Patrik Vestin, and Per Weslien

Status: open (until 07 Jan 2025)

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Jalisha Theanutti Kallingal, Marko Scholze, Paul Anthony Miller, Johan Lindström, Janne Rinne, Mika Aurela, Patrik Vestin, and Per Weslien
Jalisha Theanutti Kallingal, Marko Scholze, Paul Anthony Miller, Johan Lindström, Janne Rinne, Mika Aurela, Patrik Vestin, and Per Weslien

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Short summary
We explored the possibilities of a Bayesian-based data assimilation algorithm to improve the wetland CH4 flux estimates by a dynamic vegetation model. By assimilating CH4 observations from 14 wetland sites we calibrated model parameters and estimated large-scale annual emissions from northern wetlands. Our findings indicate that this approach leads to more reliable estimates of CH4 dynamics, which will improve our understanding of the climate change feedback from wetland CH4 emissions.