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

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Title: Assimilating multi-site eddy-covariance data to calibrate the wetland CH4 emission module in a terrestrial ecosystem model
Authors: Kallingal, Jalisha Theanutti, Scholze, Marko, Miller, Paul Anthony, Lindström, Johan, Rinne, Janne, Aurela, Mika, Vestin, Patrik, Weslien, Per
Contributors: Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), MERGE: ModElling the Regional and Global Earth system, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), MERGE: ModElling the Regional and Global Earth system, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), BECC: Biodiversity and Ecosystem services in a Changing Climate, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), BECC: Biodiversity and Ecosystem services in a Changing Climate, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator, Lund University, Faculty of Science, Dept of Physical Geography and Ecosystem Science, Lunds universitet, Naturvetenskapliga fakulteten, Institutionen för naturgeografi och ekosystemvetenskap, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Aerosols, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Aerosoler, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Nature-based future solutions, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturbaserade framtidslösningar, Originator, Lund University, Faculty of Science, Centre for Mathematical Sciences, Mathematical Statistics, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Matematisk statistik, Originator
Source: Biogeosciences. 22(16):4061-4086
Subject Terms: Natural Sciences, Earth and Related Environmental Sciences, Climate Science, Naturvetenskap, Geovetenskap och relaterad miljövetenskap, Klimatvetenskap, Mathematical Sciences, Probability Theory and Statistics, Matematik, Sannolikhetsteori och statistik, Physical Geography, Naturgeografi
Description: 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 .
Access URL: https://doi.org/10.5194/bg-22-4061-2025
Database: SwePub
Description
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 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 .
ISSN:17264189
DOI:10.5194/bg-22-4061-2025