Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The processes responsible for methane (CH4) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simu...
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ftunivhelsihelda:oai:helda.helsinki.fi:10138/574823 2024-05-19T07:36:51+00:00 Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm Kallingal, Jalisha T. Lindström, Johan Miller, Paul A. Rinne, Janne Raivonen, Maarit Scholze, Marko Institute for Atmospheric and Earth System Research (INAR) Micrometeorology and biogeochemical cycles 2024-04-23T13:15:04Z 26 application/pdf http://hdl.handle.net/10138/574823 eng eng COPERNICUS GESELLSCHAFT MBH 10.5194/gmd-17-2299-2024 Kallingal , J T , Lindström , J , Miller , P A , Rinne , J , Raivonen , M & Scholze , M 2024 , ' Optimising CH 4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm ' , Geoscientific Model Development , vol. 17 , no. 6 , pp. 2299-2324 . https://doi.org/10.5194/gmd-17-2299-2024 ORCID: /0000-0002-8987-4972/work/158618001 http://hdl.handle.net/10138/574823 832c7d47-7e60-4f39-aa37-1da117cc8bbf 85188433674 001192126000001 cc_by info:eu-repo/semantics/openAccess openAccess 114 Physical sciences Article publishedVersion 2024 ftunivhelsihelda 2024-04-30T23:51:23Z The processes responsible for methane (CH4) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) is one such model that allows quantification and understanding of the natural wetland CH4 fluxes at various scales, ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of the CH4 production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions highlight the need for a calibrated and optimised version of LPJ-GUESS. In this study, we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis-Hastings (MH)-based Markov chain Monte Carlo (MCMC) algorithm to improve predictions of CH4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method on uncertain parameters allows for a greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with a reduced risk of the sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period from 2005 to 2014 from the Siikaneva wetlands in Southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH4 flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to ... Article in Journal/Newspaper Arctic HELDA – University of Helsinki Open Repository |
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HELDA – University of Helsinki Open Repository |
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ftunivhelsihelda |
language |
English |
topic |
114 Physical sciences |
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114 Physical sciences Kallingal, Jalisha T. Lindström, Johan Miller, Paul A. Rinne, Janne Raivonen, Maarit Scholze, Marko Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm |
topic_facet |
114 Physical sciences |
description |
The processes responsible for methane (CH4) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) is one such model that allows quantification and understanding of the natural wetland CH4 fluxes at various scales, ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of the CH4 production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions highlight the need for a calibrated and optimised version of LPJ-GUESS. In this study, we formulated the parameter calibration as a Bayesian problem, using knowledge of reasonable parameters values as priors. We then used an adaptive Metropolis-Hastings (MH)-based Markov chain Monte Carlo (MCMC) algorithm to improve predictions of CH4 emission by LPJ-GUESS and to quantify uncertainties. Application of this method on uncertain parameters allows for a greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with a reduced risk of the sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period from 2005 to 2014 from the Siikaneva wetlands in Southern Finland with an estimation of measurement uncertainties. The data are used to constrain the processes behind the CH4 dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH4 flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to ... |
author2 |
Institute for Atmospheric and Earth System Research (INAR) Micrometeorology and biogeochemical cycles |
format |
Article in Journal/Newspaper |
author |
Kallingal, Jalisha T. Lindström, Johan Miller, Paul A. Rinne, Janne Raivonen, Maarit Scholze, Marko |
author_facet |
Kallingal, Jalisha T. Lindström, Johan Miller, Paul A. Rinne, Janne Raivonen, Maarit Scholze, Marko |
author_sort |
Kallingal, Jalisha T. |
title |
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm |
title_short |
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm |
title_full |
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm |
title_fullStr |
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm |
title_full_unstemmed |
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm |
title_sort |
optimising ch4 simulations from the lpj-guess model v4.1 using an adaptive markov chain monte carlo algorithm |
publisher |
COPERNICUS GESELLSCHAFT MBH |
publishDate |
2024 |
url |
http://hdl.handle.net/10138/574823 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
10.5194/gmd-17-2299-2024 Kallingal , J T , Lindström , J , Miller , P A , Rinne , J , Raivonen , M & Scholze , M 2024 , ' Optimising CH 4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm ' , Geoscientific Model Development , vol. 17 , no. 6 , pp. 2299-2324 . https://doi.org/10.5194/gmd-17-2299-2024 ORCID: /0000-0002-8987-4972/work/158618001 http://hdl.handle.net/10138/574823 832c7d47-7e60-4f39-aa37-1da117cc8bbf 85188433674 001192126000001 |
op_rights |
cc_by info:eu-repo/semantics/openAccess openAccess |
_version_ |
1799475988131741696 |