Abstract
The processes responsible for methane (CH_{4}) emissions from boreal wetlands are complex; hence, their model representation is complicated by a large number of parameters and parameter uncertainties. The arcticenabled dynamic global vegetation model LPJGUESS (LundPotsdamJena General Ecosystem Simulator) is one such model that allows quantification and understanding of the natural wetland CH_{4} fluxes at various scales, ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of the CH_{4} production, oxidation, and transport controlled by several process parameters. Complexities in the underlying environmental processes, warmingdriven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions highlight the need for a calibrated and optimised version of LPJGUESS. 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 MetropolisHastings (MH)based Markov chain Monte Carlo (MCMC) algorithm to improve predictions of CH_{4} emission by LPJGUESS 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 CH_{4} dynamics, and the posterior covariance structures are used to explain how the parameters and the processes are related. To further support the conclusions, the CH_{4} flux and the other component fluxes associated with the flux are examined. The results demonstrate the robustness of MCMC methods to quantitatively assess the interrelationship between objective function choices, parameter identifiability, and data support. The experiment using real observations from Siikaneva resulted in a reduction in the rootmeansquare error (RMSE), from 0.044 to 0.023 gC m^{2} d^{1}, and a 93.89 % reduction in the cost function value. As a part of this work, knowledge about how CH_{4} data can constrain the parameters and processes is derived. Although the optimisation is performed based on a single site's flux data from Siikaneva, the algorithm is useful for largerscale multisite studies for a more robust calibration of LPJGUESS and similar models, and the results can highlight where model improvements are needed.
Original language  English 

Pages (fromto)  22992324 
Number of pages  26 
Journal  Geoscientific Model Development 
Volume  17 
Issue number  6 
DOIs  
Publication status  Published  2024 Mar 20 
Subject classification (UKÄ)
 Probability Theory and Statistics
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LundPotsdamJena General Ecosystem Simulator (LPJGUESS)
Thomas Pugh (Manager) & Paul Miller (Manager)
MERGE: ModElling the Regional and Global Earth systemInfrastructure