POSTER: Optimising the CH4 simulations from LPJ-GUESS for the Scandinavian wetlands

Description

Processes behind the methane (CH4) emission from the boreal wetlands are
complex, and hence their model representation faces extensive hassle due to large numbers of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS is one of the models that allow quantification and understanding of the natural CH4 fluxes at various scales ranging from local to regional and global, but with several uncertainties. The model contains detailed descriptions of CH4 production, oxidation, and transportation controlled by many tunable parameters. Complexity in the underlying environmental processes, warming-driven alternative paths of meteorological phenomena, and changes in hydrological and vegetation conditions are exi- gent for a calibrated and optimised version of the LPJ-GUESS.
In this study, we used the Markov chain Monte Carlo algorithm based on the Bayes Theorem to improve the predictions and to quantify the uncertainties of LPJ-GUESS. Application of this method on uncertain parameters allows greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with reduced risk of sample impoverishment. For the assimilation, the analysis used the flux measurement data from the Siikaneva wetlands in Southern Finland (from 2005 to 2015) and from the Degerö mire in Northern Sweden (from 2014 to 2018 ). 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. The results demonstrate the reliance of Markov chain Monte Carlo methods to quantitatively examine the interrelationship between objective function choices, parameter identifiability, and data support. As a part of this work, knowledge about how the CH4 flux data can constrain the parameters and processes is de- rived. Although the optimisation in this study is performed based on only two sites, the framework is useful for larger-scale multi-site studies for more robust calibration of the LPJ-GUESS, and the results can be used for its further development.

Period	2022 May 16
Event title	Swedish Climate Symposium 2022
Event type	Conference
Location	Norrköping, SwedenShow on map