Optimising CH₄ simulations from the LPJ-GUESS model v4.1 using an adaptive MCMC algorithm

Abstract

Abstract. Processes behind methane (CH4) emission from boreal wetlands are complex, and hence their model representation is complicated by a large number of parameters and parameter uncertainties. The arctic-enabled dynamic global vegetation model LPJ-GUESS 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 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 are 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 pa- rameters 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 greater search of their posterior distribution, leading to a more complete characterisation of the posterior distribution with reduced risk of sample impoverishment that can occur when using other optimisation methods. For assimilation, the analysis used flux measurement data gathered during the period 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 quantitatively assess the interrelationship between objective function choices, parameter identifiability, and data support. As a part of this work, knowledge about how the CH4 data can constrain the parameters and processes is derived. Though the optimisation is performed based on a single site’s flux data from Siikaneva, the algorithm is useful for larger-scale multi-site studies for more robust calibration of LPJ-GUESS and similar models, and the results can highlight where model improvements are needed.

Comment on egusphere-2024-373

“…A detailed description of the wetland and CH 4 emissions module is given in Wania et al (2010) and in Kallingal et al (2023).…”

“…A part of this CH 4 is oxidised by the oxygen in the soil and the other part is eventually transported to the atmosphere through either diffusion, plantmediated transport or ebullition. As shown in Eq.1 the sum of the emissions through these three pathways constitutes the total CH 4 flux from the soil to the atmosphere (Wania et al (2010), Kallingal et al (2023)).…”

“…The parameters selected for optimisation in this study are shown in Table 1. For this study we have considered 10 out of 11 parameters calibrated by Kallingal et al (2023) in their single-site optimisation. The parameter w tiller is removed because it showed high correlation with r tiller in Kallingal et al (2023).…”

“…2) that compares the modelled observable with the observations. The details of this search algorithm and its application to a single-site optimisation have been described in Kallingal et al (2023). Efficient sampling from the target distribution requires a proposal distribution that correctly represents the dependence structure of the target, and to avoid manual tuning of the proposal we use an adaptive MCMC to tune the proposal distribution, where the Rao-Blackwellisation improves the adaptation step.…”

“…The present study's objective is to investigate the capacity of the GRaB-AM framework developed by Kallingal et al (2023) for calibrating process model parameters in a more general multi-site framework. We aim to optimise selected model parameters for examining to what extent this optimisation improves the model's ability to simulate the seasonal cycle of CH 4 from different wetlands over northern latitudes above 40 • N. We also aim to estimate the model process and parameter correlations and uncertainties.…”