Submitted as: development and technical paper
19 Apr 2023
Submitted as: development and technical paper |  | 19 Apr 2023
Status: this preprint is currently under review for the journal GMD.

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

Jalisha Theanutti Kallingal, Johan Lindström, Paul A Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze

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.

Jalisha Theanutti Kallingal et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-302', Anonymous Referee #1, 09 Jul 2023
    • AC2: 'Reply on RC1', Jalisha Theanutti Kallingal, 30 Oct 2023
  • RC2: 'Comment on gmd-2022-302', Anonymous Referee #2, 29 Aug 2023
    • AC3: 'Reply on RC2', Jalisha Theanutti Kallingal, 30 Oct 2023
  • EC1: 'Comment on gmd-2022-302', Julia Hargreaves, 12 Sep 2023
  • AC1: 'Comment on gmd-2022-302', Jalisha Theanutti Kallingal, 30 Oct 2023

Jalisha Theanutti Kallingal et al.

Jalisha Theanutti Kallingal et al.


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Short summary
This manuscript describes the development of a Bayesian data assimilation framework around the wetland CH4 module in the LPJ-GUESS DGVM. The novel approach we developed combines the Rao-Blackwellised Adaptive Metropolis algorithm with the Global Adaptive Scaling (G-RB AM) for sampling the model parameters. Further, the manuscript demonstrates the application of the DA framework for optimising model process parameters by assimilating daily CH4 flux measurement data.