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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 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 root-mean-square 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 CH4 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 larger-scale multi-site studies for a more robust calibration of LPJ-GUESS and similar models, and the results can highlight where model improvements are needed. [ABSTRACT FROM AUTHOR]

In this study, we use a data assimilation framework based on the Adaptive Markov Chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS using CH4 eddy covariance flux observations from 14 different natural boreal and temperate wetlands. The objective is to derive a single set of calibrated parameter values. These parameters are then used in the model to validate its CH4 flux output against 5 different types of natural wetlands situated in different locations, assessing their generality for simulating CH4 fluxes from different boreal and temperate wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH4 fluxes) and facilitated detailed characterisation of the posterior distribution. A reduction of around 95 % in the cost function and approximately 50 % in RMSE were observed. The validation experiment results indicate that four out of 5 sites successfully reduced RMSE, demonstrating the effectiveness of the framework for estimating CH4 emissions from wetlands not included in the study. [ABSTRACT FROM AUTHOR]

Arctic terrestrial greenhouse gas (GHG) fluxes of carbon dioxide (CO 2), methane (CH 4), and nitrous oxide (N 2 O) play an important role in the global GHG budget. However, these GHG fluxes are rarely studied simultaneously, and our understanding of the conditions controlling them across spatial gradients is limited. Here, we explore the magnitudes and drivers of GHG fluxes across fine-scale terrestrial gradients during the peak growing season (July) in sub-Arctic Finland. We measured chamber-derived GHG fluxes and soil temperature, soil moisture, soil organic carbon and nitrogen stocks, soil pH, soil carbon-to-nitrogen (C/N) ratio, soil dissolved organic carbon content, vascular plant biomass, and vegetation type from 101 plots scattered across a heterogeneous tundra landscape (5 km 2). We used these field data together with high-resolution remote sensing data to develop machine learning models for predicting (i.e., upscaling) daytime GHG fluxes across the landscape at 2 m resolution. Our results show that this region was on average a daytime net GHG sink during the growing season. Although our results suggest that this sink was driven by CO 2 uptake, it also revealed small but widespread CH 4 uptake in upland vegetation types, almost surpassing the high wetland CH 4 emissions at the landscape scale. Average N 2 O fluxes were negligible. CO 2 fluxes were controlled primarily by annual average soil temperature and biomass (both increase net sink) and vegetation type, CH 4 fluxes by soil moisture (increases net emissions) and vegetation type, and N 2 O fluxes by soil C/N (lower C/N increases net source). These results demonstrate the potential of high spatial resolution modeling of GHG fluxes in the Arctic. They also reveal the dominant role of CO 2 fluxes across the tundra landscape but suggest that CH 4 uptake in dry upland soils might play a significant role in the regional GHG budget. [ABSTRACT FROM AUTHOR]