Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2299-2024
https://doi.org/10.5194/gmd-17-2299-2024
Development and technical paper
 | 
20 Mar 2024
Development and technical paper |  | 20 Mar 2024

Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm

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

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Cited articles

Aalto, J., Aalto, P., Keronen, P., Kolari, P., Rantala, P., Taipale, R., Kajos, M., Patokoski, J., Rinne, J., Ruuskanen, T., Leskinen, M., Laakso, H., Levula, J., Pohja, T., Siivola, E., Kulmala, M., and Ylivinkka, I.: SMEAR II Hyytiälä forest meteorology, greenhouse gases, air quality and soil, University of Helsinki, Institute for Atmospheric and Earth System Research, https://doi.org/10.23729/23dd00b2-b9d7-467a-9cee-b4a122486039, 2022. a
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Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
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