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Optimising the CH4 simulations from the LPJ-GUESS model using Adaptive MCMC

descriptionPublicationkeyboard_double_arrow_right Article 16 Nov 2022 English Publisher:Zenodo
Authors: Theanutti Kallingal Jalisha; Scholze Marko; A. Miller Paul; Lindstrom Johan; Rinne Janne; Raivonen Maarit;

doi: 10.5281/zenodo.7339241 , 10.5281/zenodo.7657117 , 10.5281/zenodo.7774119 , 10.5281/zenodo.7339240

Optimising the CH4 simulations from the LPJ-GUESS model using Adaptive MCMC

Abstract

CH4 simulations from the LPJ-GUESS, a process-based dynamic global vegetation model, was calibrated using the daily CH4 flux observations collected at Siikaneva wetlands ( Finland) from 2005 to 2014. Rao-Blackwellised modification of Adaptive Metropolis MCMC sampling algorithm was used to calibrate the model. The code, data used to input the model and the data used to optimise the model are uploaded.

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Keywords

AMCMC, Data assimilation, Siikaneva, Methane, LPJ-GUESS

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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