Application of Ensemble Kalman Filter in Estimation of Global Methane Balance

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Title: Application of Ensemble Kalman Filter in Estimation of Global Methane Balance
Author: Tsuruta, Aki
Belongs to series: Finnish Meteorological Institute Contributions 141
ISSN: 0782-6117
ISBN: 978-952-336-041-9
Abstract: Ensemble Kalman filter (EnKF) is a useful Bayesian inverse modelling method to make inference of the states of interest from observations, especially in non-linear systems with a large number of states to be estimated. This thesis presents an application of EnKF in estimation of global and regional methane budgets, where methane fluxes are inferred from atmospheric methane concentration observations. The modelling system here requires a highly non-linear atmospheric transport model to convert the state space on to the observation space, and an optimization in both spatial and temporal dimensions is desired. Methane is an important greenhouse gas, strongly influenced by anthropogenic activities, whose atmospheric concentration increased more than twice since pre-industrial times. Although its source and sink processes have been studied extensively, the mechanisms behind the increase in the 21st century atmospheric methane concentrations are still not fully understood. In this thesis, contributions of anthropogenic and natural sources to the increase in the atmospheric methane concentrations are studied by estimating the global and regional methane fluxes from anthropogenic and biospheric sources for the 21st century using an EnKF based data assimilation system (CarbonTracker Europe-CH4; CTE-CH4). The model was evaluated using assimilated in situ atmospheric concentration observations and various non-assimilated observations, and the model sensitivity to several setups and inputs was examined to assess the consistency of the model estimates. The key findings of this thesis include: 1) large enough ensemble size, appropriate prior error covariance, and good observation coverage are important to obtain consistent and reliable estimates, 2) CTE-CH4 was able to identify the locations and sources of the emissions that possibly contribute significantly to the increase in the atmospheric concentrations after 2007 (the Tropical and extra Tropical anthropogenic emissions), 3) Europe was found to have an insignificant or negative influence on the increase in the atmospheric CH4 concentrations in the 21st century, 4) CTE-CH4 was able to produce flux estimates that are generally consistent with various observations, but 5) the estimated fluxes are still sensitive to the number of parameters, atmospheric transport and spatial distribution of the prior fluxes.
Date: 2017-12
Subject: bayesian inversion
ensemble Kalman filter
data assimilation
GHG flux inversion

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