Browsing by Author "Kangasaho, Vilma Eveliina"

The goal of this study is to ascertain whether methane (CH4) emissions can be estimated source-wise by utilising stable isotope observations in the CarbonTracker Data Assimilation System (CTDAS). The global CH4 budget is poorly known and there are uncertainties in the spatial and temporal distributions as well as in the magnitude of different sources. In this study CTDAS-13CH4 atmospheric inverse model is developed. CTDAS-13CH4 is based on ensemble Kalman filer (EnKF) and used to estimate CH4 fluxes on a region and weekly resolution by implementing CH4 and δ13C-CH4 observations. Anthropogenic biogenic emissions (rice cultivation, landfills and waste water treatments and enteric fermentation and manure management) and anthropogenic non-biogenic emissions (coal, residential and oil and gas) are optimised. Different emission sources can be identified by using process-specific isotopic signature values, δ13C-CH4, because different processes produce CH4 with different isotopic ratio. Optimisation of anthropogenic biogenic emissions increased the total emissions from the prior in eastern North America by 34%, while the optimisation of anthropogenic non-biogenic emissions increased only by 14%. In western North America the corresponding changes were −39% and 9%, respectively. In western parts of Europe, total emissions from prior increased in anthropogenic biogenic optimisation by 18% and decreased in non-biogenic by 3%. Optimisation of anthropogenic biogenic and non-biogenic emissions in the total CH4 budget did not give complete emission estimates, because the optimisation did not include all emission sources and source-specific δ13C-CH4 values were assumed not to vary regionally. However, the modelled concentrations from the optimisation of anthropogenic non-biogenic emissions agreed with the observations of CH4 concentration and δ13C-CH4 values better. Therefore, one could say that the optimisation of anthropogenic non-biogenic emissions was more successful. This study provides reliable information of the magnitude of anthropogenic biogenic and non-biogenic emissions in regions with sufficient observational coverage. The next step in evaluating the spatial and temporal distributions and magnitude of different CH4 sources will be optimising all emission sources simultaneously in a multi-year simulation.