Evaluating CH₄ Concentrations and Emissions in the Amazon Basin Using the Greenhouse Gases Observing Satellite and Dedicated CH₄ Models

Natural wetlands, such as those in the Amazon, are important sources of methane (CH4), which is the second most important anthropogenic greenhouse gas in terms of radiative forcing. With a short atmospheric lifetime compared to carbon dioxide, reductions in CH4 emissions have the potential to mitigate global warming on much faster time scales. Currently, our understanding of these emissions is limited, with considerable disagreement between modelled wetland emissions estimates. Satellites can provide CH4 observations with high coverage and density that can provide the required observational constraints to improve CH4 emission estimates, especially for regions where in situ observations are sparse, such as the Amazon. In this thesis, I have carried out the first validation of CH4 from the GOSAT satellite using a series of dedicated aircraft in situ profile measurements demonstrating the high quality of GOSAT CH4 observations for this region. I have then used the satellite observations in conjunction with the aircraft profiles to investigate the characteristics of CH4 emissions from wetlands in the Amazon and their representation in state-of-the-art emissions inventories when combined with a chemical transport model. GOSAT observes large methane enhancements of up to 60 ppb between the wet and dry seasons in the Amazon coinciding with large Amazonian wetlands which are underestimated by approximately 15 ppb by models, pointing towards clear shortcomings in the inventories. To further assess the regionalCH4 emissions, a simulation system has been developed using a regional transport model based on a high resolution representation of atmospheric transport. This framework allows quick comparisons of different emissions inventories to GOSAT XCH4 on regional scales while giving a better representation of transport compared to global transport models. An assessment against global models and GOSAT data has shown that the model performs well and often agrees better with GOSAT than the global models do.