Global climate change is occurring as a result of greenhouse gas emissions from human activities, with the dominant contribution from CO2 emitted by fossil fuel combustion. To calculate fossil fuel CO2 emissions of nations or provinces, a detailed accounting of economic activities is performed and used with emission factors specific to different activities. Because these calculations are subject to bias and uncertainty, they need to be validated with independent techniques, particularly as governments begin to introduce regulations to reduce emissions.
This project seeks to develop an independent approach for estimating CO2 emissions that uses atmospheric observations and models. The focus areas are the US state of California and the UK, both of which have networks of ground based atmospheric observations as well as government policies to reduce CO2 emissions. To connect observations of CO2 concentration and other related tracers of fossil fuel combustion to the locations and magnitude of emissions, regional models of atmospheric physics will be used. Observations and models will be combined with Bayesian inversion techniques to produce optimal estimates of regional emissions.