| Summary: | Carbon dioxide is the largest anthropogenic contributor to global warming and atmospheric concentrations have rapidly increased since the start of the industrial revolution. Networks of surface in-situ carbon dioxide sensors provide precise and accurate measurements of the global carbon dioxide concentration, including large scale temporal, seasonal and latitudinal variations. However, these observations are too sparse to allow the establishment of sub-continental carbon budgets, limiting the accuracy of climate change projections and the ability to mitigate future levels of atmospheric carbon dioxide. Satellite observations can provide data with dense spatial and temporal coverage over regions poorly sampled by surface networks. Specifically, observations in the shortwave infrared region are well suited for constraining carbon fluxes as they can provide total column carbon dioxide with high sensitivity to the source and sink locations at the surface. The first dedicated greenhouse gases sensor, the Greenhouse gases Observing SATellite (GOSAT), was launched in January 2009 by the Japanese Aerospace eXploration Agency (JAXA) and has successfully started to acquire global observations of greenhouse gases, including carbon dioxide. The University of Leicester Full Physics (UOL-FP) retrieval algorithm has been designed to estimate total column carbon dioxide from GOSAT shortwave infrared observations. The initial results were compared to coincident ground based measurements for a number of locations and compared on a global scale to a model. This showed an accuracy and precison that should provide improved surface flux estimates. Additionally, a bias correction scheme was developed that reduced observed geographical biases, allowing surface flux uncertainties to be potentially reduced further. To further develop the UOL-FP retrieval algorithm, a simulator capable of creating realistic GOSAT observations was built, allowing the investigation of different retrieval algorithm modifications, which may lead to reduced source and sink flux uncertainties and therefore aid future climate change forecasts.
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