Tuesday, 9 January 2018: 8:30 AM
Room 4ABC (ACC) (Austin, Texas)
Atmospheric inverse models have long been a primary tool used to determine emission rates for greenhouse gases and pollutants, based on observations of concentrations in the atmosphere. The results of inverse analyses bear on important questions, with often surprising and controversial results. How much methane is emitted to the atmosphere from natural gas infrastructure, or from coal mining? Is the Arctic releasing accumulated carbon to the atmosphere? What factors are responsible for the global shifts over time in the cycles of methane and carbon dioxide? This talk uses a set of case studies to assess how reliable are these models, what are the limiting factors, and how can we make them better. We emphasize the role of data density and design of observations to constrain independent and interdependent sources of error and bias. The role of transport errors and bias introduced by the structure of priors, and the critical need for information constraining the vertical distribution of target species, will be illustrated. We conclude by exploring how future development of remote sensing, surface networks, and aircraft observations can best inform inverse models so that they can be more effectively applied to critical scientific and societal problems.
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