It has been over three decades since the Charney report estimated that climate sensitivity is somewhere between 1.5 and 4.5°C, which remains about the same to this day. Decision-making entities now more than ever require more competent tools for climate prediction, and that competence must be gained using observational data. We will present a Bayesian approach fordetermining the utility of data types for improving climate prediction, how the Climate Absolute Radiance and Refractivity Observatory (CLARREO) is suited to the task, and what we can do in the short term to deliver a better model for weather prediction, climate prediction, and reanalysis. This talk is hosted by the the Science Visitors Colloquium Program.

Stephen received his B.A. in physics at Cornell University and his Ph.D. in planetary science at Caltech. He joined JPL in 1994 as a postdoc and joined the technical staff shortly thereafter. While at JPL, he was a member of the Atmospheric Infrared Sounder (AIRS) science integration team and participated in the development of GPS radio occultation. He took a position as a Project Scientist at Harvard in 2004 where he helped to develop the concept of the CLARREO mission, a progenitor of space-based climate monitoring.