Frontiers in the Detection and Attribution of Climate Change (12w5037)
Amy Braverman (Jet Propulsion Laboratory, California Institute of Technology)
Paul Kushner (University of Toronto)
Richard Smith (University of North Carolina Chapel Hill)
Daithi Stone (Lawrence Berkeley National Laboratory)
Claudia Tebaldi (NCAR)
Michael Wehner (Lawrence Berkeley Lab-Scientific Computing Group)
We will combine expertise in statistical climatology, epidemiology, and agricultural statistics with the aim of initiating the development of new D&A methodologies based on rigorous statistical approaches and using a hierarchy of models, including process based, statistical, and computational models. We also aim to include enough expertise in uncertainty quantification of existing and planned Earth Observation systems to allow observational uncertainty analysis to be integrated into this effort.
The invited participants and time allotted in the workshop will reflect our desire for cross fertilization between the three fields. For instance, one session we envision will treat event attribution from the perspective of climatology, epidemiology, and agriculture: In each field, how is an "event" defined and detected, how is its cause inferred quantitatively from available data, how is confidence in this inference measured, and how might newly available Earth observations improve the methodologies? Such a session will be lead by one or two chairs and will incorporate lectures, directed discussion, and undirected discussion. An ideal situation would be one in which a lecturer provides core concepts for half of his or her talk, and then initiates a moderated discussion based on "blue skies" ideas that would involve people from within and outside his or her field. Parallel breakouts might also be employed towards the end of the week, and poster presentations will be used for additional presentations.