This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 427
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #308488
Title: High-Dimensional Inference and Applications to Climate Projections
Author(s): Ian Wong*+ and Bala Rajaratnam and Claudia Tebaldi
Companies: Stanford University and Stanford University and The University of British Columbia
Address: , , ,
Keywords: climate ; high dimensional inference ; graphical models ; geostatistics
Abstract:

Developing reliable climate projections is a topic of great relevance and importance to the geo-statistics and geo-sciences community. It is also an important component of the reports of the UN Intergovernmental Panel on Climate Change(IPCC). Correspondingly, many groups from around the world have independently developed global climate models (GCM) to address this challenge. In this work, we apply current methods high dimensional inference to GCM simulation data to combine model outputs. Our results contribute to the understanding of how GCMs relate to one another and presents an approach to climate projection together with uncertainty quantification. We also compare our method to existing techniques in the literature, study its theoretical properties and scope for applicability.


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