Space-Time Global Models for Climate Ensembles

• Activity Number: 643
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Stefano Castruccio*+ and Michael L Stein and David J. McInerney and Elisabeth J. Moyer
• Companies: The University of Chicago and The University of Chicago and The University of Chicago and The University of Chicago
• Keywords: emulation ; space time global models ; climate models

Abstract

Climate models are mathematical models aimed at reproducing physical processes on a global scale and at predicting quantities like temperature and precipitation given some forcing inputs. Climate ensembles are collection of such runs with different initial physical conditions and different forcing scenarios. In previous work we used a nonlinear regression to statistically model the output to reproduce (emulate) the elements in the ensemble. The emulator was built on a coarse space resolution and focusing only on the mean structure. The purpose of this work is to build a statistical model that addresses the issue of emulating space/time dependence at grid resolution. Given the large size of the data, fitting these models requires fast algorithms for gridded data on the sphere×time domain and efficent ways of computing without storing very large matrices. The presence of independent repetitions in the climate runs based on different initial conditions leads to situations specific to computer output analysis; for example, in this setting diagnostic tools such as the variogram can be evaluated without any bias even in the presence of a spatial trend.