Statistical combination of climate models

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Abstract/Contents

Abstract
Atmosphere-ocean general circulation models (AOGCMs) are the primary tool to study how climate responds to increases in the concentration of greenhouse gases in the atmosphere. Outputs from different AOGCM's have been combined using weighting schemes related to how well they reproduce the historical data. Earlier approaches have inferred model weights in the context of a Bayes hierarchical scheme that treats both the historical record and the several model outputs as independent random samples from a distribution of possible weather data centered around the true climate. However, recent work points to evident correlations among model outputs. Our approach is based on optimizing the fit of model output combinations to historical data, allowing for weighting that is location specific with smoothing of both space-time temperature trends and model weight coefficients. Estimated model weights are extrapolated and applied to `future' AOGCM output to produce predictions of future space-time temperature trends. The approach is illustrated using observed summer temperature data for central North America for the $50$-year time period $1940-1989$, together with corresponding output from two AOGCMs.

Description

Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Copyright date 2011
Publication date 2010, c2011; 2010
Issuance monographic
Language English

Creators/Contributors

Author Chen, Yi Fang
Primary advisor Switzer, Paul
Primary advisor Walther, Guenther
Thesis advisor Switzer, Paul
Thesis advisor Walther, Guenther
Thesis advisor Zhang, Nancy R. (Nancy Ruonan)
Advisor Zhang, Nancy R. (Nancy Ruonan)
Associated with Stanford University, Department of Statistics

Subjects

Genre Theses

Bibliographic information

Statement of responsibility Yi Fang Chen.
Note Submitted to the Department of Statistics.
Thesis Thesis (Ph.D.)--Stanford University, 2011.
Location electronic resource

Access conditions

Copyright
© 2011 by Yi Fang Chen
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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