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June 2014 Fast dimension-reduced climate model calibration and the effect of data aggregation
Won Chang, Murali Haran, Roman Olson, Klaus Keller
Ann. Appl. Stat. 8(2): 649-673 (June 2014). DOI: 10.1214/14-AOAS733


How will the climate system respond to anthropogenic forcings? One approach to this question relies on climate model projections. Current climate projections are considerably uncertain. Characterizing and, if possible, reducing this uncertainty is an area of ongoing research. We consider the problem of making projections of the North Atlantic meridional overturning circulation (AMOC). Uncertainties about climate model parameters play a key role in uncertainties in AMOC projections. When the observational data and the climate model output are high-dimensional spatial data sets, the data are typically aggregated due to computational constraints. The effects of aggregation are unclear because statistically rigorous approaches for model parameter inference have been infeasible for high-resolution data. Here we develop a flexible and computationally efficient approach using principal components and basis expansions to study the effect of spatial data aggregation on parametric and projection uncertainties. Our Bayesian reduced-dimensional calibration approach allows us to study the effect of complicated error structures and data-model discrepancies on our ability to learn about climate model parameters from high-dimensional data. Considering high-dimensional spatial observations reduces the effect of deep uncertainty associated with prior specifications for the data-model discrepancy. Also, using the unaggregated data results in sharper projections based on our climate model. Our computationally efficient approach may be widely applicable to a variety of high-dimensional computer model calibration problems.


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Won Chang. Murali Haran. Roman Olson. Klaus Keller. "Fast dimension-reduced climate model calibration and the effect of data aggregation." Ann. Appl. Stat. 8 (2) 649 - 673, June 2014.


Published: June 2014
First available in Project Euclid: 1 July 2014

zbMATH: 06333771
MathSciNet: MR3262529
Digital Object Identifier: 10.1214/14-AOAS733

Rights: Copyright © 2014 Institute of Mathematical Statistics


Vol.8 • No. 2 • June 2014
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