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International Journal for Uncertainty Quantification

Impact factor: 0.967

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2012003829
pages 341-355

STATISTICAL SURROGATE MODELS FOR PREDICTION OF HIGH-CONSEQUENCE CLIMATE CHANGE

Richard V. Field Jr.
Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
Paul Constantine
Colorado School of Mines
M. Boslough
Sandia National Laboratories, Albuquerque, New Mexico 87185, USA

ABSTRACT

In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the lowprobability, high-consequence tail of the distribution of possible events, as opposed to best estimates based on central tendencies. We frame the climate change problem and its associated risks in a similar manner. To properly explore the tails of the distribution requires extensive sampling, which is not possible with existing coupled atmospheric models due to the high computational cost of each simulation. We therefore propose the use of specialized statistical surrogate models (SSMs) for the purpose of exploring the probability law of various climate variables of interest. An SSM is different than a deterministic surrogate model in that it represents each climate variable of interest as a space/time random field. The SSM can be calibrated to available spatial and temporal data from existing climate databases, e.g., the program for climate model diagnosis and intercomparison (PCMDI), or to a collection of outputs from a general circulation model (GCM), e.g., the community Earth system model (CESM) and its predecessors. Because of its reduced size and complexity, the realization of a large number of independent model outputs from an SSM becomes computationally straightforward, so that quantifying the risk associated with low-probability, high-consequence climate events becomes feasible. A Bayesian framework is developed to provide quantitative measures of confidence, via Bayesian credible intervals, in the use of the proposed approach to assess these risks.