skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.


Abstract not provided.

; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the Third Santa Fe Conference on Global and Regional Climate Change held October 31 - November 4, 2011 in Santa Fe, NM.
Country of Publication:
United States

Citation Formats

Field, Richard V.,, Boslough, Mark Bruce Elrick, and Constantine, Paul G. Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.. United States: N. p., 2011. Web.
Field, Richard V.,, Boslough, Mark Bruce Elrick, & Constantine, Paul G. Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.. United States.
Field, Richard V.,, Boslough, Mark Bruce Elrick, and Constantine, Paul G. 2011. "Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.". United States. doi:.
title = {Statistical Surrogate Models for Estimating Probability of High-Consequence Climate Change.},
author = {Field, Richard V., and Boslough, Mark Bruce Elrick and Constantine, Paul G},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2011,
month =

Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the low-probability, 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.more » A 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 a 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.« less
  • The authors present methods for developing regional scale climate change scenarios by use of regional general circulation models, which take into account the atmospheric moisture content variations which can accompany ambient temperature variations. These cases can then be used in the process of calibrating regional climate change models against more global models, and for process studies.
  • Nuclear power plant accidents, toxic spills, and chemical plant explosions have given rise to a new intellectual endeavor: LP/HC risk analysis, which involve the cooperation of physicists, biologists, engineers, sociologists, and others. The papers in this volume by authors from various disciplines all focus on such generic questions as: How good are the knowledge base and methods for estimating LP/HC risks and uncertainties. How are estimates incorporated into decisionmaking. How do features of the institutional context affect decisionmaking bodies concerned with LP/HC events. How are perceptions of LP/HC risks incorporated into public policies.
  • PRAs often require quantification of the probabilities of various low-probability events, such as accident-initiating events and hardware-fault events. A two-stage Bayes/empirical Bayes data pooling procedure is presented for use in combining as many as five different types of relevant data. A Poisson model is assumed for the event in question. Empirical Bayes methods are used to determine the population variability curve, while Bayesian methods are used to specialize this curve to the specific event in question. The procedure is illustrated by an example in which we estimate the probability of failure of a hypothetical large dam based on (1) amore » deductive event-tree-type analysis of the probability, (2) historical US dam failure data, (3) the opinions of a committee of several dam experts, and (4) the operating history for the dam in question. A Stage-2 posterior distribution is produced which incorporates these data sources. Similar distributions are produced for various combinations of data types and used to assess the contribution of each data source.« less