Statistical surrogate models for prediction of high-consequence climate change.

Constantine, Paul ; Field, Richard V., Jr. ; Boslough, Mark Bruce Elrick

Sandia National Laboratories

关键词: Safety Engineering;    Probability;    Forecasting;    Climates;    Sampling;   

DOI  :  10.2172/1029816
RP-ID  :  SAND2011-6496
RP-ID  :  AC04-94AL85000
RP-ID  :  1029816

美国|英语

来源: UNT Digital Library

【 摘 要 】

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. 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.