Statistical Surrogate Models for Prediction of High-Consequence Climate Change

Abstract

In safety engineering, performance metrics are defined using probabilistic risk assessments focused on the low-probability, high-consequence tail of the distribution of 3 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.4

Emerging Directions in Geophysical Inversion

“…Latterly, surrogate models (also known as emulators) have been explicitly adopted for geophysical studies. Similar to the Neighbourhood Algorithm, Field et al, 2011;Castruccio et al, 2014), and applications in hydrology (Hussain et al, 2015) and planetary geophysics (Agarwal et al, 2020).…”

Risk assessment of climate systems for national security.

“…Due to its reduced size and complexity, the realization of a large number of independent model outputs from a SSM becomes computationally straightforward, so that estimates of lowprobability, high-consequence climate events becomes feasible. A Bayesian framework was also developed to provide quantitative measures of confidence, via Bayesian credible intervals, in the use of the proposed SSM as a statistical replacement for the associated GCM (Field et al 2012(Field et al , 2011a(Field et al , 2011b. Figure 3.5 shows the results of an example exceedance probability for precipitation where the concern in for the probability of exceeding a reduction in precipitation below one standard deviation.…”