Failure analysis of parameter-induced simulation crashes in climate models

Abstract

Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Quantitative analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation crashes within the Parallel Ocean Program (POP2) component of the Community Climate System Model (CCSM4). About 8.5% of our CCSM4 simulations failed for numerical reasons at combinations of POP2 parameter values. We apply support vector machine (SVM) classification from machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. A committee of SVM classifiers readily predicts model failures in an independent validation ensemble, as assessed by the area under the receiver operating characteristic (ROC) curve metric (AUC > 0.96). The causes of the simulation failures are determined through a global sensitivity analysis. Combinations of 8 parameters related to ocean mixing and viscosity from three different POP2 parameterizations are the major sources of the failures. This information can be used to improve POP2 and CCSM4 by incorporating correlations across the relevant parameters. Our method can also be used to quantify, predict, and understand simulation crashes in other complex geoscientific models

Detection of Climate Crashes using Fuzzy Neural Networks

“…These model parameters were subjected to different parameterizations on a sub-grid scale. The main emphasis behind such parameterization was to determine the resultant outcome of vertical and horizontal oceanic turbulent after simulation [18], [26], [27]. Table I provides details of the uncertainty ranges of the model parameters used in this study.…”

“…The parameters are taken according to the [18]. These are: spatial anisotropic viscosity that was used to determine the horizontal momentum and was represented by the parameters 13 to 18, isopycnal eddy-induced transport of the horizontal mixers that were for parameters 10 to 12, the parameters 7 to 9 that can be used to simulate mixed layer eddies and submesoscale, and were used for the abyssal tidal mixing.…”

“…Such algorithms are used in a set of circumstances and time and have solid, liquid and gaseous elements [16], [17] showed that crashes occur at a high rate. Lucas et al have considered predictions of climate models [18]. A research is required to add and refurbish existing information about crashes in climate models.…”

Semisupervised Tangent Space Discriminant Analysis

“…BCWD consists of 569 data points from two classes in R 30 . CMSC consists of 540 data points from two classes in R 18 . CTG consists of 2126 data points from ten classes in R 23 .…”

A genetic algorithm approach to optimising random forests applied to class engineered data

“…A C C E P T E D M A N U S C R I P T Classification and Bank Notes Authentication sets [7]), Physical and Social Science (Ionosphere Data Set [7] , Climate Model Simulation Crashes [29], Balance Scale Data Set [7]) and one set from business category (Blood Transfusion Service Center [40]). …”