Inferring climate system properties using a computer model

Sansó, Bruno; Forest, Chris E.; Zantedeschi, Daniel

doi:10.1214/08-ba301

Cited by 51 publications

(27 citation statements)

References 26 publications

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“…calibrate a climate model (Sanso et al 2008;Sanso and Forest 2009;Sham Bhat et al 2012;Drignei et al 2008). The GP approach to computer model emulation assumes that the output of interest is a Gaussian process in some set of inputs that vary across model runs.…”

Section: Alternative Emulation Strategiesmentioning

confidence: 99%

“…This fitting is aided by the fact that most of the input parameters appear to have little impact on the output of interest. Emulation over physical parameters that are globally constant has been done with very few model runs by exploiting the information available in a spatially resolved climate model that provides many informative outputs about these parameters from each run (Sanso et al 2008;Sanso and Forest 2009;Sham Bhat et al 2012). In contrast, for the forcing scenario emulation, we should not assume that any of the statistical parameters in our emulators Eqs.…”

Section: Alternative Emulation Strategiesmentioning

confidence: 99%

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Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*

et al. 2014

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The authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO 2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.

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Section: Alternative Emulation Strategiesmentioning

confidence: 99%

Section: Alternative Emulation Strategiesmentioning

confidence: 99%

Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*

et al. 2014

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show abstract

“…In this regard, the statistical model can be regarded as an emulator of an initial condition ensemble, under the assumption that runs are independent for different initial conditions. This is, to our knowledge, the first time an emulator is used in this context, as it is traditionally used for calibration and sensitivity analysis (Sansó et al, 2008;Sansó and Forest, 2009;Bhat et al, 2012;Drignei et al, 2008;Chang et al, 2015) or scenario extrapolation (Holden and Edwards, 2010;Castruccio and Stein, 2013;Holden et al, 2013;Castruccio et al, 2014). The key difference with traditional emulators is that we do not assume correlation among inputs, as different initial conditions sensibly sampled from the spin-up run generate effectively independent runs.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

Castruccio

Genton

2016

Technometrics

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One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10 18 entries.

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“…Consistent with the attribution literature, ASK provides classical ("frequentist") confidence intervals -that is, ranges over which models match observations better than a given threshold for goodness-of-fit. Early implementations of FKM were also frequentist in character, while recent implementations (Sansó et al 2008;Meinshausen et al 2009) have used more explicitly Bayesian approaches, exploring sensitivities to prior distributions but still generally avoiding any claim to accurate representation of actual subjective prior beliefs. In contrast, the studies in references (Murphy et al 2004;Murphy et al 2007;) have generally aimed to provide credible intervals, or Bayesian posterior probabilities -ranges within which the forecast quantity of interest is expected to lie given both the prior expectations of the investigators and the constraints of the observations.…”

Section: Overviewmentioning

confidence: 99%

Towards a typology for constrained climate model forecasts

et al. 2014

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In recent years several methodologies have been developed to combine and interpret ensembles of climate models with the aim of quantifying uncertainties in climate projections. Constrained climate model forecasts have been generated by combining various choices of metrics used to weight individual ensemble members, with diverse approaches to sampling the ensemble. The forecasts obtained are often significantly different, even when based on the same model output. Therefore, a climate model forecast classification system can serve two roles: to provide a way for forecast producers to self-classify their forecasts; and to provide information on the methodological assumptions underlying the forecast generation and its uncertainty when forecasts are used for impacts studies. In this review we propose a possible classification system based on choices of metrics and sampling strategies. We illustrate the impact of some of the possible choices in the uncertainty quantification of large scale projections of temperature and precipitation changes, and briefly discuss possible connections between climate forecast uncertainty quantification and decision making approaches in the climate change context.

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Inferring climate system properties using a computer model

Cited by 51 publications

References 26 publications

Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*

Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*

Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

Towards a typology for constrained climate model forecasts

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