Constraints on Model Response to Greenhouse Gas Forcing and the Role of Subgrid-Scale Processes

Sanderson, Benjamin M.; Knutti, Reto; Aina, T.; Christensen, C.; Faull, N.; Frame, David J.; Ingram, William; Piani, C.; Stainforth, David A.; Stone, Dáithí A.; Allen, Myles

doi:10.1175/2008jcli1869.1

Cited by 62 publications

(67 citation statements)

References 26 publications

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“…to climate and ocean models (Sanderson et al, 2008;Goldstein and Rougier, 2006). The mathematics behind the Gaussian process emulator is explained in Appendix A1 and in Sect.…”

Section: Choose Model Parametersmentioning

confidence: 99%

“…The climateprediction.net ensemble was used in Ackerley et al (2009) to study the climate responses to changes in atmospheric aerosol, albeit with a simpler aerosol scheme than we use here. In Sanderson et al (2008) an emulator was used together with the many climateprediction.net runs to carry out sensitivity analysis. The number of ensembles produced by climateprediction.net is seldom possible in practice.…”

Section: Introductionmentioning

confidence: 99%

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Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

et al. 2011

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Abstract. Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison of driving processes, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space, using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN) sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95 % of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects) and is dominated by the uncertainty in the sulphur emissions, which explains 80 % of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40 % of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interacCorrespondence to: L. A. Lee (l.a.lee@leeds.ac.uk) tion effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process emulation is shown to be an efficient and useful technique for quantifying parameter sensitivity in complex global atmospheric models.

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“…to climate and ocean models (Sanderson et al, 2008;Goldstein and Rougier, 2006). The mathematics behind the Gaussian process emulator is explained in Appendix A1 and in Sect.…”

Section: Choose Model Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

et al. 2011

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“…This is illustrated graphically in Sanderson et al (2008Sanderson et al ( , 2010, where nominally similar parameters were varied over nominally similar ranges in two GCMs obtaining a very broad distribution of responses in one case and a relatively narrow one in the second case.…”

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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“…Instead, atmospheric parameters were chosen by interpolation. A combined root mean square error (RMSE) was derived from three different observation types: surface temperatures; top of atmosphere radiative fluxes; and total precipitation (Sanderson et al 2008). For each observation, Empirical Orthogonal Functions (EOFs) were taken in the space defined by the area weighted Giorgi regions in the 'spatial' dimension and the ensemble in the 'temporal' dimension.…”

Section: (A ) Forcings In the Bbc Ccementioning

confidence: 99%

“…Each model in the ensemble is associated with three sets of errors-each set comprising the difference between the amplitude of each mode in the truncated EOF set in that model and the reanalysis projection of that mode. These numbers were then used to train a neural network (Sanderson et al 2008), where the input was the perturbed parameters for each model and the output was the set of observational errors described above. The neural network was also trained to predict the climate sensitivity.…”

Section: (A ) Forcings In the Bbc Ccementioning

confidence: 99%

The climate prediction .net BBC climate change experiment: design of the coupled model ensemble

Frame¹,

Aina

Christensen

et al. 2008

Phil. Trans. R. Soc. A.

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Perturbed physics experiments are among the most comprehensive ways to address uncertainty in climate change forecasts. In these experiments, parameters and parametrizations in atmosphere-ocean general circulation models are perturbed across ranges of uncertainty, and results are compared with observations. In this paper, we describe the largest perturbed physics climate experiment conducted to date, the British Broadcasting Corporation (BBC) climate change experiment, in which the physics of the atmosphere and ocean are changed, and run in conjunction with a forcing ensemble designed to represent uncertainty in past and future forcings, under the A1B Special Report on Emissions Scenarios (SRES) climate change scenario.

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Constraints on Model Response to Greenhouse Gas Forcing and the Role of Subgrid-Scale Processes

Cited by 62 publications

References 26 publications

Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

Towards a typology for constrained climate model forecasts

The climate prediction .net BBC climate change experiment: design of the coupled model ensemble

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