A comparison of Latin hypercube and grid ensemble designs for the multivariate emulation of an Earth system model

Urban, Nathan M.; Fricker, Thomas E.

doi:10.1016/j.cageo.2009.11.004

Cited by 60 publications

(43 citation statements)

References 21 publications

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“…The emulation is based on a second order polynomial, following the approach of Kennedy and O'Hagan (2000). The training runs comprise 25 model simulations in a Latin hypercube design, according to Urban and Fricker (2010). Figure 7 shows very similar results for the emulator and for the full model.…”

Section: Statistical Emulatorssupporting

confidence: 52%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling.We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time-and spacedependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

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Section: Statistical Emulatorssupporting

confidence: 52%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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“…Parameters for these initial model runs were selected from the prior through Latin hypercube sampling. This form of sampling has been shown to be an effective method for selecting emulator training data (Urban and Fricker 2010). We generate 100 initial hypercubes and utilize the one with the maximum minimum distance between its members, although more developed algorithms are available for this task; c.f., Grosso et al (2008).…”

Section: Methodsmentioning

confidence: 99%

Artificial neural network assisted Bayesian calibration of climate models

2011

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We demonstrate and validate a Bayesian approach to model calibration applicable to computationally expensive General Circulation Models (GCMs) that includes a posterior estimate of the intrinsic structural error of the model. Bayesian artificial neural networks (BANNs) are trained with output from a GCM and used as emulators of the full model to allow a computationally efficient Markov Chain Monte Carlo (MCMC) sampling of the posterior for the GCM parameters calibrated against seasonal climatologies of temperature, pressure, and humidity. We validate the methodology by calibrating to targets produced by a model run with added noise. We then demonstrate a calibration of five GCM parameters against an observational data set. The approach accounts for both parametric and structural uncertainties of the model as well as uncertainties associated with the observational calibration data. This enables the generation of statistically rigorous probabilistic forecasts for future climate states. All calibration experiments are performed with emulators trained using a maximum of one hundred model runs, in accord with typical resource restrictions imposed by computationally expensive models. We conclude by summarizing remaining issues to address in order to create a complete and validated operational methodology for objective calibration of computationally expensive models.

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“…A Latin hypercube is the generalization of this concept to an arbitrary number of dimensions, in that each sample is the only one in each axis-aligned hyperplane and each parameter has equally spaced, although different, values-a permutation of the values between 0 and 1. An evaluation of Latin hypercube designs versus regular grid sampling is explored in Urban and Fricker (2010). We conduct a twostage experiment.…”

Section: B Ensemble Design and Creationmentioning

confidence: 99%

“…GP emulators have the advantage that they are more flexible than regression emulators and as flexible as neural networks but are easier to interpret. Gaussian process emulators have been used in ocean-atmosphere work either with simulators of intermediate complexity (Challenor et al 2006;Urban and Fricker 2010;Challenor 2011) or with ensembles of opportunity, rather than formally designed ensembles (Rougier and Sexton 2007;Holden and Edwards 2010). However, these papers do not address, specifically, highly nonlinear or bimodal outcomes that might result.…”

Section: Introductionmentioning

confidence: 99%

“…Examples include the Bayes hierarchical models (Berliner et al 2003;Clark 2005) and stochastic dynamical models (Sapsis and Lermusiaux 2009;Leslie et al 2008;Strounine et al 2010;Frolov et al 2009) as well as emulators. We use emulators based on Gaussian processes (GP), but others have used simple regression models (Logemann et al 2004;Murphy et al 2004) and neural networks (van der Merwe et al 2007). GP emulators have the advantage that they are more flexible than regression emulators and as flexible as neural networks but are easier to interpret.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the Use of Emulators with Extreme and Highly Nonlinear Geophysical Simulators

Tokmakian

Challenor

Andrianakis

2012

Journal of Atmospheric and Oceanic Technology

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Gaussian process emulators are a powerful tool for understanding complex geophysical simulators, including oceanic and atmospheric general circulation models. Concern has been raised about their ability to emulate complex nonlinear systems. For the first time, using the simple Stommel model, the way in which emulators can reasonably represent the full sampling space of an extreme nonlinear, bimodal system is illustrated. This simple example also shows how an emulator can help to elucidate interactions between parameters. The ideas are further illustrated with a second, more realistic, intermediate complex climate simulator. The paper describes what is meant by an emulator, the methodology of emulators, how emulators can be assessed, and why they are useful. It is shown how simple emulators can be useful to explore the parameter space (initial conditions, process parameters, and boundary conditions) of complex computer simulators, such as ocean and climate general circulation models, even when simulator outcomes contain steps in the response.

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A comparison of Latin hypercube and grid ensemble designs for the multivariate emulation of an Earth system model

Cited by 60 publications

References 21 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Artificial neural network assisted Bayesian calibration of climate models

On the Use of Emulators with Extreme and Highly Nonlinear Geophysical Simulators

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