‘Superparameterization’ and statistical emulation in the Lorenz '96 system

Wilks, Daniel S.

doi:10.1002/qj.1866

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…However, interest is growing in alternative approaches, and it is therefore useful to compare our technique with more complex emulation strategies proposed in the recent literature (Rougier et al 2009;Holden and Edwards 2010;Wilks 2012;Vecchi et al 2011;Murphy et al 2007). These strategies include the empirical orthogonal function (EOF) regression of Holden and Edwards (2010) and Gaussian process (GP) modeling, a standard method for emulating the output of deterministic computer models (Sacks et al 1989;Santner et al 2003;Kennedy and O'Hagan 2001;Oakley and O'Hagan 2002;Rougier et al 2009;O'Hagan 2006).…”

Section: Alternative Emulation Strategiesmentioning

confidence: 99%

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%

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

et al. 2014

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“…Lee and Majda (2015) have recently investigated a range of multiscale assimilation methods in a highly condensed model where the "large scale" consists of a single scalar with no spatial extent. Wilks (2012) developed an SP approximation for the two-scale Lorenz-'96 system, which has the following form (Lorenz, 1996(Lorenz, , 2006:…”

Section: A Multiscale Lorenz-'96 Model With Superparameterizationmentioning

confidence: 99%

A framework for variational data assimilation with superparameterization

Grooms

Lee

2015

Nonlin. Processes Geophys.

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Abstract. Superparameterization (SP) is a multiscale computational approach wherein a large scale atmosphere or ocean model is coupled to an array of simulations of small scale dynamics on periodic domains embedded into the computational grid of the large scale model. SP has been successfully developed in global atmosphere and climate models, and is a promising approach for new applications, but there is currently no practical data assimilation framework that can be used with these models. The authors develop a 3D-Var variational data assimilation framework for use with SP; the relatively low cost and simplicity of 3D-Var in comparison with ensemble approaches makes it a natural fit for relatively expensive multiscale SP models. To demonstrate the assimilation framework in a simple model, the authors develop a new system of ordinary differential equations similar to the two-scale Lorenz-'96 model. The system has one set of variables denoted {Y i }, with large and small scale parts, and the SP approximation to the system is straightforward. With the new assimilation framework the SP model approximates the large scale dynamics of the true system accurately.

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“…Furthermore, to guarantee a strongly mixing turbulent dynamical system the number of large-scale modes K should be roughly forty with strong forcing [20], while the number of small-scale modes J should also be relatively large. Fatkullin and Vanden-Eijnden [67] applied HMM to this system with strong scale separation, Crommelin and Vanden-Eijnden [68] used it as a test-bed for the development of stochastic parameterizations based on Markov chains, and Kang and Harlim [69] and Wilks [70] used the system to investigate aspects of multi-scale data assimilation.…”

Section: Mathematical Test Modelsmentioning

confidence: 99%

New perspectives on superparameterization for geophysical turbulence

Majda

Grooms

2014

Journal of Computational Physics

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This is a research expository paper regarding superparameterization, a class of multi-scale numerical methods designed to cope with the intermittent multi-scale effects of inhomogeneous geophysical turbulence where energy often inverse-cascades from the unresolved scales to the large scales through the effects of waves, jets, vortices, and latent heat release from moist processes. Original as well as sparse space time superparameterization algorithms are discussed for the important case of moist atmospheric convection including the role of multi-scale asymptotic methods in providing self-consistent constraints on superparameterization algorithms and related deterministic and stochastic multi-cloud parameterizations. Test models for the statistical numerical analysis of superparameterization algorithms are discussed both to elucidate the performance of the basic algorithms and to test their potential role in efficient multi-scale data assimilation. The very recent development of grid-free seamless stochastic superparameterization methods for geophysical turbulence appropriate for "eddy-permitting" mesoscale ocean turbulence is presented here including a general formulation and illustrative applications to two-layer quasigeostrophic turbulence, and another difficult test case involving one-dimensional models of dispersive wave turbulence. This last test case has randomly generated solitons as coherent structures which collapse and radiate wave energy back to the larger scales, resulting in strong direct and inverse turbulent energy cascades.

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‘Superparameterization’ and statistical emulation in the Lorenz '96 system

Cited by 5 publications

References 39 publications

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

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

A framework for variational data assimilation with superparameterization

New perspectives on superparameterization for geophysical turbulence

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