Stochasticity of convection in Giga-LES data

The poor representation of tropical convection in general circulation models (GCMs) is believed to be responsible for much of the uncertainty in the predictions of weather and climate in the tropics. The stochastic multicloud model (SMCM) was recently developed by Khouider et al. (Commun Math Sci 8(...

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Vydáno v:	Climate dynamics Ročník 47; číslo 5-6; s. 1845 - 1861
Hlavní autoři:	De La Chevrotière, Michèle, Khouider, Boualem, Majda, Andrew J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2016 Springer Springer Nature B.V
Témata:	Atmospheric circulation Atmospheric models Bayesian statistical decision theory Brackish Climate Climatology Convection Convection (Meteorology) data collection Earth and Environmental Science Earth Sciences Eddies General Circulation Models Geophysics/Geodesy Marine Oceanography Parameter estimation prediction simulation models Stochastic models stochastic processes Time series Time-series analysis Tropical environments tropics uncertainty weather United States Parameter estimation Parallel and high performance computing General circulation models Stochastic cumulus parameterization Giga-LES Markov Chain Monte Carlo Bayesian inference Tropical convection Stochastic multicloud model Large matrix exponential
ISSN:	0930-7575, 1432-0894
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Abstract	The poor representation of tropical convection in general circulation models (GCMs) is believed to be responsible for much of the uncertainty in the predictions of weather and climate in the tropics. The stochastic multicloud model (SMCM) was recently developed by Khouider et al. (Commun Math Sci 8(1):187–216, 2010 ) to represent the missing variability in GCMs due to unresolved features of organized tropical convection. The SMCM is based on three cloud types (congestus, deep and stratiform), and transitions between these cloud types are formalized in terms of probability rules that are functions of the large-scale environment convective state and a set of seven arbitrary cloud timescale parameters. Here, a statistical inference method based on the Bayesian paradigm is applied to estimate these key cloud timescales from the Giga-LES dataset, a 24-h large-eddy simulation (LES) of deep tropical convection (Khairoutdinov et al. in J Adv Model Earth Syst 1(12), 2009 ) over a domain comparable to a GCM gridbox. A sequential learning strategy is used where the Giga-LES domain is partitioned into a few subdomains, and atmospheric time series obtained on each subdomain are used to train the Bayesian procedure incrementally. Convergence of the marginal posterior densities for all seven parameters is demonstrated for two different grid partitions, and sensitivity tests to other model parameters are also presented. A single column model simulation using the SMCM parameterization with the Giga-LES inferred parameters reproduces many important statistical features of the Giga-LES run, without any further tuning. In particular it exhibits intermittent dynamical behavior in both the stochastic cloud fractions and the large scale dynamics, with periods of dry phases followed by a coherent sequence of congestus, deep, and stratiform convection, varying on timescales of a few hours consistent with the Giga-LES time series. The chaotic variations of the cloud area fractions were captured fairly well both qualitatively and quantitatively demonstrating the stochastic nature of convection in the Giga-LES simulation.
AbstractList	The poor representation of tropical convection in general circulation models (GCMs) is believed to be responsible for much of the uncertainty in the predictions of weather and climate in the tropics. The stochastic multicloud model (SMCM) was recently developed by Khouider et al. (Commun Math Sci 8(1):187-216, 2010 (See CR16)) to represent the missing variability in GCMs due to unresolved features of organized tropical convection. The SMCM is based on three cloud types (congestus, deep and stratiform), and transitions between these cloud types are formalized in terms of probability rules that are functions of the large-scale environment convective state and a set of seven arbitrary cloud timescale parameters. Here, a statistical inference method based on the Bayesian paradigm is applied to estimate these key cloud timescales from the Giga-LES dataset, a 24-h large-eddy simulation (LES) of deep tropical convection (Khairoutdinov et al. in J Adv Model Earth Syst 1(12), 2009 (See CR13)) over a domain comparable to a GCM gridbox. A sequential learning strategy is used where the Giga-LES domain is partitioned into a few subdomains, and atmospheric time series obtained on each subdomain are used to train the Bayesian procedure incrementally. Convergence of the marginal posterior densities for all seven parameters is demonstrated for two different grid partitions, and sensitivity tests to other model parameters are also presented. A single column model simulation using the SMCM parameterization with the Giga-LES inferred parameters reproduces many important statistical features of the Giga-LES run, without any further tuning. In particular it exhibits intermittent dynamical behavior in both the stochastic cloud fractions and the large scale dynamics, with periods of dry phases followed by a coherent sequence of congestus, deep, and stratiform convection, varying on timescales of a few hours consistent with the Giga-LES time series. The chaotic variations of the cloud area fractions were captured fairly well both qualitatively and quantitatively demonstrating the stochastic nature of convection in the Giga-LES simulation. The poor representation of tropical convection in general circulation models (GCMs) is believed to be responsible for much of the uncertainty in the predictions of weather and climate in the tropics. The stochastic multicloud model (SMCM) was recently developed by Khouider et al. (Commun Math Sci 8(1):187–216, 2010) to represent the missing variability in GCMs due to unresolved features of organized tropical convection. The SMCM is based on three cloud types (congestus, deep and stratiform), and transitions between these cloud types are formalized in terms of probability rules that are functions of the large-scale environment convective state and a set of seven arbitrary cloud timescale parameters. Here, a statistical inference method based on the Bayesian paradigm is applied to estimate these key cloud timescales from the Giga-LES dataset, a 24-h large-eddy simulation (LES) of deep tropical convection (Khairoutdinov et al. in J Adv Model Earth Syst 1(12), 2009) over a domain comparable to a GCM gridbox. A sequential learning strategy is used where the Giga-LES domain is partitioned into a few subdomains, and atmospheric time series obtained on each subdomain are used to train the Bayesian procedure incrementally. Convergence of the marginal posterior densities for all seven parameters is demonstrated for two different grid partitions, and sensitivity tests to other model parameters are also presented. A single column model simulation using the SMCM parameterization with the Giga-LES inferred parameters reproduces many important statistical features of the Giga-LES run, without any further tuning. In particular it exhibits intermittent dynamical behavior in both the stochastic cloud fractions and the large scale dynamics, with periods of dry phases followed by a coherent sequence of congestus, deep, and stratiform convection, varying on timescales of a few hours consistent with the Giga-LES time series. The chaotic variations of the cloud area fractions were captured fairly well both qualitatively and quantitatively demonstrating the stochastic nature of convection in the Giga-LES simulation. The poor representation of tropical convection in general circulation models (GCMs) is believed to be responsible for much of the uncertainty in the predictions of weather and climate in the tropics. The stochastic multicloud model (SMCM) was recently developed by Khouider et al. (Commun Math Sci 8(1):187–216, 2010 ) to represent the missing variability in GCMs due to unresolved features of organized tropical convection. The SMCM is based on three cloud types (congestus, deep and stratiform), and transitions between these cloud types are formalized in terms of probability rules that are functions of the large-scale environment convective state and a set of seven arbitrary cloud timescale parameters. Here, a statistical inference method based on the Bayesian paradigm is applied to estimate these key cloud timescales from the Giga-LES dataset, a 24-h large-eddy simulation (LES) of deep tropical convection (Khairoutdinov et al. in J Adv Model Earth Syst 1(12), 2009 ) over a domain comparable to a GCM gridbox. A sequential learning strategy is used where the Giga-LES domain is partitioned into a few subdomains, and atmospheric time series obtained on each subdomain are used to train the Bayesian procedure incrementally. Convergence of the marginal posterior densities for all seven parameters is demonstrated for two different grid partitions, and sensitivity tests to other model parameters are also presented. A single column model simulation using the SMCM parameterization with the Giga-LES inferred parameters reproduces many important statistical features of the Giga-LES run, without any further tuning. In particular it exhibits intermittent dynamical behavior in both the stochastic cloud fractions and the large scale dynamics, with periods of dry phases followed by a coherent sequence of congestus, deep, and stratiform convection, varying on timescales of a few hours consistent with the Giga-LES time series. The chaotic variations of the cloud area fractions were captured fairly well both qualitatively and quantitatively demonstrating the stochastic nature of convection in the Giga-LES simulation.
Audience	Academic
Author	Majda, Andrew J. De La Chevrotière, Michèle Khouider, Boualem
Author_xml	– sequence: 1 givenname: Michèle surname: De La Chevrotière fullname: De La Chevrotière, Michèle organization: Department of Mathematics and Statistics, University of Victoria, Department of Mathematics, Pennsylvania State University – sequence: 2 givenname: Boualem surname: Khouider fullname: Khouider, Boualem email: khouider@uvic.ca organization: Department of Mathematics and Statistics, University of Victoria – sequence: 3 givenname: Andrew J. surname: Majda fullname: Majda, Andrew J. organization: Department of Mathematics and Center for Atmosphere and Ocean Sciences, Courant Institute of Mathematical Sciences, New York University
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Cites_doi	10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2 10.1175/JAS-D-13-065.1 10.1175/JCLI3735.1 10.1175/JAS-D-11-0148.1 10.1175/JCLI-D-12-00541.1 10.1073/pnas.1634951100 10.4310/CMS.2014.v12.n8.a1 10.1175/BAMS-D-12-00157.1 10.1175/1520-0469(1972)029<1109:DOGSCC>2.0.CO;2 10.1073/pnas.242741499 10.1175/JAS-D-13-031.1 10.4310/CMS.2010.v8.n1.a10 10.1137/13094267X 10.1002/qj.49712354002 10.1175/1520-0477(1992)073<1377:TCTCOR>2.0.CO;2 10.1175/1520-0469(1975)032<1977:AEMFNS>2.0.CO;2 10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2 10.1175/1520-0469(1974)031<1232:FSOTPO>2.0.CO;2 10.1175/1520-0493(1967)095<0155:SCOAGC>2.3.CO;2 10.1016/j.dynatmoce.2006.03.003 10.1002/qj.49712556006 10.3894/JAMES.2009.1.15 10.1093/oso/9780195066302.001.0001 10.1007/s00162-016-0407-8 10.1029/2002GL016203 10.1029/2008GM000838 10.1175/JAS-D-15-0178.1 10.1175/1520-0493(1965)093<0769:SCOAGC>2.3.CO;2
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SubjectTerms	Atmospheric circulation Atmospheric models Bayesian statistical decision theory Brackish Climate Climatology Convection Convection (Meteorology) data collection Earth and Environmental Science Earth Sciences Eddies General Circulation Models Geophysics/Geodesy Marine Oceanography Parameter estimation prediction simulation models Stochastic models stochastic processes Time series Time-series analysis Tropical environments tropics uncertainty weather
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Title	Stochasticity of convection in Giga-LES data
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