Climate science in the tropics: waves, vortices and PDEs

Khouider, Boualem; Majda, Andrew J.; Stechmann, Samuel N.

doi:10.1088/0951-7715/26/1/r1

Cited by 71 publications

(52 citation statements)

References 144 publications

(492 reference statements)

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“…It represents a major source of predictability on intraseasonal time scales [Waliser et al, 2003;Waliser, 2011;Pegion and Kirtman, 2008;Gottschalck et al, 2010;NAS, 2010]. It challenges our understanding of tropical convection and its interaction with the large-scale environment [Wang, 2011;Khouider et al, 2013], and global climate models have particular difficulties in reproducing this phenomenon [Slingo et al, 1996[Slingo et al, , 2005Lin et al, 2006;Kim et al, 2009;Sperber et al, 2011;Hung et al, 2012].…”

Section: Introductionmentioning

confidence: 99%

Cracking the MJO nut

Zhang

Gottschalck

Maloney

et al. 2013

Geophysical Research Letters

166

126

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[1] The Madden-Julian oscillation poses great challenges to our understanding and prediction of tropical convection and the large-scale circulation. Several internationally coordinated activities were recently formed to meet the challenges from the perspectives of numerical simulations, prediction, diagnostics, and virtual and actual field campaigns. This article provides a brief description of these activities and their connections, with the motivation in part to encourage the next generation of physical scientists to help solve the grand challenging problem of the Madden-Julian oscillation.

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Section: Introductionmentioning

confidence: 99%

Cracking the MJO nut

Zhang

Gottschalck

Maloney

et al. 2013

Geophysical Research Letters

166

126

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“…In this section we describe two stochastic lattice models, the simplest tropical climate model developed in [15,30,33] and the stochastic skeleton model for the MJO developed and applied in [34,35,41,42]. They both consist of an ODE system U t , which describes the dry dynamics based on continuum thermal-dynamical PDEs, and a stochastic jump process Á t , which describes the intermittent tropical variability.…”

Section: Stochastic Lattice Models For the Tropicsmentioning

confidence: 99%

“…In [15,30,33], the simplest tropical climate model is derived to capture the impact of tropical moisture variability. Here we discuss a simplified setup of flows above the equator, which follows a PDE: Here the periodic nondimensional variable x denotes the longitude.…”

Section: The Simplest Tropical Climate Model Deterministic Modelmentioning

confidence: 99%

“…While it is possible for the same result to hold under other conditions, (2.5) has probably the simplest form. Moreover, condition (2.5) is rather mild, as it holds for most models in [15,29,30] and especially when…”

Section: Geometric Ergodicitymentioning

confidence: 99%

“…In Section 2 we formulate and present two stochastic lattice models for moist tropical convection in climate science as motivation for further developments in the paper. One model is the stochastic skeleton model for the MJO [41,42] with small nonzero damping; the second model is a stochastic lattice model designed to capture intermittent fluctuations [29,31,32] in the simplest tropical climate models [15,30,33]. With this background, the general theorem on contracting PDMPs is formulated and proved in Sections 3 and 4 using Lyapunov functions and perturbation analysis.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Geometric Ergodicity for Piecewise Contracting Processes with Applications for Tropical Stochastic Lattice Models

Majda

Tong

2015

Comm Pure Appl Math

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Stochastic lattice models are increasingly prominent as a way to capture highly intermittent unresolved features of moist tropical convection in climate science and as continuum mesoscopic models in material science. Stochastic lattice models consist of suitably discretized continuum partial differential equations interacting with Markov jump processes at each lattice site with transition rates depending on the local value of the continuum equation; they are a special case of piecewise deterministic Markov processes but often have an infinite state space and unbounded transition rates. Here a general theorem on geometric ergodicity for piecewise deterministic contracting processes is developed with full generality to apply to stochastic lattice models. A highly nontrivial application to the stochastic skeleton model for the Madden-Julian oscillation (Thual et al., 2013) is developed here where there is an infinite state space with unbounded and also degenerate transition rates. Geometric ergodicity for the stochastic skeleton model guarantees exponential convergence to a unique invariant measure that defines the statistical tropical climate of the model. Another application of the general framework is developed here for stochastic lattice models designed to capture intermittent fluctuation in the simplest tropical climate models. Other straightforward applications to models motivated by material science are mentioned briefly here.

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Parameterization Interactions in Global Aquaplanet Simulations

Bhattacharya

Bordoni

Sušelj

et al. 2018

J Adv Model Earth Syst

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Global climate simulations rely on parameterizations of physical processes that have scales smaller than the resolved ones. In the atmosphere, these parameterizations represent moist convection, boundary layer turbulence and convection, cloud microphysics, longwave and shortwave radiation, and the interaction with the land and ocean surface. These parameterizations can generate different climates involving a wide range of interactions among parameterizations and between the parameterizations and the resolved dynamics. To gain a simplified understanding of a subset of these interactions, we perform aquaplanet simulations with the global version of the Weather Research and Forecasting (WRF) model employing a range (in terms of properties) of moist convection and boundary layer (BL) parameterizations. Significant differences are noted in the simulated precipitation amounts, its partitioning between convective and large‐scale precipitation, as well as in the radiative impacts. These differences arise from the way the subcloud physics interacts with convection, both directly and through various pathways involving the large‐scale dynamics and the boundary layer, convection, and clouds. A detailed analysis of the profiles of the different tendencies (from the different physical processes) for both potential temperature and water vapor is performed. While different combinations of convection and boundary layer parameterizations can lead to different climates, a key conclusion of this study is that similar climates can be simulated with model versions that are different in terms of the partitioning of the tendencies: the vertically distributed energy and water balances in the tropics can be obtained with significantly different profiles of large‐scale, convection, and cloud microphysics tendencies.

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Climate science in the tropics: waves, vortices and PDEs

Cited by 71 publications

References 144 publications

Cracking the MJO nut

Cracking the MJO nut

Geometric Ergodicity for Piecewise Contracting Processes with Applications for Tropical Stochastic Lattice Models

Parameterization Interactions in Global Aquaplanet Simulations

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