Hamiltonian Lorenz-like models

IF 2.9 3区 数学 Q1 MATHEMATICS, APPLIED Physica D: Nonlinear Phenomena Pub Date : 2025-02-01 Epub Date: 2024-12-14 DOI:10.1016/j.physd.2024.134494
Francesco Fedele , Cristel Chandre , Martin Horvat , Nedjeljka Žagar
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Abstract

The reduced-complexity models developed by Edward Lorenz are widely used in atmospheric and climate sciences to study nonlinear aspect of dynamics and to demonstrate new methods for numerical weather prediction. A set of inviscid Lorenz models describing the dynamics of a single variable in a zonally-periodic domain, without dissipation and forcing, conserve energy but are not Hamiltonian. In this paper, we start from a general continuous parent fluid model, from which we derive a family of Hamiltonian Lorenz-like models through a symplectic discretization of the associated Poisson bracket, which preserves the Jacobi identity. A symplectic-split integrator is also formulated. These Hamiltonian models conserve energy and maintain the nearest-neighbor couplings inherent in the original Lorenz model. As a corollary, we find that the Lorenz-96 model can be seen as a result of a poor discretization of a Poisson fluid bracket. Hamiltonian Lorenz-like models offer promising alternatives to the original Lorenz models, especially for the qualitative representation of non-Gaussian weather extremes and wave interactions, which underscore many phenomena of the climate system.

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类哈密顿洛伦兹模型
洛伦兹提出的简化模型在大气和气候科学中广泛应用于研究动力学的非线性方面,并为数值天气预报提供了新的方法。一组无粘的洛伦兹模型描述了在无耗散和强迫的带周期域中单个变量的动力学,守恒能量但不是哈密顿模型。本文从一般的连续母流体模型出发,通过对相关泊松括号的辛离散化,得到一类保持Jacobi恒等式的类哈密顿模型。并给出了辛分裂积分器的公式。这些哈密顿模型保存了能量,并保持了原始洛伦兹模型中固有的最近邻耦合。作为推论,我们发现Lorenz-96模型可以看作是泊松流体支架离散化不良的结果。哈密顿类洛伦兹模型为原始洛伦兹模型提供了有希望的替代方案,特别是对于非高斯极端天气和波浪相互作用的定性表示,这强调了气候系统的许多现象。
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来源期刊
Physica D: Nonlinear Phenomena
Physica D: Nonlinear Phenomena 物理-物理:数学物理
CiteScore
7.30
自引率
7.50%
发文量
213
审稿时长
65 days
期刊介绍: Physica D (Nonlinear Phenomena) publishes research and review articles reporting on experimental and theoretical works, techniques and ideas that advance the understanding of nonlinear phenomena. Topics encompass wave motion in physical, chemical and biological systems; physical or biological phenomena governed by nonlinear field equations, including hydrodynamics and turbulence; pattern formation and cooperative phenomena; instability, bifurcations, chaos, and space-time disorder; integrable/Hamiltonian systems; asymptotic analysis and, more generally, mathematical methods for nonlinear systems.
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