Hamiltonian Lorenz-like models

TL;DR

This paper introduces Hamiltonian Lorenz-like models derived from a fluid model that conserve energy and are symplectic, providing better tools for studying nonlinear climate dynamics and weather extremes.

Contribution

It develops a family of Hamiltonian Lorenz-like models via symplectic discretization, preserving key physical properties and offering improved qualitative analysis of climate phenomena.

Findings

Models conserve energy and maintain nearest-neighbor couplings.

Lorenz-96 model is a poor discretization of a Poisson bracket.

Hamiltonian models better represent non-Gaussian weather extremes.

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 that 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 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 are key factors in understanding many phenomena of the climate system.