Dynamical Landscape and Multistability of a Climate Model

TL;DR

This paper combines energy landscape theory and data science techniques to identify and analyze multiple stable climate states, revealing a third intermediate state and the impact of feedback mechanisms on climate dynamics.

Contribution

It introduces a novel combined methodology using quasipotential analysis and manifold learning to study climate multistability and landscape topography.

Findings

Identification of a third intermediate stable climate state.

Agreement between energy landscape and data science approaches.

Insights into feedback effects on climate landscape topography.

Abstract

We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyze their interplay. First, drawing from the theory of quasipotentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states, and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, specifically manifold learning, we characterize the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth's climate.