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Updated: May 12, 2026

Using Generative Art to Convey Past and Future Climate Transitions

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

Analysis and modelling of glacial climate transitions using simple dynamical systems.

Frank Kwasniok¹

¹College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK. f.kwasniok@exeter.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|April 17, 2013

Summary

A nonlinear stochastic relaxation oscillator model accurately captures glacial climate variability and transitions between cold and warm states, as seen in Greenland ice-core data. This model outperforms simpler alternatives in explaining climate dynamics without external forcing.

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Simulating Impacts of Ice Storms on Forest Ecosystems

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Published on: June 30, 2020

Area of Science:

Paleoclimatology
Climate Dynamics
Nonlinear Stochastic Systems

Background:

Understanding glacial climate variability is crucial for predicting future climate change.
Palaeoclimatic records, such as Greenland ice cores, provide valuable long-term climate data.
Previous models have struggled to fully capture the complex dynamics of glacial climate shifts.

Purpose of the Study:

To develop and validate a nonlinear stochastic dynamical system model for glacial climate variability.
To compare the efficacy of different modeling approaches using palaeoclimatic data.
To identify key dynamical regimes within glacial climate transitions.

Main Methods:

Formulation of a two-dimensional stochastic relaxation oscillator model with proxy temperature as the fast variable.
Estimation of system parameters and noise levels using Greenland ice-core data.
Comparison of the relaxation oscillator model with a one-dimensional bistable potential model and mixture models.

Main Results:

The relaxation oscillator model, operating near a Hopf bifurcation, successfully captures transitions between cold and warm states.
The model exhibits excitable behavior under stochastic forcing, replicating key statistical characteristics of glacial climate shifts.
Three distinct dynamical regimes were identified, corresponding to different phases of the relaxation oscillator, and mixture models showed high likelihood.

Conclusions:

The stochastic relaxation oscillator model provides a superior explanation for glacial climate variability compared to simpler models.
The model effectively captures the time-reversal asymmetry and waiting time distributions of Dansgaard-Oeschger events without external forcing.
This approach offers a robust framework for understanding complex climate dynamics and transitions.