How to cope with climate's complexity

TL;DR

This paper proposes a novel climate modeling approach combining Bayesian statistics and low-order nonlinear dynamics to better understand climate complexity and predict future glaciation timing.

Contribution

It introduces a new modeling strategy that leverages paleoclimate archives and uncertainty quantification, challenging the reliance solely on high-resolution simulations.

Findings

Predicts glaciation in approximately 50,000 years

Highlights the importance of macroscopic information in climate models

Demonstrates the effectiveness of Bayesian and nonlinear dynamical methods

Abstract

Climate exhibits a vast range of dissipative structures. Some have characteristic times of a few days; others evolve on thousands of years. All these structures are interdependent; in other words, they communicate. It is often considered that the only way to cope with climate complexity is to integrate the equations of atmospheric and oceanic motion with the finer possible mesh. Is this the sole strategy? Aren't we missing another characteristic of the climate system: its ability to destroy and generate information at the macroscopic scale? Paleoclimatologists consider that much of this information is present in palaeoclimate archives. It is therefore natural to build climate models such as to get the most of these archives. The strategy proposed here is based on Bayesian statistics and low-order non-linear dynamical systems, in a modelling approach that explicitly includes the effects of uncertainties. Its practical interest is illustrated through the problem of the timing of the next great glaciation. Is glacial inception overdue, or do we need to wait for another 50,000 years before ice caps grow again? Our results indicate a glaciation inception in 50,000 years.