Network science disentangles internal climate variability in global spatial dependence structures

TL;DR

This paper uses network science to analyze internal climate variability by examining spatial dependence structures in temperature data, introducing a new metric to quantify connectivity differences across climate model ensembles.

Contribution

It introduces the 'Connectivity Ratio' (R), a novel quantifier for spatial connectivity in climate models, revealing variability and potential shifts in climate predictability.

Findings

Significant differences in temperature network structures across ensemble members.

The Connectivity Ratio (R) effectively characterizes internal climate variability.

Potential shifts in spatial connectivity under climate change scenarios.

Abstract

A comprehensive characterization of internal climate variability and irreducible uncertainty through initial-condition large ensembles of Earth system models across different spatiotemporal scales remains a significant challenge in climate science. In this study, we find significant differences in the spatial connectivity structures of temperature networks across ensemble members, with variations in long-range connections providing a distinguishing feature across the outcomes of initial conditions. Based on this, we introduce a novel quantifier, the 'Connectivity Ratio' (R), to encapsulate the spatial connectivity structure of each ensemble member by investigating the influence of internal climate variability on the global connectivity patterns in air temperatures. R allows us to characterize the variability of spatial dependence structure across the initial condition ensemble members as well as multiple models. Furthermore, we examine changes in spatial connectivity between near-term and long-term projections using R, which shows a potential shift in climate predictability under anthropogenic influence on a spatial scale.