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<p>A comprehensive characterization of internal climate variability (ICV) in initial-condition (IC) large ensembles of Earth system models (ESMs) remains a significant challenge in climate science. In this study, we leverage the spatial connectivity structures of temperature networks to characterize ICV, observing substantial differences across ensemble members, particularly in the prevalence of long-range connections. Based on this feature, we introduce the ‘Connectivity Ratio’ (CR), a new quantifier that captures long-range spatial connectivity within climate networks. CR is applied to two ESMs, EC-Earth3 and MPI-ESM1-2-LR, to evaluate structural variability across IC ensemble members, models, and climate time horizons. CR reveals systematic differences in long-range connectivity between forced and unforced simulations, as well as across future climate periods. As such, CR provides an interpretable measure for capturing ICV across ensemble members and models. It has the potential to support the quantification of irreducible uncertainty and contributes to a robust evaluation of climate models.</p>

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Network science disentangles internal climate variability in global spatial dependence structures

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月4日

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  • Arnob Ray,
  • Abhirup Banerjee,
  • Rachindra Mawalagedara,
  • Auroop R. Ganguly

摘要

A comprehensive characterization of internal climate variability (ICV) in initial-condition (IC) large ensembles of Earth system models (ESMs) remains a significant challenge in climate science. In this study, we leverage the spatial connectivity structures of temperature networks to characterize ICV, observing substantial differences across ensemble members, particularly in the prevalence of long-range connections. Based on this feature, we introduce the ‘Connectivity Ratio’ (CR), a new quantifier that captures long-range spatial connectivity within climate networks. CR is applied to two ESMs, EC-Earth3 and MPI-ESM1-2-LR, to evaluate structural variability across IC ensemble members, models, and climate time horizons. CR reveals systematic differences in long-range connectivity between forced and unforced simulations, as well as across future climate periods. As such, CR provides an interpretable measure for capturing ICV across ensemble members and models. It has the potential to support the quantification of irreducible uncertainty and contributes to a robust evaluation of climate models.