Network science disentangles internal climate variability in global spatial dependence structures
Abstract 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 charac...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Complexity |
| Online Access: | https://doi.org/10.1038/s44260-025-00048-w |
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| author | Arnob Ray Abhirup Banerjee Rachindra Mawalagedara Auroop R. Ganguly |
| author_facet | Arnob Ray Abhirup Banerjee Rachindra Mawalagedara Auroop R. Ganguly |
| author_sort | Arnob Ray |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-8d573ffb42844d3d99ef90133ee8cf71 |
| institution | DOAJ |
| issn | 2731-8753 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Complexity |
| spelling | doaj-art-8d573ffb42844d3d99ef90133ee8cf712025-08-20T03:04:10ZengNature Portfolionpj Complexity2731-87532025-08-01211910.1038/s44260-025-00048-wNetwork science disentangles internal climate variability in global spatial dependence structuresArnob Ray0Abhirup Banerjee1Rachindra Mawalagedara2Auroop R. Ganguly3Artificial Intelligence for Climate and Sustainability, The Institute for Experiential Artificial Intelligence, Northeastern UniversityCentrum für Erdsystemforschung und Nachhaltigkeit (CEN), Universität HamburgArtificial Intelligence for Climate and Sustainability, The Institute for Experiential Artificial Intelligence, Northeastern UniversityArtificial Intelligence for Climate and Sustainability, The Institute for Experiential Artificial Intelligence, Northeastern UniversityAbstract 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.https://doi.org/10.1038/s44260-025-00048-w |
| spellingShingle | Arnob Ray Abhirup Banerjee Rachindra Mawalagedara Auroop R. Ganguly Network science disentangles internal climate variability in global spatial dependence structures npj Complexity |
| title | Network science disentangles internal climate variability in global spatial dependence structures |
| title_full | Network science disentangles internal climate variability in global spatial dependence structures |
| title_fullStr | Network science disentangles internal climate variability in global spatial dependence structures |
| title_full_unstemmed | Network science disentangles internal climate variability in global spatial dependence structures |
| title_short | Network science disentangles internal climate variability in global spatial dependence structures |
| title_sort | network science disentangles internal climate variability in global spatial dependence structures |
| url | https://doi.org/10.1038/s44260-025-00048-w |
| work_keys_str_mv | AT arnobray networksciencedisentanglesinternalclimatevariabilityinglobalspatialdependencestructures AT abhirupbanerjee networksciencedisentanglesinternalclimatevariabilityinglobalspatialdependencestructures AT rachindramawalagedara networksciencedisentanglesinternalclimatevariabilityinglobalspatialdependencestructures AT aurooprganguly networksciencedisentanglesinternalclimatevariabilityinglobalspatialdependencestructures |