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Network science disentangles

Network science disentangles internal climate variability in global spatial dependence structures


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.

Discussion


We introduce CR as a single number to quantify ensemble behavior, enabling the characterization of ICV across the IC large ensemble of an ESM. As an application, CR can function as a comparative tool for evaluating multiple IC ensemble members and different models, facilitating direct model comparisons. This method is compatible with descriptive statistics and can provide additional insights into the spatial connectivity structure, thereby enhancing model evaluation. In addition to spatial diagnostics, CR captures changes in the variability of climate network structures over time. Probability density plots of CR effectively distinguish near-term and end-of-century climate networks from climate projections. Additional applications of CR beyond those presented here can be explored in future studies, some of which are outlined below.

This measure can be extended in future work to facilitate a comprehensive quantification of both reducible and irreducible uncertainty in climate projections, incorporating ICV, structural differences among climate models, and uncertainties arising from emission scenarios. Such extensions would offer a more refined understanding of the fundamental limits of climate predictability and support more robust decision-making in the context of deep uncertainty.

Beyond uncertainty quantification, this quantifier also holds promise in advancing the characterization of ICV using a range of climate variables. It can be applied within the framework of climate networks constructed from both linear and nonlinear dependency measures, as well as networks based on event synchronization techniques. In addition to continuous time series data, the application can focus specifically on the timing and frequency of extreme events. By capturing multiple dimensions of climate system behavior from typical variability to rare, high-impact phenomena, this measure would facilitate a more holistic assessment of internal variability and deepen our understanding of the dynamical mechanisms shaping future climate outcomes.

Network, Node, Edge, Graph, Degree, Centrality, Betweenness, Closeness, Eigenvector, Modularity, Community, Clustering, Path, Shortest-path, Network-flow, Scale-free, Small-world, Connectivity, Homophily, Robustness

#NetworkScience, #ComplexNetworks, #GraphTheory, #NodesAndEdges, #Centrality, #Betweenness, #Closeness, #Eigenvector, #Modularity, #CommunityDetection, #Clustering, #ShortestPath, #NetworkFlow, #ScaleFree, #SmallWorld, #Connectivity, #Homophily, #NetworkRobustness, #SystemsScience, #DataScience

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