Tipping detection using climate networks

Article Article scientifique Article Tipping detection using climate networks eng 2024-12-19 2024-12-01 author Moinat Laure Agnès Liliane 0000-0002-4831-731X Group of Applied Physics and Institute for Environmental Sciences, University of Geneva, 66 Bd Carl-Vogt, CH-1211 Geneva 4, Switzerland. 889095 Serv com physique appliquée (FIGCO) author Kasparian Jérôme 0000-0003-2398-3882 Group of Applied Physics and Institute for Environmental Sciences, University of Geneva, 66 Bd Carl-Vogt, CH-1211 Geneva 4, Switzerland. 662563 Serv com physique appliquée (FIGCO) author Brunetti Maura 0000-0001-8199-223X Group of Applied Physics and Institute for Environmental Sciences, University of Geneva, 66 Bd Carl-Vogt, CH-1211 Geneva 4, Switzerland. 649277 Serv com physique appliquée (FIGCO) 123161 10.1063/5.0230848 39700522 1054-1500 https://pubs.aip.org/cha/article/34/12/123161/3327582/Tipping-detection-using-climate-networks The development of robust Early Warning Signals (EWSs) is necessary to quantify the risk of crossing tipping points in the present-day climate change. Classically, EWSs are statistical measures based on time series of climate state variables, without exploiting their spatial distribution. However, spatial information is crucial to identify the starting location of a transition process and can be directly inferred by satellite observations. By using complex networks constructed from several climate variables on the numerical grid of climate simulations, we seek for network properties that can serve as EWSs when approaching a state transition. We show that network indicators such as the normalized degree, the average length distance, and the betweenness centrality are capable of detecting tipping points at the global scale, as obtained by the MIT general circulation model in a coupled-aquaplanet configuration for CO2 concentration-driven simulations. The applicability of such indicators as EWSs is assessed and compared to traditional methods. We also analyze the ability of climate networks to identify nonlinear dynamical patterns. Dewey 500.2 Dewey 333.7-333.9 Swiss National Science Foundation Long-term evolution of the Earth from the base of the mantle to the top of the atmosphere: Understanding the mechanisms leading to ‘greenhouse’ and ‘icehouse’ regimes 213539 CH Programmes 18 Institut des sciences de l'environnement 0DB 35 Groupe de physique appliquée 135 false 803 Groupe Kasparian 938 ISE Pôle Sciences 941 ISE Pôle Informatique environnementale 943 ISE Climat https://github.com/MITgcm https://www.pik-potsdam.de/~donges/pyunicorn Chaos 34 12 4a4c24d4-5fc5-4942-997e-6f3d38bbe78b Article (Published version) Public 123161_1_5.0230848.pdf 14776560 application/pdf CC BY-4.0 4fa361dea87f5e195297e9a09fd1faa664a8a6f06cdf201f0a65545346cbc338 openAccess Centres et instituts / Institut des sciences de l'environnement Faculté des sciences / Section de physique / Groupe de physique appliquée

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Tipping detection using climate networks	Chaos			2024	80	27

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