- Group of Applied Physics ans Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland
The climate system is prone to various tipping mechanisms at the global scale, such as the abrupt changes induced by the potential shutdown of the Atlantic meridional overturning circulation. Thus, it is essential to develop robust Early Warning Signals (EWSs) to assess the risk of crossing tipping points. Classically, EWSs are statistical measures based on time series of climate state variables, and their spatial distribution is not exploited. However, spatial information is crucial to identifying the starting location and development of a transition process. Methods that use spatial information become particularly relevant in the current era, when satellite observations with high spatiotemporal coverage produce huge amounts of data.
We use complex networks constructed from several climate variables (like surface air temperature, specific humidity and cloud cover) on the numerical grid of climate simulations. Using the pyUnicorn Python package [1], we construct networks based on linear and nonlinear spatial correlations of time series at each grid point. We seek for network properties that can serve as EWS when approaching a state transition at the planetary scale, as obtained by the MIT general circulation model in a coupled-aquaplanet configuration for CO2 concentration-driven simulations.
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 [2]. We assess and compare the applicability as EWS of these indicators to traditional methods. Moreover, we analyse climate networks’ ability to identify nonlinear dynamical patterns. Finally, we discuss the generalisation to network indicators that include causal relationships.
References
[1] J. Donges et al., Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015)
[2] L. Moinat, J. Kasparian, M. Brunetti, Tipping detection using climate networks, Chaos 34, 123161 (2024)
How to cite: Brunetti, M., Kasparian, J., and Moinat, L.: Detection of global-scale tipping using climate networks , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7369, https://doi.org/10.5194/egusphere-egu25-7369, 2025.
