Speaker
Description
Early Warning Signals (EWS) are indicators that can be used to anticipate tipping points i.e. abrupt changes of the climate dynamics. Detecting EWS is a crucial part of climate science, especially in the context of climate change. Several methods are used to identify tipping points using time series of climate state variables (e.g. temperature, precipitation, etc ...), but a few consider spatial correlations. Spatial detection could identify the starting location of a transition process from a state to another and can be directly applied to satellite observations. We consider different state variables on the numerical grid as a complex network where grid points displaying correlation are connected and their temporal evolution is studied. We seek for statistical indicators that can be used as EWS when approaching the state transition. The complex network is generated and analysed using the pyUnicorn package on Python [1], and compared to classical statistical methods [2]. These indicators are applied to the results of numerical simulations showing tipping points at the global scale, and their application as EWS is discussed.
[1] Donges, Jonathan F., et al. “Unified Functional Network and Nonlinear Time Series Analysis for Complex Systems Science: The Pyunicorn Package.” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 25, no. 11, Nov. 2015, p. 113101, https://doi.org/10.1063/1.4934554. Accessed 11 Nov. 2021.
[2] van der Mheen, Mirjam, et al. “Interaction Network Based Early Warning Indicators for the Atlantic MOC Collapse.” Geophysical Research Letters, vol. 40, no. 11, 4 June 2013, pp. 2714–2719, https://doi.org/10.1002/grl.50515.
