Date(s) - 6. May 2022
11:00 - 14:00
Categories No Categories
11:00 am CET
Keynote speaker: Michael Ghil – ENS Paris and University of California, Los Angeles
The “death of stationarity” poses a substantial challenge to climate predictability and to the climate sciences in general. This challenge is addressed herein by formulating the problems of change in the climate’s intrinsic variability within the framework of the theory of non-autonomous and random dynamical systems (NDS and RDS) with time-dependent forcing. A key role in this theory is played by the pullback attractors (PBAs) that replace the strange attractors of the more familiar theory of autonomous dynamical systems, in which there is no explicit time dependence of either forcing or coefficients.
The concepts and methods of the NDS and RDS approach will be introduced and will be illustrated using a stochastically perturbed version of the Lorenz (1963) convection model. This illustration will be followed by applications to models of the wind-driven ocean circulation. One finds that two local PBAs, a quiescent and a chaotic one, coexist within the wind-driven ocean model’s decadally modulated global PBA.
Implications for the climate sciences in general and for atmospheric, oceanic and coupled ocean-atmosphere dynamics in particular will be discussed from the perspective of the Anthropocene.
Ghil, M. and V. Lucarini, 2020: The physics of climate variability and climate change, /Rev. Mod. Phys/., *92*(3), 035002, doi:10.1103/RevModPhys.92.035002.
Pierini, S., and M. Ghil*,* 2021: Climate tipping points induced by parameter drift: an excitable system study, /Scientific Reports/, *11*, 11126, doi:10.1038/s41598-021-90138-1.
Vannitsem, S., J. Demaeyer, and M. Ghil, 2021: Extratropical low-frequency variability with ENSO forcing: A reduced-order coupled model study, /Journal of Advances in Modeling Earth Systems/, *13*, e2021MS002530, doi:10.1029/2021MS002530.
*This talk reflects work under the Tipping Points in the Earth System (TiPES) project; TiPES has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820970.