Speaker
Description
The climate is a nonlinear dynamical system driven by the inhomogeneous energy received from the Sun. The redistribution of energy involves a multitude of processes acting at various spatial and temporal scales. These processes are interconnected, leading to the existence of a multitude of feedback loops that can amplify or compensate each other.
A steady state is reached when a balance between the input of energy, dissipation and feedbacks occurs. Because of the presence of several feedbacks, there are several ways for these mechanisms to equilibrate. Thus, in general, there is no a unique resulting steady state under the same forcing, and the final state only depends on the initial conditions. A situation referred to as ‘multistability’.
The study of alternative steady states is relevant for deep-time climate modelling, where large uncertainties exist on forcing conditions used to initialize simulations. We propose applying the multistability framework to the paleogeography of the Permian-Triassic boundary (250 Ma). This period is marked by a huge magmatic activity in the Siberian Traps and one of the largest mass extinction, followed by oscillations in climatic conditions during the Early Triassic.
Simulations are performed using the MIT general circulation model in a coupled atmosphere-ocean-sea ice-land configuration, with the paleogeographic reconstruction provided by PANALESIS. We first show the existence of two robust alternative steady states under the same forcing. Then, by varying the atmospheric CO2 content, we construct a bifurcation diagram, a 2D-plot allowing us to identify the stability range of each steady state and the position of tipping points. Such a tool helps identifying the required conditions for the system to shift from one state to another and possible tipping mechanisms for the observed climatic oscillations in the Early Triassic. Finally, additional feedbacks acting on millennial time scales, namely dynamical vegetation and air-sea carbon exchanges, are included in our setup with numerical solutions that reduce the computational cost of the simulations.
