RESCHEDULED from 03/23/2022 to 03/29/2022.

Exploring the climate attractor via manifold learning

Abstract:
The threat of global warming and the demand for reliable climate predictions pose a formidable challenge being the climate system multiscale, high-dimensional and nonlinear. Spatiotemporal recurrences of the system hint to the presence of a low-dimensional manifold containing the highdimensional climate trajectory that could make the problem more tractable. Here we argue that reproducing the geometrical and topological properties of the low-dimensional attractor should be a key target for models used in climate projections. In doing so, we propose a general data-driven framework to characterize the climate attractor and showcase it in the tropical Pacific Ocean using a reanalysis as observational proxy and two state-of-the-art models. The analysis spans four variables simultaneously over the periods 1979-2019 and 2060-2100. The dynamics is confined on a manifold with dimension lower than the full state space that we characterize through manifold learning algorithms, both linear and nonlinear. Nonlinear algorithms describe the attractor through fewer components than linear ones by considering its curved geometry, allowing for visualizing the high-dimensional dynamics through low-dimensional projections. The local geometry and local stability of the high-dimensional, multi-variable climate attractor are quantified through the local dimension and persistence metrics. Model biases that hamper climate predictability are identified and found to be similar in the multivariate attractor of the two models during the historical period while diverging under the warming scenario considered. Finally, the relationships between different sub-spaces (univariate fields), and therefore among climate variables, are evaluated. The proposed framework provides a comprehensive, physically based, test for assessing climate feedbacks and opens new avenues for improving their model representation.

About this Series

The Atmosphere, Ocean and Climate Sack Lunch Seminar Series is an informal seminar series within PAOC that focuses on more specialized topics than the PAOC Colloquium. Seminar topics include all research concerning the science of atmosphere, ocean and climate. The seminars usually take place on Wednesdays from 12-1pm in 54-915. The presentations are either given by an invited speaker or by a member of PAOC and can focus on new research or discussion of a paper of particular interest.

Contact: sacklunch-committee@mit.edu for Zoom information