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• W3204689062 abstract "The threat of global warming and the demand for confident climate predictions pose a formidable challenge. The climate system is multiscale, high-dimensional and nonlinear. However, spatiotemporal recurrences of the system hint to the presence of low-dimensional manifolds containing the high-dimensional climate trajectory. Reproducing the geometrical and topological properties of the low-dimensional attractor is an important constraint for models to generate meaningful probabilistic climate projections. Here we propose a framework to characterize and compare the climate attractor across observations and models. This framework is showcased in the tropical Pacific ocean using a reanalysis as observational proxy and two state-of-the-art models. The analysis spans four variables over the periods 1979-2019 and 2060-2100. At each time t, the system can be uniquely described by a state space vector parameterized by N variables and their spatial variability. The dynamics is confined on a manifold with dimension lower than the full state space that can be characterized through manifold learning algorithms, both linear and nonlinear. The manifold learning step allows for quantifying nonlinear contributions as well as the relative role of each variable in the system's dynamics. Geometric properties of the high-dimensional attractor are further quantified through the local dimension and persistence metrics, recently proposed in the climate literature. The metrics quantify the local geometry of the climate attractor and its local stability. Both models underestimate the average dimension and overestimate persistence compared to observations. These biases limit climate predictability and are nearly identical among the chosen models during the historical period while diverging in the near future under the warming scenario considered." @default.
• W3204689062 created "2021-10-11" @default.
• W3204689062 creator A5073045368 @default.
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• W3204689062 date "2021-10-07" @default.
• W3204689062 modified "2023-09-27" @default.
• W3204689062 title "Exploring the climate system through manifold learning." @default.
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• W3204689062 hasPublicationYear "2021" @default.
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