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NP2.2 | Koopman Theory Methods in Atmospheric and Ocean Science
Koopman Theory Methods in Atmospheric and Ocean Science
Convener: Paula Lorenzo Sánchez | Co-conveners: Antonio Navarra, Stefan Klus, Joseph Tribbia
The Earth's climate system is characterized by the intricate interplay of atmospheric and oceanic processes evolving at various timescales, exhibiting complex behaviors and nonlinear interactions. Gaining a deeper insight into the underlying dynamics of this system is crucial for understanding the physical origins of weather and climate variability, as well as for predicting climate trends and extreme events. In this context, Koopman theory has emerged as a powerful mathematical framework with promising applications in atmospheric and ocean sciences, offering a novel perspective for analyzing complex, high-dimensional nonlinear systems.

Koopman theory, rooted in ergodic operator theory, characterizes nonlinear systems operating within a nonlinear state space using linear operators that act on vector spaces of observables (functions on the state space). By representing the evolution of observables as linear operators acting within a function space, Koopman analysis facilitates the identification of coherent structures, recurrent patterns, and dominant modes of variability within these systems. This capability is particularly valuable for comprehending climate behavior across various timescales, encompassing short-term weather patterns to long-term climate trends.

The recent advancement of efficient numerical methods for estimating the Koopman operator from data has led to a proliferation of studies employing this methodology within climate sciences, showcasing its utility in extracting meaningful information from both observational data and climate model simulations. Indeed, Koopman theory holds a great potential for enhancing our understanding of complex atmospheric and oceanic climate processes.
This session welcomes contributions that explore the application of Koopman Theory in atmospheric and oceanic sciences, including (but not limited to) the following topics:

-Climate predictability and forecasting
-Spatiotemporal feature extraction
-Climate mode identification
-Climate network analysis
-Exploration of climate attractors
-Development of reduced-order models
-Extreme event analysis