2017/11/05 by Joanna Slawinska, Joanna Sławińska, Eniko Szekely +5
Earth and Planetary Sciences · Environmental Science · Physics and Astronomy · #Atmospheric and Oceanic Physics (physics.ao-ph) #FOS: Physical sciences #Meteorological Phenomena and Simulations #Model Reduction and Neural Networks #Plant Water Relations and Carbon Dynamics #physics.ao-ph
paper · pdf · doi:10.48550/arxiv.1711.02526
arxiv created 2017/11/05 · openalex publication_date 2017/11/05 · arxiv updated 2017/11/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study nonlinear dynamics of the Earth's tropical climate system. For that, we apply a recently developed technique for feature extraction and mode decomposition of spatiotemporal data generated by ergodic dynamical systems. The method relies on constructing low-dimensional representations (temporal patterns) of signals using eigenfunctions of Koopman operators governing the evolution of observables in ergodic dynamical systems. We apply this technique to a variety of tropical climate datasets and extract a multiscale hierarchy of spatiotemporal patterns on diurnal to interannual timescales. In particular, we detect without prefiltering the input data modes operating on intraseasonal and shorter timescales that correspond to propagation of organized convection. We discuss the salient properties of these propagating features and in particular we focus on how the activity of certain types of these traveling patterns is related to lower-frequency dynamics. As an extension of this work, we discuss their potential predictability based on a range of nonparametric techniques and potential advances related to understanding the deterministic and stochastic aspects of the variability of these modes.