Lagrangian attractors from satellite observations of stratocumulus clouds, and pullback attractor dissection from stochastic delay models

Abstract:

The emergence of organised multiscale patterns resulting from convection is ubiquitous, observed throughout different cloud types around the world. The nonlinear dynamics understanding of such cloud patterns by cloud-resolving models such as large eddy simulation models remains a grand challenge. In this work, we present an alternative approach based on conceptual stochastic delay differential models. We show that with the suitable stochastic parameterization accounting for the missing physics, the delay model's response to stochastic perturbations can indeed reproduces with fidelity the rich variability of cloud oscillations such as extracted from Lagrangian analysis of high-resolution satellite images.

Our approach employs Lagrangian attractors obtained by tracking oscillatory features from satellite images  that we confront to ensemble and pullback attractors from stochastic delay models experiencing a stochastic Hopf bifurcation.  Our analysis reveals that while the closed-cell dynamics corresponds to that of  a random steady state, the open-cell dynamics is much richer, as associated to that of a random limit cycle, and dominated by different types of phase-locked oscillations that unfold in the course of the day.

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Last updated on 12/23/2023