How Organized Convection Evolves in Latent Space

Deep convection exhibits substantial variability even under fixed large-scale forcing, challenging deterministic descriptions of convective organization. Using idealized radiative–convective equilibrium simulations with imposed low-level shear, we quantify this intrinsic variability through a reduced-order stochastic framework. Convective transport is characterized by isentropic mass flux and embedded in a low-dimensional latent space using a variational autoencoder. The temporal evolution of convection in this space is modeled as a Markov chain, yielding a data-driven representation of convective states and their transition probabilities.

This framework demonstrates that internal feedbacks alone generate a broad ensemble of admissible convective trajectories within a single environment, which we interpret as the system’s intrinsic stochasticity. The leading latent dimensions correspond to the convective life cycle and degree of organization, while state transitions identify the constrained pathways through which organized convection emerges and evolves. Comparison of individual storm trajectories in latent space exposes systematic differences in dynamical behavior that are difficult to diagnose in physical space. However, departures from strictly Markovian behavior indicate that the instantaneous state representation does not fully capture slow memory effects associated with convective organization, which likely condition transition probabilities.

These results show that organized convection is best understood as one realization drawn from a constrained distribution of possible trajectories and establish a general machine-learning-enabled framework for quantifying variability and limits of predictability in multiscale atmospheric systems.