An Ising model to reproduce mesoscale cloud field organizations. Atmospheric Research

Preprint, 2026

The organization of mesoscale cloud fields is crucial for understanding atmospheric dynamics and their modeling.
The role played by these cloud structures on their direct environment and, more generally, on the climate remains challenging to incorporate in climate models.
In this paper, we introduce a new framework for investigating mesoscale cloud-field organization based on an Ising-type statistical model, the Bidimensional Cloud Ising Model (BICIM).
In this approach, a cloud is defined not as an ensemble of droplets, but as a thermodynamic state emerging from local interactions between temperature, humidity, and aerosol concentrations.
Starting from simple initial conditions and a minimal set of physical parameters, BICIM reproduces a wide variety of cloud morphologies observed over western Sweden, from dispersed to aggregated regimes.
The model demonstrates that mesoscale organization can arise spontaneously from stochastic, thermodynamically constrained interactions, without invoking explicit dynamical equations.
Sensitivity experiments highlight the central role of convective fluxes in maintaining structured cloud fields, while diffusive and surface fluxes play a comparatively minor role at 2 km altitude. 
Despite its stochastic formulation, BICIM robustly converges toward reproducible equilibrium states, reflecting the coexistence of randomness and deterministic thermodynamic forcing that characterizes atmospheric self-organization.
The model’s physically grounded simplicity and adjustable grid resolution make it computationally efficient and adaptable to a wide range of conditions.