arxiv:2604.13481

Monthly Diffusion v0.9: A Latent Diffusion Model for the First AI-MIP

Published on Apr 15

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Abstract

A climate emulator using a spherical Fourier neural operator-inspired conditional variational autoencoder and latent diffusion models low-frequency atmospheric variability at monthly timesteps with modest compute.

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Here, we describe Monthly Diffusion at 1.5-degree grid spacing (MD-1.5 version 0.9), a climate emulator that leverages a spherical Fourier neural operator (SFNO)-inspired Conditional Variational Auto-Encoder (CVAE) architecture to model the evolution of low-frequency internal atmospheric variability using latent diffusion. MDv0.9 was designed to forward-step at monthly mean timesteps in a data-sparse regime, using modest computational requirements. This work describes the motivation behind the architecture design, the MDv0.9 training procedure, and initial results.

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