Assessing Emulator Design and Training for Modal Aerosol Microphysics Parameterizations in E3SMv2

TL;DR

This study evaluates the design and training of neural network emulators for aerosol microphysics in a climate model, highlighting how architecture choices and training strategies affect accuracy.

Contribution

It systematically investigates emulator design choices, such as complexity and normalization, providing practical guidance for future aerosol process emulation in climate models.

Findings

Effective scaling and convergence are crucial for accurate emulation.

Simple neural network architectures can effectively reproduce aerosol microphysics.

Training convergence and network complexity significantly influence emulation accuracy.

Abstract

Toward the goal of using Scientific Machine Learning (SciML) emulators to improve the numerical representation of aerosol processes in global atmospheric models, we explore the emulation of aerosol microphysics processes under cloud-free conditions in the 4-mode Modal Aerosol Module (MAM4) within the Energy Exascale Earth System Model version 2 (E3SMv2). To develop an in-depth understanding of the challenges and opportunities in applying SciML to aerosol processes, we begin with a simple feedforward neural network architecture that has been used in earlier studies, but we systematically examine key emulator design choices, including architecture complexity and variable normalization, while closely monitoring training convergence behavior. Our results show that optimization convergence, scaling strategy, and network complexity strongly influence emulation accuracy. When effective scaling is applied and convergence is achieved, the relatively simple architecture, used together with a moderate network size, can reproduce key features of the microphysics-induced aerosol concentration changes with promising accuracy. These findings provide practical clues for the next stages of emulator development; they also provide general insights that are likely applicable to the emulation of other aerosol processes, as well as other atmospheric physics involving multi-scale variability.