Stochastic Emulation of a Fully Coupled Preindustrial E3SMv3 Simulation

2026-08-10

AI Summary

This study addresses the challenge of efficiently simulating long-timescale coupled climate dynamics while preserving internal variability, particularly extreme events and air–sea interactions. The authors propose the SamudrACE framework, which for the first time integrates a stochastic atmospheric emulator (ACE2S) with a full-depth ocean model (Samudra) within a fully coupled system. By fine-tuning the coupling through a probabilistic objective function, atmospheric forcing is optimized to drive realistic oceanic internal variability. The approach substantially improves the fidelity of simulated ENSO, eddy-related sea surface temperature anomalies, and marginal sea ice variability. Over 105 years of training and 400 years of evaluation, the model accurately reproduces the E3SMv3 climatology—within observational uncertainty—and captures daily precipitation extremes up to the 99.99th percentile, with only slight underestimation of the rarest tropical extremes.

Abstract

We present a stochastic coupled emulator of E3SM version 3, built on the SamudrACE framework, which couples an atmosphere emulator (ACE2) with a full-depth ocean emulator (Samudra). We replace the deterministic atmosphere emulator with its stochastic counterpart, ACE2S, and fine-tune the coupled system with a probabilistic objective, so that the atmosphere acts as a source of internal variability for the ocean. Trained on 105 years of a pre-industrial control simulation and evaluated on an independent 400 years, the emulator reproduces E3SMv3's mean climate state with biases much smaller than existing model-to-observation differences. Relative to a deterministic baseline, stochastic training maintains internal variability across timescales, most notably in the ENSO power spectrum, eddy-rich SST anomalies, and sea ice variability in the marginal ice zone. The emulator captures daily precipitation accurately up to the 99.99th percentile, but underestimates the rarest tropical extremes. These results show that stochastic coupled emulators can reproduce long-timescale variability with high fidelity, while extrapolation to unseen extremes remains a key challenge.

Problem

• internal variability
• climate emulation
• stochastic modeling
• extreme precipitation
• coupled climate system

Innovation

• stochastic emulation
• coupled climate modeling
• internal variability
• E3SMv3
• probabilistic training