Hybrid physics-ML & parameterization★ Highlight
Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations
Why it matters. Extends coarse-model ML correction from fine-resolution simulations to multiple climates and multi-year runs, improving land precipitation and temperature.
In one sentence. Machine-learned corrective tendencies from 25 km simulations improve a 200 km FV3GFS model's land precipitation pattern error by 8–28% across four SST-perturbed climates.
Abstract
Abstract Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794 ) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a fine‐resolution model, at the scales resolved by both. This study extends that work for application in multiple climates and multi‐year ML‐corrected simulations. Here four fine‐resolution (∼25 km) 2 year reference simulations are run using FV3GFS with climatological sea surface temperatures perturbed uniformly by −4, 0, +4, and +8 K. A data set of state‐dependent corrective tendencies is then derived through nudging the ∼200 km model to the coarsened state of the fine‐resolution simulations in each climate. Along with the surface radiative fluxes, the corrective tendencies of temperature and specific humidity are machine‐learned as functions of the column state. ML predictions for the fluxes and corrective tendencies are applied in 5.25 years ∼200 km resolution simulations in each climate, and improve the spatial pattern errors of land precipitation by 8%–28% and land surface temperature by 19%–25% across the four climates. The ML has a neutral impact on the pattern error of oceanic precipitation.
Labels
- Topic
- Hybrid physics-ML & parameterization
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- km-scale or high-res simulation
- Status
- Research
- Identifiers
- DOI:10.1029/2022ms003219 OpenAlex:W4294219199
- Added
- 2026-10-22
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