Old Dog, New Trick: Reservoir Computing Advances Machine Learning for Climate Modeling
In one sentence. Commentary on reservoir-computing hybrid models coupling ML to a coarse climate model and SST predictor, arguing ML could disrupt conventional climate modeling.
Abstract
Abstract Physics‐informed machine learning (ML) applied to geophysical simulation is developing explosively. Recently, graph neural net and vision transformer architectures have shown 1–7 days global weather forecast skill superior to any conventional model with integration times over 1,000 times faster, but longer simulations rapidly degrade. ML that achieves high skill in both weather and climate applications is a tougher goal. This Commentary was inspired by Arcomano et al. (2023, https://doi.org/10.1029/2022GL102649 ), who show impressive progress toward that goal using hybrid ML, combining reservoir computing (RC) to a coarse‐grid climate model and coupling to a separate data‐driven RC model that interactively predicts sea‐surface temperature. This opens new horizons; where will the next ML breakthrough come from, and is conventional climate modeling about to be disrupted?
Labels
- Topic
- Climate emulation, Hybrid physics-ML & parameterization
- Type
- Position or perspective paper
- Method
- Other
- Time scale
- Climate projections / centennial
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1029/2023gl104174 OpenAlex:W4386274382
- Added
- 2026-10-22
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