Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation
In one sentence. Perspective reviewing machine learning combined with data assimilation for climate surrogate models and model error correction, with open challenges for operational use.
Abstract
The outstanding breakthroughs of deep learning in computer vision and natural language processing have been the horn of plenty for many recent developments in the climate sciences. These methodological advances currently find applications to subgrid-scale parameterization, data-driven model error correction, model discovery, surrogate modeling, and many other uses. In this perspective article, I will review recent advances in the field, specifically in the thriving subtopic defined by the intersection of dynamical systems in geosciences, data assimilation, and machine learning, with striking applications to physical model error correction. I will give my take on where we are in the field and why we are there and discuss the key perspectives. I will describe several technical obstacles to implementing these new techniques in a high-dimensional, possibly operational system. I will also discuss open questions about the combined use of data assimilation and machine learning and the short- vs. longer-term representation of the surrogate (i.e., neural network-based) dynamics, and finally about uncertainty quantification in this context.
Labels
- Topic
- Data assimilation, Hybrid physics-ML & parameterization, Explainability, uncertainty & physics
- Type
- Position or perspective paper
- Method
- Classical ML (trees, GP, linear, kernels), Physics-informed / physics-constrained
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.3389/fams.2023.1133226 OpenAlex:W4323315742
- Added
- 2026-10-22
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