As we embark on the third year of M²LInES, we want to share our progress and what comes next.
M²LInES’ mission is to improve coupled climate models by reimagining physics model development through innovative use of data and AI. We aim to accelerate the pace of climate model development by learning physics from data with scientific machine learning, and ultimately enhance climate model fidelity and reliability for future projections.
As we continue to develop and generalize AI-enhanced models of ocean, sea-ice, and atmospheric processes from data, we can now begin to assess their impact on the large-scale climate in a suite of global model configurations.
Climate models are known to have stubborn biases (model error relative to observations) due to incorrect representations of unresolved physics. We can now demonstrate in GFDL OM4, Global Ocean and Sea Ice Model at 1/4 degree horizontal resolution:
- A reduction in upper ocean temperature biases in the summer, through the enhancement of a physics-based parameterization with a data-driven vertical diffusivity profile across many forcing regimes (Sane et al., 2023);