Project Goal
We aim to reduce biases at the air-sea-ice interface in existing global climate models for reliable seasonal to multidecadal timescale projections, focusing on fundamental ocean, atmosphere, and sea-ice processes.
Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations. Another error source arises from the climate models’ numerics, which include spatial and temporal discretizations and numerical dissipation. These errors can accumulate or compensate for each other, making improving climate models intricate and requiring a range of approaches.
To tackle these biases and reduce the potential sources of error, M²LInES’ strategy is to leverage advances in machine learning & “interrogate” the data to:
- Develop data-informed, interpretable & generalizable subgrid physics models (ocean, ice, atm);
- Produce error corrections derived from observational products for climate model components.
By improving model physics, this strategy ensures a more faithful representation of feedbacks and sensitivities under different climates.
Our vision
💡 Generate new scientific knowledge in climate science from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction?
💻 Accelerate end-to-end, from development to delivery, for a new generation of climate models; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models.
⚙️ Drive a change of direction in the field by building models and tools centered around data-driven methods for the community to advance climate science discovery.
👩🏫 Enable a new generation of versatile scientists working at the interface of machine learning, climate science & numerical modeling.
