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
By improving model physics, this strategy ensures a more faithful representation of feedbacks and sensitivities under different climates.
Despite drastic improvements in climate model development, current simulations have difficulty capturing the...
Learning from the high resolution data, as well as from model errors (Data Assimilation Increments), we will develop...
We will diagnose small-scale physical processes such as:
Predicting future climate conditions on earth, and in particular, the impacts of climate change, would not be possible...
Your can find most of our past talks, and much more, on our Youtube Channel
Themes of the talks: