An international collaborative project to better understand the impact of amplified warming in polar regions, through the development of a new sea ice modelling paradigm. Through SASIP, the Scale-Aware Sea Ice Project, we propose to develop a truly innovative, scale-aware continuum sea ice model for climate research; one that faithfully represents sea ice dynamics and thermodynamics and that is physically sound, data-adaptive, highly parallelized and computationally efficient. SASIP will exploit large datasets from both granular process models and remote sensing to constrain sea ice properties and optimize continuum model parameters, jointly using data assimilation and machine learning methods. Coupling this multi-scale modeling framework to an ocean mixed-layer model, we will open up a new regime for polar oceanography via an examination of currently unresolved or poorly understood ice–ocean interactions across physical scales. In this systematic merger of models, observations, and numerical techniques, SASIP will reform sea ice modeling, a crucial leap needed to improve regional and larger-scale predictions of polar climate. Through the further development of neXtSIM and the MEB rheological framework, SASIP will build a data-constrained model that is rigorously based on sea ice solid-like physics. This model will allow improved high resolution and large- scale predictions of Arctic and Antarctic sea ice, and the propagation of sea ice related climate feedbacks. Employing hybrid data assimilation and machine learning approaches as a native part of the model architecture will allow for objective combinations of model and data. Ultimately, SASIP will lead to reduced uncertainties related to the impact of
