Towards a mass-conservative global sea ice emulator that generalizes across climates

We introduce FloeNet, a data-driven emulator architecture trained on the Geophysical Fluid Dynamics Laboratory (GFDL) global sea ice model, SIS2. FloeNet is an auto-regressive graph neural network (GNN) which marks a step forward in sea ice emulation as the first model to dynamically evolve the state of sea ice and snow-on-sea-ice by mass and area budget decompositions. Specifically, FloeNet receives mechanical and thermodynamic forcing inputs from the atmosphere and ocean, and predicts ice and snow mass tendencies due to growth, melt, and advection. This yields a mass-conservative and interpretable model, as timestep-to-timestep changes in sea ice area and mass can now be attributed to each term in their respective budget.

Sea ice is often seen as a barometer for climate change. It is therefore crucial that data-driven sea ice models show an accurate response to different climate forcings. To this end, we show how FloeNet successfully reproduces sea ice trends and variability of pre-industrial and 1% CO2 climates, despite being trained only on a present-day climate; FloeNet also reaches globally ice-free conditions under 1% CO2 forcing, with consistent timing to that of the original numerical model. In summary, FloeNet is a fast global sea ice emulator, taking 4.75 hours to generate a 140-year simulation on 1 GPU. It is also stable and accurate, reproducing critical features of long-term sea ice evolution under different forcings. We expect that FloeNet will substantially improve the representation of atmosphere-ice-ocean interactions in existing climate emulators.