Modeling Snow on Sea Ice using Physics Guided Machine Learning

TL;DR

This paper introduces a physics-guided machine learning emulator for snow on sea ice, significantly speeding up complex snow process simulations while maintaining high accuracy and physical consistency.

Contribution

It develops a physics-informed LSTM model that emulates SnowModel processes with over 9000 times faster computation, improving efficiency and physical realism in climate modeling.

Findings

Physics-Guided LSTM outperforms other models in accuracy and generalizability.

Achieves over 9000 times speedup compared to SnowModel.

Maintains high fidelity to physical laws in snow process simulation.

Abstract

Snow is a crucial element of the sea ice system, affecting sea ice growth and decay due to its low thermal conductivity and high albedo. Despite its importance, present-day climate models have an idealized representation of snow, often including only single-layer thermodynamics and omitting several processes that shape its properties. Although advanced snow process models like SnowModel exist, they are often excluded from climate modeling due to their high computational costs. SnowModel simulates snow depth, density, blowing-snow redistribution, sublimation, grain size, and thermal conductivity in a multi-layer snowpack. It operates with high spatial (1 meter) and temporal (1 hour) resolution. However, for large regions like the Arctic Ocean, these high-resolution simulations face challenges such as slow processing and large resource requirements. Data-driven emulators are used to address these issues, but they often lack generalizability and consistency with physical laws. In our study, we address these challenges by developing a physics-guided emulator that incorporates physical laws governing changes in snow density due to compaction. We evaluated three machine learning models: Long Short-Term Memory (LSTM), Physics-Guided LSTM, and Random Forest across five Arctic regions. All models achieved high accuracy, with the Physics-Guided LSTM showing the best performance in accuracy and generalizability. Our approach offers a faster way to emulate SnowModel with a speedup of over 9000 times, maintaining high fidelity.