Modeling snow on sea ice using physics-guided machine learning

Snow is a crucial element of the sea ice system, affecting the sea ice growth and decay due to its low thermal conductivity and high albedo. Despite its importance, present-day climate models have a very idealized representation of snow, often including just one-layer thermodynamics, omitting several processes that shape its properties. Even though sophisticated snow process models exist, they tend to be excluded in climate modeling due to their prohibitive computational costs. For example, SnowModel is a numerical snow process model developed to simulate the evolution of snow depth and density, blowing snow redistribution and sublimation, snow grain size, and thermal conductivity in a spatially distributed, multilayer snowpack framework. SnowModel can simulate snow distributions on sea ice floes in high spatial (1-m horizontal grid) and temporal (1-hour time step) resolution. However, for simulations spanning over large regions, such as the Arctic Ocean, high-resolution runs face challenges of slow processing speeds and the need for large computational resources. To address these common issues in high-resolution numerical modeling, data-driven emulators are often used. However, these emulators have their caveats, primarily a lack of generalizability and inconsistency with physical laws. In our study, we address these challenges by using a physics-guided approach in developing our emulator. By integrating physical laws that govern changes in snow density due to compaction, we aim to create an emulator that is efficient while also adhering to essential physical principles. We evaluated this approach by comparing three machine learning models: long short-term memory (LSTM), physics-guided LSTM, and Random Forest, across five distinct Arctic regions. Our evaluations indicate that all models achieved high accuracy, with the physics-guided LSTM model demonstrating the most promising results in terms of accuracy and generalizability. Our approach offers a computationally faster way to emulate the SnowModel with high fidelity and a speedup of over 9000 times.