FirnLearn: A neural network-based approach to firn density modeling in Antarctica

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Abstract Understanding firn densification is essential for interpreting ice core records, predicting ice sheet mass balance, elevation changes and future sea-level rise. Current models of firn densification on the Antarctic ice sheet (AIS), such as the Herron and Langway (1980) model are either simple semi-empirical models that rely on sparse climatic data and surface density observations or complex physics-based models that rely on poorly understood physics. In this work, we introduce a deep learning technique to study firn densification on the AIS. Our model, FirnLearn, evaluated on 225 cores, shows an average root-mean-square error of 31 kg m−3 and explained variance of 91%. We use the model to generate surface density and the depths to the $550\,\mathrm{kg\,m}^{-3}$ and $830\,\mathrm{kg\,m}^{-3}$ density horizons across the AIS to assess spatial variability. Comparisons with the Herron and Langway (1980) model at ten locations with different climate conditions demonstrate that FirnLearn more accurately predicts density profiles in the second stage of densification and complete density profiles without direct surface density observations. This work establishes deep learning as a promising tool for understanding firn processes and advancing towards a universally applicable firn model.