Predicting Greenland Ice Albedo Using A Physically-Based Convolutional Long Short-Term Memory Network

Global mean sea level rise has been accelerating significantly over the past decades, a substantial part of which is attributed to increased surface melting from the Greenland ice sheet (GrIS). Climate models project the GrIS to contribute 9-18 cm to global mean sea level rise by 2100 for the Shared Socioeconomic Pathway SSP5-8.5. The significant uncertainty in this projection prevents accurate mitigation of the effects of sea level rise. The uncertainty stems from a not-comprehensive understanding of the physical processes controlling surface melting. In particular, we lack understanding of ice albedo evolution/variability, a crucial factor in surface melt processes. Ice albedo is a complex and highly variable property of the ice surface that is not well represented in climate model projections, leading to imprecise predictions of sea level rise. The high complexity and number of drivers and feedbacks responsible for ice albedo variability prevent us from building a comprehensive predictive ice albedo model that accurately incorporates all these processes.

From this point of view, we adopt a machine learning-based approach to predict ice albedo variability on the GrIS. We use daily regional climate model output of atmospheric, radiative, and glaciological variables from the Modèle Atmosphérique Régional (MAR) as input data and daily broadband albedo data from the Moderate Resolution Imaging Spectroradiometer (MODIS) as output data. From these data, we construct a Convolutional Long Short-Term Memory (CNN-LSTM) network that models daily ice albedo variability on 6.5 km spatial resolution. A CNN is a neural network that works particularly well for extracting patterns from spatial data. An LSTM is a special kind of recurrent neural network (RNN) that is well-suited for finding patterns and trends in temporal data on a much longer time scale than classic RNNs. Preliminary results show a significant improvement of the correlation between observed and simulated bare ice albedo, with the CNN-LSTM outperforming MAR. Besides the predictive ability of this physically-based machine learning ice albedo model and its suitability for implementation in climate models, it also allows us to gain understanding of what variables drive ice albedo variability on the GrIS, now and in the future.