Modelling Future Evolution of the Antarctic Ice Sheet With Explainable Machine Learning

The accelerating effects of climate change, particularly rising sea levels resulting from melting land ice, require accurate predictions of ice sheet dynamics. Traditional numerical models, such as the Parallel Ice Sheet Model (PISM), are widely used; however, they rely on simplifying assumptions and are computationally intensive, limiting their ability to make long-term predictions. This study addresses these limitations by employing machine learning techniques to emulate ice sheet dynamics, offering a faster, more efficient alternative to traditional numerical models. Using data generated by PISM for the Antarctic ice sheet spanning 2015 to 2100, we develop proof-of-concept machine learning models that predict key ice properties-ice mask, ice thickness, and ice velocity-based on climate inputs. Our models tackle both spatial and temporal challenges by incorporating the ice properties of neighbouring cells and past temporal values, thereby improving predictive accuracy. Additionally, explainable AI (XAI) techniques are applied to enhance trust and explainability. Our best-performing model, a Random Forest with an expanding window, achieves an average RMSE of 3.36 for thickness and 65.6 for velocity and misclassifies on average only 3 grid points per year for the ice mask out of 2257, demonstrating good accuracy comparable to that of the original simulator at a fraction of the computational cost. This approach not only improves the computational efficiency of scenario modelling but also contributes to the broader field of climate science by enabling more frequent and extensive analyses of climate change impacts on Antarctic ice sheets.