ISEFlow: A Flow-Based Neural Network Emulator for Improved Sea Level Projections and Uncertainty Quantification

Abstract. Ice sheets are the primary contributors to global sea level rise, yet projecting their future contributions remains challenging due to the complex, nonlinear processes governing their dynamics and uncertainties in future climate scenarios. This study introduces ISEFlow, a neural network-based emulator of the ISMIP6 ice sheet model ensemble designed to accurately and efficiently predict sea level contributions from both ice sheets while quantifying the sources of projection uncertainty. By integrating a normalizing flow architecture to capture data coverage uncertainty and a deep ensemble of LSTM models to assess emulator uncertainty, ISEFlow separates uncertainties arising from training data from those inherent to the emulator. Compared to existing emulators such as Emulandice and LARMIP, ISEFlow achieves substantially lower mean squared error and improved distribution approximation while maintaining faster inference times. This study investigates the drivers of increased accuracy and emission scenario distinction and finds that the inclusion of all available climate forcings, ice sheet model characteristics, and higher spatial resolution significantly enhances predictive accuracy and the ability to capture the effects of varying emissions scenarios compared to other emulators. We include a detailed analysis of importance of input variables using Shapley Additive Explanations, and highlight both the climate forcings and model characteristics that have the largest impact on sea level projections. ISEFlow offers a computationally efficient tool for generating accurate sea level projections, supporting climate risk assessments and informing policy decisions.