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arXiv2026

Neural Operators Reveal Scale-Dependent Limits on Recovering Greenland Ice Loss from Sea Level Fingerprints

Sea level fingerprints connect the spatial distribution of rapid ice mass change to sea surface height (SSH) change, but the spatial resolving power of this inverse problem remains poorly quantified. Here we provide the first systematic, scale-resolved quantification of Greenland Ice Sheet mass change structure recoverable from its SSH fingerprint when ice loss geometry varies. We develop a physics-informed neural operator, constrained by an accurate neural-operator emulator of the forward sea level equation, to probe the information content of this ill-conditioned inverse problem. Across synthetic ice change ensembles, broad ice loss patterns can be recovered, but distinct ice fields can produce nearly identical fingerprints, so fine-scale structure depends on the assumed a-priori ice change distribution. Inversion fidelity varies with the wavelength and amplitude of ice mass change. An empirical error law separates these effects and defines a scale-dependent accuracy ceiling. For a satellite-altimetry-derived reference field, the estimated fingerprint-only ceiling is $R^2\sim0.79$ under the present SSH observation geometry and matched-prior assumption. The leading three principal components explain 99.76% of fingerprint variance, yet higher-order components retain information needed to distinguish finer ice change structure. SSH observations near Greenland carry most information on ice loss locations. Applying the leading fingerprint principal components to the observed 1993--2019 SSH rate recovers the large-scale Greenland fingerprint, while the residual field is correlated with basin-scale bathymetry, consistent with unresolved sterodynamic variability. These results establish a quantitative empirical bound on fingerprint-only ice-loss resolving power and identify how independent observations and ice sheet constraints can improve inference.

physics.geo-ph↗

arXiv2026

A scalable Bayesian framework for modern sea-level inference

This paper presents a scalable Bayesian framework for the inference of modern-day sea-level change and surface mass redistribution. To motivate this approach, we first review and quantify the performance of some standard methods for the analysis of satellite gravity and ocean altimetry observations. We find that these methods substantially underestimate uncertainties and are subject to systematic biases, deficiencies that stem from their incomplete treatment of sea-level physics and from uncertainty estimates based solely on the propagation of observational noise. To address these limitations, our approach combines three advances. First, we use recent developments in adjoint sea-level theory to embed the full physics into the forward and inverse modelling. Second, we formulate the Bayesian inverse problem in an infinite-dimensional setting, thereby avoiding discretisation artefacts and the underestimation of uncertainties inherent in truncated model spaces. Finally, our computational methods render such inversions tractable at full observational resolution while supporting joint model spaces and multiple data types. By employing a matrix-free approach with iterative solvers and randomised low-rank decompositions -- implemented in the linked open-source libraries pygeoinf and pyslfp -- basic calculations are possible on a single laptop, with the most intensive tasks parallelising trivially across available cores. We demonstrate the methodology through a series of synthetic experiments, culminating in a joint inversion of satellite gravity and ocean altimetry data that decomposes regionally averaged sea-level change into steric and manometric components with quantified uncertainties, including the degeneracies that remain.

physics.geo-ph↗