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Abstract Two commonly used ice models that are constructed using glacial isostatic adjustment (GIA) modeling are the ICE‐6G and ANU ice models. In this study, we examined the relationships among mantle viscosity, ice models and relative sea level (RSL) data through an analytic GIA model. In general, almost all the pairs of RSL data sets and ice models we considered appear to be consistent with a mantle viscosity structure with a factor of 10–20 viscosity increase from the upper to lower mantles. By using the GIA model with the viscosity structure that produces a minimum model‐data misfit, we constructed the temporal and spatial distributions of misfit (i.e., misfit maps) to different RSL data sets, for both the ICE‐6G and ANU ice models. While the misfit maps at different times clearly show that more observations are desired to constrain ice models, we propose that the spatial and temporal misfit maps should be used to revise the existing ice models to further improve the fit to RSL data. In our initial proof of concept attempts to modify ICE‐6G by adding more ice to it, the three modified ICE‐6G ice models we considered all significantly improve the fit to the far‐field RSL data, although additional effort is needed to reduce misfit to near field RSL data. Finally, we emphasize that RSL at different far‐field sites may differ by up to ∼25 m at the Last Glacial Maximum (LGM) (∼26 ka), suggesting the need for a sufficiently large number of far‐field RSL data in determining the total melt ice volume since the LGM. 

Abstract This article presents a comprehensive benchmark study for the newly updated and publicly available finite element code CitcomSVE for modeling dynamic deformation of a viscoelastic and incompressible planetary mantle in response to surface and tidal loading. A complete description of CitcomSVE’s finite element formulation including calculations of the sea‐level change, polar wander, apparent center of mass motion, and removal of mantle net rotation is presented. The 3‐D displacements and displacement rates and the gravitational potential anomalies are solved with CitcomSVE for three benchmark problems using different spatial and temporal resolutions: (a) surface loading of single harmonics, (b) degree‐2 tidal loading, and (c) the ICE‐6G GIA model. The solutions are compared with semi‐analytical solutions for error analyses. The benchmark calculations demonstrate the accuracy and efficiency of CitcomSVE. For example, for a typical ICE‐6G GIA calculation with a 122‐ky glaciation‐deglaciation history, time increment of 100 years, and ∼50 km (or ∼0.5°) surface horizontal resolution, it takes ∼4.5 hr on 96 CPU cores to complete with about 1% and 5% errors for displacements and displacement rates, respectively. Error analyses shows that CitcomSVE achieves a second order accuracy, but the errors are insensitive to temporal resolution. CitcomSVE achieves the parallel computational efficiency >75% for using up to 6,144 CPU cores on a parallel supercomputer. With its accuracy, computational efficiency and its open‐source public availability, CitcomSVE provides a powerful tool for modeling viscoelastic deformation of a planetary mantle with 3‐D mantle viscous and elastic structures in response to surface and tidal loading problems.