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Abstract

The measurement of glacial isostatic adjustment (GIA) is one of the key ways in which geophysicists probe the long-term mantle rheology and Pleistocene ice history. GIA models are also tied to global and regional relative sea-level (RSL) histories, to 20th century tide-gauge (TG) data and to space and terrestrial geodetic measurements. Two new types of observation are related to the high-resolution space–gravity data recovered from the Gravity and Climate Experiment (GRACE) satellite pair and the soon-to-be launched Gravity and Ocean Circulation Experiment (GOCE) with on-board three-component gradiometer. Gravity mapping has the unique capability of isolating those regions that lack isostatic equilibrium. When coupled with other space and terrestrial geodetic measurements, such as those of the Global Positioning System (GPS) networks and with multi-decade terrestrial gravity data, new constraints on GIA are in the offing and should soon illuminate new interpretations of ice-sheet history and mantle response. GIA studies also incorporate space-based altimetry data, which now provide multi-decadal coverage over continents, oceans and lakes. As we are approaching 72 monthly solutions of GRACE gravity coefficients for determining the Earth's secular component of gravity change over the continents, a new issue has surfaced: the problem of relying on interannual hydrological modeling to determine the hydrological contribution to the linear trend in the gravity field. Correctly extracting this contribution is germane to using the GIA-driven component for modeling solid-Earth and paleo-climatic parameters. Seismic and heat-flux-based models of the Earth's interior are emerging with ever higher levels of sophistication regarding material strength (or viscosity). A basic question raised is: how good are traditional Newtonian and non-Newtonian viscosity models that only allow radial variations of Earth parameters? In other words: under what circumstances must this assumption be abandoned for joint interpretations of new and traditional data sets. In this short review we summarize the issues raised in the papers forming this special issue (SI) dedicated to GIA.