The impact of water loading on postglacial decay times in Hudson Bay

摘要

Ongoing glacial isostatic adjustment (GIA) due to surface loading (ice and water) variations during the last glacial cycle has been contributing to sea-level changes globally throughout the Holocene, especially in regions like Canada that were heavily glaciated during the Last Glacial Maximum (LGM). The spatial and temporal distribution of GIA, as manifested in relative sea-level (RSL) change, are sensitive to the ice history and the rheological structure of the solid Earth, both of which are uncertain. It has been shown that RSL curves near the center of previously glaciated regions with no ongoing surface loading follow an exponential-like form, with the postglacial decay times associated with that form having a weak sensitivity to the details of the ice loading history. Postglacial decay time estimates thus provide a powerful datum for constraining the Earth's viscous structure and improving GIA predictions. We explore spatial patterns of postglacial decay time predictions in Hudson Bay by decomposing numerically modeled RSL changes into contributions from water and ice loading effects, and computing their relative impact on the decay times. We demonstrate that ice loading can contribute a strong geographic trend on the decay time estimates if the time window used to compute decay times includes periods that are temporally close to (i.e. contemporaneous with, or soon after) periods of active loading. This variability can be avoided by choosing a suitable starting point for the decay time window. However, more surprisingly, we show that across any adopted time window, water loading effects associated with inundation into, and postglacial flux out of, Hudson Bay and James Bay will impart significant geographic variability onto decay time estimates. We emphasize this issue by considering both maps of predicted decay times across the region and site-specific estimates, and we conclude that variability in observed decay times (whether based on existing or future data