Postglacial relative sea level change in Norway

We present the first comprehensive postglacial relative sea-level (RSL) database for the Norwegian coast from Oslo to the Kola Peninsula. The database spans the last 20 kyrs and is composed of 413 index points and 610 limiting data points derived from raised beaches, archeological data, glaciomarine terraces, and sedimentary indicators from isolation basins, salt marshes, and peat bogs. The data are quality controlled, assigned standardized indicative meanings, and recalibrated to current standards. We use an ensemble of Bayesian statistical models, trained on sea-level index points and weighted by their fit to both index and sea-level limiting points, to assess the spatiotemporal patterns of Norwegian RSL change. Continuous RSL fall driven by isostatic rebound in response to Eurasian ice sheet collapse dominates the RSL signal at every inland location in Norway. A first transgression (episode of RSL rise), which occurred in southwest Norway during the Younger Dryas (14-11.7 ka), increased RSL by as much as 15 m in some locations. A second transgression, named the Tapes transgression, occurred during the early-mid Holocene between 10 and 5.5 ka. The spatiotemporal model ensemble constrains the timing, amplitude, and spatial distribution of the Younger Dryas and Tapes transgressions. Based on our modeling results, we speculate that the Tapes Transgression was the result of global mean sea level rise temporarily outpacing isostasydriven RSL fall, while the Younger Dryas Transgression was likely the result of local ice sheet readvance combined with low viscosity asthenosphere and weak lithosphere in the region. We also describe the effects of peripheral bulge migration on Norwegian RSL, which caused increased RSL in the early Holocene and a delayed Tapes transgression. We show that postglacial RSL data in Norway contain complex spatiotemporal patterns of nearfield RSL change that can best be estimated by combining a high-quality data compilation with glacial isostatic adjustment modeling via a robust statistical model. (c) 2022 Elsevier Ltd. All rights reserved.