Fossil Java Sea corals record Laurentide ice sheet disappearance

• Published in: Geology
• Main Authors: Mann, Thomas, Schöne, Tilo, Kench, Paul, Lambeck, Kurt, Ashe, Erica, Kneer, Dominik, Beetham, Eddie, Illigner, Julia, Rovere, Alessio, Marfai, Muh Aris, Westphal, Hildegard
• Other Authors: Mann, Thoma, Marfai, Muh Ari
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2023
• Subjects: Settore GEO/04 - Geografia Fisica e Geomorfologia; Ice Sheet
• Online Access: https://hdl.handle.net/10278/5019908; https://doi.org/10.1130/G51038.1

The Laurentide ice sheet was the largest late Pleistocene ice mass and the largest contributor to Holocene pre-industrial sea-level rise. While glaciological dates suggest final ice sheet melting between 8 and 6 ka, inversion of sea-level data indicates deglaciation at ca. 7 ka. Here, we present new chronostratigraphic constraints on Laurentide ice sheet disappearance based on Holocene relative sea-level observations from the tectonically stable north coast of Java, Indonesia. Age-elevation data from the flat upper surfaces of 13 fossil intertidal corals (i.e., microatolls) indicate that the Java Sea experienced a relative sea level of 1.3 ± 0.7 m above present between 6.9 and 5.3 ka. To determine uncaptured relative sea-level trends within the observational uncertainties of this apparently constant highstand, we analyzed the internal structure of three sliced microatolls from the same site to produce a high-resolution data set. These data were used to statistically model relative sea-level rates and trends. Employing the data with the model provided evidence for a short-lived rise of relative sea level from 1.0 ± 0.3 m above present at 6.7 ± 0.1 ka to 1.9 ± 0.3 m above present at 6.4 ± 0.1 ka. The end of this rise likely represents the last input of meltwater from the vast Laurentide ice sheet, which, consequently, collapsed at least 400 yr later than assumed by some widely used models of glacial isostatic adjustment. Incorporating these new results into such predictive models will help to better understand the geographical variability of future sea-level rise as a result of global warming.