Uncertainties of glacial isostatic adjustment model predictions in North America associated with 3D structure

• Published in: Geophysical Research Letters
• Main Authors: Li, Tanghua, Wu, Patrick, Wang, Hansheng, Steffen, Holger, Khan, Nicole S., Engelhart, Simon E., Vacchi, Matteo, Shaw, Timothy Adam, Peltier, W. Richard, Horton, Benjamin Peter
• Other Authors: Asian School of the Environment, Earth Observatory of Singapore
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2020
• Subjects: Science::General; Glacial Isostatic Adjustment; Uncertainties; Hudson; Hudson Bay; Moe; Pacific
• Online Access: https://hdl.handle.net/10356/145214; https://doi.org/10.1029/2020GL087944

We quantify GIA prediction uncertainties of 250 1D and 3D glacial isostatic adjustment (GIA) models through comparisons with deglacial relative sea‐level (RSL) data from North America and rate of vertical land motion ( urn:x-wiley:00948276:media:grl60497:grl60497-math-0001) and gravity rate of change ( urn:x-wiley:00948276:media:grl60497:grl60497-math-0002) from GNSS and GRACE data, respectively. Spatially, the size of the RSL uncertainties varies across North America with the largest from Hudson Bay and near previous ice margins along the northern Atlantic and Pacific coasts, which suggests 3D viscosity structure in the lower mantle and laterally varying lithospheric thickness. Temporally, RSL uncertainties decrease from the Last Glacial Maximum to present except for west of Hudson Bay and the northeastern Pacific coast. The uncertainties of both these regions increase from 30 to 45 m between 15 and 11 ka BP, which may be due to the rapid decrease of surface loading at that time. Present‐day urn:x-wiley:00948276:media:grl60497:grl60497-math-0003 and urn:x-wiley:00948276:media:grl60497:grl60497-math-0004 uncertainties are largest in southwestern Hudson Bay with magnitudes of 2.4 mm/year and 0.4 μGal/year, mainly due to the 3D viscosity structure in the lower mantle. Ministry of Education (MOE) National Research Foundation (NRF) Published version We thank Donald Argus and an anonymous reviewer for their very constructive comments, which have helped to improve the manuscript. Patrick Wu received GRF grant 17315316 from the Hong Kong Research Grants Council. Tanghua Li and Benjamin P. Horton are supported by the Singapore Ministry of Education Academic Research Fund MOE2019‐T3‐1‐004 and MOE2018‐T2‐1‐030, the National Research Foundation Singapore, and the Singapore Ministry of Education, under the Research Centers of Excellence initiative. Hansheng Wang is funded by the National Key R & D Program of China (2017YFA0603103) and National Natural Science Foundation of China (41431070 and 41974009). The ...