Measurements of Iceberg Melt Rates Using High‐Resolution GPS and Iceberg Surface Scans

• Published in: Geophysical Research Letters
• Main Authors: Schild, Kristin M., Sutherland, David A., Elosegui, Pedro, Duncan, Daniel
• Other Authors: Agencia Estatal de Investigación (España), National Science Foundation (US)
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union 2021
• Subjects: Arctic; Greenland; Ice Sheet; Iceberg*; North Atlantic
• Online Access: http://hdl.handle.net/10261/228362; https://doi.org/10.1029/2020GL089765; https://doi.org/10.13039/100000001; https://doi.org/10.13039/501100011033

9 pages, 4 figures, 1 table, supporting information https://doi.org/10.1029/2020GL089765.-- Data Availability Statement Processed: SfM drone pointclouds, raw GPS data, multibeam pointclouds (doi:10.18739/A2QF8JK6B), and CTD casts (doi:10.18739/A2NG4GS8C) are uploaded to the Arctic Data Center Increasing freshwater input to the subpolar North Atlantic through iceberg melting can influence fjord‐scale to basin‐scale ocean circulation. However, the magnitude, timing, and distribution of this freshwater have been challenging to quantify due to minimal direct observations of subsurface iceberg geometry and melt rates. Here we present novel in situ methods capturing iceberg change at high‐temporal and ‐spatial resolution using four high‐precision GPS units deployed on two large icebergs (>500 m length). In combination with measurements of surface and subsurface geometry, we calculate iceberg melt rates between 0.10–0.27 m/d over the 9‐day survey. These melt rates are lower than those proposed in previous studies, likely due to using individual subsurface iceberg geometries in calculations. In combining these new measurements of iceberg geometry and melt rate with the broad spatial coverage of remote sensing, we can better predict the impact of increasing freshwater injection from the Greenland Ice Sheet With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI) This work was supported by NSF OPP grant 1552232 Peer reviewed