The International Bathymetric Chart of the Arctic Ocean Version 4.0
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Published:2020-07-09
Issue:1
Volume:7
Page:
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ISSN:2052-4463
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Container-title:Scientific Data
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language:en
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Short-container-title:Sci Data
Author:
Jakobsson MartinORCID, Mayer Larry A., Bringensparr Caroline, Castro Carlos F.ORCID, Mohammad Rezwan, Johnson Paul, Ketter Tomer, Accettella Daniela, Amblas DavidORCID, An Lu, Arndt Jan Erik, Canals MiquelORCID, Casamor José Luis, Chauché Nolwenn, Coakley Bernard, Danielson SethORCID, Demarte Maurizio, Dickson Mary-Lynn, Dorschel BorisORCID, Dowdeswell Julian A.ORCID, Dreutter Simon, Fremand Alice C.ORCID, Gallant Dana, Hall John K., Hehemann Laura, Hodnesdal Hanne, Hong Jongkuk, Ivaldi Roberta, Kane Emily, Klaucke IngoORCID, Krawczyk Diana W., Kristoffersen Yngve, Kuipers Boele R., Millan Romain, Masetti Giuseppe, Morlighem MathieuORCID, Noormets RikoORCID, Prescott Megan M., Rebesco MicheleORCID, Rignot Eric, Semiletov Igor, Tate Alex J., Travaglini Paola, Velicogna Isabella, Weatherall Pauline, Weinrebe Wilhelm, Willis Joshua K., Wood MichaelORCID, Zarayskaya YuliaORCID, Zhang TaoORCID, Zimmermann Mark, Zinglersen Karl B.
Abstract
AbstractBathymetry (seafloor depth), is a critical parameter providing the geospatial context for a multitude of marine scientific studies. Since 1997, the International Bathymetric Chart of the Arctic Ocean (IBCAO) has been the authoritative source of bathymetry for the Arctic Ocean. IBCAO has merged its efforts with the Nippon Foundation-GEBCO-Seabed 2030 Project, with the goal of mapping all of the oceans by 2030. Here we present the latest version (IBCAO Ver. 4.0), with more than twice the resolution (200 × 200 m versus 500 × 500 m) and with individual depth soundings constraining three times more area of the Arctic Ocean (∼19.8% versus 6.7%), than the previous IBCAO Ver. 3.0 released in 2012. Modern multibeam bathymetry comprises ∼14.3% in Ver. 4.0 compared to ∼5.4% in Ver. 3.0. Thus, the new IBCAO Ver. 4.0 has substantially more seafloor morphological information that offers new insights into a range of submarine features and processes; for example, the improved portrayal of Greenland fjords better serves predictive modelling of the fate of the Greenland Ice Sheet.
Funder
The Nippon Foundation of Japan, grant Seabed 2030 NASA | Jet Propulsion Laboratory Open access funding provided by Stockholm University
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
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