Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-259-2022
https://doi.org/10.5194/tc-16-259-2022
Research article
 | 
24 Jan 2022
Research article |  | 24 Jan 2022

Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements

Arttu Jutila, Stefan Hendricks, Robert Ricker, Luisa von Albedyll, Thomas Krumpen, and Christian Haas

Data sets

Airborne sea ice parameters during the PAMARCMIP2017 campaign in the Arctic Ocean, version 1 Arttu Jutila, Stefan Hendricks, Robert Ricker, von Luisa Albedyll, and Christian Haas https://doi.org/10.1594/PANGAEA.933883

Airborne sea ice parameters during the IceBird Winter 2019 campaign in the Arctic Ocean, version 1 Arttu Jutila, Stefan Hendricks, Robert Ricker, von Luisa Albedyll, and Christian Haas https://doi.org/10.1594/PANGAEA.933912

Airborne sea ice plus snow thickness during the PAMARCMIP 2017 aircraft campaign in the Arctic Ocean Stefan Hendricks, Robert Ricker, Christian Haas, and Andreas Herber https://doi.org/10.1594/PANGAEA.924848

EASE-Grid Sea Ice Age, Version 4 M. Tschudi, W. N. Meier, J. S. Stewart, C. Fowler, and J. Maslanik https://doi.org/10.5067/UTAV7490FEPB

Canadian Ice Service Arctic Regional Sea Ice Charts in SIGRID-3 Format, Version 1 Canadian Ice Service https://doi.org/10.7265/N51V5BW9

Model code and software

kingjml/pySnowRadar: IEEE TGRS Submission J. King, M. Brady, and T. Newman https://doi.org/10.5281/ZENODO.4071801

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Short summary
Sea-ice thickness retrieval from satellite altimeters relies on assumed sea-ice density values because density cannot be measured from space. We derived bulk densities for different ice types using airborne laser, radar, and electromagnetic induction sounding measurements. Compared to previous studies, we found high bulk density values due to ice deformation and younger ice cover. Using sea-ice freeboard, we derived a sea-ice bulk density parameterisation that can be applied to satellite data.