Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework
For ice concentrations less than 85%, internal ice stresses in the sea ice pack are small and sea ice is said to be in free drift. The sea ice drift is then the result of a balance between Coriolis acceleration and stresses from the ocean and atmosphere. We investigate sea ice drift using data from...
Main Authors: | , , , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://discovery.ucl.ac.uk/id/eprint/10081852/1/Heorton_et_al-2019-Journal_of_Geophysical_Research__Oceans.pdf https://discovery.ucl.ac.uk/id/eprint/10081852/ |
id |
ftucl:oai:eprints.ucl.ac.uk.OAI2:10081852 |
---|---|
record_format |
openpolar |
spelling |
ftucl:oai:eprints.ucl.ac.uk.OAI2:10081852 2023-12-24T10:14:03+01:00 Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework Heorton, HDBS Tsamados, M Cole, ST Ferreira, AMG Berbellini, A Fox, M Armitage, TWK 2019-08 text https://discovery.ucl.ac.uk/id/eprint/10081852/1/Heorton_et_al-2019-Journal_of_Geophysical_Research__Oceans.pdf https://discovery.ucl.ac.uk/id/eprint/10081852/ eng eng https://discovery.ucl.ac.uk/id/eprint/10081852/1/Heorton_et_al-2019-Journal_of_Geophysical_Research__Oceans.pdf https://discovery.ucl.ac.uk/id/eprint/10081852/ open Journal of Geophysical Research: Oceans , 124 (8) pp. 6388-6413. (2019) sea ice drift observations inverse modeling drag coefficients Article 2019 ftucl 2023-11-27T13:07:37Z For ice concentrations less than 85%, internal ice stresses in the sea ice pack are small and sea ice is said to be in free drift. The sea ice drift is then the result of a balance between Coriolis acceleration and stresses from the ocean and atmosphere. We investigate sea ice drift using data from individual drifting buoys as well as Arctic-wide gridded fields of wind, sea ice, and ocean velocity. We perform probabilistic inverse modeling of the momentum balance of free-drifting sea ice, implemented to retrieve the Nansen number, scaled Rossby number, and stress turning angles. Since this problem involves a nonlinear, underconstrained system, we used a Monte Carlo guided search scheme—the Neighborhood Algorithm—to seek optimal parameter values for multiple observation points. We retrieve optimal drag coefficients of CA=1.2×10−3 and CO=2.4×10−3 from 10-day averaged Arctic-wide data from July 2014 that agree with the AIDJEX standard, with clear temporal and spatial variations. Inverting daily averaged buoy data give parameters that, while more accurately resolved, suggest that the forward model oversimplifies the physical system at these spatial and temporal scales. Our results show the importance of the correct representation of geostrophic currents. Both atmospheric and oceanic drag coefficients are found to decrease with shorter temporal averaging period, informing the selection of drag coefficient for short timescale climate models. Article in Journal/Newspaper Arctic ice pack Sea ice University College London: UCL Discovery Arctic |
institution |
Open Polar |
collection |
University College London: UCL Discovery |
op_collection_id |
ftucl |
language |
English |
topic |
sea ice drift observations inverse modeling drag coefficients |
spellingShingle |
sea ice drift observations inverse modeling drag coefficients Heorton, HDBS Tsamados, M Cole, ST Ferreira, AMG Berbellini, A Fox, M Armitage, TWK Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework |
topic_facet |
sea ice drift observations inverse modeling drag coefficients |
description |
For ice concentrations less than 85%, internal ice stresses in the sea ice pack are small and sea ice is said to be in free drift. The sea ice drift is then the result of a balance between Coriolis acceleration and stresses from the ocean and atmosphere. We investigate sea ice drift using data from individual drifting buoys as well as Arctic-wide gridded fields of wind, sea ice, and ocean velocity. We perform probabilistic inverse modeling of the momentum balance of free-drifting sea ice, implemented to retrieve the Nansen number, scaled Rossby number, and stress turning angles. Since this problem involves a nonlinear, underconstrained system, we used a Monte Carlo guided search scheme—the Neighborhood Algorithm—to seek optimal parameter values for multiple observation points. We retrieve optimal drag coefficients of CA=1.2×10−3 and CO=2.4×10−3 from 10-day averaged Arctic-wide data from July 2014 that agree with the AIDJEX standard, with clear temporal and spatial variations. Inverting daily averaged buoy data give parameters that, while more accurately resolved, suggest that the forward model oversimplifies the physical system at these spatial and temporal scales. Our results show the importance of the correct representation of geostrophic currents. Both atmospheric and oceanic drag coefficients are found to decrease with shorter temporal averaging period, informing the selection of drag coefficient for short timescale climate models. |
format |
Article in Journal/Newspaper |
author |
Heorton, HDBS Tsamados, M Cole, ST Ferreira, AMG Berbellini, A Fox, M Armitage, TWK |
author_facet |
Heorton, HDBS Tsamados, M Cole, ST Ferreira, AMG Berbellini, A Fox, M Armitage, TWK |
author_sort |
Heorton, HDBS |
title |
Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework |
title_short |
Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework |
title_full |
Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework |
title_fullStr |
Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework |
title_full_unstemmed |
Retrieving Sea Ice Drag Coefficients and Turning Angles From In Situ and Satellite Observations Using an Inverse Modeling Framework |
title_sort |
retrieving sea ice drag coefficients and turning angles from in situ and satellite observations using an inverse modeling framework |
publishDate |
2019 |
url |
https://discovery.ucl.ac.uk/id/eprint/10081852/1/Heorton_et_al-2019-Journal_of_Geophysical_Research__Oceans.pdf https://discovery.ucl.ac.uk/id/eprint/10081852/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic ice pack Sea ice |
genre_facet |
Arctic ice pack Sea ice |
op_source |
Journal of Geophysical Research: Oceans , 124 (8) pp. 6388-6413. (2019) |
op_relation |
https://discovery.ucl.ac.uk/id/eprint/10081852/1/Heorton_et_al-2019-Journal_of_Geophysical_Research__Oceans.pdf https://discovery.ucl.ac.uk/id/eprint/10081852/ |
op_rights |
open |
_version_ |
1786189004101648384 |