Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies th...

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Published in:Remote Sensing
Main Authors: Nasonova, Sasha, Scharien, Randall K., Haas, Christian, Howell, Stephen E. L.
Format: Article in Journal/Newspaper
Language:English
Published: Remote Sensing 2017
Subjects:
Online Access:https://doi.org/10.3390/rs10010037
https://dspace.library.uvic.ca//handle/1828/10355
id ftuvicpubl:oai:dspace.library.uvic.ca:1828/10355
record_format openpolar
spelling ftuvicpubl:oai:dspace.library.uvic.ca:1828/10355 2023-05-15T14:54:26+02:00 Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice Nasonova, Sasha Scharien, Randall K. Haas, Christian Howell, Stephen E. L. 2017 application/pdf https://doi.org/10.3390/rs10010037 https://dspace.library.uvic.ca//handle/1828/10355 en eng Remote Sensing Nasonova, S., Scharien, R.K., Haas, C. & Howell, S.E.L. (2018). Linking regional winter sea ice thickness and surface roughness to spring melt pond fraction on landfast artic sea ice. Remote Sensing, 10(1), 37. https://doi.org/10.3390/rs10010037 https://doi.org/10.3390/rs10010037 https://dspace.library.uvic.ca//handle/1828/10355 Arctic sea ice thickness roughness melt pond fraction object-based image analysis (OBIA) Article 2017 ftuvicpubl https://doi.org/10.3390/rs10010037 2022-05-19T06:14:33Z The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (fp) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two ~5.2 km long profiles over FYI- and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring fp using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring fp. A stronger negative correlation was observed between FYI thickness and fp (Spearman rs = −0.85) compared to FYI roughness and fp (rs = −0.52). The association between MYI thickness and fp was also negative (rs = −0.56), whereas there was no association between MYI roughness and fp. 47% of spring fp variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high fp) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low fp). This work provides an important contribution towards the parameterizations of fp in seasonal and long-term prediction models by quantifying linkages ... Article in Journal/Newspaper Arctic Sea ice University of Victoria (Canada): UVicDSpace Arctic Remote Sensing 10 2 37
institution Open Polar
collection University of Victoria (Canada): UVicDSpace
op_collection_id ftuvicpubl
language English
topic Arctic
sea ice thickness
roughness
melt pond fraction
object-based image analysis (OBIA)
spellingShingle Arctic
sea ice thickness
roughness
melt pond fraction
object-based image analysis (OBIA)
Nasonova, Sasha
Scharien, Randall K.
Haas, Christian
Howell, Stephen E. L.
Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice
topic_facet Arctic
sea ice thickness
roughness
melt pond fraction
object-based image analysis (OBIA)
description The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (fp) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two ~5.2 km long profiles over FYI- and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring fp using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring fp. A stronger negative correlation was observed between FYI thickness and fp (Spearman rs = −0.85) compared to FYI roughness and fp (rs = −0.52). The association between MYI thickness and fp was also negative (rs = −0.56), whereas there was no association between MYI roughness and fp. 47% of spring fp variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high fp) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low fp). This work provides an important contribution towards the parameterizations of fp in seasonal and long-term prediction models by quantifying linkages ...
format Article in Journal/Newspaper
author Nasonova, Sasha
Scharien, Randall K.
Haas, Christian
Howell, Stephen E. L.
author_facet Nasonova, Sasha
Scharien, Randall K.
Haas, Christian
Howell, Stephen E. L.
author_sort Nasonova, Sasha
title Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice
title_short Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice
title_full Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice
title_fullStr Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice
title_full_unstemmed Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice
title_sort linking regional winter sea ice thickness and surface roughness to spring melt pond fraction on landfast arctic sea ice
publisher Remote Sensing
publishDate 2017
url https://doi.org/10.3390/rs10010037
https://dspace.library.uvic.ca//handle/1828/10355
geographic Arctic
geographic_facet Arctic
genre Arctic
Sea ice
genre_facet Arctic
Sea ice
op_relation Nasonova, S., Scharien, R.K., Haas, C. & Howell, S.E.L. (2018). Linking regional winter sea ice thickness and surface roughness to spring melt pond fraction on landfast artic sea ice. Remote Sensing, 10(1), 37. https://doi.org/10.3390/rs10010037
https://doi.org/10.3390/rs10010037
https://dspace.library.uvic.ca//handle/1828/10355
op_doi https://doi.org/10.3390/rs10010037
container_title Remote Sensing
container_volume 10
container_issue 2
container_start_page 37
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