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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ftdoajarticles:oai:doaj.org/article:6206b2fa198147cbb5fa99610c0d7c92 2023-05-15T14:54:27+02:00 Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice Sasha Nasonova Randall K. Scharien Christian Haas Stephen E. L. Howell 2017-12-01T00:00:00Z https://doi.org/10.3390/rs10010037 https://doaj.org/article/6206b2fa198147cbb5fa99610c0d7c92 EN eng MDPI AG https://www.mdpi.com/2072-4292/10/1/37 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs10010037 https://doaj.org/article/6206b2fa198147cbb5fa99610c0d7c92 Remote Sensing, Vol 10, Iss 1, p 37 (2017) Arctic sea ice thickness roughness melt pond fraction object-based image analysis (OBIA) Science Q article 2017 ftdoajarticles https://doi.org/10.3390/rs10010037 2022-12-31T10:54:20Z 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 Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 10 2 37 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic sea ice thickness roughness melt pond fraction object-based image analysis (OBIA) Science Q |
spellingShingle |
Arctic sea ice thickness roughness melt pond fraction object-based image analysis (OBIA) Science Q Sasha Nasonova Randall K. Scharien Christian Haas Stephen E. L. Howell 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) Science Q |
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 |
Sasha Nasonova Randall K. Scharien Christian Haas Stephen E. L. Howell |
author_facet |
Sasha Nasonova Randall K. Scharien Christian Haas Stephen E. L. Howell |
author_sort |
Sasha Nasonova |
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 |
MDPI AG |
publishDate |
2017 |
url |
https://doi.org/10.3390/rs10010037 https://doaj.org/article/6206b2fa198147cbb5fa99610c0d7c92 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing, Vol 10, Iss 1, p 37 (2017) |
op_relation |
https://www.mdpi.com/2072-4292/10/1/37 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs10010037 https://doaj.org/article/6206b2fa198147cbb5fa99610c0d7c92 |
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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1766326179444293632 |