Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean

We used deep learning networks to establish a relationship model among MODIS daily surface reflectance product (MOD09GA) and Arctic melt ponds fraction (MPF), ice fraction (IF), and open water fraction (OWF). We applied this model to MODIS 8-day surface reflectance (MOD09A1) to derive Arctic 8-day M...

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Published in:International Journal of Applied Earth Observation and Geoinformation
Main Authors: Jiajun Feng, Yuanzhi Zhang, Qiuming Cheng, Kapo Wong, Yu Li, Jin Yeu Tsou
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2021
Subjects:
geo
Online Access:https://doi.org/10.1016/j.jag.2021.102297
https://doaj.org/article/a98f54b8e30a4d5b907a46891d529799
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record_format openpolar
spelling fttriple:oai:gotriple.eu:oai:doaj.org/article:a98f54b8e30a4d5b907a46891d529799 2023-05-15T14:43:16+02:00 Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean Jiajun Feng Yuanzhi Zhang Qiuming Cheng Kapo Wong Yu Li Jin Yeu Tsou 2021-06-01 https://doi.org/10.1016/j.jag.2021.102297 https://doaj.org/article/a98f54b8e30a4d5b907a46891d529799 en eng Elsevier 1569-8432 doi:10.1016/j.jag.2021.102297 https://doaj.org/article/a98f54b8e30a4d5b907a46891d529799 undefined International Journal of Applied Earth Observations and Geoinformation, Vol 98, Iss , Pp 102297- (2021) Arctic sea ice Melt ponds fraction Sea ice extent in September Air temperatures Satellite data geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.1016/j.jag.2021.102297 2023-01-22T17:50:04Z We used deep learning networks to establish a relationship model among MODIS daily surface reflectance product (MOD09GA) and Arctic melt ponds fraction (MPF), ice fraction (IF), and open water fraction (OWF). We applied this model to MODIS 8-day surface reflectance (MOD09A1) to derive Arctic 8-day MPF and SIF (SIF as the sum of IF and MPF). The results demonstrate that our model improved MPF estimation accuracy to an RMSE of 3.7%, compared with previous models. The characteristics of MPF spatiotemporal changes seen in early summer (May-July) indicate that MPF increased first from May-June, reaching its peak around early July, and then decreased. In addition, early summer MPF was significantly negatively correlated with sea ice extent (SIE) in September. We also found that early summer MPF caused sea ice in the Beaufort Sea, the Chukchi Sea, and the East Siberian Sea to move to warm water. Moreover, the movement of sea ice from the marginal sea to the center of the Arctic was shown to be conducive to the reduction of SIE in September. Early summer MPF was also related to Arctic oscillation (AO) during June to July, and significantly positively related to air temperature in the East Siberian and Chukchi Seas in September. As a consequence, these areas produced more open water and absorbed more heat, reducing the extent of sea ice in September, while increasing their air temperatures. The results also show that early summer MPF has a high negative correlation with air temperature in northern China, and MPF can be used to predict air temperature in northern China. These new findings should be investigated in future studies with additional data collection and field observations. Article in Journal/Newspaper Arctic Arctic Ocean Beaufort Sea Chukchi Chukchi Sea East Siberian Sea Sea ice Unknown Arctic Arctic Ocean Chukchi Sea East Siberian Sea ENVELOPE(166.000,166.000,74.000,74.000) International Journal of Applied Earth Observation and Geoinformation 98 102297
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic Arctic sea ice
Melt ponds fraction
Sea ice extent in September
Air temperatures
Satellite data
geo
envir
spellingShingle Arctic sea ice
Melt ponds fraction
Sea ice extent in September
Air temperatures
Satellite data
geo
envir
Jiajun Feng
Yuanzhi Zhang
Qiuming Cheng
Kapo Wong
Yu Li
Jin Yeu Tsou
Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean
topic_facet Arctic sea ice
Melt ponds fraction
Sea ice extent in September
Air temperatures
Satellite data
geo
envir
description We used deep learning networks to establish a relationship model among MODIS daily surface reflectance product (MOD09GA) and Arctic melt ponds fraction (MPF), ice fraction (IF), and open water fraction (OWF). We applied this model to MODIS 8-day surface reflectance (MOD09A1) to derive Arctic 8-day MPF and SIF (SIF as the sum of IF and MPF). The results demonstrate that our model improved MPF estimation accuracy to an RMSE of 3.7%, compared with previous models. The characteristics of MPF spatiotemporal changes seen in early summer (May-July) indicate that MPF increased first from May-June, reaching its peak around early July, and then decreased. In addition, early summer MPF was significantly negatively correlated with sea ice extent (SIE) in September. We also found that early summer MPF caused sea ice in the Beaufort Sea, the Chukchi Sea, and the East Siberian Sea to move to warm water. Moreover, the movement of sea ice from the marginal sea to the center of the Arctic was shown to be conducive to the reduction of SIE in September. Early summer MPF was also related to Arctic oscillation (AO) during June to July, and significantly positively related to air temperature in the East Siberian and Chukchi Seas in September. As a consequence, these areas produced more open water and absorbed more heat, reducing the extent of sea ice in September, while increasing their air temperatures. The results also show that early summer MPF has a high negative correlation with air temperature in northern China, and MPF can be used to predict air temperature in northern China. These new findings should be investigated in future studies with additional data collection and field observations.
format Article in Journal/Newspaper
author Jiajun Feng
Yuanzhi Zhang
Qiuming Cheng
Kapo Wong
Yu Li
Jin Yeu Tsou
author_facet Jiajun Feng
Yuanzhi Zhang
Qiuming Cheng
Kapo Wong
Yu Li
Jin Yeu Tsou
author_sort Jiajun Feng
title Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean
title_short Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean
title_full Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean
title_fullStr Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean
title_full_unstemmed Effect of melt ponds fraction on sea ice anomalies in the Arctic Ocean
title_sort effect of melt ponds fraction on sea ice anomalies in the arctic ocean
publisher Elsevier
publishDate 2021
url https://doi.org/10.1016/j.jag.2021.102297
https://doaj.org/article/a98f54b8e30a4d5b907a46891d529799
long_lat ENVELOPE(166.000,166.000,74.000,74.000)
geographic Arctic
Arctic Ocean
Chukchi Sea
East Siberian Sea
geographic_facet Arctic
Arctic Ocean
Chukchi Sea
East Siberian Sea
genre Arctic
Arctic Ocean
Beaufort Sea
Chukchi
Chukchi Sea
East Siberian Sea
Sea ice
genre_facet Arctic
Arctic Ocean
Beaufort Sea
Chukchi
Chukchi Sea
East Siberian Sea
Sea ice
op_source International Journal of Applied Earth Observations and Geoinformation, Vol 98, Iss , Pp 102297- (2021)
op_relation 1569-8432
doi:10.1016/j.jag.2021.102297
https://doaj.org/article/a98f54b8e30a4d5b907a46891d529799
op_rights undefined
op_doi https://doi.org/10.1016/j.jag.2021.102297
container_title International Journal of Applied Earth Observation and Geoinformation
container_volume 98
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