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...
Published in: | International Journal of Applied Earth Observation and Geoinformation |
---|---|
Main Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Elsevier
2021
|
Subjects: | |
Online Access: | https://doi.org/10.1016/j.jag.2021.102297 https://doaj.org/article/a98f54b8e30a4d5b907a46891d529799 |
id |
fttriple:oai:gotriple.eu:oai:doaj.org/article:a98f54b8e30a4d5b907a46891d529799 |
---|---|
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 |
container_start_page |
102297 |
_version_ |
1766314950033145856 |