Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T58
The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environment temperature SIMBA-ET and temperature change around the thermistors after a weak heating applied to each sensor (SIMBA-HT). Totally, there were 22 SIMBAs deployed in the A...
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ftpangaea:oai:pangaea.de:doi:10.1594/PANGAEA.938227 2024-09-30T14:28:13+00:00 Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T58 Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu MEDIAN LATITUDE: 85.432658 * MEDIAN LONGITUDE: 62.871909 * SOUTH-BOUND LATITUDE: 81.027367 * WEST-BOUND LONGITUDE: -0.717505 * NORTH-BOUND LATITUDE: 88.803913 * EAST-BOUND LONGITUDE: 139.519755 * DATE/TIME START: 2019-10-08T19:01:00 * DATE/TIME END: 2020-07-19T19:01:00 2021 text/tab-separated-values, 572 data points https://doi.pangaea.de/10.1594/PANGAEA.938227 https://doi.org/10.1594/PANGAEA.938227 en eng PANGAEA https://doi.org/10.1594/PANGAEA.938244 Lei, Ruibo; Cheng, Bin; Hoppmann, Mario; Zhang, Fanyi; Zuo, Guangyu; Hutchings, Jennifer K; Lin, Long; Lan, Musheng; Wang, Hangzhou; Regnery, Julia; Krumpen, Thomas; Haapala, Jari; Rabe, Benjamin; Perovich, Donald K; Nicolaus, Marcel (2022): Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic transpolar drift during 2019–2020. Elementa - Science of the Anthropocene, 10(1), 000089, https://doi.org/10.1525/elementa.2021.000089 Jackson, Keith; Wilkinson, Jeremy; Maksym, Ted; Meldrum, David T; Beckers, Justin; Haas, Christian; Mackenzie, David (2013): A Novel and Low-Cost Sea Ice Mass Balance Buoy. Journal of Atmospheric and Oceanic Technology, 30(11), 2676-2688, https://doi.org/10.1175/jtech-d-13-00058.1 https://doi.pangaea.de/10.1594/PANGAEA.938227 https://doi.org/10.1594/PANGAEA.938227 CC-BY-4.0: Creative Commons Attribution 4.0 International Access constraints: unrestricted info:eu-repo/semantics/openAccess 2019T58 AF-MOSAiC-1 AF-MOSAiC-1_115 Akademik Fedorov Akademik Tryoshnikov Arctic Ocean AT-MOSAiC-1 AT-MOSAiC-1_2 DATE/TIME Ice thickness LATITUDE LONGITUDE Manual identification method mass balance MOSAiC MOSAiC20192020 AF122/1 MOSAiC expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate North Greenland Sea PS122/1_1-177 FMI_05_09 SAMS Ice Mass Balance buoy Sea ice SIMBA snow depth Snow thickness dataset 2021 ftpangaea https://doi.org/10.1594/PANGAEA.93822710.1594/PANGAEA.93824410.1525/elementa.2021.00008910.1175/jtech-d-13-00058.1 2024-09-11T00:15:19Z The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environment temperature SIMBA-ET and temperature change around the thermistors after a weak heating applied to each sensor (SIMBA-HT). Totally, there were 22 SIMBAs deployed in the Arcitic Ocean over the Distributed Network (DN) and the Central Observatory during the Legs 1a, 1 and 3 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign. The SIMBA thermistor chain is 5.12 m long, and equipped with 256 thermistors (Maxim Integrated DS28EA00) at 0.02 m spacing. Based on a manual identification method, the SIMBA-ET and SIMBA-HT were processed to yield snow depth and ice thickness. Here, we combined the two optimal methods (the ET vertical gradient and HT rise ratio) to reduce the uncertainty. To keep the consistency, we use the snow or ice surface, consequentially the snow depth, determined by the ET vertical gradient. The formations of snow ice and superposed ice are not considered in this data set. That is to say, the value of snow depth includes the layers of snow ice at two sites (2019T56 and 2019T72). The superposed ice was generally negligible. We used the HT rise ratio to determine the ice-water interface, consequentially the ice thickness. Overall, the measurement accuracy was 0.02 m for both the snow depth and ice thickness. After the snow cover melted over, the negative values for the snow depth indicate the onset of ice surface melt. Dataset Arctic Arctic Arctic Ocean Greenland Greenland Sea North Greenland Sea ice PANGAEA - Data Publisher for Earth & Environmental Science Arctic Arctic Ocean Greenland ENVELOPE(-0.717505,139.519755,88.803913,81.027367) |
institution |
Open Polar |
collection |
PANGAEA - Data Publisher for Earth & Environmental Science |
op_collection_id |
ftpangaea |
language |
English |
topic |
2019T58 AF-MOSAiC-1 AF-MOSAiC-1_115 Akademik Fedorov Akademik Tryoshnikov Arctic Ocean AT-MOSAiC-1 AT-MOSAiC-1_2 DATE/TIME Ice thickness LATITUDE LONGITUDE Manual identification method mass balance MOSAiC MOSAiC20192020 AF122/1 MOSAiC expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate North Greenland Sea PS122/1_1-177 FMI_05_09 SAMS Ice Mass Balance buoy Sea ice SIMBA snow depth Snow thickness |
spellingShingle |
2019T58 AF-MOSAiC-1 AF-MOSAiC-1_115 Akademik Fedorov Akademik Tryoshnikov Arctic Ocean AT-MOSAiC-1 AT-MOSAiC-1_2 DATE/TIME Ice thickness LATITUDE LONGITUDE Manual identification method mass balance MOSAiC MOSAiC20192020 AF122/1 MOSAiC expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate North Greenland Sea PS122/1_1-177 FMI_05_09 SAMS Ice Mass Balance buoy Sea ice SIMBA snow depth Snow thickness Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T58 |
topic_facet |
2019T58 AF-MOSAiC-1 AF-MOSAiC-1_115 Akademik Fedorov Akademik Tryoshnikov Arctic Ocean AT-MOSAiC-1 AT-MOSAiC-1_2 DATE/TIME Ice thickness LATITUDE LONGITUDE Manual identification method mass balance MOSAiC MOSAiC20192020 AF122/1 MOSAiC expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate North Greenland Sea PS122/1_1-177 FMI_05_09 SAMS Ice Mass Balance buoy Sea ice SIMBA snow depth Snow thickness |
description |
The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environment temperature SIMBA-ET and temperature change around the thermistors after a weak heating applied to each sensor (SIMBA-HT). Totally, there were 22 SIMBAs deployed in the Arcitic Ocean over the Distributed Network (DN) and the Central Observatory during the Legs 1a, 1 and 3 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign. The SIMBA thermistor chain is 5.12 m long, and equipped with 256 thermistors (Maxim Integrated DS28EA00) at 0.02 m spacing. Based on a manual identification method, the SIMBA-ET and SIMBA-HT were processed to yield snow depth and ice thickness. Here, we combined the two optimal methods (the ET vertical gradient and HT rise ratio) to reduce the uncertainty. To keep the consistency, we use the snow or ice surface, consequentially the snow depth, determined by the ET vertical gradient. The formations of snow ice and superposed ice are not considered in this data set. That is to say, the value of snow depth includes the layers of snow ice at two sites (2019T56 and 2019T72). The superposed ice was generally negligible. We used the HT rise ratio to determine the ice-water interface, consequentially the ice thickness. Overall, the measurement accuracy was 0.02 m for both the snow depth and ice thickness. After the snow cover melted over, the negative values for the snow depth indicate the onset of ice surface melt. |
format |
Dataset |
author |
Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu |
author_facet |
Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu |
author_sort |
Lei, Ruibo |
title |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T58 |
title_short |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T58 |
title_full |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T58 |
title_fullStr |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T58 |
title_full_unstemmed |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T58 |
title_sort |
snow depth and sea ice thickness derived from the measurements of simba buoy 2019t58 |
publisher |
PANGAEA |
publishDate |
2021 |
url |
https://doi.pangaea.de/10.1594/PANGAEA.938227 https://doi.org/10.1594/PANGAEA.938227 |
op_coverage |
MEDIAN LATITUDE: 85.432658 * MEDIAN LONGITUDE: 62.871909 * SOUTH-BOUND LATITUDE: 81.027367 * WEST-BOUND LONGITUDE: -0.717505 * NORTH-BOUND LATITUDE: 88.803913 * EAST-BOUND LONGITUDE: 139.519755 * DATE/TIME START: 2019-10-08T19:01:00 * DATE/TIME END: 2020-07-19T19:01:00 |
long_lat |
ENVELOPE(-0.717505,139.519755,88.803913,81.027367) |
geographic |
Arctic Arctic Ocean Greenland |
geographic_facet |
Arctic Arctic Ocean Greenland |
genre |
Arctic Arctic Arctic Ocean Greenland Greenland Sea North Greenland Sea ice |
genre_facet |
Arctic Arctic Arctic Ocean Greenland Greenland Sea North Greenland Sea ice |
op_relation |
https://doi.org/10.1594/PANGAEA.938244 Lei, Ruibo; Cheng, Bin; Hoppmann, Mario; Zhang, Fanyi; Zuo, Guangyu; Hutchings, Jennifer K; Lin, Long; Lan, Musheng; Wang, Hangzhou; Regnery, Julia; Krumpen, Thomas; Haapala, Jari; Rabe, Benjamin; Perovich, Donald K; Nicolaus, Marcel (2022): Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic transpolar drift during 2019–2020. Elementa - Science of the Anthropocene, 10(1), 000089, https://doi.org/10.1525/elementa.2021.000089 Jackson, Keith; Wilkinson, Jeremy; Maksym, Ted; Meldrum, David T; Beckers, Justin; Haas, Christian; Mackenzie, David (2013): A Novel and Low-Cost Sea Ice Mass Balance Buoy. Journal of Atmospheric and Oceanic Technology, 30(11), 2676-2688, https://doi.org/10.1175/jtech-d-13-00058.1 https://doi.pangaea.de/10.1594/PANGAEA.938227 https://doi.org/10.1594/PANGAEA.938227 |
op_rights |
CC-BY-4.0: Creative Commons Attribution 4.0 International Access constraints: unrestricted info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1594/PANGAEA.93822710.1594/PANGAEA.93824410.1525/elementa.2021.00008910.1175/jtech-d-13-00058.1 |
_version_ |
1811633997120274432 |