Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T62
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.938228 2024-10-06T13:45:01+00:00 Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T62 Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu MEDIAN LATITUDE: 85.667929 * MEDIAN LONGITUDE: 59.918958 * SOUTH-BOUND LATITUDE: 81.671278 * WEST-BOUND LONGITUDE: 5.871424 * NORTH-BOUND LATITUDE: 88.582866 * EAST-BOUND LONGITUDE: 124.246748 * DATE/TIME START: 2019-10-30T08:30:00 * DATE/TIME END: 2020-07-05T20:30:00 2021 text/tab-separated-values, 482 data points https://doi.pangaea.de/10.1594/PANGAEA.938228 https://doi.org/10.1594/PANGAEA.938228 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.938228 https://doi.org/10.1594/PANGAEA.938228 CC-BY-4.0: Creative Commons Attribution 4.0 International Access constraints: unrestricted info:eu-repo/semantics/openAccess 2019T62 PRIC_09_01 Arctic Ocean DATE/TIME Ice thickness LATITUDE LONGITUDE Manual identification method mass balance MOSAiC MOSAiC20192020 MOSAiC expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern PS122/1 PS122/1_1-125 PS122/4 PS122/4_43-156 SAMS Ice Mass Balance buoy Sea ice SIMBA snow depth Snow thickness dataset 2021 ftpangaea https://doi.org/10.1594/PANGAEA.93822810.1594/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 Sea ice PANGAEA - Data Publisher for Earth & Environmental Science Arctic Arctic Ocean ENVELOPE(5.871424,124.246748,88.582866,81.671278) |
institution |
Open Polar |
collection |
PANGAEA - Data Publisher for Earth & Environmental Science |
op_collection_id |
ftpangaea |
language |
English |
topic |
2019T62 PRIC_09_01 Arctic Ocean DATE/TIME Ice thickness LATITUDE LONGITUDE Manual identification method mass balance MOSAiC MOSAiC20192020 MOSAiC expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern PS122/1 PS122/1_1-125 PS122/4 PS122/4_43-156 SAMS Ice Mass Balance buoy Sea ice SIMBA snow depth Snow thickness |
spellingShingle |
2019T62 PRIC_09_01 Arctic Ocean DATE/TIME Ice thickness LATITUDE LONGITUDE Manual identification method mass balance MOSAiC MOSAiC20192020 MOSAiC expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern PS122/1 PS122/1_1-125 PS122/4 PS122/4_43-156 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 2019T62 |
topic_facet |
2019T62 PRIC_09_01 Arctic Ocean DATE/TIME Ice thickness LATITUDE LONGITUDE Manual identification method mass balance MOSAiC MOSAiC20192020 MOSAiC expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern PS122/1 PS122/1_1-125 PS122/4 PS122/4_43-156 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 2019T62 |
title_short |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T62 |
title_full |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T62 |
title_fullStr |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T62 |
title_full_unstemmed |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T62 |
title_sort |
snow depth and sea ice thickness derived from the measurements of simba buoy 2019t62 |
publisher |
PANGAEA |
publishDate |
2021 |
url |
https://doi.pangaea.de/10.1594/PANGAEA.938228 https://doi.org/10.1594/PANGAEA.938228 |
op_coverage |
MEDIAN LATITUDE: 85.667929 * MEDIAN LONGITUDE: 59.918958 * SOUTH-BOUND LATITUDE: 81.671278 * WEST-BOUND LONGITUDE: 5.871424 * NORTH-BOUND LATITUDE: 88.582866 * EAST-BOUND LONGITUDE: 124.246748 * DATE/TIME START: 2019-10-30T08:30:00 * DATE/TIME END: 2020-07-05T20:30:00 |
long_lat |
ENVELOPE(5.871424,124.246748,88.582866,81.671278) |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Arctic Ocean Sea ice |
genre_facet |
Arctic Arctic Arctic Ocean 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.938228 https://doi.org/10.1594/PANGAEA.938228 |
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.93822810.1594/PANGAEA.93824410.1525/elementa.2021.00008910.1175/jtech-d-13-00058.1 |
_version_ |
1812173439618777088 |