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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Main Authors: Lei, Ruibo, Cheng, Bin, Hoppmann, Mario, Zuo, Guangyu
Format: Dataset
Language:English
Published: PANGAEA 2021
Subjects:
Online Access:https://doi.pangaea.de/10.1594/PANGAEA.938228
https://doi.org/10.1594/PANGAEA.938228
id ftpangaea:oai:pangaea.de:doi:10.1594/PANGAEA.938228
record_format openpolar
spelling 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
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