Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2020T76

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...

Full description

Bibliographic Details
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.938241
https://doi.org/10.1594/PANGAEA.938241
id ftpangaea:oai:pangaea.de:doi:10.1594/PANGAEA.938241
record_format openpolar
spelling ftpangaea:oai:pangaea.de:doi:10.1594/PANGAEA.938241 2023-05-15T14:27:57+02:00 Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2020T76 Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu MEDIAN LATITUDE: 83.032712 * MEDIAN LONGITUDE: 9.622723 * SOUTH-BOUND LATITUDE: 80.686852 * WEST-BOUND LONGITUDE: -0.765437 * NORTH-BOUND LATITUDE: 84.636609 * EAST-BOUND LONGITUDE: 17.394724 * DATE/TIME START: 2020-04-05T14:01:00 * DATE/TIME END: 2020-07-18T14:31:00 2021-11-16 text/tab-separated-values, 204 data points https://doi.pangaea.de/10.1594/PANGAEA.938241 https://doi.org/10.1594/PANGAEA.938241 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, Mushen; Wang, Hangzhou; Regnery, Julia; Krumpen, Thomas; Haapala, Jari; Rabe, Benjamin; Perovich, Donald K; Nicolaus, Marcel (submitted): Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic Transpolar Drift during 2019/20. 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.938241 https://doi.org/10.1594/PANGAEA.938241 CC-BY-4.0: Creative Commons Attribution 4.0 International Access constraints: unrestricted info:eu-repo/semantics/openAccess CC-BY 2020T76 PRIC_10_04 Arctic Ocean DATE/TIME ice thickness LATITUDE LONGITUDE Manual identification method mass balance Mosaic MOSAiC20192020 Multidisciplinary drifting Observatory for the Study of Arctic Climate Polarstern PS122/3 PS122/3_28-94 SAMS Ice Mass Balance buoy Sea ice SIMBA snow depth Snow thickness Dataset 2021 ftpangaea https://doi.org/10.1594/PANGAEA.938241 https://doi.org/10.1594/PANGAEA.938244 https://doi.org/10.1175/jtech-d-13-00058.1 2022-12-22T21:54:36Z 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(-0.765437,17.394724,84.636609,80.686852)
institution Open Polar
collection PANGAEA - Data Publisher for Earth & Environmental Science
op_collection_id ftpangaea
language English
topic 2020T76
PRIC_10_04
Arctic Ocean
DATE/TIME
ice thickness
LATITUDE
LONGITUDE
Manual identification method
mass balance
Mosaic
MOSAiC20192020
Multidisciplinary drifting Observatory for the Study of Arctic Climate
Polarstern
PS122/3
PS122/3_28-94
SAMS Ice Mass Balance buoy
Sea ice
SIMBA
snow depth
Snow thickness
spellingShingle 2020T76
PRIC_10_04
Arctic Ocean
DATE/TIME
ice thickness
LATITUDE
LONGITUDE
Manual identification method
mass balance
Mosaic
MOSAiC20192020
Multidisciplinary drifting Observatory for the Study of Arctic Climate
Polarstern
PS122/3
PS122/3_28-94
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 2020T76
topic_facet 2020T76
PRIC_10_04
Arctic Ocean
DATE/TIME
ice thickness
LATITUDE
LONGITUDE
Manual identification method
mass balance
Mosaic
MOSAiC20192020
Multidisciplinary drifting Observatory for the Study of Arctic Climate
Polarstern
PS122/3
PS122/3_28-94
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 2020T76
title_short Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2020T76
title_full Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2020T76
title_fullStr Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2020T76
title_full_unstemmed Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2020T76
title_sort snow depth and sea ice thickness derived from the measurements of simba buoy 2020t76
publisher PANGAEA
publishDate 2021
url https://doi.pangaea.de/10.1594/PANGAEA.938241
https://doi.org/10.1594/PANGAEA.938241
op_coverage MEDIAN LATITUDE: 83.032712 * MEDIAN LONGITUDE: 9.622723 * SOUTH-BOUND LATITUDE: 80.686852 * WEST-BOUND LONGITUDE: -0.765437 * NORTH-BOUND LATITUDE: 84.636609 * EAST-BOUND LONGITUDE: 17.394724 * DATE/TIME START: 2020-04-05T14:01:00 * DATE/TIME END: 2020-07-18T14:31:00
long_lat ENVELOPE(-0.765437,17.394724,84.636609,80.686852)
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, Mushen; Wang, Hangzhou; Regnery, Julia; Krumpen, Thomas; Haapala, Jari; Rabe, Benjamin; Perovich, Donald K; Nicolaus, Marcel (submitted): Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic Transpolar Drift during 2019/20.
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.938241
https://doi.org/10.1594/PANGAEA.938241
op_rights CC-BY-4.0: Creative Commons Attribution 4.0 International
Access constraints: unrestricted
info:eu-repo/semantics/openAccess
op_rightsnorm CC-BY
op_doi https://doi.org/10.1594/PANGAEA.938241
https://doi.org/10.1594/PANGAEA.938244
https://doi.org/10.1175/jtech-d-13-00058.1
_version_ 1766302052637474816