Seasonal evolution of Ku- and Ka-band backscattering horizon over snow on first-year and multiyear sea ice
第一年和多年海冰雪上 Ku 和 Ka 波段后向散射地平线的季节演变
基本信息
- 批准号:NE/S002510/1
- 负责人:
- 金额:$ 25.79万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2019
- 资助国家:英国
- 起止时间:2019 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Arctic sea ice area has been mapped for nearly four decades using the long-term data record provided by successive passive microwave satellite missions; showing an accelerated pace of ice loss since 1979. Less is known about how much the ice has also thinned, in part because of the lack of a similarly long-term and consistent data record on sea ice thickness. Radar altimeters, such as the one flown on the European Space Agency (ESA)'s CryoSat-2 (CS2) since April 2010, and the SARAL/AltiKa satellite, launched in February 2013 as part of a joint mission by the Centre National d'Etudes Spatiales (CNES) and the Indian Space Research Organization (ISRO), are now providing pan-Arctic (or up to 81.5N for AltiKa) thickness observations. However, one key uncertainty in using these data is how far the radar actually penetrates into the overlying snow cover. The general assumption has been that the radar return is from the snow-ice interface at Ku-band (CS2) frequencies, and from the snow-air interface at Ka-band (AltiKa) frequencies. Using this information together with assumptions on the depth of the overlying snow pack and its density, scientists can then convert the radar returns into total ice thickness assuming hydrostatic equilibrium. However, field evidence has put this general assumption into question, even for a homogeneous snowpack. A further complication is the lack of knowledge on how deep the snow pack is and its density. Typically, snow depth and density information based on a climatology constructed over thick multiyear ice in the 1980s have been used. However, as the total area in the sea ice cover has declined, there is now a larger proportion of first-year sea ice in the Arctic Basin. Snow over first-year ice tends to be more saline than over multiyear ice, and as such it has the potential for a significant impact on the radar returns. In addition, autumn and winter freeze-up has been delayed by several weeks to months in certain regions of the Arctic, shortening the duration for accumulation of snow. Given these current uncertainties, it is difficult to accurately assess how sea ice thickness is changing from year to year and over the long-term.Because sea ice is an important indicator of climate change, plays a fundamental role in the Arctic energy and freshwater balance, and is a key component of the marine ecosystem, it is essential that we improve the accuracy of thickness retrievals from radar altimetry. This project aims to do just that by making ground-based observations of the radar penetration depth over a full annual cycle at both Ku- and Ka-band frequencies, from autumn freeze-up, through winter snow metamorphism and summer melt. This information, together with detailed snow pack characteristics, will allow us to assess how changes in snow accumulation, snow morphology and snow salinity impact Ku- and Ka-band penetration factors. The MOSAiC drifting station provides a unique opportunity, possibly the only opportunity, to obtain a benchmark dataset that involves coherent field, airborne and satellite data. Analysis of this information will enable scientists to better characterize how the physical properties of the snow pack (above different ice types) influence the penetration of Ka and Ku band radar. Importantly, we will be able to evaluate the seasonal evolution of the snow pack over first-year (sea ice greater than a few cm) and multiyear sea ice. MOSAiC additionally provides the opportunity for year-round observations of snow depth and density that will allow for assessment of the validity of climatological assumptions typically employed in thickness retrievals from radar altimetry and provide data for validation of snow depth products. These activities are essential in order to improve sea ice thickness retrievals from radar altimetry over the many ice and snow conditions found in the Arctic.
近四十年来,利用连续的被动微波卫星任务提供的长期数据记录,北极海冰面积已被绘制成地图;显示自1979年以来,冰损失的速度加快。人们对海冰变薄的程度知之甚少,部分原因是缺乏类似的长期和一致的海冰厚度数据记录。雷达高度计,如2010年4月以来在欧洲空间局(欧空局)CryoSat-2(CS2)上飞行的雷达高度计,以及2013年2月作为法国国家空间研究中心(法国空研中心)和印度空间研究组织(印度空研组织)联合使命的一部分发射的SARAL/AltiKa卫星,目前正在提供泛北极(或AltiKa高达81.5 N)厚度观测。然而,使用这些数据的一个关键不确定性是雷达实际穿透覆盖的积雪的距离。一般的假设是,雷达回波在Ku波段(CS2)频率来自冰雪界面,在Ka波段(AltiKa)频率来自雪-空气界面。利用这些信息,再加上对上覆积雪深度及其密度的假设,科学家们就可以在假设流体静力平衡的情况下,将雷达回波转换为总冰厚。然而,现场证据使这一一般假设受到质疑,即使是对于均匀的积雪。另一个复杂的问题是缺乏关于积雪有多深及其密度的知识。通常情况下,雪的深度和密度的信息的基础上建立在厚厚的多年冰在20世纪80年代的气候。然而,随着海冰覆盖总面积的减少,北极盆地第一年海冰的比例现在有所增加。第一年冰上的雪往往比多年冰上的雪更咸,因此它有可能对雷达回波产生重大影响。此外,在北极某些地区,秋季和冬季的封冻推迟了几周到几个月,缩短了积雪的持续时间。鉴于目前的这些不确定性,很难准确评估海冰厚度每年和长期的变化。由于海冰是气候变化的重要指标,在北极能源和淡水平衡中起着重要作用,是海洋生态系统的关键组成部分,因此我们必须提高雷达测高厚度反演的准确性。该项目的目的正是通过在Ku和Ka波段频率上对雷达穿透深度进行地面观测,从秋季冻结到冬季积雪变质和夏季融化,整个年度周期都是如此。这些信息,连同详细的积雪特征,将使我们能够评估雪的积累,雪的形态和雪的盐度的变化如何影响Ku和Ka波段的穿透系数。MOSAiC漂移站提供了一个独特的机会,可能是唯一的机会,以获得一个基准数据集,包括连贯的现场,空中和卫星数据。对这些信息的分析将使科学家能够更好地描述积雪的物理特性(在不同类型的冰之上)如何影响Ka和Ku波段雷达的穿透力。重要的是,我们将能够评估积雪在第一年(海冰大于几厘米)和多年海冰的季节性演变。MOSAiC还提供了全年观测积雪深度和密度的机会,这将有助于评估雷达测高厚度反演中通常采用的气候学假设的有效性,并为验证积雪深度产品提供数据。为了改善北极地区许多冰雪条件下雷达测高的海冰厚度检索,这些活动至关重要。
项目成果
期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Platelet Ice Under Arctic Pack Ice in Winter
冬季北极浮冰下的血小板冰
- DOI:10.1029/2020gl088898
- 发表时间:2020
- 期刊:
- 影响因子:5.2
- 作者:Katlein, Christian;Mohrholz, Volker;Sheikin, Igor;Itkin, Polona;Divine, Dmitry V.;Stroeve, Julienne;Jutila, Arttu;Krampe, Daniela;Shimanchuk, Egor;Raphael, Ian
- 通讯作者:Raphael, Ian
CryoSat-2 Significant Wave Height in Polar Oceans Derived Using a Semi-Analytical Model of Synthetic Aperture Radar 2011-2019
- DOI:10.3390/rs13204166
- 发表时间:2021-10-01
- 期刊:
- 影响因子:5
- 作者:Heorton, Harold;Tsamados, Michel;Landy, Jack
- 通讯作者:Landy, Jack
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas
北冰洋边缘海海冰厚度下降速度更快且变化更大
- DOI:10.5194/tc-2020-282
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Mallett R
- 通讯作者:Mallett R
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover
考虑动态积雪时,北冰洋边缘海海冰厚度的下降速度更快,变化更大
- DOI:10.5194/tc-15-2429-2021
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Mallett R
- 通讯作者:Mallett R
A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations
根据 CryoSat-2 和 Sentinel-3 卫星观测数据,采用贝叶斯方法对每日泛北极海冰干舷进行估计
- DOI:10.5194/egusphere-egu21-11462
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Gregory W
- 通讯作者:Gregory W
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Julienne Stroeve其他文献
Sea level trends along the South African coast from 1993 to 2022 using XTRACK altimetry, tide gauges, and GNSS measurements
使用 XTRACK 测高仪、验潮仪和全球导航卫星系统测量的 1993 年至 2022 年南非沿海海平面趋势
- DOI:
10.1038/s41598-025-89258-9 - 发表时间:
2025-02-10 - 期刊:
- 影响因子:3.900
- 作者:
Franck Eitel Kemgang Ghomsi;Muharrem Hilmi Erkoç;Roshin P. Raj;Atinç Pirti;Antonio Bonaduce;Babatunde J. Abiodun;Julienne Stroeve - 通讯作者:
Julienne Stroeve
Mapping of sea ice in 1975 and 1976 using the NIMBUS-6 Scanning Microwave Spectrometer (SCAMS)
1975年和1976年使用雨云 - 6号扫描微波光谱仪(SCAMS)对海冰进行的测绘
- DOI:
10.1016/j.rse.2025.114815 - 发表时间:
2025-10-01 - 期刊:
- 影响因子:11.400
- 作者:
Wiebke Margitta Kolbe;Rasmus T. Tonboe;Julienne Stroeve - 通讯作者:
Julienne Stroeve
Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning
利用无监督和有监督机器学习相结合的方法提升ICESat-2卫星数据的海冰分类能力
- DOI:
10.1016/j.rse.2025.114607 - 发表时间:
2025-03-01 - 期刊:
- 影响因子:11.400
- 作者:
Wenxuan Liu;Michel Tsamados;Alek Petty;Taoyong Jin;Weibin Chen;Julienne Stroeve - 通讯作者:
Julienne Stroeve
Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977
使用 1972 年至 1977 年 NASA NIMBUS 5 电扫描微波辐射计数据绘制海冰浓度图
- DOI:
10.5194/essd-16-1247-2024 - 发表时间:
2024 - 期刊:
- 影响因子:11.4
- 作者:
Wiebke M. Kolbe;R. Tonboe;Julienne Stroeve - 通讯作者:
Julienne Stroeve
Long-term prediction of Arctic sea ice concentrations using deep learning: Effects of surface temperature, radiation, and wind conditions
- DOI:
10.1016/j.rse.2024.114568 - 发表时间:
2025-03-01 - 期刊:
- 影响因子:
- 作者:
Young Jun Kim;Hyun-cheol Kim;Daehyeon Han;Julienne Stroeve;Jungho Im - 通讯作者:
Jungho Im
Julienne Stroeve的其他文献
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{{ truncateString('Julienne Stroeve', 18)}}的其他基金
EAGER: Collaborative Research: Autonomous retrieval of impurity-laden Arctic sea ice and hyperspectral surface properties through innovative robotics
EAGER:合作研究:通过创新机器人技术自主检索充满杂质的北极海冰和高光谱表面特性
- 批准号:
2218835 - 财政年份:2022
- 资助金额:
$ 25.79万 - 项目类别:
Standard Grant
DEFIANT: Drivers and Effects of Fluctuations in sea Ice in the ANTarctic
挑战:南极海冰波动的驱动因素和影响
- 批准号:
NE/W004712/1 - 财政年份:2021
- 资助金额:
$ 25.79万 - 项目类别:
Research Grant
NSFGEO-NERC Advancing Predictability of Sea Ice: Phase 2 of the Sea Ice Prediction Network (SIPN2)
NSFGEO-NERC 提高海冰的可预测性:海冰预测网络 (SIPN2) 第二阶段
- 批准号:
NE/R017123/1 - 财政年份:2018
- 资助金额:
$ 25.79万 - 项目类别:
Research Grant
Collaborative Research: Phytoplankton Phenology in the Antarctic: Drivers, Patterns, and Implications for the Adelie Penguin
合作研究:南极浮游植物物候学:对阿德利企鹅的驱动因素、模式和影响
- 批准号:
1341547 - 财政年份:2014
- 资助金额:
$ 25.79万 - 项目类别:
Continuing Grant
Collaborative Research: An innovative network to improve sea ice prediction in a changing Arctic
合作研究:改善北极变化中海冰预测的创新网络
- 批准号:
1304246 - 财政年份:2013
- 资助金额:
$ 25.79万 - 项目类别:
Standard Grant
Collaborative Research: Assessing the Impact of Arctic Sea Ice Variability on the Greenland Ice Sheet Surface Mass and Energy Balance
合作研究:评估北极海冰变化对格陵兰冰盖表面质量和能量平衡的影响
- 批准号:
1304807 - 财政年份:2013
- 资助金额:
$ 25.79万 - 项目类别:
Standard Grant
CDI-Type I: Data Rods: Enabling Time-Series Analysis of Massive Multi-Modality Cryospheric Data
CDI-I 型:数据棒:实现大规模多模态冰冻圈数据的时间序列分析
- 批准号:
0941442 - 财政年份:2009
- 资助金额:
$ 25.79万 - 项目类别:
Standard Grant
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