Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Abstract. Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release dataset is typically one month, due to the time required to determine precise satellite orbits. We use a new fast delivery CryoSat-2 dataset based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea ice thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with an average thickness difference of 5 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast delivery product also provides measurements of Arctic sea ice thickness within three days of acquisition by the satellite, and a measurement is delivered, on average, within 10, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.

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Investigating the variability of the Arctic sea ice thickness in response to a stochastic thermodynamic atmospheric forcing

Climate Dynamics ◽

10.1007/s003820000096 ◽

2001 ◽

Vol 17 (2-3) ◽

pp. 107-125 ◽

Cited By ~ 14

Author(s):

B. L'Hévéder ◽

M.-N. Houssais

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Atmospheric Forcing ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

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Estimating Arctic Sea Ice Thickness with CryoSat-2 Altimetry Data Using the Least Squares Adjustment Method

Sensors ◽

10.3390/s20247011 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7011

Author(s):

Feng Xiao ◽

Fei Li ◽

Shengkai Zhang ◽

Jiaxing Li ◽

Tong Geng ◽

...

Keyword(s):

Sea Ice ◽

Least Squares ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Altimetry Data ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Least Squares Adjustment ◽

Arctic Sea Ice Thickness

Satellite altimeters can be used to derive long-term and large-scale sea ice thickness changes. Sea ice thickness retrieval is based on measurements of freeboard, and the conversion of freeboard to thickness requires knowledge of the snow depth and snow, sea ice, and sea water densities. However, these parameters are difficult to be observed concurrently with altimeter measurements. The uncertainties in these parameters inevitably cause uncertainties in sea ice thickness estimations. This paper introduces a new method based on least squares adjustment (LSA) to estimate Arctic sea ice thickness with CryoSat-2 measurements. A model between the sea ice freeboard and thickness is established within a 5 km × 5 km grid, and the model coefficients and sea ice thickness are calculated using the LSA method. Based on the newly developed method, we are able to derive estimates of the Arctic sea ice thickness for 2010 through 2019 using CryoSat-2 altimetry data. Spatial and temporal variations of the Arctic sea ice thickness are analyzed, and comparisons between sea ice thickness estimates using the LSA method and three CryoSat-2 sea ice thickness products (Alfred Wegener Institute (AWI), Centre for Polar Observation and Modelling (CPOM), and NASA Goddard Space Flight Centre (GSFC)) are performed for the 2018–2019 Arctic sea ice growth season. The overall differences of sea ice thickness estimated in this study between AWI, CPOM, and GSFC are 0.025 ± 0.640 m, 0.143 ± 0.640 m, and −0.274 ± 0.628 m, respectively. Large differences between the LSA and three products tend to appear in areas covered with thin ice due to the limited accuracy of CryoSat-2 over thin ice. Spatiotemporally coincident Operation IceBridge (OIB) thickness values are also used for validation. Good agreement with a difference of 0.065 ± 0.187 m is found between our estimates and the OIB results.

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Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products

International Journal of Remote Sensing ◽

10.1080/01431161.2019.1637961 ◽

2019 ◽

Vol 41 (1) ◽

pp. 152-170 ◽

Cited By ~ 2

Author(s):

Mengmeng Li ◽

Chang-Qing Ke ◽

Hongjie Xie ◽

Xin Miao ◽

Xiaoyi Shen ◽

...

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

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Multidecadal Arctic sea ice thickness and volume derived from ice age

The Cryosphere ◽

10.5194/tc-14-1325-2020 ◽

2020 ◽

Vol 14 (4) ◽

pp. 1325-1345 ◽

Cited By ~ 3

Author(s):

Yinghui Liu ◽

Jeffrey R. Key ◽

Xuanji Wang ◽

Mark Tschudi

Keyword(s):

Sea Ice ◽

Global Climate ◽

Arctic Sea Ice ◽

Ice Age ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Volume ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

Abstract. Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.

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Winter Arctic Sea Ice Thickness From ICESat‐2 Freeboards

Journal of Geophysical Research Oceans ◽

10.1029/2019jc015764 ◽

2020 ◽

Vol 125 (5) ◽

Cited By ~ 3

Author(s):

Alek A. Petty ◽

Nathan T. Kurtz ◽

Ron Kwok ◽

Thorsten Markus ◽

Thomas A. Neumann

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

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Coincident multiscale estimates of Arctic sea ice thickness

Eos ◽

10.1029/2012eo060001 ◽

2012 ◽

Vol 93 (6) ◽

pp. 57-58 ◽

Cited By ~ 8

Author(s):

Joan Gardner ◽

Jackie Richter-Menge ◽

Sinead Farrell ◽

John Brozena

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

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On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses

The Cryosphere ◽

10.5194/tc-13-521-2019 ◽

2019 ◽

Vol 13 (2) ◽

pp. 521-543 ◽

Cited By ~ 3

Author(s):

Leandro Ponsoni ◽

François Massonnet ◽

Thierry Fichefet ◽

Matthieu Chevallier ◽

David Docquier

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Length Scales ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration ◽

Arctic Sea Ice Thickness

Abstract. The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

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