Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data

Abstract. Sea-ice thickness on a global scale is derived from different satellite sensors using independent retrieval methods. Due to the sensor and orbit characteristics, such satellite retrievals differ in spatial and temporal resolution as well as in the sensitivity to certain sea-ice types and thickness ranges. Satellite altimeters, such as CryoSat-2 (CS2), sense the height of the ice surface above the sea level, which can be converted into sea-ice thickness. Relative uncertainties associated with this method are large over thin ice regimes. Another retrieval method is based on the evaluation of surface brightness temperature (TB) in L-band microwave frequencies (1.4 GHz) with a thickness-dependent emission model, as measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. While the radiometer-based method looses sensitivity for thick sea ice (> 1 m), relative uncertainties over thin ice are significantly smaller than for the altimetry-based retrievals. In addition, the SMOS product provides global sea-ice coverage on a daily basis unlike the altimeter data. This study presents the first merged product of complementary weekly Arctic sea-ice thickness data records from the CS2 altimeter and SMOS radiometer. We use two merging approaches: a weighted mean (WM) and an optimal interpolation (OI) scheme. While the weighted mean leaves gaps between CS2 orbits, OI is used to produce weekly Arctic-wide sea-ice thickness fields. The benefit of the data merging is shown by a comparison with airborne electromagnetic (AEM) induction sounding measurements. When compared to airborne thickness data in the Barents Sea, the merged product has a root mean square deviation (RMSD) of about 0.7 m less than the CS2 product and therefore demonstrates the capability to enhance the CS2 product in thin ice regimes. However, in mixed first-year (FYI) and multiyear (MYI) ice regimes as in the Beaufort Sea, the CS2 retrieval shows the lowest bias.

Download Full-text

The relation between sea ice thickness and freeboard in the Arctic

The Cryosphere Discussions ◽

10.5194/tcd-4-641-2010 ◽

2010 ◽

Vol 4 (2) ◽

pp. 641-661 ◽

Cited By ~ 10

Author(s):

V. Alexandrov ◽

S. Sandven ◽

J. Wahlin ◽

O. M. Johannessen

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Empirical Relation ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Radar Altimeter ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

Abstract. Retrieval of Arctic sea ice thickness from radar altimeter freeboard data, to be provided by CryoSat-2, requires observational data to verify the relation between the two variables. In this study in-situ ice and snow data from 689 observation sites obtained during the Sever expeditions in the 1980s have been used to establish an empirical relation between ice thickness and freeboard. Estimates of mean and variability of snow depth, snow density and ice density were produced based on many field observations, and have been used in the isostatic equilibrium equation to estimate ice thickness as a function of ice freeboard, snow depth and snow/ice density. The accuracy of the ice thickness retrieval has been calculated from the estimated variability in ice and snow parameters and error of ice freeboard measurements. It is found that uncertainties of ice density and freeboard are the major sources of error in ice thickness calculation. For FY ice, retrieval of ≈1.0 m (2.0 m) thickness has an uncertainty of 60% (41%). For MY ice the main uncertainty is ice density error, since the freeboard error is relatively smaller than for FY ice. Retrieval of 2.4 m (3.0 m) thick MY ice has an error of 24% (21%). The freeboard error is ±0.05 m for both the FY and MY ice. If the freeboard error can be reduced to 0.01 m by averaging a large number of measurements from CryoSat, the error in thickness retrieval is reduced to about 32% for a 1.0 m thick FY floe and to about 18% for a 2.3 m thick MY floe. The remaining error is dominated by uncertainty in ice density. Provision of improved ice density data is therefore important for accurate retrieval of ice thickness from CryoSat data.

Download Full-text

A Weekly Arctic Sea-Ice Thickness Data Record from merged CryoSat-2 and SMOS Satellite Data

10.5194/tc-2017-4 ◽

2017 ◽

Cited By ~ 2

Author(s):

Robert Ricker ◽

Stefan Hendricks ◽

Lars Kaleschke ◽

Xiangshan Tian-Kunze ◽

Jennifer King ◽

...

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Optimal Interpolation ◽

Altimeter Data ◽

Ice Thickness ◽

Weighted Mean ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness ◽

Smos Satellite

Abstract. Sea-ice thickness on global scale is derived from different satellite sensors using independent retrieval methods. Due to the sensor and orbit characteristics, such satellite retrievals differ in spatial and temporal resolution as well as in the sensitivity to certain sea-ice types and thickness ranges. Satellite altimeters, such as CryoSat-2 (CS2), sense the height of the ice surface above the sea level, which can be converted into sea-ice thickness. However, relative uncertainties associated with this method are large over thin ice regimes. Another retrieval strategy is realized by the evaluation of surface brightness temperature in L-Band microwave frequencies (1.4 GHz) with a thickness-dependent emission model, as measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. While the radiometer based method looses sensitivity for thick sea ice (> 1 m), relative uncertainties over thin ice are significantly smaller than for the altimetry-based retrievals. In addition, the SMOS product provides global sea-ice coverage on a daily basis unlike the narrow-swath altimeter data. This study presents the first merged product of complementary weekly Arctic sea-ice thickness data records from the CS2 altimeter and SMOS radiometer. We use two merging approaches: a weighted mean and an optimal interpolation scheme (OI). While the weighted mean leaves gaps between CS2 orbits, OI is used to produce weekly Arctic-wide sea-ice thickness fields. The benefit of the data merging is shown by a comparison with airborne electromagnetic induction sounding measurements. When compared to airborne thickness data in the Barents Sea, the merged products reveal a reduced root mean square deviation of about 0.7 m compared to the CS2 retrieval and therefore demonstrate the capability to enhance the CS2 retrieval in thin ice regimes.

Download Full-text

Near Real Time Arctic sea ice thickness and volume from CryoSat-2

10.5194/tc-2016-21 ◽

2016 ◽

Cited By ~ 3

Author(s):

R. L. Tilling ◽

A. Ridout ◽

A. Shepherd

Keyword(s):

Sea Ice ◽

Real Time ◽

Arctic Sea Ice ◽

Arctic Climate ◽

The Arctic ◽

Ice Thickness ◽

Satellite Orbits ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

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.

Download Full-text

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

Download Full-text

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.

Download Full-text

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

Download Full-text

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.

Download Full-text