scholarly journals Sensitivity of CryoSat-2 Arctic sea-ice volume trends on radar-waveform interpretation

2014 ◽  
Vol 8 (2) ◽  
pp. 1831-1871 ◽  
Author(s):  
R. Ricker ◽  
S. Hendricks ◽  
V. Helm ◽  
H. Skourup ◽  
M. Davidson
Keyword(s):  
Sea Ice ◽  
Sea Level ◽  
Global Scale ◽  
Arctic Sea Ice ◽  
Radar Altimeter ◽  
Ice Volume ◽  
Ice Surface ◽  
Arctic Sea ◽  
Volume Decrease ◽  
Ku Band

Abstract. Several studies have shown that there is considerable evidence that the Arctic sea-ice is thinning during the last decades. When combined with the observed rapid reduction of ice-covered area this leads to a decline in sea-ice volume. The only remote sensing technique capable of quantifying this ice volume decrease at global scale is satellite altimetry. In this context the CryoSat-2 satellite was launched in 2010 and is equipped with the Ku-band SAR radar altimeter SIRAL, which we use to derive sea-ice freeboard defined as the height of the ice surface above the local sea level. In the context of quantifying Arctic ice-volume decrease at global scale, the CryoSat-2 satellite was launched in 2010 and is equipped with the Ku-band SAR radar altimeter SIRAL, which we use to derive sea-ice freeboard defined as the height of the ice surface above the sea level. Accurate CryoSat-2 range measurements over open water and the ice surface in the order of centimeters are necessary to achieve the required accuracy of the freeboard to thickness conversion. Besides uncertainties of the actual sea-surface height and limited knowledge of ice and snow properties, the penetration of the radar signal into the snow cover and therefore the interpretation of radar echoes is crucial. This has consequences in the selection of retracker algorithms which are used to track the main scattering horizon and assign a range estimate to each CryoSat measurement. In this paper we apply a retracker algorithm with thresholds of 40%, 50% and 80% of the first maximum of radar echo power, spanning the range of values used in current literature. For the 40% threshold we assume that the main scattering horizon lies at a certain depth between the surface and snow-ice interface as verified through coincident CryoSat-2 and airborne laser altimetry measurements. This contrasts with the 50% and 80% thresholds where we assume the ice-snow interface as the main scattering horizon similar to other published studies. Using the selected retrackers we evaluate the uncertainties of trends in sea-ice freeboard and higher level products that arise from the choice of the retracker threshold only, independently from the uncertainties related to snow and ice properties. Our study shows that the choice of retracker thresholds does have a non-negligible impact on magnitude estimates of sea-ice freeboard, thickness and volume, but that the main trends in these parameters are less affected. Specifically we find declines of Arctic sea-ice volume of 9.7% (40% threshold), 10.9% (50% threshold) and 6.9% (80% threshold) between March 2011 and March 2013. In contrast to that we find increases in Arctic sea-ice volume of 27.88% (40% threshold), 25.71% (50% threshold) and 32.65% (80% threshold) between November 2011 and November 2013. Furthermore we obtain a significant increase of freeboard from March 2013 to November 2013 in the area for multi-seasonal sea-ice north of Greenland and the Canadian Archipelago. Since this is unlikely it gives rise to the assumption that applying different retracker thresholds depending on seasonal properties of the snow load is necessary in the future.

scholarly journals Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation

2014 ◽  
Vol 8 (4) ◽  
pp. 1607-1622 ◽  
Author(s):  
R. Ricker ◽  
S. Hendricks ◽  
V. Helm ◽  
H. Skourup ◽  
M. Davidson
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Global Scale ◽  
Significant Rise ◽  
Arctic Sea Ice ◽  
Synthetic Aperture ◽  
Radar Altimeter ◽  
Roughness Effects ◽  
Snow Layer ◽  
Ice Surface

Abstract. In the context of quantifying Arctic ice-volume decrease at global scale, the CryoSat-2 satellite was launched in 2010 and is equipped with the Ku band synthetic aperture radar altimeter SIRAL (Synthetic Aperture Interferometric Radar Altimeter), which we use to derive sea-ice freeboard defined as the height of the ice surface above the sea level. Accurate CryoSat-2 range measurements over open water and the ice surface of the order of centimetres are necessary to achieve the required accuracy of the freeboard-to-thickness conversion. Besides uncertainties of the actual sea-surface height and limited knowledge of ice and snow properties, the composition of radar backscatter and therefore the interpretation of radar echoes is crucial. This has consequences in the selection of retracker algorithms which are used to track the main scattering horizon and assign a range estimate to each CryoSat-2 measurement. In this study we apply a retracker algorithm with thresholds of 40, 50 and 80% of the first maximum of radar echo power, spanning the range of values used in the current literature. By using the selected retrackers and additionally results from airborne validation measurements, we evaluate the uncertainties of sea-ice freeboard and higher-level products that arise from the choice of the retracker threshold only, independent of the uncertainties related to snow and ice properties. Our study shows that the choice of retracker thresholds does have a significant impact on magnitudes of estimates of sea-ice freeboard and thickness, but that the spatial distributions of these parameters are less affected. Specifically we find mean radar freeboard values of 0.121 m (0.265 m) for the 40% threshold, 0.086 m (0.203 m) for the 50% threshold and 0.024 m (0.092 m) for the 80% threshold, considering first-year ice (multiyear ice) in March 2013. We show that the main source of freeboard and thickness uncertainty results from the choice of the retracker and the unknown penetration of the radar pulse into the snow layer in conjunction with surface roughness effects. These uncertainties can cause a freeboard bias of roughly 0.06–0.12 m. Furthermore we obtain a significant rise of 0.02–0.15 m of freeboard from March 2013 to November 2013 in the area for multiyear sea ice north of Greenland and Canada. Since this is unlikely, it gives rise to the assumption that applying different retracker thresholds depending on seasonal properties of the snow load is necessary in the future.

Investigating how platform height affects sea ice radar returns with KuKaSim

2021 ◽  
Author(s):  
Thomas Newman ◽  
Rosemary Willatt ◽  
Julienne Stroeve ◽  
Robbie Mallet ◽  
Michel Tsamados ◽  
...  
Keyword(s):  
Surface Morphology ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Airborne Lidar ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Ice Surface ◽  
Arctic Sea ◽  
Airborne Lidar Data

<p>Current, and ongoing observations, of Arctic sea ice, indicate a trend towards a younger, thinner and more mobile pack that exhibits significant inter-annual variability. Satellite and airborne radar altimeters have been used extensively to quantify these changes by deriving sea ice freeboard to infer sea ice thickness. Radar returns from altimeters are impacted by both the morphology of snow and ice features on the sea ice surface, in addition to the radar properties of the snowpack, with both contributing to uncertainties in radar-derived sea ice freeboard. Here we make use of airborne lidar data, collected as part of the MOSAiC expedition in the winter of 2019/2020, to investigate the effect of sea ice surface morphology on radar altimeter measurements. We quantify these effects using 'KuKaSim' a forward-modelling approach based on the KuKa instrument deployed at MOSAiC, which allows us to investigate how simulated radar returns vary with radar height. Our results allow us to better constrain the altimetric uncertainty resulting from ice surface morphology, with respect to both radar height and sea ice type, leading to an enhanced understanding of sources of uncertainty in altimeter-derived sea ice thickness products.</p>

scholarly journals Ku-band radar penetration into snow cover on Arctic sea ice using airborne data

2011 ◽  
Vol 52 (57) ◽  
pp. 197-205 ◽  
Author(s):  
Rosemary Willatt ◽  
Seymour Laxon ◽  
Katharine Giles ◽  
Robert Cullen ◽  
Christian Haas ◽  
...  
Keyword(s):  
Sea Ice ◽  
Field Measurements ◽  
Normal Incidence ◽  
Arctic Sea Ice ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Validation Experiment ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness ◽  
Ku Band

AbstractSatellite radar altimetry provides data to monitor winter Arctic sea-ice thickness variability on interannual, basin-wide scales. When using this technique an assumption is made that the peak of the radar return originates from the snow/ice interface. This has been shown to be true in the laboratory for cold, dry snow as is the case on Arctic sea ice during winter. However, this assumption has not been tested in the field. We use data from an airborne normal-incidence Ku-band radar altimeter and in situ field measurements, collected during the CryoSat Validation Experiment (CryoVEx) Bay of Bothnia, 2006 and 2008 field campaigns, to determine the dominant scattering surface for Arctic snow-covered sea ice. In 2006, when the snow temperatures were close to freezing, the dominant scattering surface in 25% of the radar returns appeared closer to the snow/ice interface than the air/snow interface. However, in 2008, when temperatures were lower, the dominant scattering surface appeared closer to the snow/ice interface than the air/snow interface in 80% of the returns.

Intercomparison of Arctic Sea Ice Backscatter and Ice Type Classification Using Ku-Band and C-Band Scatterometers

2021 ◽  
pp. 1-18
Author(s):  
Zhilun Zhang ◽  
Yining Yu ◽  
Mohammed Shokr ◽  
Xinqing Li ◽  
Yufang Ye ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Type Classification ◽  
Ku Band

Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period 

2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Volume Growth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Concentration ◽  
Marginal Seas ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration

<p><span>We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.</span></p>

scholarly journals Retrievals of Arctic Sea-Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability

2018 ◽  
Vol 45 (21) ◽  
pp. 11,751-11,759 ◽  
Author(s):  
Felix Bunzel ◽  
Dirk Notz ◽  
Leif Toudal Pedersen
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Volume ◽  
Arctic Sea

scholarly journals The European Space Agency’s Earth Explorer Mission CryoSat: measuring variability in the cryosphere

2004 ◽  
Vol 39 ◽  
pp. 313-320 ◽  
Author(s):  
Mark R. Drinkwater ◽  
Richard Francis ◽  
Guy Ratier ◽  
Duncan J. Wingham
Keyword(s):  
European Space Agency ◽  
Ice Sheets ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Radar Altimeter ◽  
Operating Characteristics ◽  
Space Agency ◽  
Explorer Mission ◽  
Arctic Sea ◽  
Ku Band

AbstractCryoSat is currently being prepared for a 2005 launch as the first European Space Agency Earth Explorer Opportunity mission. It is a dedicated cryospheric mission equipped with a Ku-band SIRAL (SAR/Interferometric Radar ALtimeter), whose primary objectives are to measure the variability and trends in the mass of the Arctic sea-ice cover and large terrestrial ice sheets. In this paper, an overview is provided of the mission and of the measurement characteristics of the new SIRAL instrument. Examples of data acquired on recent preparatory campaigns are presented, illustrating the operating characteristics of the key SIRAL modes. Preparatory plans for calibration and validation of CryoSat data are described.

scholarly journals Multidecadal Arctic sea ice thickness and volume derived from ice age

2020 ◽  
Vol 14 (4) ◽  
pp. 1325-1345 ◽  
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.

scholarly journals Arctic Sea Ice Volume Variability over 1901–2010: A Model-Based Reconstruction

2019 ◽  
Vol 32 (15) ◽  
pp. 4731-4752 ◽  
Author(s):  
Axel J. Schweiger ◽  
Kevin R. Wood ◽  
Jinlun Zhang
Keyword(s):  
Twentieth Century ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ocean Modeling ◽  
The Arctic ◽  
Error Statistics ◽  
Atlantic Sector ◽  
Ice Volume ◽  
Sea Ice Decline ◽  
Arctic Sea

Abstract PIOMAS-20C, an Arctic sea ice reconstruction for 1901–2010, is produced by forcing the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) with ERA-20C atmospheric data. ERA-20C performance over Arctic sea ice is assessed by comparisons with measurements and data from other reanalyses. ERA-20C performs similarly with respect to the annual cycle of downwelling radiation, air temperature, and wind speed compared to reanalyses with more extensive data assimilation such as ERA-Interim and MERRA. PIOMAS-20C sea ice thickness and volume are then compared with in situ and aircraft remote sensing observations for the period of ~1950–2010. Error statistics are similar to those for PIOMAS. We compare the magnitude and patterns of sea ice variability between the first half of the twentieth century (1901–40) and the more recent period (1980–2010), both marked by sea ice decline in the Arctic. The first period contains the so-called early-twentieth-century warming (ETCW; ~1920–40) during which the Atlantic sector saw a significant decline in sea ice volume, but the Pacific sector did not. The sea ice decline over the 1979–2010 period is pan-Arctic and 6 times larger than the net decline during the 1901–40 period. Sea ice volume trends reconstructed solely from surface temperature anomalies are smaller than PIOMAS-20C, suggesting that mechanisms other than warming, such as changes in ice motion and deformation, played a significant role in determining sea ice volume trends during both periods.

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