Conference Agenda

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Session Overview
Session
Session 2B - Sea-ice thickness retrieval and validation #2
Time:
Tuesday, 21/Mar/2017:
10:40am - 12:20pm

Session Chair: Christian Haas
Session Chair: Amandine Guillot

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Presentations
10:40am - 11:00am

Validation of CryoSat-2 Sea Ice Thickness with Upward-Looking Sonar Measurements in the Eastern Canadian Arctic

Ingrid Peterson, Yongsheng Wu, Jim Hamilton

Fisheries and Oceans Canada, Canada

Sea ice thickness estimates from CryoSat-2 are compared with ice draft measurements collected with Upward-Looking Sonar (ULS) moorings in Davis Strait and in the Canadian Arctic Archipelago (Barrow Strait) in 2010-2013. In western Davis Strait, the mean ice thicknesses inferred from the ULS ice draft measurements in spring (March-April) 2012 and 2013 were 2.8 m and 2.4 m respectively. The mean CryoSat ice thickness to the north in Baffin Bay was also higher in 2012 than in 2013. However the magnitude of the ULS measurements was about 50% higher than the CryoSat estimates, probably due in large part to the close proximity of the mooring to the coast. The difference in ice thickness between the two years is consistent with a higher winter North Atlantic Oscillation (NAO) index in 2012 than in 2013. Differences in spring CryoSat ice thicknesses in Baffin Bay for all years between 2011 and 2016 are also discussed. In Barrow Strait, the mean ULS ice thicknesses in fall 2010 and spring 2011 were 0.9 and 1.6 m respectively, and were in reasonable agreement with the mean CryoSat ice thicknesses in the surrounding area.


The Development of a Dynamic Snow Load for Cryosat-2 Sea Ice Thickness Retrievals

Rachel Tilling, Andy Ridout, Michel Tsamados, Isobel Lawrence, Andrew Shepherd

University of Leeds, United Kingdom

CryoSat-2 data are now being used internationally to produce estimates of Arctic-wide sea ice thickness and volume [1-5]. However, current estimates rely (to varying degrees) on the use of a snow climatology in the conversion of ice freeboard to thickness, and this is currently the largest source of error in sea ice thickness and volume estimates[6, 7]. To reduce this uncertainty we have developed a dynamic snow load for application with our sea ice processor. The snow load is initialised using precipitation and evaporation data from the ERA-Interim reanalysis [8], and developed with a dependence on sea ice concentration, drift, and atmospheric temperature. This enables us to apply a snow load that varies in space and time, rather than relying on a constant monthly snow climatology. To perform an initial evaluation of our dynamic snow load, we compared estimates of sea ice thickness that we obtained using the climatological and dynamic snow loads, to ice thickness measurements from NASA’s Operation IceBridge (OIB) campaign. Although both of our sea ice thickness datasets agree well with OIB in the region north of Greenland, there is some spatial variation in the thickness differences at lower latitudes. This presentation will summarise the development, application, and evaluation of the new snow load in relation to our sea ice thickness estimates and comment on future considerations.

[1] S. Laxon, K. A. Giles, A. Ridout, D. J. Wingham, R. C. Willatt, R. Cullen, et al., "CryoSat-2 estimates of Arctic sea ice thickness and volume," Geophysical Research Letters, vol. 40, pp. 732-737, Feb 28 2013.

[2] R. Ricker, S. Hendricks, V. Helm, H. Skourup, and M. Davidson, "Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation," The Cryosphere, vol. 8, pp. 1607-1622, Aug 28 2014.

[3] N. T. Kurtz, N. Galin, and M. Studinger, "An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting," The Cryosphere, vol. 8, pp. 1217-1237, July 15 2014.

[4] R. Kwok and G. F. Cunningham, "Variability of Arctic sea ice thickness and volume from CryoSat-2," Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences, vol. 373, p. 20140157, Jul 13 2015.

[5] S. Lee, J. Im, J. Kim, M. Kim, M. Shin, H. C. Kim, et al., "Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection," Remote Sensing, vol. 8, pp. 1-20, Sep 2016.

[6] R. L. Tilling, A. Ridout, A. Shepherd, and D. J. Wingham, "Increased Arctic sea ice volume after anomalously low melting in 2013," Nature Geoscience, vol. 8, pp. 643-646, 2015.

[7] R. L. Tilling, A. Ridout, and A. Shepherd, "Near Real Time Arctic sea ice thickness and volume from CryoSat-2," The Cryosphere, vol. 10, pp. 2003-2016, 2016.

[8] D. P. Dee, S. M. Uppalaa, A. J. Simmonsa, P. Berrisforda, P. Polia, S. Kobayashib, et al., "The ERA-Interim reanalysis: Configuration and performance of the data assimilation system," Quarterly Journal of the Royal Meteorological Society, vol. 137, pp. 553-597, April 2011.


11:00am - 11:20am

Using Ice Thickness Distribution from Cryosat to Initialise Sea Ice Models

Michel Tsamados1, David Schroeder2, Daniel Feltham2, Andy Ridout1

1University College London, United Kingdom; 2University of Reading, UK

We extract for the first time the local sea ice thickness distribution (ITD) from the along track Cryosat individual thickness measurements and compare these distributions with high resolution airborne data from Operation IceBridge.

We use the state of the art sea ice model CICE that was previously used to successfully forecast September sea ice extent from the melt onset pond coverage in May to assess its sensitivity to a sub-grid scale ITD initialised from a distribution derived from Cryosat.

CICE model runs initialized from a Cryosat ITD in November and April are compared with the corresponding model runs without initialisation and with the observed Cryosat thickness maps for the following April (lead time of 6 and 12 months respectively).

We demonstrate that this type of ITD initialisation from Cryosat thickness data shows potential to improve sea ice forecast both in term of its concentration and thickness With lead times of up to a year.


11:20am - 11:40am

A Physical Approach for Freeboard Computation from CryoSat-2

Jean Christophe Poisson1, Pierre Thibaut1, Duc Hoang1, Amandine Guillot2, François Boy2, Nicolas Picot2

1Collecte Localisation Satellite, France; 2Centre National d'Etudes Spatiales

The Arctic region is an important component of the climate system and its exact influence is still not clearly understood today. For several years, radar measurements from Low Resolution Mode (LRM) satellite altimetry missions have been processed in Arctic sea ice region, providing valuable data on the sea ice freeboard and its evolution. However, the observation in these areas is not simple due to the presence of multiple surface types which make LRM altimetry measurements very complex with multiple off-nadir reflections that degrade the surface height retrieval. With the arrival of delay-doppler altimetry embarked onboard the CryoSat-2 mission (and Sentinel-3), this new technique has provided very promising results opening a new area for the observation of the sea ice regions.

For several years, specific processings have been developed to extract and monitor sea ice extent, sea level in the leads and freeboard height of the ice covered ocean. In the CS-2 ground segment, the methods developed for the LRM altimetry, although empirical, have been adapted to SAR waveforms with their robustness but also with their drawbacks. In this talk, we propose to present a new physical approach to classify and retrack the sea ice waveforms combined with a lead-oriented method to compute the freeboard heights. The retrievals are then compared with the results obtained with the same physical approach applied on SARAL/AltiKa and Sentinel-3A data.


11:40am - 12:00pm

Retrievals of Lake Ice Thickness Using CryoSat-2

Justin Francis Beckers1, J. Alec Casey1,2, Christian Haas1,2,3

1Department of Earth and Atmospheric Sciences, University of Alberta, Canada; 2Department of Earth and Space Science and Engineering, York University, Canada; 3Alfred Wegener Institute for Polar and Marine Sciences, Germany

Satellite observations have revealed decreases in the duration of the seasonal snow and ice coverage of lakes in northern Canada and modelling studies suggest that these decreases will continue and that there will be an associated decrease in ice thickness. However, there is limited ice thickness information for these lakes due to their remoteness. Here we present and validate a method to retrieve lake ice thickness using CryoSat-2 L1B waveform data. Under optimal conditions, the CryoSat-2 signal penetrates trough the freshwater ice and is scattered from both the snow-ice and the ice-water interfaces, with returns from each interface being of sufficient power to be detected. The distance between the scattering horizons is used to determine the ice thickness, similar to ground-penetrating radar profiling. The observed seasonal evolution of ice thickness of Great Bear Lake and Great Slave Lake agrees well with in-situ measurements, modelled ice thicknesses, and previous studies. Thickness retrievals of thin ice are limited by a minimum waveform peak separation of 2 range bins, approximately 0.26 m in ice. A comparison of maximum waveform power to lake ice thickness is also presented for the retrieval of phenological ice and snow information. Although not designed for lake ice observations, CryoSat-2 and future SAR satellite altimeter missions offer new possibilities to monitor the ice and water levels of climatically sensitive and influential lakes.



 
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