The relation between sea ice thickness and freeboard in the Arctic

Alexandrov, V.; Sandven, Stein; Wå̊hlin, Johan; Johannessen, Ola M.

doi:10.5194/tc-4-373-2010

Cited by 138 publications

(190 citation statements)

References 20 publications

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“…(3) to determine monthly snow depth fields. Based on the results by Alexandrov et al (2010), recent studies attribute a lower density to MYI (882 kg m −3 ) than to FYI (917 kg m −3 ). To account for the difference in ice density between MYI and FYI, we use the same definition for ice density as in Kwok and Cunningham (2015):…”

Section: Freeboard-to-thickness Conversionmentioning

confidence: 99%

Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

et al. 2017

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Abstract. Over the past decade, sea-ice freeboard has been monitored with various satellite altimetric missions with the aim of producing long-term time series of ice thickness. While recent studies have demonstrated the capacity of the CryoSat-2 mission (2010-present) to provide accurate freeboard measurements, the current estimates obtained with the Envisat mission (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) still require some large improvements.In this study, we first estimate Envisat and CryoSat-2 radar freeboard by using the exact same processing algorithms. We then analyse the freeboard difference between the two estimates over the common winter periods (November 2010-April 2011 and November 2011-March 2012. The analysis of along-track data and gridded radar freeboard in conjunction with Envisat pulse-peakiness (PP) maps suggests that the discrepancy between the two sensors is related to the surface properties of sea-ice floes and to the use of a threshold retracker.Based on the relation between the Envisat pulse peakiness and the radar freeboard difference between Envisat and CryoSat-2, we produce a monthly CryoSat-2-like version of Envisat freeboard. The improved Envisat data set freeboard displays a similar spatial distribution to CryoSat-2 (RMSD = 1.5 cm) during the two ice growth seasons and for all months of the period of study.The comparison of the altimetric data sets with in situ ice draught measurements during the common flight period shows that the improved Envisat data set (RMSE = 12-28 cm) is as accurate as CryoSat-2 (RMSE = 15-21 cm) and much more accurate than the uncorrected Envisat data set (RMSE = 178-179 cm).The comparison of the improved Envisat radar freeboard data set is then extended to the rest of the Envisat mission to demonstrate the validity of PP correction from the calibration period. The good agreement between the improved Envisat data set and the in situ ice draught data set (RMSE = 13-32 cm) demonstrates the potential of the PP correction to produce accurate freeboard estimates over the entire Envisat mission lifetime.

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Section: Freeboard-to-thickness Conversionmentioning

confidence: 99%

Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

et al. 2017

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“…For first-year ice, retrieval of 1.0 m (2.0 m) thickness has an uncertainty of 46 % (37 %), assuming that the freeboard error is ±0.03 m. If the freeboard error can be reduced to 0.01m by averaging measurements from CryoSat-2, the error in thickness retrieval is reduced to about 32 % for a 1.0 m thick firstyear floe. The remaining error is dominated by uncertainty in ice density (Alexandrov et al, 2010).…”

Section: Sea Ice Thicknessmentioning

confidence: 99%

“…The difference in snow properties between multi-year and firstyear ice is therefore related to snow depth, not to snow density (Alexandrov et al, 2010).…”

Section: Sea Ice Thicknessmentioning

confidence: 99%

“…The Cryosphere, 6, 1411-1434, 2012 www.the-cryosphere.net/6/1411/2012/ Equation (5) is applicable for level first-year ice in the Eurasian Arctic in the period March-May, but has to be modified for deformed first-year and multi-year ice (Alexandrov et al, 2010). However, taking into consideration that the snow climatology of Warren et al (1999) is not valid for firstyear ice, ice thickness can be calculated during the whole winter, assuming that ice density and snow loading on the ice do not substantially change.…”

Section: Sea Ice Thicknessmentioning

confidence: 99%

“…The mean ice density for first-year ice from the Sever data is 917 ± 36 kg m −3 (Alexandrov et al, 2010). In the analysis of ice thickness-freeboard relation the data from all landings were divided into two groups.…”

Section: Sea Ice Thicknessmentioning

confidence: 99%

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Remote sensing of sea ice: advances during the DAMOCLES project

et al. 2012

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Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMO-CLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer AS-CAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

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Impact of snow accumulation on CryoSat‐2 range retrievals over Arctic sea ice: An observational approach with buoy data

Ricker

Hendricks

Perovich

et al. 2015

Geophysical Research Letters

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Radar altimetry measurements of the current satellite mission CryoSat‐2 show an increase of Arctic sea ice thickness in autumn 2013, compared to previous years but also related to March 2013. Such an increase over the melting season seems unlikely and needs to be investigated. Recent studies show that the influence of the snow cover is not negligible and can highly affect the CryoSat‐2 range retrievals if it is assumed that the main scattering horizon is given by the snow‐ice interface. Our analysis of Arctic ice mass balance buoy records and coincident CryoSat‐2 data between 2012 and 2014 adds observational evidence to these findings. Linear trends of snow and ice freeboard measurements from buoys and nearby CryoSat‐2 freeboard retrievals are calculated during accumulation events. We find a positive correlation between buoy snow freeboard and CryoSat‐2 freeboard estimates, revealing that early snow accumulation might have caused a bias in CryoSat‐2 sea ice thickness in autumn 2013.

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The relation between sea ice thickness and freeboard in the Arctic

Cited by 138 publications

References 20 publications

Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

Remote sensing of sea ice: advances during the DAMOCLES project

Impact of snow accumulation on CryoSat‐2 range retrievals over Arctic sea ice: An observational approach with buoy data

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