ICESat measurements of sea ice freeboard and estimates of sea ice thickness in the Weddell Sea

Zwally, H. Jay; Yi, Donghui; Kwok, R.; Zhao, Yunhe

doi:10.1029/2007jc004284

Cited by 233 publications

(309 citation statements)

References 37 publications

(46 reference statements)

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“…Along the eastern side of the Antarctic Peninsula, the modelled ice thickness is greater than in the rest of the pack, as observed. Nonetheless, the ice thickness there appears underestimated by more than 2 m during both winter and summer, compared to both the ASPeCt climatology and satellite freeboard-based ice thickness estimates (Zwally et al, 2008). This is clasically attributed to a poor representation of the cold barrier winds along the Antarctic Peninsula in the forcing fields (Timmermann et al, 2005;Vancoppenolle et al, 2009;Massonnet et al, 2011).…”

Section: Southern Hemispherementioning

confidence: 90%

Better constraints on the sea-ice state using global sea-ice data assimilation

et al. 2012

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Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using iceocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an ensemble Kalman filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged) data into a global coupled oceansea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the use of synthetic data (i.e. model-generated data), and secondly, through the assimilation of real satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005-2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

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Section: Southern Hemispherementioning

confidence: 90%

Better constraints on the sea-ice state using global sea-ice data assimilation

et al. 2012

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“…We first filter and pre-process ICESat surface elevations following [15,31]. Subsequently, we apply the lowest-level elevation method as described in [16] to approximate the sea surface height and compute the total freeboard [14][15][16]. The retrieved freeboard is quite sensitive to the parameters used in the lowest-level elevation method.…”

Section: Methodsmentioning

confidence: 99%

Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

Kern

Özsoy

2016

Remote Sensing

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Snow on Antarctic sea ice plays a key role for sea ice physical processes and complicates retrieval of sea ice thickness using altimetry. Current methods of snow depth retrieval are based on satellite microwave radiometry, which perform best for dry, homogeneous snow packs on level sea ice. We introduce an alternative approach based on in-situ measurements of total (sea ice plus snow) freeboard and snow depth, which we use to compute snow depth on sea ice from Ice, Cloud, and land Elevation Satellite (ICESat) total freeboard observations. We compare ICESat snow depth for early winter and spring of the years 2004 through 2006 with the Advanced Scanning Microwave Radiometer aboard EOS (AMSR-E) snow depth product. We find ICESat snow depths agree more closely with ship-based visual and air-borne snow radar observations than AMSR-E snow depths. We obtain average modal and mean ICESat snow depths, which exceed AMSR-E snow depths by 5-10 cm in winter and 10-15 cm in spring. We observe an increase in ICESat snow depth from winter to spring for most Antarctic regions in accordance with ground-based observations, in contrast to AMSR-E snow depths, which we find to stay constant or to decrease. We suggest satellite laser altimetry as an alternative method to derive snow depth on Antarctic sea ice, which is independent of snow physical properties.

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“…The capability of sea-ice thickness retrieval using satellite radar and laser altimetry data has been demonstrated for Arctic and Antarctic sea ice (Ricker et al, 2014;Laxon et al, 2013;Kurtz et al, 2014;Zwally et al, 2008;. The altimetry sea-ice thickness retrieval algorithm is based on estimations of freeboard, the height of the ice (ice freeboard) or snow surface (total or snow freeboard) above the local sea level.…”

Section: Introductionmentioning

confidence: 99%

“…They found that the choice of SSH estimation has the highest sensitivity, but reasonable alternatives for lead detections do not result in significant differences. At the same time that the first ICESat snow freeboard maps were developed by Zwally et al (2008), Giles et al (2008) also computed freeboard out of radar altimeter data from the European Remote Sensing satellite 2 (ERS-2). In their study they could show that the winter mean freeboard from ERS-2 shows a reasonable distribution and good qualitative agreement with ship-based observations.…”

Section: Introductionmentioning

confidence: 99%

“…Over sea ice in the Southern Ocean, Zwally et al (2008) and Yi et al (2011) provided a first estimate of snow freeboard and sea-ice thickness distribution and its seasonal evolution in the Weddell Sea using the laser altimeter data from ICESat. They found the highest snow freeboard and the thickest ice in the western Weddell Sea and a clear seasonal cycle of the snow freeboard with the highest values in summer (since all the thin ice is melted away) and lower values in the beginning of winter (due to new ice formation).…”

Section: Introductionmentioning

confidence: 99%

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About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

et al. 2016

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Abstract. Knowledge about Antarctic sea-ice volume and its changes over the past decades has been sparse due to the lack of systematic sea-ice thickness measurements in this remote area. Recently, first attempts have been made to develop a sea-ice thickness product over the Southern Ocean from space-borne radar altimetry and results look promising. Today, more than 20 years of radar altimeter data are potentially available for such products. However, the characteristics of individual radar types differ for the available altimeter missions. Hence, it is important and our goal to study the consistency between single sensors in order to develop long and consistent time series. Here, the consistency between freeboard measurements of the Radar Altimeter 2 on board Envisat and freeboard measurements from the SyntheticAperture Interferometric Radar Altimeter on board CryoSat-2 is tested for their overlap period in 2011. Results indicate that mean and modal values are in reasonable agreement over the sea-ice growth season (May-October) and partly also beyond. In general, Envisat data show higher freeboards in the first-year ice zone while CryoSat-2 freeboards are higher in the multiyear ice zone and near the coasts. This has consequences for the agreement in individual sectors of the Southern Ocean, where one or the other ice class may dominate. Nevertheless, over the growth season, mean freeboard for the entire (regionally separated) Southern Ocean differs generally by not more than 3 cm (8 cm, with few exceptions) between Envisat and CryoSat-2, and the differences between modal freeboards lie generally within ±10 cm and often even below.

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ICESat measurements of sea ice freeboard and estimates of sea ice thickness in the Weddell Sea

Cited by 233 publications

References 37 publications

Better constraints on the sea-ice state using global sea-ice data assimilation

Better constraints on the sea-ice state using global sea-ice data assimilation

Satellite Remote Sensing of Snow Depth on Antarctic Sea Ice: An Inter-Comparison of Two Empirical Approaches

About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

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