Error assessment of satellite-derived lead fraction in the Arctic

Ivanova, N. P.; Rampal, Pierre; Bouillon, Sylvain

doi:10.5194/tc-10-585-2016

Cited by 24 publications

(22 citation statements)

References 30 publications

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“…The mean differences over FYI are within 3-5 cm, while locally these can be more than 10 cm. Since the lead fraction in the areas of seasonal ice is higher than over thick MYI (Willmes and Heinemann, 2016;Ivanova et al, 2016;Röhrs et al, 2012;Bröhan and Kaleschke, 2014), one can expect that the observed difference in freeboard estimates does not come from the same causes mentioned above for MYI areas. The positive differences most likely reflect the underestimation of freeboard retrieved by the TP method as was found by Kwok et al (2007) from comparison with the freeboards adjacent to leads detected on satellite images and collocated with ICESat data.…”

Section: The Original Algorithm Used In the Tp Methodsmentioning

confidence: 94%

On retrieving sea ice freeboard from ICESat laser altimeter

Khvorostovsky¹,

Rampal²

2016

The Cryosphere

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Abstract. Sea ice freeboard derived from satellite altimetry is the basis for the estimation of sea ice thickness using the assumption of hydrostatic equilibrium. High accuracy of altimeter measurements and freeboard retrieval procedure are, therefore, required. As of today, two approaches for estimating the freeboard using laser altimeter measurements from Ice, Cloud, and land Elevation Satellite (ICESat), referred to as tie points (TP) and lowest-level elevation (LLE) methods, have been developed and applied in different studies. We reproduced these methods for the ICESat observation periods (2003)(2004)(2005)(2006)(2007)(2008) in order to assess and analyse the sources of differences found in the retrieved freeboard and corresponding thickness estimates of the Arctic sea ice as produced by the Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). Three main factors are found to affect the freeboard differences when applying these methods: (a) the approach used for calculation of the local sea surface references in leads (TP or LLE methods), (b) the along-track averaging scales used for this calculation, and (c) the corrections for lead width relative to the ICESat footprint and for snow depth accumulated in refrozen leads. The LLE method with 100 km averaging scale, as used to produce the GSFC data set, and the LLE method with a shorter averaging scale of 25 km both give larger freeboard estimates comparing to those derived by applying the TP method with 25 km averaging scale as used for the JPL product. Two factors, (a) and (b), contribute to the freeboard differences in approximately equal proportions, and their combined effect is, on average, about 6-7 cm. The effect of using different methods varies spatially: the LLE method tends to give lower freeboards (by up to 15 cm) over the thick multiyear ice and higher freeboards (by up to 10 cm) over first-year ice and the thin part of multiyear ice; the higher freeboards dominate. We show that the freeboard underestimation over most of these thinner parts of sea ice can be reduced to less than 2 cm when using the improved TP method proposed in this paper. The corrections for snow depth in leads and lead width, (c), are applied only for the JPL product and increase the freeboard estimates by about 7 cm on average. Thus, different approaches to calculating sea surface references and different along-track averaging scales from one side and the freeboard corrections as applied when producing the JPL data set from the other side roughly compensate each other with respect to freeboard estimation. Therefore, one may conclude that the difference in the mean sea ice thickness between the JPL and GSFC data sets reported in previous studies should be attributed mostly to different parameters used in the freeboard-to-thickness conversion.

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Section: The Original Algorithm Used In the Tp Methodsmentioning

confidence: 94%

On retrieving sea ice freeboard from ICESat laser altimeter

Khvorostovsky¹,

Rampal²

2016

The Cryosphere

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“…Synthetic Aperture Radar (SAR) or microwave imagery can be used to obtain sea ice surface details with minimum cloud influence. Kwok (1998) used the RADARSAT Geophysical Processor System (RGPS) to estimate the deformation of sea ice and identify the linear kinematics features, i.e., sea ice leads. Röhrs and Kaleschke (2012) presented an algorithm applied to the passive microwave imagery from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to detect sea ice leads wider than 3 km.…”

Section: Methodsmentioning

confidence: 99%

“…One reason is a lack of observations of sea ice leads with sufficient spatial and temporal coverage. This hampers our understanding of the variability of sea ice leads in the Arctic Ocean, and their relationship with Arctic sea ice cover (Ivanova et al, 2016;Wernecke and Kaleschke, 2015). Another reason is that sea ice leads constitute an unrepresented process in numerical prediction systems and climate models due to their highly nonlinear, small-scale and intermittent characteristics (Spreen et al, 2017;Wang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

The potential of sea ice leads as a predictor for summer Arctic sea ice extent

et al. 2018

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The Arctic sea ice extent throughout the melt season is closely associated with initial sea ice state in winter and spring. Sea ice leads are important sites of energy fluxes in the Arctic Ocean, which may play an important role in the evolution of Arctic sea ice. In this study, we examine the potential of sea ice leads as a predictor for summer Arctic sea ice extent forecast using a recently developed daily sea ice lead product retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Our results show that July pan-Arctic sea ice extent can be predicted from the area of sea ice leads integrated from midwinter to late spring, with a prediction error of 0.28 million km 2 that is smaller than the standard deviation of the observed interannual variability. However, the predictive skills for August and September pan-Arctic sea ice extent are very low. When the area of sea ice leads integrated in the Atlantic and central and west Siberian sector of the Arctic is used, it has a significantly strong relationship (high predictability) with both July and August sea ice extent in the Atlantic and central and west Siberian sector of the Arctic. Thus, the realistic representation of sea ice leads (e.g., the areal coverage) in numerical prediction systems might improve the skill of forecast in the Arctic region.Published by Copernicus Publications on behalf of the European Geosciences Union.

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“…The lack of existing modeling capacity has meant that our understanding of linear kinematics of sea ice is mainly based on buoys and satellite observations of ice drift [e.g., Kwok et al , ; Lindsay , ; Weiss and Marsan , ; Marsan et al , ; Rampal et al , ; Stern and Lindsay , ; Hutchings et al , ; Herman and Glowacki , ] and satellite as well as airborne measurements for sea ice leads [ Fily and Rothrock , ; Stone and Key , ; Lindsay and Rothrock , ; Miles and Roger , ; Tschudi et al , ; Onana et al , ; Broehan and Kaleschke , ; Willmes and Heinemann , , ]. Here we exploit the fact that lead area fraction data sets for the last decade have become available [ Roehrs and Kaleschke , ; Wernecke and Kaleschke , ; Willmes and Heinemann , , ; Ivanova et al , ], which can be used to evaluate sea ice models.…”

Section: Introductionmentioning

confidence: 99%

Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends

Wang

Danilov

Jung

et al. 2016

Geophysical Research Letters

117

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Sea ice leads in the Arctic are important features that give rise to strong localized atmospheric heating; they provide the opportunity for vigorous biological primary production, and predicting leads may be of relevance for Arctic shipping. It is commonly believed that traditional sea ice models that employ elastic‐viscous‐plastic (EVP) rheologies are not capable of properly simulating sea ice deformation, including lead formation, and thus, new formulations for sea ice rheologies have been suggested. Here we show that classical sea ice models have skill in simulating the spatial and temporal variation of lead area fraction in the Arctic when horizontal resolution is increased (here 4.5 km in the Arctic) and when numerical convergence in sea ice solvers is considered, which is frequently neglected. The model results are consistent with satellite remote sensing data and discussed in terms of variability and trends of Arctic sea ice leads. It is found, for example, that wintertime lead area fraction during the last three decades has not undergone significant trends.

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Error assessment of satellite-derived lead fraction in the Arctic

Cited by 24 publications

References 30 publications

On retrieving sea ice freeboard from ICESat laser altimeter

On retrieving sea ice freeboard from ICESat laser altimeter

The potential of sea ice leads as a predictor for summer Arctic sea ice extent

Sea ice leads in the Arctic Ocean: Model assessment, interannual variability and trends

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