Sea-Ice Wintertime Lead Frequencies and Regional Characteristics in the Arctic, 2003–2015

Willmes, Sascha; Heinemann, Günther

doi:10.3390/rs8010004

Cited by 91 publications

(116 citation statements)

References 46 publications

(54 reference statements)

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“…4, leads are mainly located in the area of the Beaufort Sea and north of Greenland (shear zones), which can be mainly attributed to their relatively high spatial and temporal persistence. Frequent lead occurrences in the East Siberian Sea found by Willmes and Heinemann (2016), for example, are not reflected in our study. In some regions, however, the influence of (shelf) bathymetry and associated ocean currents on the spatial distribution of polynya and lead occurrences is also visible in the TIT frequencies derived here (e.g., eastern exit Vilkitsky Strait, Hanna Shoal on the northern Chukchi Shelf, northern ESS).…”

Section: Derivation Of Ice Production and Polynya Areacontrasting

confidence: 50%

“…Compared to the MODIS-derived lead product from Willmes and Heinemann (2016), the SFR algorithm used in the present study is not able to adequately reconstruct leads with low spatial and temporal persistence. A thoughtful combination of both concepts is therefore a goal worth achieving for future investigations of thinice regions in the polar regions using thermal infrared data from MODIS or other comparable satellite sensors.…”

Section: Resultsmentioning

confidence: 75%

“…Compared to the study of Willmes and Heinemann (2016), leads are only weakly visible in these long-term averages (frequencies below 0.05-0.1). In Fig.…”

Section: Derivation Of Ice Production and Polynya Areamentioning

confidence: 99%

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Circumpolar polynya regions and ice production in the Arctic: results from MODIS thermal infrared imagery from 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea

et al. 2016

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Abstract. High-resolution MODIS thermal infrared satellite data are used to infer spatial and temporal characteristics of 17 prominent coastal polynya regions over the entire Arctic basin. Thin-ice thickness (TIT) distributions ( ≤ 20 cm) are calculated from MODIS ice-surface temperatures, combined with ECMWF ERA-Interim atmospheric reanalysis data in an energy balance model for 13 winter seasons (2002/2003to 2014/2015 November to March). From all available MODIS swath data, daily thin-ice thickness composites are computed in order to derive quantities such as polynya area and total thermodynamic (i.e., potential) ice production. A gap-filling approach is applied to account for cloud and data gaps in the MODIS composites. All polynya regions combined cover an average thin-ice area of 226.6 ± 36.1 × 10 3 km 2 in winter. This allows for an average total winter-accumulated ice production of about 1811 ± 293 km 3 , whereby the Kara Sea region, the North Water polynya (both 15 %), polynyas on the western side of Novaya Zemlya (20 %), as well as scattered smaller polynyas in the Canadian Arctic Archipelago (all combined 12 %) are the main contributors. Other wellknown sites of polynya formation (Laptev Sea, Chukchi Sea) show smaller contributions and range between 2 and 5 %. We notice distinct differences to earlier studies on pan-Arctic polynya characteristics, originating in some part from the use of high-resolution MODIS data, as the capability to resolve small-scale (> 2 km) polynyas and also large leads are increased. Despite the short record of 13 winter seasons, positive trends in ice production are detected for several regions of the eastern Arctic (most significantly in the Laptev Sea region with an increase of 6.8 km 3 yr −1 ) and the North Water polynya, while other polynyas in the western Arctic show a more pronounced variability with varying trends. We emphasize the role of the Laptev Sea polynyas as being a major influence on Transpolar Drift characteristics through a distinct relation between increasing ice production and ice area export. Overall, our study presents a spatially highly accurate characterization of circumpolar polynya dynamics and ice production, which should be valuable for future modeling efforts of atmosphere-ice-ocean interactions in the Arctic.

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Section: Derivation Of Ice Production and Polynya Areacontrasting

confidence: 50%

Section: Resultsmentioning

confidence: 75%

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Circumpolar polynya regions and ice production in the Arctic: results from MODIS thermal infrared imagery from 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea

et al. 2016

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“…As shown in Zhou et al (2017), the presence of sea ice leads has a profound effect in lowering the overall TB on the scale of SMOS observations. Leads can be treated as small-scale heterogeneity of the sea ice cover, and the incorporation of lead maps such as Willmes and Heinemann (2015b) effectively reduces the overestimation of TB, as studied by Zhou et al (2017). Specifically, the lead map can be adopted by the retrieval through the integration with the forward radiation model.…”

Section: Outlook Of Satellite-based Retrievalmentioning

confidence: 99%

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Zhou

Liu

et al. 2018

The Cryosphere

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Abstract. The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

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“…The thin ice concentration maps (Rohrs and Kaleschke, 2012) and the lead fraction maps using CryoSat-2 (Wernecke and Kaleschke, 2015) are available on their website (http://icdc.cen.uni-hamburg.de/1/daten/cryosphere.html). Willmes and Heinemann (2016) also produced daily lead maps over the entire Arctic Region, classifying land, cloud, sea ice, lead-artefact, and lead fraction maps using MODIS data with the spatial resolution less than 2 km. The lead-artefact class was excluded to calculate monthly lead fraction maps.…”

Section: Monthly Lead Fraction Mapsmentioning

confidence: 99%

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

Lee¹,

Kim²,

Im³

2017

Preprint

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Abstract. Arctic sea ice leads play a major role in exchanging heat and momentum between the Arctic atmosphere and ocean 10 as well as in the retrieval of sea ice thickness. Although leads cover only a small portion of the Arctic Ocean, they affect the heat budget in the Arctic region considerably. In this study, we propose a waveform mixture analysis to detect leads, which is novel and different from the existing threshold-based lead detection methods. The waveform mixture analysis adopts the concept of linear mixture analysis that is widely used in the field of hyperspectral image analysis. This lead detection method based on the waveform mixture analysis was evaluated with high resolution (250m) MODIS images and showed better 15 performance in detecting leads than previous methods. The robustness of the proposed approach also lies in the fact that it does not require the rescaling of parameters, as it directly uses L1B waveform data unlike the existing threshold-based methods.Monthly lead fraction maps derived by the waveform mixture analysis were produced, which show a strong inter-annual variability of recent sea ice cover during 2006-2011 excluding the summer season (i.e., June to September). We also compared the lead fraction maps to other lead fraction maps generated from the literature, resulting in similar spatiotemporal patterns. 20

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Sea-Ice Wintertime Lead Frequencies and Regional Characteristics in the Arctic, 2003–2015

Cited by 91 publications

References 46 publications

Circumpolar polynya regions and ice production in the Arctic: results from MODIS thermal infrared imagery from 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea

Circumpolar polynya regions and ice production in the Arctic: results from MODIS thermal infrared imagery from 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea

On the retrieval of sea ice thickness and snow depth using concurrent laser altimetry and L-band remote sensing data

Arctic lead detection using a waveform mixture algorithm from CryoSat-2 data

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