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The Arctic Ocean domain. Also shown are the boundaries of the perennial ice zone (PIZ) on October 1, 1996, and May 1, 1997.
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We use National Aeronautics and Space Administration scatterometer (NSCAT), RADARSAT, and ice motion data to examine the perennial ice zone (PIZ) of the Arctic Ocean between October 1996 and April 1997. The PIZ is identified by a simple backscatter-based classification of the gridded NSCAT backscatter fields. The area of the PIZ at the beginning of...
Contexts in source publication
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... analyze the area of the PIZ within the boundaries of the Arctic Ocean domain shown in Figure 1. The area of the PIZ is defined as the sum of the area of all pixels above a certain backscatter threshold. ...
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... variability in the PIZ area superimposed on the downward trend is 62,000 km 2 , 1.3% of the average PIZ area. The boundaries of the PIZ at the beginning of October 1996 and May 1997 (Figure 1) show the westward advection of ice as part of the Beaufort Gyre and the north- ward motion of the PIZ edge in the Eurasian Basin. ...
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... of the NSCAT backscat- ter fields reveals outflows of ice through the Nares Strait be- tween the northwest coast of Greenland and Ellesmere Island. Figure 10 shows an NSCAT backscatter field and a coincident RADARSAT image of the opening into Nares Strait (30-km wide) in the Arctic Ocean. The high-resolution SAR image shows the characteristic "arch" feature, also evident in the NSCAT data, formed from leads at the opening into this nar- row passage. ...
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... compute this ice area within a spatial window using the thresholds de- fined above. The area of the PIZ ice in this region is shown Figure 11. Note that the region within the window excludes any possible outflow of PIZ ice from Lancaster Sound. ...
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... trajectories of the points are estimated using 1-day ice motion fields derived from sequen- tial SSM/I data. Figure 12 shows the polygon at the beginning and end of the 7-month period. The final shape of the polygon seems to give a fair characterization of the motion and deformation of the PIZ interior compared to the boundaries of the PIZ. Figure 13 shows the area of the polygon and PIZ area over the period. ...
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... final shape of the polygon seems to give a fair characterization of the motion and deformation of the PIZ interior compared to the boundaries of the PIZ. Figure 13 shows the area of the polygon and PIZ area over the period. The initial area of the polygon is 3.25 10 6 km 2 . ...
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... we compare our PIZ area with the MY coverage of the Arctic Ocean derived from the SSM/I data. Figure 14 shows the ice area within our Arctic Ocean domain covered by more than 80, 60, and 30% concentrations of MY ice. Several features are evident: (1) our PIZ area estimates correspond to approximately the 30% coverage curve; (2) the variability of the two time series are correlated with the SSM/I-derived data having the higher of the two; and (3) both time series exhibit a downtrend over the 7-month period. ...
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Citations
... Что касается разделения типов льда, то до недавнего времени считалось, что измерения скаттерометров можно использовать только для разделения (по мощности сигнала) многолетнего и однолетнего льдов (Kwok et al., 1999). Однако в одном из недавних исследований обсуждается возможность выделения также класса двухлетнего льда из многолетних льдов. ...
... исследование (Rivas et al., 2018) и цитируемую литературу), о возможности использования данных скаттерометра для картирования торосов в опубликованных до настоящего времени работах не упоминается. При классификации льда иногда для выявления причин повышенных значений УЭПР (многолетний или торосистый однолетний лёд) используются географические маски прикромочной ледовой зоны (Kwok et al., 1999). ...
... The coarse resolution of satellite observations does not allow us to accurately quantify the sea ice loss in relation to sea ice deformation. However, in winter, sea ice area change due to deformation is expected to be negligible due to solid pack ice (approximately 1 %-2 %) (Kwok et al., 1999). In summer, the deformation may be larger. ...
The Pacific sector of the Arctic Ocean (PA, hereafter) is a region sensitive
to climate change. Given the alarming changes in sea ice cover during recent
years, knowledge of sea ice loss with respect to ice advection and melting
processes has become critical. With satellite-derived products from the
National Snow and Ice Center (NSIDC), a 38-year record (1979–2016) of the
loss in sea ice area in summer within the Pacific-Arctic (PA) sector due to
the two processes is obtained. The average sea ice outflow from the PA to the
Atlantic-Arctic (AA) Ocean during the summer season (June–September) reaches
0.173×106 km2, which corresponds to approximately 34 %
of the mean annual export (October to September). Over the investigated
period, a positive trend of 0.004×106 km2 yr−1 is also
observed for the outflow field in summer. The mean estimate of sea ice
retreat within the PA associated with summer melting is 1.66×106 km2, with a positive trend of 0.053×106 km2 yr−1. As a result, the increasing trends of ice
retreat caused by outflow and melting together contribute to a stronger
decrease in sea ice coverage within the PA (0.057×106 km2 yr−1) in summer. In percentage terms, the melting
process accounts for 90.4 % of the sea ice retreat in the PA in summer,
whereas the remaining 9.6 % is explained by the outflow process, on
average. Moreover, our analysis suggests that the connections are relatively
strong (R=0.63), moderate (R=-0.46), and weak (R=-0.24) between
retreat of sea ice and the winds associated with the dipole anomaly (DA),
North Atlantic Oscillation (NAO), and Arctic Oscillation (AO), respectively.
The DA participates by impacting both the advection (R=0.74) and melting
(R=0.55) processes, whereas the NAO affects the melting process (R=-0.46).
... The presence of the peak-valley structure in the histogram during the sea ice growth season (Fig. 4) indicates that there are two major types of sea ice, allowing us to obtain a threshold for discriminating MY ice and FY ice from the scatterometer data, not only for the Arctic sea ice classification, but also for the Antarctic MY ice and FY ice discrimination. The FY sea ice usually has lower backscatter because of its relatively high salinity, which can cause surface backscatter to be the dominant component of the radar return rather than volume backscatter (Kwok et al. 1999). The scattering from the inhomogeneity of the low-salinity MY sea ice volume, hummocks and ridges within the scatterometer resolution element presents a characteristic of high backscatter. ...
Information on sea ice type is an important factor for deriving sea ice parameters from satellite remote sensing data, such as sea ice concentration, extent and thickness. In this study, sea ice in the Weddell Sea was classified by the histogram threshold (HT) method, the Spreen model (SM) method from satellite scatterometer data and the strong contrast (SC) method from radiometer data, and this information was compared with Antarctic Sea Ice Processes and Climate (ASPeCt) sea ice-type ship-based observations. The results show that all three methods can distinguish the multi-year (MY) ice and first-year (FY) ice using Ku-band scatterometer data and radiometer data during the ice growth season, while C-band scatterometer data are not suitable for MY ice and FY ice discrimination using HT and SM methods. The SM model has a smaller MY ice classification extent than the HT method from scatterometer data. The classification accuracy of the SM method is the higher compared to ship-based observations. It can be concluded that the SM method is a promising method for discriminating MY ice from FY ice. These results provide a reference for further retrieval of long-term sea ice-type information for the whole of Antarctica.
... However, in winter, sea ice area change due to deformation is expected to be negligible due to solid pack ice (approximately 1~2%) (Kwok et al., 1999). In summer, the deformation may be larger. ...
The Pacific–Arctic (PA) Ocean is a region sensitive to climate change. Given the alarming changes in sea ice cover during recent years, knowledge of sea ice loss with respect to ice advection and melting processes has become critical. With satellite-derived products from the National Snow and Ice Center (NSIDC), a 38-yr record (1979–2016) of the loss in sea ice area in summer within the Pacific-Arctic (PA) sector due to the two processes is obtained. The average sea ice outflow from the PA to the Atlantic-Arctic (AA) Ocean during the summer season (June–September) reaches 173×103km², which corresponds to approximately 34% of the mean annual export (October to September). Over the investigated period, a positive trend of 4.2×103km²/yr is also observed for the outflow field in summer. The mean estimate of sea ice retreat within the PA associated with summer melting is 1.66×106km², with a positive trend of 53.1×103km²/yr. As a result, the increasing trends of ice retreat caused by outflow and melting together contribute to a stronger decrease in sea ice coverage within the PA (57.3×103km²/yr) in summer. In percentage terms, the melting process accounts for 90.4% of the sea ice retreat in the PA in summer, whereas the remaining 9.6% is explained by the outflow process, on average. Moreover, our analysis suggests that the connections are relatively strong (R=0.63), moderate (R=−0.46), and weak (R=−0.24) between retreat of sea ice and the winds associated with the Dipole Anomaly (DA), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO), respectively. The DA participates by impacting both the advection (R=0.74) and melting (R=0.55) processes, whereas the NAO affects the melting process (R=−0.46).
... We also introduce a geographical mask to screen the high-backscatter response from MIZ sea ice, which has been attributed to surface deformation by compression and irreversible snow/ice metamorphism after melt-freeze events (Voss et al., 2003;Willmes et al., 2011). The geographical mask delimits the Arctic Basin (see red contours in Fig. 11) across the Fram Strait and Svalbard, to Severnaya Zemlya through Franz Josef Land (Kwok et al., 1999). An additional line from Point Barrow to Wrangel Island also excludes the Chukchi Sea from the MY area estimations. ...
This paper
presents the first long-term climate data record of sea ice extents and
backscatter derived from intercalibrated satellite scatterometer missions
(ERS, QuikSCAT and ASCAT) extending from 1992 to the present date (Verhoef et
al., 2018). This record provides a valuable independent account of the
evolution of Arctic and Antarctic sea ice extents, one that is in excellent
agreement with the passive microwave records during the fall and winter
months but shows higher sensitivity to lower concentration and melting sea
ice during the spring and summer months. The scatterometer record also
provides a depiction of sea ice backscatter at C- and Ku-bands, allowing the
separation of seasonal and perennial sea ice in the Arctic and further
differentiation between second-year (SY) and older multiyear (MY) ice
classes, revealing the emergence of SY ice as the dominant perennial ice type
after the historical sea ice loss in 2007 and bearing new evidence on the
loss of multiyear ice in the Arctic over the last 25 years. The relative good
agreement between the backscatter-based sea ice (FY, SY and older MY) classes
and the ice thickness record from Cryosat suggests its applicability as a
reliable proxy in the historical reconstruction of sea ice thickness in the
Arctic.
... We also introduce a geographical mask to screen the high backscatter response from MIZ sea ice, which has been attributed to surface deformation by compression and irreversible snow/ice metamorphism after melt-freeze events (Voss et al., 2003) (Willmes et al., 2011). The geographical mask delimits the Arctic Basin (see red contours in Fig. 11) across the Fram Strait and Svalbard, to Severnaya 15 Zemlya through Franz Josef Land (Kwok, Cunningham and Yueh, 1999). An additional line from Point Barrow to Wrangel Island also excludes the Chukchi Sea from the MY area estimations. ...
This paper presents the first long-term climate data record of sea ice extents and backscatter derived from inter-calibrated satellite scatterometer missions (ERS, QuikSCAT and ASCAT) extending from 1992 to present date. This record provides a valuable independent account of the evolution of Arctic and Antarctic sea ice extents, one that is in excellent agreement with the passive microwave records during the fall and winter months but shows higher sensitivity to lower concentration and melting sea ice during the spring and summer months, providing a means to correct for summer melt ponding errors. The scatterometer record also provides a depiction of sea ice backscatter at C and Ku-band, allowing the separation of seasonal and perennial sea ice in the Arctic, and further differentiation between second year (SY) and older multiyear (MY) ice classes, revealing the emergence of SY ice as the dominant perennial ice type after the record sea ice loss in 2007, and bearing new evidence on the loss of multiyear ice in the Arctic over the last 25 years. The relative good agreement between the backscatter-based sea ice (FY, SY and older MY) classes and the ice thickness record from Cryosat suggests its applicability as a reliable proxy in the historical reconstruction of sea ice thickness in the Arctic.
... Simplifying the observed patterns, high signals in sea-ice backscatter maps correspond to multiyear ice and lower signals correspond to first-year ice. As noted by [19] the persistent contrast in scatterometer data between two types of ice during the winter allows the straightforward delineation of the boundary between the two ice classes with the simple backscatter threshold. Establishing the boundary between first-year and multiyear ice is the main problem that scatterometer data helps to solve however, the stable scatterometer threshold value becomes fixed only after some time in winter. ...
Though changes of the full ice cover in the Arctic are well described due to availability for processing SMMR/SSMI passive microwave data, changes of the composition of the Arctic ice cover, in terms of ice types still need to be quantified, at least for winter season. Algorithms for mapping multiyear ice concentrations based on passive microwave data suffer from a number of problems. In this paper, those problems are described and it has been shown that QuikSCAT scatterometer data can add complimentary information to that available from passive microwave, which can assist in fixing the boundary between first-year and multiyear ice and in removing erroneously classified by passive microwave algorithm multiyear ice in the ice edge area.
... Numerical sea ice models are at a stage of development where their spatial and temporal resolution and underlying physics have evolved far enough to simulate the discontinuities described above, in particular, models by Hibler (2001), Aksenov and Hibler (2001), Zhang and Hibler (1997), Hopkins (2001), Hopkins (1996), Hunke (2001), Pritchard (2001), Heil and Hibler (2002) and others. While a few example data sets including the RADARSAT Geophysical Processing System (RGPS) data set over the western Arctic (Kwok et al., 1998a) currently exist, there are still an enormous number of research topics that can be explored both in the visualization and understanding of the physics of these data. ...
Using Synthetic Aperture Radar (SAR) images from ERS-1, we render high resolution motion fields of sea ice using a multi-resolution processing system. The results are provided at a 400 m resolution, which is an order of magnitude greater than the standard SAR motion products (5–10 km). An error propagation experiment shows a standard deviation of 1.3% day− 1 for the noise in invariant shear resulting from position uncertainties and processing techniques. We use this noise level to determine a significant lower threshold when identifying shear zone discontinuities. As example, a 24-day sequence of images is processed using this system to examine the development and evolution of a shear zone. This evolution is in response to the topographic steering caused by ocean circulation and wind forcing along a continental shelf break. In addition, we adapt the Line Integral Convolution (LIC) to depict flow patterns present in the motion field. Collectively, these motion products provide valuable descriptions of the non-rigid dynamics taking place within the sea ice. Our goal is to complement the existing RADARSAT Geophysical Processing System (RGPS) motion products and aid in the validation and further development of the most progressive “lead-resolving” sea ice models currently available. This form of sea ice visualization is important for understanding air–ice–sea momentum transfer processes that transcend through small-scale to large-scale fracture events with application to ship navigation.
... Early research was carried out to investigate the use of passive microwave data for sea ice classification [Eppler et al., 1986]; however, there are significant uncertainties in passive microwave results [Maslanik, 1992; Thomas, 1993]. The capability of Ku-band scatterometer to map sea ice, including the multi-year ice class, was demonstrated and results for Arctic sea ice were published [Kwok et al., 1999; Nghiem and Neumann, 2002; Nghiem, 2004; Nghiem et al., 2005]. [4] Arctic sea ice consists of two major classes: perennial or multi-year sea ice defined as sea ice that survives at least one summer and can be as thick as 3 m or more [Armstrong et al., 1973], and seasonal or first-year sea ice that grows in the current freezing season and is significantly thinner (0.3 – 2 m) [Armstrong et al., 1973] than perennial ice. ...
The extent of perennial sea ice in the East Arctic Ocean (0-180°E) decreased by nearly one half with an abrupt reduction of 0.95 × 106 km2, while the West Arctic Ocean (0-180°W) had a slight gain of 0.23 × 106 km2 between 2004 and 2005, as observed by satellite scatterometer data during November-December. The net decrease in the total perennial ice extent is 0.72 × 106 km2, about the size of Texas. Perennial ice in the East Arctic Ocean continued to be depleted with an areal reduction of 70% from October 2005 to April 2006. With the East Arctic Ocean dominated by seasonal sea ice, a strong summer melt may open a vast ice-free region with a possible record minimum ice extent largely confined to the West Arctic Ocean. Simultaneous scatterometer measurements of sea ice and winds will be crucial for sea ice monitoring and forecasts.
... The freeze-thaw cycle in the boreal forests is a primary science driver in the combined active-passive Hydrosphere State Mission (HYDROS) mission (Entekhabi et al., 2003). Key sea ice applications include routine generation of sea ice motion maps (Zhao et al., 2002), of value for determining the heat flux between the ocean and atmosphere, and the assessment of changes in the extent of the perennial and seasonal ice packs in the Arctic (Kwok et al., 1999). Both measurements provide a means for assessing polar climate change, particularly related to the large-scale transport of ice in response to shifting atmospheric oscillations and subsequent impacts on ice thickness. ...
Thousands of scientific publications and dozens of textbooks include data from instruments derived from NASA's Seasat. The Seasat mission was launched on June 26, 1978, on an Atlas-Agena rocket from Vandenberg Air Force Base. It was the first Earth-orbiting satellite to carry four complementary microwave experiments—the Radar Altimeter (ALT) to measure ocean surface topography by measuring spacecraft altitude above the ocean surface; the Seasat-A Satellite Scatterometer (SASS), to measure wind speed and direction over the ocean; the Scanning Multichannel Microwave Radiometer (SMMR) to measure surface wind speed, ocean surface temperature, atmospheric water vapor content, rain rate, and ice coverage; and the Synthetic Aperture Radar (SAR), to image the ocean surface, polar ice caps, and coastal regions. While originally designed for remote sensing of the Earth's oceans, the legacy of Seasat has had a profound impact in many other areas including solid earth science, hydrology, ecology and planetary science.
