Fram Strait satellite image‐derived ice motions

Emery, William J.; Fowler, Charles W.; Hawkins, Jeffery D.; Preller, Ruth H.

doi:10.1029/90jc02273

Cited by 125 publications

(83 citation statements)

References 11 publications

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“…Emery et al, 1991;Kwok and Rothrock., 1999;Girard-Ardhuin and Ezraty, 2012). Ice drift information obtained from satellites has been applied in various studies to examine Arctic-wide spatial trends in sea ice drift velocity and area flux (e.g.…”

Section: Sea Ice Drift Datasetmentioning

confidence: 99%

Variability and trends in Laptev Sea ice outflow between 1992–2011

et al. 2013

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Abstract. Variability and trends in seasonal and interannual ice area export out of the Laptev Sea between 1992 and 2011 are investigated using satellite-based sea ice drift and concentration data. We found an average total winter (October to May) ice area transport across the northern and eastern Laptev Sea boundaries (NB and EB) of 3.48 × 10 5 km 2 . The average transport across the NB (2.87 × 10 5 km 2 ) is thereby higher than across the EB (0.61 × 10 5 km 2 ), with a less pronounced seasonal cycle. The total Laptev Sea ice area flux significantly increased over the last decades (0.85 × 10 5 km 2 decade −1 , p > 0.95), dominated by increasing export through the EB (0.55 × 10 5 km 2 decade −1 , p > 0.90), while the increase in export across the NB is smaller (0.3 × 10 5 km 2 decade −1 ) and statistically not significant. The strong coupling between across-boundary SLP gradient and ice drift velocity indicates that monthly variations in ice area flux are primarily controlled by changes in geostrophic wind velocities, although the Laptev Sea ice circulation shows no clear relationship with large-scale atmospheric indices. Also there is no evidence of increasing wind velocities that could explain the overall positive trends in ice export. The increased transport rates are rather the consequence of a changing ice cover such as thinning and/or a decrease in concentration. The use of a back-propagation method revealed that most of the ice that is incorporated into the Transpolar Drift is formed during freeze-up and originates from the central and western part of the Laptev Sea, while the exchange with the East Siberian Sea is dominated by ice coming from the central and southeastern Laptev Sea. Furthermore, our results imply that years of high ice export in late winter (February to May) have a thinning effect on the ice cover, which in turn preconditions the occurence of negative sea ice extent anomalies in summer.

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Section: Sea Ice Drift Datasetmentioning

confidence: 99%

Variability and trends in Laptev Sea ice outflow between 1992–2011

et al. 2013

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“…The satellite-derived drift vectors are calculated by tracking features from one satellite image to the next [Emery et al, 1991[Emery et al, , 1995[Emery et al, , 1997Fowler et al, 2003;Kwok et al, 1998]. Successive images are geo-located, shifted by discrete pixel-widths up to 10 pixels, and compared.…”

Section: Input Datamentioning

confidence: 99%

Increasing transnational sea‐ice exchange in a changing Arctic Ocean

et al. 2017

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The changing Arctic sea-ice cover is likely to impact the trans-border exchange of sea ice between the exclusive economic zones (EEZs) of the Arctic nations, affecting the risk of ice-rafted contamination. We apply the Lagrangian Ice Tracking System (LITS) to identify sea-ice formation events and track sea ice to its melt locations. Most ice (52%) melts within 100 km of where it is formed; ca. 21% escapes from its EEZ. Thus, most contaminants will be released within an ice parcel's originating EEZ, while material carried by over 1 00,000 km 2 of ice-an area larger than France and Germany combined-will be released to other nations' waters. Between the periods 1988-1999 and 2000-2014, sea-ice formation increased by ∼17% (roughly 6 million km 2 vs. 5 million km 2 annually). Melting peaks earlier; freeze-up begins later; and the central Arctic Ocean is more prominent in both formation and melt in the later period. The total area of ice transported between EEZs increased, while transit times decreased: for example, Russian ice reached melt locations in other nations' EEZs an average of 46% faster while North American ice reached destinations in Eurasian waters an average of 37% faster. Increased trans-border exchange is mainly a result of increased speed (∼14% per decade), allowing first-year ice to escape the summer melt front, even as the front extends further north. Increased trans-border exchange over shorter times is bringing the EEZs of the Arctic nations closer together, which should be taken into account in policy development-including establishment of marine-protected areas. Plain Language SummaryWe use data from satellite images to identify the formation, drift tracks, and melt locations of sea ice in the Arctic. Most ice melts locally: only about 21% is exported from the exclusive economic zone (EEZ) in which it is formed. That export is nonetheless about 1,000,000 km 2 each year. As the ice cover has thinned and the summer sea ice has retreated in a warming Arctic, formation and melt locations have moved further north, ice drifts have accelerated, and the area of ice formation and melt has increased. We looked at ice formation and transport between the EEZs of the Arctic nations, and broke the record into two periods : 1988-1999 and 2000-2014. As the Arctic warms, more ice is transported between EEZs and it is arriving at the receiving EEZ faster, than in the past. Between the two study periods: Sea ice velocity increased by about 14%/decade; Russian ice reached melt locations in other nations' EEZs 46% faster; and North American ice reached Eurasian destinations 37% faster. Exchanges of ice have increased as a result. For example, export of ice from Russia to Norway increased by 11% and export from Alaska to Russia by 16%.

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“…The code works directly in the spatial domain, to allow more flexibility in fine-tuning the computational parameters (Emery et al, 1991) and to allow easy parallelization. Critical parts of the algorithm were imple-120 mented on GPU calculation units and programmed using the Open Computing Language (OpenCL) C. OpenCL is a portable language for writing code that can be run in a parallel fashion on a variety of devices (Stone et al, 2010).…”

Section: The Motion Estimation Approachmentioning

confidence: 99%

Comparing C- and L-band SAR images for sea ice motion estimation

2015

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Abstract.Pairs of consecutive C-band synthetic-aperture radar (SAR) images are routinely used for sea ice motion estimation. The L-band radar has a fundamentally different character, as its longer wavelength penetrates deeper into sea ice. L-band SAR provides information on the seasonal sea ice inner structure in addition to the surface roughness that dominates C-band images. This is especially useful in the Baltic Sea, which lacks multiyear ice and icebergs, known to be confusing targets for L-band sea ice classification. In this work, L-band SAR images are investigated for sea ice motion estimation using the well-established maximal crosscorrelation (MCC) approach. This work provides the first comparison of L-band and C-band SAR images for the purpose of motion estimation. The cross-correlation calculations are hardware accelerated using new OpenCL-based source code, which is made available through the author's web site. It is found that L-band images are preferable for motion estimation over C-band images. It is also shown that motion estimation is possible between a C-band and an L-band image using the maximal cross-correlation technique.

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Fram Strait satellite image‐derived ice motions

Cited by 125 publications

References 11 publications

Variability and trends in Laptev Sea ice outflow between 1992–2011

Variability and trends in Laptev Sea ice outflow between 1992–2011

Increasing transnational sea‐ice exchange in a changing Arctic Ocean

Comparing C- and L-band SAR images for sea ice motion estimation

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