scholarly journals Comparison of different ridge formation models of Arctic Sea ice with observations from laser profiling

2006 ◽  
Vol 44 ◽  
pp. 403-410 ◽  
Author(s):  
Torge Martin
Keyword(s):  
Sea Ice ◽  
Deformation Energy ◽  
Arctic Sea Ice ◽  
Climate Modelling ◽  
Ice Surface ◽  
Arctic Sea ◽  
Shear Motion ◽  
Redistribution Function ◽  
Ridged Ice ◽  
Ice Surface Roughness

AbstractSea ice deforms under convergent and Shear motion, causing rafting and ridging. This results in thicker ice than could be formed by thermodynamic growth only. Three different approaches to Simulating the formation of pressure ridges in a dynamic–thermodynamic continuum model are considered. They are compared with and evaluated by airborne laser profiles of the Sea-ice Surface roughness. The respective characteristic of each of the three ridging Schemes is (1) a prognostic equation for deformation energy from which ridge parameters are derived; (2) a redistribution function, Shifting ice between two categories, level and ridged, combined with a Monte Carlo Simulation for ridge parameters; and (3) prognostic equations for ridge density and height, resulting in the formation of ridged-ice volume. The model results Show that the ridge density is typically related to the State of ice motion, whereas the mean Sail height is related to the parent ice thickness. In general, all of the three models produce realistic distributions of ridges. Finally, the Second ridging Scheme is regarded as the most appropriate for climate modelling, while the third Scheme has advantages in Short-term Sea-ice forecasting.

Late Summer Arctic Sea Ice Surface Roughness Signatures in C-Band SAR Data

2016 ◽  
Vol 9 (3) ◽  
pp. 1199-1215 ◽  
Author(s):  
Ane S. Fors ◽  
Camilla Brekke ◽  
Sebastian Gerland ◽  
Anthony P. Doulgeris ◽  
Justin F. Beckers
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Late Summer ◽  
Arctic Sea Ice ◽  
Ice Surface ◽  
Arctic Sea ◽  
Sar Data ◽  
Ice Surface Roughness

scholarly journals Arctic Sea Ice Surface Roughness Derived from Multi-Angular Reflectance Satellite Imagery

2018 ◽  
Author(s):  
Anne W. Nolin
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Ice Surface ◽  
Ice Roughness ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Angular Reflectance ◽  
Ice Surface Roughness

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer melt ponds, and ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters meters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values are nearly identical for areas with roughness <20 cm but that for rougher regions, the MISR-derived roughness has a narrower range of values than the ATM data. The algorithm is able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-derived roughness data have a variance of about half that ATM roughness data.

scholarly journals Arctic Sea Ice Surface Roughness Estimated from Multi-Angular Reflectance Satellite Imagery

2018 ◽  
Vol 11 (1) ◽  
pp. 50 ◽  
Author(s):  
Anne Nolin ◽  
Eugene Mar
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Ice Surface ◽  
Ice Roughness ◽  
Arctic Sea ◽  
Angular Reflectance ◽  
Ice Surface Roughness ◽  
Range Of Values

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer meltponds, and affects ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values were nearly identical for areas with a roughness < 20 cm, but for rougher regions, the MISR-estimated roughness had a narrower range of values than the ATM data. The algorithm was able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-estimated roughness data have a variance about half that of ATM roughness data.

scholarly journals Parameterization of Arctic Sea-ice Surface roughness for application in ice type classification

2006 ◽  
Vol 44 ◽  
pp. 224-230 ◽  
Author(s):  
Carola Von Saldern ◽  
Christian Haas ◽  
Wolfgang Dierking
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Classification Rule ◽  
First Year ◽  
Roughness Parameters ◽  
Ice Surface ◽  
Arctic Sea ◽  
Ice Surface Roughness ◽  
Induction Sounding

AbstractStatistics of Arctic Sea-ice Surface roughness have been investigated in order to improve classification of ice-thickness regimes. The data consist of Surface roughness and thickness profiles, acquired Simultaneously by helicopter-borne laser altimetry and electromagnetic induction Sounding. Five thickness classes were identified using the modal thickness as a criterion. For each class, the Statistical properties of the Surface roughness profiles were analyzed. A classification algorithm was designed, which assigns profiles to the thickness classes on the basis of a Set of Selected Statistical roughness parameters. The algorithm was applied to profiles of different lengths. Best results were obtained for 2 km long profiles, for which it was possible to discriminate well between thick first-year and multi-year ice, and to distinguish these classes from thinner ice. The classification rule was tested on data obtained under winter and Summer conditions. The results Suggest that Statistical Surface roughness properties are different for thinner and thicker ice classes. However, individual thin-ice classes cannot be discriminated on the basis of the Selected roughness parameters.

scholarly journals Seasonal changes in Arctic sea-ice morphology

2001 ◽  
Vol 33 ◽  
pp. 171-176 ◽  
Author(s):  
Donald K. Perovich ◽  
Jacqueline A. Richter-Menge ◽  
Walter B. Tucker
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Surface ◽  
Arctic Sea ◽  
Dynamic Forcing ◽  
Break Up

AbstractThe morphology of the Arctic sea-ice cover undergoes large changes over an annual cycle. These changes have a significant impact on the heat budget of the ice cover, primarily by affecting the distribution of the solar radiation absorbed in the ice-ocean system. In spring, the ice is snow-covered and ridges are the prominent features. The pack consists of large angular floes, with a small amount of open water contained primarily in linear leads. By the end of summer the ice cover has undergone a major transformation. The snow cover is gone, many of the ridges have been reduced to hummocks and the ice surface is mottled with melt ponds. One surface characteristic that changes little during the summer is the appearance of the bare ice, which remains white despite significant melting. The large floes have broken into a mosaic of smaller, rounded floes surrounded by a lace of open water. Interestingly, this break-up occurs during summer when the dynamic forcing and the internal ice stress are small During the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment we had an opportunity to observe the break-up process both on a small scale from the ice surface, and on a larger scale via aerial photographs. Floe break-up resulted in large part from thermal deterioration of the ice. The large floes of spring are riddled with cracks and leads that formed and froze during fall, winter and spring. These features melt open during summer, weakening the ice so that modest dynamic forcing can break apart the large floes into many fragments. Associated with this break-up is an increase in the number of floes, a decrease in the size of floes, an increase in floe perimeter and an increase in the area of open water.

scholarly journals Meltwater sources and sinks for multiyear Arctic sea ice in summer

2021 ◽  
Vol 15 (9) ◽  
pp. 4517-4525
Author(s):  
Don Perovich ◽  
Madison Smith ◽  
Bonnie Light ◽  
Melinda Webster
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Arctic Sea Ice ◽  
Sources And Sinks ◽  
Ice Melt ◽  
Ice Surface ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Ice Mass Balance ◽  
Surface Heat Budget

Abstract. On Arctic sea ice, the melt of snow and sea ice generate a summertime flux of fresh water to the upper ocean. The partitioning of this meltwater to storage in melt ponds and deposition in the ocean has consequences for the surface heat budget, the sea ice mass balance, and primary productivity. Synthesizing results from the 1997–1998 SHEBA field experiment, we calculate the sources and sinks of meltwater produced on a multiyear floe during summer melt. The total meltwater input to the system from snowmelt, ice melt, and precipitation from 1 June to 9 August was equivalent to a layer of water 80 cm thick over the ice-covered and open ocean. A total of 85 % of this meltwater was deposited in the ocean, and only 15 % of this meltwater was stored in ponds. The cumulative contributions of meltwater input to the ocean from drainage from the ice surface and bottom melting were roughly equal.

A multi-model CMIP6 study of Arctic sea ice at 127 ka: Sea ice data compilation and model differences

2020 ◽  
Author(s):  
Louise Sime ◽  
Masa Kageyama ◽  
Marie Sicard ◽  
Maria-Vittoria Guarino ◽  
Anne de Vernal ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Climate Modelling ◽  
Last Interglacial ◽  
Nordic Seas ◽  
Arctic Sea ◽  
The Arctic Ocean ◽  
Intercomparison Project

&lt;p&gt;The Last interglacial (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models&amp;#8217; representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 12 climate models in terms of Arctic sea ice. The mean pre-industrial to LIG reduction in minimum sea ice area (SIA) reaches 59% (multi-model mean LIG area is 2.21 mill. km2, compared to 5.85 mill. km2 for the PI), and the range of model results for LIG minimum sea ice area (from 0.02 to 5.65 mill. km2) is larger than for PI (from 4.10 to 8.30 mill. km2). On the other hand there is little change for the maximum sea ice area (which is 12 mill. km2 for both the PI and the LIG, with a standard deviation of 1.04 mill. km2 for PI and 1.21 mill. km2 for LIG). To evaluate the model results we synthesize LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. South of 78&lt;sup&gt;o&lt;/sup&gt;N, in the Atlantic and Nordic seas, the LIG was seasonally ice-free. North of 78&lt;sup&gt;o&lt;/sup&gt;N there are some discrepancies between sea ice reconstructions based on dinocysts/foraminifers/ostracods and IP25: some sites have both seasonal and perennial interpretations based on the same core, but different indicators. Because of the con&amp;#64258;icting interpretations it is not possible for any one model to match every data point in our data synthesis, or say whether the Arctic was seasonally ice-free. Drivers for the inter-model differences are: different phasing of the up and down short-wave anomalies over the Arctic ocean, associated with differences in model albedo; possible cloud property differences, in terms of optical depth; LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally we note that inter-comparisons between the LIG simulations, and simulations with moderate CO2 increase (during the transition to high CO2 levels), may yield insight into likely 21C Arctic sea ice changes using these LIG simulations.&lt;/p&gt;

scholarly journals Comparison of Leads Mapping in the Arctic Ocean Between Landsat and MODIS Ice Surface Temperature Products

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Xiaoping Pang ◽  
Pei Fan ◽  
Xi Zhao ◽  
Qing Ji
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Single Channel ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Landsat 8 ◽  
Ice Surface ◽  
Arctic Sea ◽  
Split Window Algorithm ◽  
Ice Surface Temperature

<p><strong>Abstract.</strong> Leads are linear or wedge-shaped openings in the sea ice cover. They account for about half of the sensible heat transfer from the Arctic Ocean to the atmosphere in winter, though the sea surface area covered by them is only 1%~2% of the total sea ice area, thus monitoring leads changes and mapping leads distributions become an essential role on Arctic researches. Sea ice surface temperature (IST) product from Moderate Resolution Imaging Spectroradiometer (MODIS) is the most used source of leads monitoring and mapping, however, due to the coarse spatial resolution (1km at swath level), it suffers from mixed pixel effect when describes the temperature variations on thin leads (10m~100m), thus an IST product with a finer spatial resolution is needed. Though several surface temperature retrieval algorithms had been introduced based on Landsat 8 thermal imagery, none of them were validated in Arctic sea ice region. Given that the special weather conditions such as air temperature inversion were not taken into consideration, these algorithms may not always suitable for IST acquisition in Arctic. In this paper, we applied five mainstream IST algorithms (three split window algorithms and two single channel methods) on Arctic sea ice, compared the Landsat 8 IST with corresponding MODIS IST product, and validated all the satellite ISTs by in situ temperature measurements from drifting buoys. Compared to the buoy ISTs, the single channel method through web-based atmosphere correction tool provided by Barsi et al. (2003) offers the best accuracy. The split window algorithm proposed by Du et al. (2015) ranks the second, but constrained by the banding effect due to the stripe noise. Split window algorithm introduced by Jiménez-Muñoz et al. (2014) coincides with MODIS IST product best. All of the three methods mentioned above have slightly better accuracy than MODIS IST, particular in thin leads areas, which indicated that Landsat based leads map will provide us a better insight of Arctic sea ice. All the satellite ISTs tend to underestimate the surface temperature than those measured by buoys.</p>

Investigating how platform height affects sea ice radar returns with KuKaSim

2021 ◽  
Author(s):  
Thomas Newman ◽  
Rosemary Willatt ◽  
Julienne Stroeve ◽  
Robbie Mallet ◽  
Michel Tsamados ◽  
...  
Keyword(s):  
Surface Morphology ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Airborne Lidar ◽  
Ice Thickness ◽  
Radar Altimeter ◽  
Sea Ice Thickness ◽  
Ice Surface ◽  
Arctic Sea ◽  
Airborne Lidar Data

&lt;p&gt;Current, and ongoing observations, of Arctic sea ice, indicate a trend towards a younger, thinner and more mobile pack that exhibits significant inter-annual variability. Satellite and airborne radar altimeters have been used extensively to quantify these changes by deriving sea ice freeboard to infer sea ice thickness. Radar returns from altimeters are impacted by both the morphology of snow and ice features on the sea ice surface, in addition to the radar properties of the snowpack, with both contributing to uncertainties in radar-derived sea ice freeboard. Here we make use of airborne lidar data, collected as part of the MOSAiC expedition in the winter of 2019/2020, to investigate the effect of sea ice surface morphology on radar altimeter measurements. We quantify these effects using 'KuKaSim' a forward-modelling approach based on the KuKa instrument deployed at MOSAiC, which allows us to investigate how simulated radar returns vary with radar height. Our results allow us to better constrain the altimetric uncertainty resulting from ice surface morphology, with respect to both radar height and sea ice type, leading to an enhanced understanding of sources of uncertainty in altimeter-derived sea ice thickness products.&lt;/p&gt;

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