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.

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.

Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry

2020 ◽  
Author(s):  
Thomas Johnson ◽  
Michel Tsamados ◽  
Jan-Peter Muller ◽  
Julienne Stroeve
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Distribution Functions ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Training Dataset ◽  
Support Vector ◽  
Laser Measurements ◽  
Ice Surface ◽  
Ice Surface Roughness

&lt;div&gt; &lt;div&gt; &lt;p&gt;Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extended over multi-decadal time-scales. &amp;#160;The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery at 275m (red channel) and 1.1 km (all channels) resolutions from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident probability distribution functions of lidar derived elevations from the Airborne Topographic Mapper (ATM) is generated. Surface roughness, defined as the standard deviation of the within-pixel elevations to a best-fit plane, is modelled using Support Vector Regression with a Radial Basis Function kernel, hyperparameters are tuned using GridSearchCV, and performance is assessed using nested cross-validation. We present derived instantaneous and monthly averaged sea ice roughness products at 1.1km and 17.6km resolution over the timespan of IceBridge campaigns (March and April for 2009-2018) on an EASE-2 (Equal-Area Scalable Earth) grid. These show considerable promise in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.&lt;/p&gt; &lt;/div&gt; &lt;/div&gt;&lt;div&gt;&amp;#160;&lt;/div&gt;

scholarly journals Observations of Sea Ice Melt from Operation IceBridge Imagery

2019 ◽  
Author(s):  
Nicholas C. Wright ◽  
Chris M. Polashenski ◽  
Scott T. McMichael ◽  
Ross A. Beyer
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
First Year ◽  
Ice Melt ◽  
Melt Pond ◽  
Ice Surface ◽  
Optical Images ◽  
Arctic Sea ◽  
Ice Floes ◽  
Multiyear Ice

Abstract. The summer albedo of Arctic sea ice is heavily dependent on the fraction and color of melt ponds that form on the ice surface. This work presents a new dataset of sea ice surface fractions along Operation IceBridge (OIB) flight tracks derived from the Digital Mapping System optical imagery set. This dataset was created by deploying version 2 of the Open Source Sea-ice Processing (OSSP) algorithm to NASA’s Advanced Supercomputing Pleiades System. These new surface fraction results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. Observations herein show that first-year ice does not ubiquitously have a higher melt pond fraction than multiyear ice under the same forcing conditions, contrary to established knowledge in the sea ice community. We discover and document a larger possible spread of pond fractions on first year ice leading to both high and low pond coverage, in contrast to the uniform melt evolution that has been previously observed on multiyear ice floes. We also present a selection of optical images that captures both the typical and atypical ice types, as observed from the OIB dataset. We hope to demonstrate the power of this new dataset and to encourage future collaborative efforts to utilize the OIB data to explore the behavior of melt pond formation Arctic sea ice.

Bacterial Production in the Bottom Surface of Sea Ice in the Canadian Subarctic

1990 ◽  
Vol 47 (10) ◽  
pp. 1986-1995 ◽  
Author(s):  
J. N. Bunch ◽  
R. C. Harland
Keyword(s):  
Organic Carbon ◽  
Sea Ice ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Trophic Levels ◽  
Bottom Surface ◽  
First Year ◽  
Bacterial Assemblage ◽  
Ice Surface ◽  
Arctic Sea

Standing stocks of bacteria in the bottom of first-year sea ice at Frobisher Bay, N.W.T., increased fivefold between March and May (1985 and 1986) and constituted up to 5% of particulate organic carbon (POC). Autoradiography demonstrated that approximately one-third of the bacterial assemblage incorporated radioactive thymidine. The mean volume of cells was six times larger than that in the underlying water, and the assemblage was dominated by rod-shaped cells rather than the coccus-shaped cells prevalent in the water column. Bacterial carbon production by 3H-thymidine incorporation amounted to 0.04 mg carbon m−2∙h−1, or a doubling time of about 22 h, in the bottom ice surface and 0.01 mg carbon m−3∙h−1 in the underlying water. The concentration of dissolved organic carbon (DOC) was generally much higher in the bottom ice surface than in the underlying water, and was closely related to rate of cell production. A model of bacterial dependancy on DOC derived from primary production suggests that bacteria are important in the localized production of POC in the bottom of arctic sea ice, and contribute to an early source of nutrition for higher trophic levels before summer production in open water.

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.

scholarly journals Observations of sea ice melt from Operation IceBridge imagery

2020 ◽  
Vol 14 (10) ◽  
pp. 3523-3536
Author(s):  
Nicholas C. Wright ◽  
Chris M. Polashenski ◽  
Scott T. McMichael ◽  
Ross A. Beyer
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
First Year ◽  
Ice Melt ◽  
Melt Pond ◽  
Ice Surface ◽  
Optical Images ◽  
Arctic Sea ◽  
Ice Floes ◽  
Multiyear Ice

Abstract. The summer albedo of Arctic sea ice is heavily dependent on the fraction and color of melt ponds that form on the ice surface. This work presents a new dataset of sea ice surface fractions along Operation IceBridge (OIB) flight tracks derived from the Digital Mapping System optical imagery set. This dataset was created by deploying version 2 of the Open Source Sea-ice Processing (OSSP) algorithm to NASA's Advanced Supercomputing Pleiades System. These new surface fraction results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. Observations herein show that first-year ice does not ubiquitously have a higher melt pond fraction than multiyear ice under the same forcing conditions, contrary to established knowledge in the sea ice community. We discover and document a larger possible spread of pond fractions on first-year ice leading to both high and low pond coverage, in contrast to the uniform melt evolution that has been previously observed on multiyear ice floes. We also present a selection of optical images that capture both the typical and atypical ice types, as observed from the OIB dataset. The derived OIB data presented here will be key to explore the behavior of melt pond formation Arctic sea ice.

Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry

2021 ◽  
Author(s):  
Thomas Johnson ◽  
Michel Tsamados ◽  
Jan-Peter Muller ◽  
Julienne Stroeve
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Model Performance ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Training Dataset ◽  
Support Vector ◽  
Laser Measurements ◽  
Ice Surface ◽  
Ice Surface Roughness

&lt;p&gt;Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extended over multi-decadal time-scales. The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery at 275m (red channel) and 1.1 km (all channels) resolutions from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident roughness is generated. Surface roughness, defined as the standard deviation of the within-pixel elevations to a best-fit plane, is modelled using several techniques and Support Vector Regression with a Radial Basis Function kernel selected. Hyperparameters are tuned using grid optimisation, model performance is assessed using nested cross-validation, and product performance is assessed with independent validation. We present a derived sea ice roughness product at 1.1km resolution over a two-decade timespan (1999 &amp;#8211; 2020) and a corresponding time series analysis by region. These show considerable promise in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads. &lt;/p&gt;

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