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;

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

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;

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 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.

scholarly journals C-Band Polarimetric Coherences and Ratios for Discriminating Sea Ice Roughness

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mukesh Gupta ◽  
Randall K. Scharien ◽  
David G. Barber
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Two Dimensions ◽  
The Arctic ◽  
Rapid Decline ◽  
Ice Surface ◽  
Ice Roughness ◽  
Direct Measurements ◽  
Ice Surface Roughness ◽  
Spaceborne Radar

The rapid decline of sea ice in the Arctic has resulted in a variable sea ice roughness that necessitates improved methods for efficient observation using high-resolution spaceborne radar. The utility of C-band polarimetric backscatter, coherences, and ratios as a discriminator of ice surface roughness is evaluated. An existing one-dimensional backscatter model has been modified to two-dimensions (2D) by considering deviation in the orientation (i.e., the slopes) in azimuth and range direction of surface roughness simultaneously as an improvement in the model. It is shown theoretically that the circular coherence (ρRRLL) decreases exponentially with increasing surface roughness. The crosspolarized coherence (ρHHVH) is found to be less sensitive to surface roughness, whereas the copolarized coherence (ρVVHH) decreases at far-range incidence angles for all ice types. A complete validation of the adapted 2D model using direct measurements of surface roughness is suggested as an avenue for further research.

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.

Importance of variable neutral drag coefficients for ocean-ice and air-ice fluxes in polar regions

2021 ◽  
Author(s):  
Jean Sterlin ◽  
Thierry Fichefet ◽  
Francois Massonnet ◽  
Michel Tsamados
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Surface Fluxes ◽  
The Arctic ◽  
Climate Modelling ◽  
Form Drag ◽  
Drag Coefficients ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Surface Roughness

&lt;p&gt;Sea ice features a variety of obstacles to the flow of air and seawater at its top and bottom surfaces. Sea ice ridges, floe edges, ice surface roughness and melt ponds, lead to a form drag that interacts dynamically with the air-ice and ocean-ice fluxes of heat and momentum. In most climate models, surface fluxes of heat and momentum are calculated by bulk formulas using constant drag coefficients over sea ice, to reflect the mean surface roughness of the interfaces with the atmosphere and ocean. However, such constant drag coefficients do not account for the subgrid-scale variability of the sea ice surface roughness. To study the effect of form drag over sea ice on air-ice-ocean fluxes, we have implemented a formulation that estimates drag coefficients in ice-covered areas comprising the effect of sea ice ridges, floe edges and melt ponds, and ice surface skin (Tsamados et al., 2013) into the NEMO3.6-LIM3 global coupled ice-ocean model. In this work, we thoroughly analyse the impacts of this improvement on the model performance in both the Arctic and Antarctic. A particular attention is paid to the influence of this modification on the air-ice-ocean fluxes of heat and momentum, and the characteristics of the oceanic surface layers. We also formulate an assessment of the importance of variable drag coefficients over sea ice for the climate modelling community.&lt;/p&gt;

scholarly journals Freshwater Sources and Sinks for Arctic Sea Ice in Summer 

2021 ◽  
Author(s):  
Don Perovich ◽  
Madison Smith ◽  
Bonnie Light ◽  
Melinda Webster
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Arctic Sea Ice ◽  
Freshwater Input ◽  
Sources And Sinks ◽  
Ice Melt ◽  
Ice Surface ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Ice Mass Balance

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 freshwater 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 SHEBA field experiment, we calculate the sources and sinks of freshwater produced during summer melt. The total freshwater input to the system from snow melt, 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. 85 % of this freshwater was deposited in the ocean and only 15 % of this freshwater was stored in ponds. The cumulative contributions of freshwater input to the ocean from drainage from the ice surface and bottom melting were roughly equal.

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