scholarly journals Mapping Sea Ice Surface Topography in High Fidelity with ICESat-2

2020 ◽  
Author(s):  
Sinéad Farrell ◽  
Kyle Duncan ◽  
Ellen Buckley ◽  
Jacqueline Richter-Menge ◽  
Ruohan Li
2020 ◽  
Vol 47 (21) ◽  
Author(s):  
S. L. Farrell ◽  
K. Duncan ◽  
E. M. Buckley ◽  
J. Richter‐Menge ◽  
R. Li

2021 ◽  
Author(s):  
Roberta Pirazzini ◽  
Henna-Reetta Hannula ◽  
David Brus ◽  
Ruzica Dadic ◽  
Martin Scnheebeli

<p>Aerial albedo measurements and detailed surface topography of sea ice are needed to characterize the distribution of the various surface types (melt ponds of different depth and size, ice of different thicknesses, leads, ridges) and to determine how they contribute to the areal-averaged albedo on different horizontal scales. These measurements represent the bridge between the albedo measured from surface-based platforms, which typically have metre-to-tens-of-meters footprint, and satellite observations or large-grid model outputs.</p><p>Two drones were operated in synergy to measure the albedo and map the surface topography of the sea ice during the leg 5 of the MOSAiC expedition (August-September 2020), when concurrent albedo and surface roughness measurements were collected using surface-based instruments. The drone SPECTRA was equipped with paired Kipp and Zonen CM4 pyranometers measuring broadband albedo and paired Ocean Optics STS VIS (350 – 800 nm) and NIR (650-1100 nm) micro-radiometers measuring visible and near-infrared spectral albedo, and the drone Mavic 2 Pro was equipped with camera to perform photography mapping of the area measured by the SPECTRA drone.</p><p>Here we illustrate the collected data, which show a drastic change in sea ice albedo during the observing period, from the initial melting state to the freezing and snow accumulation state, and demonstrate how this change is related to the evolution of the different surface features, melt ponds and leads above all. From the data analysis we can conclude that the 30m albedo is not significantly affected by the individual surface features and, therefore, it is potentially representative of the sea ice albedo in satellite footprint and model grid areas.</p><p>The Digital Elevation Models (DEMs) of the sea ice surface obtained from UAV photogrammetry are combined with the DEMs based on Structure From Motion technique that apply photos manually taken close to the surface. This will enable us to derive the surface roughness from sub-millimeter to meter scales, which is critical to interpret the observed albedo and to develop correction methods to eliminate the artefacts caused by shadows.</p><p>The UAV-based albedo and surface roughness are highly complementary also to analogous helicopter-based observations, and will be relevant for the interpretation of all the physical and biochemical processes observed at and near the sea ice surface during the transition from melting to freezing and growing.</p>


1993 ◽  
Vol 45 (2) ◽  
pp. 127-142 ◽  
Author(s):  
John E. Lewis ◽  
Matti LeppäRanta ◽  
Hardy B. Granberg

2017 ◽  
Author(s):  
Wolfgang Dierking ◽  
Oliver Lang ◽  
Thomas Busche

Abstract. Quantitative parameters characterizing the sea ice surface topography are needed in geophysical investigations such as studies on atmosphere-ice interactions or sea ice mechanics. Recently, the use of space-borne single-pass interferometric synthetic aperture radar (InSAR) for retrieving the ice surface topography has attracted notice among geophysicists. In this paper the potential of InSAR measurements is examined for several satellite configurations and radar frequencies, considering statistics of heights and widths of ice ridges as well as possible magnitudes of ice drift. It is shown that theoretically surface height variations can be retrieved with relative errors ≤ 0.5 m. In practice, however, the sea ice drift and open water leads may contribute significantly to the measured interferometric phase. Another essential factor is the dependence of the achievable interferometric baseline on the satellite orbit configurations. Possibilities to assess the influence of different factors on the measurement accuracy are demonstrated: signal-to-noise ratio, presence of a snow layer, and the penetration depth into the ice. Practical examples of sea surface height retrievals from bistatic SAR images collected during the TanDEM-X Science Phase are presented.


2017 ◽  
Vol 11 (4) ◽  
pp. 1967-1985 ◽  
Author(s):  
Wolfgang Dierking ◽  
Oliver Lang ◽  
Thomas Busche

Abstract. Quantitative parameters characterizing the sea ice surface topography are needed in geophysical investigations such as studies on atmosphere–ice interactions or sea ice mechanics. Recently, the use of space-borne single-pass interferometric synthetic aperture radar (InSAR) for retrieving the ice surface topography has attracted notice among geophysicists. In this paper the potential of InSAR measurements is examined for several satellite configurations and radar frequencies, considering statistics of heights and widths of ice ridges as well as possible magnitudes of ice drift. It is shown that, theoretically, surface height variations can be retrieved with relative errors  ≤  0.5 m. In practice, however, the sea ice drift and open water leads may contribute significantly to the measured interferometric phase. Another essential factor is the dependence of the achievable interferometric baseline on the satellite orbit configurations. Possibilities to assess the influence of different factors on the measurement accuracy are demonstrated: signal-to-noise ratio, presence of a snow layer, and the penetration depth into the ice. Practical examples of sea surface height retrievals from bistatic SAR images collected during the TanDEM-X Science Phase are presented.


2021 ◽  
Author(s):  
Ruzica Dadic ◽  
Martin Schneebeli ◽  
Henna-Reeta Hannula ◽  
Amy Macfarlane ◽  
Roberta Pirazzini

<p>Snow cover dominates the thermal and optical properties of sea ice and the energy fluxes between the ocean and the atmosphere, yet data on the physical properties of snow and its effects on sea ice are limited. This lack of data leads to two significant problems: 1) significant biases in model representations of the sea ice cover and the processes that drive it, and 2) large uncertainties in how sea ice influences the global energy budget and the coupling of climate feedback. The  MOSAiC research initiative enabled the most extensive data collection of snow and surface scattering layer (SSL) properties over sea ice to date. During leg 5 of the MOSAiC expedition, we collected multi-scale (microscale to 100-m scale) measurements of the surface layer (snow/SSL) over first year ice (FYI) and MYI on a daily basis. The ultimate goal of our measurements is to determine the spatial distribution of physical properties of the surface layer. During leg 5 of the MOSAiC expedition, that surface layer changed from the  surface scattering layer (SSL),   characteristic for the melt season, to an early autumn snow pack. Here,  we will present data showing both a) the physical properties and the spatial distribution of the SSL during the late melt season and b) the transition of the sea ice surface from the SSL to the fresh autumn snowpack. The structural properties of this transition period are poorly documented, and this season is critical  for the initialization of sea ice and snow models. Furthermore, these data are crucial to interpret simultaneous observations of surface energy fluxes, surface optical and remote sensing data (microwave signals in particular), near-surface biochemical activity, and to understand the sea ice  processes that occur as the sea ice transitions from melting to freezing.</p>


2021 ◽  
Author(s):  
Marc Oggier ◽  
Hajo Eicken ◽  
Robert Rember ◽  
Allison Fong ◽  
Dmitry V. Divine ◽  
...  

<p>Sea ice affects the exchange of energy and matter between the atmosphere and the ocean from local to hemispheric scales. Salt fluxes across the ice-ocean interface that drive thermohaline mixing beneath growing sea ice are important elements of upper ocean nutrient and carbon exchange. Sea-ice melt releases freshwater into the upper ocean and results in formation of melt ponds that affect gas and energy transfer across the atmosphere-ice interface. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provided an opportunity to follow sea-ice evolution and exchange processes over a full seasonal cycle in a rapidly changing ice cover. To this end, approximately 25 sea-ice cores were collected at 2 distinct sites, representing first-year and multi-year ice, to monitor physical, biological and geochemical processes relevant to atmosphere-ice-ocean exchange processes. Here we compare the growth and decay of first-year ice in the Central Arctic during the winter 2019-2020 to that of landfast first-year ice at Utqiaġvik, Alaska, from 1998 to 2016. Ice stratigraphy was similar at both sites with about 15 cm of granular ice on top of columnar ice, with a comparable growth history with a similar maximum ice thickness of 1.6-1.7 m. We aggregated the sea-ice bulk salinity and temperature profiles using a degree-day approach, and examined brine and freshwater fluxes at lower and upper interfaces of the ice, respectively. Preliminary results show lower sea-ice bulk salinity during the growth season and greater desalination at the ice surface during the melt season at the MOSAiC floe in comparison to Utqiaġvik.</p>


2021 ◽  
Author(s):  
Dorsa Nasrollahi Shirazi ◽  
Michel Tsamados ◽  
Isobel Lawrence ◽  
Sanggyun Lee ◽  
Thomas Johnson ◽  
...  

<p>The Copernicus operational Sentinel-3A since February 2016 and Sentinel-3B since April 2018 build on the CryoSat-2 legacy in terms of their synthetic aperture radar (SAR) mode altimetry providing high-resolution radar freeboard elevation data over the polar regions up to 81N. This technology combined with the Ocean and Land Colour Instrument (OLCI) imaging spectrometer offers the first space-time collocated optical imagery and radar altimetry dataset. We use these joint datasets for validation of several existing surface classification algorithms based on Sentinel-3 altimeter echo shapes. We also explore the potential for novel AI techniques such as convolutional neural networks (CNN) for winter and summer sea ice surface classification (i.e. melt pond fraction, lead fraction, sea ice roughness). For lead surface classification we analyse the winters of 2018/19 and 2019/20 and for summer sea ice feature classification we focus on the Sentinel-3A &3B tandem phase of the summer 2018. We compare our CNN models with other existing surface classification algorithms.</p>


1979 ◽  
Vol 22 (88) ◽  
pp. 473-502 ◽  
Author(s):  
Seelye Martin

AbstractFrom field observations this paper describes the growth and development of first-year sea ice and its interaction with petroleum. In particular, when sea ice initially forms, there is an upward salt transport so that the ice surface has a highly saline layer, regardless of whether the initial ice is frazil, columnar, or slush ice. When the ice warms in the spring, because of the eutectic condition, the surface salt liquifies and drains through the ice, leading to the formation of top-to-bottom brine channels and void spaces in the upper part of the ice. If oil is released beneath winter ice, then the oil becomes entrained in thin lenses within the ice. In the spring, this oil flows up to the surface through the newly-opened brine channels and distributes itself within the brine-channel feeder systems, on the ice surface, and in horizontal layers in the upper part of the ice. The paper shows that these layers probably form from the interaction of the brine drainage with the percolation of melt water from surface snow down into the ice and the rise of the oil from below. Finally in the summer, the oil on the surface leads to melt-pond formation. The solar energy absorbed by the oil on the surface of these melt ponds eventually causes the melt pond to melt through the ice, and the oil is again released into the ocean.


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