Evolution of Backscattering Coefficients of Drifting Multi-Year Sea Ice during End of Melting and Onset of Freeze-up in the Western Beaufort Sea

Backscattering coefficients of Sentinel-1 synthetic aperture radar (SAR) data of drifting multi-year sea ice in the western Beaufort Sea during the transition period between the end of melting and onset of freeze-up are analyzed, in terms of the incidence angle dependence and temporal variation. The mobile sea ice surface is tracked down in a 1 km by 1 km region centered at a GPS tracker, which was installed during a field campaign in August 2019. A total of 24 Sentinel-1 images spanning 17 days are used and the incidence angle dependence in HH- and HV-polarization are −0.24 dB/deg and −0.10 dB/deg, respectively. Hummocks and recently frozen melt ponds seem to cause the mixture behavior of surface and volume scattering. The normalized backscattering coefficients in HH polarization gradually increased in time at a rate of 0.15 dB/day, whereas the HV-polarization was relatively flat. The air temperature from the ERA5 hourly reanalysis data has a strong negative relation with the increasing trend of the normalized backscattering coefficients in HH-polarization. The result of this study is expected to complement other previous studies which focused on winter or summer seasons in other regions of the Arctic Ocean.

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Drone-based sea ice albedo measurements and photogrammetry during the Arctic freeze-up in the MOSAiC expedition

10.5194/egusphere-egu21-11377 ◽

2021 ◽

Author(s):

Roberta Pirazzini ◽

Henna-Reetta Hannula ◽

David Brus ◽

Ruzica Dadic ◽

Martin Scnheebeli

Keyword(s):

Surface Roughness ◽

Sea Ice ◽

Surface Topography ◽

Near Infrared ◽

Snow Accumulation ◽

The Arctic ◽

Ocean Optics ◽

Surface Features ◽

Ice Surface ◽

Melt Ponds

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.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 &#8211; 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.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.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.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.

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Average cosine coefficient and spectral distribution of the light field under sea ice: Implications for primary production

Elem Sci Anth ◽

10.1525/elementa.363 ◽

2019 ◽

Vol 7 ◽

Cited By ~ 4

Author(s):

L. C. Matthes ◽

J. K. Ehn ◽

S. L.-Girard ◽

N. M. Pogorzelec ◽

M. Babin ◽

...

Keyword(s):

Sea Ice ◽

Primary Production ◽

Radiation Field ◽

Light Field ◽

The Arctic ◽

Daily Production ◽

Ice Surface ◽

Melt Ponds ◽

The Impact ◽

Average Cosine

The Arctic spring phytoplankton bloom has been reported to commence under a melting sea ice cover as transmission of photosynthetically active radiation (PAR; 400–700 nm) suddenly increases with the formation of surface melt ponds. Spatial variability in ice surface characteristics, i.e., snow thickness or melt pond distributions, and subsequent impact on transmitted PAR makes estimating light-limited primary production difficult during this time of year. Added to this difficulty is the interpretation of data from various sensor types, including hyperspectral, multispectral, and PAR-band irradiance sensors, with either cosine-corrected (planar) or spherical (scalar) sensor heads. To quantify the impact of the heterogeneous radiation field under sea ice, spectral irradiance profiles were collected beneath landfast sea ice during the Green Edge ice-camp campaigns in May–June 2015 and June–July 2016. Differences between PAR measurements are described using the downwelling average cosine, μd, a measure of the degree of anisotropy of the downwelling underwater radiation field which, in practice, can be used to convert between downwelling scalar, E0d, and planar, Ed, irradiance. A significantly smallerμd(PAR) was measured prior to snow melt compared to after (0.6 vs. 0.7) when melt ponds covered the ice surface. The impact of the average cosine on primary production estimates, shown in the calculation of depth-integrated daily production, was 16% larger under light-limiting conditions when E0d was used instead of Ed. Under light-saturating conditions, daily production was only 3% larger. Conversion of underwater irradiance data also plays a role in the ratio of total quanta to total energy (EQ/EW, found to be 4.25), which reflects the spectral shape of the under-ice light field. We use these observations to provide factors for converting irradiance measurements between irradiance detector types and units as a function of surface type and depth under sea ice, towards improving primary production estimates.

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Arctic summer sea-ice SAR signatures, melt-season characteristics, and melt-pond fractions

Polar Record ◽

10.1017/s003224740001442x ◽

1997 ◽

Vol 33 (185) ◽

pp. 101-112 ◽

Cited By ~ 15

Author(s):

M. O. Jeffries ◽

K. Schwartz ◽

S. Li

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Late Summer ◽

Beaufort Sea ◽

Early Summer ◽

The Arctic ◽

Late Winter ◽

Melt Pond ◽

Melt Ponds ◽

The Arctic Ocean

AbstractVariations in multiyear sea-ice backscatter from the synthetic aperture radar (SAR) aboard the ERS-1 satellite are interpreted in terms of melt-season characteristics (onset of melt in spring and of freeze-up in autumn, and the duration of the snow-decay period, the melt season, and the melt-pond season) from late winter to early autumn 1992 in two regions of the Arctic Ocean: the northeastern Beaufort Sea adjacent to the Queen Elizabeth Islands in the Canadian high Arctic and the western Beaufort Sea north of Alaska. In the northeastern Beaufort Sea, the onset of melt occurs later, and the periods of snow-cover decay and the occurrence of melt ponds are shorter than in the western Beaufort Sea. These melt-season characteristics of each area are consistent with previous observations that the northeastern Beaufort Sea has one of the most severe summer climates in the Arctic Ocean. A model, which assumes that the backscatter from multiyear floes is the sum of backscatter from bare ice and melt ponds, is used to derive the melt-pond fraction during the summer. The results show that melt-pond fractions decrease from an early-summer maximum of about 60% to a late-summer minimum around 10%. The magnitude of the melt-pond fractions and their decline during the summer is consistent with previous, more qualitative data. The SAR model, which gives melt-pond fractions with lower variability and less uncertainty than previous data, offers an improved approach to the reliable estimation of the areal extent of water on ice floes. Suggestions for further improvement of the model include accounting for the consequences of wind-speed variations, summer snowfall, and freeze/thaw cycles and their effects on melt-pond and ice-surface roughness.

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

10.5194/egusphere-egu21-12274 ◽

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

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.

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Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

Remote Sensing ◽

10.3390/rs13193882 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3882

Author(s):

Jiechen Zhao ◽

Yining Yu ◽

Jingjing Cheng ◽

Honglin Guo ◽

Chunhua Li ◽

...

Keyword(s):

Sea Ice ◽

The Arctic ◽

High Concentration ◽

Sea Ice Concentration ◽

Melt Pond ◽

Ice Surface ◽

Melt Ponds ◽

Ice Concentration ◽

Microwave Imager

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

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Inter-comparison of melt pond products with melt/freeze-up dates and sea ice concentration data

10.5194/egusphere-egu2020-20874 ◽

2020 ◽

Author(s):

Sanggyun Lee ◽

Julienne Stroeve ◽

Michel Tsamados

Keyword(s):

Machine Learning ◽

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Solar Heat ◽

Melt Pond ◽

Ice Surface ◽

Melt Ponds ◽

Ice Concentration

&#160;Melt ponds are a dominant feature on the Arctic sea ice surface in summer, occupying up to about 50 &#8211; 60% of the sea ice surface during advanced melt. Melt ponds normally begin to form around mid-May in the marginal ice zone and expand northwards as the summer melt season progresses. Once melt ponds emerge, the scattering characteristics of the ice surface changes, dramatically lowering the sea ice albedo. Since 96% of the total annual solar heat into the ocean through sea ice occurs between May and August, the presence of melt ponds plays a significant role in this transfer of solar heat, influencing not only the sea ice energy balance, but also the amount of light available under the sea ice and ocean primary productivity. Given the importance melt ponds play in the coupled Arctic climate-ecosystem, mapping and quantification of melt pond variability on a Pan-Arctic basin scale are needed. Satellite-based observations are the only way to map melt ponds and albedo changes on a pan-Arctic scale. R&#246;sel et al. (2012) utilized a MODIS 8-day average product to map melt ponds on a pan-Arctic scale and over several years. In another approach, melt pond fraction and surface albedo were retrieved based on the physical and optical characteristics of sea ice and melt ponds without a priori information using MERIS.Here, we propose a novel machine learning-based methodology to map Arctic melt ponds from MODIS 500m resolution data. We provide a merging procedure to create the first pan-Arctic melt pond product spanning a 20-year period at a weekly temporal resolution. Specifically, we use MODIS data together with machine learning, including multi-layer neural network and logistic regression to test our ability to map melt ponds from the start to the end of the melt season. Since sea ice reflectance is strongly dependent on the viewing and solar geometry (i.e. sensor and solar zenith and azimuth angles), we attempt to minimize this dependence by using normalized band ratios in the machine learning algorithms. Each melt pond retrieval algorithm is different and validation ways are different as well producing somewhat dissimilar melt pond results. In this study, we inter-compare melt ponds products from different institutes, including university of Hamburg, university of Bremen, and university college London. The melt pond maps are compared with melt onset and freeze-up dates data and sea ice concentration. The melt pond maps are evaluated by melt pond fraction statistics from high resolution satellite (MEDEA) images that have not been used for the evaluation in melt pond products.&#160;

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Increasing Multiyear Sea Ice Loss in the Beaufort Sea: A New Export Pathway for the Diminishing Multiyear Ice Cover of the Arctic Ocean

10.1002/essoar.10509833.1 ◽

2021 ◽

Author(s):

David Gareth Babb ◽

Ryan J. Galley ◽

Stephen E. L. Howell ◽

Jack Christopher Landy ◽

Julienne Christine Stroeve ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Beaufort Sea ◽

Ice Cover ◽

The Arctic ◽

Export Pathway ◽

The Arctic Ocean ◽

Multiyear Ice

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A Field Study of Brine Drainage and Oil Entrainment in First-Year Sea Ice

Journal of Glaciology ◽

10.3189/s0022143000014477 ◽

1979 ◽

Vol 22 (88) ◽

pp. 473-502 ◽

Cited By ~ 5

Author(s):

Seelye Martin

Keyword(s):

Sea Ice ◽

Growth And Development ◽

Salt Transport ◽

First Year ◽

Field Observations ◽

Melt Pond ◽

Ice Surface ◽

Melt Ponds ◽

Brine Channel ◽

Horizontal Layers

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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Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

Atmospheric Measurement Techniques ◽

10.5194/amt-8-4025-2015 ◽

2015 ◽

Vol 8 (10) ◽

pp. 4025-4041 ◽

Cited By ~ 4

Author(s):

H.-J. Kang ◽

J.-M. Yoo ◽

M.-J. Jeong ◽

Y.-I. Won

Keyword(s):

Sea Ice ◽

The Arctic ◽

Data Sets ◽

High Latitudes ◽

Ice Surface ◽

Moderate Resolution ◽

Microwave Sounding ◽

Polar Oceans ◽

Moderate Resolution Imaging Spectroradiometer ◽

Ice Surface Temperature

Abstract. Uncertainties in the satellite-derived surface skin temperature (SST) data in the polar oceans during two periods (16–24 April and 15–23 September) 2003–2014 were investigated and the three data sets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice boundary and colder down to −2.04 K in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a less accurate GCM forecast over the seasonally varying frozen oceans than the microwave data. The three data sets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ~ 2.8 ± 1.9 K decade−1) in the northern high regions (70–80° N) as expected from the ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

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Investigation of spatiotemporal variability of melt pond fraction and its relationship with sea ice extent during 2000–2017 using a new data

10.5194/tc-2019-208 ◽

2019 ◽

Author(s):

Yifan Ding ◽

Xiao Cheng ◽

Jiping Liu ◽

Fengming Hui ◽

Zhenzhan Wang

Keyword(s):

Sea Ice ◽

Arctic Basin ◽

Beaufort Sea ◽

Arctic Sea Ice ◽

Spatiotemporal Variability ◽

First Year ◽

Spatial And Temporal Variability ◽

Sea Ice Extent ◽

Melt Pond ◽

Melt Ponds

Abstract. The accurate knowledge of variations of melt ponds is important for understanding Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) from the MODIS surface reflectance. We construct an ensemble-based deep neural network and use in-situ observations of MPF from multi-sources to train the network. The results show that our derived MPF is in good agreement with the observations, and relatively outperforms the MPF retrieved by University of Hamburg. Built on this, we create a new MPF data from 2000 to 2017 (the longest data in our knowledge), and analyze the spatial and temporal variability of MPF. It is found that the MPF has significant increasing trends from late July to early September, which is largely contributed by the MPF over the first-year sea ice. The analysis based on our MPF during 2000–2017 confirms that the integrated MPF to late June does promise to improve the prediction skill of seasonal Arctic sea ice minimum. However, our MPF data shows concentrated significant correlations first appear in a band, extending from the eastern Beaufort Sea, through the central Arctic, to the northern East Siberian and Laptev Seas in early-mid June, and then shifts towards large areas of the Beaufort Sea, Canadian Arctic, the northern Greenland Sea and the central Arctic basin.

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