SVALBARD 2006 EXPERIMENTAL OIL SPILL UNDER ICE: REMOTE SENSING, OIL WEATHERING UNDER ARCTIC CONDITIONS AND ASSESSMENT OF OIL REMOVAL BY IN-SITUBURNING

2008 ◽  
Vol 2008 (1) ◽  
pp. 681-688 ◽  
Author(s):  
David Dickins ◽  
Per Johan Brandvik ◽  
John Bradford ◽  
Liv-Guri Faksness ◽  
Lee Liberty ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Field Experiments ◽  
New Technologies ◽  
Time Window ◽  
The Arctic ◽  
Small Scale ◽  
Oil Removal ◽  
Arctic Conditions

ABSTRACT This paper describes the findings from an experimental spill of 3,400 liters of Statfjord crude under first-year sea ice in Svalbard, Norway in March 2006. The objectives were to:1. Test commercially available radar and acoustics systems for detecting oil spilled under ice.2. Document the weathering processes governing crude oil behaviour in ice.3. Confirm the effectiveness of in-situ burning as an oil removal strategy. The results of this project will be used in planning new Arctic oil exploration and development programs. With the growing awareness of the Arctic basin as a potentially important province for new oil and gas discoveries, there is a critical need to: (1) develop new technologies to detect and map spills under ice; (2) increase the understanding of oil behaviour in ice and: (3) continue to demonstrate the capabilities of in-situ burning as an important and safe Arctic response tool. Tank tests conducted in 2004 (Dickins et al., 2005) showed that radar systems could detect and map oil pools as thin as 2 to 3 cm under controlled conditions under model sea ice up to 40 cm thick. This field experiment created a much larger-scale spill under thicker 65 cm natural sea ice to further evaluate potential remote sensing systems as practical operational spill response tools. The findings of the 2006 experiment: (1) demonstrated for the first time the ability of ground penetrating radar to detect and map oil under natural sea ice from the surface; (2) documented oil weathering with a relatively warm ice sheet under spring conditions; and (3) confirmed the effectiveness of in situ burning as a primary oil removal strategy under Arctic conditions. Oil weathering results are discussed and compared with small-scale field experiments performed on Svalbard during the period 2003–2006. Low temperatures and lack of waves in ice act to reduce oil spreading, evaporation, emulsification and dispersion. As a result, the operational time window for several spill response strategies such as dispersants and in-situ burning may be significantly extended compared to oil spills in open water.

scholarly journals Remote sensing of sea ice: advances during the DAMOCLES project

2012 ◽  
Vol 6 (6) ◽  
pp. 1411-1434 ◽  
Author(s):  
G. Heygster ◽  
V. Alexandrov ◽  
G. Dybkjær ◽  
W. von Hoyningen-Huene ◽  
F. Girard-Ardhuin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Estimation Method ◽  
Satellite Observations ◽  
Microwave Emission ◽  
The Arctic ◽  
Ice Thickness ◽  
In Situ Observation ◽  
Microwave Emissivity

Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the European Union's (EU) project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). In order to better understand the seasonal variation of the microwave emission of sea ice observed from space, the monthly variations of the microwave emissivity of first-year and multi-year sea ice have been derived for the frequencies of the microwave imagers like AMSR-E (Advanced Microwave Scanning Radiometer on EOS) and sounding frequencies of AMSU (Advanced Microwave Sounding Unit), and have been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. In addition, a sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emissivities from 6 GHz to 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but is more difficult to observe directly. The size of the snow grains on top of the sea ice influences both its albedo and the microwave emission. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and demonstrated in an application on the Ross ice shelf. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMOCLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer ASCAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

scholarly journals Remote sensing of sea ice: advances during the DAMOCLES project

2012 ◽  
Vol 6 (1) ◽  
pp. 37-88
Author(s):  
G. Heygster ◽  
V. Alexandrov ◽  
G. Dybkjær ◽  
F. Girard-Ardhuin ◽  
W. von Hoyningen-Huene ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Estimation Method ◽  
Satellite Observations ◽  
The Arctic ◽  
Visible Range ◽  
Ice Thickness ◽  
In Situ Observation ◽  
Microwave Emissivity

Abstract. In the Arctic, global warming is particularly pronounced so that we need to monitor its development continuously. On the other hand, the vast and hostile conditions make in situ observation difficult, so that available satellite observations should be exploited in the best possible way to extract geophysical information. Here, we give a résumé of the sea ice remote sensing efforts of the EU project DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies). The monthly variation of the microwave emissivity of first-year and multiyear sea ice has been derived for the frequencies of the microwave imagers like AMSR-E and sounding frequencies of AMSU, and has been used to develop an optimal estimation method to retrieve sea ice and atmospheric parameters simultaneously. A sea ice microwave emissivity model has been used together with a thermodynamic model to establish relations between the emisivities at 6 GHz and 50 GHz. At the latter frequency, the emissivity is needed for assimilation into atmospheric circulation models, but more difficult to observe directly. A method to determine the effective size of the snow grains from observations in the visible range (MODIS) is developed and applied. The bidirectional reflectivity distribution function (BRDF) of snow, which is an essential input parameter to the retrieval, has been measured in situ on Svalbard during the DAMOCLES campaign, and a BRDF model assuming aspherical particles is developed. Sea ice drift and deformation is derived from satellite observations with the scatterometer ASCAT (62.5 km grid spacing), with visible AVHRR observations (20 km), with the synthetic aperture radar sensor ASAR (10 km), and a multi-sensor product (62.5 km) with improved angular resolution (Continuous Maximum Cross Correlation, CMCC method) is presented. CMCC is also used to derive the sea ice deformation, important for formation of sea ice leads (diverging deformation) and pressure ridges (converging). The indirect determination of sea ice thickness from altimeter freeboard data requires knowledge of the ice density and snow load on sea ice. The relation between freeboard and ice thickness is investigated based on the airborne Sever expeditions conducted between 1928 and 1993.

In-situ automatic observations of ice thickness of seas

2012 ◽  
Vol 19 (3) ◽  
pp. 583-592 ◽  
Author(s):  
Yinke Dou ◽  
Xiaomin Chang
Keyword(s):  
Sea Ice ◽  
New Technologies ◽  
Capacitive Sensor ◽  
Automatic Monitoring ◽  
Ice Thickness ◽  
In Situ Observation ◽  
Glacier Surface ◽  
Sea Ice Thickness ◽  
Surface Ice

Abstract Ice thickness is one of the most critical physical indicators in the ice science and engineering. It is therefore very necessary to develop in-situ automatic observation technologies of ice thickness. This paper proposes the principle of three new technologies of in-situ automatic observations of sea ice thickness and provides the findings of laboratory applications. The results show that the in-situ observation accuracy of the monitor apparatus based on the Magnetostrictive Delay Line (MDL) principle can reach ±2 mm, which has solved the “bottleneck” problem of restricting the fine development of a sea ice thermodynamic model, and the resistance accuracy of monitor apparatus with temperature gradient can reach the centimeter level and research the ice and snow substance balance by automatically measuring the glacier surface ice and snow change. The measurement accuracy of the capacitive sensor for ice thickness can also reach ±4 mm and the capacitive sensor is of the potential for automatic monitoring the water level under the ice and the ice formation and development process in water. Such three new technologies can meet different needs of fixed-point ice thickness observation and realize the simultaneous measurement in order to accurately judge the ice thickness.

scholarly journals Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement

2021 ◽  
Vol 9 (7) ◽  
pp. 755
Author(s):  
Kangkang Jin ◽  
Jian Xu ◽  
Zichen Wang ◽  
Can Lu ◽  
Long Fan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sea Ice ◽  
Convolutional Neural Network ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Small Scale ◽  
Acoustic Tomography ◽  
Arctic Sea ◽  
Current Inversion

Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion.

scholarly journals Sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2

2014 ◽  
Vol 8 (1) ◽  
pp. 845-885 ◽  
Author(s):  
R. K. Scharien ◽  
K. Hochheim ◽  
J. Landy ◽  
D. G. Barber
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Temporal Frequency ◽  
Model Performance ◽  
Free Water ◽  
Light Transmittance ◽  
The Arctic ◽  
Ice Melt ◽  
Melt Pond

Abstract. Observed changes in the Arctic have motivated efforts to understand and model its components as an integrated and adaptive system at increasingly finer scales. Sea ice melt pond fraction, an important summer sea ice component affecting surface albedo and light transmittance across the ocean-sea ice–atmosphere interface, is inadequately parameterized in models due to a lack of large scale observations. In this paper, results from a multi-scale remote sensing program dedicated to the retrieval of pond fraction from satellite C-band synthetic aperture radar (SAR) are detailed. The study was conducted on first-year sea (FY) ice in the Canadian Arctic Archipelago during the summer melt period in June 2012. Approaches to retrieve the subscale FY ice pond fraction from mixed pixels in RADARSAT-2 imagery, using in situ, surface scattering theory, and image data are assessed. Each algorithm exploits the dominant effect of high dielectric free-water ponds on the VV/HH polarisation ratio (PR) at moderate to high incidence angles (about 40° and above). Algorithms are applied to four images corresponding to discrete stages of the seasonal pond evolutionary cycle, and model performance is assessed using coincident pond fraction measurements from partitioned aerial photos. A RMSE of 0.07, across a pond fraction range of 0.10 to 0.70, is achieved during intermediate and late seasonal stages. Weak model performance is attributed to wet snow (pond formation) and synoptically driven pond freezing events (all stages), though PR has utility for identification of these events when considered in time series context. Results demonstrate the potential of wide-swath, dual-polarisation, SAR for large-scale observations of pond fraction with temporal frequency suitable for process-scale studies and improvements to model parameterizations.

scholarly journals First in situ estimations of small phytoplankton carbon and nitrogen uptake rates in the Kara, Laptev, and East Siberian seas

2018 ◽  
Author(s):  
Bhavya P. Sadanandan ◽  
Jang Han Lee ◽  
Ho Won Lee ◽  
Jae Joong Kaang ◽  
Jae Hyung Lee ◽  
...  
Keyword(s):  
Sea Ice ◽  
Nitrogen Uptake ◽  
N Uptake ◽  
Total Carbon ◽  
The Arctic ◽  
Carbon And Nitrogen ◽  
Uptake Rates ◽  
Wide Range ◽  
Small Phytoplankton

Abstract. Carbon and nitrogen uptake rates by small phytoplankton (0.7–5 μm) in the Kara, Laptev, and East Siberian seas in the Arctic Ocean were quantified using in situ isotope labelling experiments for the first time as part of the NABOS (Nansen and Amundsen Basins Observational System) program during August 21 to September 22, 2013. The depth integrated C, NO3−, and NH4+ uptake rates by small phytoplankton showed a wide range from 0.54 to 15.96 mg C m−2 h−1, 0.05 to 1.02 and 0.11 to 3.73 mg N m−2 h−1, respectively. The contributions of small phytoplankton towards the total C, NO3−, and NH4+ was varied from 24 to 89 %, 32 to 89 %, and 28 to 89 %, respectively. The turnover times for NO3− and NH4+ by small phytoplankton during the present study point towards the longer residence times (years) of the nutrients in the deeper waters, particularly for NO3−. Relatively, higher C and N uptake rates by small phytoplankton obtained during the present study at locations with less sea ice concentrations points towards the possibility of small phytoplankton thrive under sea ice retreat under warming conditions. The high contributions of small phytoplankton towards the total carbon and nitrogen uptake rates suggest capability of small size autotrophs to withstand in the adverse hydrographic conditions introduced by climate change.

scholarly journals Tensile fracture of a single crack in first-year sea ice

2018 ◽  
Vol 376 (2129) ◽  
pp. 20170346 ◽  
Author(s):  
J. P. Dempsey ◽  
D. M. Cole ◽  
S. Wang
Keyword(s):  
Sea Ice ◽  
Field Trips ◽  
Cohesive Crack ◽  
The Arctic ◽  
Tensile Fracture ◽  
First Year ◽  
Crack Model ◽  
Separation Curve ◽  
Ice Phenomena

The break-up of sea ice in the Arctic and Antarctic has been studied during three field trips in the spring of 1993 at Resolute, NWT, and the fall of 2001 and 2004 on McMurdo Sound via in situ cyclic loading and fracture experiments. In this paper, the back-calculated fracture information necessary to the specification of an accurate viscoelastic fictitious (cohesive) crack model is presented. In particular, the changing shape of the stress separation curve with varying conditions and loading scenarios is revealed. This article is part of the theme issue ‘Modelling of sea-ice phenomena’.

The Copernicus global 1/12° oceanic and sea ice reanalysis

2021 ◽  
Author(s):  
Jean-Michel Lellouche ◽  
Romain Bourdalle-Badie ◽  
Eric Greiner ◽  
Gilles Garric ◽  
Angelique Melet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Real Time ◽  
Sea Level ◽  
Observation System ◽  
Small Scale ◽  
Free Access ◽  
In Situ Observation ◽  
Seasonal Forecasts ◽  
Concentration Data

<p>The GLORYS12V1 system is a global eddy-resolving physical ocean and sea ice reanalysis at 1/12° resolution covering the 1993-present altimetry period, designed and implemented in the framework of the Copernicus Marine Environment Monitoring Service (CMEMS). All the essential ocean physical variables from this reanalysis are available with free access through the CMEMS data portal.</p><p>The GLORYS12V1 reanalysis is based on the current CMEMS global real-time forecasting system, apart from a few specificities that are detailed in this manuscript. The model component is the NEMO platform driven at the surface by atmospheric conditions from the ECMWF ERA-Interim reanalysis. Ocean observations are assimilated by means of a reduced-order Kalman filter. Along track altimeter sea level anomaly, satellite sea surface temperature and sea ice concentration data and in situ temperature and salinity (T/S) vertical profiles are jointly assimilated. A 3D-VAR scheme provides an additional correction for the slowly-evolving large-scale biases in temperature and salinity.</p><p>The performance of the reanalysis is first addressed in the space of the assimilated observations and shows a clear dependency on the time-dependent in situ observation system, which is intrinsic to most reanalyses. The general assessment of GLORYS12V1 highlights a level of performance at the state-of-the-art and the reliability of the system to correctly capture the main expected climatic interannual variability signals for ocean and sea ice, the general circulation and the inter-basins exchanges. In terms of trends, GLORYS12V1 shows a higher than observed  warming trend together with a lower than observed global mean sea level rise.</p><p>Comparisons made with an experiment carried out on the same platform without assimilation show the benefit of data assimilation in controlling water masses properties and their low frequency variability. Examination of the deep signals below 2000 m depth shows that the reanalysis does not suffer from artificial signals even in the pre-Argo period.</p><p>Moreover, GLORYS12V1 represents particularly well the small-scale variability of surface dynamics and compares well with independent (non-assimilated) data. Comparisons made with a twin experiment carried out at ¼° resolution allows characterizing and quantifying the strengthened contribution of the 1/12° resolution onto the downscaled dynamics.</p><p>In conclusion, GLORYS12V1 provides a reliable physical ocean state for climate variability and supports applications such as seasonal forecasts. In addition, this reanalysis has strong assets to serve regional applications and should provide relevant physical conditions for applications such as marine biogeochemistry. In a near future, GLORYS12V1 will be maintained to be as close as possible to real time and could therefore provide a relevant reference statistical framework for many operational applications.</p>

scholarly journals Reflective properties of melt ponds on sea ice

2018 ◽  
Vol 12 (6) ◽  
pp. 1921-1937 ◽  
Author(s):  
Aleksey Malinka ◽  
Eleonora Zege ◽  
Larysa Istomina ◽  
Georg Heygster ◽  
Gunnar Spreen ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Atmospheric Correction ◽  
Scattering Coefficient ◽  
Spherical Particles ◽  
The Arctic ◽  
Melt Pond ◽  
Melt Ponds ◽  
Bottom Albedo

Abstract. Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. In this study, the melt pond reflectance is considered in the framework of radiative transfer theory. The melt pond is modeled as a plane-parallel layer of pure water upon a layer of sea ice (the pond bottom). We consider pond reflection as comprising Fresnel reflection by the water surface and multiple reflections between the pond surface and its bottom, which is assumed to be Lambertian. In order to give a description of how to find the pond bottom albedo, we investigate the inherent optical properties of sea ice. Using the Wentzel–Kramers–Brillouin approximation approach to light scattering by non-spherical particles (brine inclusions) and Mie solution for spherical particles (air bubbles), we conclude that the transport scattering coefficient in sea ice is a spectrally independent value. Then, within the two-stream approximation of the radiative transfer theory, we show that the under-pond ice spectral albedo is determined by two independent scalar values: the transport scattering coefficient and ice layer thickness. Given the pond depth and bottom albedo values, the bidirectional reflectance factor (BRF) and albedo of a pond can be calculated with analytical formulas. Thus, the main reflective properties of the melt pond, including their spectral dependence, are determined by only three independent parameters: pond depth z, ice layer thickness H, and transport scattering coefficient of ice σt.The effects of the incident conditions and the atmosphere state are examined. It is clearly shown that atmospheric correction is necessary even for in situ measurements. The atmospheric correction procedure has been used in the model verification. The optical model developed is verified with data from in situ measurements made during three field campaigns performed on landfast and pack ice in the Arctic. The measured pond albedo spectra were fitted with the modeled spectra by varying the pond parameters (z, H, and σt). The coincidence of the measured and fitted spectra demonstrates good performance of the model: it is able to reproduce the albedo spectrum in the visible range with RMSD that does not exceed 1.5 % for a wide variety of melt pond types observed in the Arctic.

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