Although the presence of a gas phase in sea ice creates the potential for
gas exchange with the atmosphere, the distribution of gas bubbles and
transport of gases within the sea ice are still poorly understood. Currently
no straightforward technique exists to measure the vertical distribution of
air volume fraction in sea ice. Here, we present a new fast and
non-destructive X-ray computed tomography technique to quantify the air
volume fraction and produce separate images of air volume inclusions in sea
ice. The technique was performed on relatively thin (4–22 cm) sea ice
collected from an experimental ice tank. While most of the internal layers
showed air volume fractions
Sea ice is a multi-phase system consisting of ice crystals, salt precipitates, brine, and gas bubbles (i.e., air inclusions). The abundance and morphology of brine and air inclusions are strongly dependent on the temperature and salinity of the sea ice (Cox and Weeks, 1983; Weeks and Ackley, 1986). Microscale studies of sea ice inclusions have in large part focused on the formation and morphology of brine inclusions (as pockets and/or channels) (e.g., Bennington, 1967; Bock and Eicken, 2005; Cole and Shapiro, 1998; Cox and Weeks, 1975; Eicken et al., 2000; Eide and Martin, 1975; Galley et al., 2015; Hunter et al., 2009; Notz and Worster, 2008). Inclusions in large part control the transfer of heat, salt, gases, and radiation between the ocean and atmosphere (Light et al., 2003). Brine and air inclusions in sea ice also affect the optical and electromagnetic properties of sea ice, and are often sites of biological activity (Fritsen et al., 1994; Krembs et al., 2000; Vancoppenolle et al., 2013).
Studies on the formation and morphology of gas inclusions and gas transport
within sea ice are sparse. The air porosity quantitatively defined by the
air volume fraction (
Previous studies of air inclusions morphology in sea ice were based on horizontal thin sections (e.g., Grenfell, 1983; Perovich and Gow, 1991, 1996; Light et al., 2003; Cole et al., 2004). Grenfell (1983), Perovich and Gow (1996) and Cole et al. (2004) highlighted that the columnar ice is usually depleted in air inclusions while top granular ice is described as bubbly with larger air inclusions. Grenfell (1983) measured bubble number distributions in small samples cut from first year sea ice, observing diameters ranging from 0.2 to 4 mm. Perovich and Gow (1996) reported mean bubble diameters ranging from 0.036 to 0.56 mm for 30 cm-thick pancake ice and mean diameter of 2.6 mm on a multi-year hummock. Light et al. (2003) recorded 100 images from thin sections in transmitted light and reported bubble diameters between 0.008 to 0.14 mm in ice columnar ice that was 175 cm thick (Light et al., 2003).
Limitations of current methods have resulted in a lack of details on determination of air volume fraction. Those methods provide inadequate profiles of the vertical distribution of air inclusions in sea ice, especially in the context of ocean–sea-ice atmosphere exchange of gas. The sea ice air volume fraction is most often determined empirically from bulk temperature, salinity and density measurements (after Cox and Weeks, 1983). However, small errors associated with sea ice density measurements result in large errors in the calculated air volume fraction. Perovich and Gow (1996), and Light et al. (2003) used sea ice sections imaged using transmitted light to describe air inclusions within sea ice, given the caveats that undisturbed microstructure required careful thermal control, size may be limited, and the distinction between gas and brine can be ambiguous in transmitted images. While thin section studies are relevant to detail morphometric analysis of inclusions, profile of air volume fraction cannot be deduced from thin section analysis. Another approach is high resolution measurements of the total gas content along a vertical profile using techniques initially developed for continental ice cores (melting–refreezing and toepler pump extraction or summing individual gases concentrations measured using gas chromatography (GC); Tison et al., 2002). These techniques however operate under vacuum, and therefore collect both the dissolved and gaseous phases. Also, this technique does not provide information on the morphology of the bubble content. A third approach used previously is to melt the ice sample in a gas tight container and quantify total gas volume (Rysgaard and Glud, 2004). A problem with this approach, however, is that gases equilibrate to a new bulk gas concentration depending on the salinity and temperature of the melting ice and hence do not represent the actual gas volume at in situ conditions.
We propose a methodological advancement employing computed tomography (CT)
X-ray imaging for measurement of air inclusions within sea ice. For many
years CT X-ray has been widely used as a medical diagnostic tool. This
non-invasive technique has largely contributed to the study of rock
fractures and rock porosity, and has recently been applied to the sea ice
field, advancing percolation theory for the brine system (Golden et al.,
2007; Pringle et al., 2009, Obbard et al., 2009). Here we present
high-resolution profiles of the distribution of air inclusions in sea ice,
which are derived from CT X-ray images of whole ice cores at the
sub-millimeter scale. A detailed statistical analysis of the air volume
fraction in experimental sea ice is presented, as well as comparisons to the
air volume equations of Cox and Weeks (1983) and measurement of total gas
content. Throughout this work, we highlight the parameters and processes
influencing the air porosity (air volume fraction,
The Sea-ice Environmental Research Facility (SERF) at the University of
Manitoba (Winnipeg, Canada) is an in-ground concrete pool with dimensions of
23.3 m (length)
Effect of dimensional error on brine volume and air volume fraction computed by mass–volume density measurement using state equation from Cox and Weeks (1983).
At least four ice cores were extracted on each sampling occasion using a
Mark II core barrel with an internal diameter of 9 cm (Kovacs Ent., Lebanon
NH, USA). One of the cores was destructively interrogated to measure an in
situ ice temperature profile at a depth resolution of 2 cm using a
calibrated probe (Testo 720, precision
The bulk ice concentration of argon (Ar), oxygen (O
The saturation level of a gas affects bubble nucleation in brine inclusions
and is therefore a crucial parameter determining gas flux at the ice–air
interface. Theoretically, nucleation occurs when the sum of the partial
pressures of dissolved gases is higher than the local hydrostatic pressure.
We therefore compared (i) the gas concentrations profile measured in bulk
ice;
To compute the brine volume fraction and the air volume fraction, the bulk
ice density of 5 cm core sections was measured with the mass–volume
technique in a cold lab (
To limit error induced by imperfect sample dimensions, we used a precision
diamond wire saw. The length of each edge (the number of edges per cube
The brine volume was calculated according to Cox and Weeks (1983) using in
situ temperature, bulk ice salinity, and bulk ice density measurements from
the cores. Brine salinity (
To describe the ice crystal texture, horizontal thin sections of maximum
10 cm length were produced in a cold lab at
CT scanning is a non-destructive radiographic approach to examine materials
by creating a three-dimensional image of density contrasts. Ice cores were
imaged using a third generation Siemens Somatom Volume Access sliding gantry
medical CT-Scanner (Siemens SOMATOM Definition AS
Hounsfield (1973) and Knoll (1989) describe the X-ray technique in detail.
The Hounsfield Unit (HU) value for each voxel corresponds to linear X-ray
attenuation (Duliu, 1999), where higher density and higher atomic numbers
result in greater X-ray attenuation. Ice core density was calculated in
terms of tomographical intensity (TI) (in Hounsfield units for each voxel):
The process of pixel selection to create binary images of air inclusions,
thereby defining the air volume fraction (air porosity,
Determination of the most applicable threshold is therefore of the utmost importance here, as in all image classifications in the multitude of fields that employ the technique. Three approaches are typical for determining an optimal threshold; manual threshold selection based on the human visual system, automated threshold selection based on image data, usually employing the image histogram, and a threshold based on a mixture model approach.
There are many automated segmentation techniques described in the literature. In this study segmentation algorithms representing a selection of established thresholding techniques chosen on the basis that they (i) suited a unimodal histogram (Fig. 1d), and they (ii) showed potential for automated characterization of pore space in geomaterials. Global thresholding specifically was selected on the basis of comparative reviews by Sezgin and Sankur (2004) and Iassonov et al. (2009). Global thresholding may be divided into several subcategories depending on the applied approach. These subcategories include those based on signal entropy considerations (Shannon and Weaver, 1948; Pal and Pal, 1989; Pal, 1996) to separate background and foreground voxels, including EN-Kapur and EN-Yen (Kapur et al., 1985; Yen et al., 1995). There exist global thresholding methods that analyze histogram shape (HS), including HS-Zack and HS-Tsai (Zack et al., 1997; Tsai, 1995). Finally, segmentation may be accomplished by clustering (CL) methods, which separate background (i.e., ice) and foreground voxels (i.e., air) by approximating the histogram with a combination of two or more statistical distributions, including CL-Otsu and CL-Ridler (Ridler and Calvard, 1978; Otsu, 1979).
Estimation of the HU value of a pixel containing at least 50 %
of air. Assuming the HU value of air, of ice and brine are
Each segmentation method was tested on the three core image sets (633 total image slices), as well as on selected parts of each image set to insure that the algorithm response was stable. The results of each segmentation method were visually evaluated by comparing the raw and segmented images (Fig. 2) and by computing linear profiles of HU value (Fig. 3) through cross-sectional images and examining them visually to determine the efficacy of various thresholds in identifying air inclusions.
Analysis of variance (ANOVA) demonstrated significant (
Manual segmentation thresholds were defined by inspecting a variety of
different bubbles in different slices (e.g., Fig. 3). Figure 3 indicates
that visual thresholds were subjective; the pixel scale actually makes
visual bubble delineation more ambiguous. Bubble number 2 (Fig. 3) is best
delineated by TI
Finally, the tomographic intensity of “mixed pixels” which appear as
varying shades of grey is dependent of the proportion of air (
The CL-Ridler (TI
Hereafter, the air volume fraction is presented as the mean of the air
volume fraction results computed using the three selected thresholds. The
potential range of the
Our method endeavors to meet the challenge of CT X-ray image threshold
selection in porous materials while lacking knowledge of the optimal
segmentation result. Selecting the most applicable threshold is imperfect
because the resolution of the CT-imaged used will almost always be
insufficient to resolve every object of interest (in this case air
inclusions in sea ice). When the object of interest is smaller than the
spatial resolution of the imager, it appears as a mixed pixel, where the voxel
TIs is function of the amount of air, of ice and/or brine in the voxel,
resulting in voxel TIs different than that of pure ice (or pure air) by some amount. In this
way delineation of an object using TI thresholds is complicated by the TIs
of adjacent pixels/materials. If an air bubble (TI
At the Sea-ice Environmental Research Facility (SERF), the ambient air
temperature varied between
Sea ice microstructural images overlain by the air volume fraction
(red curve) for 14, 16 and 25 January. The
Summarizes the main sea ice characteristics and sea ice properties.
Ice in situ temperature (
Profiles of the total gas content in bulk sea ice measured by gas
chromatography as the sum of O
Sea ice temperature, bulk salinity, brine volume, and bulk ice density profiles for cores sampled on 14 January (4 cm thick), 16 January (8 cm thick) and 25 January (22 cm thick) are shown in Fig. 5 and Table 3.
On 14 January, the bulk salinity profile was approximately linear, and
evolved to a more a C-shaped profile on 16 and 25 January as the granular
top layer remained saline and the top of the columnar layer desalinated
through the experiment (Fig. 5). Calculated brine volume (
Bulk ice densities ranged from 0.84
The total gas content in the sea ice volume increased from its minimum in
the bottom permeable columnar layer to its maximum in the top granular layer
on 14, 16 and 25 January. The total gas content in the sea ice volume also
increased over time (Fig. 6). The total gas content of the permeable
columnar bottom of each of the ice cores (and the entire core on 14 January)
were close to the concentration at saturation with respect to calculated
theoretical atmospheric gas concentrations, leading to saturation factor
ranging from 0.8 to 1.2. This will be referred to as “subsaturated”
(SAT
For each of the three dates sampled, the air volume fraction increased from the bottom columnar ice layer to the granular surface ice layer and the CT-derived air volume fraction in the sea ice increased overall from 14 to 25 January (Figs. 4 and 7a–c) in the same way as was shown by the total gas content analysis (Fig. 6).
In columnar ice, we distinguish permeable (
On all three dates, the maximum air volume fraction occurred in the granular
layers nearest the atmosphere interface increasing from the base of the
granular layer (Fig. 7c and d). As the granular ice layer thickened by snow
ice formation from 0.7 to 4 cm,
Transversal slice at different depth highlighting the proportion
of micro (yellow), large (red) and macro (green) bubbles in each slice
(e.g., [Nbr micro/(Nbr micro
The morphology of air inclusions is characterized quantitatively using their
diameters (Ø, mm) in the transverse (
For each ice core, the bubble size increased from the bottom columnar layer, to the top granular layer and increased over time in the same way as observed by the total gas content measured using the GC method (Fig. 9b). The bottom permeable subsaturated columnar ice contained almost exclusively micro bubbles on all three dates (Fig. 9a and b). Large bubbles occurred more frequently in the intermediate impermeable supersaturated columnar layer than in the bottom permeable subsaturated columnar layer. Macro bubbles were exclusively found close to the ice–atmosphere interface in the snow ice layer (Fig. 9b).
By using computed tomography X-ray imaging with a voxel size of 0.0056 mm
CT X-ray images (of porous materials in particular) are of such high
resolution (in this case voxel
Correlation between CT-derived air volume fraction and the bulk ice gas
content (mL L
The temperature during storage potentially influences our
computation. Storing sea ice at
The cumulated contribution of the macro, large and micro
bubbles to the cumulated relative air volume fraction for the whole ice
core
Although microstructural analysis of sea ice may produce reliable
morphological results for air inclusions, thin sections only represent a
small subsample of the ice core, are time consuming, can be
operator-dependent, and the area and thickness of a thin section limit these
studies to the measurement of intact bubbles within a thin section.
Density-derived air volume fraction results from the mass–volume technique
generally have large errors and very low vertical resolution because they
require large core subsample volumes (e.g., 5 cm
While large and macro bubbles account for less than 17, 22 and 27 % of the bubble population observed for 14 %, 16 and 25 January respectively (Fig. 11a), the large and macro bubbles contribute systematically to more than 50 % of the total air volume fraction produced (Fig. 11a). Even in bottom columnar ice where large bubbles represent only 10 % of the bubble population (Fig. 11d), they contributed to 40 and 22 % of the air porosity of bottom columnar ice on January 14 and 25 respectively (Fig. 11d). For each ice type – granular (Fig. 11b), columnar impermeable (Fig. 11c) and columnar permeable (Fig. 11d) – it is clear that the largest bubbles contribute most to the air porosity (Fig. 11, Table 4), which is not surprising as the latter depends on air bubble size cubed. However air porosity in the permeable columnar layer where the proportion of large bubbles decreased (Fig. 12) seems largely to be controlled by the amount of bubbles (i.e., bubble density number). Increasing the number of bubbles produces also a linear increase in the air volume fraction (Fig. 12) in columnar ice.
Classification and properties of the air inclusions. The
“abundance” is the proportion of micro, large, and macro bubbles on the
total number of air inclusions observed (100 % is the total number of
inclusions in the three data sets (14 Jan
For the air volume fraction to increase above 3 % (e.g., Fig. 12 in the granular layer), the presence of large and macro bubbles are required (Fig. 12).
Large bubbles were more prevalent when brine volume increased (Fig. 13a, red and grey circles). Light et al. (2003) observed that bubbles were contained within brine and concluded that bubble size was limited by the size of the brine inclusion in which they resided. In several slices, we observed lighter pixels around air inclusions indicating these bubbles likely formed in a brine pocket. The CT-scanner used here cannot unambiguously identify these pixels as brine inclusions. To visualize both air and brine inclusions in the same images, finer resolution with respect to sample density and finer spatial resolution are required. For example, Obbard et al. (2009) showed that micro-X-ray-computed tomography with a higher voxel resolution of 1 order of magnitude is suitable for visualization of brine and air inclusions.
The relationship between bubble density: number of bubbles per
mm
In our sea ice samples, the top granular layers are supersaturated, have
large air volume fractions (
In the multiphase sea ice system, the ratio between dissolved gas and bubbles should depend on the bulk ice gas saturation state. In a closed system, when bubble nucleation is exclusively solubility driven, we expect the air volume fraction to be a function of the saturation factor, which would lead to subsaturated sea ice being bubble-free, and high air volume fractions in supersaturated sea ice. However, the observed relationship between air volume fraction and saturation factor is not straightforward (Fig. 13b) and highlights difference between the type of ice (i) bottom permeable columnar ice, (ii) intermediate impermeable columnar ice and (iii) top granular ice.
Physical characteristics of the various ice types. Where the brine
volume exceeds the permeability threshold for columnar ice of 5 % V
Main parameters influencing the air volume fraction. The
“abundance” is the proportion of the inclusions concerned by the processes
on the total of inclusion observed (100 % is the total of inclusions
observed in the three data sets (14 Jan
Within the permeable subsaturated columnar layer near the sea ice bottom,
the air volume fraction is lower than 1 % due to the subsaturated state
of the ice, and independent of the brine volume fraction (Fig. 13a and b,
blue circles). As long as the brine is able to exchange with the underlying
seawater (i.e., when the
On 16 and 25 January, we observed a slight increase of air volume fraction
at the transition between the subsaturated permeable columnar sea ice
and the supersaturated impermeable columnar sea ice at two-thirds of the
total sea ice thickness (isotherm
Within the supersaturated impermeable columnar layer, bubble nucleation is
solubility driven and we expect the air volume fraction to be a function of
the saturation factor. Within the supersaturated impermeable columnar layer,
the air volume fraction becomes increasingly a function of the saturation
factor as the brine volume increases (Fig. 13b, red circles). At low brine
volumes, the air volume fraction is low regardless of the saturation factor,
as indicated by the accumulation of red circles in the top left corner of
Fig. 13b. As brine volume increases in the impermeable supersaturated
intermediate columnar layer, both air volume fraction and bubble size
increase (Fig. 13a and b). At a given SAT
Four successive slices in the snow ice layer on 25 January from
We observed an increase of air volume fraction nearest the ice–atmosphere
interface and generally within the ice surface granular ice layer
(Fig. 7c and d). This granular surface layer had the highest gas content, the
highest saturation factor, the highest air volume fraction
(5 %
The formation of frazil ice is known to contain more gas than columnar ice because it traps gas directly from the atmosphere (Tsurikov, 1979; Cole et al., 2004; Zhou et al., 2013); this explains the increase of air volume fraction in the frazil ice formed on 14 January. Snow-ice formation observed thereafter on 16 and 25 January trapped gas initially contained within the snow. Moreover, rapid freezing of slush forces gas out of solution, building up the air volume fraction nearest the ice–atmosphere interface.
Macro bubbles are exclusively found in granular layer. They seem to be the
result of aggregation of discrete bubbles, like an aggregation of soap bubbles. A
succession of 0.6 mm thick transversal slices from
Bulk salinity and bulk ice total gas content (mL L
The presence of large bubbles and air volume fraction
We used computed tomography X-ray imaging to quantify air inclusion
distribution in sea ice, from which we derive the air volume fraction. Air
inclusions are quickly and easily identified by X-ray tomography and
quantitatively analyzed using segmentation techniques. The threshold
selection is a crucial step requiring careful examination to provide
successful results. The results from the CT X-ray analysis showed similar
trends to conventional density and bulk ice total gas content (mL L
We differentiate between micro bubbles, large bubbles, and macro bubbles
based on their diameters. Micro bubbles are found both in the bottom
columnar permeable layers (
We suggest that bubbles observed in the bottom subsaturated permeable layers are formed by convection-driven nucleation. Here the amount and size of the bubbles are limited by the low saturation state of the brine. Bubbles observed in impermeable columnar supersaturated sea ice are formed by solubility-driven nucleation, where the amount and bubble size is limited by the amount of brine. In growing sea ice, a maximum exists at a given depth just above the permeability transition, confirming the important role of this transition zone in shaping the vertical air volume fraction distribution. Macro bubbles located in the near-surface sea ice are linked to the presence of granular ice and the formation of snow ice (Table 6).
We conclude that processes regulating the vertical distribution of salts do
not control the vertical distribution of gases, because most of the total
gas content (mL L
As a result of the presence of large bubbles and higher air volume fraction measurements in sea ice we introduce new perspectives on processes regulating gas exchange at the ice–atmosphere interface, and note that further work should investigate the effect of air volume fraction on sea ice permeability parameterizations. CT-X-ray imaging may allow for visualizations of transport pathways, for example the upward migration of bubbles. CT-X-ray imaging could be used to investigate the effect of different thermal and crystal texture regimes on bubble formation, dimensions, and their vertical and horizontal distribution in a large number of replicate cores from the same ice cover. This information is vital to the improvement of models involving transport of biochemical compounds and gas transfer between the ocean and the atmosphere in polar oceans.
We gratefully acknowledge the contributions of the Canada Excellence Research Chair (CERC) and Canada Research Chair (CRC) programs. Support was also provided by the Natural Sciences and Engineering Research Council (NSERC), the Canada Foundation for Innovation, and the University of Manitoba. R. J. Galley thanks the NSERC discovery grant program. B. Delille is a research associate of the Fonds de la Recherche Scientifique – FNRS (Belgium). This work is a contribution to the ArcticNet Networks of Centres of Excellence and the Arctic Science Partnership (ASP) asp-net.org. This work is also a contribution to the BIGSOUTH project funded by the Belgian Science Federal Policy Office, and FNRS project 2.4517.11. Edited by: M. Schneebeli