TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-11-1075-2017Location and distribution of micro-inclusions in the EDML and NEEM ice cores using optical microscopy and in situ Raman spectroscopyEichlerJanjan.eichler@awi.deKleitzInaBayer-GiraldiMaddalenaJansenDanielaKipfstuhlSeppShigeyamaWataruWeikusatChristianWeikusatIlkahttps://orcid.org/0000-0002-3023-6036Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, 27568 Bremerhaven, GermanyDepartment of Geosciences, Eberhard Karls University Tübingen, 72074 Tübingen, GermanyDepartment of Polar Science, SOKENDAI (The Graduate University for Advanced Studies), 10-3 Midori-cho, Tachikawa, Tokyo, 190-8518, JapanNational Institute of Polar Research, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8518, JapanJan Eichler (jan.eichler@awi.de)5May20171131075109022October201630November201621March201724March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/11/1075/2017/tc-11-1075-2017.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/11/1075/2017/tc-11-1075-2017.pdf
Impurities control a variety of physical properties of polar
ice. Their impact can be observed at all scales – from the microstructure
(e.g., grain size and orientation) to the ice sheet flow behavior (e.g.,
borehole tilting and closure). Most impurities in ice form micrometer-sized
inclusions. It has been suggested that these µ inclusions control
the grain size of polycrystalline ice by pinning of grain boundaries (Zener
pinning), which should be reflected in their distribution with respect to the
grain boundary network. We used an optical microscope to generate
high-resolution large-scale maps (3µmpix-1, 8×2cm2) of the distribution of micro-inclusions in four polar ice
samples: two from Antarctica (EDML, MIS 5.5) and two from Greenland (NEEM,
Holocene). The in situ positions of more than 5000 µ inclusions
have been determined. A Raman microscope was used to confirm the extrinsic
nature of a sample proportion of the mapped inclusions. A superposition of
the 2-D grain boundary network and µ-inclusion distributions shows no
significant correlations between grain boundaries and µ inclusions.
In particular, no signs of grain boundaries harvesting µ inclusions
could be found and no evidence of µ inclusions inhibiting grain
boundary migration by slow-mode pinning could be detected. Consequences for
our understanding of the impurity effect on ice microstructure and rheology
are discussed.
Introduction
Polar meteoric ice is one of the purest materials on Earth. Impurity mass
concentrations in the Antarctic ice sheet vary between ppbm during
warm periods and ppmm in the most dusty layers in cold periods
. Values for the Greenland ice sheet
are approximately 10 times higher. Impurities enter the ice sheet during
deposition in the form of terrestrial dust, salt particles and other
aerosols,
e.g., from bio-activity, ocean, volcanoes or combustion, or in the form of gas
inclusions from air bubbles. Trace substances are a subject of intense ice
core studies for many reasons. Their chemical and isotopic compositions can
be linked to atmospheric and climatic processes (climate proxies), and their
analysis provides an important insight into processes and constraints of the
past of Earth's climate . When linked to geologic events ,
they can serve as absolute chronological markers for ice core dating
sulfate and tephra layers from volcanic eruptions;. And
finally, many material properties of ice are controlled or modulated by its
impurity content. This is the case for the dielectric constant and electrical
conductivity, measured systematically using dielectric profiling (DEP) and
electrical conductivity method (ECM) , but it also applies to mechanical properties of ice
– such as creep behavior and viscosity – which are of great interest with
respect to ice rheology and ice sheet dynamics. The influence of various
impurities on deformation rate has been observed in laboratory tests
e.g., as well as in the field from borehole
tilting and closure e.g.,. The link between impurity
content and strain rate becomes most evident when comparing ice-age ice with
ice from warm periods. Observations show that the impurity-rich ice-age ice
deforms on average 2.5 times faster in simple shear .
Several explanations for this effect have been proposed, but the
responsible mechanisms still remain under discussion. Since ice-age ice
usually develops a different microstructure – e.g., stronger crystal
preferred orientation (CPO) or smaller grains
– the higher strain rates may result from an interplay between impurities,
microstructure and recrystallization.
With increasing interest in the impurity content, analysis methods have been
refined over the past years. Continuous flow analysis (CFA) became a standard
part of ice core processing due to its effectivity and
high depth resolution. However, the aim to understand impurity-related
processes in polycrystalline ice requires the application of approaches
dedicated to the solid state. Thus, advanced analytical techniques, such as
scanning electron microscopy (SEM), Raman spectroscopy or laser ablation
inductively coupled plasma mass spectrometry (LA-ICPMS), became popular with
glaciologists. However, each of these methods brings its own specifications and
constraints and the results often lead to contradictory conclusions.
Comparative studies may be necessary to clear these discrepancies in future.
The composition, form, location and distribution of impurities are different
in ice compared to liquid water. Due to the electric dipole moment of the
H2O molecule, water is a good solvent. In contrast, the ice
crystal is not and will reject most extrinsic substances out of the matrix,
forming a second phase . Only a few elements are
theoretically able to be incorporated into the ice lattice. According to
and , F-, Cl-, K+ and
NH4+ in low concentrations are able to substitute oxygen atoms or
enter the ice lattice interstitials. In both cases they would introduce ionic
and Bjerrum defects and significantly affect the charge carrier density of
the ice crystal. Since protonic defects can alter dislocation mobilities, ice
doped with these species manifests higher strain rates than pure ice
. However, the doping experiments by Jones and Glen probably
represent upper bounds with respect to natural ice, which is formed from snow
flakes inheriting impurities from atmospheric processes. Another widely
discussed form of ice impurities was proposed by , who
interpreted DC conductivity as caused by conduction through acidic
environments along grain boundaries and triple junctions. The authors
suggested that acids like HCl, HNO3 and H2SO4 would
concentrate at grain boundaries lowering the eutectic point of the solute and
thus forming liquid veins. The existence of an acidic vein network was
supported by , who found sulfur at three triple junctions
using energy-dispersive X-ray spectroscopy (EDX), and , who
measured a sulfate peak in a triple junction using a Raman microscope.
However, other Raman-spectroscopic studies by and
found most (or all) sulfates forming salt particles. In
contrast, EDX experiments by , ,
, and found traces of
sodium, chlorine and sulfur in filaments, which would grow out of grain
boundaries after controlled surface sublimation of natural ice samples.
analyzed the distribution of a variety of elements in
discrete samples from the glacial part of the NGRIP ice core using
UV-LA-ICPMS. No correlation was found between impurities and grain boundaries
in cloudy bands, but the authors observed concentration peaks at grain
boundaries in the cleaner parts of the ice. There is no consensus yet about
the abundance and relevance of impurity segregation to grain boundaries.
In terms of mass fraction, most impurities form second phase inclusions,
i.e., inclusions of extrinsic material or another lattice-incoherent phase
. Due to their typical size of a
few micrometers we call them “µ inclusions” in the following.
Water-insoluble dust particles as well as water-soluble particulate salts are
the most abundant µ inclusions. Their concentrations are highly
variable along the ice cores. For instance, dust concentrations in the EDML
ice core measured by CFA vary between 103 and
105 particles per milliliter. Strata with high concentrations of
µ inclusions are visible in ice cores and are often called “cloudy
bands” . µ inclusions appear under an optical
microscope as dark spots near the optical detection limit – commonly
referred to as “black dots” (see Fig. ). When using optical
microscopy it is impossible to examine their state of aggregate, shape, color
or composition, and thus the term “black dot” reflects not only their
visual appearance but also our uncertainty about their nature and
composition.
(a) Schematic of positioning stacked microstructure maps from surface and inside the sample.
The focus distance between the two mapping planes in general depends on the application of this method
(e.g., investigation of air inclusions or µ inclusions).
In order to correlate µ inclusions and grain boundary grooves, for this study a distance
of 300 µm has been chosen.
(b) Surface-focused photomicrograph from EDML-2371.9 (2371 m, drilled in 2006). The image shows
three grain boundaries as black thin curves and a triple junction.
“Black dots” in this map type
are due to surface pollution by frost particles
and should not be confused with µ inclusions in the impurity maps.
Below the surface – and slightly out of focus – a clathrate hydrate (marked with blue circle) and three
plate-like inclusions (hexagonal objects) are visible. Roundish black objects of the size of up to 100 µm
are secondary gas inclusions formed by relaxation of the material. At the surface they are clearly attached to
the left grain boundary.
(c) Photomicrograph focused ca. 300 µm below the surface. The grain boundary grooves disappeared but objects
in the sample volume came into focus – the clathrate hydrate and plate-like inclusions are sharp now.
“Black dots” in this image (pinpointed with arrows) are chemical impurities in the form of µ inclusions.
High concentrations of µ inclusions are usually associated with
certain changes in ice microstructure. On the large scale, ice-age ice was
reported to be characterized by stronger CPO e.g., and cloudy ice exhibits in general smaller grain sizes
than clean ice. Since grain growth involves grain boundary migration,
impeding grain boundary movement by µ inclusions would consequently
have a grain-size-reducing effect. The attractive force between
µ inclusions and grain boundaries is known from material sciences
and has been first modeled by Zener in – Zener pinning.
When a migrating grain boundary passes a particle, its energy is reduced by
the portion of the cross-sectional area. Depending on the grain boundary
driving force, the pinning pressure and the mobility of the particles, grain
boundary can be completely stopped (pinned), free itself after a while from
the particles and continue its motion, or drag the particles with it.
reviewed the Zener theory and available data
with respect to the pinning effect on normal grain growth (NGG) in cold ice. They
differentiate between a low-velocity regime, where the grain boundary is
pinned by the impurities, and a high-velocity regime, where the grain
boundary continues its motion leaving the impurities behind.
conclude that pinning on µ inclusions occurs in
high-velocity regimes and the concentration of microparticles is too low in
general to significantly affect grain growth (the only exceptions are tephra
layers from volcanic eruptions). simulated grain size
evolution along the Dome Concordia ice core by modeling NGG
controlled by pinning on dust particles. They suggest that dust particles
will indeed impede grain growth if they concentrate at grain boundaries.
However, a direct proof for such a particle distribution was not provided,
since grain boundaries could not be imaged. In contrast,
Raman measurements of µ inclusions in the Dome Fuji ice core,
presented by , showed that major part of the
particles (mostly sulfate salts) were located in grain interiors.
evaluated relevant microstructure and impurity data
concerning the integrity of the EDML ice core. According to their report,
black dots in the microstructure mapping images do not accumulate along grain
boundaries for depths down to 2300 m. Only below this depth, and
particularly in the deepest 200 m of the core, were accumulations at grain
boundaries and on the surface of clathrate hydrates observed.
So far, studies on spatial distributions of µ inclusions in ice have
been
based on discrete observations of a few dust or salt particles. In order to
discuss general concepts, such as the formation of the CFA signal,
predominant form of impurities in situ and their effect on recrystallization
(e.g., grain boundary pinning), more systematic and statistically relevant
approaches are necessary. The aim of this study is to provide a more detailed
insight into the in situ concentrations and distributions of
µ inclusions. We mapped over 5000 µ inclusions within four
different ice samples from polar ice cores. Overall and local concentrations
are estimated and compared with the available CFA data. Special attention is
payed to the correlation between µ inclusions and the grain boundary
network.
Methods and sample material
The state in which impurities are included in ice is different from their
form in meltwater (CFA). In order to reveal their distribution and in situ
form, more data based on direct measurements of ice samples are needed.
Optical microscopy and Raman spectroscopy provide the opportunity to explore
the interior of ice samples in a non-destructive way. This is a significant
advantage over surface-based methods (e.g., SEM), where contamination and
sublimation-related redistribution of impurities have to be considered
e.g.,. We use microstructure mapping to create large
area maps (8×2cm2) of the specimens surfaces and
interiors. This microscopical technique was introduced by
and it enables us to pinpoint visible
µ inclusions as well as grain and subgrain boundaries. We use a
Raman microscope to prove that we are dealing with extrinsic material and to
investigate the composition of selected µ inclusions.
As the lower impurity content in warm-period ice provide a better chance to
observe and characterize the majority of µ inclusions over a large
sample area and analyze their correlation with the microstructure, we focused
our study on samples from warmer periods. Furthermore, one of the very few
studies which found evidence for a connection of spatial distribution of
impurities along grain boundaries reported this evidence as a characteristic
of “clean” interstadial ice . Four samples were
analyzed: two from the EDML ice core (Antarctica) and two from the NEEM ice
core (Greenland). The EDML samples (EDML-2371.4 and EDML-2371.9) originate from
2370 m depth which corresponds to the early Eemian (MIS5.5). The NEEM
samples (NEEM-1346.2 and NEEM-1346.5) originate from the Holocene, 740 m below
surface and around 4000 years b2k .
Impurity maps
Double impurity layer in EDML-2371.9 (horizon 1 in Fig. ).
(a) Surface map with grain boundaries visible as thin dark lines highlighted with blue bands.
Blurred lines are grain boundaries at the bottom side of the specimen which are out of camera focus.
(b) Impurity map with marked µ inclusions (yellow circles) and grain boundary network from the surface map.
Roundish black objects with the size of several tens of µm are secondary gas inclusions (micro-bubbles)
formed due to relaxation . While µ inclusions follow horizontal layering, micro-bubbles trace the
3-D shape of the grain boundaries.
(c) A high-resolution uninterpreted detail of the upper impurity layer is indicated by the red rectangle in (b).
(d) Same detail with interpreted µ inclusions.
(e) Example Raman spectrum of one µ inclusion
from the upper layer (red circle). Parts of the signal marked with asterisk correspond to the ice spectrum.
The positions of the proper peaks are quoted.
The inclusion is a gypsum particle (CaSO4⋅2H2O).
EDML-2371.9: (a) impurity map of the whole sample (17 mm × 76 mm) with 2527 µ inclusion
(yellow circles) and grain boundaries from the surface map (blue bands).
Blurred lines are grain boundaries at the bottom side of the specimen.
Horizontal layers (1–3) of µ inclusion are clearly visible.
(b) A detail from the line scanner image of the same part of the ice core. Horizon 1 is visible as a cloudy band.
(c)C-axis orientations of individual grains projected into a horizontal plane. Vertical c axes appear
white, while
horizontal c axes appear in full colors depending on their azimuth (see color code in the legend).
(a) and (c) refer to different surfaces as shown in the cutting plan (d) and thus the grain
boundary networks are not corresponding (3-D effect).
(d) Cutting plan of the sample preparation.
The sample preparation has been described by . Specimens
of the thickness of around 1 cm are cut with a band saw (see cutting
plan in Fig. d). Both surfaces are polished with a microtome
knife and exposed to air for a few hours. Sublimation smoothens the surface
and creates grooves at sites of high energy, where grain and subgrain
boundaries intersect the surface. In this way 2-D maps of grain boundary
networks and subgrain structures can be created . When
focusing into the ice volume and choosing transmission light mode, surface
features such as etching grooves fade out but other objects inside the sample
come into focus. Typical features are gas inclusions (air bubbles and
clathrate hydrates), relaxation features (secondary bubbles, plate-like
inclusions) and µ inclusions (see examples in
Figs. b, c and c, d). µ inclusions
appear as dark spots near the resolution limit of the microscope.
We use an optical microscope (Leica DMLM) with a CCD camera (Hamamatsu
C5405), frame grabber and a software-controlled x–y stage. Ice samples of
up to 10 cm side length can be scanned at the resolution of 3 µmpix-1. The final microstructure map is stitched from up to 1500
individually captured photomicrographs. We combine two of these scans for
each sample – a microstructure map focused onto the surface to reveal grain
boundaries and an impurity map focused ca. 300 µm into the sample
volume to visualize µ inclusions (see Fig. a). Such
a stack of maps allows us to study inclusions in direct relation to the grain
boundary network. Since µ inclusions are mapped within the sample
volume, contamination is not an issue. Due to the obliquity and 3-D shape of
grain boundaries, their positions inside the sample are not exactly the same
as the etching grooves on the surface. To keep this uncertainty low the
second scan must be focused close below the surface. Black dots in impurity
maps were detected manually. The low contrast and size of black dots in the
images did not support the application of automatic detection filters. Visual
detection and manual counting of µ inclusions is a time-consuming
process which requires a certain amount of patience and discipline. Since it
is based on observer's subjective judgment, the results may contain an
observer-dependent variance. The impurity maps presented in this study were
generated by three observers independently, but using the same criteria.
Partial comparison of the results at overlapping regions showed a good
consistency within the data and thus confirmed the observer-dependent factor
being minimal.
Raman spectroscopy
A confocal Raman microscope was used to analyze the composition and
mineralogy of selected µ inclusions. The AWI cryo-Raman system
consists of a WITec alpha 300 M+ with an UHTS 300 spectrometer and a Nd:YAG
laser (532 nm) set up in the cryolab at -15∘C. Within the scope of this paper, the measured Raman
spectra shall serve as a proof that the mapped black dots are in fact
chemical impurities in the form of µ inclusions. A detailed analysis
of the detected minerals is being prepared for publication.
Results
Microstructure and impurity maps were generated for the four samples:
EDML-2371.4, EDML-2371.9, NEEM-1346.2 and NEEM-1346.5. We localized 5784
µ inclusions in total (in Figs. , ,
, marked with yellow circles). Their size of
2–3 pixels in diameter would correspond to 6–9 µm, but their
appearance as “black dots” is probably produced by optical effect of much
smaller particles of the typical dust size ca. 1–2 µm. Raman measurements and correlation with CFA dust and
Ca2+ peaks, however, confirm our assumption that these features are
real µ inclusions.
Raman spectroscopy
A limited number of µ inclusions (20–40 per sample) were selected
for the Raman analysis. Using a 50× lens and confocal mode, a great
majority (around 90 %) of the µ inclusions could be found again in
the Raman microscope. Additionally, their distance from the sample surface
(z coordinate) could be measured due to the confocal setting. The
z coordinates of µ inclusions are Gaussian-distributed around the
focal plane with a half maximum width of 200 µm. We accept this
value as the depth of field of the mapping microscope and use it for the
calculation of the volume fraction of the impurity maps.
Around 70 % of analyzed µ inclusions showed a Raman spectrum of
sufficiently high intensity to separate it from the overall present ice
spectrum. The quality of obtained Raman signal depends on several factors.
The most limiting ones are the size of the µ inclusion, the path
length of the laser beam through the crystal, the quality of polished surface
and the acquisition time. Due to time constraint given the large amount of
measured points, integration time was in the range 5–10 s with 10
repetitions. Most of the measured inclusions in EDML-2371.4 and EDML-2371.9
– around 40 spectra – could be identified as sulfate salts (see example
spectrum in Fig. e). The NEEM samples showed a higher content
of water-insoluble inclusions, such as quartz and black carbon. A detailed
description of the composition statistics is still in progress and will be
shown elsewhere. However, the Raman measurements show that the counted black
dots are chemical impurities and thus verify microstructure mapping as a
valid method to map the visible in situ impurity content.
Concentration
The number of counted µ inclusions per sample are shown in
Table . Knowing the depth of field being
200 µm we calculated the volume fraction of the mapped area. In
this way, the average concentration of µ inclusions per ml of water
equivalent could be estimated. Comparison with the CFA dust concentration
shows a clear difference between the deep EDML ice and the shallow NEEM ice.
In EDML, the number of visible µ inclusions in situ is 2–3 times
higher than the content of insoluble particles (dust) in the meltwater. In
contrast the amount of µ inclusion in the NEEM samples is comparable
or even less than the CFA dust concentration.
Black dots from optical microscopy versus dust concentration from CFA .
With the sample size, the focus range of 200 µm and the density of the ice we estimate the concentration
of µ inclusions. The density of the samples has been estimated using images of air bubble density.
Ice coreEDMLNEEMSample2371.92371.41346.21346.5Depth (m)2370.92370.4739.9740.2Sample size (mm)76 × 1780 × 2171 × 2174 × 23Total number of µ inclusions252711951145917Ice density (g mL-1)0.91670.91670.90790.9079Number of µ inclusions/mL water10668497241973019Dust particles/mL water (CFA)3575264554503823DistributionGeneral spatial distribution
The distributions of µ inclusions within the four samples are highly
inhomogeneous. Similarly to ice-age ice (cloudy ice), the cleaner
Holocene and Eemian ice (MIS5.5) also contains horizontal layers of increased
impurity concentrations (analogous to cloudy bands) instead of
µ inclusions being distributed homogeneously.
In the EDML-2371.9 sample more than 50 % of the counted black dots can be
allocated to one of these horizons. We can distinguish a sharp double horizon
at 2370.95 m (Fig. a, central part of the sample). This
sharp double layer correlates with a cloudy band in the visual stratigraphy
scanning image (Fig. b) and with dust and calcium peaks in
the CFA profile. Around 9 mm below, there is another (disrupted) impurity
layer at 2370.96 m and one more at the bottom part of the section around
2370.99 m. In the sample EDML-2371.4 (Fig. ) the layers are
not as sharp as in 2371.9 but horizons of higher and lower concentrations are
clearly visible. The NEEM samples (Fig. ) also show
horizontal layering, but they are not so well defined and more continuous.
While the distribution of µ inclusions on the centimeter scale occurs in
horizontal or sub-horizontal layers, on the micrometer scale they are often
aggregated in clusters. These groups of two or more adjacent black dots are
typical for both – regions with low concentration as well as high-impurity
layers.
Distribution with respect to microstructure
Grain boundary grooves from microstructure mapping of the sample surfaces are
highlighted in Figs. , ,
with blue lines of a thickness of 300 µm. This width was chosen to
represent the possible grain boundary position error rising from the unknown
inclination of the grain boundary below the sample surface. In
Table , µ inclusions located within this blue
range are considered as potentially interacting with the grain boundary. This
is an upper-limit assumption, considering the fact that a real grain boundary
is not thicker than only a few nm. Table shows
the fraction of µ inclusions found within a range of 300 µm around a grain boundary. The percentages clearly show that the vast
majority of µ inclusions are located away from grain boundaries. In
EDML-2371.4, 89 % of µ inclusions are situated further than
150 µm away from any grain boundary. In EDML-2371.9, the
percentage of 93 % of µ inclusions not located in the vicinity of
grain boundaries is even higher. Slightly higher percentages of µ
inclusions related to grain boundaries are observed in the NEEM samples: 24
and 15 %. The “grain boundary region” in both sample types has been kept
constant for observational reasons such as same imaging resolution, similar
focus depth of impurity maps and unknown grain boundary inclination. However,
the NEEM samples show a significantly smaller grain size (mean radius
1.5 mm) compared to the EDML samples mean radius
2.5 mm;.
In general no correlation between µ inclusions
and grain boundaries could be detected in any of the analyzed samples. Instead, the distinctive
horizons 2 and 3 in EDML-2371.9 are located inside big grains, several
millimeters away from the nearest grain boundaries.
In both EDML samples we observe high accumulations of secondary gas
inclusions along grain boundaries, which are formed due to relaxation of the
material . Their high densities allow us to partly
reconstruct the 3-D shape of the grain boundary just by means of
the micro-bubbles (Figs. , ).
The concentration of µ inclusions and clusters seems not to depend
on shape, size or crystal orientation of individual grains
(Fig. c). Black dots follow sub-horizontal layering as
mentioned above rather than any microstructural feature.
DiscussionImpurities in the form of µ inclusions
We used a microscopic method to map visible impurities within the ice sample
volume. Since µ inclusions are mapped in situ, surface contamination
of the sample does not affect the results. The method is limited mainly by
the optics of our system, in particular contrast and resolution. Objects
smaller than a certain size limit would be virtually not resolved and thus a
fraction of small-sized µ inclusions would be excluded from the
analysis. analyzed size distributions of dust particles in
the EDML ice core using a laser particle detector. The average particle
diameter varied between 2 and 3 µm and only a small fraction of dust
particles were smaller than 1 µm in diameter. Thus, the majority
of microparticles were indeed within the resolution range of an optical
microscope and our results should be comparable to the CFA dust data.
Individual µ inclusions were selected for Raman measurements. The
obtained spectra confirmed that the optically detected “black dots” are
chemical impurities, mainly salt and dust particles. This supports previous
studies by and who used Raman
microscopy to analyze µ particles in ice from Dome Fuji. Detailed
quantitative studies on composition and size of µ inclusions are
ongoing to estimate the actual proportion of substances present as inclusions
or dissolved within the ice lattice respectively.
Counted µ inclusions in the vicinity of grain boundaries (region of 300 µm thickness
along grain boundaries) obtained from stacked microstructure maps with grain boundary grooves on the surface
and impurity maps focused inside the sample (ca. 300 µm below the sample surface).
Ice coreEDMLNEEMSample2371.92371.41346.21346.5Depth (m)2370.92370.4739.9740.2Total number of µ inclusions252711951145917µ inclusions within 300 µm around a grain boundary183127278164Percentage7 %11 %24 %18 %
No signal other than ice spectrum could be detected when focusing into grain
interiors, grain boundaries or triple junctions. In all four samples
presented in our study, impurity spectra could be detected only when focusing
onto visible µ inclusions. Thus we cannot confirm observations by
and , who measured acidic
environments in triple junctions and grain boundaries via Raman spectroscopy,
nor EDX analyses by e.g., and , who also
found trace elements in grain boundaries. The AWI cryo-Raman should be
considered one of the most powerful Raman systems applied to ice. Still, its
spatial resolution and sensitivity are limited by the applied optics and by
the physics of Raman scattering. Thus, we cannot rule out segregation of
trace elements to grain boundaries if their concentrations were very low.
High-resolution CFA dust concentrations measured by were
taken as reference for comparison with our concentrations of
µ inclusions (Table ). The ratios dust vs.
µ inclusions differ significantly between the two ice cores. The
deep EDML ice in solid state contains 2–3 times more µ inclusions
per volume unit than dust in the meltwater CFA;. In
contrast, the NEEM samples contain comparable amounts of
µ inclusions and dust. However, the NEEM CFA data still need to be
flux-calibrated (Wegner, personal communication). Additionally, the presence
of air bubbles in the shallower NEEM samples may cause an underestimation of
µ-inclusion concentration. As air inclusions appear dark in the
impurity maps (Fig. ), they may cover a substantial part of
µ inclusions in the image. Another possible explanation is to assume
that the NEEM samples contain predominantly insoluble dust particles which
are detected in the CFA, whereas the EDML µ inclusions consist
mainly of water-soluble substances. The preliminary analysis of the collected
Raman spectra points in this direction. While the two Antarctic samples
feature primarily sulfate salts in agreement with, we
mainly found terrestrial minerals and black carbon in the Greenland samples.
EDML one meter core section (2370–2371 m). From left to right: visual stratigraphy ,
DEP and CFA conductivity, NH4+, Ca2+ and dust profile CFA;, and c-axis orientations projected into the horizontal
plane with the corresponding Schmidt diagrams.
bag2371
Microstructure and impurity map of the sample EDML-2371.4. Layering of µ inclusions (yellow) is less pronounced than
in EDML-2371.9. Secondary gas inclusions (small, black and roundish) tend to follow the shapes of grain
boundaries. In contrary, the µ inclusions seem not to accumulate at grain boundaries.
Microstructure and impurity maps of the two NEEM samples (740 m). Horizontal layering of µ inclusions
is present in both maps. The samples contain a higher density of grain boundaries, the average grain radius is 1.5 mm.
NEEM-1346.2 is cracked in the central part. In contrast to the EDML samples, the gas inclusions (black) are homogeneously
distributed, since they are primary air bubbles, albeit deformed due to relaxation.
µ inclusions are distributed in horizontal bands or layers of higher
concentration. The annual layer thickness at 740 m in the NEEM ice core was
estimated as 12–16 cm. Thus at most one annual
layer would fit in one section and the layering of µ inclusions in
NEEM-1346.2 and NEEM-1346.5 is probably attributed to seasonal variability.
The annual layer thickness of the EDML samples (2371 m) is only
5–10 mm so that more than 10 annual layers should
be present in each sample. However, the stratigraphy at this depth of the
EDML ice core is strongly disrupted, strata are tilted by up to 30∘
and centimeter-sized z folds are present . The visual
stratigraphy, CFA profile and c-axis orientations along the whole EDML bag
2371 are plotted in Fig. . Impurity layers are visible in
the line scanner image and correlate with peaks in the dust and Ca2+
record. A sharp impurity peak is visible in the EDML-2371.9 section, which
corresponds to the double horizon of µ inclusions shown in
Figs. b and . The DEP and CFA conductivities
show significant differences, as DEP is recorded on the ice core in solid
state while CFA measures the electrolytic conductivity of the meltwater. The
DEP variability is independent on dust and Ca2+ concentration,
while the CFA conductivity shows a sharp peak in EDML-2371.9 correlated to
the dust and Ca2+ horizon. This supports our conclusion from
the last paragraph stating that a large portion of the µ inclusions
in the EDML samples are water-soluble salts, which will increase the CFA
conductivity. Another dust peak arises in sample EDML-2371.4, but no
correlated signal is found in Ca2+ nor CFA conductivity.
Furthermore, it is unclear whether the NH4+ peak in EDML-2371.4 is
correlated to the mentioned dust peak and the shift is due to inaccuracy in
depth assignment or the signals are independent. Further evaluation of the
Raman spectra, which will be presented elsewhere, should help to better
understand the origin of the signals.
Zener pinning
The attractive force between a grain boundary and µ inclusions
results from the reduction of grain boundary energy. Assuming a random
distribution of spherical inclusions of radius r, the
maximal pinning pressure on a grain boundary can be derived:
PZ=2πr2γNV,
where γ is the grain boundary energy and NV number of
µ inclusions per volume unit. If the driving force for grain
boundary migration PGBM<PZ, then the grain boundary stays in
contact with the pinning particles and its migration rate adapts to the
mobility of the particles (slow-mode pinning). In contrast, if
PGBM>PZ, the interaction time is short and grain boundary
proceeds with its motion, leaving the particles behind (fast-mode pinning).
One of the objectives of our study was to catch µ inclusions “in
flagrante” – i.e., in the very act of pinning a grain boundary. However,
this aim resulted to be cumbersome since grain boundaries are usually
invisible inside the sample volume. In general, we can only estimate
positions of grain boundaries from the surface images (microstructure maps).
Sometimes, if the curvature and convexity are favorable with respect to the
image plane, 3-D shapes of grain boundaries are indeed visible within the
sample volume. In such cases, we could observe clathrate hydrates sticking to
grain boundary interfaces, deforming their shapes due to the pinning force.
However, no µ inclusions were observed to produce such kind of
effects. We studied the distribution of µ inclusions in a plane of
focus over the whole sample area. The density of µ inclusions is
inhomogeneous but exhibits no correlation with grain boundaries. The
typically clustered distribution could in principle be interpreted as caused
by sudden release of µ inclusions from accelerating grain boundary.
However, the clusters which are within themselves unordered also are of no
significant shape, e.g., rows, planes or ellipsoids. We would expect some kind
of alignment in rows or planes or at least some sort of graduation (size,
type) of the clusters if they would result from release by a moving grain
boundary.
The observations listed above lead us to the conclusion that only fast-mode
pinning can take place in all four analyzed samples:
PGBM>PZ.
This is in agreement with , and others, who
suggest that particle concentrations in ice are in general too low to induce
slow-mode pinning. However, we cannot confirm the assumption by
that µ inclusions would accumulate in high
concentrations at grain boundaries. Our results also confirm general
observations by and , who concluded that
impurity layering and thus climatic record would stay preserved even in the
deep parts of the EDML ice core and the NGRIP ice core respectively.
identified annual layering of impurities down to the
Eemian part of the NGRIP ice core. observed no
redistribution of µ inclusions due to pinning or dragging down to
2300 m of the EDML ice core. However, below this depth, and particularly in
the deepest 200 m, the authors found accumulations of “black dots” along
grain boundaries and concluded that pinning and dragging is relevant only in
the deepest part of the EDML ice core. This finding mismatched our
observations in EDML-2371.4 and EDML-2371.9, where we only found secondary
bubbles accumulated along grain boundaries. Since microstructure maps
described by were made only few days or hours after drilling
the ice core, we decided to record new microstructure maps of the same spots
now after ca. 10 years of storage. The comparison indicates that significant
changes occur during the relaxation of the material (see example in
Fig. ). The image of the freshly drilled ice shows a group of
“black dots” accumulated at a grain boundary. The image of a re-measurement of
the same sample shows that with time these “black dots” have grown and
filled with gas and now they are forming typical relaxation air bubbles.
There are two possible explanations. (1) Grain boundaries in deep EDML do
collect µ inclusions by dragging but after retrieving the core they
serve as seeds for the growing relaxation bubbles and get surrounded with
gas. It remains unclear why µ inclusions in the interior of grains
do not evolve into bubbles. (2) Another option is that the “black dots” at
grain boundaries observed by in fact are small micro-bubbles
forming due the abrupt drop of pressure after logging the ice core.
analyzed compositions of secondary microbubbles using
Raman spectroscopy. The authors also remark that secondary bubbles tend to
form at locations of “black dots”. However, to date there are no data
available concerning the composition of these original “black dots” as
Raman spectroscopy is currently not available on-site, viz. right after
drilling.
Two images of the same spot in EDML-2376.0. Two clathrate hydrates are highlighted
(blue circles) in both images for comparison. (a) Photomicrograph taken at the site immediately after drilling the ice core in 2006.
An accumulation of “black dots” within the grain boundary plane can be recognized. These “black dots” are slightly
larger than the µ inclusions counted in our impurity maps. (b) The same spot after ca. 10 years of relaxation.
Almost all “black dots” at the grain boundary evolved into secondary gas inclusions.
observed impurity concentration peaks at grain
boundaries in the clean parts of the Greenland Stadial 22, NGRIP ice core,
using LA-ICPMS. However, fundamental differences between the experimental
techniques as well as the ice samples impede a direct comparison to our
study. Dust concentrations in GS-22 determined by vary
between 2×105mL-1 in cloudy bands and 2×104mL-1 in the clean bands, which is 1 order of magnitude more
than the average concentrations in our samples. Furthermore, it remains
unclear what portion of the LA-ICPMS signal is due to visible
µ inclusions. A comparative study applying both techniques to the
same specimen could help resolve the contradiction.
The highest local µ-inclusion density in our sample material was
found in the double horizon of sample EDML-2371.9 (Figs. ,
) and could be estimated as NV=37206cm-3. When inserting this value in Eq. (1), assuming the
mean particle radius r=1.5µmin agreement
with and using γ=65mJm-2 for high
angle boundaries , we obtain the maximal pinning pressure
PZ=0.034Nm-2. The driving force for grain boundary
migration can be written as
PGBM=ΔH-2γR,
where ΔH is the gradient in stored strain energy density across the
grain boundary (i.e., strain-induced driving pressure) and the second term
represents the curvature-driven pressure with the radius R of grain
boundary local curvature. Assuming no difference in stored strain energy
(ΔH=0), the boundary migration will be driven only by its
curvature. Inserting PZ and Eq. (3) into Eq. (2) we obtain R<3.8m the radius of local curvature necessary to unpin the grain
boundary from its surrounding µ inclusions. Most grain boundaries
along the NEEM and EDML ice cores indeed fulfill this condition
e.g.,.
In a second marginal case, let us consider a planar grain boundary (R→∞) whose migration is only driven by the difference in
stored strain energy as function of the local dislocation density Δρdis. Following and ,
ΔH can be approximated as
ΔH=Δρdis12Gb2,
where Gb2/2 is the energy per unit length of a single dislocation line
consisting of the shear modulus G and the magnitude of the Burgers vector
b. Considering only the basal slip system the mean dislocation energy can
be estimated as Gb2/2=3.6×10-10Jm-1. Inserting
this in Eq. (4) and combining Eqs. (4), (3) and (2) we obtain an estimation
for the minimal difference in dislocation density required for unpinning:
Δρdis>108m-2. This value is 2–3 orders
of magnitude smaller than absolute dislocation densities modeled by
and and also smaller than a
minimum dislocation density access calculated from grain boundary curvatures
by Fig. 11b.
In the above considerations we made a variety of assumptions and the derived
values are only for approximation. Furthermore, in the case of a real grain
boundary ΔH acts against the curvature-driven force as described in
Eq. (3) and thus the final motion and shape are determined by the ratio of
these forces. However, the exercise demonstrated that applying Zener's theory
to our particular µ-inclusion concentration the pinning effect is
comparatively small and will hardly affect grain boundary migration. This is
indeed in agreement with what we observe.
Grain size controlling mechanisms
It is difficult to make general conclusions based on the analysis of four
discrete samples. Our study indicates that pinning on µ inclusion
occurs in fast mode in a large part of the ice sheets, as already stated by
, and will not significantly affect grain boundary
migration. At the same time, with our Raman microscope we find no other form
of, for example, dissolved impurities segregated to grain boundaries. However,
negative correlations between average grain size and impurity content were
found in virtually all ice cores e.g.,.
High-impurity ice exhibits generally smaller grains than low-impurity ice at
the same depth. This negative correlation can be found at all scales: in
seasonal variabilities (cloudy bands, cm), during rapid climatic fluctuations
such as Dansgaard–Oeschger events (tens of meters) or comparing glacial and
interglacial periods (hundreds of meters).
Grain size has an impact on a variety of ice physical properties
; vice versa it is controlled by
thermodynamic conditions and processes within the ice sheet. Without
deformation and under purely static conditions, mean grain area increases
linearly with time. This NGG is driven by the reduction
of grain boundary surface energy due to the optimization
of volume versus interfaces. This model is especially relevant for very small
grain sizes well below the equilibrium or steady-state grain size see,
e.g.,, such as smallest grain sizes in the
uppermost part of the ice sheet , but may also apply to some
areas where topographic depressions in the bedrock below ice sheets inhibit
deformation and NGG can produce extraordinary large grain sizes
stagnant ice;. If deformation introduces additional
energy into the system, dynamic recrystallization processes are activated,
driven by the energy reduction. Rotation recrystallization (RRX) splits
grains into subgrains and thus has a grain-size-reducing effect
. During RRX recovery orders
dislocations of a deformed grain into subgrain boundaries, which are lower
energy states of dislocation assemblages .
Subgrain boundaries can develop into grain boundaries by further rotation. In contrast, strain-induced boundary migration (SIBM) occurs when grain
boundaries propagate into regions of high dislocation densities. The
effectiveness of SIBM increases with the heterogenous distribution of
dislocations in polycrystalline ice, which is a direct
consequence of the high mechanical anisotropy of the ice crystal. SIBM leads
to huge grain sizes in the deep ice but can also lead to
grain size reduction, e.g., by nucleation of new grains
SIBM-N; or by dissection of highly irregular grains.
Recent microstructural studies e.g., show
that all recrystallization mechanisms (NGG, RRX and SIBM) concur all over the
depth range of an ice sheet rather than being dominantly active in separate
depth zones. The grain size is then a product of the interplay between these
processes dynamic grain growth;.
It is widely accepted that the grain size is modulated by some impurity
effect. The two most manifest candidates for such an effect are
(1) reduction of grain boundary mobility via dissolved impurities being
dragged along by migrating grain boundaries ; (2) Zener pinning , that is, interaction between µ inclusions and grain
boundaries as discussed in Sect. . However, experimental
evidences for both models are controversial in ice, as demonstrated by our
study. Furthermore, both interpretations are based on the assumption that it
is solely NGG, which suffers under the effect of impurities leading to smaller
grain sizes. However, grain size is a product of all recrystallization
processes together, as discussed previously. Therefore we
hypothesize that an indirect impurity effect, for instance enhanced
deformation and/or effect on dynamic recrystallization triggered by high
impurity content, could be an alternative candidate responsible for the
changes in grain size. Impurities could have a significant influence on
strain distribution within grains as well as dislocation mobilities and
densities, e.g., via dislocation multiplication. The microstructure effects
of increasing dynamic recrystallization versus viscoplastic deformation have
recently been tested by a microstructural evolution model (Llorens, 2016a,
b). In these model runs the impact is significant on, e.g., the grain shape
evolution because deformation tends to flatten grains while
recrystallization tends to make them equidimensional. First evidences of
changes in the deformation–recrystallization interplay have been observed by
means of grain shape analyses . However, to
test these hypotheses is far beyond the scope of this study.
Summary
We present high-resolution large-scale maps (3µmpix-1,
8×2cm2) of µ inclusions within four samples from
polar ice cores: two from the EDML (2371 m) and two from the NEEM ice core
(740 m). For the first time, in situ distributions of a representative
number (more than 5000) of µ inclusions have been studied. A
confocal Raman microscope has been used to prove the impurity origin of the
inclusions. Discrete µ inclusions are the only impurity form
detected in this study; i.e., we measured no signal attributed to dissolved
impurities neither in grain interiors nor in grain boundaries. The comparison
with grain boundary network shows no correlation between
µ inclusions and grain boundaries. Therefore we observed evidence
for neither redistribution of impurities by dragging nor slow-mode grain
boundary pinning as defined by . The link between grain size
and impurities may not be (only) due to hindered normal grain growth in
impurity-rich ice. Deformation and dynamic recrystallization enhanced by
impurities possibly also have a grain-size-reducing effect.
The four impurity maps in original resolution and positions
of individual micro-inclusions are available upon request and will be
published in PANGAEA in the future. Thick-section images along the whole EDML
ice core are available at 10.1594/PANGAEA.663141.
J. Eichler, I. Kleitz and W. Shigeyama performed the measurements, processing
and interpretation. M. Bayer-Giraldi, D. Jansen, S. Kipfstuhl, C. Weikusat
and
I. Weikusat supported the processing and interpretation of data by providing
specialists knowledge as well as the general glaciological framework.
I. Weikusat provided the initial concept. Under the lead of J. Eichler all
authors contributed to writing of the manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was funded by HGF grant VH-NG-802 to J. Eichler and
I. Weikusat, SPP 1158 DFG grant WE4711/2 to C. Weikusat, as well as DFG grant
SPP 1158 BA 3694/2-1 and JSPS fellowship ID PE16746 to M. Bayer-Giraldi. The
Microdynamics of Ice (MicroDICE) research network, funded by the European
Science Foundation, is acknowledged for funding research visits of J. Eichler
and I. Kleitz (short visit grant). We thank Anna Wegner, Melanie Behrens and
Maria Hörhold for discussions on solubility of impurities and CFA-related
issues. The visual stratigraphy line scan image has been made available at
www.pangaea.de. We thank the logistics and drilling team of the Kohnen
and NEEM stations. NEEM is directed and organized by the Center of Ice and
Climate at the Niels Bohr Institute and US NSF, Office of Polar Programs. It
is supported by funding agencies and institutions in Belgium (FNRS-CFB and
FWO), Canada (NRCan/GSC), China (CAS), Denmark (FIST), France (IPEV,
CNRS/INSU, CEA and ANR), Germany (AWI), Iceland (RannIs), Japan (NIPR), Korea
(KOPRI), the Netherlands (NWO/ALW), Sweden (VR), Switzerland (SNF), UK (NERC)
and the USA (US NSF, Office of Polar Programs). This work is a contribution
to the European Project for Ice Coring in Antarctica (EPICA), a joint
European Science Foundation/ European Commission (EC) scientific programme,
funded by the EC and by national contributions from Belgium, Denmark, France,
Germany, Italy, the Netherlands, Norway, Sweden, Switzerland and the
UK.The article processing charges for this
open-access publication were covered by a Research
Centre of the Helmholtz
Association.Edited by: F. Dominé
Reviewed by: J. L. Urai and A. Svensson
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