TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-12-3293-2018Carbonaceous material export from Siberian permafrost tracked across the
Arctic Shelf using Raman spectroscopySiberian carbonaceous material exportSparkesRobert B.r.sparkes@mmu.ac.ukhttps://orcid.org/0000-0003-0756-0150MaherMelissaBlewettJeromeDoğrul SelverAyçaGustafssonÖrjanhttps://orcid.org/0000-0002-1922-0527SemiletovIgor P.van DongenBart E.School of Science and the Environment, Manchester Metropolitan
University, Manchester, UKSchool of Earth and Environmental Sciences and Williamson Research
Centre for Molecular Environmental Science, University of Manchester, Manchester, UKBalıkesir University, Geological Engineering Department,
Balıkesir, TurkeyDepartment of Environmental Science and Analytical Chemistry (ACES)
and the Bolin Centre for Climate Research, Stockholm University, Stockholm, SwedenPacific Oceanological Institute Far Eastern Branch of the Russian
Academy of Sciences, Vladivostok, RussiaInternational Arctic Research Center, University of Alaska, Fairbanks, USANational Tomsk Research Polytechnic University, Tomsk, Russianow at: Organic Geochemistry Unit, School of Chemistry, Cabot Institute, University of Bristol, Bristol, UKRobert B. Sparkes (r.sparkes@mmu.ac.uk)11October201812103293330919January20189March201817August20186September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://tc.copernicus.org/articles/12/3293/2018/tc-12-3293-2018.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/12/3293/2018/tc-12-3293-2018.pdf
Warming-induced erosion of permafrost from Eastern Siberia mobilises large
amounts of organic carbon and delivers it to the East Siberian Arctic Shelf
(ESAS). In this study Raman spectroscopy of carbonaceous material (CM) was used
to characterise, identify and track the most recalcitrant fraction of the
organic load: 1463 spectra were obtained from surface sediments collected across
the ESAS and automatically analysed for their Raman peaks. Spectra were
classified by their peak areas and widths into disordered, intermediate, mildly graphitised and highly graphitised groups and the distribution of these classes
was investigated across the shelf. Disordered CM was most prevalent in a
permafrost core from Kurungnakh Island and from areas known to have high rates
of coastal erosion. Sediments from outflows of the Indigirka and Kolyma rivers
were generally enriched in intermediate CM. These
different sediment sources were identified and distinguished along an E–W transect using their Raman
spectra, showing that sediment is not homogenised on the ESAS. Distal samples,
from the ESAS slope, contained greater amounts of highly graphitised CM compared
to the rest of the shelf, attributable to degradation or, more likely, winnowing
processes offshore. The presence of all four spectral classes in distal
sediments demonstrates that CM degrades much more slowly than lipid biomarkers and
other traditional tracers of terrestrial organic matter and shows that
alongside degradation of the more labile organic matter component there is also
conservative transport of carbon across the shelf toward the deep ocean. Thus,
carbon cycle calculations must consider the nature as well as the amount of
carbon liberated from thawing permafrost and other erosional settings.
Introduction
Extensive Northern Hemisphere permafrost deposits contain approximately
40 % of the global soil organic carbon (OC) budget
. The majority of this carbon is currently
freeze-locked, but rapid warming at high latitudes is making it increasingly
vulnerable to mobilisation via fluvial and coastal erosion. Thawing also
leads to degradation, both in situ and post-mobilisation, to greenhouse
gases, providing a positive feedback response to warming
. The Eurasian Arctic region contains
the majority of Northern Hemisphere permafrost OC, and the release rate of
both OC and sediment from this area into the Arctic Ocean is predicted to
rise during the next century
.
Deepening hydrological flow paths and thermokarst erosion events are
mobilising “old” carbon that has been stored in deep permafrost for
thousands of years
. OC
delivered by fluvial erosion and transport has been identified offshore using
molecular biomarkers and has been shown to deposit and/or degrade rapidly on the
shelf
.
In addition to fluvial erosion, coastal erosion delivers a significant amount
of sediment and OC (44±10TgOCyr-1) to the Arctic Ocean
. Poorly lithified coastal cliffs, combined with stormy
weather, have caused erosion rates of up to 10 myr-1. Reducing sea ice cover, and therefore increased wave
fetch and storm impact, is also likely to increase coastal erosion rates
during the next century .
Recalcitrant organic matter export from permafrost
Erosion of ancient carbon from Arctic permafrost may lead to degradation of
carbon into greenhouse gases and a positive feedback effect on global
climate, or the absence of degradation may allow this material to be used to
track eroded sediment across the continental shelf. Alongside modern OC
transport, erosion mobilises petrogenic OC from rocks and soils (also known
as carbonaceous material, CM) and delivers this to the ocean. CM consists of
recalcitrant material, such as coal, lignite, combustion-derived black
carbon, kerogen insoluble hydrocarbons; and graphite,
which is stable over millions of years. CM is (apart from black carbon)
formed from the maturation of buried organic matter, with the degree of
diagenesis and metamorphism controlling the molecular and crystalline
structure . High temperatures
during burial and metamorphism drive the transition from disordered kerogen
and lignite to highly crystalline graphite, allowing CM from different areas
to be identified due to differences in the geological history of the source
rocks . CM represents a major fraction of the global OC
budget, sequestered in sedimentary and metamorphic rocks over million-year
timescales . Any oxidation
of CM during erosion, transport and burial represents a movement of carbon
between the long-term and short-term carbon cycles. During transport,
bioavailability of CM is believed to be low, but there is evidence that
extremely long transport distances can degrade disordered CM, leaving only
crystalline graphite . Previous work has
investigated CM in tropical and subtropical settings, but, in contrast to
extractable OC, no work has been done to characterise or trace CM in the
Arctic. The only studies on recalcitrant Arctic OC involve (usually
submicron-sized) soot black carbon particles
.
measured the distribution of soot black carbon (SBC)
across the ESAS. SBC was isolated using chemothermal oxidation at
375 ∘C, a method that has been thoroughly tested to isolate
combustion-derived SBC from other recalcitrant species such as coal of
various degree of maturity, pollen and other biomacromolecules;
. The highest concentrations (up to
0.22 wt%SBC) and proportions (11% of the total OC) of SBC were
found at the mouths of the Lena and Kolyma rivers, with concentrations and
proportions decreasing offshore as SBC was deposited and marine primary
productivity became the dominant OC source. However, the relative importance
of the recalcitrant SBC within the permafrost-derived carbon pool increased
as more labile material degraded during transport. This pattern was seen
consistently along a W–E transect from the Laptev Sea to the Eastern ESAS.
The authors concluded that the source of SBC was not atmospheric deposition
(e.g. via biomass burning) but permafrost erosion. report
anomalously old radiocarbon ages for distal sediments on the Beaufort Shelf,
attributed to highly recalcitrant terrestrial OC that has been matured to
kerogen grade or higher, but the nature and origin of the material has not
been investigated in detail. Therefore, there is a need to assess whether the
various inputs of carbon to the Arctic Ocean system can explain these old
radiocarbon ages, and whether they form an active or passive part of the
carbon cycle.
This study uses Raman spectroscopy to identify the sources of CM in the
Eastern Siberian region and tracks the export of CM from these sources across the
East Siberian Arctic Shelf (ESAS). This is the first Raman study of
sedimentary CM in the Arctic. Automated Raman spectra fitting procedures were
used to analyse >1400 spectra measured from CM in sediments collected
across 1 million km2 of the (ESAS). Multiple heterogeneous
sources of sediment were studied, including several of the world's largest
river catchments and thousands of kilometres of shoreline experiencing rapid coastal
erosion .
Raman spectroscopy
Raman spectroscopy is a precise and powerful tool for quantifying the degree
of crystallinity within CM . In this study,
it is used to identify permafrost-sourced CM offshore. Monochromatic incident
light interacts with the crystal lattice of the targeted particle and
reflects with a changed wavelength due to the energy shift introduced by
lattice vibrations (phonons). When analysed with in a Raman spectrometer,
graphitic and disordered carbon can be characterised by studying the response
within the range of 800–2000 wave numbers. In a pure graphite crystal, lattice
vibrations are restricted to bond stretching of sp2 atom pairs only,
leading to only one Raman peak at 1580 cm-1 (known as G1).
Disordered CM allows for “breathing” of aromatic rings, and longitudinal
oscillations . This introduces more peaks into a Raman
spectrum, at 1350 cm-1 (D1), 1620 cm-1 (D2),
1500 cm-1 (D3) and 1200 cm-1 (D4). These peaks appear
and grow as the degree of disorder increases. Highly graphitised CM is
dominated by the G peak, with minor contributions from D1 and D2. In a highly
disordered sample the largest peak will be D1, the D2 and G peaks will
overlap to form a single peak at approximately 1600 cm-1, and the
D3 and D4 peaks will form a noticeable part of the spectrum (see Supplement
Fig. S1). Advances in computing power have allowed the analysis of these
spectra to transition from manual peak fitting to an automated approach, in
which a combination of difference-minimising algorithms and defined limits
fits multiple peaks to a Raman spectrum. CM ranging from highly disordered to
highly graphitised can be characterised and differentiated
. Further details of the fitting script are found
in Sect. 2.4.
Since Raman spectroscopy can differentiate CM in lithologies that have
experienced different metamorphic conditions, it has been used as a
geothermometer in orogenic belts . Peak area ratios have
been calibrated against metamorphic temperatures for mildly to highly
graphitised CM and for more disordered CM
. Furthermore, the recalcitrance of CM allows it to be
identified downstream, following weathering and erosion. CM, especially highly
graphitised material eroded from the Himalaya and Andes, has been identified
thousands of kilometres downstream in the Bengal Fan and Amazon River
. During long-distance transport, disordered CM
appears to be preferentially degraded. Over shorter distances, the relative
distribution of disordered and graphitised CM allows the use of Raman
spectroscopy as a tool for tracing erosion from separate lithologies. This
technique has been applied in single river catchments in Taiwan and New
Zealand but up to now has not been applied
to wider, continental shelf systems. Given the complex interplay between
fluvial and coastal erosion in the Eastern Siberian region, this is the most
geologically complex application of Raman spectroscopy of CM to date. Raman
spectroscopy was used to characterise the CM in each sediment sample
(disordered through to extremely graphitised), with the changing distribution
of spectral groups being used to (i) identify whether extremely
heterogeneous sedimentary systems can be characterised using Raman
spectroscopy of CM, (ii) differentiate the CM present in sediments delivered
to the ESAS via fluvial and/or coastal erosion, (iii) track the CM (and
therefore sediment) from these various inputs across the ESAS and
(iv) evaluate the impact of CM erosion and transport on the carbon cycle in
Arctic permafrost systems
Materials and methodsStudy area
Samples used in this study were collected from across the East Siberian
Arctic Shelf, from 130 to 175∘ E and from 70 to 77∘ N. This
area includes the outflows of four of the major rivers: the Lena, Yana,
Indigirka and Kolyma rivers (see Fig. 1). Collectively, these rivers drain
3680×103km2 of Siberia, including tundra and taiga
environments. This area is largely underlain by permafrost, where soil
temperatures remain below 0 ∘C year round. Northern and eastern
areas contain continuous permafrost, where frozen ground forms a layer
impenetrable to water, while discontinuous permafrost in the southern and
eastern parts of the catchment allows water ingress to lower soil levels
. The uppermost portion (also known as the “active
layer”) freezes and thaws on an annual basis and contains both open tundra
and taiga forests. The active layer is expected to deepen as the climate
warms in the next century, which will also lead to a reduction in continuous
permafrost area and an increase in fluvial erosion .
Additionally, the Eastern Siberian coastline contains large ice complex deposits
(ICD, also known as “Yedoma”). These Plio-Pleistocene permafrost deposits
are weakly lithified and rich in well-preserved OC, providing a major influx
of sediment and carbon to the Arctic Ocean
.
Sediment sourcing in Eastern Siberia is due to fluvial and coastal erosion.
Run-off from the surface carries soil from the active layer into lakes and
rivers. Lateral cutting can mobilise sediment from deep permafrost layers
directly into the rivers. The Lena, Yana, Indigirka and Kolyma rivers deliver
523, 32, 54 and 122 km3yr-1 of water and 21, 4, 11 and
10 Mtyr-1 sediment respectively . Coastal
erosion is another major source of sediment to the ESAS. Coastal erosion
rates of up to 10 myr-1 have been measured (see Fig. 1) and are among
the fastest in the Arctic . Erosion rates from ICD are
5 to 7 times greater than other coastal permafrost
.
The river catchments in this region extend as far south as 52∘ N,
underlain by a wide range of lithologies. Carbonaceous material exported to
the ESAS could have been transported over 4000 km from the Lena River
headwaters. This is a much larger system than has been studied using the
Raman sedimentary provenance tool before .
Potential CM source lithologies include extensive coal basins within the Lena
catchment and metamorphic zones within the Chersky
collision belt that have experienced greenschist to amphibolite metamorphic
conditions, with temperatures up to 620 ∘C . This
is enough to form crystalline graphite .
The ESAS is extremely shallow, generally less than 100 m (see
Fig. 1). The seabed consists of permafrost that developed sub-aerially, was
flooded during Holocene sea-level rise and is now being buried by sediment
sourced from fluvial and coastal erosion . Geochemical
studies investigating the sources and offshore distribution of organic matter
have noted differences between east and west, nearshore and offshore sections
of the shelf
.
Map of the East Siberian Arctic Shelf (ESAS) showing the location of
surface sediment and permafrost samples used in this study. The thin black line
represents the -100m bathymetric contour. Grey to white colours
offshore represent the transition from -100 to 0 m water depth,
highlighting both the shallow nature of the ESAS and the minor bathymetric
features present. Major rivers are labelled. Areas of rapid coastal erosion
>1myr-1; are shown in red. Groups of
samples are denoted by marker shape and colour: black circles is terrestrial
ICD and permafrost cores, white triangles is nearshore Lena River
outflow/Buor-Khaya Bay, black squares is offshore Lena River
outflow/Buor-Khaya Bay, white squares is areas of high coastal erosion and
little fluvial input, white circles is distal ESAS areas, black pentagons
is Indigirka River outflow and black triangles is Kolyma River outflow.
Sample collection
This study analysed 56 samples collected from terrestrial and marine settings
in Eastern Siberia, 47 of which were surface sediment samples (samples YS-XX and
TB-XX) collected from across the ESAS during the International Siberian Shelf
Study research cruise in 2008 (ISSS-08; Semiletov and Gustafsson, 2009).
GEMAX sediment cores were sliced into 1 cm sections and transferred
into pre-cleaned polyethylene containers, while van Veen grab samples were
subsampled using stainless steel instruments into pre-cleaned polyethylene
containers. Only the top 0–1 cm of cores and the upper 2 cm
of grab samples were used in this study. Three terrestrial locations
provided information on permafrost material from the Lena, Indigirka and
Kolyma river catchments, although single locations cannot, of course, be
representative of such large catchments. In the Lena Delta, this comprised
subsamples from a permafrost core collected from Kurungnakh Island
samples KUR-XXXX; Russian-German LENA 2002
expedition;. Subsamples at 0.34, 14.4 and
22.0 m were analysed. Six ICD samples were collected from the upper,
middle and lower portions of riverbank profiles in the Indigirka River
(samples KY-XXX) and Kolyma River (samples CH-XXX) catchments
. All samples were kept frozen below -18∘C before
being stabilised by freeze-drying. Sample locations are shown in Fig. 1 and
reported in the Sample Metadata table in the Supplement.
A minority of sediments used in this study were solvent extracted for
biomarker analysis prior to collecting Raman spectroscopy measurements
. An ultrasonic extraction
process used methanol, dichloromethane and pH-buffered distilled water in
order to remove extractable material. This represents approximately 5 % of
the total OC content, OC that is not likely to be Raman-amenable. Comparison
of Raman spectra collected from extracted and non-extracted sediments showed
that there was no noticeable effect on the distribution of spectra; for
completeness the extracted sediments have been identified in the Sample
Metadata table.
Raman spectroscopy
Raman spectra were collected following the procedure of .
Approximately 1 g of sediment from each sample was collected and
homogenised by being stirred in a glass vial. Samples with sand- or silt-sized
grains were crushed using a pre-cleaned pestle and mortar to obtain a
fine-grained sediment. Core samples (KUR-XXXX) were ground using a planetary
mill. It has been shown that physical grinding, even over several hours, does
not affect carbonaceous material crystallinity, nor does it reduce grain sizes
to below the analytical scope of the instrument
. Sedimentary particles smaller than
1 µm would not have been easily measurable, but are also unlikely
to be deposited in these sediments; instead they are likely to be winnowed
out and transported to the deep ocean. Atmospheric soot particles on the
nanometre scale are smaller than the analytical window.
A subsample of 0.25 g was taken and placed onto a glass slide,
forming a circle of diameter 1–2 cm. This was pressed below another
slide to produce a uniform horizontal surface in order to aid focussing and
align tabulate particles with the incident light beam. The sample was
rastered under the microscope and all potential CM particles were
investigated using a brief exposure (2 s at 10 % laser power).
Those confirmed as being carbonaceous were analysed using an extended
acquisition, using the following settings. Samples were exposed to a 514 nm Argon
ion laser set at ∼1W power, under a 50× magnifying lens.
Laser spot size was approximately 5 µm. The reflected light was
analysed using a Renishaw InVia Raman spectrometer with a grating spacing of
1800 Lmm-1. Synchroscan mode was used to allow a spectral window
of 800 to 2200 cm-1. Three 20 s acquisitions were made at
10 % laser power for a total of 60 s acquisition time. Where
possible, 30 spectra were collected from each sample. Data were exported
as a text file (wave number, intensity) for subsequent analysis.
Automated processing of Raman spectra
Raman spectra from carbonaceous material are often complex, consisting of up
to five overlapping peaks with an additional offset increasing with
wave number (see Supplement Fig. S1). The peak width and number of peaks is
highest in the most disordered CM
. An automated
computer script was used to quantify, quickly and objectively, the peak
locations, heights, widths and areas for further analysis
. Briefly, the script determines whether it is appropriate
to use three Voigt peaks or five Lorentzian
peaks to best fit the spectrum. These peaks are adjusted
to find the optimum combination of peak location, width and height using the
best-fit algorithms implemented by the open-source software package Gnuplot
(version 4.6). A linear background subtraction was made as part of this
automated fitting. For full details of the fitting process, see
. The fitting script used in this study is included as a
supporting file and developed at
https://github.com/robertsparkes/raman-fitting (last access: 29 September 2018).
As well as peak shape parameters (location, height, width, area), the fitting
script calculates two additional values. The implied maximum burial temperature
experienced by the carbonaceous material is calculated based on the peak area
ratio. For mildly and highly graphitised material, fitted using three Voigt
peaks, the R2 ratio compares the G, D1 and D2 peaks :
R2=D1G+D1+D2.
For more disordered CM fitted using five Lorentzian peaks, the RA2 ratio
compares the G, D1, D2, D3 and D4 Lorentzian peaks :
RA2=(D1+D4)(G+D2+D3).
The temperature value derived from these calculations represents the degree
of graphite crystallinity in the carbonaceous material. Highly graphitic
samples have higher metamorphic temperatures, up to 650 ∘C
. Less graphitised samples have lower temperatures,
calibrated down to 200 ∘C . It relates to the peak
metamorphic conditions experienced by the CM , and when
dealing with sedimentary samples is used as a tracer for CM from different
lithologies . The second parameter is the sum of peak
widths parameter, which is the sum of the G, D1 and D2 peak widths at
half-maximum. This value is lowest for highly graphitised CM and increases
with increasing CM disorder. Although it is a non-standard method for
classifying Raman spectra, this parameter differentiates CM better than other
potential parameters. For example, measuring just the width of peak D1 can
limit the ability to differentiate between moderately and extremely
disordered CM, as the D1 peak width saturates at high amounts of disorder.
When analysing only mildly and highly graphitised CM, the D1 width parameter
could be used instead of total width. Extremely disordered CM has the highest
total width value, and this factor can be used to discriminate between
samples that are mildly metamorphosed (and legitimately sit on the
calibration of ) and those that have undergone only
diagenetic alteration (e.g. lignite-grade CM). These extremely low-grade CM
particles, for which metamorphic temperature calibrations are not available,
can still be distinguished and identified via their Raman spectra despite the
lack of calibrated temperature . Using a combination of
these two parameters, spectra were divided into four classes: disordered,
intermediate, mildly graphitised and highly graphitised (see Table 1).
Parameters for classifying Raman spectra into four groups based on
their metamorphic temperature determined from the R2 or RA2 peak area
ratio; see and total width parameter (G+D1+D2). Note that there are overlaps between the defined regions. Mildly graphitised takes precedence over intermediate grade if a sample plots in
the overlapping region.
The 1463 spectra collected span the entire range from perfectly crystalline
graphite to highly disordered CM. Maximum implied metamorphic temperatures
were 641 ∘C, i.e. spectra with no discernible D1 peak. The highest total
peak width values were 366 cm-1, i.e. extremely disordered spectra
implying minimal CM crystallinity. Between these two endmembers, a complete
range of spectra was collected. When rastering the microscope focus point
across the samples, CM was easiest to find and measure in nearshore samples.
CM in samples collected from the distal shelf was more disperse and often
consisted of smaller particles. However, since samples were ground before
analysis, the size of each CM particle was not recorded.
Raman spectroscopy results showing the relative amount of each
carbonaceous material spectral class within each sample as pie charts.
Grouping spectra by Raman parameters
For each sample, Raman spectra were classified into disordered, intermediate,
mildly graphitised and highly graphitised CM as described above. This allowed
the proportion of each spectral type in each sample to be investigated
statistically. The most common group of spectra was intermediate, comprising
50 % of the data set, 27 % of the spectra were classified as disordered,
18 % as mildly graphitised and 5 % as highly graphitised. Based on the
location of samples on the shelf and guided by the proportion of spectra in
each group, groupings were defined for various regions of the ESAS (Fig. 1)
as follows:
Terrestrial samples.
These comprised the Kurungnakh Island permafrost core, and permafrost samples collected from the Indigirka and Kolyma catchments.
Nearshore Buor-Khaya Bay. This group contained samples that were collected
within 55 km of outflows from the Lena River (as measured by ArcGIS – see values in the Sample Metadata table in the Supplement).
Offshore Buor-Khaya Bay. These samples were collected further than 55 km from outflows of the Lena River.
Coastal erosion. This group comprised samples that were collected from areas known to have high rates of coastal erosion . This includes the Dmitry
Laptev Strait, samples YS-31, YS-32 and YS-33, which lie to the west of the
Kolyma River, and sample YS-37, which lies near Ayon Island. According to
, Ayon Island is eroding slowly (0–1 myr-1)
compared to the Dmitry Laptev Strait and regions west of Kolyma
(2–10 myr-1). However, sample YS-37 was included in the coastal
erosion group since the proximity of sample YS-37 to the island
(20 km) is significantly lower than its proximity to the Kolyma River
(245 km), and the spectral distribution in sample YS-37 is significantly
different if compared to neighbouring samples in the Kolyma region (YS-34 to
YS-41; see Fig. 2).
Distal ESAS. This group contained the samples located furthest offshore,
at the edge of the shelf and the start of the continental slope. The ESAS is
particularly shallow for hundreds of kilometres offshore, and these samples were collected in water depths ranging from 32 to 69 m.
Indigirka outflow. This group comprised a transect of samples from the mouth of the Indigirka River onto the ESAS.
Kolyma outflow. This group comprised a transect of samples from the mouth of the Kokyma River onto the ESAS.
Within each of these sample groups, the distribution of spectra is shown in
Fig. 3 and Table 2.
Distribution of spectral classes within each group of samples
The terrestrial group contains the largest proportion of disordered CM
(42 %) and the smallest proportion of mildly graphitised (5 %) and highly graphitised CM (2 %). There is no apparent difference with depth in the
terrestrial samples (KUR, KY, CH). The Buor-Khaya Bay nearshore and offshore
samples contain much less disordered CM than the terrestrial Kurungnakh
Island core samples (23 % and 18 % for the BKB vs. 68 % for the
Kurungnakh Island core samples) and contain an increasing amount of mildly graphitised CM offshore (16 % and 29 %). Indigirka River and Kolyma River
outflow samples contain the highest amounts of intermediate CM (61 % and
63 % respectively) and the lowest proportion of disordered CM (14 % and
15 %). In contrast, coastal erosion samples contain relatively high
proportions of disordered CM (36 %) and the joint-lowest amount of
intermediate CM (42 %). Distal ESAS samples contain 43 % intermediate CM
and the highest amount of highly graphitised CM (13 %). The highest amount
of highly graphitised CM was found in the two samples positioned furthest
from the continental mainland, excluding the New Siberian Islands, (YS-104
(56 %) and YS-102 (33 %); see Fig. 2).
Box and whisker chart showing the mean, median and quartile
proportions of each spectral class within each group of samples. Sample
groups are mapped in Fig. 1 and listed in the Sample Metadata table in
the Supplement.
Distribution of spectral classes within each group of samples.
Percentage values are averages of the proportions within each group, rather
than the proportion of the entire group. This corrects for samples in which
it was not possible to collect 30 Raman spectra.
In order to observe patterns in the spectral classes between samples and
sample groups, principal component analysis (PCA) was applied using the
software package R . For each sample, the proportion of each
spectral class was analysed (e.g. Ndisordered/Ntotal), and principal components were derived using the
prcomp function. Due to the extreme amounts of highly graphitised CM
in samples YS-102 and YS-104, the analyses were run with (see Supplement
Fig. S3) and without (see Fig. 4) these two samples to investigate whether
they biased the results. Considering that there was no significant difference
between the two PCA analysis runs, except that the large amount of highly graphitised CM in samples YS-102 and YS-104 created a greater amount of
scatter in the distal ESAS group, the PCA calculations excluding samples
YS-102 and YS-104 are used in further discussions.
Principal component 1, explaining 74 % of the variance, shows the interplay
between intermediate and disordered CM. These two spectral classes are plotted in
opposition to each other. Principal component 2, explaining 22 % of the
variance, denotes the relative enrichment in mildly graphitised CM. Principal
component 3, representing only 4 % of the variance, contains the varying
amount of highly graphitised CM, but is a minor component and the
distribution of this material will be discussed in Sect. 4.3. The various
sample groups defined in Table 1 are plotted in well-defined regions (see Fig. 4).
The Kurungnakh Island core is plotted in the top left corner of Fig. 4a. This
position is noticeably different to the Buor-Khaya Bay samples, suggesting
that bulk sediment delivered from the Lena River is not the same as
permafrost in the downstream area. Since the Lena catchment is one of the
largest on Earth, this is not surprising. The nearshore BKB samples are
noticeably different to the offshore BKB samples, as shown by their
confidence ellipses. Nearshore samples have higher values of PC1 compared to
the Kurungnakh Island core (average 0.06±0.12 c.f. -0.5; all errors
given as 1 standard deviation), but show a range of PC2 values ranging from
nearly equivalent to substantially lower (average 0.05±0.06, range
-0.03 to 0.13, c.f. 0.16). Offshore BKB samples have similar PC1 values to
the nearshore samples (average 0.04±0.10), with a lower PC2 value
(average -0.11±0.11). This denotes a high amount of disordered CM and
a low amount of intermediate and mildly graphitised CM in the Kurungnakh
Island core compared to the BKB samples and that the offshore BKB samples
contain more mildly graphitised CM than the nearshore BKB samples. This could
be a degradation or distribution effect, or it could be that the BKB offshore
samples have less influence from the Lena outflow and more influence from
coastal erosion within the Buor-Khaya Bay, such as Moustakh Island
.
Principal component analysis plots of PC2 vs. PC1 for the sample
groups. Terrestrial samples are shown individually. Vectors showing the
contribution of spectral classes to the principal components are shown in
red. Confidence ellipses (1 standard deviation) are shown in colours matching
the sample groups. (a) Buor-Khaya Bay nearshore and offshore groups.
(b) Kolyma and Indigirka river outflows. (c) Coastal
erosion and distal ESAS groups. Data labels showing sample names are included
in Supplement Fig. S2.
Onshore permafrost samples from the Indigirka and Kolyma catchments have high
values of PC2 (0.171 and 0.218 respectively), which is similar to the
Kurungnakh Island core and denotes a low amount of mildly graphitised CM.
Offshore, the Indigirka and Kolyma outflows are plotted in similar locations on
Fig. 4b. Their respective PC1 values are 0.14±0.16 and 0.15±0.14;
PC2 values are -0.02±0.10 and 0.01±0.10. These PC1 values are the
highest of all sample groups, denoting that the river outflows are the
richest in intermediate CM and poorest in disordered CM of the whole ESAS
data set. As seen in the Bour-Khaya Bay, the offshore Indigirka and Kolyma
samples have lower PC2 values than the corresponding terrestrial samples,
suggesting a higher amount of mildly graphitised CM offshore.
The coastal erosion data set has relatively low values of PC1 (average -0.15±0.16) and PC2 values (-0.00±0.08), which are similar to the Kolyma,
Indigirka, distal ESAS and nearshore BKB groups. The low PC1 values denote a
high amount of disordered CM in this group, which is supported by the trend
within the sample set as a whole – the confidence ellipse in Fig. 4c for
this group lies along the vector for disordered CM.
Samples from the distal ESAS have similar principal component values to the
coastal erosion samples. PC1 averages -0.08±0.15; PC2 0.00±0.07.
This set of values denotes that, similar to the coastal erosion group, the
distal ESAS sediments contain high proportions of disordered CM, low
proportions of intermediate CM and moderate amounts of mildly graphitised CM
(more than the terrestrial samples, but less than the offshore Buor-Khaya
Bay). It is noticeable that the distal ESAS confidence ellipse in Fig. 4c
almost overlays the coastal erosion ellipse and is different to
the ellipses from river outflow groups (Buor-Khaya Bay, Indigirka, Kolyma).
This suggests that the distal ESAS sediments are mostly sourced from coastal
erosion and that on-shelf homogenisation processes are not sufficient to mix
coastal-erosion- and river-derived sediments see sedimentological data
from.
Spatial distribution of principal component 1 across the ESAS. An
interpolated map of the distribution is included as Supplement Fig. S4.
Spatial pattern of principal component 1
To investigate further, the value principal component 1 was plotted across
the ESAS (Fig. 5; Supplement Fig. S4). It shows that the Dmitry Laptev
Strait, the region between the Indigirka and Kolyma rivers, and the north-eastern part of the distal ESAS have the lowest values of PC1. Highest
values of PC1 are in the outflows of the Indigirka and Kolyma rivers, and in
the central distal part of the ESAS (around 150∘ E).
In the offshore area of the Lena River, the variation is more moderate, trending
from mildly positive (somewhat enriched in intermediate OC) near the river to
mildly negative (somewhat enriched in disordered OC) on the ESAS at
130∘ E. These patterns demonstrate that there are longitudinal zones
of disordered and intermediate CM across the entire ESAS. In the western
section, disordered CM is prevalent offshore in contrast to the Buor-Khaya
Bay sediments. The permafrost core from Kurungnakh Island is enriched in
disordered CM, as are the samples from the Dmitry Laptev Strait, which is
known to have high rates of coastal erosion. This suggests that distal
sediment in this region may be dominated by coastal erosion of permafrost,
and that sediment from the Lena River is less likely to be deposited in the
area north of the Lena Delta.
In the central section, from 140 to 160∘ E, PC1 has a positive value
on the distal ESAS; the proportion of disordered CM is low, while
intermediate CM dominates. This distribution is similar to that of the
Indigirka River outflow at 150∘ E. It is likely that the sediments
from the Indigirka have been transported out to this part of the shelf.
However, there is a possibility that the New Siberian Islands, which are much
closer to the distal ESAS than the Indigirka Outflow, could be the source of
this intermediate CM. Unfortunately there are no samples from the New
Siberian Islands for comparison.
In the eastern part of the distal ESAS, beyond 160∘ E, PC1 has a
negative value that is similar to samples collected to the west of the Kolyma
River outflow, but different to the samples collected east of the outflow.
Pyrolysis-GC-MS studies of macromolecular OC in this region suggest that most
of the OC delivered by the Kolyma River is distributed to the east of the
outflow and that the samples to the west of the outflow are likely dominated
by coastal erosion (Sparkes et al., 2016). This would imply that the distal
ESAS sediments east of 160∘ E are mostly composed of material eroded
from the coastline between the Indigirka and Kolyma rivers, known to have
high rates of coastal erosion . Material delivered by the
Kolyma River may be travelling even further to the east, and future sample
campaigns beyond 170∘ E may find material that is more enriched in
intermediate CM.
The distribution of highly graphitised CM across the ESAS. An
interpolated map is included as Supplement Fig. S5.
Distribution of highly graphitised CM
The relative proportion of highly graphitised CM in the distal ESAS sample
group was much higher than the other samples (see Table 2 and Fig. 3).
Throughout the distal ESAS group, highly graphitised CM represents 12 % of
all spectra collected (38 spectra out of 325). Averaging the proportion of
highly graphitised CM in each of these samples, the distal ESAS group has
13 % ± 15 % (1 s.d.) highly graphitised CM, with a range from 0 %
to 55 %. This heterogeneity is seen in Fig. 2, but there is still
significantly (P=0.0001) more highly graphitised CM in the distal ESAS
group than the remaining samples (3 % ± 3 %), and the greatest
proportion of highly graphitised CM found in any other sample is 10 %
(sample YS-22). As mentioned earlier, two samples, YS-102 and YS-104, have
extremely high amounts of highly graphitised CM, far exceeding the proportion
measured in any other sample (33 % and 56 % respectively). Even without
these samples in the calculation, the distal ESAS group is still
significantly different from the remaining samples (7.4 % ± 5.5 %
c.f. 3 % ± 3 %, P=0.0001).
There is more highly graphitised CM in the distal ESAS samples than in both
the nearshore ESAS samples and the terrestrial samples
(1.7 % ± 1.9 % for terrestrial samples c.f. 7.4 % ± 5.5 %
in the distal ESAS; P<0.05). There is an increased amount of highly graphitised CM in the nearshore ESAS than the terrestrial samples, but this
increase is not significant (2.7 % ± 2.9 % for the non-slope
offshore samples, 1.7 % ± 1.9 % for terrestrial samples; P=0.19).
Therefore, there must be a process leading to an enhanced amount of highly graphitised CM present in the furthest offshore samples. Three possibilities
exist for this: unseen sources of graphite, preferential preservation of
graphite and sorting of sediment particles.
The terrestrial samples available for this project may not represent all of the eroded material from the ESAS. Eastern Siberia contains some of the
world's largest drainage basins and longest rivers, and therefore CM could be
eroded from thousands of kilometres inland before being delivered to the
ESAS. Highly graphitised CM has been shown to survive long-distance river
transport . There could be a source of graphite
inland that, coupled with sorting effects, is responsible for the offshore
graphite. In this case, rivers would deliver highly graphitised CM that is
diluted by erosion of non-graphitic CM close to the river mouth and along the
coastline. Further processing offshore, either the loss of non-graphitic CM
or the concentration of graphitic CM, means that, while the graphite is a
minor component of the nearshore samples, it can be seen in the distal
samples.
However, samples collected near to the Lena and Indigirka river mouths show
no increase in graphite compared to nearby areas dominated by coastal
erosion, suggesting that there is not even a diluted graphite signature
coming from the rivers. Nearshore BKB samples (2.3 % ± 2.9 %) and
the sample nearest to the Indigirka River (YS-30; 0 %) have little or no
highly graphitised CM. The sample closest to the Kolyma River outflow has
some highly graphitised CM (YS-34; 7 %), so there may be graphite erosion
from within the Kolyma catchment. report graphite
bearing ore bodies from multiple locations within the Kolyma catchment, which
form part of the Kolyma–Omolon superterrane metamorphic belt. This material
could survive transport through the Kolyma River ,
explaining the increase in highly graphitised CM in the north-eastern
part of the ESAS. Given the increasing proportion of highly graphitised CM
observed offshore across the entire E–W transect, it is more likely that the
graphite is sourced in low concentrations across the region and that offshore
processes are responsible for increasing the proportion in distal areas.
Regardless of the graphite source, other processes are required to explain
the offshore trend in highly graphitised CM. There is a clear offshore
transition from lower to greater amounts of highly graphitised CM (Fig. 6;
Supplement Fig. S5), which could have been caused by a preservation effect.
As seen in transects along the Bengal Fan, there is often a trend to more
crystalline graphite with transport distance . This has been
explained as a resistance to physical, chemical and/or biological
degradation. If disordered, intermediate or mildly graphitised CM is
preferentially degraded during transport across the ESAS, distal samples
would be relatively enriched in highly graphitised CM even if the proportion
of this material in the sediment delivered to the shelf is low, but there is
not an offshore trend in the other classes of spectra. If graphitisation
increases resilience to degradation, it would be expected that disordered CM
was the least resistant group of CM, and that this would be seen as a trend
away from disordered CM offshore. However, this is not the case and the
distal ESAS samples contain large amounts of disordered CM. All CM analysed
in this study must be autochthonous, even disordered CM. In situ production
or flocculation of marine organic matter could not produce Raman-amenable CM
particles.
There could also be a sorting effect across the shelf, rather than
degradation. Whereas solvent extractable biomarkers of terrestrial processes
have been shown to reduce rapidly across the ESAS
, most of the Raman-amenable CM
present in these samples is likely to be resistant to degradation
. Therefore, an alternative reason for highly graphitised CM to
be present in high concentrations on the distal ESAS is that it is
preferentially delivered and deposited there. showed that
organic carbon was mostly associated with fine particles in the distal ESAS,
and that larger particulate OC was deposited close to the coastline. Graphite
flakes have a lower density than many silicate minerals and have a tabulate
form. Winnowing is used commercially to separate graphite flakes from bulk
sediments using liquid froth or air jet . Stokes' law
predicts that low-density graphite flakes travelling independently should
settle slower than denser silicate minerals or large conglomerate grains.
Less graphitic CM may be part of a sedimentary conglomerate or the individual
particles may be larger and have a higher settling velocity. Thus, even if
highly graphitised CM is present as only a minor fraction in the bulk
sedimentary input to the ESAS, if other fractions are preferentially
deposited on the shelf, then the most distal samples will be enriched in
highly graphitised CM.
The current data set does not allow a definitive determination of whether
degradation or distribution is the primary cause of the offshore trend
towards highly graphitised CM. The distal ESAS samples are located in deeper
water compared to the other sample groups (see the Sample Metadata table
in the Supplement). The deeper water setting allows increased settling time
before burial, which would enhance both degradation and winnowing effects. It
is noticeable, however, that there are no offshore trends in the distribution
of disordered, intermediate or mildly graphitised CM. Therefore, whichever
process is driving the highly graphitised CM pattern must be mostly affecting
only the crystalline particles.
Comparison with soot black carbon
Soot black carbon (SBC) is a complementary portion of the recalcitrant OC
load in the ESAS. studied SBC using chemical methods
(chemothermal oxidation to remove non-SBC, followed by elemental analysis and
stable/radiocarbon isotope mass spectrometry), which allowed for
quantification of SBC and source apportionment. This method does not
directly measure the nature of SBC particles, in contrast to Raman analysis,
but allows for quantification rather than investigating relative changes. They
showed that, while the total amount of SBC diminished offshore, along with
the proportion of SBC as a fraction of total OC, the proportion of SBC as a
fraction of permafrost-sourced OC increased offshore, especially in the
eastern ESAS. This pattern matches our findings regarding the proportion of
highly graphitised CM in the ESAS sediments (see Figs. 3 and 4). The authors
concluded that SBC is recalcitrant compared to other forms of permafrost OC
and avoids degradation during transport. Our Raman study supports this
finding, while also adding information about the longitudinal distribution
of CM. Longitudinal variations in SBC amount, proportion and radiocarbon
signature are small, but Raman suggests that river-sourced and coastal-erosion-sourced
recalcitrant organic matter can be differentiated and tracked
across the shelf.
One consideration when comparing the chemical SBC and spectroscopic Raman
studies is whether there is crossover between the two carbon pools. SBC
particles can have a wide range of chemical structures and grain sizes. Some
SBC grains may be large enough to be seen using a Raman microscope (greater
than approx. 5 µm), but most SBC particles are smaller than
submicron size and would not be analysed. Some CM, especially highly graphitised CM, may survive the chemothermal oxidation process and be counted
amongst the SBC pool. Intermediate and disordered grade CM are more likely to
be lost during the chemothermal oxidation, and it is these two forms of CM
that demonstrated longitudinal variation, highlighting the difference between
river and coastal permafrost carbon. Thus, there is value in measuring both
the chemical properties and crystallographic form of recalcitrant organic
matter as a way of tracking exported carbon and sediment.
Implications for carbon cycling in the ESAS
The results of this study indicate that terrestrially sourced CM in the East
Siberian Arctic region can be found in sediments hundreds of kilometres
offshore. Whilst direct quantification of CM as a fraction of total OC is not
possible using Raman spectroscopy, chemical analyses show that SBC could
account for 14 % of terrestrial OC in distal sediments .
Highly graphitised CM is structurally similar to SBC and the chemothermal
treatment used in these studies likely removes low-grade CM, implying that CM
concentrations could be similar to, if not higher than, SBC offshore.
Whereas biomarker studies suggest that terrestrially sourced organic
compounds are often absent or have very low concentrations in offshore
settings in this region
, CM was
observed in every offshore sample and there does not seem to be a consistent
decline in any of the CM groups – there are distal ESAS samples with
significant proportions of each of these classes. Halfway between extractable
lipids and Raman-amenable CM particles is macromolecular organic matter, such
as lignin. extracted these same samples and found that
concentrations of lignin phenols were also very low in the outer shelf. Thus,
the CM observed using Raman behaves very differently to extractable and
pyrolysable OC. While there are observable offshore trends, it appears that
this material is much more resilient than the labile organic matter
traditionally used to track OC export from terrestrial settings. This
observation likely accounts for the old radiocarbon values observed in
ESAS sediments , where carbon eroded from coastal
sediments is thought to be a major contributor to offshore sedimentary OC,
whereas modern terrestrial OC is a minor component far offshore. CM has
undergone diagenesis and/or metamorphism, a process that takes a significant
amount of time and should therefore have no radiocarbon remaining.
Conservative offshore transport also addresses the anomalous radiocarbon
values measured in the Beaufort Sea by . If CM can be
transported for large distances offshore without significant degradation, it
could lead to unusually old radiocarbon in seafloor sediments but
potentially also suspended particulate matter.
Long-distance CM transport also has implications for the carbon cycle in this
region. estimated, using bulk isotope analysis, that
66 % ± 16 % of the 44 ± 10 Tg OC eroded from ice
complexes along the ESAS coastline is released as CO2, with the
remaining third buried in the shelf or deep ocean. Distal shelf sediments
were estimated as containing up to 50 % of their OC from coastal erosion
sources. However, subsequent molecular analyses have suggested that organic
matter degradation is extremely prevalent by the outer shelf. Solvent
extractable material, while only accounting for 5 % to 10 % of the total
OC content of exported sediment, have predicted a significant loss of
terrestrial OC during transport across the ESAS
.
Pyrolysis GC-MS, investigating larger, non-extracted, biomolecules, also found
that terrestrial markers were much diminished on the outer shelf
. Therefore, this study provides a systematic
characterisation of the most likely form of terrestrial carbon in distal
offshore settings. Erosion of recalcitrant OC from permafrost, without
subsequent degradation, will transpose carbon from land into ESAS sediments
or the deep Arctic Ocean basin, with no net effect on atmospheric CO2
levels. If the recalcitrant CM analysed in this study forms a significant
proportion of the OC load of the ESAS, then the warming-induced carbon cycle
feedbacks will not be as severe as if all terrestrial OC was degraded. Thus,
when modelling the impact of permafrost erosion on climate change, and vice
versa, researchers should not consider the total organic carbon content of
the mobilised sediment but its labile fraction. Note that some of
this labile fraction could be ancient carbon, currently locked in permafrost
deposits across Eastern Siberia , and therefore
permafrost thaw can still cause a net increase in atmospheric CO2
levels and an associated positive feedback relationship with climate
warming.
Conclusions
Raman spectroscopy of carbonaceous material has been successfully applied
across the East Siberian Arctic Shelf, a complex and heterogeneous
sedimentary system. By grouping collected spectra into classes and applying
principal component analysis, CM from river and coastal erosion has been
differentiated – coastal erosion delivers more disordered CM, while rivers
deliver more intermediate CM. Across the ESAS, two main processes have been
identified. Firstly, there is a trend across the entire shelf towards highly graphitised CM in the furthest offshore samples, collected from the
distal ESAS. This could be due to degradation of weaker CM during transport
or preferential winnowing of crystalline graphite to more distal settings;
given the presence of disordered CM in the most distal settings, winnowing is
the preferred explanation. In addition to this, an east–west pattern is
observed in the distribution of disordered and intermediate CM. This suggests
that there are areas where sedimentation is dominated by material sourced
from rivers and areas dominated by coastal erosion sediment. Despite the
shelf being extremely wide and shallow, sediment transport processes have not
homogenised the sediments and three individual sections can be identified
along the shelf. Raman spectroscopy is thus a valuable tool for identifying
and tracking eroded sediment in complex environments. Furthermore, the
presence of CM ranging from disordered to highly graphitised in distal
sediments, hundreds of kilometres from the coastline, shows that
Raman-amenable carbon is generally resistant to oxidation and thus a
conservative part of the organic carbon cycle in this region.
Supplementary information is supplied with this paper.
Supplementary information included with the article:
Sample Metadata: a spreadsheet containing sample locations, previously measured geochemical parameters and results from principal component analysis.
Supplementary Figure S1: an example of peak-fitting results for disordered/intermediate, mildly graphitised and highly graphitised
spectra.
Supplementary Figure S2: principal component analysis results, as shown in Fig. 4, but with each sample location
identified.
Supplementary Figure S3: principal component analysis results if samples YS-102 and YS-104 are also included in the
procedure.
Supplementary Figure S4: interpolated map of principal component 1 across the ESAS. Interpolation was carried out using the nearest-neighbour algorithm within
ArcMap.
Supplementary Figure S5: interpolated map of percentage highly graphitised CM across the ESAS. Interpolation was carried out using the nearest-neighbour
algorithm within ArcMap. Colour scale transition has a geometric profile to enable regions with smaller amounts of highly graphitised CM to be differentiated.
Supporting data and code underlying this study are available via the MMU research repository:
The shell script used to fit Raman data:
https://doi.org/10.23634/MMUDR.00620208
Unprocessed Raman spectra as x,y text files:
https://doi.org/10.23634/MMUDR.00620205
Fitted spectra, showing the automatically fitted peaks, as a PDF file:
https://doi.org/10.23634/MMUDR.00620207
Parameters of the fitted peaks including height, width, location and area:
https://doi.org/10.23634/MMUDR.00620209
Raman fitting procedures are also published and maintained at
https://github.com/robertsparkes/raman-fitting (last access: 29 September 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-12-3293-2018-supplement.
ÖG, BEvD, and IPS collected samples along with the crew of ISSS-08. RBS designed the
study. ADS assisted in preparing the samples for analysis. Raman spectroscopy
measurements were carried out by MM, JB and RBS. Data analysis was carried
out by RBS. RBS and BEvD prepared the manuscript with contributions from all
co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
We gratefully acknowledge receipt of NERC research grants (NE/I024798/1 and
NE/P006221/1) to Bart E. van Dongen, an MMU studentship to Robert B.
Sparkes/Melissa Maher, an MMU-EERC research grant to Robert B. Sparkes,
Swedish Research Council (VR contracts 621-2007-4631, 621-2013-5297 and the
Distinguished Professors Grant 2017-01601) and the European Research Council
(ERC-AdG CC-TOP project #695331) support to Örjan Gustafsson, and
support from the Government of the Russian Federation (mega-grant
14.Z50.31.0012) to Igor P. Semiletov. We thank the crew and personnel of the
R/V Yakob Smirnitskyi and all colleagues in the International Siberian Shelf
Study (ISSS) Program for support, including sampling. We thank Tomasso Tesi for
providing the Yedoma samples for the Kolyma and Indigirka catchment areas. We
thank Juliane Bischoff and Dirk Wagner for providing the Kurungnakh Island core.
Hayley Andrews provided technical support for the MMU Raman Spectrometer. The
ISSS program is supported by the Knut and Alice Wallenberg Foundation, the
Far Eastern Branch of the Russian Academy of Sciences, the Swedish Research
Council (VR contract no. 621-2004-4039, 621-2007-4631 and 621-2013-5297), the
US National Oceanic and Atmospheric Administration (OAR Climate Program
Office, NA08OAR4600758/Siberian Shelf Study), the Russian Foundation of Basic
Research (08-05-13572, 08-05-00191-a, and 07-05-00050a), the Swedish Polar
Research Secretariat, the Nordic Council of Ministers and the US National
Science Foundation (OPP ARC 0909546). Finally, we thank the editor and two
anonymous reviewers for their comments and
suggestions.Edited by: Florent Dominé
Reviewed by: two anonymous referees
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