Introduction
Understanding natural processes and feedbacks within the global organic carbon cycle is
necessary for a comprehensive understanding of Earth system dynamics and of continuing climate change. High
latitudes account for nearly half of the global soil carbon stores
and are a poorly understood region. Arctic permafrost
carbon, in the form of tundra and taiga soils (∼1000 PgC), terrestrial
ice
complexes (∼400 PgC) and submarine permafrost (∼1400 PgC)
significantly outweighs the atmospheric CO2 pool (∼760 PgC;
), and is liable to
become an active part of the carbon cycle in the region during the next century
. Observations and predictions
of global climate change have shown that the polar regions are
disproportionately affected by temperature increases
, leading to increased permafrost
thawing, erosion of coastal permafrost and destabilisation of submarine
permafrost .
Recent experiments have shown that long-term warming of permafrost reorganises the
soil carbon stock, increasing decomposer activity in the mineral soil layer
while also increasing the vegetation stock at the surface
. Changing pervasiveness of permafrost (i.e.
from continuous to discontinuous coverage) introduces permeability, and allows
groundwater flow to interact with deeply buried carbon
.
This activation of deep carbon will lead not only to direct oxidation and
CO2 release but also to increased erosion and offshore transport from the
permafrost layer to the Arctic Ocean, enhanced by (already observed) increased
river discharge . Ultimately these processes
will lead to increased input of terrestrial organic carbon (terrOC) to the
Arctic Ocean, where it can interact with the biosphere. However, the fate of
terrOC in the Arctic Ocean remains poorly understood.
Carbon stored within frozen soils and ice complexes is only released to the
atmosphere if it becomes an active part of the carbon cycle. Inert transport
from terrestrial to submarine storage (e.g. deposition as organic-rich sediment)
has no net effect on global atmospheric carbon dioxide levels. However, any
degradation during transport and deposition of carbon, previously sequestered
for thousands of years, will release CO2 to the atmosphere
, causing a positive feedback effect on
climate change. Some studies of global offshore terrOC burial have argued that
there is extensive remineralisation once terrestrial material is delivered to
the oceans , whilst others have
documented significant offshore terrOC burial, even over long transport
distances .
Therefore, understanding the fate of terrOC after it is transported to the
Arctic marine environment is critical to quantify the carbon cycle in the polar
region.
Recently, a number of studies have been published focusing on bulk and molecular
level investigations of sediments exported from the Canadian and Siberian
regions, attempting to better understand the behaviour of terrOC in lakes,
rivers, estuaries and shelves
.
These studies have shown the presence of terrOC in marine settings offshore
major Arctic rivers, and a transition from terrestrial- to marine-dominated
geochemical signatures with increasing distance offshore. TerrOC input from
coastal erosion is also a significant part of the Arctic carbon cycle
, and up to
44±10 Mt of terrOC may be mobilised from permafrost coastal erosion
each year. The distribution of stable carbon isotopes in sedimentary organic
carbon (δ13C) in marine sediments was used to
distinguish between the two biogeochemical provinces, western and eastern, in
the East Siberian Arctic Shelf (ESAS; ).
Glycerol dialkyl glycerol tetraethers (GDGTs) have been identified as
biomarker molecules for terrestrial and marine organic matter
. Sourced from the cell membranes of
bacteria and thaumarchaeota, they have been found in a range of terrestrial
and marine sediments dating back millions of years .
Branched GDGTs (brGDGTs) contain 4–6 methyl branches along two C28
alkyl chains (Fig. S1 in the Supplement) and are produced by terrestrial
bacteria in peats and soils . They have also
been found to be abundant in other terrestrial settings, including lakes and
rivers . Isoprenoidal GDGTs contain two C40
isoprenoid chains with varying number of cyclopentane rings. One of these,
crenarchaeol (cren), which is dominantly produced by marine thaumarchaeota,
contains a cyclohexane unit in addition to four cyclopentane rings (Fig. S1).
The ratio of brGDGTs to cren forms the basis of the branched and isoprenoidal
tetraether (BIT) index , a proxy for tracing terrestrial
material in marine sediments. The BIT index has been used to infer
terrestrial to marine transitions along river–ocean transects in (sub)-Arctic
and non-Arctic regions . Recent
studies have inferred that a portion of the brGDGTs in the Arctic region may
be produced within rivers, rather than being harvested entirely from soil
erosion during the freshet, and that brGDGTs and BIT can be used to trace
fluvial erosion offshore . Therefore
the relationship between river outflows and the Arctic Shelf is worth
investigation to understand the delivery of organic matter to the Arctic
Ocean and its eventual fate. Differences in amount, distribution and eventual
fate between coastal and fluvial OC delivery can have severe implications for
climate change and feedbacks.
This study aims to use a combination of GDGT biomarkers and stable carbon isotope proxies
measured on a series of surface sediments from across the entire ESAS, including transects of the major Russian Arctic rivers in this area
(Lena, Indigirka and Kolyma) and areas of coastal erosion, to investigate the
transport and fate of terrestrial organic carbon in a region which has
experienced little scientific investigation but is likely to experience extreme
climate change in the next century. Combining these proxies allows us to (i)
differentiate between the different fractions of terrOC (coastal ice complex OC
and river-transported terrOC) which will likely have different degradation
potentials and (ii) observe whether bulk terrOC and a specific fraction of the
terrOC behave similarly.
Results and discussion
Sedimentary organic carbon (OC) concentrations (from )
ranged from 0.68 to 2.25 wt.%C. OC concentration was highest in the
Buor-Khaya
Bay, and relatively uniform across the rest of the ESAS. 0–100 km from
the
river outflows, TOC averaged 1.81±0.10 %, 100–800 km away it
averaged 0.88±0.06 % (Table 2).
Summed brGDGTs, crenarchaeol concentrations, BIT, δ13C
and TOC values on the East Siberian Arctic Shelf, grouped by distance from
river mouths.
Distance from
n
ΣbrGDGTs
Crenarchaeol
BIT
δ13C**
TOC
rivers*, km
nggsed-1
nggsed-1
‰
%
0–100
46
668
475
0.58
-26.05
1.81
100–200
13
227
781
0.32
-25.95
0.79
200–300
12
306
815
0.37
-26.55
0.96
300–400
5
129
3595
0.04
-24.60
0.94
400–500
5
136
2611
0.05
-24.60
0.91
500–600
3
84
2164
0.04
-23.70
0.78
600–700
6
62
1984
0.03
-22.88
0.83
700–800
2
13
971
0.01
-21.35
0.97
* This distance was measured radially in kilometres from a series of
outflows shown in the NOAA GSHGG river data set. ** As reported in
.
GDGT concentrations
Liquid chromatography mass spectrometry analysis showed a wide range of concentrations for both brGDGTs
and cren throughout the sediments (Fig. 2a and b). BrGDGT
concentrations
ranged from below detection limit (BDL) to 180 µggOC-1 (2046 nggsed-1), with the highest concentrations observed
close to river
mouths – especially the Lena River, which is the largest of the rivers in the
study area and exports the largest amount of sediment (20×106 tyr-1; ). Within the Buor-Khaya Bay, brGDGT
concentrations were highest in the south-western corner of the bay, beside the
major outflows of the Lena Delta, and reduced with distance across the bay.
Nearshore ESAS samples, less than 150 km from the river mouths, averaged 30 µggOC-1 (203 nggsed-1), whilst samples >150
km offshore
averaged 14 µggOC-1 (136 nggsed-1; Fig. S2a).
Maps of (a) summed brGDGTs and (b) crenarchaeol
concentrations, and (c) the BIT index on the ESAS. Maps were
interpolated using a kriging algorithm, and the locations of ISSS-08 stations
are shown with black dots.
When plotted against the distance from river outflows, the offshore trend in
brGDGT concentrations showed a rapid decrease in concentration (Fig. 3a).
Samples within 100 km of the river mouths had an average brGDGT
concentration of 38±3µggOC-1
(668±65nggsed-1), by 300–400 km offshore
the concentration was only 15±4µggOC-1
(129±31nggsed-1), and 700–800 km
offshore the average was 1.3±1.0µggOC-1
(13±11nggsed-1; Tables 1 and S1 in the
Supplement). found similarly rapid decreases in the
concentration of lignin phenols and cutin acids along the same sample
transect. The brGDGT concentration per gram of sediment had a power-law
reduction (y=axb) with an exponent of b=-0.92 and an r2
value of 0.52. In a similar analysis, found power-law
relationships between water depth and concentration for GDGTs and other
biomarkers in the East China Sea. Given that the bathymetry of the ESAS is
very flat, the equivalent in this case is to plot against distance offshore.
Our results show that rapid offshore decreases in brGDGT concentrations are
not an isolated observation. This sharp decrease in brGDGT concentration
could be due to either a rapid sedimentation of brGDGT-rich material close to
the shoreline, or the remineralisation of GDGT compounds during transport to
the more distal locations.
Box plots summarising the concentrations of (a) brGDGTs and
(b) crenarchaeol on the ESAS, grouped by distance from river mouths.
Concentrations in ice complex samples are also shown. Thick lines show the
median values, boxes the 25th and 75th percentiles, whiskers the maximum and
minimum values within 1.5 times the interquartile range, and square symbols
the outliers beyond this threshold.
Cren ranged from 2.05 to
656 µg
gOC-1 (24 to 8116 nggsed-1) with the lowest values in the coastal areas and the highest
cren concentration at site YS-40, 391 km offshore from the Kolyma
River
outflow (Figs. 3b and S2b, location details in Table S1). Other regions of
high cren concentration were the area east of longitude 160∘ E, and north of the Lena Delta. The increase in cren east of 160∘ E corresponds to the “eastern ESS” region defined by
, and suggests a region more affected by marine processes
than the remainder of the ESAS. The most distal sediments showed a reduction in
cren concentration, with mean values of ∼197 µggOC-1 (970 nggsed-1) among the samples collected at the edge of the shelf.
Cren trends offshore were also non-linear, with the concentration
peaking 300–400 km from the river mouths (average concentration 358±65µggOC-1,
3600 ± 1200 nggsed-1). Nearshore and far offshore the average values were much
lower
(0–100 km: 38±8µggOC-1, 480±50nggsed-1; 700–800 km:
95±73µggOC-1 970±774nggsed-1 – see Table 2). A similar pattern in marine production has
been
observed in other transects of the Arctic coast, such as offshore northern
Alaska and may be due to
a combination of (local) factors. Close to the shore the presence of fast ice for
most of the year could reduce primary productivity, whilst far offshore the ice
cap may have the same effect
Measurements in the Laptev Sea of dinosterol and brassicasterol, biomarkers for
open-water phytoplankton , showed a similar
pattern, although
the maximum concentrations of these biomarkers were 76–79∘ N,
further offshore than the cren peak. The authors suggested that maximum primary
productivity is in the open water and polynyas between the terrestrially bound
fast ice and the permanent open-ocean ice sheet.
Onshore, in ice complex samples, total brGDGT concentrations were 129 nggsed-1, and cren concentrations 124 nggsed-1. These
values are very low compared to the ESAS samples, especially the brGDGT
concentration compared to samples collected in the Buor-Khaya Bay or close to
river outflows (Fig. S2). report comparable
results: brGDGT concentrations of 77±50
ng gsed-1, cren concentrations of 16±11
ng gsed-1 and BIT values of 0.83±0.02. These
results both suggest that erosion of ice complex is unlikely to
be the main source of brGDGTs or cren to the ESAS.
Spatial GDGT distributions and BIT
BrGDGTs and cren had very different concentration relationships across
the shelf (Figs. 2a and b and 3). Plotting cren concentration against
brGDGT concentration shows that all nearshore samples are grouped together,
having low cren concentrations, whilst all offshore ESAS samples are in
a distinct group with high cren and low brGDGT concentrations (Fig. 4). The
existence of these two groups is visible in the BIT index – Fig. 2c shows
a map of BIT index across the ESAS. BIT was highest in the
Buor-Khaya Bay, especially close to the Lena River outflows (Fig. 2c). The
stations closest to the Lena, TB-30, 40, 46, 47 and 48, had an average BIT value
of 0.91, compared to the bay as a whole, which averaged 0.58 (Table S1). Given
a terrestrial BIT value of 1 , this
strongly suggests a terrestrial source of the sediment deposited here, and
a fluvial source to the brGDGTs , and
is similar to patterns seen in other locations
.
The BIT index values averaged 0.58±0.03 in the 100 km closest to
all river outflows, dropping to 0.04±0.01 when 300–400 km
offshore. A strong relationship is observed when the BIT index is plotted
against the distance from the outflows of major rivers (Fig. 5a). The BIT
index decreased rapidly in the first 150 km offshore before reducing more
slowly across the ESAS. This was seen for the Lena, Indigirka and
Kolyma offshore regions, as well as the open shelf. report BIT
values from the Laptev Sea that show similar trends, reducing quickly
offshore. However, their results are not quantitatively comparable to this
study since a correction factor was not applied during
analysis. In contrast, the rapidly decreasing pattern was not seen in
the DLS. Although the DLS is influenced by freshwater from the Lena River , it is a long distance from any river outflows and yet
has a relatively high BIT value of 0.55±0.06. Excluding data from the
DLS, which will be discussed separately, there is a strong
power-law correlation (y=axb) between BIT and distance from rivers, with
a value for exponent b of -1.209 (Fig. 5a, (r2)=0.85p≤0.00001). To
test this further, the BIT indices of offshore regions from the Lena (Laptev Sea),
Indigirka (East Siberian Sea <160∘ E)
and Kolyma (East Siberian Sea >160∘ E) rivers were plotted against
distance from river outflows in log–log
space (Fig. 5b). The gradients of the associated trend lines correspond to the
exponential value (b) of each transect. The values for the Lena (b=0.903)
and Indigirka (b=0.953) are comparable, but the values for the Kolyma region
seem
substantially higher (b=1.302), denoting a more rapid shift to
a marine-dominated system. The offshore Kolyma region showed linear rather than
power-law reductions in high/low-molecular-weight n-alkanes
. Measurements of lignin phenols from the same region showed
rapid offshore decline but did not show the spatial variance in reduction rates
. The sediments from the most distal part of the
Kolyma offshore region appear to have abnormally low BIT values compared to
the
nearshore sediments. These sediments are in a region that can potentially be
influenced by inflow of Pacific Ocean water from the Bering Strait
, where incoming nutrients could stimulate primary
productivity, as indicated by the extremely high cren values.
The nearshore section of the Kolyma region gives a value (b=0.945)
comparable to the other two regions. The similarity of each region
studied, each showing a power-law reduction in BIT with distance despite
a spatial separation of hundreds of kilometres, suggests that the processes affecting
brGDGT degradation and cren production are similar across the whole
ESAS. The absolute amounts of brGDGTs and cren differ for each river
(Figs. 2a and b and S3a), and each region has a different BIT value for
a given distance offshore (Figs. 2c and 5b), yet the rate of reduction offshore is
remarkably comparable.
Plot of crenarchaeol vs. brGDGT concentration. Nearshore samples
from the Buor-Khaya Bay, DLS and nearshore (<150 km from river
mouths) ESAS have low crenarchaol concentrations. Offshore ESAS samples
(>150 km from river mouths) have high crenarchaeol concentrations.
Labelled contours show the BIT index values.
The DLS is unusual for its relatively high BIT index compared
to its location, over 200 km from a major river outflow. This area is
a region of high coastal erosion and the outflow of the Lena and Yana rivers is
channelled through the DLS – the eastward branch of the Lena River outflow determines the fresh water balance and thermal
regime of the strait, but particulate matter is dominated by coastal erosion . Given that the BIT index appears to
decrease based on the distance from fluvial outflows (Fig. 5a), and therefore
brGDGTs are likely delivered by rivers, one possibility could be that either the Lena River outflow or minor
rivers discharging into this area are providing the brGDGTs, giving an enhanced
BIT index. However, Figs. 2a and S2a show that brGDGT concentrations in this
area are not especially high, and that there is a decreasing trend going
eastward from the Lena Delta. The cren concentrations in this region are
very low (Figs. 2b and S2b), and it is this that is driving the high BIT index in
the area. The DLS may be poor in cren due to sea-ice cover
reducing primary productivity.
found that later melting times for sea-ice cover reduced seasonal primary
productivity. Retreating ice causes a plankton bloom and initiates the growing
season in that area. report that the
boundary between sea ice and continentally anchored fast ice forms open-water
polynyas roughly equivalent to the peak cren regions, and the fast ice then
retreats throughout the summer. Summer sea-ice concentrations are higher in the
DLS than other coastal areas, which could lead to the extremely
low cren concentrations. Future changes in ice cover will likely lead to
increased marine productivity in this region, and may therefore reduce BIT
values . Alternatively, because the ESAS is characterised by very low transparency, which
limits euphotic layer thickness , increasing river discharge will further decrease
transparency, affecting marine productivity, and may therefore increase BIT values in the future.
Plot of BIT index vs. linear distance from river mouths.
(a) Plotted in linear space, showing the strong power-law
relationship between the BIT and distance (with the exception of the DLS
samples) and (b) plotted in log–log space. Outflows from the Lena,
Indigirka and Kolyma rivers are comparable, with power-law coefficients
labelled.
Stable carbon isotopes and BIT
Stable carbon isotope values (δ13C) can be used as a bulk
proxy for marine vs. terrestrial influence on sediment organic carbon
composition. Marine productivity produces material with a more positive δ13C
value compared to terrOC. δ13C values of the
surface sediments, sourced from were
analysed in combination with the GDGT results. δ13C ranged
from -21.2 to -27.5 ‰, with most depleted values in the DLS, and most enriched values on the distal shelf, again showing
a transition from terrestrial to marine dominance offshore. The Buor-Khaya Bay
samples were also depleted, although less so than the DLS, and
showed no significant variation across the Buor-Khaya Bay, in contrast to the BIT values (Fig. 6).
There was a linear relationship between δ13C and distance
offshore. For samples from the Indigirka and Kolyma regions, and across the offshore
ESAS, the correlation was very strong (r2=0.90). This is in contrast to
the BIT index, which had a strongly non-linear relationship. The relationship
between δ13C and BIT was therefore also non-linear, albeit
with a strong correlation between the two (Fig. 6). This was observed in the
Kolyma River transect and attributed to the higher degradation rate of brGDGTs
compared to other fractions of terrOC and/or a significantly higher cren
addition compared to addition of other marine compounds
. Here, for the first time,
decoupled offshore trends in BIT and δ13C were observed.
Plot of δ13C vs. BIT index. Typical values for
terrestrial and marine endmember samples are shown
. Note the non-linearity of the relationship; the BIT
index drops significantly before a shift in isotope ratio to more marine
values.
In the Buor-Khaya Bay, DLS and within 150 km of the
coastline, the δ13C value was between -25
and -28 ‰ and showed no significant trend, whilst the BIT value
dropped from 1 to 0.28 in an offshore direction. Greater than 150 km
offshore, the BIT value decreases from 0.22 to 0, and the δ13C value enriches from -26 to -21 ‰, creating an inflection at δ13C = -26 ‰ and BIT = 0.25. Considering that both δ13C
and BIT are used as proxies to quantify the proportion of terrestrial and marine
material in offshore sediments
, this apparent disagreement, which has not been seen in
studies elsewhere, may suggest that on the ESAS they are measuring different
aspects of the terrestrial sediment export. showed that the ice
complexes that dominate the East Siberian coastline are at least as rich in
organic carbon as topsoil, yet our analysis showed low concentrations of GDGTs (Table S1), confirming results from . Therefore, erosion of coastal ice complexes would
affect the δ13C value of the sediments without significantly
changing BIT values. Thus BIT may be measuring input from GDGT-rich fluvial
sources, whilst δ13C integrates both fluvial influx and
coastal erosion. An alternate explanation is that the brGDGTs responsible for
the BIT index were degrading at a different rate compared to the bulk
terrestrial organic carbon signal . If brGDGTs, which made up
a small proportion of the OC load of the sampled sediments (averaging 30 mggOC-1), degraded more rapidly than bulk organic
matter,
which may contain large amounts of resistant molecules such as lignin phenols or
plant wax lipids , then the two proxies were likely to
have a non-linear relationship. However, showed rapid
offshore reduction in the concentration of lignin phenols and cutin acids among
the same samples, which would suggest that the BIT results are not unique, and
may be representing at least a portion of the bulk OC signal. This finding
raises suspicion about the
usefulness of the BIT index as a proxy for the proportion of terrestrial carbon
in a bulk sediment sample where coastal erosion plays a large part, but
introduces the possibility of its use as a more specific proxy for fluvial
input.
Comparison plots of sample parameters with modelled values. Grey
symbols represent observed data from this study and ; black
symbols are modelled values. (a) BIT index vs. distance from river
outflows. Samples from the DLS are shown separately, demonstrating how this
region is offset from the general offshore-reduction trend in BIT, and
showing the model recreating this trend. (b) δ13C vs.
distance from river outflow. (c) δ13C vs. BIT index.
Modelling OC and GDGT delivery
To investigate the sources and offshore behaviour of GDGTs and OC on the ESAS
further, a simple model was created to simulate the deposition and degradation of
terrestrial and marine material (Fig. S3). Apart from δ13C, which has been shown to vary across the ESAS, single uniform
values were applied across the entire ESAS rather than tuning the model to
particular rivers or regions. A full description of the model is
available in Appendix A.
Our data set, and other recent studies, has shown that fluvial systems in this
region contain large amounts of brGDGTs and OC .
Fluvial endmember values were defined using surface sediment samples closest to
the great Russian Arctic river (GRAR) mouths. It is assumed that these samples represent an integrated signal from the
river catchments, delivering mainly active layer soil material and, in the case of brGDGTs, in situ river production. We modelled fluvial delivery of sediment,
OC and GDGTs from GRARs as a series of
point sources, using the same sediment delivery conditions, from which material
spread across the ESAS in a radial pattern. This leads to concentrations
decreasing across the shelf in a 1/distance pattern. The Siberian Arctic
coastline experiences rapid coastal erosion, delivering large amounts of
sediment and OC to the Arctic Ocean each year . This process was
modelled as a linear source of material stretching along the entire longitudinal
range of this study, with a single value for OC and GDGT concentrations and
sediment delivery rate. Endmember values were defined using ice complex samples,
since these represent the majority of the sediment eroded from the East Siberian
coastline . The OC, GDGTs and sediment delivered by coastal erosion
decreased proportional to the distance from the coastline. Cren production
peaked in the mid-latitude samples as discussed previously (Fig. 3b). This
feature was
reproduced simply in the model, with low marine OC and cren deposition
close to the coastline and far offshore and a peak at 290 km
offshore.
A degradation factor was applied to the model in order to simulate oxidation of organic
matter in the water column. In the absence of more detailed studies, a simple
rule was applied in which OC and biomarkers were degraded proportional to the
distance travelled from source, and were assumed to have degraded completely by 800 km offshore. Initial conditions for sediment supply, OC concentration
and δ13C for both fluvial and coastal erosion were
defined using values from previous studies . GDGT
concentrations were defined using a single representative value based on samples
from this study but were not “tuned” to specific regions, in order to avoid
circularity (see Table S2 for model parameters). The model is based on simple principles and
is applicable in other areas if the relevant endmember values (GDGT concentrations, δ13C) and model parameters (e.g. fluvial/coastal sedimentary input rates) are known.
Through application of the uniform parameters described above as well as simple
processes, the model reproduced measured offshore distributions of brGDGTs,
cren, TOC, δ13C and BIT (Fig. 7). Transects from the river outflows were successfully reproduced, and the
low-cren,
high-BIT behaviour of the DLS was also qualitatively
replicated. The model was then applied to the whole ESAS region included in this
study in order to avoid sampling bias. In the model, rivers delivered 13 % of the sediment to the ESAS but 72 % of
the brGDGTs, which supports the use of the BIT index as a proxy for fluvial
rather than coastal sediment and terrOC delivery. As suggested by our measurements, brGDGTs are primarily delivered by rivers, which have eroded them
from soils. There is the potential for in situ production within the river, but this cannot be quantified in this study.
OC supply to the shelf was 40 % fluvial, 44 % coastal and 16 % marine primary productivity. These findings
are comparable to , although in their
numerical model the role of coastal erosion was slightly greater (estimated 57 % contribution from ice complexes). Using the
degradation functions provided above, the model predicts that 23 % of the
exported terrOC was degraded between delivery and sampling. (This study
only considers surface sediments, so the sampled material should at most be only a few years old. Subsequent diagenesis is ignored, but likely to be
substantial; .) Using published sediment delivery
estimates this degradation equates to 0.7 Tgyr-1 across the whole shelf, whilst 2.79 Tgyr-1 is
deposited. Of this deposition, 1.13 Tgyr-1 comes from
fluvial
erosion, 1.23 Tgyr-1 from coastal erosion of ice complexes and 0.43 Tgyr-1 from burial of marine primary productivity. These
figures
are comparable to the values published by , who found 4 Tgyr-1 of terrOC delivered to the Laptev and East Siberian
seas, of
which 0.38 Tgyr-1 was sourced from the Lena River.
produced higher estimates for terrOC delivery, 27 Tgyr-1 of
which 7 Tgyr-1 is from fluvial sources and 20 Tgyr-1 from
coastal erosion. These figures are higher than both our model and previous
estimates due to the high sediment deposition rate measured on the ESAS by
. Since their study suggests both a higher sedimentation
rate and a proportionally greater influence of coastal erosion, further study
of OC source and deposition rates is clearly needed in this complex environment.
Use of brGDGTs as a tracer for river-derived sediment
The patterns observed in the BIT and δ13C proxies, and the modelling results, support suggestions that the BIT index may be used not as a proxy for bulk soil export but for
fluvial sediment delivery . Observations of large-scale ice complex erosion and mobilisation
in this area are not carried forward into GDGT measurements, despite being identified in isotopic analyses . Both the power-law reduction in BIT in an offshore
direction and the non-linear relationship between BIT and δ13C can be explained by the interaction of three carbon pools. The
model suggests that the majority of the brGDGTs are due to input of OC from
rivers discharging to the East Siberian Sea, whilst BIT is less representative
of coastal erosion. As a bulk proxy, δ13C is measuring the
integrated effect of coastal erosion of terrestrial material, fluvial input and
marine productivity, and therefore follows a different trend. Thus near-outflow
samples are river-dominated, nearshore samples are coastal-erosion-dominated and
offshore samples are marine-enriched. The west–east decrease in BIT values
(Fig. 2c), while it may be influenced by inflow of water through the Bering Strait, may also be explained by a fluvial signal, since the easternmost
rivers are both smaller and will deliver lower amounts of brGDGTs during the spring
freshet .