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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NGEO2007

Ebullition and storm-induced methane release from the East Siberian Arctic Shelf

Shakhova et al. Ebullition and storm-induced methane release from the East Siberian Arctic Shelf

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SUPPLEMENTARY INFORMATION

Contents

Supplementary Figures S1-7

FigureS1.jpg: Snapshots of the winter drilling campaign performed in the study area in

April 2011 (238 KB)

FigureS2.jpg: Primary structural elements of the Laptev Rift System (166 KB)

FigureS3.jpg: Laboratory Delta-T calibration data (208 KB)

FigureS4.jpg: Seep plume class occurrence in the study area (summer 2009) (148 KB)

FigureS5.jpg: Summer 2009 sonar data from the study area (179 KB)

FigureS6.jpg: Best estimate contribution of each seep class to total flux in the study area.

(214 KB)

Figure S7.jpg: Data from in-situ bubble observations made in the study area (340 KB)

Supplementary Methods

This supplement contains detailed information on the sampling campaigns and data

processing and analysis, a description of modeling assumptions used to simulate the

current state of subsea permafrost in the coastal area of the ESAS, and details about the

sonar data acquisition and analysis used for estimating ebullition-induced flux in the

study area based on seep classification and apportioning the contribution of each seep

class to the total flux in the study area.

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SUPPLEMENTARY INFORMATION DOI: 10.1038/NGEO2007Shakhova et al. Ebullition and storm-induced methane release from the East Siberian Arctic Shelf

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1. Supplementary Methods

1.1. Estimation of population parameters using the maximum likelihood (ML) method41.

The best fitting function to describe data sets is a lognormal distribution. The maximum

likelihood estimates of the mean (m*) and variance (s*2) were calculated using equations (1)

and (2), respectively:

(1)

where n is the number of observations and xi is the methane (CH4) concentration, and

(2)

The parameters m* and s*2 were then used to estimate the arithmetic mean and median of the

lognormal distribution. For a lognormally-distributed population, the arithmetic mean (a) was

estimated by

(3)

From the definition of the arithmetic mean of a lognormal distribution, this value is always

greater than zero, guaranteeing that confidence interval estimates are always positive. This is a

major advantage compared to the confidence intervals of a normal mean, where, in contrast,

the lower bound of the confidence interval can be assigned a nonsensical negative value.

Because of the positive skewness of the lognormal distribution, the confidence interval of a

lognormal arithmetic mean is also skewed positively. The median of lognormally distributed

datasets was estimated by: *mex � (4)

1.2. Quantifying the dissolved CH4 concentration in the water column.

Conductivity/temperature/depth (CTD) profiles and water samples were collected with Niskin

bottles during upcasts at a series of stations. For CH4 measurements, water samples were

drawn immediately from Niskin bottles into replicate 500-ml glass bottles, overfilling 1.5-2

times with sample water. Sub-sampling was done carefully to avoid introducing air bubbles.

Bottles were sealed with silicon stoppers and metallic crimps. In the first analytical step of the

headspace technique, part of the bottle sample water was replaced with helium. Then, samples

were placed in a thermostatic water-bath shaker and the dissolved and gaseous phases were

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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NGEO2007Shakhova et al. Ebullition and storm-induced methane release from the East Siberian Arctic Shelf

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equilibrated for 30 minutes. The replicate samples were kept at ambient laboratory temperature

and analyzed within a few hours. CH4 concentrations were measured with a MicroTech-8160

gas chromatograph (GC) using a flame ionization detector (FID) with a helium carrier gas. The

GC analysis was isothermal (40˚C) and the maximum FID temperature was held at ≈250˚C.

Calibration used certified CH4 gas standards in a balance of air (Air Liquide, USA). The

standard deviation of duplicate analyses (3-5 replicates) was less than 2% and GC precision

was 1%. The concentration of dissolved CH4 in the water samples was calculated with the

Bunsen solubility coefficient for CH4 at the appropriate equilibration temperature using the

fit42:

� �2321321 )100/()100/()100/ln()/100(ln TBTBBSTATAA �� (5)

where β is the Bunsen solubility coefficient, T is temperature in degrees Kelvin, and S is the

salinity in per mil. Calculated data were analyzed statistically with the Statistics 7.0 and Matlab

7.0 software packages. For graphical representation of horizontal surfaces, irregularly-spaced

data were interpolated onto uniform grids (using Krieging algorithms), allowing an area-

weighted mean to be calculated. Topographic plots of vertical profiles were generated by

mapping the irregularly-spaced station data using a minimum-curvature algorithm (Surfer 8.0).

1.3. CH4 mixing ratio in the atmosphere. CH4 concentrations were measured with a pre-

calibrated (by manufacturer) high-accuracy fast CH4 analyzer (HAFMA, DLT-100; response

time: <0.05 seconds; accuracy: better than 1% of reading; concentration range: 0.01-25ppmv;

www.lgrinc.com) at 10-12 Hz; two sonic anemometers (CSAT3, Campbell Scientific Inc.)

measured the 3D wind vector and sonic temperature; a Li-Cor 1400 meteorological station

measured wind speed and direction, moisture, and temperature; and a Li-Cor 7500 open path

infrared gas analyzer measured H2O and CO2. Other instruments included a pressure transducer

(PTB 101, Vaisala) and a motion package (NAV440, Crossbow) that measured all 6

components of ship motion and the 3 components of acceleration, magnetic field, and position.

The package was mounted on a meteorological mast affixed to the vessel’s bow at 10-12 m

height.

1.4. Drilling of subsea permafrost from the fast ice. A heavy drilling technique was used for

drilling the subsea permafrost in the study area in April 2011. Fieldwork was accomplished

through the use of an equipment caravan which traveled over the sea ice to the drilling location

(Supplementary Fig. S1). The drilling rig, well tubes, boring casing, and additional equipment





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(including equipment for geochemical and microbial measurements) was delivered to Tiksi by

two cargo air freighters (AN-12s). The drilling was performed using a drilling rig (URB-2A-2)

with a hydraulic rotary-pressure mechanism operating without drilling fluid. Well tubes and

borehole casing 4 m long and 147 mm in diameter were used to preserve an undisturbed core

structure and prevent sea water infiltration. After extraction from the borehole the sediment

core was cleaned and generally described as frozen or unfrozen and including or not including

ice structures, debris, organic layers, and grain-size variations.

1.5. Thermal measurements in the boreholes. Temperature in each borehole was

measured 3 days after drilling using a chain of calibrated thermistors, according to the

Global Terrestrial Network for Permafrost (GTN-P) protocol (http://www.gtn-p.org). A

temperature logger (HOBO Temp PRO V2) was installed in the head of the probe,

allowing the temperature gradient within the frozen and unfrozen layers of both Holocene

marine sediments and Pleistocene permafrost deposits to be documented. The obtained data

was used in the model to constrain permafrost geophysical features.

1.6. Sea water temperature measurements. A pre-calibrated shipboard CTD sond attached to

a large metal rosette wheel was used to monitor variation of water temperature, salinity, and

density. Temperature of sea water at different water horizons was measured by lowering the

rosette on a cable down to the seafloor, and observing the water properties in real time via a

conducting cable connecting the CTD sond to a computer on the ship. A standard CTD sond

cast was performed at each of 570 oceanographic stations.

1.7. Modeling subsea permafrost in the study area. A modification of the model that has

been described in detail13 was performed as a case study. Recently obtained data on

temperature dynamics of bottom water during the last 14 years (1999-2012) were employed to

update the historical data set and to determine the thermodynamic state of sediments over

decadal and multi-decadal time scales. Thermal conductivity and the heat capacity of the

ground material were parameterized as functions of ice, liquid water, and salt concentration

(values of key parameters established from field data for the studied area). The thermodynamic

model was forced by seawater temperature dynamics computed by global circulation models

(GCMs). Since GCMs provide coarse-resolution temperature dynamics, we incorporated local

seawater warming effects. We used a 1-D realization of the thermodynamic model (the size of

a computational domain is 100 meters). The initial temperature distribution was set to





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measured values collected during drilling. To compute temperature dynamics at sites within

tectonics fault zones we utilized a 2-D realization of the thermodynamic model, which allows

the formation and evolution of an open talik to be simulated. The computational domain and

heat fluxes at its boundaries were determined as described13.

1.8. Sonar data acquisition and analysis. A 260 kHz 8-bit Imagenex Delta-T multibeam

sonar was used for the surveys because of its portability (shipped via aircraft carry-on

luggage), its durability (no moving parts, sensitive power transformers, or external beam-

forming computers), and ability to be run by a laptop computer. The system continuously

recorded water-column returns for all beams during the entire survey. The Delta-T has 120

across-track beams by 3 degree along-track swaths; beams were formed in real time and in

post-processing. One advantage of multibeam sonar data over single beam data for plume

characterization is that the plume geometry relative to the beam swath is precisely known, and

thus bubble plumes cannot be lost due to missing the beam, as is illustrated in sonar lander data

for the study area shown in Fig. 1b. These data show bubble plumes extending to near the sea

surface. Boat-based survey data reveal similar details (Fig. S6).

A lab calibration experiment with the Delta-T confirmed a positive correlation between

summed sonar return and bubble plume gas volume (Fig. S7). These data were collected by

rotating the sonar around a vertical axis in a 5-m-deep tank at a range of 6 m from a flow-

controlled air bubble plume. In this orientation, the sonar swath extended from below the tank

floor to above the water surface such that the long axis of the swath was parallel to the bubble

plume. Fig. S7 shows summed sonar returns from four rotations across the air bubble plume for

four flows. These data demonstrate that Delta T sonar returns are strongly sensitive to bubble

plume emission strengths. However, note the increase in sonar return with height above tank

bottom, even though the total number of plume bubbles remains approximately constant,

because hydrostatic pressure decreases are minimal and dissolution negligible. This signifies

complexity in the relationship between bubble plume spatial density and bubble size and the

return signal, preventing simple calibration. Thus, sonar return strength absent additional

information is only a quasi-quantitative value.

For the survey, a sonar pole-mount was fabricated such that the transducer head was

fixed 1 m below the sea surface amidships on the starboard gunwale and directed ~20 degrees

forward of vertical with the swath extending 60 degrees orthogonal to the boat’s direction of





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travel. This orientation improved the data by enabling easy discrimination between seeps and

electrical noise (noise is along-beam, which is tilted from vertical, while seeps rise vertically),

reduction of seabed return signal, and increased pings per seep plume.

Water-column multibeam sonar data were recorded throughout the survey as pings of raw

sonar return for 120 beams with 500 samples per beam regardless of range. This coordinate

system (range vs. beam angle, theta) is referred to as “R-Theta” space. Sonar returns were

normalized for acquisition gain to facilitate comparison across different data blocks. Sonar data

acquisition recorded 157 survey lines of variable duration that were imported from the

Imagenex Delta-T data files in intervals of 6000 sequential pings, hereafter referred to as

“blocks.” A total of 1178 blocks were created, representing between 100 m and 3175 m of

linear “along-track” survey distance. The mean length of the blocks was ~1600 m. Blocks were

subdivided into 200 m along-track intervals termed “sub-blocks”; a total of 8203 sub-blocks

were analyzed. Sub-blocks were filtered for erroneous pings and processed in R-Theta space.

Each block was manually inspected and assigned a density class and intensity class.

Conversion to a flux involved assigning a representative flux to each intensity class; this

process focused on several plume characteristics visible in the sonar data, including

persistence, spreading rate, and “clumpiness.” “Clumpiness” is the well-known tendency for

bubbles to aggregate in persistent groups from a few centimeters to half a meter in size19. This

estimation was based on saltwater laboratory experiments for bubbles from a sand and seashell

sediment bed over a range of different bubble fluxes. These laboratory experiments confirmed

a strong sensitivity of appearance to flux, in agreement with previous field measurement data

of natural marine hydrocarbon seep bubbles18,31 and laboratory data32 and showed that bubble

plume appearance in video and multibeam is strongly sensitive to flux.

Each data block was classified by two characteristics: spatial concentration of seeps, d,

which characterized spatial density, and the largest seep, which served as a proxy for seep

intensity. Spatial density equaled the number of seeps per sub-block averaged over the data

block. Seep density classes were d1: ~1-2.9 seeps, d2: ~3-4.9 seeps, d3: ~5-9.9 seeps, d4: ~10-

19 seeps, d5: ~20-39 seeps, and d6: ≥40 seeps (×10-4 m-2). Seep intensity classes were i1: short

clumps of bubbles, i2: bubble pulses that were >50% active, i3: thin continuous bubble

streams, and i4: thick continuous bubble streams (Supplementary Table S1). The magnitude of

sonar return integrated over the spatial domain was generally stronger for plume classes i3 and





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i4 than for i1 or i2. These classes are further described below. All block data with some

seepage exhibited examples of the smallest intensity seep class, i1. Therefore, the intensity

classification is the upper end of the range of seep intensities within each data block. Values of

seepage spatial density, d, were corrected for approximate sonar swath width (based on range

to seabed) to an area-corrected spatial density, D (seep number per km2).

Due to the large number of sonar blocks analyzed (8203) the number of seeps in each

class is well characterized except for the smallest, where misclassification of noise could lead

to an undercount. Fortunately, the contribution of the smallest intensity class was not dominant.

However, there is significant uncertainty in the flux assignment for each intensity class which

was addressed by estimating maximum likely limits that the flux classification probability

could be biased towards larger or smaller classes (Supplementary Table S1, calculations B and

C) or that the dominant bubble size could be different, while still maintaining the same bubble

plume appearance. Plume misclassification could have led to biases in the intensity

classification scheme, resulting in undercounting or overcounting in each of the classes with a

maximum likely shift of 30% assumed, but note that bias was not assumed to preserve the

number of plumes. Also note that the repeatability of the classification approach was tested by

repeating some of the analyses which were found to agree with the initial analysis to better than

10%, so 30% is a conservative limit on error bias. This caused a much smaller uncertainty of

about 10-25%. Shifts in bubble size were assumed to conserve the number of observed bubble

plumes. In these cases, the driving factor for the uncertainty limits used in this uncertainty

assessment was the results of laboratory experiments in which the levels were chosen because

larger uncertainties/biases would change the characteristics of the sonar images of the bubble

plumes in the data and thus would be apparent in the data. These maximum uncertainty limits

were then averaged to estimate the total flux in a range of 0.99 x102 to 6.3 x102 mg m-2 d-1.

1.9. Ebullition-induced flux estimates in the study area.

The probability of each class was determined for all datablocks in the study area and

found to be 43%, 29%, 25%, and 4% for intensity classes i1, i2, i3, and i4, respectively. The

probability of each class up to the highest seep class observed in the segment was calculated

for all data blocks in the study area, covering 32 km of linear tracks. Emission rates were then

calculated per square meter based on each segment’s along-track distance and swath width.





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Plume occurrence rates were 2.16×10-3, 8.56×10-4, 2.9×10-4, and 4.09×10-6 plumes m-2 for seep

classes i1, i2, i3, and i4, respectively (Fig. S5).

An effort then was made to assign a flux to each seep plume classification. Based on

experience with bubble observations and emission modes, best estimate bubble emission rates

of 3, 15, 25, and 100 bubbles s-1 per plume were assigned to density class i1, i2, i3, and i4,

respectively. Using an average bubble size of 3700 µm equivalent spherical radius based on

video bubble images obtained in 2012 (Fig. S7), seep density classes i1, i2, i3, and i4

correspond to emissions of 0.7, 3.3, 5.5, and 22 cm3 s-1, respectively. Combining seep plume

occurrences and seep plume emission rates yields a best estimate total seabed flux of ~290 mg

m-2 d-1. There are a number of significant uncertainties in these estimates, including bubble

size, seep plume bubble rates, and how representative the seep plume probabilities are (Fig.

S5).

References:

41. Jonsson, A., Gustafsson, O., Axelman, J., Suindberg, H. Global accounting of PCBs in the

continental shelf sediments. Environ. Sci. Technol. 37, 245 (2003).

42. Wiesenburg, D.A., Guinasso, N.L. Equilibrium solubility of methane, carbon monoxide,

and hydrogen in water and seawater. J. Chem. Eng. Data 24(4), 356 (1979).



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