Geophysical Research Letters
Supporting Information for
Effects of post-depositional processing on nitrogen isotopes of nitrate in the Greenland Ice
Sheet Project 2 (GISP2) ice core
Authors: Lei Geng1, Maria C. Zatko1, Becky Alexander1, T. J. Fudge2, Andrew J. Schauer2,
Lee T. Murray3,4, Loretta J. Mickley5
1
Department of Atmospheric Sciences, University of Washington, Seattle, WA, 98195, USA
2
Department of Earth and Space Sciences, University of Washington, Seattle, WA, 98195, USA
3
NASA Goddard Institute for Space Studies, New York, NY, USA
4
Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA
5
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Contents of this file
Text S1, S2 and S3
Table S1
Additional Supporting Information (Files uploaded separately)
Caption for Dataset S1
1
Introduction
In Text S1, we give details on sample preparation and the analytical procedures regarding
nitrogen isotope analysis of the samples.
In Text S2, we give the details related to the calculations of actinic flux within the snow
photic zone using the parameterization in Zatko et al. [2013], and the time that nitrate remains in
the photic zone (Tz). We also describe the Rayleigh fractionation model [Blunier et al., 2005]
used to calculate snow δ15N(NO3-) with respect to fractional loss of nitrate from snowpack. The
calculated e-folding depth of actinic flux in snow (and the parameters related to this calculation),
snow accumulation rate, Tz, and observed and calculated δ15N(NO3-) values are listed in Table
S1.
In Text S3, we conduct sensitivity studies and discuss what could lead to the discrepancies
between the calculated and observed δ15N(NO3-) values during the D-O events. The
discrepancies are mainly due to the uncertainty in the reconstructed snow accumulation rate, with
additional contributions from the uncertain snow concentration of organics which influences the
calculation of the e-folding depth of actinic flux in snow.
Text S1:
S1.1. Discrete samples from 1-100ka BP
We took ~100 mL aliquots of melt water from each of the 15 discrete samples (total
volume was ~2000 mL each), which each contains over 90 nmol nitrate as estimated from the
calculated mean nitrate concentration. These aliquots were then concentrated using the resin
method as described in Frey et al. [2009] to reduce the sample volume to 10 mL. The δ15N value
of nitrate in these concentrated aliquots was then measured in triplicate using the denitrifier
(Bacteria: P. chlororaphis) method [Sigman et al., 2001] which converts nitrate to N2O. The N2O
2
was purified and masses 44, 45 and 46 were measured using a Delta Advantage isotope ratio
mass spectrometer. International standards used to calibrate the measured values to the Air-N2
scale were USGS34 (δ15N = -1.8 ‰) and USGS32 (δ15N = 180 ‰) [Bohlke et al., 1993; Kaiser
et al., 2007]. Another international standard, IAEA-NO-3 with δ15N = 4.7 ‰, was used as a
quality control, and the standard deviation of δ15N was ± 0.25 ‰ as indicated by repeated
measurements of IAEA-NO-3.
The remaining volume of each sample was processed following the procedures described
in Geng et al. [2013] to concentrate and separate nitrate from other ions in the sample matrix
using ion chromatography. In order to correct for the interference of oxygen-17 in determining
δ15N [Hastings et al., 2003], Δ17O of nitrate in the IC-concentrated samples was determined
using the combined denitrifier (bacteria: P. aureofaciens) and gold tube method [Kaiser et al.,
2007] (details in the next section). The correction resulted in a mean difference of (-3.6 ± 0.5) ‰
from the standard correction assuming Δ17O = 0 [Sigman et al., 2001].
S1.2. Continuous samples from 43-49 ka BP
The volume of melt water from each of the 112 continuous samples between 43-49 ka BP
was ~900 mL. The amount of nitrate in each sample was estimated to be over 700 nmol. After
nitrate pre-concentration as described in the last section, δ15N of nitrate in these samples was
measured in triplicate using the combined denitrifier and gold tube method[Kaiser et al., 2007].
N2O from bacterial reduction of NO3- was decomposed to N2 and O2 in the gold tube at 800 ⁰C,
and masses of 28 and 29 of N2, and masses of 32, 33 and 34 of O2 were measured after
separation. Measurements of δ17O and δ18O will be reported in a follow-up paper. The δ15N
values were calibrated against two international standards USGS34 (δ15N = -1.8 ‰) and
USGS32 (δ15N = 180 ‰) [Bohlke et al., 1993; Kaiser et al., 2007]. The standard deviation of
3
δ15N was ± 0.24 ‰ as indicated by repeated measurements of USGS32.
The nitrate mass in each sample estimated from its calculated mean concentration from
Mayewski et al. [1997] was consistent with the mass indicated by peak areas of N2O or N2 during
IRMS analysis, suggesting little to no nitrate contamination during sample processing.
Text S2:
The vertical profile of actinic flux in snowpack is calculated using the following equation
from Zatko et al. [2013]:


 I ( , z ) 
  I ( , z ) 

I 0 ( , z )    0
 f dif   0
 (1  f dif )  [ Finc ( )] tot




 Finc ( )  direct
 Finc ( )  diffuse

(1)
I0(λ, z) is the depth (z, cm) and wavelength (λ, nm) dependent actinic flux in snow. Finc(λ)tot
(photons cm−2 s −1) is the sum of the direct (Finc(λ)direct) and diffuse (Finc(λ)diffuse) downwelling
irradiance at the snow surface over a given λ bin (e.g., 298- 307 nm). fdif represents the fraction
of diffuse downwelling irradiance to total (direct + diffuse) downwelling irradiance. Below the
first 2 cm in snow, both the diffuse and direct components of the actinic flux decay exponentially
within the snowpack as a function of snow UV-LAI (e.g., dust, black carbon, and organics)
concentrations, snow density (g cm−3), radiation equivalent mean ice grain radius (cm), and solar
zenith angle (θ) [Zatko et al., 2013]. UV-LAI influence actinic flux in snow through its effect on
the inherent optical properties (IOPs) of the snowpack, including extinction coefficients (Kexttot,
unit: cm-1) and co-albedos of single scattering (c  eff, unitless), which are calculated throughout
the UV wavelength region (λ =298-345 nm) in (2) and (3), respectively, following Zatko et al.
[2013],
𝐾𝑒𝑥𝑡𝑡𝑜𝑡 = ∑𝑖 𝐾𝑒𝑥𝑡𝑖
(2)
4
𝑐𝜛𝑡𝑜𝑡 =
∑𝑖 𝑐𝜛𝑖 ∙𝐾𝑒𝑥𝑡𝑖
𝐾𝑒𝑥𝑡𝑡𝑜𝑡
(3)
In equations (2) and (3), i indicates the two components of the snowpack: ice and UV-LAI. The
extinction coefficients for black carbon, dust and organics used in (2) and (3) are given below:
Kextbc 
 bc  C bc   snow
c bc
𝐾𝑒𝑥𝑡𝑑𝑢𝑠𝑡 =
𝐾𝑒𝑥𝑡𝑜𝑟𝑔 =
𝛽𝑑𝑢𝑠𝑡 ∙𝐶𝑑𝑢𝑠𝑡 ∙𝜌𝑠𝑛𝑜𝑤
𝑐𝜛𝑑𝑢𝑠𝑡
𝐴
𝑀
𝛵𝑜𝑟𝑔 ∙( )∙𝜌𝑠𝑛𝑜𝑤
𝑐𝜛𝑜𝑟𝑔
(4)
(5)
(6)
In the above equations βbc and βdust are the mean average mass absorption efficiencies of BC and
dust (units: m2 g-1); Cbc and Cdust are the concentrations of BC and dust in snow (units: ng g-1); c
 bc, c nonBC, c org are the co-albedos of single scattering for BC, dust, and organics in snow
(unitless), respectively. ρsnow is the density of snow (g m-3).
In equations (4) and (5), the concentrations of black carbon and dust are known. The
concentration of organic species in the snow is unknown, but its absorption in snow was
previously deduced using an ISSW spectrophotometer [Grenfell et al., 2011], which is
represented by optical depth (𝛵𝑜𝑟𝑔 , unitless). The spectrophotometer measures the absorption of
UV-LAI on Nucleopore filters, and the optical depth is multiplied by the area of the filter (A, in
cm2) and the mass of meltwater passed through the filter (M, in g). Since ISSW measurements of
organics are not present during the Holocene and the glacial period in Greenland, we scale the
relative absorption of organics to black carbon according to observations from Greenland snow
in the present day and by assuming an Ångstrom exponent of 5 for organic material [Zatko et al.,
2013]. The relative importance of organics and BC for UV absorption in snow may vary over
time, but this is yet unknown. We note that the relative importance of organics and BC is
5
relatively constant throughout both Greenland and Antarctica, and in clean and remote (from
research stations) polar snow [Zatko et al., 2013], suggesting that anthropogenic activities do not
significantly influence their relative importance for UV absorption. The average e-folding depth
of actinic flux in snowpack (ze) at a wavelength range of 298 to 345 nm (peak wavelength of
snow nitrate photolysis at 320 nm [Chu and Anastasio, 2003]) is then calculated as the depth
where the actinic flux is 1/e of the surface value [Warren et al., 2006]. The full depth of the snow
photic zone is 3 times the e-folding depth [Zatko et al., 2013], and Tz is calculated as:
Tz 
3 z e
ac
(7)
where ac is snow accumulation rate with units of cm snow a-1. A snow density of 0.36 g cm−3
and deep ice density of 0.9 g cm−3 were used to convert snow accumulation rate from units of m
ice a-1 to units of cm snow a-1.
In this study, for calculating the vertical profile of actinic flux and its e-folding depth in
snowpack at Summit, Greenland, we use the approximate mean summer noon solar zenith angle
of 60 ⁰, with snow density ranging from 0.26 g cm−3 at the surface to 0.36 g cm−3 at, and below,
20 cm. Snow grains are assumed to be spherical [Zatko et al., 2013], and the radiation equivalent
mean ice grain radii range from 86 um at the snow surface to 460.5 um at 500 cm depth [Gallet
et al., 2011]. Downwelling surface irradiance at Summit, Greenland is calculated from the Fast-J
radiative transfer algorithm [Wild et al., 2000] in the GEOS-Chem model (www.geos-chem.org)
for wavelengths from 298 to 345 nm. All of these parameters remain constant when calculating
the e-folding depth in each period (i.e., the Holocene vs. the last glacial period, and the period
before and after the abrupt warming ~ 45.4 ka BP). Concentrations of UV-LAI, black carbon
(1.7 µg L -1) and organics (scaled according to black carbon concentrations [Zatko et al., 2013])
6
are kept constant in each time period considered while dust concentrations differ in each time
period as described in the main text.
The Rayleigh fractionation equation used to calculate δ15N(NO3-) in snow is from Blunier et
al. [2005]:
 15N( NO3  )  ( 15N( NO3  ) 0  1)  f   1
(8)
where f is the fraction of nitrate remaining in snow after the UV-driven loss, ε is the fractionation
constant of nitrogen isotopes associated with snow nitrate photolysis (- 47.9 ‰ [Berhanu et al.,
2014]). δ15N(NO3-)0 is the δ15N value of nitrate originally deposited to the snow, which is
assumed to be 0 ‰ in this study, and is within the range of observed tropospheric δ15N(NO3-) of
(- 4.5 ± 5.4) ‰ [Savarino et al., 2007] that is representative of the natural background of
tropospheric nitrate.
In order to calculate snow δ15N(NO3-), we need to know f . In this study, f in the Holocene
is calculated to be 0.86 using Equation (8) according to its mean δ15N(NO3-) value of 8.6 ‰. To
simplify the calculation, we assume a linear relationship between the fractional loss and Tz (a
longer Tz leads to more UV-driven nitrate loss). A scaling factor of 0.24 (the ratio of the
fractional loss (i.e., 1-f) to Tz in the Holocene) is then applied to Tz in all other periods to
calculate their relative f values.
The calculated Tz, f, and snow δ15N(NO3-) are shown in Table S1. For the glacialinterglacial cycle, the calculated δ15N(NO3-) values are consistent with observations. In the D-O
cycles, although the calculated δ15N(NO3-) reflects a similar magnitude change (~ 12 ‰) as the
observations ((10.2 ± 2.3) ‰), during the abrupt warming, the absolute values are in general ~10
‰ lower than observations. In addition, as shown in this table that in the four periods, although
the variations in dust concentrations are large (one order of magnitude), the e-folding depth (ze)
7
varies only slightly (~ 7 % difference between the smallest and largest ze) due to the variability
in dust concentrations. In contrast, Tz differs by ~ 170 %, and is almost entirely caused by
variations in the snow accumulation rate (the difference between the largest and smallest snow
accumulation rate over the studies periods is also ~ 170 %). This suggests that the changes in
δ15N(NO3-) over these major climate transitions are mainly caused by varying snow
accumulation rate which dominates the changes in Tz.
Text S3:
Due to the lack of information on past solar irradiance, snow grain size and structure, firn
density, and snow LAI other than dust, we have assumed that these parameters are constant in
the different periods considered here. These may all contribute to the discrepancies between the
modeled and observed δ15N(NO3-) values during the D-O cycles. In the calculations of Zatko et
al. [2013], the effect of snow acidity is also not considered. Laboratory experiments have
indicated that snow acidity facilitates nitrate photolysis [Abida and Osthoff, 2011], and thus the
lower snow acidity in the glacial period would tend to reduce the degree of post-depositional
processing resulting in lower glacial δ15N(NO3-). Further knowledge of the response of snow
nitrate photolysis to snow pH is required to quantify the importance of this effect. In addition,
uncertainties in the reconstructed snow accumulation rate, as well as that in the fractionation
constant, could also lead to the discrepancies between the model and observations. In the
discussion as follows, we describe how uncertainties in snow accumulation rate and the
concentration of UV-LAI could lead to the discrepancies between the modeled and observed
δ15N(NO3-) before and after the abrupt warming ~ 45.4 ka BP as an example.
8
The accumulation rate by Cuffey and Clow [1997] depends on accurately measured
annual layer thicknesses. The thickness of the layers in the GISP2 timescale during the D-O 12
cycle are likely too thick. Comparisons of the GISP2 timescale to the Greenland Ice Core
Chronology 2005 (GICC05, [Rasmussen et al., 2006; Svensson et al., 2006; 2008; Vinther et al.,
2006]) indicate that the GISP2 timescale missed about 20% of the years for ages older than 40 ka
BP [Svensson et al., 2008]. If those additional layers had been identified in the GISP2 timescale,
the measured layer thickness would be 20% smaller and the reconstructed accumulation rate
would be approximately 20% smaller as a result. How these missed years are distributed is not
known, but larger variations in layer thickness are possible over shorter intervals.
The accumulation-rate inference for GISP2 also depends on the amount of vertical
thinning the ice has experienced. The ice of D-O 12 is at about 80% depth in the ice sheet and
has been thinned to about ~15% of its original thickness. The amount of thinning depends on the
vertical strain rate in the ice sheet since the snow was deposited. The vertical strain rate will vary
based on a variety of factors including the history of ice thickness, temperature profile, ice fabric,
and divide position. Cuffey and Clow [1997] give accumulation rates inferred from two ice-sheet
retreat scenarios (50 and 100 km) in addition to the preferred scenario (200 km) used here. Those
scenarios inferred accumulation rates lower by up to 12% and do not represent the full
uncertainty. Cuffey and Clow [1997] note that a larger uncertainty results from poor knowledge
of how vertical strain rates vary in an ice sheet. Therefore, the Cuffey and Clow [1997]
reconstruction of the snow accumulation rate is overestimated, especially in the period older than
40 ka BP (by up to 20 %) that overlaps with the focused period of the D-O 12 and 13 cycles in
this study.
9
In order to assess whether the uncertainties associated with the snow accumulation rate can
account for the ~ 10 ‰ lower calculated δ15N(NO3-) than the observations in the D-O 12 & 13
cycles, we conduct sensitivity studies by varying the snow accumulation rate data within the
estimated range of uncertainty (lower by 10 to 20 %) when calculating δ15N(NO3-). The result
indicates that, when the snow accumulation rate is lowered by ~ 18 %, the calculated mean
δ15N(NO3-) value prior to 45.4 ka BP (33.5 ‰) becomes consistent with the observations ((33.5 ±
2.3) ‰). However, with this level of decrease in the snow accumulation rate, the calculated
δ15N(NO3-) value after the 45.4 ka BP warming is still 6 ‰ lower than the observations (see
values in the gray-shaded areas in Table S1).
The snow accumulation rate is not the only uncertain term related to calculations of
δ15N(NO3-). Due to reasons stated in the main text, the calculations of the e-folding depth (ze)
assume constant snow concentrations of black carbon and organics before and after the abrupt
warming. However, global model simulations indicate that before and after the abrupt warming
of an idealized D-O event, VOC emissions are enhanced by 10 - 30 % [Hopcroft et al., 2011].
While during the same period, snow accumulation rate is increased by a factor of ~ 2 [Cuffey and
Clow, 1997]. Ideally, this implies a reduction in the snow organic concentrations by 30 - 40 % in
the warming interval relative to the cold interval of a D-O cycle. Applying this level of
difference in the concentration of organics in the Zatko et al. [2013] model used for e-folding
depth calculations, a deeper ze in the warming D-O interval is calculated, and thus the calculated
δ15N(NO3-) values are higher (20 - 22 ‰) and approach the observed values of (23.3 ± 0.5) ‰
(see values in the blue-shaded areas in Table S1).
In summary, there are many other variables not considered in the model due to the large
uncertainty in estimating their past variability and/or the magnitude of their isotopic effects.
10
However, even while neglecting potential variability in these other parameters, the model is still
able to predict the magnitude of δ15N(NO3-) variations in the glacial-interglacial cycle and abrupt
climate change events while considering only the effects of varying snow accumulation rate and
dust concentrations on post-depositional loss of snow nitrate. This supports the dominance of the
post-depositional processing in δ15N(NO3-) variations over climate transitions.
Table S1: Observed dust concentrations (using equivalent Ca2+concentrations) [Mayewski et al.,
1997], observed δ15N(NO3-), calculated e-folding depth (ze), snow accumulation rates [Cuffey
and Clow, 1997], calculated Tz, the calculated fraction of nitrate loss through photolysis, and
calculated δ15N(NO3-) in the four climate scenarios.
Dust
(µg L -1)
Observed
δ15N(NO3-)
(‰)
ze
(cm)
Snow
accumulation
rate (m ice a-1)
Tz
(a)
Fraction
loss
(1- f )
Calculated
δ15N(NO3-)
(‰)
Holocene
7.4 ± 3.5
8.6 ± 1.3
12.3
(0.22 ± 0.03)b
0.67
0.16
8.6
Glacial
222 ± 260
(28.5 ± 3.6)a
11.8
(0.08 ± 0.01)b
1.83
0.45
27.5
(0.08 ± 0.01)b
1.67
0.41
25.5
0.068c
2.04
0.49
33.5
(0.15 ± 0.01)b
0.99
0.24
13.5
0.12c
1.21
0.30
17.0
14.3d
0.12
1.41
0.34
20.4
15.1d
0.12
1.49
0.36
21.9
Prior to
45.4 ka BP
316 ± 88
33.5 ± 2.3
11.5
12.3
After 45.4
ka BP
34.2 ± 0.8
23.3 ± 0.5
a.
This is the average of the discrete samples only during 16-50 ka BP, consistent with the
coverage of the snow accumulation rate data;
b.
The accumulation rate from Cuffey and Clow [1997] reconstruction with 200 km ice sheet
retreat;
c.
Adjusted accumulation rates to be 18 % lower than the Cuffey and Clow [1997] reconstruction
(details in Text S3);
d.
Values in these two rows represent the results of the sensitivity studies that lower the
concentrations of organics in the warm D-O period by 30 % (top row) and 40 % (bottom row)
relative to the cold D-O interval for the e-folding depth calculations (details in Text S3).
Data Set S1. δ15N(NO3-) observations from the GISP2 core shown in Figures 1 and 2.
11
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13