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Supplemental Materials
S1. General model architecture
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The model was implemented in a spreadsheet using hourly timesteps. Beginning with an
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initial HOOH concentration, the change in HOOH concentration (Δ[HOOH]) was determined for
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the following hour by calculating the impact of the various factors. Δ[HOOH] was then applied
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to the initial [HOOH] to give the final [HOOH] for that hour; this value was in turn used as the
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initial [HOOH] for the next hour of the simulation.
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S2. Surface actinic flux
We used the direct and downwelling components of the actinic flux from TUV (i.e.,
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ignoring the upwelling component) under clear-sky conditions to estimate actinic flux in surface
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snow at Summit, Greenland (3200 m elevation, 0.93 snow albedo, 308 DU column ozone, no
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column SO2, no column NO2, 0.235 aerosol optical depth, defaults used for all other settings).
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We ran TUV for each hour of the first day of each week from 21 June 2011 through 31
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December 2011. To obtain values for an entire year, we assumed the actinic flux was
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symmetrical for the 26-week period before 21 June 2011. We used the hourly actinic fluxes
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from the first day of each week to represent hourly values for the entire week.
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S3. Impact of snowfall
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For our baseline simulation, we assume that snow falls continuously at the rate of 60 cm
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y-1, without seasonal variation [Steffen and Box, 2001]. The actinic flux at a particular date and
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time (i.e., depth) in the snowpack is a function of how much snow has fallen:
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Iz = I0 × exp(-z/efd)
(S1)
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where I0 is the surface actinic flux for a particular date and time, z is the depth of the snowpack
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above the snow parcel (z = 5 cm / 30 d × number of days since start), and efd is the e-folding
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depth for HOOH photodegradation (13 cm) [Galbavy et al., 2007].
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Although some studies find no seasonal variation in snowfall intensity at Summit [Dibb
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and Fahnestock, 2004; Steffen and Box, 2001], others have found higher snowfall rates in
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summer [Bromwich et al., 1993; Shuman et al., 1995]. Accordingly, we have included
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simulations to study the impact of snowfall seasonality. For this evaluation, we used data from
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Bromwich et al. Figure 15 [1993], and assumed a sinusoidally varying snowfall of 5 ± 1.67 cm
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mo-1 (Supplemental Figure S1). Because snowfall rates can vary dramatically in polar areas (see,
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for example, Bromwich et al. [1993] and Hamilton [2004]), we have run simulations at 12, 30,
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120, and 300 cm y-1 as well.
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S4. Calculation of photolysis rate constant
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We determined the rate constant for HOOH photolysis, j(HOOH)z, for each hour in the
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model using the appropriate temperature (T) and depth (z) of the snow parcel using equation (1)
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in the main text:
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j(HOOH)z = 103 cm3 L-1 / NA × 2.303 × ∫ (Iz,λ × ΦT × εT,λ) dλ
(1)
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For simplicity, we do not explicitly call out the temperature-dependence of the rate constant and
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instead refer to it as j(HOOH)z in the manuscript. The variables in equation (1) are: NA,
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Avogadro’s number; Iz,λ, the actinic flux at depth z; ΦT, the wavelength-independent quantum
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efficiency for HOOH photolysis; and εT,λ, the base-10 molar absorptivity for HOOH. Values of
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ε for HOOH have not been determined in frozen aqueous samples, so we used ε274, λ from Chu
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and Anastasio [2005] along with their quantum yields for OH formation from HOOH photolysis
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in liquid-like regions of ice: ln(Φ(HOOHOH)) = -(684 ± 17)(1/T) + (2.27 ± 0.064). Later
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work by Beine and Anastasio [2011] found lower quantum yields when HOOH was located
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primarily in the bulk ice matrix, with an expression based on initial rates of HOOH loss of
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ln(Φ(HOOHOH)) = -(1244 ± 375)(1/T) + (3.86 ± 3.4). They also found the OH quantum
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yield after several hours of illumination was best represented by a temperature-independent value
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of 0.29. As we are interested in HOOH destruction in this work, we focus on the quantum
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efficiency of HOOH photodegradation, which is half the value of the quantum yield for OH
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formation because of the stoichiometry of HOOH photolysis (HOOH + h → 2 OH). We
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distinguish the HOOH quantum efficiencies from different studies by their values at 258 K: Φ258
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= 0.34 [Chu and Anastasio, 2005]; Φ258 = 0.19 ([Beine and Anastasio, 2011], based on initial rate
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of HOOH loss); and Φ258 = 0.15 ([Beine and Anastasio, 2011], based on final rate). Estimated
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errors for these measurements are ±0.04 (1 standard error), ±0.02 (1 σ), and ±0.05 (1 σ)
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respectively.
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S5. Temperature modeling
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To estimate the temperature at depth in the snowpack we used the expression of Carslaw
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and Jaeger [1959] for a conductive half-space with a sinusoidal temperature variation at the
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surface,
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T  Ae
z

2



cost  z
  
2


(S2)
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where A is the amplitude of the oscillation (measured as the maximum difference from the
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average), z is the depth, κ is the thermal diffusivity of the snow (assumed to be 3.2 x 10-7 m2 s-1,
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Dr. Mary Albert, personal communication), and ω is the angular frequency of the temperature
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oscillation (2π/period of the oscillation). We used hourly temperature data for Summit from
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1996 through 2011, collected at a height of approximately 1 m [Steffen et al., 1996], to determine
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both diel and yearly patterns in surface temperature. Figure S2 gives average daily temperatures
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at Summit. Figure S3 shows the average temperature, as well as the average range (difference
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between maximum and minimum temperatures for each day) measured for each month.
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Using the Carslaw and Jaeger method, Figure S4 shows the diel temperature variation at
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various depths in the snowpack, assuming a surface temperature variation (difference between
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diel maximum and minimum temperatures) of 15 °C, the largest monthly average range found in
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our surface temperature analysis (Figure S3). Changes in quantum efficiencies due to the diel
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temperature variation were smaller than the reported errors in Φ [Beine and Anastasio, 2011;
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Chu and Anastasio, 2005], so we did not include diel variations in quantum efficiencies in the
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model. In contrast, we included the yearly temperature variation (Figure S5) – and its effect on
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HOOH quantum efficiencies – in the model since the temperature variation in the snowpack due
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to yearly temperature variation at the surface (27 °C; Figure S3) is significantly greater than the
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diel variation (Figure S4). Based on the measured temperature data we represented the yearly
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temperatures using a sinusoidal cycle with an average temperature of -29° C, amplitude of 13.5°
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C, and maximum yearly temperature occurring on 21 July. Figure S6 shows the snowpack
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temperature used in the model throughout the course of the year, determined by applying the
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sinusoidal surface temperature cycle to the Carslaw and Jaeger equation. Because the modeled
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snowpack temperature in our continually buried parcel includes the impact of snowfall, the
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temperature after one year (at z = 60 cm) is slightly less than the initial temperature (at z = 0 cm).
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Finally, Figure S7 shows the impact of temperature on the HOOH quantum efficiency in the
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parcel over a year for the case of Φ258 = 0.19.
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S6. Uncertainties
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To evaluate the uncertainty of our estimates, we ran two additional summer simulations
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accounting for uncertainties in the estimates of Φ258 and Rf(HOOH). In the first scenario, we
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used Φ258 = 0.21 (Φ258 + 1 σ, i.e., 0.19 + 0.02) and Rf(HOOH) = 0.3 nM h-1 (Rf(HOOH) – 1
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standard error, i.e., 5.3 nM h-1 – 5.0 nM h-1), choosing the signs for the uncertainties that would
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result in the least preservation of HOOH. In the second scenario, we used the opposite
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uncertainties to result in the most preservation of HOOH. Preservation percentages in the two
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cases were 40 and 78%, respectively, while the baseline summer preservation percentage (for
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Φ258 = 0.19 and Rf(HOOH) = 5.3 nM h-1) is 59%. This suggests that our HOOH preservation
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results have a relative uncertainty of approximately ± 30% (i.e., ± 19%/59%).
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S7. Antarctic model
To test the sensitivity of our model to simulated quasi-Antarctic conditions, we used an average
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yearly temperature of -50 C, snowfall of 2 cm per month, and RE and Rf(HOOH) values 20% (1/5) of our
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standard values; all other model parameters remained the same (e.g., summer deposited snow with an
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initial HOOH concentration of 10 µM and Summit sunlight at the surface). After 26 weeks of the
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simulation, the Summit values showed preservation of 5.9 µM HOOH, while the Antarctic values gave a
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concentration of 5.0 µM HOOH. So, although HOOH loss might be higher, the difference is small
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because the lower recycling efficiency and HOOH photoformation rate is largely offset by less efficient
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photolysis due to lower temperatures.
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References
Beine, H., and C. Anastasio (2011), The photolysis of flash-frozen dilute hydrogen peroxide
solutions, J. Geophys. Res.-Atmos., 116, D14302, doi: 10.1029/2010jd015531.
Bromwich, D. H., F. M. Robasky, R. A. Keen, and J. F. Bolzan (1993), Modeled variations of
precipitation over the Greenland ice sheet, J. Clim., 6(7), 1253-1268, doi: 10.1175/15200442(1993)006<1253:mvopot>2.0.co;2.
Carslaw, H., and J. Jaeger (1959), Conduction of Heat in Solids, Oxford University Press,
Oxford.
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Dibb, J. E., and M. Fahnestock (2004), Snow accumulation, surface height change, and firn
densification at Summit, Greenland: Insights from 2 years of in situ observation, J.
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Galbavy, E. S., C. Anastasio, B. L. Lefer, and S. R. Hall (2007), Light penetration in the
snowpack at Summit, Greenland: Part I - Nitrite and hydrogen peroxide photolysis,
Atmos. Environ., 41(24), 5077-5090, doi: 10.1016/j.atmosenv.2006.04.072.
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Pole and implications for ice-core interpretation, in Annals of Glaciology, Vol 39, 2005,
edited by J. Jacka, pp. 214-218, Int Glaciological Soc, Cambridge.
Shuman, C. A., R. B. Alley, S. Anandakrishnan, J. W. C. White, P. M. Grootes, and C. R.
Stearns (1995), Temperature and accumulation at the Greenland Summit - comparison of
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trends, J. Geophys. Res.-Atmos., 100(D5), 9165-9177, doi: 10.1029/95jd00560.
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Captions
Figure S1: Estimated seasonal variation in snowfall rate assuming maximum snowfall on 21
July (based on data of Bromwich et al. [1993]) for examining the sensitivity of HOOH
concentrations on snowfall timing. For most scenarios (i.e., in our baseline condition), the
snowfall rate was assumed constant at 5 cm mo–1 (0.0069 cm h–1) throughout the year; see text
for details.
Figure S2: Average daily temperature at Summit, Greenland, using hourly data collected from
1996 through 2011 [Steffen et al., 1996]. We calculated the average of all valid data points for
each day.
Figure S3: Monthly average temperature (with error bars showing ± 0.5 times the monthly
average range) at Summit, Greenland, using hourly data collected from 1996 through 2011
[Steffen et al., 1996]. We calculated the monthly average temperature using all data available for
each month. We determined the monthly range by first subtracting the low temperature from the
high temperature for each day to give a range for each day of the month. We then averaged the
daily ranges to get the monthly average range. The average monthly range is shown by the entire
bar length in the figure, with the average monthly temperature in the center of the bar.
Figure S4: Impact of the diel surface temperature variation (15 °C) at various snow depths,
showing deviation from average surface temperature. Snowpack temperature variation decreases
with depth and also shows a time lag relative to surface temperature variation.
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Figure S5: Impact of yearly surface temperature variation (27 °C) at various snow depths,
showing deviation from the average surface temperature. Compared to the diel temperature
variation (Figure S3), annual temperature variations are noticeable at greater depths and with a
larger time lag.
Figure S6: Modeled snowpack temperature in our snow parcel deposited 21 June for the first
year of the simulation. We modeled snowpack temperature assuming a sinusoidal surface
temperature variation of 27 °C and a snowfall rate of 5 cm mo-1. Note the modeled temperature
after 1 year (z = 60 cm) does not equal the starting temperature (z = 0 cm) due to the impact of
snowfall.
Figure S7: Calculated Φ values (for the case of Φ258 = 0.19) in the snow parcel for the first year
of the simulation, where the parcel moves from z = 0 to z = 60 cm. Φ is a function of
temperature and follows the temperature pattern shown in Figure S6.
Figure S8: Seasonal variation of hourly actinic flux at the snow surface. Actinic fluxes shown
are the total flux between 240 and 380 nm at each hour. As described in the text, we calculated
hourly actinic fluxes using the TUV model for each week of the simulation. Week 1 begins on
21 June.
Figure S9: Sensitivity of modeled HOOH concentrations in the snow parcel to the initial HOOH
concentration. Snow is deposited 21 June and modeled using baseline conditions (Φ258 = 0.19,
RE = 0.10, and Rf(HOOH)0 = 5.3 nM h-1). The percentages of initially deposited HOOH that are
preserved after 6 months are 48, 51, 59, and 77% for initial concentrations of 30, 20, 10, and 5
µM, respectively.