S1. Updates to the GEOS-Chem Hg model
S1.1 Anthropogenic emissions
Anthropogenic emissions of Hg0 and HgII are derived from the global historical Hg emissions
inventory of Streets et al. [2011]. Total present-day global emissions from this inventory of 2000
Mg a-1 are similar to those from the recent United Nations Environment Programme report of
1960 Mg a-1 [UNEP, 2013]. However, trends since 1990 in the two inventories differ from one
another [Wilson et al., 2010], and from a third inventory currently in preparation that shows a
decline in Hg emissions to the atmosphere when commercial product use is included [Horowitz
et al., 2013]. Here, we use the Streets et al. [2011] inventory as it is the only published inventory
that covers our entire simulation period (1979-2008), and we discuss the sensitivity of our trend
results to assumed emission trends. The choice of emissions inventory does not affect simulation
of interannual variability, which is the main focus of this work. The inventory provides regional
totals for each decade. We distribute these totals within each region on the basis of present-day
distributions [Pacyna et al., 2010] and then interpolate between decades to create annuallyresolved emissions at the 4°x5° resolution of the GEOS-Chem grid. The emissions do not vary
seasonally. Following Amos et al. [2012], we emit particulate Hg as HgII and allow it to partition
thermodynamically between the gas and aerosol phases.
S1.2 Polar bromine chemistry
As in Fisher et al. [2012], we assume that fast atmospheric bromine radical generation driving
AMDEs takes place in GEOS-Chem grid squares with (1) at least 10% sea ice cover in at least
50% of native resolution (0.5°x0.667°) grid squares and less than 100% total sea ice cover, (2)
surface incident shortwave radiation greater than 100 W m-2 [Pöhler et al., 2010], and (3) surface
temperature less than 273 K. Fisher et al. [2012] used a step function for the relationship
between BrO concentration and 2-meter air temperature T, but this is not well-suited for
investigating interannual variability. Here we have updated it to the following linear function:
[BrO] = 20 pptv
[BrO] = (253 K – T) + 20 pptv
[BrO] = 0 pptv
T ≤ 253 K
253 K < T ≤ 273 K
273 K < T
(S1a)
(S1b)
(S1c)
The slope and endpoints of this relationship were chosen to optimize simulation of both current
spring Hg0 concentrations (as in Fisher et al. [2012]) and the shift of peak depletion at Alert
from May in the late 1990s to April in the mid 2000s [Cole and Steffen, 2010]. The resulting
range of BrO concentrations is consistent with the limited observations in the Arctic boundary
layer [Pöhler et al., 2010; Friess et al., 2011; Prados-Roman et al., 2011]. BrO is assumed to be
well-mixed in the atmospheric boundary layer diagnosed from the MERRA meteorological
fields. Br concentrations are inferred from local Br-BrO-O3 photochemical equilibrium assuming
5 ppbv O3 [Holmes et al., 2010; Fisher et al., 2012].
S1.3 Oceanic photoreduction
Aqueous HgII photoreduction in marine environments is induced by radiation at UV-B (280-315
nm), UV-A (315-400 nm), and visible wavelengths, with high-energy UV-B radiation being the
most efficient [Amyot et al., 1997; Bonzongo and Donkor, 2003; O'Driscoll et al., 2006; Qureshi
et al., 2010; Oh et al., 2011]. Depletion of Arctic stratospheric ozone has increased the UV-B
flux over the past 30 years [Bais et al., 2011]. Laboratory studies show that 40-50% more Hg0 is
produced under irradiation by UV-B than UV-A at light intensities characteristic of natural
systems [O'Driscoll et al., 2006; Oh et al., 2011]. Based on the results of O’Driscoll et al.
[2006], we assume that half of the total reducible pool (40% of dissolved oceanic HgII for present
day [Soerensen et al., 2010]) is reducible by all wavelengths of solar radiation and half is
reducible only by UV-B.
The reducible fraction of HgII (fred) for each ocean grid square is thus computed as:
fred = 0.2 + 0.2(Ω / Ω0 )-1.23
(S2)
where Ω is the observed ozone column for each month and Ω0 is the present-day (2006-2010)
mean ozone column for that location. The exponent in Equation S2 reflects the relationship
between UV Index (weighted to UV-B radiation) and Ω [Madronich, 2007]. For Ω and 0 we
use monthly mean data from the TOMS/SBUV satellite instrument (available from http://acdbext.gsfc.nasa.gov/Data_services/merged/), regridded to 4°x5°. Missing data are replaced with the
climatological (1971-2010) mean value for the given location and month.
In calculating the total HgII photoreduction that occurs in the surface ocean, all solar radiation
(visible and UV) is attenuated with depth as described by Soerensen et al. [2010]. We do not
compute separate attenuation coefficients for visible and UV-B radiation.
S1.4 Arctic Ocean productivity
Net primary productivity (NPP) in the ocean impacts surface ocean Hg cycling through both
biological reduction and export of particulate Hg to subsurface waters, with increased NPP
generally leading to decreased surface ocean Hg. In previous versions of GEOS-Chem, NPP
distributions were based on observations from one year of MODIS satellite data [Soerensen et
al., 2010], with missing data replaced by a constant Arctic value. Here we replace missing
MODIS values (mainly in the central Arctic Ocean) with monthly mean NPP estimates from the
POP-ICE ice-ocean-ecosystem model for the year 2009 [Jin et al., 2011].
Increased light availability in the Arctic Ocean driven by recent declines in sea ice cover has
resulted in increased NPP in ice-free waters [Arrigo et al., 2008; Wassmann et al., 2010; Arrigo
and van Dijken, 2011]. Phytoplankton growth in the Arctic is also limited by nutrients [Popova
et al., 2010; Tremblay et al., 2012], and this is likely to play an increasingly important role for
whole basin productivity in future [Wassmann, 2011; Hill et al., 2013]. Here we focus on the
change in productivity between ice-free and ice-covered waters since this appears to be the
dominant source of variability in NPP over the past several decades [Arrigo and van Dijken,
2011]. We simulate IAV in Arctic NPP based on the work of Arrigo and van Dijken [2011], who
used 1998-2009 observations to derive a relationship between annual Arctic NPP and maximum
summer sea ice extent, which they then applied to estimate NPP from 1979-1997. We use their
observed and estimated NPP to construct an annual scaling factor that we apply to the gridded
monthly mean Arctic NPP from MODIS/POP-ICE.
S1.5 Riverine inputs
Riverine inputs of HgII to the Arctic Ocean are as described by Fisher et al. [2012]. The source
extends from May to October and depends on both the riverine Hg concentration and the total
freshwater discharge. We use riverine Hg concentrations that are a factor of three higher during
freshet (May-June) than during the rest of the year [Leitch et al., 2007; Graydon et al., 2009;
Emmerton et al., 2013]. There is evidence that permafrost melt has increased the export of Hg to
Arctic lakes [Klaminder et al., 2008; Rydberg et al., 2010] but the implications for Hg
mobilization in river basins and associated riverine Hg concentrations remain unclear. More
work has addressed the impacts of permafrost melt on dissolved organic carbon (DOC), which is
strongly associated with Hg [Ravichandran, 2004; Schuster et al., 2011]. Results from these
studies have been mixed. Most basins indicate decreased DOC export with increased permafrost
melt while others (principally in West Siberia) indicate increased DOC export [Frey and
McClelland, 2009]. Recent work by O’Donnell et al. [2012] in the Yukon basin shows
decreasing DOC with increasing permafrost-induced groundwater flow, potentially driven by
increased sorption of DOC to soils and thermokarst formation [Kawahigashi et al., 2006;
Prokushkin et al., 2007]. Given the substantial uncertainty surrounding the relationship between
riverine Hg concentrations and permafrost melt, we are unable to estimate how river Hg inputs
have varied based on changes in the Arctic landscape over the last 30 years. We thus simulate
variability in river Hg inputs based only on changes in freshwater discharges, with constant
concentrations inferred as described in Fisher et al. [2012]. The sensitivity of our results to the
assumed river Hg concentrations is discussed in Section S3.
Freshwater discharge amounts are from the Arctic Rapid Integrated Monitoring System (ArcticRIMS) database (http://rims.unh.edu/index.shtml). Since our simplified Hg model for the Arctic
Ocean does not include lateral flows in the surface mixed layer, we use a mean riverine input
term rather than basin-specific estimates [Fisher et al., 2012]. The three largest Russian rivers
(Yenisei, Lena, Ob) account for on average 78% of the total freshwater discharge term and so
their discharge amounts are used to define IAV, applied as an interannual scaling factor
calculated independently for each month.
S2. Statistical Methods
In sections 4 and 5, we use multiple linear regression (MLR), principal component analysis
(PCA), and principal component regression (PCR) to relate variability in observed and simulated
Arctic Hg to a range of meteorological drivers. Here we briefly describe the methods involved.
We apply the following standardized MLR model:
y(t) - y
x (t) - xk
(S3)
= å bk k
sy
sk
k
where t is the year, y(t) represents the 2-month mean Hg concentrations (ng m-3 for atmospheric
Hg0, pM for surface ocean HgT), xk(t) are the meteorological variables (Table S1), 𝑦̅ and 𝑥̅ 𝑘 are
the multi-year temporal means, sy and sk are the standard deviations, and βk are the normalized
regression coefficients. Because the variables are standardized during the MLR, the βk are
dimensionless and can be directly compared for different independent variables [Tai et al.,
2012]. Non-standardized regression coefficients βk* in units of ng m-3 dk-1 or pM dk-1 (where dk is
the unit of variable xk) can be computed from βk* = (sy/sk)βk. To account for correlation between
the meteorological variables, we select a subset of relevant variables for use in a stepwise
regression with terms added and deleted repeatedly to obtain a best fit based on the Akaike
Information Criterion [Tai et al., 2010].
We apply a PCA to the standardized meteorological variables listed in Table S1, following the
method described previously by Tai et al. [2012] for analysis of the normal climatic modes
driving variability of particulate matter concentrations in the U.S. The principal components
(PCs) represent orthogonal (uncorrelated) linear combinations of the meteorological variables
and thus can be viewed as representing the normal modes of the system. We construct a time
series for each of the PCs:
x (t) - xk
U j (t) = åa kj k
sk
k
(S4)
where Uj(t) represents the 2-month mean value of PC j in year t, and αkj are the elements of the
orthogonal transformation matrix from the PCA, which represent the relative importance of each
variable to the PC. We then perform a principal component regression (PCR; [Jeffers, 1967]) to
identify the relationships between Hg concentrations and climatic modes:
y(t) - y
= åg kU j (t)
sy
k
(S5)
where γk are the coefficients of the PCR.
S3. Sensitivity to assumed riverine Hg
The Hg flux from circumpolar rivers to the Arctic Ocean is highly uncertain. Measurements of
Arctic riverine Hg are extremely rare, especially in the very large Russian rivers, and are
unlikely to have captured periods of peak Hg flux as discussed in Fisher et al. [2012]. In this
work, we assume a significantly higher total riverine flux (80 Mg yr-1; [Fisher et al., 2012]) than
previously calculated from bottom-up estimates (5-39 Mg yr-1; [Outridge et al., 2008]). To test
the influence of our riverine Hg assumptions on our results, we performed a sensitivity
simulation with the riverine Hg flux decreased by a factor of 10 for the entire 30-year simulation
(plus spin-up).
As expected from Fisher et al. [2012], without the high riverine flux, the model is unable to
reproduce the magnitude of observed concentrations in the Arctic atmosphere and ocean in
summer. However, the large decrease in riverine Hg has very little impact on the simulation of
interannual variability (IAV), the focus of this work. This largely reflects the fact that in summer
when freshwater discharge is at its peak, its IAV is less than 10% of the total magnitude. IAV is
higher in May (when we find it has some influence on ocean HgT in the original simulation), but
overall fluxes are much smaller then. Table S2 compares the statistical results given in Sections
3, 4 and 5 for the original simulation with those computed from the sensitivity simulation. As
seen in the table, the change in river Hg has little impact on the ability of the model to simulate
observed IAV at Alert and Zeppelin, with virtually no change in spring and a small reduction in
skill in summer.
The principal components (PCs) used to describe atmospheric variability are computed solely
from the environmental drivers given in Table S1 and therefore do not change between the two
simulations. Principal component regression between the PCs and atmospheric Hg0 is nearly
identical between the original and sensitivity simulations. In the ocean, simulated HgT is even
more strongly correlated with the simulated meltwater flux in the sensitivity simulation than in
the original simulation, reflecting the reduced influence from the variable river flux. In both
cases, the meltwater term is clearly dominant. The multiple linear regression results from the
original simulation (with contributions from wind speed, solar radiation, May surface air
temperature, and May freshwater discharge) can explain 54% of simulated HgT variability in the
sensitivity simulation (versus 55% in the original simulation). Stepwise multiple linear
regression of ocean HgT from the sensitivity simulation shows solar radiation and wind speed (in
that order) to be the dominant drivers of ocean HgT variability when the river flux is decreased,
together explaining 48% of the variability. This result further reinforces the importance of the
meltwater and deposition fluxes, as described in the main text.
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