Iodine chemistry has noteworthy impacts on the oxidising capacity of the
marine boundary layer (MBL) through the depletion of ozone (O3) and
changes to HOx (OH/HO2) and NOx (NO/NO2) ratios.
Hitherto, studies have shown that the reaction of atmospheric O3 with
surface seawater iodide (I-) contributes to the flux of iodine species
into the MBL mainly as hypoiodous acid (HOI) and molecular iodine (I2).
Here, we present the first concomitant observations of iodine oxide (IO),
O3 in the gas phase, and sea surface iodide concentrations. The results
from three field campaigns in the Indian Ocean and the Southern Ocean during
2015–2017 are used to compute reactive iodine fluxes in the MBL.
Observations of atmospheric IO by multi-axis differential
optical absorption spectroscopy (MAX-DOAS) show active iodine chemistry in
this environment, with IO values up to 1 pptv (parts per trillion by volume)
below latitudes of 40∘ S. In order to compute the sea-to-air
iodine flux supporting this chemistry, we compare previously established
global sea surface iodide parameterisations with new region-specific
parameterisations based on the new iodide observations. This study shows
that regional changes in salinity and sea surface temperature play a role in
surface seawater iodide estimation. Sea–air fluxes of HOI and I2,
calculated from the atmospheric ozone and seawater iodide concentrations
(observed and predicted), failed to adequately explain the detected IO in
this region. This discrepancy highlights the need to measure direct fluxes
of inorganic and organic iodine species in the marine environment. Amongst
other potential drivers of reactive iodine chemistry investigated,
chlorophyll a showed a significant correlation with atmospheric IO (R=0.7
above the 99 % significance level) to the north of the polar front. This
correlation might be indicative of a biogenic control on iodine sources in
this region.
Introduction
Iodine chemistry in the troposphere has gained interest over the last
4 decades after it was first discovered to cause depletion of tropospheric
ozone (O3)
(Chameides and
Davis, 1980; Jenkin et al., 1985) and cause changes to the atmospheric
oxidation capacity
(Davis
et al., 1996; Read et al., 2008). Iodine studies in the remote open ocean
are important considering its role in tropospheric ozone destruction
(Allan et al., 2000), the formation of potential cloud
condensation nuclei, and impact on cloud radiative properties
(McFiggans, 2005; O'Dowd
et al., 2002). However, iodine chemistry in the remote open ocean is still
not completely understood, with uncertainties remaining around the sources
and impacts of atmospheric iodine
(Saiz-Lopez et al., 2012;
Simpson et al., 2015).
Recent studies of atmospheric iodine chemistry have focused on the detection
of iodine oxide (IO) in the marine boundary layer (MBL) as a fingerprint for
active iodine chemistry. IO itself may also participate in particle
nucleation if present at high concentrations (Saiz-Lopez
et al., 2006). Iodine-containing precursor compounds undergo photo-dissociation to produce iodine atoms (I), which rapidly react with ambient
ozone, forming IO (Chameides and Davis, 1980).
Until recently, fluxes of volatile organic iodine (e.g. CH3I,
CH2ICl, CH2I2) compounds including those originating from
marine algae (Saiz-Lopez and Plane, 2004) were considered to be
the primary source of iodine in the marine atmosphere
(Carpenter, 2003; Vogt et al., 1999). However, the
biogenic sources of atmospheric iodine could not account for the levels of
IO detected in the tropical MBL
(Mahajan
et al., 2010b; Read et al., 2008). Currently, inorganic iodine emissions are
considered to be the dominant sources contributing to the open-ocean
boundary layer iodine (Carpenter et al., 2013). A
recent study by
Koenig et
al. (2020) concluded that inorganic iodine sources play a major role in
comparison to the organic iodine sources in contributing to the upper
troposphere iodine budget. Laboratory investigations revealed that at the
ocean surface, iodide (I-) dissolved in the seawater reacts with the
deposited gas-phase ozone to release hypoiodous acid (HOI) and molecular
iodine (I2) via the following reactions
(Carpenter et al.,
2013; Gálvez et al., 2016; MacDonald et al., 2014):
R1aI-+O3→IOOO-,R1bIOOO-→IO-+O2,R1cIO-+H+⇋HOI,H++HOI+I-⇋I2+H2O.
The reaction of sea surface iodide (SSI) with ozone in Reaction (R1) is considered a
major contributor (600–1000 Tg yr-1; Ganzeveld et al., 2009) to the loss of
ozone at the surface ocean, contributing between 20 %
(Garland et al., 1980) and 100 % (Chang et al., 2004) of the oceanic
ozone dry deposition velocity. Reactions (R1) and (R2) result in the release
of reactive iodine (HOI and I2) to the atmosphere, where they quickly
photolyse to yield I atoms, which react with ozone in the gas phase to form
IO (Carpenter, 2003;
Saiz-Lopez et al., 2012). Carpenter et al. (2013)
showed that the Reactions (R1) and (R2) could account for about 75 % of
the IO levels detected over the tropical Atlantic Ocean. Further studies
have shown that including these reactions and the resulting fluxes of HOI
and I2 in atmospheric chemistry models has results in good agreement
between observed and modelled iodine levels over the Atlantic and the
Pacific Ocean but not for the Indian and Southern Ocean. For example, the
sea–air flux of HOI and I2 could explain the observed levels of
molecular iodine and IO at Cape Verde
(Lawler et al., 2014), and observed
IO levels over the eastern Pacific were in reasonable agreement with those
modelled from estimated I2 and HOI fluxes
(MacDonald et al., 2014). In contrast, the inorganic
iodine fluxes estimated for the Indian Ocean and Indian sector of the
Southern Ocean marine boundary layer could not fully explain the observed IO
concentrations (Mahajan et al., 2019a, b).
Similarly, in the Pacific observations of IO and halocarbons have shown that
the contribution of combined iodocarbon fluxes to IO is between 30 % and 80 %,
assuming an inorganic iodine lifetime of between 1 and 3 d
(Hepach et al., 2016).
Predicted global emissions of iodine compounds show a large sensitivity
(∼50 %) to the SSI field used
(Saiz-Lopez
et al., 2014; Sherwen et al., 2016a, c); an improved and accurate system
for simulating SSI concentration is imperative. Existing global
parameterisations discussed in this study follow three different methods for
SSI estimation. The first is a linear regression approach against
biogeochemical and oceanographic variables (Chance et
al., 2014); the second uses an exponential relationship with sea surface
temperature as a proxy for SSI (MacDonald et al.,
2014), and the third is a recent machine-learning-based model
(Sherwen et al., 2019)
that predicts monthly global SSI fields for the present day. Where such
approaches are based on large-scale relationships, they may not properly
capture smaller-scale regional differences in SSI
(as observed for Chance et al., 2014;
MacDonald et al., 2014) or underestimate the surface iodide concentration
(in the case of Sherwen et
al., 2019). Furthermore, there are large differences in predicted iodide
concentrations between these parameterisations in some regions (refer to Sect. 3.2). Thus, estimation of seawater iodide based on the existing
parameterisations may not always be sufficiently accurate.
At present, there is a paucity of measurements of SSI, and remote sensing
techniques cannot detect iodine species in seawater
(Chance et
al., 2014; Sherwen et al., 2019). In particular, regions of the Indian
Ocean and the Southern Ocean have been under-sampled in terms of iodine
observations in the atmosphere and ocean
(Chance
et al., 2014; Mahajan et al., 2019a, b). It is important to remember
that the most widely used parameterisation (MacDonald et al., 2014) is built on a
limited observational dataset from the Atlantic and Pacific Ocean completely
excluding the Indian Ocean and Southern Ocean. As they have not been
tested in the Indian Ocean, they may not be suitable for accurate estimation
of SSI in the distinct and highly variable salinity and temperature regimes
of the Indian Ocean region. The parameterisations presented in
Chance et al. (2014) are based on a larger dataset
including Southern Ocean observations but still only make use of two data
points in the Indian Ocean. Furthermore, the
Sherwen et al. (2019)
parameterisation uses the updated dataset including the new Indian Ocean
SSI observations used in this study. Compounding the lack of Indian Ocean
SSI observations is the fact that parts, in particular the Arabian Sea
and the Bay of Bengal, do not follow the same seasonal trends in salinity
(D'Addezio et al., 2015)
and sea surface temperature
(Dinesh Kumar
et al., 2016) as each other on the same latitudinal band, and hence the
currently used global iodide parameterisations in models, i.e.
MacDonald et al. (2014), may not be appropriate for
these areas. Here we use new SSI observations made as part of this study
(described in full in Chance et al., 2020, and
included in Chance et al., 2019) to test whether the existing parameterisations can be directly
applied to the Indian Ocean and if regionally specific parameterisations are
more accurate compared to the former.
Although several measurements of IO have been reported around the globe,
including in the open ocean
(Alicke
et al., 1999; Allan et al., 2000; Frieß et al., 2001; Großmann et
al., 2013; Mahajan et al., 2009, 2010a, b; Prados-Roman et al., 2015),
the remote open ocean still remains under-sampled. The two documented
observations of IO in the Indian Ocean and the Indian sector (January–February 2015
and December 2015) of the Southern Ocean were interpreted using
parameterisations to estimate the SSI concentrations in combination with
observed ozone concentrations to subsequently calculate the resulting
inorganic iodine fluxes. This approach suggested that the observed
atmospheric IO may not be well correlated with the inorganic fluxes and that
biogenic fluxes could play an important role
(Mahajan et al., 2019a, b). Here, we
present measurements of IO in the MBL of the Indian Ocean and the Southern
Ocean during the 9th Indian Southern Ocean Expedition (ISOE-9)
conducted in January–February 2017, alongside the first simultaneous SSI
observations along the cruise track
(Chance et al., 2019).
The iodide observations were used to compute the inorganic iodine fluxes to
compare with IO observations along the cruise tracks. Further, observed SSI
concentrations are used to compute region-specific parameterisations for SSI
concentrations, following the approaches taken by
Chance et al. (2014) and
MacDonald et al. (2014). The iodide concentrations
obtained with these region-specific modified parameterisations are compared
to the iodide estimates using their original counterparts and the global
machine-learning-based prediction of SSI concentration
(Sherwen et al., 2019). The resulting estimated reactive iodine fluxes (HOI and I2) are
then used to see if the inorganic fluxes can explain the IO loading in the
atmospheric MBL.
Details of the three expeditions contributing to the IO and
seawater iodide dataset in this study. Expeditions are listed in
chronological order from 2015 to 2017.
ExpeditionResearch vesselDurationLocationMeridional transectObservations8th Indian Southern Ocean Expedition (ISOE-8)SagarNidhi, India7 January 2015 to 22 February 2015Indian Ocean from Chennai, India, to Port Louis, Mauritius13∘ N to 56∘ SIO, O32nd International Indian Ocean Expedition (IIOE-2)SagarNidhi, India4 to 22 December 2015Indian Ocean from Goa, India, to Port Louis, Mauritius15∘ N to 20∘ SIO, O3Bay of Bengal Boundary Layer Experiment (BoBBLE)RV SindhuSadhana23 June 2016 to 24 July 2016Southern Bay of Bengal8 to 10∘ NSeawater samples for I-Sagar Kanya-333 (SK-333)SagarKanya, India5 to 20 September 2016Southern Arabian Sea and southern Bay of Bengal1.6∘ N to 4∘ SSeawater samples for I-9th Indian Southern Ocean Expedition (ISOE-9)S.A.Agulhas, South Africa6 January 2017 to 26 February 2017Indian and Southern Ocean from Port Louis, Mauritius, to Antarctica20 to 70∘ SIO, O3, I-
List of existing global (italicised reference column) and
new region-specific (regular font in reference column) parameterisations for
sea surface iodide concentration indicating data location and number of data
points used to formulate each equation. Here [iodide] represents the sea surface
iodide concentration (nM), and sea surface temperature is SST (in
degrees Celsius for Eqs. (1) to (3) and in Kelvin for Eqs. 4 to 5).
The nitrate concentration NO3- is given in micromoles (µM), the mixed layer depth is
MLDpt in metres, the subscript
“pt” indicates potential temperature implying a temperature change of 0.5 ∘C from the ocean surface (Monterey and
Levitus, 1997), and salinity is in practical salinity units (PSU). Further details on individual
parameters and the choice of Eq. (1) over others proposed in Chance et al. (2014) are discussed in the Supplement. R2∗ represents the initial coefficient of determination (COD) while
deriving each parameterisation, and R2 represents the COD
from correlation analysis of the calculated iodide with observations in this
study (ISOE-9, SK-333, BoBBLE).
Eq. no.Database locationReferenceParametric equation ([iodide]; nM)Data pointsR2∗R2Eq. (1)Majorly Atlantic and Pacific OceanChance et al. (2014)iodide=0.28±0.002×SST2+1.7±0.2×latitude+0.9±0.4×NO3--0.02±0.002×MLDpt+7±2×salinity-309±75n=6730.6760.758Eq. (2)Indian and Southern OceanThis studyiodide=0.36±0.04×SST2-2.7±0.5×latitude+0.28±0.57×NO3-+0.64±0.17×MLDpt-5.4±3.82×salinity+22±137n=1280.7940.794aEq. (3)Southern OceanThis studyiodide=0.25±0.017×SST2-0.6±0.4×latitude+2.2±0.4×NO3--5.5±3.3×salinity+212±123n=1100.8590.859aEq. (3a)Indian OceanThis studyiodide=4.56±6.45×latitude-23.7±31×salinity+944±1096n=180.325NAEq. (4)Atlantic, central, and western Pacific OceanMacDonald et al. (2014)[iodide]=1.46×1015×exp-9134SSTn=∼880.710.739Eq. (5)Indian and Southern OceanThis study[iodide]=3.6×107×exp-3763SSTn=1290.7020.697aEq. (6)Atlantic, Pacific, Indian, and Southern OceanSherwen et al. (2019)Machine-learning-based regression approachn=1293NA0.842
a Higher R2 values for
the modified parameterisations reflect the fact that they have been derived
using the same observational data as they are tested on.
Map of the Indian Ocean and the Southern Ocean (a) with
cruise tracks for campaigns conducted during the austral summer of
2014–2016. Green circles indicate the cruise track for ISOE-8, red circles
show the cruise track for IIOE-2, and blue circles indicate the cruise track
for ISOE-9. Magenta and cyan circles indicate sample locations for the
BoBBLE and SK-333 expeditions, respectively. (b) Boxes represent 129 seawater
iodide sampling locations from three expeditions following the colour code in (a).
Measurement techniques and methodology
The 9th Indian Southern Ocean Expedition (ISOE-9) was conducted from
January to February 2017 in the Southern Ocean and the Indian Ocean sector
of the Southern Ocean. The expedition started from Port Louis, Mauritius,
and spanned the remote open-ocean area to the coast of Antarctica.
Observations of IO, SSI, and O3 were made along the cruise track
during ISOE-9. For further analysis we also include IO observations from the
2nd International Indian Ocean Expedition (IIOE-2) and the 8th
Indian Southern Ocean Expedition (ISOE-8) conducted in the Indian and
Southern Ocean region during austral summer of 2015
(Mahajan et al., 2019a, b). We also
include SSI observations in the northern Indian Ocean from two expeditions,
namely the Sagar Kanya-333 cruise (SK-333) and the Bay of Bengal Boundary
Layer Experiment (BoBBLE) conducted during June–July and September 2016,
respectively (Chance et al., 2020). Table 1 shows the
details of the expeditions, including the locations, dates of the
expeditions, and the meridional transect for each expedition. Figure 1a shows
a map with the cruise tracks for the five expeditions. Figure 1b shows the
seawater iodide sampling locations during the ISOE-9, SK-333, and BoBBLE
expeditions. The track of the ship during ISOE-9, along with the air mass
back trajectories arriving at noon each day, is given in the Supplement in Fig. S1. The HYbrid Single-Particle Lagrangian Integrated Trajectory
(HYSPLIT) model (Rolph et al.,
2017; Stein et al., 2015) was used to calculate the back trajectories.
Similar back-trajectory plots and full cruise tracks for ISOE-8 and IIOE-2
are given in Mahajan et al. (2019a, b).
During the three expeditions, meteorological parameters of the ocean and
atmosphere were measured using an on-board automatic weather station
(WeatherPak®-2000 v3), which is specially built for shipboard
observations and manual observation techniques. The WeatherPak system was
installed in the front of the ship, with the sensors approximately 10 m from
the sea surface. The weather system is equipped with a GPS system for
measuring the true wind speed and direction along with the apparent data.
The SST and salinity were measured manually through bucket sampling.
Sea surface iodide (SSI)
In this section, we focus on developing a region-specific parameterisation for
SSI estimation by adapting previously established methods. The SSI
concentrations obtained from the original and newly developed
region-specific parameterisation as well as SSI model predictions are used for a
comparison study and to calculate the inorganic iodine emissions.
Observed SSI in the Indian Ocean and the Southern Ocean
Historically, few observations of SSI are available for the Indian Ocean
basin, with reports of only three data points in the open ocean from the Arabian
Sea sector of the Indian Ocean (Farrenkopf and Luther, 2002). Two
of these values are coastal, and they lack supporting sea surface
temperature and salinity data; thus, they have been excluded from this
study. However, recent work has led to a large increase in the number of SSI
observations available for the Indian Ocean and Southern Ocean (Indian Ocean
sector) (Chance et al., 2020). Specifically, 111 new
observations were made during the 2016 ISOE-9 and 18 during the SK-333 and
BoBBLE. During the ISOE-9, SSI measurements in seawater were made
concomitant with observations of O3 and IO in the gas phase for the
first time. Observations of SSI made during this expedition used the
cathodic stripping voltammetry method with a hanging mercury drop electrode
as a working electrode
(Campos, 1997; Luther et
al., 1988). The errors reported on the concentrations reflect the standard
deviation of the repeat scans and the standard error on the intercept and
slope of the calibration. The seawater samples were collected during the
ISOE-9 at a 3–6 h interval between 23 and
70∘ S. Seawater samples from the SK-333 cruise and BoBBLE
were analysed following the same technique for surface iodide
concentrations. Iodide data from SK-333 and BoBBLE contributed to 18
additional data points between 10∘ N and 4∘ S, making a total of 129 new locations (excluding coastal and extremely high
values above 400 nM; see Chance et al., 2020, for
details) for observed SSI in the Indian Ocean and Southern Ocean region.
This is a major sample size compared to the global 2014 database (n=925)
across all the global oceans (Chance et al., 2014), and
these data points contribute substantially to the recently updated iodide
dataset (Chance et
al., 2019) (n=1342). From here onwards, the iodide concentrations obtained
from sampling observations will be referred to as measured SSI as opposed to
modelled SSI to differentiate between the observed iodide concentrations and
those calculated using the parameterisations. All available observations made
in the Indian Ocean basin as presented in
Chance et al. (2019) have been included for the development of the region-specific
parameterisation presented in this work. Further details about the
measurement technique and the observations used can be found in
Chance et al. (2020).
Iodide parameterisations
Due to the sparsity of SSI measurements, different empirical
parameterisations have been proposed to estimate SSI concentrations.
Parameters like SST and salinity (only for SK-333 and BoBBLE; R2=0.3, P=0.018) show a positive correlation with SSI concentrations.
However, a global parameterisation scheme may not capture the specificities
of these required for regional studies. The northern Indian Ocean has
markedly different sea surface salinity
(D'Addezio et al., 2015)
and SST (Dinesh
Kumar et al., 2016) in its two basins, the Arabian Sea and the Bay of
Bengal, that share the same latitude bands separated by the Indian
subcontinental land mass. These basins experience the biannually reversing
monsoonal winds, which greatly influence their SST and salinity structure.
Strong winds in the Arabian Sea associated with the summer monsoon dissipate
heat via overturning and turbulent mixing, whereas weaker winds in the Bay
of Bengal imply high SST due to the formation of a stable and shallow surface
mixed layer (Shenoi, 2002). The Arabian Sea
exhibits much higher salinity compared to the Bay of Bengal due to greater
evaporation and lower river runoff (Rao and
Sivakumar, 2003). As mentioned earlier, the current global SSI
parameterisations are based almost entirely on observations from the
Atlantic, Pacific, and Southern Ocean and have not been tested in the
Indian Ocean region.
Arrhenius form plot of sea surface iodide concentrations
against SST from all available seawater iodide field observations in the
Indian Ocean and Southern Ocean. The red line represents a linear fit; the
shaded region in dark red (inner) indicates the 95 % confidence bands, and
the shaded area in light red (outer) indicates the 95 % prediction bands.
Here, we aim to create region-specific parameterisations for the Indian and
Southern Ocean and conduct a comparison between these and the existing
global parameterisations, further discussed in Sect. 4.2. The existing (Eqs. 1, 4, and 6) global and the new region-specific
parameterisations are listed in Table 2. Below we briefly describe the
modified parameterisations. Details about the original parameterisations can
be found in their respective publications
(Chance
et al., 2014; MacDonald et al., 2014; Sherwen et al., 2019).
Linear regression analysis was performed on each parameter, namely
SST, mixed layer depth (MLD), latitude, sea surface nitrate concentration
(as it has been suggested that iodate could be reduced by nitrate-based
enzymes; Chance et al., 2014), and salinity, against the
measured SSI concentrations from the ISOE-9, SK-333, and BoBBLE campaigns,
similar to the Chance et al. (2014) technique but using in situ SST and
salinity observations instead of climatological values. More details on the
approach taken can be found in the Supplement. The combination with
the largest R2 and a uniform distribution of residuals from the
statistically significant dependent variables, as detailed in Table S1,
resulted in Eq. (2) in Table 2. Equation (2) thus represents a region-specific
(the Indian Ocean and Southern Ocean region abbreviated as Ind. O. + Sou. O. in the figures) variant of the Chance et al. (2014) parameterisation for the estimation of SSI concentrations. Similarly,
keeping in mind the difference in the SST and salinity for the Indian Ocean
and Southern Ocean, another parameterisation was derived only for the
Southern Ocean region using the ISOE-9 iodide observations and for the
Indian Ocean using the SK-333 and BoBBLE iodide observations, respectively.
The parameterisation for the Southern Ocean region using ISOE-9 iodide
observations is given in Table 2 as Eq. (3). A similar Indian Ocean
parameterisation is formulated and listed in the last row of Table 2 as Eq. (3a). However, this parameterisation is not valid, and it is omitted from
analysis in this text due to statistical insignificance inferred from an analysis of variance (ANOVA)
test using StatPlus statistical analysis software. In this method, the F
ratio from ANOVA analysis is compared with the critical F value from the
standard f-distribution table (at 0.05 significance level) to confirm the
statistical robustness. Results of the ANOVA test on the datasets for Eqs. (2),
(3), and (3a) are discussed in the Supplement.
A second method for the estimation of SSI concentration was proposed by
MacDonald et al. (2014) that uses the correlation
between sea surface iodide and SST. At present, this is the most commonly
used parameterisation in global models
(Sherwen
et al., 2016c, b, a; Stone et al., 2018). Similar to MacDonald et al. (2014) (Table 2, Eq. 4), we
derived an Arrhenius-type, region-specific expression using iodide and SST
data from ISOE-9, SK-333, and BoBBLE. Figure 2 shows the typical linear
dependence of ln[I-] for observed SSI in the Indian Ocean and
Southern Ocean, with SST-1, which resulted in the Arrhenius form
expression given as Eq. (5) in Table 2.
Figure 3 shows the iodide concentrations for the three campaigns, ISOE-8,
IIOE-2 and ISOE-9, calculated using Eqs. (1) to (5), the measured
iodide concentrations from ISOE-9, SK-333, and BoBBLE, and the global iodide
model predictions obtained from Sherwen et al. (2019) (Table 2). From here
on, region-specific parameterisations developed for SSI concentrations are
referred to as the modified versions of the original parameterisations; Eqs. (2) and (3) are called the modified Chance et al. (2014) parameterisation
for the Indian Ocean and Southern Ocean region and only the Southern Ocean
region, respectively. Equation (5) is called the modified MacDonald et al. (2014)
parameterisation. The machine-learning-based model proposed in Sherwen et al. (2019) is referred to as the “SSI model”.
Latitudinal averages of calculated sea surface
iodide (SSI) concentrations for each campaign using (a) existing and (b) new
parameterisation tools and observations from ISOE-9, SK-333, and BoBBLE.
Filled markers represent combined SSI from ISOE-8 and ISOE-9; unfilled
markers represent SSI from the IIOE-2 campaign.
Ozone
Surface ozone was monitored using a US EPA approved nondispersive
photometric UV analyser (Ecotech EC9810B) installed on the ship during the
expeditions to detect surface ozone values at a 1 min temporal
resolution. A Teflon tube ∼4 m long fixed towards the front
of the ship acted as an inlet for the analyser. The analyser is equipped
with a selective ozone scrubber, which was alternatively switched in and out
of the measuring stream. The analyser has a lower detection limit of 0.5 ppbv and a precision of 1 ppbv. A 5 µm polytetrafluoroethylene (PTFE) filter membrane installed in
the sample inlet tube was changed regularly. Zero and span calibrations were
done every alternate day to ensure accurate O3 measurements. The ozone
data collected were cleaned to remove the data points under the influence of
the ship's smokestack by referring to the measured apparent wind direction
on the ship. Apparent wind approaching the ship from 0 to
90∘ or 27 to 360∘ (front
hemisphere of the ship) was considered free from smokestack emission
influence; 0 or 360∘ represents the
bow of the ship. Ozone data recorded when the ship was stationary showed
major smokestack emission influence and were excluded.
Estimation of inorganic iodine fluxes
In order to estimate the contribution of inorganic iodine chemistry to
active iodine chemistry in the atmosphere, the atmospheric fluxes for the
main product species, I2 and HOI, need to be calculated, since direct
flux measurements of I2 and HOI have not been done anywhere in the
world to date. While there are reported observations of marine I2
emissions, they are few in number and mostly from coastal regions
(Atkinson
et al., 2012; Huang et al., 2010; Saiz-Lopez et al., 2006) with one
observation in the open ocean (Lawler et
al., 2014), although these are all observations of atmospheric
concentrations and not fluxes. As observed SSI is not available for all
cruises, we used the following scenarios for SSI to estimate the inorganic
iodine fluxes:
using measured SSI from observations of sea surface iodide from ISOE-9, SK-333,
and BoBBLE;
using calculated SSI from
the Chance et al. (2014) parameterisation in Eq. (1),
the modified Chance et al. (2014) parameterisation for the Indian Ocean and
Southern Ocean (Ind. O. + Sou. O.) in Eq. (2),
the modified Chance et al. (2014) parameterisation for the Southern Ocean (Sou.
O.) in Eq. (3),
the MacDonald et al. (2014) parameterisation using SST in Eq. (4),
the modified MacDonald et al. (2014) parameterisation in Eq. (5), and
a machine-learning-based model prediction
(Sherwen et al., 2019) in
Eq. (6).
Ozone was measured on all three cruises (ISOE-9, IIOE-2, and ISOE-8). The
fluxes for HOI and I2 were then calculated for all the above scenarios
except for the observations from SK-333 and BoBBLE as IO observations were
not taken during these cruises. The following algorithm was used for
estimating iodine fluxes (Carpenter et al., 2013):
1fluxI2=O3(g)×[I(aq)-]1.3×1.74×109-6.54×108×ln(ws),2fluxHOI=O3(g)×4.15×105×Iaq-ws-20.6ws-2.36×104×I(aq)-,
where the fluxes are in nanomoles per square metre per day (nmol m-2 d-1), [O3] is in nanomoles per mole (nmol mol-1) (ppbv), [I-] in moles per cubic decimetre (mol dm-3), and the wind speed (WS) is in metres per second (m s-1). Carpenter et al. (2013) did not consider the effect of temperature
in the interfacial layer of the sea surface model on activation energies for
the Reaction (R1) (i.e. assumed the temperature dependence for k (I-+O3) to be zero). Although I2 and HOI fluxes are expected to
increase with the temperature of the interfacial layer, I2 production
has a negative activation energy, as noted by MacDonald et al. (2014). In
Carpenter et al. (2013) (specific to the tropical Atlantic), a seawater
temperature of 15 ∘C and air temperature of
20 ∘C were used to calculate Henry's law constants,
diffusion constants, and mass transfer velocities. Again assuming the
temperature dependence of k(I-+O3) to be zero but including
the temperature dependence of Henry's law constants, diffusion constants,
and mass transfer velocities, the same interfacial layer model predicted
effective activation energies for I2 and HOI emissions of -2 and 25 kJ mol-1 (MacDonald et al. (2014). Using these
activation energies, MacDonald et al. (2014) calculated differences in
I2 and HOI fluxes of 3 % and 31 %–41 %, respectively, at SSTs of
10 and 30 ∘C compared to
the room-temperature parameterisations presented in Carpenter et al. (2013).
Experimentally derived activation energies for I2 and HOI emissions
were -7± 18 and 17±50 kJ mol-1
(MacDonald et al., 2014). As HOI represents the
larger iodine flux, the higher relative uncertainty in the activation energy
should be kept in mind when calculating temperature-dependent emissions. It
should be noted that a recent study suggested that the activation energies
from MacDonald et al. (2014) are better summarised as
approximately zero (e.g. Moreno and
Baeza-Romero, 2019) as the overall temperature dependence remains
unresolved.
HOI and I2 fluxes are also influenced by the wind speed as seen from
Eqs. (7) and (8), and the modelled iodine fluxes (HOI and I2) are
highest for high [O3], high [I-], and low wind speed. This is
explained by the assumption that wind shear drives mixing of the interfacial
layer to bulk seawater, reducing the efflux of HOI and I2 into the
atmosphere (Carpenter et al., 2013). Negative fluxes
are obtained from Eqs. (7) and (8) for both HOI and I2 when the
wind speed is higher than 14 m s-1, which is not physically possible,
and therefore the model output is limited to wind speeds below 14 m s-1
(Mahajan et al., 2019a). Iodine fluxes calculated
from Eqs. (7) and (8) using SSI concentrations from the scenarios (a)
and (b: 1–6) are shown in Fig. 4c and d.
Daily averaged atmospheric and oceanic parameters combined
from the ISOE-8, IIOE-2, and ISOE-9 field campaigns. Data marked with “ISOE” represent
combined data from ISOE-8 and ISOE-9. Unfilled markers and dotted lines show
values for IIOE-2. (a) IO above the detection limit from ISOE-8, ISOE-9. and
IIOE-2. (b) Surface IO values from the GEOS-Chem and CAM-Chem models. Panels (c) and
(d) comprise HOI and I2 fluxes estimated from
Eqs. (7) and (6), respectively. Fluxes are colour-coded for different sea
surface iodide (SSI) datasets used for their estimation. Black,
blue, red, and green correspond to fluxes calculated using SSI estimation
from Eqs. (1) to (5); purple represents the use of model SSI
predictions
(Sherwen et
al., 2019), and filled circles in dark blue correspond to measured SSI
from ISOE-9 for each observation. (e) Chlorophyll a observations from ISOE-8 and ISOE-9
(blue circles) as well as satellite data for all campaigns (red circles). (f) Ozone
mixing ratios from campaigns ISOE and IIOE-2. The dashed line marks the
polar front at 47∘ S. Observational plots for ISOE-8 and
IIOE-2 were adapted from Mahajan et al. (2019a, b). The vertical dashed
line through the figure indicates the PF (polar front).
Iodine oxideObservations
Ship-based measurements of IO were made using the multi-axis differential
optical absorption spectroscopy (MAX-DOAS) technique
(Hönninger et al., 2004; Platt and
Stutz, 2008). The MAX-DOAS device was installed at the bow of the ship with a
direct line of sight towards the front of the ship to avoid the ship's plume
in the detection path of the telescope. The MAX-DOAS device was programmed to
capture scattered sunlight spectra every 1 s at set elevation angles
of 0, 1, 2, 3, 5, 7, 20, 40, and 90∘ during daylight hours. Mercury
line calibration offset and dark current spectra were recorded after sunset
each day. Elevation angles outside a range of ±0.2∘ from the
set value were eliminated from the 30 min averaged spectra for increased
accuracy. Figure S2 shows the resultant IO and O4 differential slant
column densities (DSCDs) for the ISOE-9 campaign; similar plots are available
for ISOE-8 (Mahajan et al., 2019a) and IIOE-2
(Mahajan et al., 2019b). The QDOAS software
(Danckaert et al., 2017) was used for DOAS retrieval of
IO from the spectra using the optical density fitting analysis method. The
spectra were fitted with a third-order polynomial using a fitting interval
of 415 to 440 nm with cross sections of NO2
(Vandaele et al.,
1998), O3 (Bogumil et al.,
2003), O4 (Thalman and Volkamer, 2013), H2O
(Rothman
et al., 2013), two ring spectra, first as recommended by
Chance and Spurr (1997) and second following
Wagner et al. (2009), and a liquid water spectrum for
seawater (Pope and Fry, 1997). To remove the influence of
stratospheric absorption a spectrum corresponding to 90∘
(zenith) from each scan was used as a reference for the analysis. The raw
spectra were analysed to obtain differential slant column densities (DSCDs),
and values with a root mean square error (RMSE) greater than 10-3
were eliminated. Similarly, DOAS retrieval of O4 in the 350 to 386 nm
spectral window was performed, and DSCDs were obtained. The optical density
fits for IO and O4 from ISOE-9 are shown in Fig. S3. The IO DSCDs were
then converted to volume mixing ratios using the O4 slant columns
following the previously used “O4 method”
(Mahajan
et al., 2012; Prados-Roman et al., 2015; Sinreich et al., 2010; Wagner et
al., 2004). Further details on the instrument, retrieval procedure, and
conversion into mixing ratios can be found in previous works (Mahajan et al., 2019a, b).
Modelled atmospheric IO
We use outputs from two global models for a comparison with the observations
conducted during the three cruises. The first model is the GEOS-Chem
chemical transport model (version 10-01, 4×5∘ horizontal resolution,
http://www.geos-chem.org, last access: 1 April 2019), which includes detailed
HOx–NOx–VOC–ozone–halogen–aerosol (VOC: volatile organic carbon) tropospheric chemistry
(Sherwen
et al., 2016c, 2017) and is driven by offline meteorology from the NASA Global
Modelling and Assimilation Office (http://gmao.gsfc.nasa.gov, last access: 5 April 2019)
forward processing product (GEOS-FP).
The second model is the 3D chemistry–climate model CAM-Chem version 4
(Community Atmospheric Model with Chemistry) https://www2.acom.ucar.edu/gcm/cam-chem, last access: 8 April 2019), which is included in the CESM
framework (Community Earth System Model, CAM-Chem, version 4.0). The model
includes a state-of-the-art halogen chemistry scheme (chlorine, bromine, and
iodine) (Saiz-Lopez and Fernandez, 2016). The current
configuration includes an explicit scheme for organic and inorganic iodine
emissions and photochemistry. These halogen sources comprise the
photochemical breakdown of five very short-lived bromocarbons (CHBr3,
CH2Br2, CH2BrCl, CHBrCl2, and CHBr2Cl) naturally
emitted by phytoplankton from the oceans
(Ordóñez et al., 2012). The model was run
in specified dynamic mode (Ordóñez et al.,
2012), with a spatial resolution of 1.9∘ latitude by
2.5∘ longitude and 26 vertical levels from the surface to up
to 40 km.
Both models include biotic emissions of four iodocarbons (CH3I,
CH2ICl, CH2IBr, and CH2I2) as described by
Ordóñez et al. (2012) and abiotic oceanic
sources of HOI and I2 based on the Carpenter et al. (2013) and
MacDonald et al. (2014) laboratory studies of the oxidation of aqueous
iodide by atmospheric ozone at the ocean surface. Both models here use the
MacDonald parameterisation expression (Eq. 4;
MacDonald et al., 2014) discussed in Sect. 2.1.2
to predict surface iodide used for calculating iodine emissions and the
organo-halogen emissions from Ordóñez et al. (2012). IO surface
concentrations for the three campaigns (IIOE-2, ISOE-8, and ISOE-9) were
extracted from the model runs and used for comparison. Currently, these two
global models include reactive iodine chemistry (along with TOMCAT, which
includes the tropospheric iodine chemistry; Hossaini et al.,
2016).
Latitudinal plot of hourly averaged field measurements of
wind speed, ozone mixing ratios, SST, and salinity
(salinity data
for IIOE-2 are monthly climatological means from the World Ocean Atlas as
described in the Supplement) from the ISOE-8, IIOE-2,
and ISOE-9 campaigns. Data markers in red are for the IIOE-2 campaign;
those in blue are for ISOE-8, and markers in black are from ISOE-9 for
all the panels. Observational plots for ISOE-8 and IIOE-2 were adapted from
Mahajan et al. (2019a, b).
ResultsOzone, meteorological, and oceanic parameters
The latitudinal distributions of hourly average values of U10 wind speed
(WS), O3, SST, and salinity from all the campaigns are shown in Fig. 5.
Winds arriving at the ship, shown in Fig. 5a, remained low
for most of the duration of all three expeditions, with wind speed ranging
from 1 m s-1 to stronger winds of 24 m s-1 on a few days. Even
stronger winds (above 30 m s-1) were observed during ISOE-9 in the
region between 64 and 65∘ S, with the
highest wind speed of 32 m s-1 at 66∘ S on the night of
8 February 2017. Ozone mixing ratios (Fig. 5b) during all three
expeditions showed a similar trend, exhibiting a large reduction in values in
the open-ocean environment compared to coastal environments. The back
trajectories (Supplement) show that for most of the expeditions, air
masses arriving at the cruise location were from the open-ocean environment and did
not have any anthropogenic influence for the last 5 d. This is
reflected in the O3 values, which range between 8 and 20 ppbv in the
open ocean but were between 30 and 50 ppbv near the coastal regions, where
the air mass back trajectories confirm anthropogenic origins. Close to the
Indian Subcontinent ozone levels peaked at about 50 ppbv during ISOE-8.
They also showed a distinct diurnal variation, with higher ozone values during
the daytime due to photochemical production. However, in the open-ocean
environment, ozone mixing ratios did not show this diurnal variation, and
indeed values of ozone dropped during daytime, indicating photochemical
destruction during both ISOE-8 and ISOE-9 (Fig. 5b).
As already noted, SST is widely used to predict SSI (Eqs. 4 and 5). Combined
SST data (Fig. 5c) reveal a steady decrease in sea surface temperature from
15 to 68∘ S for all the campaigns. During
January 2015 (ISOE-8) seawater north of 6∘ N displays
slightly lower SST (∼3∘C) compared to that
in December 2015 (IIOE-2). Salinity is also an important parameter for the
prediction of SSI (higher coefficient in Eqs. 1, 2, and 3). The Southern Ocean
region explored during ISOE-8 and ISOE-9 reveals similar salinity values
(Fig. 5d) for the austral summer months of 2015 and 2016 (January–February).
The salinity data show relatively lower values for ISOE-8 compared to those
for IIOE-2 for the region 15∘ N to 20∘ S.
Despite the inter-annual differences in the northern Indian Ocean region,
salinity values of ∼35 PSU overlap for IIOE-2 and ISOE-8
in a small window of 7∘ N to the Equator. Below the Equator,
the salinity values for IIOE-2 increase, while for ISOE-8 salinity remains
lower than 35 PSU until 20∘ S. Seawater between
20 and 44∘ S has a nearly constant salinity
of 35 PSU, which decreases to ∼33.5 PSU after
44∘ S and remains the same until 65∘ S after
which the salinity begins to drop to 31.5 PSU near 67∘ S
close to Antarctica.
Sea surface iodide concentration
Latitudinal averages of SSI concentrations estimated from seven scenarios
(listed in Sect. 2.3) are shown in Fig. 3. SSI estimates from the IIOE-2
campaign are marked separately to differentiate them from the ISOE estimates for
the Indian Ocean region. There is a clear difference in the estimated SSI in
different scenarios. All the estimates and the model follow a similar
pattern, showing elevated levels in the tropics compared to the higher
latitudes. SSI estimates from parameterisations (Eqs. 1, 3, 4, and 5) show
nearly constant values for SSI from 15∘ N to
25∘ S, after which a steady decline is noted until
70∘ S. Thus, the parameterisations based on Eqs. 1, 3, 4, and 5
do not capture the decreasing trend observed for iodide around the Equator.
Equation (2), which was derived specifically for the Indian Ocean and Southern
Ocean region, better captures this trend and also shows a better match to
the measured SSI from SK-333 and BoBBLE in the Indian Ocean. Equation (6) also
predicts lower concentrations around the Equator than in the northern Indian
Ocean. SSI concentrations estimated using the Chance et al. (2014)
parameterisation (Eq. 1) show a small increase in iodide concentrations
south of 47∘ S (polar front), which is not observed in the
other parameterisations, but there is some suggestion of an increase in the
observations. Equation (1) also resulted in a large difference (∼50 nM) of SSI estimates north of 10∘ N between the IIOE-2 and
ISOE-8 cruises, while this difference was lower for the other
parameterisations. This difference between the SSI estimates for the IIOE-2
and ISOE-8 cruises is due to the large difference in salinity values for
this region (Sect. 4.1). SSI estimates using Eq. (2) show good agreement with
the model prediction of Sherwen et al. (2019), both indicating a decrease in
SSI concentrations near the Equator during the IIOE-2 and ISOE-8
expeditions. Some high SSI concentrations (up to ∼250 nM)
were observed around 10∘ N; these were best replicated by Eq. (3). The
highest SSI concentrations estimated using Eq. (3) were 244 nM at
7∘ N during IIOE-2 and 242 nM at 12∘ S
during ISOE-8. At the Equator, Eq. (2) performs better in predicting the SSI
concentrations, with a difference of ∼75 nM compared to the
observations. SSI estimates from Eq. (4), i.e. the MacDonald et al. (2014)
parameterisation, were lower than the measured iodide concentrations and all
other parameterisation, including the model (Eq. 7) predictions. Overall,
all modified parameterisations (Eqs. 2, 3, and 5) estimate higher SSI compared
to the original parameterisation (Eqs. 1 and 4), with the exception of the
region south of 20∘ S, where Eq. (3) predicts lower SSI than
Eq. (1). The modified MacDonald parameterisation (Eq. 5) estimated iodide
concentrations to be greater by 50 nM for the entire dataset in comparison
to the existing MacDonald parameterisation given by Eq. (4). For Eq. (5), the
uncertainty in the iodide concentration from the 95 % prediction band is
∼15 % of the predicted value.
Iodine fluxes
Figure 4 shows the latitudinal variation in IO mixing ratios, inorganic
iodine emissions (HOI and I2), chl a, and ozone mixing ratios for the
entire dataset comprising the three campaigns. All the panels in Fig. 4
are plots of daily averaged values during each expedition, except for the
HOI and I2 fluxes; these are latitudinal averages from each campaign.
Emissions calculated using the measured SSI concentrations (represented by
filled spheres in Fig. 4c, d) from ISOE-9 correspond to the data points
of the measured SSI concentration. Oceanic inorganic iodine emission fluxes
of HOI and I2 were estimated using the Carpenter et al. (2013)
parameterisation given in Eqs. (7) and (8) limited to wind speeds below 14 m s-1. Thus, the fluxes estimated from the measured SSI concentrations
were reduced to 56 points (out of 111 measured SSI data points). The seven
different datasets of iodide concentrations (listed in Sect. 2.3) have been
used for the estimation of HOI and I2 fluxes. For the entire dataset, the
highest fluxes were obtained when using the SSI concentrations from the
modified Chance et al. (2014) parameterisation (Eq. 3) derived from
measured SSI in the Southern Ocean region, i.e. during ISOE-9. The second
highest fluxes were estimated using SSI from Eq. (2), obtained from measured
SSI in the Indian Ocean and Southern Ocean. Comparatively lower iodine
emissions were estimated using SSI concentration from the MacDonald et al. (2014) parameterisation (Eq. 4). The estimated inorganic iodine fluxes in
the Southern Ocean region (30∘ S and below) are much lower
compared to the Indian Ocean (Fig. 5), driven by the higher estimated SSI in
the latter. Maximum inorganic emissions are predicted in the tropical
region, specifically north of the Equator. HOI is the dominant reactive
iodine precursor species for the entire dataset, with calculated flux values
20 times higher than those for I2. Emissions estimated using SSI from
Eq. (3) resulted in a peak HOI flux of 1.5×109 molec. cm-2 s-1 at 9∘ N during ISOE-8. The lowest HOI flux of
1.7×106 molec. cm-2 s-1 was obtained at
61∘ S during ISOE-9. For the same latitudes (9∘ N and
61∘ S), a maximum I2 flux of 7.0×107 molec. cm-2 s-1 and a minimum of 1.3×105 molec. cm-2 s-1 were estimated, respectively. Flux estimates
from Eq. (2) are slightly lower, with a maximum HOI flux of 1.3×109 and a minimum of 5.8×105 molec. cm-2 s-1 as well as a maximum I2 flux of 5.2×107 with
a minimum of 8.3×104 molec. cm-2 s-1 at the
same latitudes. The estimated HOI and I2 emissions are notably
lower (by ∼50 %) during IIOE-2 to the north of
5∘ S compared to emissions from ISOE-8. Between
5 and 20∘ S, the emissions from IIOE-2
and ISOE-8 are similar. Fluxes estimated using measured SSI concentrations
for the ISOE-9 campaign (20 to 70∘ S)
show no strong latitudinal trend for both HOI and I2 emissions. The
maximum calculated HOI flux was 5.8×108 molec. cm-2 s-1 at 68∘ S and the minimum was 1.1×107 molec. cm-2 s-1 at 33∘ S. Similarly,
I2 fluxes estimated from measured SSI concentrations peaked at
1.5×107 molec. cm-2 s-1 at 32∘ S
with a minimum of 3.5×105 molec. cm-2 s-1 at
67∘ S. Inorganic iodine emissions estimated using model
predictions for SSI concentrations from Sherwen et al. (2019) match
the fluxes estimated using the iodide parameterisation tools. Despite
the differences in SSI concentrations from existing and region-specific
parameterisations, all result in similar values for iodine fluxes. The
fluxes were calculated using the hourly wind speeds for the results to be
comparable with model outputs as described below. This would result in a
loss of high-temporal-resolution emission variability, but considering the
frequency of the iodide and IO observations, computing the fluxes at a
higher resolution will not give any extra information.
Iodine oxideObservations
IO was detected above the instrument detection limit (2.1–3.5×1013 molec. cm-2, i.e. 0.4–0.7 pptv) in all three campaigns. The
expeditions covered a track from the Indian Ocean to the Antarctic coast in
the Southern Ocean and showed lower IO DSCDs in the tropics compared to the
Southern Ocean, with a peak of about 3×1013 molec. cm-2 at
40∘ S. Figure 4a shows daily averaged IO mixing ratios for all
three cruises combined. IO mixing ratios of up to 1 pptv were observed in
the region 50–55∘ S, and slightly higher values of IO mixing
ratios were observed in the region below 65∘ S close to the Antarctic
coast. North of the polar front region, the maximum IO average mixing ratio
of ∼1 pptv was observed at 40∘ S. The highest values of
IO were observed close to the Antarctic coast, with up to 1.5 pptv measured
during ISOE-9, and similar values are reported for the ISOE-8 expedition
south of the polar front (Mahajan et al., 2019a). The
IO mixing ratios in the Southern Ocean region for ISOE-9 ranged between 0.1
and a maximum of 1.57 pptv (±0.37 pptv) observed on 18 February 2017 at
50∘ S on a clear-sky day. This maximum value was observed only on
1 d and was preceded by foggy and misty days, later followed by several overcast
days evidencing the role of photochemistry in IO production from its
precursor gases.
Modelled IO
Based on the current understanding of iodine chemistry, regional and global
models consider inorganic fluxes of iodine (HOI and I2) to be major
contributors of iodine in the marine boundary layer. It is important to
verify if the models using the existing parameterisation for these source
gases can replicate observations of IO in the region of study. Thus, we have
included model IO output from GEOS-Chem and CAM-Chem, both of which use the
SST-based MacDonald et al. (2014) parameterisation for SSI (Fig. 4b). The
surface IO output from GEOS-Chem predicts the highest levels of IO up to 1.7 pptv to the north of the Equator at 11∘ N for the time
period of the IIOE-2 campaign. For the same latitudes, the model suggests
lower IO levels of less than 0.5 pptv during the ISOE-8 campaign.
Conversely, south of the Equator to 10∘ S, the model
predicts higher IO levels during ISOE-8 and lower IO values during
IIOE-2, in agreement with the observations. Below 10∘ S, IO
predictions for both campaigns match well until 20∘ S, which
was the latitudinal limit for the IIOE-2 campaign. To the south of
20∘ S, modelled IO levels remained below 1 pptv and
exhibited a decreasing trend to the south of the polar front, in
disagreement with IO observations. At locations between 40 and 43∘ S, GEOS-Chem underestimates the observed IO levels
by 50 %. These locations are close to the Kerguelen Islands, and high IO
values were observed here only during ISOE-8. These locations have been
omitted in the correlation study between modelled and observed IO as they
could be impacted by coastal or upwelling emissions, which are not well
prescribed in the models.
The CAM-Chem IO surface output suggests consistently higher levels of IO
during IIOE-2 compared to ISOE-8 for the same latitudinal band (Fig. 4b). Contrary to the observations, the CAM-Chem model suggests that IO
levels during IIOE-2 are up to 1 pptv higher than the ISOE-8 campaign
near 7∘ S latitude. The model also shows elevated IO levels
of 2.7 pptv at 7.9∘ N during the IIOE-2 campaign, which does
not match the observations during IIOE-2 or ISOE-8 for that region.
IO levels below 1.5 pptv (11∘ N to 20∘ S)
are indicated for the ISOE-8 campaign. In addition, the region between
0 and 1.5∘ S has similar IO levels for the
IIOE-2 and ISOE-8 campaigns. The model predicts lower IO levels for the
southern Indian Ocean and the Southern Ocean (less than 1 pptv), with decreasing
IO to the south of the polar front. However, at 43∘ S, the
model suggests higher IO (2.4 pptv) during ISOE-9, which matches the
increase in observed IO for that region during the ISOE-8 expedition, with
this region being close to the Kerguelen Islands. Both models show
consistently higher absolute concentrations overall compared to the
observations north of the polar front.
Linear fit analysis of estimated sea surface iodide (SSI)
concentrations (y axis) from parameterisation methods in Eqs. (1) to (5) and
model prediction (Sherwen et al., 2019) against measured SSI concentration
(x axis) from ISOE-9, SK-333, and BoBBLE. (c) SSI values are compared only
with ISOE-9 observations for the Southern Ocean parameterisation. R
represents the Pearson's correlation coefficient, and N is the size of the
dataset. The dashed blue line represents the identity (1:1) line.
DiscussionSeawater iodide
To improve the estimation of SSI in the study region, previously established
parameterisations (Eqs. 1 and 4) were modified to obtain a region-specific
parameterisation for SSI concentrations. SSI estimated using these modified
parameterisations was less sensitive to seasonal salinity and SST changes
for the northern Indian Ocean basin compared to the existing parameterisation
(Fig. 3). Figure 6 shows the correlations of all the calculated SSI
concentrations with the observations. The SSI estimates from Eqs. (1) to (6)
correlate positively (significantly) with the measured SSI concentrations
(observations) from ISOE-9 (Fig. 6). Out of the six parameterisation tools
compared in this study, as expected, SSI from Eq. (2), i.e. the modified
Chance equations for the Indian Ocean and Southern Ocean, showed the best
correlation with the measured SSI because it was created using datasets
from these campaigns (Fig. 6 and Table 2). Although the region-specific
parameterisations were expected to match the observations they are
based on, there was a notable difference between predictions and
observations when this approach was applied only to Indian Ocean SSI
measurements from SK-333 and BoBBLE (R2=0.5 for Indian Ocean
parameterisation; analysis not shown). This could be attributed to the lack
of SSI measurements in this region (n=18), and it highlights the fact that
there may be not only seasonally but also regionally varying complexities in SSI
which should be considered when estimating SSI. All parameterisation methods
used for SSI estimations show that SSI concentrations are directly
proportional to seawater salinity (listed in Sect. 2.3). It is evident from
Figs. 5d and 3a that to the north of the Equator, the parameterisations
(Eqs. 1 to 5) show lower SSI concentrations in regions with lower salinity
(up to 5∘ N during ISOE-8 – filled symbols Fig. 3) and
higher SSI concentrations in regions with comparatively higher salinity
(during IIOE-2 – unfilled symbols Fig. 3). Only the modelled SSI
concentrations using Eq. (6) (Fig. 3a, data in purple) reveal an inversely
proportional relationship for salinity and SSI concentration in this region.
The Sherwen et al. (2019) parameterisation (Eq. 6) produces lower SSI
concentrations in high-salinity Arabian Sea waters during IIOE-2 (Fig. 3a)
north of 5∘ N compared to the low-salinity Bay of Bengal
waters during ISOE-8, which contradicts all the other parameterisations (Eqs. 1
to 5). Further, the SSI concentrations obtained from Sherwen et al. (2019)
reverse their trend to the south of 6∘ N, with higher
concentrations during IIOE-2 and lower during ISOE-8. It should be noted
that only a few observations of SSI exist in this region to confirm this
trend. Further discussion on the relationship between salinity and other
biogeochemical variables with SSI concentrations at a global and regional
scale can be found elsewhere
(Chance et al.,
2014, 2019).
SSI estimates considering only SST as a proxy for iodide concentration (Eq. 4) reveal positive correlations with measured SSI concentration (R=0.86, P<0.001, n=129; Fig. 6d). The modified MacDonald
parameterisation (Eq. 5) also correlates positively with the measured SSI
concentration but has a slightly lower coefficient of correlation (R=0.83, P<0.001, n=129; Fig. 6e). When using the SST as a proxy
for SSI, a large intercept was obtained for the SSI values, evidencing the
discrepancy in absolute value between this parameterisation and the
observations. Equation (5) resulted in a lower intercept, approximately half of
that for Eq. (4), and a lower absolute slope value of |-3763±218| compared to the |-9134±613| of Eq. (4)
given in MacDonald et al. (2014). The lower absolute slope value for Eq. (5)
implies that the SSI concentrations for this region were less sensitive to
the changes in SST compared to those in Eq. (4).
Despite the lower R value, the SSI estimates from Eq. (5) in Fig. 3 are closer
to the measured SSI concentration than the estimates from Eqs. (2) and (3) for
the region from 25 to 70∘ S. However,
north of 25∘ S, the SSI estimates from Eqs. (3) and (5)
differ by ∼40 %. Both SST-based parameterisations (Eqs. 4
and 5) did not show the observed latitudinal variation in the SSI
concentrations near the Equator. Linear regression of SSI with SST for only
the Indian Ocean region revealed that there was no correlation between the
two (R2=0.07, P=0.3, n=18). The SSI in this region only
showed dependence on the salinity and latitude; correlations with the other
parameters were not significant. This highlights the fact that SST may not be a very
good proxy for SSI in the Indian Ocean, especially near the Equator. This is
explored further in Chance et al. (2020). The
original Chance et al. (2014) parameterisation displays higher sensitivity
to seasonal salinity changes compared to the existing and modified
parameterisation in the Indian Ocean region (Sect. 3.3). However, this
method predicted an increasing iodide concentration to the south of the polar
front (47∘ S), which is not supported by observations in
this region (Fig. 3). In conclusion, considering the correlation with the
measured SSI concentration and dependence on seawater salinity, the
region-specific modified Chance parameterisation (Eq. 2) is a suitable
method to estimate SSI concentration for the Indian Ocean and Southern Ocean
region. The modelled SSI estimates by
Sherwen et al. (2019) capture the SSI trend close to the Equator better than other existing schemes,
but it fails to replicate higher SSI observations at locations
8∘ N, 40∘ S and to the south of
65∘ S close to the Antarctic coast (Fig. 3).
Atmospheric iodine
Combined IO observations from IIOE-2, ISOE-8, and ISOE-9 (Fig. 4a) show that
the Indian Ocean region has comparatively less IO in its MBL than the
Southern Ocean region. IO remained below 1 pptv up to 40∘ S
and reached a maximum IO of 1.6 pptv south of the polar front. Modelled
surface IO output from GEOS-Chem and CAM-Chem using the MacDonald et al. (2014) parameterisation (Fig. 4b) does not match the observations of IO,
although they generally show good agreement with each other. The models show
similar spatial patterns across the entire dataset, except for two periods
of very high IO levels predicted by CAM-Chem (Fig. 4b). As well as
structural differences between CAM-Chem and GEOS-Chem, there are many
halogen-specific differences in rate constants, heterogeneous parameters,
cross sections, and photolysis of species (e.g. higher iodine oxides) which
could explain differences in predicted gas-phase IO. Considering the
generally lower wind speeds and higher ozone concentrations seen in IIOE-2
versus SOE-8 and SOE-9, the calculated fluxes are higher and therefore more
sensitive to assumptions, such as minimum wind speeds provided to the
Carpenter et al. (2013) parameterisation. GEOS-Chem uses a minimum wind
speed of 5 m s-1; however, CAM-Chem uses a minimum wind speed of 3 m s-1, and hence fluxes calculated using the surface winds in these models
are expected to be slightly different.
Both models suggest higher than observed IO levels in the Indian Ocean
region but underpredict IO for the Southern Ocean region. The highest
detected IO levels, both in the Southern Ocean and in a narrow band around
43∘ S, were not reflected in the model predictions. We note that
these occurred in regions of elevated chl a values (Fig. 4e) close to the
Kerguelen Islands. Mahajan et al. (2019a) also reported positive
correlations for IO with chl a for the Indian Ocean region above the polar
front for a subset of the dataset (ISOE-8). Calculated fluxes of HOI and
I2 (Fig. 4c and d) fail to directly explain trends in the detected IO
levels for the entire dataset, regardless of the method used to estimate
SSI. Maximum levels of HOI and I2 predicted to the north of
5∘ N correspond to rather low levels of IO (<0.5 pptv) in this region. However, this has been attributed to NOx
titration of IO (Mahajan et al., 2019b). The models, however, do not capture
this iodine titration by NOx as seen in the observations, even though
the reactions of IO with NOx are included
(Ordóñez et al., 2012). Similarly, for the
region south of the polar front, the calculated iodine fluxes remain low in
the region of the maximum detected IO concentrations during the ISOE-8 and
ISOE-9 campaigns. Iodine fluxes estimated for the Indian Ocean region
(15 to 5∘ N) during IIOE-2 and ISOE-8
show large differences, with much higher values during ISOE-8. However, the
modelled IO is in fact higher for IIOE-2 than during ISOE-8
(5–15∘ N). Considering that the models do not reflect the fluxes,
this indicates that either photochemistry or dynamical dilution of the
fluxes led to this difference in the model. Additionally, the elevated
levels of IO predicted in the models suggest that CAM-Chem and GEOS-Chem
overestimate the impact of iodine chemistry in the northern Indian Ocean.
Linear fit of daily average sea surface iodide (SSI)
concentration, wind speed, and ozone mixing ratio (y axis) against the calculated
I2 and HOI flux (x axis) for the entire
campaign. HOI and I2 are calculated with SSI
estimated using the modified Chance parameterisation for the Indian Ocean and
Southern Ocean in Eq. (2).
In Fig. 7, correlations of iodine fluxes estimated using the measured SSI
concentrations (Eq. 2) show that fluxes of HOI correlate positively with
tropospheric ozone (R=0.56, P<0.001) and negatively to wind
speed (R=-0.62, P<0.001), and I2 fluxes correlate
positively with SSI concentration (R=0.56, P=P<0.001) and
ozone (R=0.59, P<0.001) and negatively with wind speed (R=-0.4, P<0.001). This indicates that there is a positive
correlation of I2 with SSI, the dominant inorganic iodine flux (i.e. HOI
does not show a significant correlation with the SSI concentration), although the
flux equation includes an iodide term (Eq. 8). We analysed the correlation
of daily averaged observed IO during the three campaigns with daily averaged
values of oceanic parameters (SST, chl a, salinity, SSI concentration),
meteorological parameters (wind speed, ozone), and calculated inorganic
iodine fluxes. We divided the combined dataset from three campaigns into two
regional subsets for the north (Fig. 8a) and south (Fig. 8b) of the polar
front (47∘ S). The correlation for SSI concentrations is
included for all seven methods for SSI estimation listed in Sect. 2.3.
The fluxes of HOI and I2 obtained using the seven different datasets
for SSI are included and listed in Fig. 8 in the same order as the SSI
concentration (labelled 1 to 7). IO model output from GEOS-Chem (labelled 8)
and CAM-Chem (labelled 9) is included for the correlation analysis, along
with chl a data from observations during ISOE-8 and ISOE-9 and a satellite
dataset obtained from MODIS Aqua (Oceancolor, NASA-GSFC, 2017).
The Pearson's correlation coefficient of observed iodine
monoxide (IO) with oceanic and atmospheric parameters combined for the ISOE-8,
IIOE-2, and ISOE-9 campaigns. Correlations are performed for daily averages
of IO and corresponding parameters listed on the x axis. The black squares
represent the Pearson's correlation coefficients (R), the diamonds (blue) mark
the 99 % confidence limit, and the circles (red) correspond to the 95 %
confidence limits in all the panels. Panel (a) includes data from all campaigns to
the north of the polar front (PF) (n=72), panel (b) represents
combined data for the south of the polar front (n=48), and panel
(c) includes the entire dataset from three campaigns (n=120).
For the entire dataset (Fig. 8c), only wind speed shows a statistically
significant, positive correlation with observed IO above the 99 %
confidence limit (R=0.4, P<0.001, n=115). A similar
positive correlation with wind speed was found in the subset of data south
of the polar front (Fig. 8b) (R=0.49, P=0.01, n=48), with
observations north of the polar front showing a weaker positive correlation
(R=0.27, P=0.08, n=67). Mahajan et al. (2012) showed that no
correlation existed between IO and wind speed over the eastern Pacific
Ocean, contrary to the results in this study. Current estimation methods for
iodine emissions have a negative dependence on wind speed (Eqs. 7 and 8). A
positive correlation of IO with wind speed could suggest that increased
vertical mixing enables the emission of HOI, I2, and/or other iodine
gases, thus enhancing IO production in the MBL. However, the interfacial
model still overpredicts IO concentrations at low wind speeds due to overprediction of HOI and I2 emissions (MacDonald et
al., 2014). The apparently contradictory results from different studies call
for more observations of IO in the MBL over a range of wind speeds.
Salinity and SST show a weak negative correlation with atmospheric IO for
the entire dataset and for the north of the polar front region. This
indicates that even if the physical parameters are significant for the
initial parameterisation for SSI and inorganic flux estimation, there is no
direct and significant correlation of these parameters with atmospheric
IO. However, south of the polar front, SST correlates positively above the
99 % limit (R=0.52, P=0.01, n=48) and salinity correlates
positively above the 95 % limit (R=0.44, P=0.03, n=48). Ozone
correlates negatively with IO above the 95 % limit (R=-0.4, P=0.046,
n=47), which could indicate catalytic destruction of tropospheric ozone
through atmospheric iodine cycling in the south of the polar front. This
highlights the fact that although these physical parameters may be required for
iodine fluxes, IO levels may only be weakly related to them.
The calculated SSI concentrations and the HOI and I2 fluxes calculated
using these SSIs all show a significant negative correlation with the
observed IO concentrations above the 95 % confidence limit for the entire
dataset (except for the HOI flux estimated from the MacDonald et al., 2014,
parameterisation, which shows no significant correlation). The positive
correlation of the observed IO with wind speed is a potential driver for the
negative correlation of observed IO with the calculated HOI and I2
fluxes, which decrease with wind speed.
Measured iodide levels (labelled 4) and the I2 and HOI fluxes
calculated from them (also labelled 4) show no correlation with the observed
IO levels across the entire dataset, although iodide shows a significant
positive correlation (R=0.55, P=0.04, n=32) for IO measured
south of the polar front. Mahajan et al. (2019a) pointed out that SST
negatively correlated with IO for the ISOE-8 campaign, contradicting the
previous results for observations in the Pacific Ocean
(Großmann et al., 2013;
Mahajan et al., 2012). Here, SST shows a significant positive correlation
with observed IO (R=0.52, P=0.006, n=48) south of the polar
front above the 99 % confidence limit, but there is no correlation north
of the polar front and only a weak negative correlation using the combined
dataset from the three campaigns (R=-0.18, P=0.13, n=119).
Despite the above-mentioned point regarding the increase in observed IO
levels in regions of elevated chl a, there is only a weak and negative
correlation of IO with chl a (from both observations and satellite data) south
of the polar front. However, there is a strong positive relationship north
of the polar front (R=0.696, P=2.3×10-4, n=29).
In fact, for the region north of the polar front, chl a shows a significant
positive correlation with observed IO above the 99 % confidence limit (P<0.001). The GEOS-Chem and CAM-Chem output also shows a significant
positive correlation (Fig. 8), which may result from the dependency of organic
iodine species on oceanic chl a in both GEOS-Chem and CAM-Chem. Figure 8 shows
a large difference in correlation values for chl a data obtained from
observations and a satellite (MODIS Aqua, NASA, GSFC;
https://oceancolor.gsfc.nasa.gov, last access: 12 May 2019; extracted for the same locations as the in
situ data). In situ, observed chl a showed an improved correlation with IO
compared to that with satellite chl a. Figure 9 shows linear fits for chl a
from in situ observations and the satellite against IO for the entire dataset
and the subset for north of the polar front. For the entire dataset, the correlation of
chl a with IO from both observations and satellite data is not significant.
Chl a from in situ observations positively correlates with IO (R=0.15, P=0.32), while chl a from satellite data correlates negatively (R=-0.13,
P=0.26). Correlations of chl a with IO improve north of the polar
front for chl a from observations (R=0.696, P=0.0002), but chl a from
satellite data shows a statistically insignificant correlation with IO (R=0.08, P=0.57). The discrepancies in chl a from observations and
satellite data will make it difficult to identify links between the organic
parameter and atmospheric IO and expand this to a global scale. It should be
noted that one study in the Pacific has shown that the contribution of
combined biogenic iodocarbon fluxes to IO does not explain the observed IO
(Hepach et al., 2016).
Linear fit of daily averaged field observations of
chlorophyll a (red circles) and
chlorophyll a satellite data (blue circles) (y axis) against observed iodine monoxide (IO) (x axis) from the ISOE-8, IIOE-2, and ISOE-9 campaigns. The top panels include
chlorophyll a for the entire dataset; the bottom
panels include data to the north of the polar front.
Linear fit of daily averages of modelled surface iodine
monoxide (IO) output (y axis) from GEOS-Chem (filled blue squares) and
CAM-Chem (unfilled red diamonds) against observed IO (x axis) for the ISOE-8,
IIOE-2, and ISOE-9 campaigns. Panel (a) includes linear fits of both GEOS-Chem and
CAM-Chem for IO detected to the north of the polar front, and panel (b) shows the same
for the region south of the polar front. Two data points in (a) at
41 and 43∘ S are removed due to large
differences between observations and modelled values.
Despite the observed negative relationship of IO with wind speed noted
above, note that the GEOS-Chem IO model output (which is dependent on the
calculated HOI and I2 fluxes) shows a significant positive correlation
with observed IO above the 99 % confidence limit for data south (R=0.78, P=P<0.001, n=48) and north (R=0.69, P=P<0.001, n=68) of the polar front, although there is no
correlation across the entire dataset. Note that the model underestimates IO
values by 1 pptv south of the polar front and generally overestimates IO by
∼1.5 pptv north of the polar front (Fig. 4). A linear fit
for observed IO against modelled IO for north and south of the polar front
(Fig. 10) shows a significant positive correlation of GEOS-Chem output with
observed IO but with very different slopes north of the polar front (where
the models overestimate IO) and south of the polar front (where the models
underestimate IO). Hence, even though the correlations are good in the
individual regions, the model does not accurately reproduce the observed
absolute concentrations.
Conclusions
In this study, region-specific parameterisation tools were devised for sea
surface iodide (SSI) estimation following previous SSI estimation methods
from Chance et al. (2014) and MacDonald et al. (2014). New observations of
SSI from ISOE-9, SK-333, and BoBBLE (Indian and the Southern Ocean) were used
to create region-specific SSI parameterisations. An average difference of up
to 40 % in SSI concentration was observed among the existing
parameterisations (Eqs. 1, 4, and 6), and the difference was 21 % for the
region-specific ones (Eqs. 2, 3, and 5). Comparison of estimated SSI
concentrations from various parameterisations with observed SSI and
sensitivity to seasonal salinity changes showed that the modified Chance
parameterisation (Eq. 2) was most suitable relative to the SST-based
parameterisation (Eq. 5) for SSI estimation in the Indian Ocean and Southern
Ocean region. Since the existing global parameterisation schemes (Eqs. 1 and
3) fail to match measured SSI in this region, there is a need to
conduct more observations of SSI in the Indian Ocean and Southern Ocean
region to fully understand and estimate the impact of seasonally varying,
region-specific parameters (like salinity, reversing winds patterns)
influencing the seawater iodide concentration in this region. Alternatively,
a region-specific parameterisation scheme may be included in the global
models for better representation of seawater iodine chemistry in the Indian
and Southern Ocean region. Modelled estimates from Sherwen et al. (2019)
also captured SSI well, although some high concentrations in the northern
Indian Ocean region were not captured. SSI estimation from SST alone
underpredicts SSI for the Indian Ocean and is therefore not considered to be
suitable for SSI estimation in the Indian Ocean region. Although improving
SSI concentration in models for the Indian Ocean and Southern Ocean region
may improve the estimation of seawater iodine chemistry, it does not
translate to estimating the atmospheric iodine chemistry in this region. An
accurate estimation of inorganic iodine fluxes (HOI and I2) is hence
necessary to explain observed levels of IO in the remote open-ocean marine
boundary layer. However, these first concomitant observations of SSI and IO
show that the inorganic fluxes estimated in this study fail to explain
detected IO levels for the entire dataset. No significant correlation was
seen between the SSI from different parameterisation techniques or estimated
inorganic iodine fluxes with observed IO levels. Fluxes estimated using
iodide from different parameterisation and measured iodide did not show
large variation in values and followed a similar latitudinal trend. This is
indicative of the inorganic iodine flux parameterisation not being highly
sensitive to the SSI parameterisation. Predicted inorganic iodine fluxes did
not explain iodine chemistry, as indicated by IO levels, in the atmosphere
above the Indian and Southern Ocean (Indian Ocean sector). Chl a shows a
positive correlation with IO for the north of the polar front region,
suggesting that biologically emitted species could also play a role in
addition to ozone- and iodide-derived inorganic emissions of HOI and I2.
Finally, model predictions of IO underestimate IO levels for the Southern
Ocean region but overestimate IO in the Indian Ocean. Models greatly
underestimate IO in regions with a higher chl a concentration, which could be
indicative of organic species playing a role (close to the Kerguelen
Islands; refer to Sect. 3.4.2). This study suggests that the fluxes of iodine
in the MBL are more complex than considered at present and further studies
are necessary in order to parameterise accurate inorganic and organic fluxes
that can be used in models. Using seawater iodide measurements and
calculations from different parameterisations did not alter the inorganic
iodide flux estimate greatly. Direct observations of HOI and I2,
alongside volatile organic iodine measurements in the MBL, are necessary in
order to reduce the uncertainty in the impacts of iodine chemistry.
Data availability
All the datasets used in this study are available
from Mendeley public data repository at https://doi.org/10.17632/rrn8vpv8mj.1 (Inamdar et al., 2020). Supporting datasets are appropriately cited and linked in the above
repository page.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-12093-2020-supplement.
Author contributions
ASM conceptualised the research plan and methodology. SI did the data
curation, analysis, and writing of the original draft. LT and RC did the
iodide measurements and provided unpublished iodide data from ISOE-9, SK-333, and
BoBBLE. PS and RCo provided salinity data for ISOE-9. SCT and AUK provided
chl a data for ISOE-9. AKS and PVB provided chl a data for SK-333. AS and RR
provided chl a data from BoBBLE. CC and ASL did the CAM-Chem model run for
ISOE-9 and IIOE-2. TS did the GEOS-Chem model run for ISOE-9, IIOE-2, and
ISOE-8.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank the Ministry of Earth Sciences for funding the expeditions
and IITM for providing a research fellowship to Swaleha Inamdar. We would
particularly like to thank the ISOE and IIOE-2 teams for their tireless
contribution to manually recording and compiling atmospheric and oceanic
observations during the expedition. We express gratitude to the
officers, crew, and scientists on board RV S.A. Agulhas and RV Sagar Kanya for their support.
Financial support
Lucy J. Carpenter, Liselotte Tinel, Rosie Chance, and Tomás Sherwen received funding from the UK NERC through the grant “Iodide in the ocean: distribution and impact on iodine flux and ozone loss” (grant no. NE/N009983/1).
Review statement
This paper was edited by Andreas Engel and reviewed by Theodore Koenig and two anonymous referees.
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