Ito et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00357-9(2020) 7:42

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Evaluation of aerosol iron solubility overAustralian coastal regions based on inverse
modeling: implications of bushfires on
bioaccessible iron concentrations in theSouthern Hemisphere
Akinori Ito1* , Morgane M. G. Perron2, Bernadette C. Proemse2, Michal Strzelec2, Melanie Gault-Ringold3,
Philip W. Boyd2,3 and Andrew R. Bowie2,3

Abstract
Mineral dust is the major source of external micro-nutrients such as iron (Fe) to the open ocean. However, large
uncertainties in model estimates of Fe emissions and aerosol-bearing Fe solubility (i.e., the ratio of labile Fe (LFe) tototal Fe (TFe)) in the Southern Hemisphere (SH) hampered accurate estimates of atmospheric delivery of bioavailable Fe to
the Southern Ocean. This study applied an inverse modeling technique to a global aerosol chemistry transport model(IMPACT) in order to optimize predictions of mineral aerosol Fe concentrations based on recent observational data overAustralian coastal regions (110°E–160°E and 10°S–41°S). The optimized (a posteriori) model did not only better capture
aerosol TFe concentrations downwind from Australian dust outbreak but also successfully reproduced enhanced Fesolubility (7.8 ± 8.4%) and resulted in much better agreement of LFe concentrations with the field measurements(1.4 ± 1.5 vs. 1.4 ± 2.3 ng Fe m–3). The a posteriori model estimates suggested that bushfires contributed a large
fraction of LFe concentrations in aerosols, although substantial contribution from missing sources (e.g., coalmining activities, volcanic eruption, and secondary formation) was still inferred. These findings may haveimportant implications for the projection of future micro-nutrient supply to the oceans as increasing frequency
and intensity of open biomass burning are projected in the SH.
Keywords: Mineral dust, Bushfire, Coal mine, Climate change, Bioaccessible iron, Labile ironIntroduction
Atmospheric deposition has been widely recognized as
one of the major pathways for delivering essential
micro-nutrients such as iron (Fe) to the open ocean(Fung et al. 2000; Jickells et al. 2005; Mahowald et al.
2005). Such external supply of Fe is particularly important in high-nutrient low-chlorophyll (HNLC) oceanic
regions where even a small addition of this limiting* Correspondence: akinorii@jamstec.go.jp
1
Yokohama Institute for Earth Sciences, JAMSTEC, Yokohama, Kanagawa
236-0001, Japan
Full list of author information is available at the end of the article

nutrient can trigger disproportionately large phytoplankton blooms and therefore influence the export of organic
materials to deep oceanic layers (Martin et al. 1988;
Boyd et al. 2000, 2007). The Southern Ocean (SO) is thelargest HNLC region of the globe and receives multiple
sources of external Fe, which show different temporalsignatures and spatial gradients (de Baar et al. 1995;
Mackie et al. 2008; Boyd and Ellwood 2010). The SO
hosts a variety of distinct regional oceanic ecosystems
characterized by unique physical and biological properties as well as different dominant processes of Fe supply(Boyd and Ellwood 2010). Therefore, large variabilities in© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
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Ito et al. Progress in Earth and Planetary Science(2020) 7:42

the input of bioavailable Fe should be considered forpredicting the response of SO marine ecosystems to Fe
fertilization in ocean biogeochemistry models (Tagliabue
et al. 2017). The term of “labile” Fe (LFe) is operationallydefined as the fraction of aerosol Fe that can be leachedfrom the particulate phase in the sum of the ultra-highpurity water (UHPW) and the ammonium acetate (AA)leaches (Perron et al. 2020a). The operational definition
of LFe could be applied to measurements of both the deliquesced aerosol solution and seawater. To clarify thedifferences of chemical and physical forms in between
aerosol water and seawater, “bioaccessible” Fe in aerosol
water is defined as potentially “bioavailable” forms of Fe
in seawater (Meskhidze et al. 2019). To avoid any confusion in comparing model estimates to observations inthis study, we consider LFe as bioaccessible Fe (Myriokefalitakis et al. 2018; Ito et al. 2019; Perron et al. 2020a).
A few land masses exist in south of the Equator to act
as sources of Fe to the atmosphere, compared to theNorthern Hemisphere (NH). Aerosols emitted from landmasses can travel thousands of kilometers over periodsof more than 2 weeks before settling to the seawater surface in remote parts of the ocean (Wagener et al. 2008).
Such long-range atmospheric transport over the ocean
leads to substantial atmospheric mixing between airmasses of various origins (Sholkovitz et al. 2012). Major
sources of Fe over the SO include clay and silt soils
entrained into the atmosphere due to the impacts ofsaltating particles on bare soil surface and due to pyroconvection from vegetated lands in the Southern Hemisphere (SH) during dust and fire events, respectively(Mahowald et al. 2009; Neff et al. 2015: Ito and Kok2017). Recently, there has been increasing attention paid
to Fe-containing aerosols from underrepresented anthropogenic sources such as heavy industries and deforestation in the SH (Mahowald et al. 2018; Matsui et al.
2018; Ito et al. 2019; Conway et al. 2019). Such pyrogenic
sources can enhance the global carbon export more efficiently than lithogenic Fe sources via the marine biologicalpump (Hamilton et al. 2020; Ito et al. 2020).
Global carbon emission fluxes from open biomassburning (BB) have mostly been estimated using satellite
remote sensing (e.g., Hoelzemann et al. 2004; Ito andPenner 2004; van der Werf et al. 2017), which provides
long-term observations with high-spatial resolution. Previous studies have reported large uncertainties associatedwith the satellite observations of open fires and with themodeling methods used to calculate the emissions fluxes
from the observational data, and thus often applied a
scaling factor to the particulate emissions from BB (Itoand Penner 2005; Zhang et al. 2005; Reddington et al.
2016). Indeed, the comparison of six global biomassburning emission datasets showed a large variation
for Australian fire emissions between different data

Page 2 of 17(Pan et al. 2020). Additionally, there are large uncertainties in the Fe solubility (i.e., the ratio of LFe tototal Fe, TFe) estimates for BB between different
models (Myriokefalitakis et al. 2018; Ito et al. 2019).
Moreover, model simulations on the oceanic responseof atmospheric LFe have recently raised increasingawareness on the potential role of BB in delivering
LFe to the SO (Hamilton et al. 2020; Ito et al. 2020).
The solubility of aeolian Fe will exert a key control on
the impact of atmospheric supply on open ocean ecosystems (Baker and Croot 2010; Meskhidze et al. 2019).
The solubility of aerosol Fe is initially characterized by
the nature of the emission source (Schroth et al. 2009;
Sholkovitz et al. 2012; Ito 2013). Subsequently, relatively
insoluble Fe contained in aerosols is dynamically transformed into LFe via physical and photochemical processing of aeolian particles during the atmospheric transport(Johnson and Meskhidze 2013; Myriokefalitakis et al.
2015; Ito and Shi 2016; Hamilton et al. 2019). Such processes include the preferential gravitational removal oflarger mineral dust particles over smaller combustion
aerosols during the atmospheric transport, which leads
to smaller aerosol size particles but higher Fe solubilities
away from emission sources (Sedwick et al. 2007; Luoet al. 2008; Hsu et al. 2010; Baker et al. 2016).
Atmospheric measurements of trace metal concentrations over the SO are sparse due to the technical andfinancial issues associated with long oceanographic research cruises in remote areas, and the scarcity of islands
which are mostly inhospitable and difficult to access(Heimburger et al. 2013; Winton et al. 2015). Consequently, a small number of available field observations
currently limit our understanding of the key factors controlling atmospheric LFe concentration in aerosols overthis region (Grand et al. 2015). In that regard, modelingstudies are often used to extrapolate limited observational data both in space and time (Mahowald et al.
2009). Models are also used to project past and futurechanges in atmospheric LFe deposition fluxes resulting
from large-scale shifts in weather conditions such as
those associated with El Niño Southern Oscillation(ENSO) or glacial/interglacial cycles (Mahowald et al.
2018). Moreover, model sensitivity studies (i.e., magnitude of the model response to each parameter investigated) can shed some light on possible key processes
that warrant further investigation (Li et al. 2008).
State-of-the-art global models are continuously adapted
and implemented using new knowledge and observations
from field and laboratory studies (Myriokefalitakis et al.
2018; Ito et al. 2019). Mineral dust emissions are typicallyscaled to match filed observations of aerosol optical properties, mass concentrations, and deposition fluxes (Ginoux
et al. 2001; Huneeus et al. 2011; Adebiyi et al. 2020). However, dust events in the SH are sporadic and thus dust

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

emission fluxes are not well constrained by the available observations. A recent inter-comparison study of aerosol Fe
chemistry models under the joint Group of Experts on the
Scientific Aspects of Marine Environmental Protection(GESAMP) revealed large disparities in the magnitudes ofaerosol Fe emissions and the solubilities predicted acrossfour models (Myriokefalitakis et al. 2018; Ito et al. 2019).
Such discrepancies stem from intrinsic differences in theparametrization of aerosol Fe emissions, source-dependent
initial solubilities, and the level of complexity chosen to
represent atmospheric processes of Fe chemistry and deposition. Indeed, large uncertainties remain in model representations of Fe emission to the atmosphere and its
subsequent chemistry and deposition to the ocean, particularly south of the Equator. The GESAMP study highlighted
the necessity to improve our understanding of the key
parameters controlling the atmospheric Fe cycle, which requires additional field observations and laboratory experiments to validate model predictions (Myriokefalitakis et al.
2018; Ito et al. 2019).
Here, a recent and extensive dataset gathering aerosol
Fe measurements across the 70°E–150°E and 10°S–70°S
sector of the SH (Perron et al. 2020b; Perron et al. pers.
com.) was used to evaluate and improve the processbased understanding of enhanced aerosol Fe solubility in
the Integrated Massively Parallel Atmospheric Chemical
Transport (IMPACT) model (Ito et al. 2020 and references therein). This study presents the optimization ofaerosol Fe concentration in the model to better reproduce field observations on regional scales. Subsequently,
optimal model estimates of aerosol LFe source apportionment are qualitatively compared to measurementbased estimates of enrichment factor (EF) (Perron et al.
2020b). Enrichment factors are defined as the mass ratios of total Fe, lead (Pb), and vanadium (V) to
Aluminum (Al) measured in aerosol samples divided by
the same ratios measured in the averaged upper continental crust (McLennan 2001).

Material and methods
Aerosol chemistry transport model

The aerosol chemistry transport model used in thisstudy was a coupled gas-phase (Ito et al. 2007) andaqueous-phase chemistry version (Liu et al. 2005; Linet al. 2014) of the IMPACT model (Ito et al. 2020 andreferences therein). Simulations were performed for theperiod extending from 2016 to 2018, using a horizontal
resolution of 2.0°×2.5° and 59 vertical layers. The model
used the Modern Era Retrospective analysis for Researchand Applications 2 (MERRA-2) reanalysis meteorological
data from the National Aeronautics and Space Administration (NASA) Global Modeling and Assimilation Office(GMAO) (Gelaro et al. 2017).

Page 3 of 17

The model simulated the emissions, chemistry, transport, and deposition of major aerosol species, including
black carbon (BC), particulate organic matter (POM),
mineral dust, sulfate, nitrate, ammonium, and sea sprayaerosols, and their precursor gases (Rotman et al. 2004;
Ito et al. 2015). Atmospheric processing of Fe-ladenaerosols during atmospheric transport were projected
for four distinct aerosol size bins (0.1–1.26 μm, 1.26–2.5
μm, 2.5–5 μm, and 5–20 μm of diameter) (Ito and Feng
2010; Ito 2015). Dust emissions were dynamically simulated using a physically-based emission scheme (Koket al. 2014; Ito and Kok 2017) with the soil mineralogicalmap (Journet et al. 2014). The global scaling factor of
dust emission was determined to achieve best agreementwith aerosol optical depth (AOD) measurements near
the dust source regions from AErosol RObotic NETwork(AERONET) (Kok et al. 2014; Ito and Kok 2017). This
procedure resulted in global (Australian) dust emissionsof 4626 Tg a–1 (233 Tg a–1) in 2016 and 4751 Tg a–1(408 Tg a–1) in 2017, respectively. On the other hand,combustion emission fluxes were prescribed by themonthly estimates for each source sector (energy, industry, iron and steel smelting process, residential combustion, waste burning, and shipping) (Hoesly et al. 2018;
Ito et al. 2018). The daily estimates for BB were basedon a terrestrial biogeochemistry model in conjunction
with satellite measurements, which showed good agreement with field observations of the fuel consumption foreach biome on a global scale (Sato et al. 2007; Ito 2011;
Ito et al. 2018).
The temporal variability of annual emissions of organic carbon (OC) from BB was compared to GFED4.1s
over the period of 2004–2018 (van der Werf et al. 2017)(Fig. 1). Our global estimate of OC emission in 2008 was
17.3 Tg a–1, which fell within the range of estimates inthe six data sets (13.8–51.9 Tg a–1) (Pan et al. 2020).
Overall, our estimates of the interannual variability of
global and Australian fire emissions were similar to
GFED4.1s, mainly because the GFED burned area data
set and compiled database of emission factor were used
for both the estimates (van der Werf et al. 2017; Itoet al. 2018).
To improve the accuracy of our simulations of bioaccessible Fe deposition fluxes to the SO, we made several
upgrades to the online emission schemes from the climatological emission database. The biogenic emissionsof aerosols and the precursor gases from the sea surface
were online calculated for the primary POM (Gantt
et al. 2012), dimethylsulfide (DMS) (Keller et al. 2014),
and isoprene (Hackenberg et al. 2017), which contributed to the formation of oxalate. Particle emission factors of oxalate were used to estimate the oxalate
emissions for BB aerosols (Ferek et al. 1998). In addition
to the primary sources, the oxalate was secondarily

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

Fig. 1 Comparison of inter-annual variation in (a) global and (b)Australian organic carbon (OC) emissions (Tg a–1) from biomassburning in this work (red lines with circle symbols) to GFED4.1s(dashed black lines with square symbols)

formed in cloud water but was quickly consumed in Fecontaining aerosols due to photolysis of the Fe-oxalate
complex in aqueous chemistry (Lin et al. 2014; Ito2015). The transport and deposition of BC in the model
was used to trace the fate of pyrogenic Fe particles ofsub-micron size in the atmosphere (Luo et al. 2008; Itoand Feng 2010). The particle size distributions of BC derived from bushfires was described in Liu et al. (2005).
Transformation of relatively insoluble Fe into LFe following proton-promoted, oxalate-promoted, and photoreductive dissolution processes was simulated according
to each aerosol Fe size bin for mineral dust and combustion aerosols (Ito 2015: Ito and Shi 2016).
Aerosol dataset

Measurements of aerosol Fe concentration and solubility
used in this study resulted from an extensive ship-board
atmospheric sampling effort undertaken between theyears 2016 and 2018 in five distinct marine regions
around Australia (North West Marine Region (NWMR),
North Marine Region (NMR), Coral Sea Marine Region(CSMR), Temperate East Marine Region (TEMR), andSouth West Marine Region (SWMR)), and two regions
around the Heard Islands and McDonald Islands(HIMI), which included an international study of themarine biogeochemical cycles of trace elements andtheir isotopes (GEOTRACES) section GIpr05, and along

Page 4 of 17

the 140°E meridional transect (SR3), which was a part of
GEOTRACES section GS01. Perron et al. (2020b) intheir supplementary material Table S1 reported key information related to aerosols collected over Australianmarine regions including geographical coordinates, aerosol sample collection dates, and locations (mid-point
latitude and longitude along the sampling track) as wellas laboratory measurements of TFe and LFe concentrations in aerosols.
Aerosol sampling and laboratory processing were described in details in Perron et al. (2020a). Sampling and laboratory work were undertaken following GEOTRACES
trace metal clean procedures (Cutter et al. 2017), including sample processing in a positive-pressured clean laboratory wearing clean garments and using either ultrahigh purity commercially-available (Baseline grade, Seastar
chemicals) or in-house distilled acids.
Briefly, aerosol collection was undertaken by placing
Whatman 41 (W41) cellulose filters inside pre-cleaned
Savillex filter holders housed in a high-efficiency particulate air (HEPA)-filtered portable laminar flow hood. Air
was pumped from 18 m above the sea level through successive polished stainless-steel and anti-static conductive
tubing to prevent particle loss inside the tube walls. The
sampling manifold pumps were automatically activatedfor a wind sector between 90° and 270° facing the ship
and a wind speed within a 5–80 knots range according
to the ship’s meteorological data, in order to avoid direct
sampling of the ship’s exhaust. A volume of air filteredexceeding 20 m3 over coastal waters and 50 m3 over the
remote ocean was used as minimum thresholds to define
the time of sample change over, ensuring sufficient material on the filter to overcome the filter blank. Upon
collection, samples were stored frozen in double sealed
bags until processing in the shore-based laboratory. Itshould be noted that the 20 μm nominal pore size of theW41 cellulose filter is not representative of the effective
filter retention capacity as the layering of fibers within
the filter paper retains most < 20 μm particles by interception, impaction or diffusion (Lindsley et al. 2016).
We therefore assume that field measurements (Perron
et al. 2020b) captured all the above-mentioned size bins,
which corresponded to the sum of four size bins included in the IMPACT model.
Aerosol samples were processed through a 3-stepleaching experiment using an instantaneous flowthrough leach of UHPW followed by a batch extraction
in AA (1.1 M, pH 4.7) and a final HF/HNO3 overnightdigestion at 120 °C. The TFe concentration in aerosols
was defined as the sum of Fe concentrations measured
in the three leaches and expressed in nanogram of Fe
per cubic meter of air filtered (ng m−3). The LFe concentrations were obtained from Fe measured in the sum of
the UHPW and the AA leaches. Based on the analysis ofIto et al. Progress in Earth and Planetary Science(2020) 7:42

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three replicate samples, relative standard deviations (accounting for uncertainty of the sampling method andthe leaching protocol) were estimated for TFe (11.7%)
and LFe (10.3%) concentrations (Perron et al. 2020a).

continent for the five regions. The regional scaling constant was perturbed to minimize the cost function, θ, inorder to optimize aerosol Fe source strength from Australian mineral dust at total N data points and observational j locations:

Sensitivity experiments

The IMPACT model representation of aerosol Fe was
evaluated using field measurements of TFe and LFe concentrations in the 70°E–150°E and 10°S–70°S sector of
the SH (Perron et al. 2020b; Perron et al. pers. com.). In
addition to the a priori simulation (Experiment 1), threeexperiments were performed with different assumptions
on the aerosol Fe emissions for Australian mineral dust(Experiment 2), and on the solubility of Fe in BB (Experiment 3) and in mineral dust (Experiment 4) aerosols(Table 1). Inverse modeling techniques have been widely
applied to the optimization of emission estimates(Enting et al. 1995). The synthesis process sought to estimate the strength, σμ, of total M source/sink processes
by comparing concentrations reported in field observation, c, to modeled responses, Tμ calculated by the forward model, based on the assumption thatc¼

M
X

T μ σ μ þ ðobservational noiseÞ

ð1Þ

μ¼1

Here, the forward model was first simulated to derive
atmospheric concentration of mineral dust from
Australia which was used as “tagged” tracer consideringzero emission of mineral dust from other regions for thefour aerosol size bins. The enrichment factor for Fe(EFFe) suggested that aerosol Fe mostly displayed acrustal origin as the median EFFe of 2.5 likely reflected
the well-described Fe enrichment in soil from the outback Australia (Perron et al. 2020b) (Fig. 2a). At thesame time, the a priori model outputs suggested minor
contribution of non-Australian mineral dust to the sumof TFe concentrations originated from all the dust source
regions near the Australian continent for the five regions(Fig. 2b). Thus, inverse model calculations were performed to adjust the regional scaling constant for mineral dust emission fluxes from Australia (μ = M), in
order to maximize the agreement between TFe from themodel and from field measurements near the Australian

θ¼

N
X

cj −

j¼1

M
−1
X
μ¼1

T jμ −M
X
μ¼M!2
T jμ σ=u2j

ð2Þwhere uj was the standard deviation of the observational
noise. In this study, the a posteriori estimate of aerosol
Fe emission from Australian dust sources was undertaken using a regional scaling factor of 0.0106 (Experiment 2, Experiment 3, and Experiment 4). When weapplied this scaling factor to Australian dust emissions,the agreement with the AERONET AOD on a global
scale was slightly improved for the correlation coefficients (root mean square errors) from 0.77 (0.11) to 0.78
(0.10). The scaling factor, however, ineluctably comprises uncertainty on soil mineralogy and Fe content
partly due to the limited number of observations available over most arid and semi-arid regions. Accordingly,
the reduction of Fe emissions from mineral sources in
the a posteriori model moderated the overall impact of
the uncertainty in the Fe content on the estimate of bulk
aerosol Fe solubility in the model.
The source apportionment of LFe is extremely sensitive
to Fe solubility in BB over the SH (Ito 2012). To quantify
the effect of spatially varying solubility relative to constant solubility, Experiment 3 adjusted the uniform Fesolubility (22%) for BB aerosols only to maximize agreement with LFe measurements near the Australian continent due to the optimization through the inverse method.
The sensitivity of the IMPACT model to the initial Fe
solubility in mineral dust was previously shown on a global scale (Ito and Shi 2016). The IMPACT model assumed lower initial Fe solubility at emission for mineral
dust (0.1%) than that (0.3%) derived from Australian
dust (Mackie et al. 2005). To show the sensitivity on
simulated Fe solubility from the atmospheric processingof mineral dust, the initial solubility of 0.3% for dust wasprescribed at emissions with no atmospheric processingof dust only (Experiment 4).

Table 1 Summary of sensitivity simulations performed for Fe emission for Australian mineral dust, Fe solubility for BB aerosols, andFe solubility for mineral dustExperiment

Fe emission in dust

Fe solubility in BB

Initial dust Fe solubility

Experiment 1

A priori model

Online calculation

0.1%

Experiment 2

A posteriori model

Online calculation

0.1%

Experiment 3

A posteriori model

A posteriori estimate (22%)

0.1%

Experiment 4

A posteriori model

Online calculation

0.3%

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

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Fig. 2 Variability in (a) enrichment factor in Fe (EFFe) and (b) a priori model contribution of Australian dust sources to global dust TFe (%) in eachsampled aerosol (or associated geographical grid cell)

The averages and standard deviations were calculated
from the daily a priori and a posteriori model estimatesalong the cruise tracks during sampling periods andcompared to field observations in each sub-region tohighlight variability in the model representation of different aeolian Fe sources. The tagged tracers were also
used to estimate the relative contribution from mineraldust, coal combustion, oil combustion, and biomassburning sources at each size bin. The modeled source
apportionment of TFe and LFe was compared to the enrichment factors calculated using the field measurements(Perron et al. 2020b; Perron et al. personal comms.). Indeed, Pb and V are often enriched in anthropogenic
aerosols from coal and oil combustion and thus can beused to relate changes in LFe for each tagged tracer,
respectively.

Results and discussion
Comparison of TFe and LFe between model andobservational data

Estimates of TFe and LFe concentrations from the twosimulations (Experiment 1 and Experiment 2) were displayed in Fig. 3 along with field observations for aerosol
samples collected across the 70°E–150°E and 10°S–70°S
sector of the SH (Table S1). The a priori IMPACT
model overestimated the atmospheric TFe concentrations
and underestimated Fe solubilities (Table 2, Fig. 4). Indeed, aerosol TFe (up to 9024 ng m−3) estimated by the apriori IMPACT model during dust events were a factorof 100 higher than field measurements of TFe (up to 103

ng m−3). Moreover, the a priori model predictions often
estimated Fe solubility below 2%, as illustrated by thesolid black line in Fig. 3, while field measurements reported values of Fe solubilities up to 100%.
Much smaller discrepancies existed in both the mean
and median values of TFe and LFe between the field observations and the a posteriori model estimates (Experiment 2, Experiment 3, and Experiment 4), compared to
between the field observations and the a priori model(Experiment 1) (Table 2, Fig. 4). The better agreementof the median value rather than the mean value between
Experiment 1 and the field observations suggested thatthe a priori model did not capture extremely high values
during dust outbreak (Hamilton et al. 2019; Ito et al.
2019). On the other hand, the a posteriori IMPACT
model output (Experiment 2) showed identical averaged
LFe to field observations (1.4 ± 1.5 vs. 1.4 ± 2.3 ng Fe m–
3). The optimization of Fe solubility in aerosols originated from BB sources (Experiment 3) resulted inslightly smaller LFe concentrations in aerosols (0.8 ± 0.9
ng Fe m–3) (Table 2). Similarly, higher prescribed Fesolubility in mineral dust aerosols with no atmosphericprocessing of dust only (Experiment 4) led to slightly
lower LFe concentrations (1.1 ± 1.2 ng Fe m–3) (Table 2).
These results suggested that our conclusion was robust
even if we applied the three-fold higher initial Fe solubility for mineral dust source (Mackie et al. 2005) with noatmospheric processing or if we adjusted the Fe solubility for BB. However, the scaling factor is extremely sensitive to sporadic dust outbreak and the intensity of dust

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

Page 7 of 17

chemistry models (including IMPACT), highlighting theneed for better constraining key parameters controlling atmospheric Fe cycling including the magnitude of atmospheric emissions and the processes leading to enhanced
aerosol Fe solubilities over the SO (Myriokefalitakis et al.
2018; Ito et al. 2019).
Spatial distribution of TFe concentration and Fe solubility
in aerosols

Fig. 3 Relationship between TFe and LFe concentrations (ng Fe m–3)
in aerosols for a observations, b a priori model estimates, and c aposteriori model estimates over the 70°E–150°E and 10°S–70°S sectorof the SH. The solid black line shows a linear trend with a constantFe solubility of 2%emissions is different for different model inputs and parameters. Moreover, the optimized model did not capture the wide range of Fe solubilities reported by fieldobservations over the remote ocean (black squares inFig. 3) where low aerosol Fe concentration prevailed.
Such tendency was reported in the GESAMP intercomparison study of four widely used atmospheric Fe

Averaged TFe concentrations and Fe solubilities during
the different sampling periods along the cruise tracks were
estimated before (a priori) and after (a posteriori) model
optimizations. The spatial distribution of TFe and Fe solubility from the a priori and a posteriori model estimates
was compared to field observation in Figs. 5 and 6, respectively. Comparison of the a priori and a posteriori estimates to observational data highlighted the magnitude of
the improvements inferred by the optimization of the
IMPACT model (Figs. 5 and 6).
The initial (a priori) estimates of TFe in the model
were significantly higher than the observations in the
NWMR (Fig. 5). This region is located downwind fromthe northwestern Australian dust path (Bowler et al.
1976; Baddock et al. 2015), which suggested an exaggeration of dust emission from arid and semi-arid lands inthe a priori model output. Such overestimates of Fe
emission from Australian mineral dust suggested thatthe process-based models needed to consider the “history” of ground surface conditions explicitly (Prospero
et al. 2002; Ishizuka et al. 2008; Jickells and Moore
2015). As the distances from mineral dust source regions
increased, model overestimates of TFe were reduced in
the NMR, CSMR, TEMR, and SWMR (Fig. 5). The
optimization of the IMPACT model using the inverse
modeling technique led to significantly better agreement
between TFe from the a posteriori model output andfrom field observations in the NWMR, NMR, CSMR,
TEMR, and SWMR. Conversely, the initial (a priori)model estimates of TFe were in good agreement with thefield observations in the HIMI and along the SR3. Minorchanges in TFe between the a priori and the a posteriori
model outputs reflected the small contribution ofAustralian mineral dust to TFe concentrations in theseremote oceanic regions. Over the SO, atmospheric TFe
could either be dominated by local emissions near
Antarctica (Gao et al. 2013) or influenced by well-mixed
aerosol sources during the long-range transport of the
air masses.
The a priori model estimates of aerosol Fe solubility
were up to 10 times lower than field measurements in
the NWMR where TFe from mineral dust sources was
significantly overestimated (Fig. 6). The a priori model
output also significantly underestimated aerosol Fe solubility over the other northern (NMR and CSMR) andIto et al. Progress in Earth and Planetary Science(2020) 7:42

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Table 2 Averages and standard deviations of TFe and LFe concentrations (ng Fe m–3) and Fe solubility (%) over the 70°E–150°E and
10°S–70°S oceanic sector of the SH. The parenthesis represents the median and maximum
TFe (ng Fe m–3)

LFe (ng Fe m–3)

Fe solubility (%)

Measurement

16 ± 22 (6.4, 103)

1.4 ± 2.3 (0.7, 13)

16 ± 20 (9.0, 100)Experiment 1

908 ± 1788 (86, 9024)

5.7 ± 13 (1.6, 80)

1.8 ± 1.7 (1.4, 9.4)

Experiment 2

23 ± 32 (8.1, 143)

1.4 ± 1.5 (1.0, 4.9)

7.8 ± 8.4 (4.2, 38)

Experiment 3

23 ± 32 (8.1, 143)

0.8 ± 0.9 (0.6, 3.4)

4.9 ± 5.2 (3.2, 30)

Experiment 4

23 ± 32 (8.1, 143)

1.1 ± 1.2 (0.6, 4.2)

4.7 ± 3.8 (6.9, 57)

Data setsouthern (TEMR and SWMR) coastlines (1.9 ± 2.0% vs.
13 ± 10%). Enhanced aerosol Fe solubility in the eastern
marine regions of Australia has been attributed to large
mass emissions of anthropogenic particles (Perron et al.
2020b; Strzelec et al. 2020). Indeed, a coating of pyrogenic Fe-containing aerosols with acidic species (e.g.,sulphate) has been shown to release LFe in the acidic
deliquescent layer of aerosols and enhance Fe solubility
in aerosols during the atmospheric transport (Li et al.
2017; Ito et al. 2019). The a posteriori model output (Experiment 2) showed that the reduction in Fe emissions
from Australian mineral dust in the model led to a significant increase in aerosol Fe solubility (compared tothe a priori model output), similar to that obtained fromthe observational data near the Australian continent.
The a posteriori model output further indicated high Fe
solubility in the NMR, which resulted in better agreement with the observational data. In the NMR region,
devastating fires often occur during the austral dry season (Paton-Walsh et al. 2014; Winton et al. 2016; Mallet
et al. 2017).
Atmospheric processing including acidic reactions andphotoreduction likely influence the form of Fe minerals
and oxidation state in mineral dust aerosols and contribute to increases in aerosol Fe solubility over the North
Atlantic (Longo et al. 2016). In the SH, emissions ofacidic gases to the atmosphere are much smaller than in
the NH. We found that the a posteriori model could reproduce the high Fe solubility even when the atmospheric processing was not considered for mineral dust
only (Experiment 4) (Table 2). This reflects the slower
Fe dissolution rates for mineral dust during the shorter
lifetime of aerosols, compared to combustion aerosols.
A previous study used observations of stable Fe isotopes to constrain a model representation of atmospheric LFe over the North Atlantic downwind from theNorth African dust source region (Conway et al. 2019).
Their results suggested that improvement of modeled
LFe deposition required both a reduction of the LFe content in mineral dust and an increase in pyrogenic
sources of Fe from anthropogenic activities. In ourstudy, the single reduction of Fe emission for Australianmineral dust sources in the model led to enhanced aerosol Fe solubilities, which were in better agreement withthe field observations. Since the magnitude of anthropogenic emissions of acidic gases and Fe-containing aerosols is substantially smaller across the SH compared to
the NH, our finding is likely to be relevant for the modeling over the SO.
Aerosol Fe source apportionment in the eastern SH

The IMPACT model was used to infer dominant sourcesof TFe and LFe in distinct Australian coastal regions aswell as over the SO (Table S2). Atmospheric tracers
tagged with each source sector in the IMPACT model
included dust, oil combustion, coal combustion, and BB.
Results were shown in Table 3 for TFe and Table 4 for
LFe, respectively.
The a posteriori IMPACT model (Experiment 2) attributed between 57% and 99% of the atmospheric TFe
across the study region to dust sources (Table 3). This
projection is consistent with conventional modelingstudies reporting that dust is the dominant contributor
to oceanic deposition of TFe (Mahowald et al. 2009). Regionally, up to 92%, 93%, and 99% of the atmospheric
TFe was attributed to Fe-laden mineral aerosol sourcesin the NWMR, the HIMI, and along the SR3, respectively. In the NWMR, aerosol Fe predominantly originated from arid and semi-arid regions via the NorthWest dust path (Bowler et al. 1976; Baddock et al. 2015).
A non-negligible influence from anthropogenic emissions was found in the TEMR where 23% of the atmospheric TFe was attributed to coal and 2.9% to oil
combustion sources. Natural and human-derived biomass burning was estimated to contribute 17%, 24%, and
30% of the aeolian TFe in the TEMR, CSMR, and NMR,
respectively. Over the SO, oil combustion was pointed
out as minor sources of atmospheric TFe both in the
HIMI (1.0%) and along the SR3 (0.9%). Finally, minor
contribution of BB over the SO (up to 5%) was derived
from long-range atmospheric transport of various airmass origins, which were emitted from vegetated lands
thousands of kilometers west (upwind) of the sampled
regions (Wagener et al. 2008).
While TFe computed by the model was largely dominated by dust, the relative contribution of each of thefour emission sources showed larger variabilities when
considering LFe (Table 4). Indeed, the a posteriori IMPA

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

Page 9 of 17

Fig. 5 Spatial distribution of TFe concentrations (ng Fe m–3) inaerosols for a observations, b a priori model estimates, and c aposteriori model estimates over the 70°E–150°E and 10°S–70°S sectorof the SH

Fig. 4 Concentrations of a TFe and b LFe concentrations (ng Fe m–3),
and c Fe solubility (%) for observations, a priori model estimates,
and a posteriori model estimates over the 70°E–150°E and 10°S–70°S
oceanic sector of the SH. The red square symbol represents themean. The solid line within the box mark shows the median. The
boundaries of the box mark the 25th and 75th percentiles. The
whiskers above and below the box indicate the 1.5 × interquartile
range, and the points indicate the outside of the range

CT model only attributed between 12% and 58% of LFe
to the mineral dust around Australia (Table 4). Rather,aeolian LFe over Australian coastal waters was largely associated with BB emissions (between 15% and 82%). Thisapportionment is reasonably consistent with field studies
emphasizing the importance of biomass burning emissions to the atmospheric burden near Australia (Winton
et al. 2016; Mallet et al. 2017; Perron et al. 2020b). In
the NMR, aerosols were collected simultaneously withthe occurrence of a large fire event in northwestern
Australia which likely influenced atmospheric LFe source
apportionment (82%) estimated by the model (Perron
et al. 2020b).

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

Page 10 of 17

Table 3 A posteriori model estimates of TFe source
apportionment for each region considered, including theAustralian marine regions (CSMR, NMR, TEMR, NWMR, andSWMR), as well as regions of the SO (HIMI and SR3).
Atmospheric sources considered include dust (Dust), biomassburning (BB), oil combustion (Oil), and coal combustion (Coal).
Results are in percent

Fig. 6 Spatial distribution of aerosol Fe solubility (%) for aobservations, b a priori model estimates, and c a posteriori model
estimates over the 70°E–150°E and 10°S–70°S sector of the SH

The values of EF were calculated for each sample todiscriminate Fe from anthropogenic pollution (EFPb andEFV for coal combustion and oil combustion, respectively) over natural dust (EFFe). The relative contributionof coal and oil combustion sources to LFe as predictedby the a posteriori IMPACT model was reasonably consistent with reported variability in the EF of Pb and V
across aerosols collected near the Australian coastline(Perron et al. 2020b) (Figs. 7 and 8) using values exceeding 10 as indicators of significant enrichment in coal andoil combustion aerosols, respectively. Measurement-based
estimates of EFPb and EFv suggested that the eastern
Australian coast was largely influenced by anthropogenic
emissions (EFPb and EFv > 10).

Region

Dust

BB

Oil

Coal

CSMR

70 ± 13%24 ± 11%5.2 ± 2.7%

1.5 ± 1.0%

NMR

67 ± 9%30 ± 9%2.0 ± 1.5%

0.23 ± 0.13%

TEMR

57 ± 29%

17 ± 18%2.9 ± 4.3%

23 ± 27%NWMR

92 ± 13%7.3 ± 3.0%

0.38 ± 0.19%

0.13 ± 0.07%

SWMR

90 ± 13%3.8 ± 5.1%

0.78 ± 0.43%

5.3 ± 7.4%

HIMI

93 ± 14%5 ± 14%1.0 ± 0.6%

0.9 ± 2.6%SR399 ± 1%0.29 ± 0.22%

0.90 ±0.72%

0.29 ± 0.69%

Oil combustion sources contributed 17%, 20% and
23% of LFe over the industrial coats of the CSMR,
TEMR, and SWMR, respectively. Over the SO, aerosol
LFe sources were predicted to originate from diverse
sources including dust, oil combustion, and BB withrelative contributions of 47/31/21% in the HIMI regionand 59/28/12% along the SR3 line. Anthropogenic signatures (EFv > 10) were identified in 39% of aerosol samples collected in the two oceanic regions (23 data
points). This result is consistent with previous field studies that have found the HNLC regions in the SO are associated with low mineral dust inputs (Boyd et al. 2007;
Jickells and Moore 2015).
A posteriori model estimates of TFe and LFe provided
much better agreement with observations following theinverse modeling technique. However, the a posteriori
model did not capture the wide range of Fe solubilities
obtained from field observations over the SO. Large
uncertainties remained regarding the atmospheric Fe
cycle, which included the role of aerosol size distribution, the relative contribution of dust, and combustion
sources of LFe to the atmospheric LFe burden. An
Table 4 A posteriori model estimates of LFe for each region
including the Australian marine regions (CSMR, NMR, TEMR,
NWMR, and SWMR), as well as regions of the SO (HIMI and SR3).
Atmospheric sources considered include dust (dust), biomassburning (BB), oil combustion (oil), and coal combustion (coal)
Region

Dust

BB

Oil

Coal

CSMR

17 ± 4%65 ± 5%17 ± 4%1.1 ± 0.3%

NMR

12 ± 1%

82 ±4%

5.1 ± 2.7%

0.58 ± 0.23%

TEMR

23 ± 16%52 ± 18%

20 ± 21%4.7 ± 4.1%

NWMR

26 ± 5%68 ± 6%5.5 ± 1.5%

0.76 ± 0.24%

SWMR

58 ± 9%15 ± 17%23 ± 13%3.7 ± 1.5%

HIMI

47 ± 13%21 ± 19%31 ± 14%1.8 ± 0.6%SR359 ± 13%12 ± 6%28 ± 14%1.7 ± 0.9%

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

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Fig. 7 Variability in a enrichment factor in lead (EFPb) and b a posteriori model contribution of coal combustion sources to LFe (%) in eachsampled aerosol (or associated geographical grid cell)

additional possibility is that some Fe processing mechanisms may be missing from the model. For example,
recent analysis of aerosol measurements with the IMPA
CT model in a coastal city of China revealed Fe solubility enhanced in fog compared to other weather conditions such as haze and dust (Shi et al. 2020). The lower

water content in the haze and dust particles could leadto the lower Fe dissolution rate in acidic solution andconsequently suppress the Fe solubility (Ito and Shi
2016). On the other hand, the high Fe solubility in fog
cannot be reproduced by the model, largely due to theignorance of the fog enhancement. Continuous monitoring

Fig. 8 Variability in a enrichment factor in vanadium (EFV) and b a posteriori model contribution of oil combustion sources to LFe (%) in eachsampled aerosol (or associated geographical grid cell)

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

observations of atmospheric TFe and LFe are necessary nearsource regions to better constrain the highly episodic nature of dust and bushfire emissions in the SH. At the sametime, laboratory experiments for Fe dissolution under fog
conditions are needed to simulate the fog enhancement.
Additional ship-board observations of multiple trace metals
in aerosols (e.g., V, Pb, and Fe) are necessary to better constrain aerosol TFe and LFe sources and transport, especiallyover remote regions such as the SO. Studies of stable Fe
isotopes may offer additional constraints on the contribution from combustion or mineral dust sources to aerosol
TFe and LFe, if sources have distinct isotopic signatures(Mead et al. 2013; Kurisu et al. 2016; Conway et al. 2019).
Finally, models must be continuously evaluated andadapted according to advances in field observations and laboratory measurements.
Overall, the a posteriori model outputs supported thequalitative findings based on the EFs, which highlighted
distinct, region-dependent, dominant aerosol Fe sources
across Australia (Winton et al. 2016; Perron et al. 2020b;
Strzelec et al. 2020). However, the 0 model significantly
underestimated LFe at 28 data points out of 69 (Fig. 9a).
Missing LFe sources can be responsible for such LFe
underestimate. Thus, the unidentified source contribution was calculated from the negative bias in the model
estimates less than one standard deviation of the averages from the measurements (Fig. 9). In the developed
countries, most Fe-containing aerosols are assumed tobe collected prior to injection to the atmosphere byemission control device (e.g., scrubbers, cyclones, andelectrostatic precipitator). Thus, the coal combustion
sources generally presented a low contribution to LFearound Australia, even in highly industrial regions suchas the TEMR and the CSMR (Fig. 7). It has been suggested that the high EFPb in the NMR (Fig. 7) might
be associated with mining activities in northern
Queensland (Perron et al. 2020b). Additional field observations near the various source regions are necessary to better constrain Fe emission from the sources
and the size distribution of Fe-containing aerosols.
Volcanic emissions near HIMI at the time of fieldsampling were not included in the model and might
accentuate the under-representation of LFe sources(Perron et al. pers.com.).
The spatial distribution of bushfire deposition flux
and the contribution of sub-micron particles over the
SO for 2017 were compared to mineral dust during the
austral spring (September–October–November) whenbushfires peaked in Australia (Fig. 10). The BB plumerevealed that Australian bushfires could be a major
source of LFe to the eastern Indian Ocean, rather thanthe mineral dust during the austral spring. Sub-micronaerosols were the dominant contributor for BB in contrast to mineral dust. This is reasonably consistent with

Page 12 of 17

Fig. 9 Variability in the a posteriori model contribution of a missing
source, b lithogenic source, and c pyrogenic source to LFeconcentration in aerosols (%)

field observations, which showed that higher Fe solubilities for finer aerosols influenced by BB and fossil fuelcombustion sources but no such trend for mineral dustdominated aerosols (Siefert et al. 1999; Buck et al. 2010;
Kumar et al. 2010; Trapp et al. 2010). Recent ocean biogeochemistry models included combustion sources (e.g.,fuel combustion and biomass burning) of bioavailable Fein addition to mineral dust (Hamilton et al. 2020; Hajima
et al. 2020; Ito et al. 2020; Myriokefalitakis et al. 2020).
However, the magnitude of their response to the atmospheric input of LFe was substantially different. Further
work will be needed to constrain the key parameters
responsible for the response of ocean biogeochemistry toatmospheric deposition flux.

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

Page 13 of 17

Fig. 10 Spatial distribution of LFe deposition fluxes from a bushfire and b mineral dust (mg Fe m–2 period–1) and the contribution of sub-micronparticles from c bushfire and d mineral dust (%) in the SH for 2017 during the austral spring (September–October–November) when bushfires
peak in Australia

Conclusions and implications
The state-of-the-art GESAMP models showed the underestimates in high Fe solubilities reported in observational
data over the SO, resulting in LFe concentrations 15 times
smaller than field observations (Myriokefalitakis et al.
2018; Ito et al. 2019). If deposition fluxes calculated bythese models were to be used to estimate dissolved Fe in
marine biogeochemistry models, this could contribute to
the underestimation of marine primary production in the
SO. Such underestimates might be compensated partly by
the upscaling of mineral dust emissions in the model tomatch the available measurements of atmospheric deposition fluxes (Albani et al. 2014; Ito et al. 2020). Suchupscaling, however, could result in high biases in the predicted atmospheric concentrations over the SH (Huneeus
et al. 2011; Albani et al. 2014). Moreover, such proceduremight conceal other sources that are relatively minor in
the present day but potentially important in the past andfuture.
In this study, the inverse model technique was applied
to the IMPACT model to obtain the best agreement in
TFe concentrations between the model output andrecent field measurements in aerosols collected overAustralian coastal regions (110°E–160°E and 10°S–41°S).
The a posteriori model suggested that a large fraction(between 57% and 99%) of TFe was delivered by dust

sources, while the mineral dust aerosols only contributed
between 12% and 58% of the atmospheric LFe. Instead ofmineral dust, bushfires were major sources (between
15% and 82%) of LF over Australian coastal waters. Such
BB source may become important for the ocean biogeochemistry in the SO if policy and climate follow theintermediate Representative Concentration Pathway 4.5
trajectory (Hamilton et al. 2020). This is already evident
through the devastating bushfires which occurred in the
2019–2020 austral summer. However, the source apportionment of LFe in atmospheric chemistry models is
extremely sensitive to Fe content and solubility in BB
aerosols over the SH (Ito 2012; Hamilton et al. 2019).
The IMPACT model assumed Fe dissolution rate constants for BB aerosols to be the same as those reported
for coal fly ash, although Fe dissolution rates depended
on the pH, ambient temperature, the degree of solution
saturation, and on the competition for oxalate between
Fe on the surface of particles and dissolved Fe from theaerosols in the model (Fu et al. 2012; Chen and Grassian
2013; Ito 2015). On the other hand, the IMPACT model
considered different kinetic dissolution behaviors ofmineral dust according to different types of Fe (Ito andShi 2016). Additional laboratory experiments in conjunction with field observations are required to better constrain LFe in combustion aerosols.

Ito et al. Progress in Earth and Planetary Science(2020) 7:42

Supplementary information

Page 14 of 17

Received: 20 January 2020 Accepted: 28 July 2020

Supplementary information accompanies this paper at https://doi.org/10.
1186/s40645-020-00357-9.
Additional file 1: Table S1. Latitude (Lat), longitude (Lon), date, month,year, total Fe concentration (TFe, ng m-3), and labile Fe concentration (LFe,ng m-3) for observation (Obs) and a posteriori model estimate (Model).
Table S2. Latitude (Lat), longitude (Lon), enrichment factor in lead (EFPb),modeled contribution of coal combustion sources (Coal) to LFeconcentration (%), enrichment factor in vanadium (EFV), and modeled
contribution of oil combustion sources (Oil) to LFe concentration (%), andmodeled contribution of lithogenic (Lith) and pyrogenic (Pyro) sources to
LFe concentration in aerosols (%). The missing source contribution was
estimated from the negative bias in the model estimates less than one
standard deviation of the averages from the measurements.
Abbreviations
AA: Ammonium acetate; AERONET: AErosol RObotic NETwork; AOD: Aerosol
optical depth; BB: Biomass burning; BC: Black carbon; CSMR: Coral Sea Marine
Region; DMS: Dimethylsulfide; EF: Enrichment factor; GESAMP: Group ofExperts on the Scientific Aspects of Marine Environmental Protection;
GMAO: Global Modeling and Assimilation Office; HIMI: Heard Islands andMcDonald Islands; HNLC: High-nutrient low-chlorophyll; IMPACT: Integrated
Massively Parallel Atmospheric Chemical Transport; LFe: Labile Fe; MERRA2: Modern Era Retrospective analysis for Research and Applications 2;
MODIS: MODerate resolution Imaging Spectroradiometer; NASA: National
Aeronautics and Space Administration; NH: Northern Hemisphere;
NMR: North Marine Region; NWMR: North West Marine Region; OC: Organiccarbon; POM: Particulate organic matter; SH: Southern Hemisphere;
SMAP: Soil moisture active passive; SO: Southern Ocean; SWMR: South West
Marine Region; TEMR: Temperate East Marine Region; TFe: Total Fe;
UHPW: Ultra-high purity water
Acknowledgements
Numerical simulations were performed using the Hewlett–Packard Enterprise(HPE) Apollo at the Japan Agency for Marine-Earth Science and Technology(JAMSTEC). The Modern Era Retrospective analysis for Research andApplications 2 (MERRA-2) data have been provided by the Global Modelingand Assimilation Office (GMAO) at NASA Goddard Space Flight Center.
Authors’ contributions
AI and MMGP initiated the modeling collaboration with recent observational
data set. AI conceived the optimization and carried out the modeling study.
MMG, BCP, MS, MG-R, PWB, and ARB contributed measured data. All authors
read and approved the final manuscript.
Funding
The support for this research was provided to AI by JSPS KAKENHI Grant
Number 20H04329 and 18H04143, and Integrated Research Program for
Advancing Climate Models (TOUGOU) Grant Number JPMXD0717935715
from the Ministry of Education, Culture, Sports, Science and Technology(MEXT), Japan. This work was supported by grants from the Australian
Research Council, Future Fellowship (FT130100037 to ARB), through the
Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC)
and from fieldwork support through the Australian Marine National Facility(MNF).
Availability of data and materials
The datasets supporting the conclusions of this article are included within
the article and its additional files.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Yokohama Institute for Earth Sciences, JAMSTEC, Yokohama, Kanagawa
236-0001, Japan. 2Institute for Marine and Antarctic Studies, University of
Tasmania, Battery Point, Tasmania, Australia. 3Antarctic Climate andEcosystems CRC, University of Tasmania, Battery Point, Tasmania, Australia.

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