ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-11047-2015An empirically derived inorganic sea spray source function incorporating sea surface temperatureSalterM. E.matthew.salter@aces.su.seZiegerP.https://orcid.org/0000-0001-7000-6879Acosta NavarroJ. C.GrytheH.KirkevågA.https://orcid.org/0000-0002-3691-554XRosatiB.https://orcid.org/0000-0003-4930-3638RiipinenI.NilssonE. D.Stockholm University, Department of Environmental Science and Analytical Chemistry, 11418 Stockholm, SwedenNorwegian Institute for Air Research, P.O. Box 100, 2027 Kjeller, NorwayFinnish Meteorological Institute, Air Quality Research, Erik Palmenin aukio 1, P.O. Box 503, 00101 Helsinki, FinlandNorwegian Meteorological Institute, P.O. Box 43, Blindern, 0313 Oslo, NorwayPaul Scherrer Institute, Laboratory of Atmospheric Chemistry, 5232 Villigen, SwitzerlandM. E. Salter (matthew.salter@aces.su.se)6October20151519110471106620April201513May20154September201515September2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/11047/2015/acp-15-11047-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/11047/2015/acp-15-11047-2015.pdf
We have developed an inorganic sea spray source function that is based upon
state-of-the-art measurements of sea spray aerosol production using
a temperature-controlled plunging jet sea spray aerosol chamber. The
size-resolved particle production was measured between 0.01 and
10 µm dry diameter. Particle production decreased non-linearly with
increasing seawater temperature (between -1 and 30 ∘C) similar to
previous findings. In addition, we observed that the particle effective
radius, as well as the particle surface, particle volume and particle mass, increased with
increasing seawater temperature due to increased production of particles with
dry diameters greater than 1 µm. By combining these measurements
with the volume of air entrained by the plunging jet we have determined the
size-resolved particle flux as a function of air entrainment. Through the use
of existing parameterisations of air entrainment as a function of wind speed,
we were subsequently able to scale our laboratory measurements of particle
production to wind speed. By scaling in this way we avoid some of the
difficulties associated with defining the “white area” of the laboratory
whitecap – a contentious issue when relating laboratory measurements of
particle production to oceanic whitecaps using the more frequently applied
whitecap method.
The here-derived inorganic sea spray source function was implemented in
a Lagrangian particle dispersion model (FLEXPART – FLEXible PARTicle dispersion model). An estimated annual global
flux of inorganic sea spray aerosol of 5.9 ± 0.2 Pg yr-1 was
derived that is close to the median of estimates from the same model using
a wide range of existing sea spray source functions. When using the source
function derived here, the model also showed good skill in predicting
measurements of Na+ concentration at a number of field sites further
underlining the validity of our source function.
In a final step, the sensitivity of a large-scale model
(NorESM – the Norwegian Earth System Model) to our new
source function was tested. Compared to the previously implemented
parameterisation, a clear decrease of sea spray aerosol number flux and
increase in aerosol residence time was observed, especially over the Southern
Ocean. At the same time an increase in aerosol optical depth due to an
increase in the number of particles with optically relevant sizes was found.
That there were noticeable regional differences may have important
implications for aerosol optical properties and number concentrations,
subsequently also affecting the indirect radiative forcing by non-sea spray
anthropogenic aerosols.
Introduction
Primary marine aerosol or sea spray aerosol (SSA) particles are those
particles produced directly at the ocean surface following wave breaking, air
entrainment as bubbles, and the subsequent bubble bursting process at the
ocean surface . When considered in terms of mass, sea spray
aerosol particles constitute the largest flux of particulate matter to the
atmosphere after wind-blown dust, with a global production of 3 to
30 Pgyr-1.
Sea spray aerosol is important for the climate system where it acts as both
a direct and indirect radiative forcing component . Both
of these forcing effects are highly dependent upon the total number and size
distribution parameters of the emitted sea spray aerosol particles; the
direct effect is dominated by airborne particulate surface area, while the
indirect effect is more closely related to the number of particles above
a given size. Thus, sea spray aerosol properties have been the subject of
significant scientific debate, centred on both the environmental factors that
might affect the production of sea spray aerosol and the best experimental
approach to estimate the source function of sea spray aerosol particles
emitted .
Although wind speed is the major driver of air entrainment into surface
waters, simply parameterising sea spray aerosol production in terms of wind
speed often fails to reconcile predicted and observed sea spray aerosol
concentrations e.g.. Secondary factors such as wave
state and sea surface temperature (SST) are known to affect a host of
processes from initial air entrainment to the final production of sea spray
aerosol droplets and these may in part explain these discrepancies. They may
also explain some of the disparity between different sea spray aerosol source
parameterisations .
A number of recent findings have highlighted the potential importance of sea
surface temperature on sea spray aerosol production. have
shown that the interfacial bubble flux and bubble size spectra are strongly
dependent on water temperature and that these are strongly correlated to
total particle number flux in a laboratory setting. noted
a strong influence of sea surface temperature on sea spray aerosol production
when they compared existing sea spray aerosol source functions with a global
database of sea spray aerosol mass concentration
measurements. noted large differences between
a commonly used whitecap fraction parameterisation
derived almost entirely in low-latitude regions and a satellite estimate
derived over the entire globe. The authors postulate that the weaker wind
speed dependence observed in their global data set may in part be due to the
influence of secondary factors that co-vary with the wind geographically,
such as sea surface temperature. Their data indicated that at a given wind
speed, the satellite-derived whitecap fraction decreases with increasing sea
surface temperature see Fig. 9 in.
Much of the discussion on the role of sea surface temperature in sea spray
aerosol production has focussed on the apparent contradiction between
observations made using laboratory systems that attempt to replicate oceanic
whitecaps and observations of sea salt concentrations made in the field or
inferred from aerosol optical depth (AOD) measurements. A series of
laboratory systems designed to replicate sea spray aerosol production by
whitecaps have shown that the number production flux increases markedly as
water temperatures are
decreased e.g.. In contrast,
observational data from the field, such as chemical analysis of particulate
matter smaller than 10 µmin diameter (PM10) or total suspended mass, have often been used to infer
that sea spray aerosol production increases with higher sea surface
temperatures due to higher observed concentrations at lower
latitudes e.g..
Similarly, noted a bias between predictions of sea spray
aerosol induced aerosol optical depth and measurements of aerosol optical
depth when using a sea spray source function not dependent on sea surface
temperature. They noted that the aerosol optical depth determined near the
tropics using a sea spray aerosol source function without sea surface
temperature dependence was a factor of 2 lower than observations of aerosol
optical depth, suggesting that sea spray aerosol production was
underestimated at lower latitudes, where sea surface temperatures are higher
and wind speed is generally lower.
One explanation for the aforementioned contradiction could be the distinct
properties of the sea spray aerosol that the different approaches measure. In
the laboratory studies, emphasis has been placed on obtaining estimates of
the number production flux of particles. The majority of these studies have
focussed on particles smaller than 1 µm dry diameter, both through
system design and instrumental restrictions, but also because this size range
dominates sea spray aerosol number production. However, particles with dry
diameter larger than 1 µm provide the dominant contribution to
the fluxes of surface area and volume; thus, these particles are the most
important for applications involving light scattering and particle mass.
Consequently, studies that infer a temperature dependence of sea spray
aerosol production fluxes based upon sea salt concentrations (determined from
PM10 data) and aerosol optical depth measurements in the field are
likely to be highly influenced by the latter properties. The incongruity
between laboratory studies and aerosol optical depth/sea salt mass studies
may simply result from changes to the size distribution of sea spray aerosol
coincident with changes to the total number production flux as seawater
temperature changes.
To test this hypothesis, we have determined the particle number flux in the
size range 0.01 to 10 µm dry diameter (Dp) in
a temperature-controlled laboratory sea spray chamber. This set-up previously
highlighted a significant dependence of particle number concentration
(Dp≥0.01µm) on water temperature, with
significant increases at lower water temperatures .
However, during these experiments this system was not optimised to measure
larger particles and suffered from significant particles losses for particles
Dp≳ 3 µm. Therefore, in order to obtain
better comparisons with measurements of PM10, we have improved both the
sampling protocol and the instrumentation used to measure particles with
Dp larger than 1 µm (Sect. ).
Using this new data we have derived a sea spray aerosol source function
(Sect. ) and compared it to field
measurements using a Lagrangian particle dispersion
model FLEXPART;see Sect. . Finally, we
have deployed the new parameterisation in an Earth system
model NorESM;see Sect. to facilitate
comparison with its previous
temperature-dependent parameterisation.
MethodsThe sea spray chamber
In order to observe the effects of sea surface temperature on the source flux
of aerosol produced, we have utilised a temperature-controlled sea spray
generation chamber. This system has been described in detail
by . However, a number of modifications were made to the
system to improve estimates of the aerosol particle production flux,
especially for particles with Dp>1µm.
The sea spray chamber is fabricated from stainless steel components and
incorporates temperature control (±0.1 ∘C) so that the water
temperature can be held constant between -1 and 30 ∘C. Air was
entrained using a plunging jet that exited a stainless steel nozzle with an
inner diameter of 4.3 mm held in a vertical position 30 cm above
the air–water interface. Water was circulated from the centre of the bottom
of the tank back through this nozzle using a peristaltic pump
(Watson–Marlow, 620S) and silicone tubing. All surfaces below the water
level on the inside of the tank were coated in Teflon, and prior to all
experiments all internal surfaces were rinsed thoroughly with reagent grade
ethanol and low organic carbon (American Society for Testing and Materials
Type 1) standard deionised water (>18.2MΩ), hereafter
referred to as DIW.
Both seawater salinity and temperature were measured continuously using an
Aanderaa 4120 conductivity sensor. Seawater dissolved oxygen concentration
was measured with an Aanderaa oxygen optode 4175. This sensor also provided
an independent temperature measurement. Both sensors were placed towards the
centre of the tank approximately halfway between the tank base and the
air–water interface. Relative humidity and temperature were measured in the
headspace of the sea spray simulator using a Vaisala model HMT333 probe.
Dry zero-sweep air entered the tank at 6 Lmin-1 after passing
through an ultrafilter (Type H cartridge, MSA) and an activated carbon filter
(Ultrafilter, AG-AK). The airflow rate was maintained and quantified using
a mass flow controller (Brooks, 5851S). Aerosol particle-laden air was
sampled through a number of ports in the lid of the sea spray simulator and
transferred under laminar flow to all aerosol instrumentation. To prevent
contamination by room air, the sea spray simulator was operated under slight
positive pressure by maintaining the sweep air flow several
Lmin-1 greater than the sampling rate. Excess air was vented
through a 1-way flutter valve on the lid of the system.
Figure is a schematic of the set-up used.
Schematic of the plunging jet tank used for the experiments.
Particle size distribution measurementsDifferential mobility particle sizer and condensation particle counter
Aerosol particle-laden air was directed through 2 m of
1/4′′ stainless steel tubing and a custom made silica
diffusion dryer at which point the flow was split. Immediately following this
split, a TSI model 3010 condensation particle counter (CPC) was used to
enumerate the total number concentration at 1 Hz for particles with
Dp>0.01µm. The aerosol particle-laden air that
entered the second sampling line was first directed to a custom made impactor
(0.0707 cm nozzle, with a cut-off diameter of ∼1µm
at 1 Lmin-1), it was then passed through a bipolar charger
(neutraliser, Ni-63.) before it entered a closed-loop sheath air,
custom-built differential mobility particle sizer (DMPS) that selected
negatively charged particles using a positive high voltage in the
differential mobility analyser (DMA). The selected particles were enumerated
with a TSI 3772 CPC (1 Lmin-1 flow rate). The DMPS was used to
determine the size distribution for the size range
0.01 µm<Dp< 0.7 µm
(electrical mobility diameter) and a single scan over 37 size bins was
completed in 12 min.
A particle's mobility equivalent diameter, Dmob, is defined as
the diameter of a sphere with the same electrical mobility as the particle.
Dmob is only equal to the volume equivalent diameter,
Dve, for spherical particles. Since NaCl and the other salts
present in the artificial seawater used during our study form cubic and not
spherical particles when aerosolised and dried, we have shape corrected the
mobility diameters obtained using our DMPS. The relation between
Dmob and Dve of a particle is
f=DveDmob=1χCcDveCcDmob,
where f is the correction factor applied to each diameter measured, χ
is the dynamic shape factor of the particle, and Cc is the
Cunningham slip correction factor . For spherical particles,
χ has by definition the value 1, while for NaCl χ is equal to that
of a cube . For mobility diameters much greater than the
mean free path of air, ∼0.06µm, known as the continuum
regime, χ for a cube is 1.08 , while for particles
smaller than this in the kinetic regime χ for a cube is
6/π1/3=1.23. However,
since in the kinetic regime Cc also depends on Dmob
and the ratio of Dve to Dmob is related to the square
root of Cc, which is ∼1.1, the use of 1.08 for all sizes
will result in an inaccuracy of only a few percent. Therefore, we apply a
χ of 1.08 across all sizes. We also assume that this value holds for the
artificial sea salt used during our experiments and have used it to correct
the size distributions obtained with our DMPS system to volume equivalent
diameters.
White-light optical particle size
spectrometer
Aerosol particle-laden air was vertically sampled and drawn directly upwards,
without bends or contractions in the sample line, through 0.75 m of
1/2′′ stainless steel tubing and a custom made silica
diffusion dryer to a Palas WELAS 2300 white-light aerosol spectrometer
(WELAS; Palas GmbH) that was mounted directly above the sea spray chamber.
This is an optical particle size spectrometer (OPSS) with a white-light
source (Osram XBO-75 Xenon short arc lamp in the wavelength range of
λ≈350–750 nm) that illuminates a measuring volume of ∼7cm-3. Optical lenses collect the scattered light between 78 and
102∘ with respect to the incident beam and direct it to
a photomultiplier tube (PMT). The sensor is connected to the light source and
detector via optical fibers, which minimises heat input from the lamp and
temperature increase in the sensor. This instrument was used to obtain the
aerosol size distribution for the size range 0.2µm<Dp<10µm (polystyrene latex sphere optical
equivalent diameter) at 1 Hz, sizing particles in 59 bins.
Given that the OPSS instrument employs a white-light source, it should be less
influenced by so-called “Mie wiggles” than OPSS instruments that use
monochromatic light sources. Thus, the OPSS should be less affected by sizing
ambiguities than a single wavelength OPSS.
The OPSS reports equivalent optical diameters that were calculated by the
instrument's firmware using a preset empirical calibration curve based on
polystyrene latex (PSL) sphere measurements. In order to account for
systematic instrumental drifts caused by changes in the incident light
intensity, changes of the PMT efficiency, or degradations of the optical
fibers, we made periodic measurements of 0.85 µm monodisperse
CalDust (calibration dust provided by the
manufacturer). Using these measurements the instruments firmware applied
a correction factor to maintain a constant relation between scattered light
intensity and optical diameter.
The probability that the OPSS will detect a particle is a function of the
particle's size or cross section resulting in a size dependent counting
efficiency. For particles close to the small end of the OPSS sizing range
there is a decreased probability of detection or counting
efficiency. have determined the counting efficiency of the
OPSS used in this study and their results were similar to those
of . 100 % counting
efficiency is attained for all
particles larger than 0.3 µm, and the counting efficiency
increases to a maximum of ∼130 %. The raw counts obtained by the
OPSS were multiplied by the reciprocal of the counting efficiency curve
generated by to correct for the counting efficiency of the
instrument.
As with all OPSS instruments, the OPSS measurements depend on the
wavelength-dependent complex refractive index of the sampled aerosol. It is
this that determines the scattering response for particles of a given size
and shape. Therefore, measurement of non-PSL aerosols such as sea salt
aerosol particles with an OPSS factory calibrated with PSLs will manifest in
a diameter shift of the size distribution due to differences in the
refractive index of the materials. Since this diameter shift is likely to
have a large influence on the aerosol particle surface and volume size
distributions, we have converted the measured optical diameters to volume
equivalent diameters by assuming that the sea salt aerosol particles had
a refractive index of m=1.54-0i, which corresponds to
the value of NaCl (compared to a refractive index of m=1.588-0i for the
PSLs the instrument was calibrated with). This correction was conducted using
the software provided by the manufacturer (PDAnalyze, Palas GmbH, Version
No 2.024), which is based on instrument-specific Mie calculations.
As with the DMPS measurements, there is also an effect of
particle shape on the OPSS measurements. Therefore, these
measurements were also corrected, through the use of
PALAS PDAnalyze software, assuming that the shape factor
of 1.08 for NaCl holds for the artificial sea salt used
during these experiments.
Temperature and humidity of the sampled aerosol
The temperature and relative humidity (RH) of the sample entering the DMPS,
as well as the sheath air of the DMPS, were monitored using a Campbell
Scientific HMP50 sensor. Although the relative humidity of the air entering
the OPSS instrument was not measured directly, it is assumed that it was
always well below 30 % such that the sea spray aerosol had effloresced.
This conclusion was made on the basis that all driers were of identical
design and because the flow through the OPSS drier was significantly lower
than the flow through the DMPS drier (OPSS: 0.5 Lmin-1; DMPS:
2 Lmin-1). Based upon the dimensions of the diffusion driers
used and the flow rates of the various instruments, the residence time of the
aerosol particle-laden air in the driers was ∼6s and ∼1.5s for the OPSS and DMPS instruments, respectively. The silica
gel in each drier was replaced when the relative humidity measured at the
inlet to the DMPS exceeded 25 %. Therefore, we report our aerosol in dry
diameters.
Experimental set-up
Each experiment was conducted with artificial seawater (ASW) consisting of
Sigma sea salt (Sigma Aldrich, S9883; mass fraction: 55 % Cl-,
31 % Na+, 8 % SO42-, 4 % Mg2+, 1 %
K+, 1 % Ca2+, <1 % other) rehydrated to an absolute
salinity of 35 gKg-1 using DIW. We subjected our artificial
seawater to a purification process in the same manner as previously described
by . This consisted of activated charcoal treatments,
artificial UV exposures and hydrogen peroxide (H2O2, 30 %
solution, no stabiliser) additions. Here H2O2 acted as an oxidising
agent to remove organic matter.
Manipulating the water temperature in the sea spray chamber could potentially
have changed gas saturation levels in the water. Since there has been
speculation in the literature that sub- or super-saturations of atmospheric
gases in seawater might affect particle production through changes to the
bubble population e.g., we conducted constant
temperature experiments to ensure that gas saturations were in thermodynamic
equilibrium with the headspace of the sea spray chamber. Once the artificial
seawater purification procedure was complete, the water temperature was held
constant at a series of values between -1 and 30 ∘C whilst
measurements of the aerosol generated were conducted. The water temperatures
investigated were -1, 3, 5, 8, 10, 20, and 30 ∘C. At each water
temperature aerosol measurements were conducted over a period ≥2h following a period of at least 12 h at the desired
temperature. Measurements of oxygen concentration in the seawater confirmed
that gas saturations were in thermodynamic equilibrium and the oxygen percent
saturations were not significantly different between the experiments. The
mean oxygen saturation across all experiments was 111 % with a standard
deviation of 1 % (the reported accuracy of the Aanderaa oxygen optode
4175 is to within <5 % saturation), within the range of anomalies
typically encountered in ocean surface waters .
The second phase of the experiment consisted of measurements of the sea spray
aerosol particles generated whilst the temperature of the water was slowly
ramped downward from 30 to 2 ∘C over a period of 29 h. This
second phase was conducted 24 h after the first phase of experiments
were completed. In the interim period the chamber was kept closed with
a constant inflow of zero-particle air and the same water was used for both
experiments. At no point during the seawater cooling experiment was the water
undersaturated with respect to O2 (see Supplement), nor was it
significantly different than the mean of the constant temperature experiments
(mean oxygen saturation: 111 %).
Mean particle (a) number size distribution,
(b) surface size distribution, and (c) volume
size distribution measured at different water temperatures. The
solid lines represent the DMPS measurements (Dp<0.7µm electrical mobility diameter), while the dashed
lines show the OPSS data (Dp>0.35µm optical
equivalent diameter when m=1.54-0i).
In order to obtain estimates of the particle size distributions as a function
of water temperature during this experiment the data were binned at
a resolution of 1 ∘C. Here the data from the DMPS system and the
OPSS have been combined following corrections for particle shape and
refractive index, respectively. The two instruments both provide size-resolved particle number in the dry diameter range between ∼0.2 to
0.7 µm. Given that for particles close to the small end of the
OPSS sizing range there is a decreased probability of detection and that an
increasing number of particles close to upper size range of the DMPS system
will have been influenced by the ∼1µm impactor placed
prior to it, we have chosen to use the DMPS measurements in the range 0.01 to
0.45 µm and the OPSS measurements in the range 0.45 to
10 µm.
ResultsMeasured number size distributions during the constant temperature experiments
Over the 0.01 to 10 µm diameter size range covered by the DMPS
system and OPSS instrument, when represented in the form
dN/dlogDp, the size distributions obtained
during the constant water temperature experiments exhibit three modes
(Fig. ). A noteworthy observation is the
apparent lack of agreement between the DMPS measurements and the OPSS
measurements in the particle size range where they overlap. Most likely the
DMPS instrument was increasingly influenced by particle losses due to the
system tubing and the impactor placed before it in its upper sizing range. It
should be borne in mind that the particle size range over which the
instruments disagree is not dominating dN/dlogDp, dS/dlogDp, or
dV/dlogDp; therefore, it is unlikely to influence
the number fluxes, optical properties, or mass fluxes of the sea spray source
function derived later in this study.
Following correction for the effect of shape, the DMPS system data exhibited
a single mode centred close to 0.1 µm when plotted in the form
dN/dlogDp. The magnitude of this mode
decreased as the water temperature was increased between -1 and
30 ∘C. Following correction for the effect of both shape and
refractive index, the data obtained using the OPSS exhibited two modes when
plotted in the form dN/dlogDp. One was
centred around 0.55 µm and another was centred around
1.5 µm. The mode centred around 0.55 µm exhibited
similar behaviour to the mode centred around 0.1 µm in that its
magnitude decreased as the water temperature increased. However, the mode
centred around 1.5 µm exhibited different behaviour in that its
magnitude also increased as the water temperature was increased. This effect
is much more prominent when the size distribution is plotted in the form of
the particle surface size distribution (dS/dlogDp) or particle volume size distribution
(dV/dlogDp), which both assume that the
particles are spherical (Fig. ).
Also noteworthy is the observation that the data obtained at 30 ∘C
appears to show a sudden shift in the size distribution to larger sizes.
Although we cannot discount that this effect is real,
since we observe this effect only at
a water temperature of 30 ∘C suggests that this is more likely to
have been a measurement artefact. Given that at a water temperature of
30 ∘C in the chamber the air temperature was only slightly lower and
the sea spray chamber headspace had an RH of ∼98 %, the absolute
water content will have been high. This combined with the observed increase
in the number of larger particles (>1µm) at this temperature
relative to lower water temperatures may mean that despite the fact that the
RH at the inlet to the OPSS was below the efflorescence point of the
particles, assuming they were mainly NaCl, the particles may not have had
adequate time to fully effloresce and thus could have still been partially
liquid. The rate at which the particles were crystallising may also have
changed, a factor which is known to effect the ultimate shape NaCl particles
take when dried .
Integrated (a) number, (b) surface, and
(c) volume as a function of water temperature for the
constant water temperature experiments (crosses) and during the
temperature ramp experiment (circles). One standard deviation (1σ) is shown
for the integrated number concentration during the constant
temperature experiments. Panel (d) plots the effective
radius as a function of water temperature for all experiments.
Measured number size distributions during the temperature ramp experiments
Measured dN/dlogDp was very similar to the
constant temperature experiments, consisting of three modes centred at dry
diameters of ∼0.1, ∼0.55, and ∼1.5µm (see
Supplement). The two smallest modes decreased in magnitude with increased
water temperature whilst the mode at the largest dry diameter exhibited
opposite behaviour and increased in number as the water temperature was
increased. Once again this trend is much more apparent when the size
distribution is presented in the forms dS/dlogDp and dV/dlogDp. The sudden
shift towards larger particles observed in the constant temperature
experiments was also apparent during the temperature ramp experiments.
However, it appeared at a slightly lower temperature of ∼23∘C.
Comparison of the constant temperature experiments and
the temperature ramp experiments is facilitated in
Fig. . The integrated
total particle number concentration (integrated across
the size range 0.01 to 10 µm) in the
temperature ramp experiments was not significantly
different to the constant temperature
experiments. Figure d
plots the effective radius (reff) of both the
constant temperature experiments and the temperature ramp
experiment as a function of water temperature:
reff=3VA,
where V is the total integrated particle volume and A is the total
integrated particle surface area (assuming spherical particles). The
effective radius of both the constant temperature experiments and the
temperature ramp experiment were also very similar at comparable water
temperatures.
Given the observed similarity between the constant water temperature
experiments and the water temperature ramp experiments, as well as the higher
water temperature resolution of the latter experiments, we have chosen to use
the data from only the temperature ramp experiments to generate a new
inorganic sea spray aerosol parameterisation as a function of water
temperature in the following section.
Derivation of a model parameterisation of the sea spray aerosol production fluxAir entrainment as a function of wind speed
We have combined the number of particles in a unit logarithmic interval of
Dp produced per unit time (p(Dp,T)) as a function
of seawater temperature measured during our experiments with measurements of
the air entrained by the plunging jet as a function of temperature presented
in . This approach is based on the assumption that all air
entrained into the water column detrains as bubbles that produce particles.
This approach also assumes that there is no dependence of oceanic air
entrainment on SST and does not make allowance for other factors that may
affect air entrainment flux such as breaking wave strength or sea state. As
with nearly all laboratory-based studies of sea spray aerosol production,
another critical assumption of our approach is that the size distribution of
the aerosol produced is constant across all wind speeds.
Using this approach, the rate of particle production per unit volume of entrained air as
a function of water temperature during our experiments
(fτ(Dp,T)) is defined as
fτ(Dp,T)=p(Dp,T)τ(T),
where p(Dp,T) is the number of particles in a unit logarithmic
interval of Dp produced per unit time as a function of water
temperature (T), and τ(T) is the rate of air entrainment in
m3s-1 as a function of water temperature.
Figure depicts the rate of particle
production per unit volume of entrained air determined from the temperature
ramp data (see Sect. ) using this approach.
Mean aerosol number effective flux distribution of the
corrected temperature ramp data (coloured lines) and corresponding
log-normal fits constrained by fixed modal diameters and geometric
standard deviations (black lines).
In order to estimate the size-resolved oceanic interfacial sea spray aerosol
production flux, fint, we have combined the size-resolved
particle production rate per unit volume of entrained air from
Eq. () with an estimate of the entrainment
flux of air into the oceanic water column in the same manner as described
by :
fint(Dp,T)=fτ(Dp,T)Fent,
where Fent is the dependence of the air
entrainment flux into the oceanic water column on wind
speed measured at 10 m height (U10).
As discussed by , the air entrainment
flux into the water column (Fent) can be
estimated from
Fent=αϵd,
where ϵd is the rate of energy dissipation by wave
breaking in Wm-2 and α is the ratio of the volume of air
entrained by breaking waves to the energy dissipated by the
wind-wave field through wave breaking. As
presented by , initially we assumed a range of (4±2)×10-4m3J-1 for α and that
ϵd varies as a function of wind speed as (5±1)×10-5(U10)3.74Wm-2 giving
Fent=(2±1)×10-8⋅(U10)3.74,
where Fent is in m3m-2s-1. However, this
resulted in unrealistic over-production of sea spray aerosol at low latitudes
in the Southern Hemisphere when implemented in the Norwegian Earth system
model (NorESM) (see Sect. ). Numerous existing sea spray
aerosol parameterisations based upon the whitecap method utilise a wind speed
dependence of (U10)3.41 with recent studies advocating even lower
wind speed dependencies with a smaller exponent for
U10e.g.. Given this we have kept the scaling
to air entrainment the same as that used by but use a lower
wind speed dependency of (U10)3.41, which is the same value used
by . This results in a final dependency of air
entrainment on wind speed of
Fent=(2±1)×10-8⋅(U10)3.41,
where Fent is in m3m-2s-1.
Given that this change is arbitrary we would urge that the modelling
community first implement the parameterisation using the larger exponent of
(U10)3.74 since this has a more sound physical basis. If the model
does not compare well with observed sea spray concentrations or data from
remote sensing, re-tuning of uncertain parameters in the model (e.g.
prescribed scavenging coefficients for SSA), within the range of uncertainty
for those particular parameters, may improve the model results. If not, this
single exponent value (3.74) can then be changed as and when new research on
the dependence of air entrainment upon wind speed is available in the
literature.
Effective vs. interfacial sea spray aerosol fluxes
The aim of this study is to provide a parameterisation of sea spray aerosol
production to represent the production flux in atmospheric chemical transport
models or global circulation models. Usually such models have their lowest
atmospheric layer at 10 m and often much higher (e.g. 100 and
180 m in the FLEXible PARTicle (FLEXPART) dispersion model and NorESM, respectively). Therefore, knowledge of
the size distribution of particles that attain significant height in the
atmosphere, often referred to as the effective flux, is required. Since the
inlets to the aerosol instrumentation used during this study were sited ∼30cm above the water surface, we have determined the flux of
particles that reached this height, often referred to as the interfacial
flux. As such, consideration should be given to the difference between the
effective production flux and the interfacial production flux measured at
∼30cm.
Using an approach described by we have attempted to convert
the interfacial fluxes measured in the sea spray chamber utilised during this
study to effective fluxes at 10 m height. This approach is outlined
in detail in the Supplement accompanying this work. Since the ratio of
effective fluxes to interfacial fluxes depends on both particle size and wind
speed, computation of the effective sea spray aerosol particle flux should
take into account both variables. However, since it is non-trivial to add a
size-dependent correction to the model that can account for the difference
between effective and interfacial fluxes, we have converted the temperature-dependent interfacial fluxes measured during our study to temperature-dependent effective fluxes based upon a single wind speed (U10) of
7 ms-1, approximately the global average wind speed over the
ocean. An implication of this assumption is that effective fluxes will be
overestimated at wind speeds below 7 ms-1 and underestimated at
wind speeds above 7 ms-1.
Size distribution as a function of
temperature
Using the data presented in Sect. we have
generated a temperature-dependent sea spray source function. Since many Earth
system models utilise modal modules as input for aerosol emissions to limit
computation time, we present our source function in this manner.
(a) The here derived sea spray source function
(dF/dlogDp) for three different sea surface
temperatures compared to the parameterisations
of , , ,
and , as well as the source function previously
implemented in NorESM described
by (see legend in b). Panel (b) plots integrated
(0.029µm<Dp<0.580µm) sea salt mass
fluxes as a function of wind speed measured at 10 m height
for the same parameterisations shown in panel (a) as well as the fit
to measured data reported by .
The effective particle production flux (see
Fig. ) has been parameterised by fitting the
1 ∘C binned interfacial number fluxes obtained during the
temperature ramp experiments corrected to an effective flux at
7 ms-1 wind speed, to the sum of three log-normal distributions
of the form:
dFdlogDp=∑i=13Ni2πlogσiexp-12logDp-logD¯mod,i2(logσi)2,
where Ni is the number production flux,
D¯mod,i is the mode (median) diameter,
σi is the standard deviation of the ith log-normal mode, and
log is the logarithm with base 10.
Least-squares polynomial curve fitting was conducted to allow for the estimation of
the number production flux (Ni) of the log-normal modes, with fixed modal
diameters and geometric standard deviations, as a function of water
temperature. Therefore, in the final form of the parameterisation, the Ni of each of the three log-normal modes is a cubic
function of sea surface temperature:
Ni=Fent(U10)⋅(Ai⋅T3+Bi⋅T2+Ci⋅T+Di),
where Fent(U10) is the volume of air entrained per unit area
per unit time as a function of U10
(Eq. ) and T is the sea surface
temperature in Celsius. Table
describes the details of the three modes and the modal emission coefficients
for use in Eq. ().
Overlaid in black in Fig. are the log-normal
fits for each water temperature based on the values given in
Table and
Eq. (). Although there is a
tendency for the fits to underestimate the magnitude of the mode centred at
0.095µm, the fits are able to account for most of the
variability in the measured number effective flux distributions, with the
coefficient of determination (R2) values of the fits ranging between 0.94
and 0.97 for the effective number fluxes across the range of temperatures 2
to 30 ∘C; however, comparison between the predicted surface area
fluxes and those measured highlight discrepancies. Between 2 and 22 ∘C, the correlation
between predicted surface area fluxes and those measured is generally good
with R2 values between 0.96 and 0.99. However, at water temperatures
higher than 22 ∘C the correlation between predicted surface area
fluxes and those measured becomes much poorer, with R2 values decreasing
monotonically from 0.70 at 23 ∘C to 0.21 at 30 ∘C. This
disconnect results from the fact that the measured particles increase
considerably in size, an effect which the fits, constrained to constant modal
diameter and geometric standard deviations, cannot account for. The
observation that a transition to larger particle sizes occurred at a water
temperature of ∼23∘C was discussed in detail in
Sect. with the conclusion that we cannot
exclude that the particles had not fully effloresced at these higher water
temperatures. Given this, we have assumed that the small increase in the
number of particles with dry diameters greater than 1 µm observed
as water temperatures increased from 2 to 22 ∘C continued at higher
water temperatures by simply extrapolating the increase in the number
production flux in the fitted mode centred at 1.5 µm observed in
the water temperature between 2 and 22 up to 30 ∘C.
The source function estimated during this study is compared with a variety of
source functions from other recent studies for wind speeds of
10 ms-1 in
Fig. a
as well as the previous source function implemented in NorESM described
by . The latter source function is a slight
modification of the previous sea spray aerosol treatment in NorESM1-M
introduced by , which in turn was based on
the source function. Therefore, it includes
a dependence on sea surface temperatures. In contrast, the source functions
of , and do not
incorporate a dependence on sea surface temperature and were presumably
derived at water temperatures somewhere close to either room
temperature in the case of or to the sea surface
temperature in coastal Hawaii in the case of, since
none of the studies make specific reference to the water temperature. All the
source functions are shown for particle sizes normalised to dry diameter. The
source function obtained during this study lies within the range of the other
functions for all particle sizes measured.
Assuming the measured sea spray aerosol particles are spherical, it is
possible to integrate the sea spray aerosol mass flux to obtain mass
emissions as a function of wind speed and sea surface temperature. This can
then be compared to observations as well as previously published sea spray
aerosol source functions. Sea spray aerosol mass emissions, F¯, can be
obtained as follows:
F¯=π6ρss∫Dp,1Dp,2dFdlogDpDp3dlogDp,
where ρss is the density of sea salt (2.16 gcm-3)
assuming it is similar to that of NaCl. Measurements of sea spray aerosol
mass are often obtained using aerosol mass
spectrometers e.g., which determine the vacuum
aerodynamic diameter, Dva. When such instruments obtain mass
estimates for particles with dry diameters smaller than 1 µm,
Dva=0.05–1 µm, which is equivalent to Dp=0.029–0.580 µm. Figure b
shows F¯ integrated across the size range:
0.029 µm<Dp< 0.580 µm as
a function of wind speed for the sea spray source function derived during
this study at sea surface temperatures of 2, 15, and 30 ∘C, a number
of previously published source functions, the source function previously
implemented in NorESM described
by , as well as a fit to measurements made at the
Mace Head coastal station recently published by . It is
clear from these figures that the previously published source functions,
including the source function previously implemented in NorESM, predict much
higher sea salt mass emissions (for particles with dry diameters smaller than
1 µm) to the extent that at U10=10ms-1 they
are a factor of 2–3 higher. The source function predicts
sea salt mass emissions for particles with dry diameters smaller than
1 µm that are an order of magnitude higher at U10=10ms-1 in part due to its strong wind speed dependence of
(U10)3.74. This appears to support our decision to reduce the wind
speed dependence of our function down from (U10)3.74 to
(U10)3.41. Indeed, the new source function presented in this study
compares much better with the measurements of .
The modal diameters and geometric standard deviations (σ) for
the previous sea spray aerosol parameterisation implemented in NorESM
.
ModeModalσdiameter(µm)10.0441.5920.261.5931.482.0Model simulationsThe FLEXPART Lagrangian particle dispersion model
The FLEXPART Lagrangian particle dispersion model has been
used to simulate sea spray aerosol transport from its source to a series of
observation sites where chemical analysis of Na+ on aerosol filter
samples has been conducted. This model computes the trajectories of particles
in the atmosphere to describe the transport and turbulent diffusion of
tracers. In this study particles were released from the observation sites at
a constant rate of 15 000 particles per hour during every measurement
sampling interval and followed backwards in time for 20 days. When run in
backward mode tracing mass concentrations the output of the model is an
emission sensitivity in seconds as a function of space (1∘×1∘ with variable vertical resolution) and time (every 3 h). Here
emission sensitivity can be thought of as a statistical measure of the
fraction of time that an air mass has spent over a specific area of ocean. By
multiplying the emission sensitivity in the lowest model layer
(100 m) by a source flux, the source contribution is obtained. When
integrated over all grid cells and 3 h intervals, this provides the simulated sea spray aerosol concentration at
the measurement point averaged over the sampling interval. Further detail on
the manner in which we run this model can be found in .
In order to facilitate comparison with other commonly deployed sea spray
source functions, four log-normal modes with modal diameters of 1.3, 9.4,
13.6, and 17.8 µm and corresponding geometric standard deviations
of 1.350, 1.100, 1.075, and 1.050 were used to approximate the source
function presented in Sect. .
FLEXPART modelled sea spray aerosol concentrations using the parameterisation
presented in this study are compared with the database of observed sea spray
aerosol concentrations compiled by . This consists of
observational data obtained at 21 monitoring sites and on-board ships during
11 research cruises see Table 1 in and totals over
20 000 observations distributed over the global oceans.
The Norwegian Earth system model
We have used a modified first version of the Norwegian Earth System Model,
NorESM1-M . This model is run
with intermediate atmospheric resolution
(1.9∘× 2.5∘) and is based on the Community Climate System Model v4
(CCSM4)
developed at the National Center for Atmospheric Research (NCAR) . The model was set up to run in the same
manner as described by with only slight modifications to
the version of the atmospheric model, CAM4-Oslo. The model was set up using
prescribed sea surface temperatures and run in offline mode, so that changes
in aerosol treatment do not affect the meteorology.
Modal diameters, geometric standard deviations (σ), and the
polynomial coefficients for the number flux (Ni) of each of the
three log-normal modes in the here derived parameterisation
(Eq. ).
The aerosol module in the atmospheric model, CAM4-Oslo, describes the
size-resolved aerosol physics and transport of 20 aerosol components and
combines a life-cycle model that handles the emissions, processing and
transport of aerosol mass with a physics scheme with look-up
tables calculated by an offline
microphysics model. The look-up tables are used to compute the bulk (from
size-resolved) physical and optical properties of the aerosol population. The
differences introduced in the aerosol schemes compared
to are the modified modal median diameters and standard
deviations of the log-normal (and dry) sea spray size distributions at the
point of emission. The previous modal mean diameters and standard deviations
from the parameterisation by are listed in
Table. . The parameters for
the new parameterisation are listed in
Table .
Comparison to a Lagrangian particle dispersion model
Using European Centre for Medium-Range Weather Forecasts (ECMWF) wind fields
over a 25 yr period, sea spray aerosol production was calculated
using the source function presented here as well as a number of source
functions more commonly deployed in large-scale models. Annual mean global
sea spray aerosol production was 5.9±0.2Pgyr-1, where the
plus or minus value represents only the interannual variability. Although
this is at the low end of the range of estimates presented
by of between 1.83 and 2444 Pgyr-1 it
compares favourably with the median of the 22 source functions of
5.91 Pgyr-1. For comparison the source
functions of (defined only up to Dp=0.8µm), (an extrapolation
of ), and produced 4.5, 4.6, and 2.6 Pgyr-1, respectively. Further
comparison to existing source functions can be made using Table 2
in .
Sea spray aerosol concentrations from the FLEXPART model using the
parameterisation presented in this study can be compared with the database of
observed sea spray aerosol concentrations compiled by .
This consists of observational data obtained at 21 monitoring sites and
on-board ships during 11 research cruises see Table 1
in and totals over 20 000 observations distributed over the
global oceans.
Figure compares
FLEXPART modelled with measured Na+ concentrations using the sea spray
source function presented here for four stations included in the comparison,
Barrow, Malin Head, Valentia, and Zeppelin. When comparing measured and
modelled concentrations at these four stations R2=0.62, which compares
favourably with those of other common parameterisations when the same
comparison was conducted by of between 0.18 and 0.66.
However, the performance of the model using the source function presented
here ranged considerably across the four stations – lower skill was observed
at the two polar stations, Barrow, Alaska, and Zeppelin, Svalbard, which are
characterised by lower concentrations of Na+ overall. Their distance
from large open seawater sources relative to Malin Head and Valentia, as well
as the higher elevation of Zeppelin (475 m above sea level), may mean
that they are less representative of fresh sea spray aerosol. When comparing
the entire data set R2=0.16, whilst it is 0.09, 0.64, and 0.09 when
comparing only PM10 measurements, European
Monitoring and Evaluation Programme (EMEP) station observations, and
weekly observations, respectively. The value for the entire data set compares
favourably with the correlations between modelled and observed sea spray
aerosol concentrations for other common sea spray aerosol parameterisations
found by . Here R2 ranged between 0.03 and 0.17 when
comparing the entire data set.
Comparison of FLEXPART modelled with measured Na+
concentrations using the sea spray source function presented here
for four stations included in the comparison
by . Linear orthogonal fits are shown along with
the correlation coefficient for the whole data set as well
the individual stations. Compared to standard linear least-squares regression, which minimises the error only in the y direction, the
orthogonal fitting procedure used minimises the error in both the x and
y directions. Also presented are the normalised root
mean square errors (NRMSE) for the whole data set as well as the
individual stations. Here the NRMSE is the root mean square error
normalised to the difference between the maximum and minimum
measured values for the entire data set or individual stations.
Description of the simulated sensitivity experiments
conducted in NorESM. The simulated climate was identical in all
experiments.
RunSea surface temperatureWind speed at 10 mSSA wind speedSea spraydependency*parameterisation1Varying (from climatology)Varying (computed online)Fent=2×10-8U103.41This study2Fixed at 15 ∘C in all grid cellsVarying (computed online)Fent=2×10-8U103.41This study3Varying (from climatology)Varying (computed online)W=3.84×10-6U103.41
* Here W denotes the whitecap fraction.
It is clear from
Fig. that the
model is biased ∼50 % low compared to the measurements. A low bias
of similar magnitude was observed for many commonly deployed source functions
tested by . It may be caused by the proximity of the
observations to coastal wave breaking in the form of surf, which is not
accounted for in the models, as well as inadequate treatment of sea spray
aerosol post-production in the model. For example, errors in the rate of
below cloud aerosol scavenging in the model will have knock-on effects on the
aerosol residence time and how much of the aerosol produced by wave breaking
was predicted to reach the point of measurement. Overall, given the
uncertainty in the source function and the multitude of processes that must
be accounted for in the model such as dry deposition and cloud processing, it
is difficult to attribute too much to this disagreement.
Global simulations using an Earth system model
We ran a total of three 2-year NorESM simulations after 1 year of spin-up.
The model was set up as atmosphere only and the atmosphere was coupled with
the data ocean and sea ice model (from CCSM4). In addition, the CAM4-Oslo
aerosol life-cycle module was run offline with respect to the atmospheric
component so that the aerosol changes induced by changing sea spray aerosol
emissions in CAM4-Oslo had no effect on the meteorology in any of the
simulations. We chose not to include these feedbacks in order to obtain
a clearer causal relation between sea surface temperature and sea spray
aerosol given that all of these runs had exactly the same meteorology. All
simulations employ emissions of SO2, SO4, particulate organic matter,
and black carbon from fossil-fuel and bio-fuel combustion and biomass
burning, taken from the IPCC AR5 data sets as in . The
description of the runs and the sea spray parameterisation is presented in
Table .
Comparison of global averages (median) of sea spray aerosol
column burdens (CSSA), all-sky sea spray aerosol
optical depth, mass-specific extinctions (ME), and sea spray
atmospheric residence times between the three NorESM model runs.
Model runCSSASSA optical depthSSA MESSA residence(mgm-2)(–)(m2g-1)time (h)Current parameterisation7.440.03795.1029.6(climatology SSTs)Current parameterisation7.420.03835.1629.2(SSTs fixed at 15 ∘C)Previous parameterisation9.740.03023.1010.0
The global sea spray aerosol mass emission predicted by the model using the
sea spray source function presented in this study is 1.84±0.92Pgyr-1 whilst the global sea spray aerosol number
emission is (2.1±1.1)×105 particles m-2s-1
based on the uncertainty in oceanic air entrainment presented
by . That this uncertainty of ∼50 % only includes
the uncertainty in air entrainment suggests that the total uncertainty will
be much higher given that we include assumptions that the size distribution
is independent of wind speed and that oceanic air entrainment is also
independent of water temperature.
Zonal plots of the annually averaged (median) absolute
difference in (a) SSA number fluxes, (b) SSA mass
fluxes, and (c) clear-sky aerosol optical depth at
550 nm between the parameterisation developed here with
climatology sea surface temperatures and sea surface temperature
fixed at 15 ∘C. Each plot was generated as the variable
sea surface temperature simulation minus the fixed sea surface
temperature simulation. Shaded areas represent 25th and 75th
percentiles and the blue lines in (a) and (b) show percentage changes and refer to the right axes.
(a) Annually averaged (median) sea spray aerosol
number concentration in the lowest model layer computed during the
three NorESM runs. (b) Zonally and annually averaged
clear-sky aerosol optical depth at 550 nm computed during
the three NorESM runs (median). Shaded areas represent the 25th and
75th percentiles.
(a) Comparison of zonally (over all grid boxes) and
annually averaged (median) sea spray aerosol column burden computed
with the current parameterisation and the previous
parameterisation . (b) Comparison of
zonally (only ocean grid boxes) and annually averaged (median) sea
spray aerosol residence time computed with the current
parameterisation and the previous
parameterisation . Shaded areas represent the
25th and 75th percentiles.
The global sea spray aerosol mass emission predicted by NorESM is
significantly lower than that predicted by the Lagrangian particle dispersion
model FLEXPART. This may be because the different models have different
assumptions for the sea spray size representation or due to differences in
the wind fields and SSTs used by the different models. NorESM uses the three
modes described in Table , whilst
FLEXPART used four log-normal distributions with modal diameters of 1.3, 9.4,
13.6, and 17.8 µm and corresponding geometric standard deviations
of 1.350, 1.100, 1.075, 1.050, respectively, to approximate the source
function (as well as all others in the comparison).
To determine the influence of including a dependence on sea surface
temperature in the sea spray aerosol source function relative to no
dependence on sea surface temperature, we ran a simulation where the sea
surface temperature was fixed at 15 ∘C over the entire ocean (a
value in the range of the annual mean sea surface temperature of the global
oceans). Figure
plots the difference in sea spray aerosol number flux, mass flux and
clear-sky aerosol optical depth at 550 nm between the run with
variable sea surface temperatures and the run with sea surface temperatures
fixed at 15 ∘C (the variable sea surface temperature run minus the
fixed sea surface temperature run). Although changes in sea spray aerosol
number fluxes are small in absolute terms, there is a large relative increase
at high latitudes in both the Southern Hemisphere and the Northern Hemisphere
when a temperature dependence is included. There is no discernible difference
at lower latitudes in both hemispheres. When a temperature dependence is
included, sea spray aerosol mass fluxes are slightly higher throughout the
entire Northern Hemisphere, whilst they are significantly lower at higher
latitudes in the Southern Hemisphere. Clear-sky aerosol optical depth values
(Fig. c)
are also generally higher in the Northern Hemisphere, when a sea surface
temperature dependence is included, especially around the tropics, which is
consistent with the observations of . Averaged globally
over a year, including a dependence on sea surface temperature, the sea
spray source function decreases sea spray aerosol mass fluxes by ∼7 %, increases sea spray aerosol number fluxes by ∼14 %, and
increases clear-sky aerosol optical depth by <0.1 % relative to
a fixed sea surface temperature of 15 ∘C.
Figure a compares sea spray
aerosol number concentrations modelled by NorESM using both the previous sea
spray source function and that presented in the current study. From this
figure it is clear that changing the sea spray parameterisation decreases the
sea spray aerosol number concentration in the model in the lowest atmospheric
layer. Over the Southern Ocean the effect is particularly noticeable – there
are significantly fewer sea spray aerosol particles in the lowest layers of
the model atmosphere in the model run using the parameterisation developed
during this study when compared to the parameterisation.
Further evaluation of the new parameterisations deployment within NorESM is
facilitated through comparison of modelled clear-sky aerosol optical depth at
550 nm in Fig. b. Across
all regions in the Northern Hemisphere there is no discernible difference
between all three model runs due to the dominance of aerosols other than sea
spray aerosol. However, there are significant differences at higher latitudes
in the Southern Hemisphere. Here, the model run using the sea spray aerosol
parameterisation developed during this study and climatology sea surface
temperatures simulates increased clear-sky aerosol optical depth.
It is also useful to consider the column burden of sea spray aerosol mass
(CSSA), the sea spray aerosol residence time, which is defined as
the column (mass) burden divided by the loss (through wet and dry
deposition), as well as the sea spray aerosol mass-specific extinction (ME),
defined as the sea spray aerosol optical depth divided by the sea spray
aerosol column (mass) burden. A comparison of these parameters between the
previous parameterisation and that proposed in the current study is
facilitated in Fig. and
Table . The column
burdens of sea salt aerosol are generally lower in the parameterisation
proposed in this study compared to the previous parameterisation
of , apart from in the polar regions. Globally averaged
sea spray aerosol column burdens are 7.44 and 7.42 mgm-2 for the
parameterisation with climatology sea surface temperatures and sea surface
temperature fixed at 15 ∘C, respectively, compared to
9.74 mgm-2 with the previous parameterisation deployed in NorESM
(Table ). The
parameterisation developed during this study results in slightly increased
numbers of accumulation mode particles across all latitudes but decreased
amounts of particles with dry diameters greater than 1 µm that
dominate the mass production – hence the decreased column burden. Our
calculated sea spray aerosol column burdens fall within the range of values
reported by , which has a mean of
15.5 mgm-2 (median of 12.7 mgm-2) and an
inter-model diversity of 69 %.
The current parameterisation results in significantly longer sea spray
aerosol residence times than the previous parameterisation, which is to be
expected given that the effective radii of the sea spray aerosol are closer
to the accumulation mode in the current parameterisation. The global mean
residence time of 29.6 h for the current
parameterisation and 10 h for the previous
parameterisation can be compared
with the AeroCom model comparison study , where the model mean
residence time for sea spray aerosol
was modelled as 12 h (median of 7.2 h) with an inter-model
diversity of 59 %. The sea spray aerosol residence time resulting from
the new parameterisation is therefore outside the AeroCom model diversity
interval.
The current parameterisation results in significantly larger sea spray
aerosol mass-specific extinction than the previous
parameterisation. reported sea spray aerosol mass-specific
extinction for the AeroCom models. These values vary between 0.88 and
7.5 m2g-1 (median 3 m2g-1) for mass-specific
extinction. Therefore, our calculated sea salt aerosol mass-specific
extinction of 5.1 m2g-1 falls within the inter-model
diversities of AeroCom.
also reported sea spray aerosol optical depth for the AeroCom
models. These values vary between 0.003 and 0.067 (median 0.030). Compared
with , our calculated sea salt aerosol optical depth of
0.038 falls within the inter-model diversities of AeroCom.
In essence, the changes to the modal diameters compensate for the coincident changes to the
magnitudes of the fluxes of each mode, resulting in decreased sea
spray aerosol number, increased residence time, and increased clear-sky
aerosol optical depth compared to the previous parameterisation deployed in
the model. When viewed as a whole these changes to the sea spray aerosol
parameterisation may have important implications for aerosol optical
properties and number concentrations, subsequently also affecting the
indirect radiative forcing by (non-sea spray) anthropogenic
aerosols e.g., especially at the regional level.
Conclusions
We have developed a parameterisation for inorganic sea spray aerosol
production based upon state-of-the-art measurements of aerosol production
using a temperature-controlled laboratory sea spray aerosol chamber. Using
measurements of particle production in the size range 0.01 to
10 µm dry diameter, we observed that particle production
decreased non-linearly with increasing seawater temperature (between -1 and
30 ∘C) similar to previous findings. In addition, we observed that
the particle effective radius, as well as the particle surface, particle
volume and particle mass, increased with increasing water temperature due to
increased production of particles with dry diameters greater than
1 µm. These observations might explain the contradiction between
observations made using laboratory systems that attempt to replicate oceanic
whitecaps, where decreasing particle production with increasing seawater
temperature is observed, and observations of sea
salt concentrations made in the
field or inferred from aerosol optical depth measurements, which tend to
increase with increasing seawater temperature. They also underline the need
to model sea spray emissions separately for particles with dry diameters
smaller and larger than 1 µm when a dependence upon SST is
included.
We have combined our measurements of particle production with measurements of
the volume of air entrained by the plunging jet in order to determine the
size-resolved particle flux as a function of air entrainment. By scaling in
this way we avoid some of the difficulties associated with defining the
“white area” of the laboratory whitecap – a contentious issue when using
the more frequently applied whitecap method.
The here-derived inorganic sea spray source function was
implemented in a Lagrangian particle dispersion model. An estimated annual
global flux of inorganic sea spray aerosol of 5.9±0.2Pgyr-1 was derived that is close to the median of estimates
from the same model using a wide range of existing sea spray source
functions. When using the source function derived here, the model also showed
good skill in predicting measurements of Na+ concentration at a number
of field sites further underlining the validity of our source function.
In a final step, the sensitivity of a large-scale model to our new source
function was tested by implementing it in NorESM. Compared to the previously
implemented parameterisation, a clear decrease of sea spray aerosol number
flux and increase in aerosol residence time was observed, especially over the
Southern Ocean. At the same time an increase in aerosol optical depth due to
an increase in the number of particles with optically relevant sizes was
found. That there were noticeable regional differences may have important
implications for aerosol optical properties and number concentrations,
subsequently also affecting the indirect radiative forcing by non-sea spray
anthropogenic aerosols.
The Supplement related to this article is available online at doi:10.5194/acp-15-11047-2015-supplement.
Acknowledgements
Matt Salter and Douglas Nilsson were supported by the Swedish Research
Council (Vetenskapsrådet) and the Nordic Center of Excellence on
Cryosphere–Atmosphere (NCoE CRAICC). Paul Zieger was supported by a postdoc
fellowship of the Swiss National Science Foundation (grant
no. P300P2_147776). Alf Kirkevåg was supported by the Norwegian
Research Council through the project EVA (grant no. 229771) and the NOTUR
(nn2345k) and NordStore projects (ns2345k) as well as the NCoEs CRAICC and
eSTICC and the EU FRP7 projects PEGASOS and ACCESS. The data from this study
are available from the authors upon request.
Edited by: M. Boy
ReferencesAbo Riziq, A., Erlick, C., Dinar, E., and Rudich, Y.: Optical properties of
absorbing and non-absorbing aerosols retrieved by cavity ring down (CRD)
spectroscopy, Atmos. Chem. Phys., 7, 1523–1536, 10.5194/acp-7-1523-2007,
2007.Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A.,
Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and
Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part
1: Description and basic evaluation of the physical climate, Geosci. Model
Dev., 6, 687–720, 10.5194/gmd-6-687-2013, 2013.Bowyer, P. A., Woolf, D. K., and Monahan, E. C.: Temperature
dependence of the charge and aerosol production associated with
a breaking wave in a whitecap simulation tank, J. Geophys. Res., 95,
5313–5319,
doi:10.1029/JC095iC04p05313, 1990.Callaghan, A.: An improved whitecap timescale for sea spray aerosol
production flux modeling using the discrete whitecap method,
J. Geophys. Res., 118, 9997–10010.
doi:10.1002/jgrd.50768, 2013.Ceburnis, D., Rinaldi, M., Keane-Brennan, J., Ovadnevaite, J., Martucci, G.,
Giulianelli, L., and O'Dowd, C. D.: Marine submicron aerosol sources, sinks
and chemical fluxes, Atmos. Chem. Phys. Discuss., 14, 23847–23889,
10.5194/acpd-14-23847-2014, 2014.Clarke, A. D., Owens, S. R., and Zhou, J. C.: An ultrafine sea-salt
flux from breaking waves: Implications for cloud condensation nuclei
in the remote marine atmosphere, J. Geophys. Res., 111, D06202,
doi:10.1029/2005JD006565, 2006.Dahneke, B.: Slip correction factors for nonspherical bodies. II. Free
molecule regime, Aerosol Sci., 4, 147–161,
doi:, 1973.de Leeuw, G., Andreas, E. L., Anguelova, M. D., Fairall, C. W.,
Lewis, E. R., O'Dowd, C. D., Schulz, M., and Schwartz, S. E.:
Production flux of sea spray aerosol, Rev. Geophys., 49, RG2001,
doi:10.1029/2010RG000349, 2011.Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke,
E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J.,
Vertenstein, M., Worley, P. H., Yang, Z., and Zhang, M.: The community
climate system model version 4, J. Climate, 24, 4973–4991,
doi:10.1175/2011JCLI4083.1, 2011.Gong, S. L.: A parameterization of sea-salt aerosol source function
for sub- and super-micron particles, Global Biogeochem. Cy., 17, 1097,
doi:10.1029/2003GB002079, 2003.Grythe, H., Ström, J., Krejci, R., Quinn, P., and Stohl, A.: A
review of sea-spray aerosol source functions using a large global set
of sea salt aerosol concentration measurements, Atmos. Chem. Phys.,
14, 1277–1297,
doi:10.5194/acp-14-1277-2014, 2014.
Hinds, W.: Aerosol Technology: Properties, Behavior, and Measurement
of Airborne Particles, John Wiley & Sons, New York, 1999.Hoose, C., Kristjansson, J. E., Iversen, T., Kirkevåg, A., Seland, Ø.,
and Gettelman, A.: Constraining cloud droplet number concentration in GCMs
suppresses the aerosol indirect effect, Geophys. Res. Lett., 36, L12807,
doi:10.1029/2009GL038568,
2009.Iversen, T., Bentsen, M., Bethke, I., Debernard, J. B., Kirkevåg, A.,
Seland, Ø., Drange, H., Kristjansson, J. E., Medhaug, I., Sand, M., and
Seierstad, I. A.: The Norwegian Earth System Model, NorESM1-M – Part 2:
Climate response and scenario projections, Geosci. Model Dev., 6, 389–415,
10.5194/gmd-6-389-2013, 2013.Jaeglé, L., Quinn, P. K., Bates, T. S., Alexander, B., and Lin, J.-T.:
Global distribution of sea salt aerosols: new constraints from in situ and
remote sensing observations, Atmos. Chem. Phys., 11, 3137–3157,
10.5194/acp-11-3137-2011, 2011.Kinne, S., Schulz, M., Textor, C., Guibert,
S., Balkanski, Y., Bauer, S. E., Berntsen, T., Berglen, T. F., Boucher, O.,
Chin, M., Collins, W., Dentener, F., Diehl, T., Easter, R., Feichter, J.,
Fillmore, D., Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J.,
Herzog, M., Horowitz, L., Isaksen, I., Iversen, T., Kirkevåg, A.,
Kloster, S., Koch, D., Kristjansson, J. E., Krol, M., Lauer, A., Lamarque, J.
F., Lesins, G., Liu, X., Lohmann, U., Montanaro, V., Myhre, G., Penner, J.,
Pitari, G., Reddy, S., Seland, O., Stier, P., Takemura, T., and Tie, X.: An
AeroCom initial assessment – optical properties in aerosol component modules
of global models, Atmos. Chem. Phys., 6, 1815–1834,
10.5194/acp-6-1815-2006, 2006.Kirkevåg, A., Iversen, T., Seland, Ø., Hoose, C., Kristjánsson, J.
E., Struthers, H., Ekman, A. M. L., Ghan, S., Griesfeller, J., Nilsson, E.
D., and Schulz, M.: Aerosol–climate interactions in the Norwegian Earth
System Model – NorESM1-M, Geosci. Model Dev., 6, 207–244,
10.5194/gmd-6-207-2013, 2013.
Lewis, E. R. and Schwartz, S. E.: Sea Salt Aerosol Production: Mechanisms,
Methods, Measurements and Models – a Critical Review, Geophys. Monogr.,
Vol. 152, Amer. Geophys. Union, 2004.Long, M. S., Keene, W. C., Kieber, D. J., Erickson, D. J., and Maring, H.: A
sea-state based source function for size- and composition-resolved marine
aerosol production, Atmos. Chem. Phys., 11, 1203–1216,
10.5194/acp-11-1203-2011, 2011.Mårtensson, E. M., Nilsson, E. D., de Leeuw, G., Cohen, L. H.,
and Hansson, H. C.: Laboratory simulations and parameterization of the
primary marine aerosol production, J. Geophys. Res., 108, 4297,
doi:10.1029/2002JD002263, 2003.Monahan, E. C. and O'Muircheartaigh, I.: Optimal power-law
description of oceanic whitecap coverage dependence on wind speed,
J. Phys. Oceanogr., 10, 2094–2099,
doi:10.1175/1520-0485(1980)010<2094:OPLDOO>2.0.CO;2, 1980.
Monahan, E. C., Spiel, D. E., and Davidson, K. L.: Oceanic Whitecaps, in:
A Model of Marine Aerosol Generation Via Whitecaps and Wave Disruption,
167–174, D. Reidel Publishing Company, 1986.Mullins, B. J., Kampa, D., and Kasper, G.: Comment on “Performance
evaluation of 3 optical particle counters with an efficient multimodal
calibration method” (Heim et al., 2008) – Performance of improved
counter, J. Aerosol Sci., 49, 48–50,
doi:10.1016/j.jaerosci.2012.01.009, 2012.Najjar, R. G. and Keeling, R. F.: Analysis of the mean annual cycle of
the dissolved oxygen anomaly in the World Ocean, J. Marine Res., 55,
117–151,
doi:10.1357/0022240973224481, 1997.Rosati, B., Wehrle, G., Gysel, M., Zieger, P., Baltensperger, U., and
Weingartner, E.: The white-light humidified optical particle spectrometer
(WHOPS) – a novel airborne system to characterize aerosol hygroscopicity,
Atmos. Meas. Tech., 8, 921–939, 10.5194/amt-8-921-2015, 2015.Salisbury, D. J., Anguelova, M. D., and Brooks, I. M.: On the
variability of whitecap fraction using satellite-based observations,
J. Geophys. Res., 118, 6201–6222,
doi:10.1002/2013JC008797, 2013.Salisbury, D. J., Anguelova, M. D., and Brooks, I. M.: Global
distribution and seasonal dependence of satellite-based whitecap
fraction, Geophys. Res. Lett., 41, 1616–1623,
doi:10.1002/2014GL059246,
2014.Salter, M. E., Nilsson, E. D., Butcher, A., and Bilde, M.: On the
seawater temperature dependence of continuous plunging jet derived sea
spray aerosol, J. Geophys. Res., 119, 9052–9072,
doi:10.1002/2013JD021376,
2014.Sofiev, M., Soares, J., Prank, M., de Leeuw, G., and Kukkonen, J.:
A regional-to-global model of emission and transport of sea salt
particles in the atmosphere, J. Geophys. Res., 116, D21302,
doi:10.1029/2010JD014713, 2011.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.:
Climate Change 2013: The Physical Science Basis. Contribution of
Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge Univ. Press,
Cambridge, UK and New York, USA, 2013.Stohl, A., Forster, C., Frank, A., Seibert, P., and Wotawa, G.: Technical
note: The Lagrangian particle dispersion model FLEXPART version 6.2, Atmos.
Chem. Phys., 5, 2461–2474, 10.5194/acp-5-2461-2005, 2005.Stramska, M., Marks, R., and Monahan, E. C.: Bubble-mediated aerosol
production as a consequence of wave breaking in supersaturated
(hyperoxic) seawater, J. Geophys. Res., 95, 18281–18288,
doi:10.1029/JC095iC10p18281, 1990.Struthers, H., Ekman, A. M. L., Glantz, P., Iversen, T., Kirkevåg, A.,
Seland, Ø., Martensson, E. M., Noone, K., and Nilsson, E. D.:
Climate-induced changes in sea salt aerosol number emissions: 1870 to 2100,
J. Geophys. Res., 118, 670–682,
doi:10.1002/jgrd.50129, 2013.
Textor, C., Schulz, M., Guibert, S., Kinne, S., Balkanski, Y., Bauer, S.,
Berntsen, T., Berglen, T., Boucher, O., Chin, M., Dentener, F., Diehl, T.,
Easter, R., Feichter, H., Fillmore, D., Ghan, S., Ginoux, P., Gong, S.,
Grini, A., Hendricks, J., Horowitz, L., Huang, P., Isaksen, I., Iversen, I.,
Kloster, S., Koch, D., Kirkevåg, A., Kristjansson, J. E., Krol, M.,
Lauer, A., Lamarque, J. F., Liu, X., Montanaro, V., Myhre, G., Penner, J.,
Pitari, G., Reddy, S., Seland, Ø., Stier, P., Takemura, T., and Tie, X.:
Analysis and quantification of the diversities of aerosol life cycles within
AeroCom, Atmos. Chem. Phys., 6, 1777–1813, 10.5194/acp-6-1777-2006,
2006.Wang, Z., King, S. M., Freney, E., Rosenoern, T., Smith, M. L., Chen,
Q., Kuwata, M., Lewis, E. R., Pöschl, U., Wang, W., Buseck, P. R.,
and Martin, S. T.: The dynamic shape factor of sodium chloride
nanoparticles as regulated by drying rate, Aerosol Sci. Tech., 44,
939–953,
doi:10.1080/02786826.2010.503204, 2010.Zábori, J., Krejci, R., Ström, J., Vaattovaara, P., Ekman, A. M. L.,
Salter, M. E., Mårtensson, E. M., and Nilsson, E. D.: Comparison between
summertime and wintertime Arctic Ocean primary marine aerosol properties,
Atmos. Chem. Phys., 13, 4783–4799, 10.5194/acp-13-4783-2013, 2013.