ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-11807-2016Can simple models predict large-scale surface ocean isoprene concentrations?BoogeDennisdbooge@geomar.deMarandinoChrista A.SchlundtCathleenPalmerPaul I.https://orcid.org/0000-0002-1487-0969SchlundtMichaelAtlasElliot L.https://orcid.org/0000-0003-3847-5346BracherAstridhttps://orcid.org/0000-0003-3025-5517SaltzmanEric S.WallaceDouglas W. R.GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, GermanySchool of GeoSciences, University of Edinburgh, Edinburgh, UKRosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami, Miami, FL, USAAlfred Wegener Institute – Helmholtz Centre for Polar and Marine Research, Bremerhaven, GermanyInstitute of Environmental Physics, University Bremen, Bremen, GermanyDepartment of Earth System Science, University of California, Irvine, CA, USADepartment of Oceanography, Dalhousie University, Halifax, CanadaDennis Booge (dbooge@geomar.de)22September2016161811807118212June20122June201626August20165September2016This 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/16/11807/2016/acp-16-11807-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/11807/2016/acp-16-11807-2016.pdf
We use isoprene and related field measurements from three
different ocean data sets together with remotely sensed satellite data to
model global marine isoprene emissions. We show that using monthly mean
satellite-derived chl a concentrations to parameterize isoprene with
a constant chl a normalized isoprene production rate underpredicts
the measured oceanic isoprene concentration by a mean factor of 19 ± 12.
Improving the model by using phytoplankton functional type dependent
production values and by decreasing the bacterial degradation rate of
isoprene in the water column results in only a slight underestimation (factor
1.7 ± 1.2). We calculate global isoprene emissions of 0.21 Tg C for 2014
using this improved model, which is twice the value calculated using the
original model. Nonetheless, the sea-to-air fluxes have to be at least 1
order of magnitude higher to account for measured atmospheric isoprene mixing
ratios. These findings suggest that there is at least one missing oceanic
source of isoprene and, possibly, other unknown factors in the ocean or
atmosphere influencing the atmospheric values. The discrepancy between
calculated fluxes and atmospheric observations must be reconciled in order to
fully understand the importance of marine-derived isoprene as a precursor to
remote marine boundary layer particle formation.
Introduction
Remote marine boundary layer aerosol and cloud formation is important for
both the global climate system/radiative budget and for atmospheric chemistry
(Twomey, 1974) and has been investigated, with contentious results, for
decades. The question remains: what are the precursors to aerosol and cloud
formation over the ocean? Earlier studies pinpointed dimethyl sulfide (DMS)
as the main precursor, as described in the CLAW hypothesis (Charlson et al.,
1987). More recently, this hypothesis has been debated controversially (Quinn
and Bates, 2011) because primary organic aerosols (POA; O'Dowd et al., 2008)
and small sea salt particles (Andreae and Rosenfeld, 2008; de Leeuw et al.,
2011) have been identified as cloud condensation nuclei (CCN) precursors with
higher CCN production potential than DMS. In addition to POA, other gases
besides DMS have been hypothesized as important for remote marine secondary
organic aerosol formation (SOA), including isoprene (2-methyl-1,3-butadiene),
which has received the most attention in recent years (Carlton et al., 2009).
Isoprene is a byproduct of plant metabolism and one of the most abundant of
the atmospheric volatile non-methane hydrocarbons (NMHC). On a global basis,
as much as 90 % of atmospheric isoprene comes from terrestrial plant
emissions (400–600 Tg C yr-1; Guenther et al., 2006; Arneth et al.,
2008). Isoprene is very short lived in the atmosphere, with a lifetime
ranging from minutes to a few hours. The principal loss mechanism is
reaction with hydroxyl radicals (OH), but reactions with ozone and nitrate
radicals are also important sinks (Atkinson and Arey, 2003; Lelieveld et
al., 2008).
The importance of the ocean as a source of atmospheric isoprene is unclear,
as only few studies have directly measured isoprene concentrations in the
euphotic zone. Throughout most of the world oceans, near-surface seawater
isoprene concentrations range between < 1 and 200 pmol L-1,
depending on season and region (Bonsang et al., 1992; Milne et al.,
1995; Broadgate et al., 1997; Baker et al., 2000; Matsunaga et al.,
2002; Broadgate et al., 2004; Zindler et al., 2014; Ooki et al., 2015). Higher
isoprene levels have been measured in Southern Ocean and Arctic waters
(395 and 541 pmol L-1, respectively; Kameyama et al., 2014; Tran et al.,
2013). Atmospheric isoprene levels can be as high as
300 parts per trillion (ppt), varying with location and time of day
(Shaw et al., 2010). Generally, the mixing ratios are lower than
100 ppt in remote areas not influenced by terrestrial sources
(Yokouchi et al., 1999), but they can also increase up to
375 ppt during a phytoplankton bloom (Yassaa et al.,
2008). Matsunaga et al. (2002) found that the sea-to-air flux
estimated from measurements could not explain the atmospheric concentrations
observed in the western North Pacific. This agrees with the model
calculations of Hu et al. (2013), who found that top-down
and bottom-up models estimating isoprene emissions disagree by 2 orders of
magnitude.
Assessing the importance of isoprene for marine atmospheric chemistry and SOA
formation requires extrapolations of measurements to develop global emissions
climatologies and inventories. Model studies suggest that oceanic sources of
isoprene are too weak to control marine SOA formation (Spracklen et al.,
2008; Arnold et al., 2009; Gantt et al., 2009; Anttila et al., 2010;
Myriokefalitakis et al., 2010) and field studies indicate that the organic
carbon (OC) contribution from oceanic isoprene is less than 2 % and out
of phase with the peak of OC in the Southern Indian Ocean (Arnold et al.,
2009). In contrast, Hu et al. (2013) found that, despite sometimes low
isoprene fluxes calculated by models, oceanic isoprene emissions can increase
abruptly in association with phytoplankton blooms, resulting in regionally
and seasonally important isoprene-derived SOA formation. Further experiments
showed that isoprene oxidation products can increase the level of CCN when
the number of CCN is low (Ekström et al., 2009). Lana et al. (2012) used
both model-calculated fluxes of isoprene and remote sensing products to
investigate isoprene-derived SOA formation in the marine atmosphere. Their
results illustrated that the oxidation products of marine trace gases seemed
to influence the condensation growth and the hygroscopic activation of small
primary particles. Fluxes of isoprene (and other marine-derived trace gases)
showed greater positive correlations with CCN number and greater negative
correlations with aerosol effective radius than POA and sea salt over most of
the world's oceans.
Since isoprene concentration measurements from the open ocean are sparse, it
is essential to combine laboratory and field measurements, remote sensing,
and modeling if we want to understand marine isoprene emissions. This study
utilizes measurements of surface ocean isoprene and associated biological
and physical parameters on three oceanographic cruises to refine and
validate the model of Palmer and Shaw (2005) for estimating
marine isoprene concentrations and emissions. The resulting model, with
satellite-derived input, is used to compute monthly climatologies and annual
average estimates of isoprene in the world ocean.
List of parameters used in each model.
ParameterAbbreviationUnitModel approach ISOPS05ISOPFTISOPFT-kBIOIsoprene production ratePpmol L-1 day-1Pchloro× [chl a]Pchloro× [PFT]Pchloro× [PFT]Chemical loss ratekOH×COHday-10.05180.05180.0518kO2×CO2day-10.00090.00090.0009Biological loss ratekBIOLday-10.060.060.01Gas transfer coefficientkASm s-1Wanninkhof (1992) Mixed layer depthMLDmde Boyer Montégut et al. (2004) Mixing loss rateLMIXpmol L-1 day-10.04590.04590.0459Chl a normalizedPchloroµmol (g chl a)-1 day-11.8PFT dependent (Table 2) isoprene production rateMethodsModel description
In this study we use a simple steady-state model for surface ocean isoprene
consisting of a mass balance between biological production, chemical and
biological losses, and emission to the atmosphere (Palmer and
Shaw, 2005):
P-CW∑kCHEM,iCXi+kBIOL+kASMLD-LMIX=0,
where biological production (P) is balanced by all loss processes,
CW is the seawater concentration of isoprene,
kCHEM is the chemical rate constant for all possible loss
pathways (i) with all reactants (X) (X= OH and
O2), kBIOL is the biological loss rate constant, which
takes into account the biodegradation of isoprene, kAS is the
air–sea gas transfer coefficient that considers the loss processes due to
air–sea gas exchange scaled with the depth of the ocean mixed layer (MLD),
and LMIX is the loss due to physical mixing
(Table 1). The model equation was rearranged to
solve for CW (Eq. 2) as
follows:
CW=P-LMIX∑kCHEM,iCXi+kBIOL+kASMLD.
The air–sea flux of isoprene (F) was calculated using the equation
F=kASCW-CA/KH=∼kASCW,
where CA is the air-side concentration of isoprene and
KH is the dimensionless form of the Henry's law constant (equilibrium
ratio of CA and CW). CA is assumed
to be negligible compared to CW as noted above
(Eq. 3). As a result, the air–sea isoprene
gradient is assumed equal to the surface ocean isoprene level, and emissions
are assumed to be first order in CW. This assumption is
justified over the open ocean because of the short atmospheric lifetime of
isoprene. In coastal regions downwind of strong isoprene sources, this
assumption may not be valid. The air–sea exchange transfer coefficient
(kAS) is computed using the Wanninkhof (1992) wind-speed-based (U10) parameterization and the Schmidt number
SC of isoprene (Palmer and Shaw, 2005):
kAS=0.31U102SC660-0.5.
Further details about the rate constants and input parameters are described
in Table 1. Monthly mean wind speed
(U10) and sea surface temperature (SST) were obtained from the
Quick Scatterometer (QuickSCAT) satellite and the Moderate Resolution
Imaging Spectroradiometer (MODIS) instrument on board the Aqua satellite,
respectively, and from in situ shipboard measurements. MLDs were
obtained from climatological monthly means (de Boyer
Montégut et al., 2004) and compared to those calculated by in situ conductivity, temperature, and depth (CTD) profile measurements during
each cruise. MLD was defined as the depth at which temperature is at least
0.2 ∘C higher or lower than the temperature at 10 m depth (de
Boyer Montégut et al., 2004). Chlorophyll a (chl a)
concentrations were obtained either from the MODIS instrument on board the
Terra satellite or from in situ shipboard measurements (here
chl a is defined as the sum of monovinyl chl a,
divinyl chl a, and chlorophyllide a). Model calculations
were carried out using MATLAB (Mathworks).
The steady-state model assumption is justified by the relatively short
lifetime of isoprene in seawater as air–sea exchange is the dominant loss
term over all latitudes and seasons (lifetime: 7–14 days) followed by
kBIOL and kCHEM (Palmer and Shaw,
2005). In this study, model runs were carried out using three different sets
of model parameters (Table 1).
ISOPS05: the original configuration used by Palmer
and Shaw (2005). In this configuration, the production of isoprene is
parameterized as the product of the bulk chl a concentration and a
chl a normalized isoprene production rate (Pchloro) inferred
from laboratory phytoplankton monocultures of several cyanobacteria,
eukaryotes, and coccolithophores (Shaw et al., 2003). This
approach inherently assumes that all phytoplankton have the same isoprene
production characteristics. Palmer and Shaw (2005) also assumed that
biological degradation of isoprene occurs in the water column, based on
indirect evidence of a biological sink for isoprene (Moore
and Wang, 2006), but no isoprene loss rate constants have been published to
date. They assumed a global average lifetime of ∼ 17 days
(kBIOL=0.06 day-1) based on the biological degradation
rates of different data sets of methyl bromide (Tokarczyk et al.,
2003; Yvon-Lewis et al., 2002).
ISOPFT: different Pchloro values are
applied for different phytoplankton functional types (PFTs). Laboratory
studies have shown that isoprene production rates vary significantly across
different PFTs (Bonsang et al., 2010; Colomb et al., 2008; Exton et al.,
2013; Shaw et al., 2003; Arnold et al., 2009). We use the PFT-dependent
isoprene production rate constants and field observations of PFT
distributions to estimate isoprene production rates. The chl a normalized
isoprene production rates of the different algae species are averaged within
each PFT to obtain an estimated Pchloro value of isoprene for
each PFT. PFT distributions along our cruise tracks were derived from the
soluble organic pigment concentrations obtained from filtered water samples
through Whatman GF/F filters using high-pressure liquid chromatography (HPLC)
according to the method of Barlow et al. (1997). This method was adjusted to
our temperature-controlled instruments as detailed in Taylor et al. (2011a).
We determined the list of pigments shown in Table 2 of Taylor et al. (2011a)
and applied the method of Aiken et al. (2009) for quality control of the
pigment data. Pigment data from expedition ANT-XXV/1 have been already
published in Taylor et al. (2011a). From the HPLC pigment concentration we
calculated PFT groups using the diagnostic pigment (DP) analysis developed by
Vidussi et al. (2001) and adapted in Uitz et al. (2006) to relate the
weighted sum of seven, for each PFT representative DP. Using this approach,
the chl a concentrations for diatoms, dinoflagellates, haptophytes,
chrysophytes, cryptophytes, cyanobacteria (excluding prochlorophytes), and
chlorophytes were derived. The chl a concentration of prochlorophytes was
derived directly from the divinyl-chl a concentration (the marker pigment
for this group).
ISOPFT-kBIO: the PFT approach is utilized to
parameterize isoprene production as in ISOPFT and assumes that
biological losses of isoprene in the water column are significantly slower
than assumed by Palmer and Shaw (2005). Seawater incubation
experiments carried out in temperature-controlled water baths over periods
ranging from 48 to 72 h under natural light conditions, using deuterated
isoprene (isoprene-d5), showed significantly longer lifetimes (manuscript in
preparation). In the ISOPFT-kBIO configuration, we test a biological
degradation lifetime of minimum 100 days
(kBIOL=0.01 day-1).
Cruise tracks (black) of ANT-XXV/1 (November 2008, eastern Atlantic
Ocean), SPACES/OASIS (June–July 2014, Indian Ocean) and ASTRA-OMZ
(October 2015, eastern Pacific Ocean). Air mass back trajectories calculated for
12 h with a starting height of 50 m using HYSPLIT are superimposed on the
cruise track. Color coding indicates altitude above sea level.
Cruise tracks
Isoprene was measured in the surface seawater during three separate cruises:
the ANT-XXV/1 in the eastern Atlantic Ocean, the SPACES/OASIS cruises in the
Indian Ocean, and the ASTRA-OMZ cruise in the eastern Pacific Ocean.
ANT-XXV/1 took place in November 2008 on board the R/V Polarstern from
Bremerhaven, Germany, to Cape Town, South Africa
(Fig. 1; for details about isoprene and ancillary
data see also Zindler et al., 2014). The SPACES/OASIS cruises took place
in June–July 2014 on board the R/V Sonne from Durban, South Africa, via Port
Louis, Mauritius, to Malé, Maldives, and the ASTRA-OMZ cruise took place
in October 2015 on board the R/V Sonne from Guayaquil, Ecuador, to
Antofagasta, Chile (Fig. 1). Air mass backward
trajectories (12 h; starting altitude: 50 m) from the Hybrid
Single-Particle Lagrangian Integrated Trajectory (HYSPLIT;
http://www.arl.noaa.gov/HYSPLIT.php) model were calculated for the sampling
sites. The trajectories, in combination with atmospheric measurements,
suggest that the air masses encountered on these cruises were from over the
ocean for more than 12 h prior to sampling and are therefore unlikely to
contain significant isoprene derived from terrestrial sources
(Fig. 1).
Isoprene measurementsEastern Atlantic Ocean
The isoprene measurements from the ANT-XXV/1 (November 2008, eastern Atlantic
Ocean) cruise are described in detail in Zindler et al. (2014). Seawater from approximately 2 m depth was continuously pumped on
board and flowed through a porous Teflon membrane equilibrator. Isoprene was
equilibrated by using a counterflow of dry air and was measured using an
atmospheric pressure chemical ionization mass spectrometer (mini-CIMS),
which consists of a 63Ni atmospheric pressure ionization source coupled
to a single quadrupole mass analyzer (Stanford Research Systems, SRS
RGA200). Isoprene from a standard tank was added to the equilibrated air
stream every 12 h to calibrate the system. The precision for isoprene
measurements was ±13 %. The isoprene data used here are 5 min
averages.
Indian and eastern Pacific Oceans
The isoprene measurements on the SPACES/OASIS (June–July 2014, Indian Ocean)
and ASTRA-OMZ (October 2015, eastern Pacific Ocean) cruises have not been
published previously. Water samples (50 mL) were taken every 3 h
from a continuously running seawater pump system located in the ship's moon
pool at approximately 6 m depth. All samples were analyzed on board within
15 min of collection using a purge and trap system attached to a gas
chromatograph/mass spectrometer operating in single ion mode (GC/MS; Agilent
7890A/Agilent 5975C; inert XL MSD with triple axis detector). Isoprene was
purged from the water sample with helium for 15 min and dried using a
Nafion membrane dryer (Perma Pure; ASTRA-OMZ) or potassium carbonate
(SPACES/OASIS). Before being injected into the GC, isoprene was
preconcentrated in a trap cooled with liquid nitrogen. Gravimetrically
prepared liquid standards in ethylene glycol were measured in the same way
as the samples and used to perform daily calibrations for quantification.
Gaseous deuterated isoprene (isoprene-d5) was measured together with each
sample as an internal standard to account for possible sensitivity drift
between calibrations. The precision for isoprene measurements was ±8 %.
Air samples were collected in electropolished stainless steel flasks and
pressurized to approximately 2.5 atm with a metal bellows pump. Analysis was
conducted after samples were returned to the laboratory. Isoprene was
measured along with a range of halocarbons, hydrocarbons, and other gases
using a combined GC/MS/FID/ECD system with a modified Markes Unity II/CIA
sample preconcentrator. The modifications incorporated a water removal
system consisting of a cold trap (-20 ∘C) and a Perma Pure dryer
(MD-050-24). Isoprene and > C4 hydrocarbons were quantified using
selected ion MS and were calibrated against a whole air sample that is
referenced to a NIST hydrocarbon mixture using GC/FID. Precision for
isoprene is estimated at approximately ±0.4 ppt +5 %.
Results and discussionComparison of modeled and in situ measured isoprene data
The shipboard isoprene measurements from the ANT-XXV/1 cruise ranged from
2 to 157 pmol L-1, with the highest levels in the subtropics of the
Southern Hemisphere and lower levels in the tropics
(Fig. 2). Model simulations were carried out
along the cruise track using monthly mean satellite data from November 2008
for chl a, surface winds, SST, and MLD as input parameters. The
simulations underestimated the measured isoprene concentrations
significantly, by as much as a factor of 20 over most of the cruise track
(mean error of 19.1 pmol L-1). Simulations were also carried out using
in situ shipboard measurements (chl a, wind speed, SST,
MLD) as the input parameters. In both cases, the model simulations show a
peak in the calculated isoprene levels at 13–17∘ N which is not
present in the observations, whereas the peak, using in situ data
as input parameter, is much smaller. This peak corresponds to elevated
chl a concentrations, suggesting that while there may have been
high biological activity in this region, isoprene-producing species were not
abundant (Figs. 3, 4). These results demonstrate
that a single isoprene production factor and bulk chl a
concentration do not adequately describe the variability in isoprene
production. When isoprene-producing PFTs are dominant, however, the modeled
isoprene values follow the observed isoprene values (increasing isoprene
concentration north of 33∘ N; Figs. 2,
5). The elevated isoprene concentrations in the subtropics of the Southern
Hemisphere are not represented by the model.
Comparison of observed (black) and modeled seawater isoprene
concentrations for the ANT-XXV/1 cruise. Model calculations were carried out
using the ISOPS05 model configuration, with monthly mean
satellite data (blue) for chl a, wind speed, SST, and MLD (climatology) and
in situ shipboard measurements (red).
Satellite and in situ data for the ANT-XXV/1 cruise. Monthly mean
satellite-derived data (blue) and in situ measurements (red) of
(a) chl a, (b) wind speed, (c) SST, and
(d) monthly mean climatology values (blue) and in situ measurements
(red) of MLD.
Comparison of in situ measured isoprene (black) with model-derived
isoprene concentrations for the ANT-XXV/1 cruise using ISOPS05
(blue), ISOPFT (orange), and ISOPFT-kBIO
(red). Squares and circles indicate direct measurements; solid lines are interpolated
data.
Monthly mean satellite data cannot resolve rapid changes like short
phytoplankton blooms or wind events. We compared the satellite data to the
ship's in situ measurements of SST, wind speed, calculated MLD, and in situ
measured chl a concentration as input parameters for the model (Fig. 3) in
order to determine if the resolution of the satellite data does resolve
important features. The modeled isoprene concentrations closely follow the
variability in chl a, demonstrating that chl a has the strongest
influence of the four input parameters to the model. The differences between
modeled isoprene concentrations using in situ data vs. satellite data are
due primarily to the differences in chl a (in situ data are in general 2
times higher than satellite data) with the largest differences in the regions
from 10–25 to 40–45∘ N. As the discrepancies between in situ and
satellite data are significant, in situ measured data of chl a are used
from now on for further calculations with the ISOPS05 model.
Using monthly mean satellite data for wind speed, SST, and climatological
values for MLD does not bias the model results significantly relative to the
in situ data. Eight-day mean chl a and weekly wind speed satellite data (not
shown) are also available and could lower the discrepancies to the in situ
data. For this study, 8-day values were not useful for this region and time
due to cloud coverage (loss of 46 % of data points). A compromise between
the two would be to average the 8-day values over a larger area grid to
increase the amount of satellite-derived data, but this would lower the
resolution and therefore the accurate comparison with the cruise track.
Modeling isoprene production using PFTs and revised
kBIOL
Palmer and Shaw (2005) used a universal Pchloro value of
1.8 ± 0.7 µmoles (g chl a)-1 day-1 based on
laboratory phytoplankton monoculture experiments with several cyanobacteria,
eukaryotes, and coccolithophores (Table 1; Shaw et al., 2003).
Subsequent laboratory experiments with monocultures of different
phytoplankton species have shown generally higher isoprene production rates
with large variations between PFTs (Arnold et al., 2009; Bonsang et al.,
2010; Colomb et al., 2008; Exton et al., 2013). In addition, Tran et al. (2013) observed that isoprene concentrations in the field are highly
PFT dependent.
Chlorophyll-normalized isoprene production rates
(Pchloro) determined from analysis of phytoplankton cultures
experiments described in the literature (Exton et al., 2013 and references
therein). Pchloro values are given in
µmol (g chl a)-1 day-1.
SpeciesLiteratureAveraged Pchloro valuesReferencesPchloro valuefor specific PFTsBacillariophyceaeChaetoceros neogracilis (CCMP 1318)28.48Colomb et al. (2008)Chaetoceros neogracilis (CCMP 1318)1.26 ± 1.19Bonsang et al. (2010)Thalassiosira pseudonana (CCAP 1085/125.76 ± 0.24Exton et al. (2013)Pelagomonas calceolate (CCMP 1214)1.6 ± 1.6Shaw et al. (2003)Phaeodactylum tricornutum (Falkowski)2.85Colomb et al. (2008)Phaeodactylum tricornutum (UTEX 646)1.12 ± 0.322.54Bonsang et al. (2010)Skeletonema costatum1.32 ± 1.21Bonsang et al. (2010)Skeletonema costatum (CCMP 1332)1.8Shaw et al. (2003)Thalassiosira weissflogii (CCMP 1051)4.56 ± 0.24Exton et al. (2013)Diatoms (elsewhere)2.48 ± 1.75Arnold et al. (2009)Cylindrotheca sp.2.64Exton et al. (2013)cold adapted BacillariophyceaeFragilariopsis kerguelensis0.56 ± 0.35Bonsang et al. (2010)Chaetoceros debilis0.65 ± 0.2Bonsang et al. (2010)Chaetoceros muelleri (CCAP 1010/3)9.36 ± 1.2Excluded from theExton et al. (2013)Fragilariopsis cylindrus0.96 ± 0.24average isopreneExton et al. (2013)Nitzschia sp. (CCMP 1088)0.96 ± 0.24production rateExton et al. (2013)Synedropsis sp. (CCMP 2745)0.72 ± 0.24Exton et al. (2013)Diatoms (Southern Ocean)1.21 ± 0.57Arnold et al. (2009)DinophyceaeProrocentrum minimum10.08 ± 1.44Exton et al. (2013)Symbiodinium sp. (CCMP 2464)4.56 ± 3.12Exton et al. (2013)Symbiodinium sp. (CCMP 2469)17.04 ± 8.413.78Exton et al. (2013)Symbiodinium sp.9.6 ± 2.8Exton et al. (2013)Symbiodinium sp. (CCMP 2463)27.6 ± 1.68Exton et al. (2013)CyanophyceaeProchlorococcus sp. (axenic MED4) (high light)1.5 ± 0.91.5Shaw et al. (2003)Prochlorococcus9.66 ± 5.789.66Arnold et al. (2009)Synechococcus sp. (RCC 40)4.97 ± 2.87Bonsang et al. (2010)Synechococcus sp. (WH 8103)1.46.04Shaw et al. (2003)Synechococcus sp. (CCMP 1334)11.76 ± 0Exton et al. (2013)ChlorophyceaeDunaliella tertiolecta0.36 ± 0.22Bonsang et al. (2010)Dunaliella tertiolecta (DUN, Falkowski)2.851.47Colomb et al. (2008)Dunaliella tertiolecta (CCMP 1320)1.2Exton et al. (2013)CryptophyceaeRhodomonas lacustris (CCAP 995/3)9.36 ± 0.729.36Exton et al. (2013)PrasinophyceaeMicromonas pusilla (CCMP 489)1.4 ± 0.8Shaw et al. (2003)Prasinococcus capsulatus (CCMP 1614)32.16 ± 5.7612.47Exton et al. (2013)Tetraselmis sp. (CCMP 965)3.84 ± 0.24Exton et al. (2013)PrymnesiophyceaeCalcidiscus leptoporus (AC 365)5.4Colomb et al. (2008)Emiliania huxleyi (CCMP 371)11.54Colomb et al. (2008)Emiliania huxleyi (CCMP 371)1Bonsang et al. (2010)Emiliania huxleyi (CCMP 373)1 ± 0.56.92Shaw et al. (2003)Emiliania huxleyi (CCMP 373)2.88 ± 0.48Exton et al. (2013)Emiliania huxleyi (CCMP 1516)11.28 ± 0.96Exton et al. (2013)Gephyrocapsa oceanica15.36 ± 4.1Exton et al. (2013)
Proportion of main PFTs contributing to the total isoprene
production rate for each station during ANT-XXV/1.
We averaged the Pchloro values of different PFTs
(Table 2) and multiplied these values by the amount
of the corresponding PFT. Using PFTs instead of total biomass of
phytoplankton (chl a) in the model run results in higher isoprene
model concentrations (orange, Fig. 4), which
match the overall isoprene concentration levels measured north of
10∘ N quite well. However, there are also regions where the model
still cannot reproduce the measured isoprene concentrations. Between
10∘ N and 25∘ S, the calculated isoprene concentrations
are quite stable with only small variations between 6 and 23 pmol L-1.
Measured concentrations are slightly higher between 10∘ N and
12∘ S (15–30 pmol L-1) and sharply increase to
40–60 pmol L-1 south of 12∘ S with a maximum concentration of
150 pmol L-1 (16∘ S). As there were no significant
differences in wind speed, SST, or MLD in these two regions during the
cruise, there must be at least one additional source which is not captured
in the model. In contrast, at 15∘ N and at 22∘ N the
model overestimates the isoprene concentration
(Fig. 4). Chl a concentrations are 10–20 times higher in these two areas than elsewhere on the cruise
(Fig. 3) and dominated by diatoms. However, the
calculated isoprene is not 10–20 times higher, since diatoms have a
relatively low Pchloro value (2.54 µmol (g chl a)-1 day-1) and, therefore, using their
respective PFT value modulates the influence of the increased chl a
on isoprene concentrations (Fig. 5).
Excluding the two bloom areas, the main PFTs contributing to the modeled
isoprene concentrations were prokaryotic phytoplankton (cyanobacteria and
Prochlorococcus) and haptophytes (Fig. 5, see also Taylor et al.,
2011a). It should be noted that the PFTs considered in our study are only
part of the full phytoplankton community. In addition, these values can be
easily over- or underestimated due to a high variability in the
Pchloro values within one group of PFTs (e.g., haptophytes:
1–15.36 µmol isoprene (g chl a)-1 day-1; Table 2).
Using the ISOPFT-kBIO model approach, the isoprene
concentrations increase by a factor of 1.35, resulting in better agreement
with the observations (Fig. 4). Overall for the conditions of this cruise,
the ISOPFT-kBIO model simulation yields 12-fold higher
isoprene levels than ISOPS05 (mean error of
11.8 pmol L-1).
It is obvious that even after implementing these changes the model does not
reproduce all the measured isoprene values or their distribution pattern.
One particular problem is that marine isoprene emissions are very low in
comparison to terrestrial isoprene emissions. Coastal emissions have to be
calculated and interpreted carefully due to this terrestrial influence. We
assume no terrestrial influence in the open ocean, since the atmospheric
lifetime of isoprene is short. Despite the terrestrial influence on
atmospheric isoprene values over the ocean, calculating surface ocean
isoprene concentrations, other assumptions in the model should be
scrutinized in order to understand the discrepancies between measured and
calculated values:
The model assumes well-mixed isoprene concentrations through the MLD, which
is, in fact, not the case. Measurements of depth profiles show a vertical
gradient with a maximum of isoprene at the depth of the chl a
maximum slightly below the MLD (Bonsang et al., 1992; Milne et al.,
1995; Moore and Wang, 2006), which was also measured during our three
campaigns (data not shown). Gantt et al. (2009) tried to solve
this problem using a light-dependent isoprene production rate, but this
resulted in high fluxes in the tropics that are questionable when compared
to field measurements.
Using PFT-dependent production rates strongly improved the model by adding
more specific and realistic product information. Nonetheless, we may still
be missing some important species within the PFTs, and the average taken over
the isoprene measurements among the cultured species within one PFT carries
some uncertainty. We used up to eight different PFTs, illustrating that only
the four main groups (haptophytes, cyanobacteria, Prochlorococcus,
and diatoms) produce the most isoprene (Fig. 5).
These groups are also the only four detected by the satellite product PHYSAT
(Alvain et al., 2005), which has been used previously for predictions of
isoprene (Arnold et al., 2009; Gantt et al., 2009). However, neglecting
the other PFTs might lead to different results (others,
Fig. 5). This highlights the need to measure the
isoprene emission of more species within each PFT group under different
physiological conditions. Emissions in laboratory culture experiments can
vary depending on the growth stage of the phytoplankton species
(Milne et al., 1995). Shaw et al. (2003) showed
that the health conditions of the phytoplankton species directly influence
the emission rates of isoprene when using phage-infected cultures. However, also
environmental stress factors, such as temperature and light, influence the
ability of different species to produce isoprene (Shaw et al., 2003; Exton
et al., 2013; Meskhidze et al., 2015). More exact data would also,
potentially, lower the uncertainty of global marine isoprene emissions,
which was found to be in the range of 20 % when using the upper or lower
bounds of PFT-dependent production rates (Gantt et al., 2009).
The temporal resolution of the simple model may also not be adequate.
Gantt et al. (2009) could show that their model, using remote
sensing input in combination with the light dependence of isoprene
production, overestimated daytime isoprene concentrations and underestimated
nighttime concentrations compared to the high temporal resolution field
measurements of Matsunaga et al. (2002). The possible diurnal
cycle of isoprene could not be resolved with remote sensing data obtained
only at a specific local time during the day (e.g., 10:00 for MODIS Terra and
13:00 for MODIS Aqua).
The role of bacteria in producing isoprene is also unclear and may be a
missing variable in the steady-state equation. Alvarez et al. (2009) observed bacterial
isoprene production in estuary sediments and discovered isoprene production
using different cultures of bacteria. However, Shaw et al. (2003) could not find any evidence of bacterial isoprene production in
separate experiments.
Verification of the ISOPFT-kBIO model using data from the Indian
and eastern Pacific Oceans
Isoprene concentrations calculated with the original (ISOPS05) and
revised (ISOPFT-kBIO) model are compared to measured isoprene in the
surface ocean at two additional campaigns in two widely differing ocean
basins (Indian Ocean, SPACES/OASIS, 2014; eastern Pacific Ocean, ASTRA-OMZ,
2015). The original model ISOPS05 predicts on average 19 ± 12 times lower isoprene concentrations compared with measured values for the
additional two ship campaigns (circles, Fig. 6),
which confirms the results obtained for ANT-XXV/1. With the newly determined
(lower) value for kBIOL and PFT-dependent Pchloro values,
the ISOPFT-kBIO model predicts concentrations that are 10 times higher
than the original model ISOPS05 output (crosses,
Fig. 6). This leads to a mean underestimation of
1.7 ± 1.2 between modeled and measured isoprene concentrations. The
main cause of the better agreement between measured and modeled isoprene
concentrations is the isoprene production rate related to the production
input parameter (color coding, Fig. 6). The mean
isoprene production rate using chl a as an input parameter multiplied
by a factor of 1.8 µmol (g chl a)-1 day-1 is less
than 0.5 pmol L-1 day-1, which is insufficient to explain the
measured concentrations in all three campaigns. Using Pchloro values
multiplied with the concentration of the related PFT yields in an isoprene
production rate of 1–2 pmol L-1 day-1 in non-bloom areas and even
higher rates during phytoplankton blooms, resulting in isoprene
concentrations that are comparable to the measured ones. The opposite can
also occur, as seen on DOY 322 (Fig. 6), when PFT
specific production rates are smaller than those using chl a only,
due to the dominance of a low isoprene-producing PFT. Even though the
improved model is tested in three widely different ocean basins, there are
still different regions where the model should be tested with direct
isoprene measurements to verify the model output.
Observed isoprene concentration divided by modeled isoprene
concentration on a logarithmic scale for three different cruises: on the left is
SPACES/OASIS 2014, in the middle is ASTRA-OMZ 2015, and on the right is ANT-XXV/1 2008. Circles and
crosses represent data derived by the original ISOPS05 and
revised ISOPFT-kBIO model, respectively. Every data
point is color coded with the corresponding isoprene production rate input
parameter. Grey diamonds represent data using parameterized PFT data by
Hirata et al. (2011); the black line represents a ratio of 1.
Global marine isoprene fluxes in nmol m-2 day-1 for
2014.
Global oceanic isoprene emissions and implications for marine aerosol
formation
Monthly mean global ocean isoprene concentrations were calculated using the
revised model ISOPFT-kBIO (2∘× 2∘ grid). As there
were no PFT satellite data readily available, we used an empirical
relationship between chl a and PFTs as parameterized by
Hirata et al. (2011). The quality of this
parameterization was verified against the PFT data sets from all three
campaigns (coefficient of determination: R2=0.89, Fig. S1
in the Supplement) and is shown in Fig. 6 (grey
diamonds). Monthly mean global ocean isoprene emissions (Figs. S2–S13
in the Supplement) were averaged in order to compute global sea-to-air fluxes of
isoprene for 2014 (Fig. 7). An annual emission of
0.21 Tg C was calculated, which is 2 times higher than the value estimated
by Palmer and Shaw (2005) (0.11 Tg C yr-1). The highest emissions,
more than 100 nmol m-2 day-1, can be seen in the North Atlantic
Ocean and the Southern Ocean, associated with high biological productivity
and strong winds driving the air–sea gas exchange. The influence of regional
hot spots of biological productivity, such as the upwelling off Mauretania
or the Brazil–Malvinas Confluence Zone, can also be seen. The tropics
(23.5∘ S–23.5∘ N) account for only 28 % of global
isoprene emissions, but they represent ∼ 47 % of the world
oceans.
One-day mean measured (blue) and calculated (red) daytime isoprene
mixing ratios (ppt) during SPACES/OASIS (2014) and ASTRA-OMZ (2015).
Calculated isoprene air values were derived by using the sea-to-air flux, a
marine boundary layer height of 800 m, and the 1 h atmospheric lifetime
based on a simple box model approach for each individual measurement.
Yearly emissions of 0.21 Tg C are at the lower end of the range of
previously published studies (Arnold et al., 2009, 0.27 Tg C yr-1; Gantt et al., 2009,
0.92 Tg C yr-1). Both studies use remotely sensed PFT data instead of
chl a to evaluate the isoprene production. Unlike this study, they
implemented the Alvain et al. (2005) approach using PHYSAT data, which
uses spectral information to produce global distributions of the dominant
PFT but is limited to four phytoplankton groups (haptophytes,
Prochlorococcus, Synechococcus, and diatoms). It should be
noted that PHYSAT does not provide actual concentrations but rather only
the relative dominance of the four groups. Arnold et al. (2009) used similar
assumptions as Palmer and Shaw (2005) to calculate isoprene
loss, namely that loss in the water column by advective mixing and aqueous
oxidation is on a longer timescale than loss by air–sea gas exchange and,
therefore, negligible. Thus, their calculated emissions of 0.27 Tg C yr-1 are an upper estimate.
The approach of Gantt et al. (2009) had two main differences compared to our study. (1) Instead of using
the MLD climatology of de Boyer Montégut et al. (2004),
they used a maximum depth where isoprene production can occur as calculated
by the downwelling irradiance (using the diffuse attenuation coefficient
values at 490 nm) and the light propagation throughout the water column that
is estimated by using the Lambert–Beer law. (2) They tested two of the
detectable PFTs in laboratory experiments using monocultures of diatoms and
coccolithophores growing under different light conditions to evaluate light-intensity-dependent isoprene production rates. Light-intensity-dependent
production rates of Prochlorococcus and Synechococcus were
derived after Gantt et al. (2009) using the original production rates
at a specified wavelength measured by Shaw et al. (2003).
Their isoprene emission calculations are more than 4 times higher than
calculated with our approach, probably as a result of the light-dependent
isoprene production rates. Whereas our global map shows very low emissions
in the tropics due to a low phytoplankton productivity, the emissions
modeled by Gantt et al. (2009) are comparable to those of high
productivity areas like the Southern Ocean or the North Atlantic Ocean,
likely as a consequence of the high solar radiation in the tropics. The data
from our three cruises contradict this model-derived result and show very
low concentrations in the tropical regions, which implies a very low flux of
isoprene to the atmosphere. Furthermore, Meskhidze et al. (2015)
showed that, at a specific light intensity, the isoprene production rate of
tested monocultures sharply decreases.
Using atmospheric isoprene concentrations measured in two of the three
campaigns, we were able to use a top-down approach to calculate isoprene
emissions in order to compare with the bottom-up flux estimates. We used a
box model with an assumed marine boundary layer height (MBLH) of 800 m,
which reflected the local conditions during the two campaigns. The only
source of isoprene for the air was assumed to be the sea-to-air flux
(emission) and the atmospheric lifetime (τ) was assumed to be
determined by reaction with OH (chemical loss, 1 h). The sea-to-air flux was
calculated by multiplying kAS with the measured isoprene
concentration (CW) in the ocean (Eq. 3). We assumed
CA to be zero in order to have the highest possible
sea-to-air-flux, following a conservative approach. The concentration outside
the box was assumed to be the same as inside to neglect advection into and
out of the box. The resulting calculated steady-state isoprene air
concentration for every box (1-day mean value of all individual measurements
at daytime) is shown in Fig. 8 (for a 1 h lifetime it takes approximately
10 h to achieve steady state) and is calculated as follows:
CA=kAS×CWτMBLH.
For comparison, the mean measured concentration of isoprene in the
atmosphere during the two cruises is 2.5 ± 1.5 ppt and therefore
45 times higher than the calculated isoprene air values. The measured
concentrations match previously measured remote open ocean atmospheric
values (Shaw et al., 2003). We only used atmospheric
measurements which were obtained during daytime (to reflect reaction with
OH) and were not influenced by terrestrial sources. This was determined by
omitting data points with concomitant high levels of anthropogenic
hydrocarbons (concentrations of butane higher 20 ppt). Reported mean
atmospheric lifetime estimates of isoprene range from minutes up to 4 h, depending mainly on the atmospheric concentration of OH
(Pfister et al., 2008). We calculate that for an
estimated lifetime of 1 and 4 h, a sea-to-air flux of at least
2000 and 500 nmol m-2 day-1,
respectively, is needed to reach the atmospheric concentration measured
during SPACES/OASIS and ASTRA-OMZ, which is approximately 10–20 times higher
than computed (even when assuming CA as zero). Recent studies
showed that the measured fluxes of isoprene range from
4.6–148 nmol m-2 day-1 in June–July 2010 in the Arctic (Tran et
al., 2013) to 181.0–313.1 nmol m-2 day-1 in the productive
Southern Ocean during austral summer 2010/2011 (Kameyama et al.,
2014). Despite these high literature values, it appears that the calculated
fluxes cannot explain the measured atmospheric concentrations even when a
conservative lifetime of 4 h is assumed.
Conclusions
The revised Palmer and Shaw (2005) isoprene emission model was evaluated
against direct surface ocean isoprene measurements from three different ocean
basins, yielding comparable ocean concentrations that were slightly
underestimated (factor of 1.7 ± 1.2). The resulting annual global
oceanic isoprene emissions are 2 times higher than the calculated flux with
the original model. However, using a simple top-down approach based on
measured atmospheric isoprene levels, we calculate that emissions from the
ocean are required to be more than 1 order of magnitude greater than those
computed using the bottom-up estimate based on measured oceanic isoprene
levels. This result is consistent with a numerical evaluation of global ocean
isoprene emissions by Luo and Yu (2010). One possible explanation could be
production in the surface microlayer (SML) that is not simulated by the
model. Ciuraru et al. (2015) showed that isoprene is produced photochemically
by surfactants in an organic monolayer at the air–sea interface. As the SML
is enriched with surfactants (Wurl et al., 2011), the isoprene flux from the
SML could range from 1000 to 33 000 nmol m-2 day-1, which is much
larger (about 2 orders of magnitude) than the highest fluxes calculated from
our observations. To date, there is no evidence of such a large gradient in
the surface ocean between the surface and 10 m. Thus, further field
measurements probing the SML could be a step forward in reconciling the role
of the ocean for the atmospheric isoprene budget. Using the bottom-up
approach, isoprene emissions are much smaller and given this scenario,
isoprene consequently appears to be a relatively insignificant source of OC
in the remote marine atmosphere. Arnold et al. (2009) calculated a yield of
0.04 Tg yr-1 OC derived from marine isoprene by using yearly emissions
of 1.9 Tg yr-1 and a SOA yield of 2 % (Henze and Seinfeld, 2006).
This is equivalent to 0.5 % of estimated 8 Tg yr-1 global source
of oceanic OC (Spracklen et al., 2008). Using our bottom-up emission of
0.21 Tg C yr-1 will even lower this small influence. Until this
discrepancy between bottom-up and top-down approaches is resolved, the
question of whether isoprene is a main precursor to remote marine boundary
layer particle formation still remains open.
Data availability
All isoprene data are available from the corresponding author. Pigment data
from ANT-XXV/1 are available from PANGAEA (Taylor et al., 2011b). Pigment
data from SPACES/OASIS and ASTRA-OMZ will be available from PANGAEA but for
now can be obtained through the corresponding author.
The Supplement related to this article is available online at doi:10.5194/acp-16-11807-2016-supplement.
Acknowledgements
The authors would like to thank the captain and crew of the R/V Polarstern
(ANT-XXV/1) and R/V Sonne (SPACES/OASIS and ASTRA-OMZ) as well as the chief
scientists, Gerhard Kattner (ANT-XXV/1) and Kirstin Krüger
(SPACES/OASIS). Boris Koch and Birgit Quack also provided valuable
help. We thank Sonja Wiegmann for HPLC pigment analysis of SPACES/OASIS and
ASTRA-OMZ samples, Sonja Wiegmann and Wee Cheah for pigment sampling
during SPACES/OASIS, and Rüdiger Röttgers for helping with pigment
sampling during ASTRA-OMZ. Paul I. Palmer gratefully acknowledges his Royal
Society Wolfson Research Merit Award. Elliot Atlas acknowledges support from
the NASA UARP program and thanks Leslie Pope and Xiaorong Zhu for assistance
in canister preparation. The authors gratefully acknowledge the NOAA Air
Resources Laboratory (ARL) for the provision of the HYSPLIT transport and
dispersion model used in this publication as well as NASA for providing the
satellite MODIS Aqua and MODIS Terra data. QuikScat and SeaWinds data were
produced by Remote Sensing Systems with thanks to the NASA Ocean Vector
Winds Science Team for funding and support. This work was carried out under
the Helmholtz Young Investigator Group of Christa A. Marandino, TRASE-EC
(VH-NG-819), from the Helmholtz Association through the President's
Initiative and Networking Fund and the GEOMAR Helmholtz Centre for Ocean Research Kiel. The R/V Sonne cruises SPACES/OASIS and ASTRA-OMZ were
financed by the BMBF through grants 03G0235A and 03G0243A,
respectively.
The article processing charges for this open-access publication
were covered by a Research Centre of the Helmholtz Association.
Edited by: A. Hofzumahaus
Reviewed by: two anonymous referees
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