The project MarParCloud (Marine biological production, organic
aerosol Particles and marine Clouds: a process
chain) aims to improve our understanding of the genesis, modification and
impact of marine organic matter (OM) from its biological production, to
its export to marine aerosol particles and, finally, to its ability to
act as ice-nucleating particles (INPs) and cloud condensation nuclei (CCN). A
field campaign at the Cape Verde Atmospheric Observatory (CVAO) in the
tropics in September–October 2017 formed the core of this project that was
jointly performed with the project MARSU (MARine atmospheric
Science Unravelled). A suite of chemical,
physical, biological and meteorological techniques was applied, and
comprehensive measurements of bulk water, the sea surface microlayer (SML),
cloud water and ambient aerosol particles collected at a ground-based and a
mountain station took place.
Key variables comprised the chemical characterization of the atmospherically
relevant OM components in the ocean and the atmosphere as well as
measurements of INPs and CCN. Moreover, bacterial cell counts, mercury
species and trace gases were analyzed. To interpret the results, the
measurements were accompanied by various auxiliary parameters such as air
mass back-trajectory analysis, vertical atmospheric profile analysis, cloud
observations and pigment measurements in seawater. Additional modeling
studies supported the experimental analysis.
During the campaign, the CVAO exhibited marine air masses with low and
partly moderate dust influences. The marine boundary layer was well mixed as
indicated by an almost uniform particle number size distribution within the
boundary layer. Lipid biomarkers were present in the aerosol particles in
typical concentrations of marine background conditions. Accumulation- and
coarse-mode particles served as CCN and were efficiently transferred to the
cloud water. The ascent of ocean-derived compounds, such as sea salt and
sugar-like compounds, to the cloud level, as derived from chemical analysis
and atmospheric transfer modeling results, denotes an influence of marine
emissions on cloud formation. Organic nitrogen compounds (free amino acids)
were enriched by several orders of magnitude in submicron aerosol particles
and in cloud water compared to seawater. However, INP measurements also indicated
a significant contribution of other non-marine sources to the local INP
concentration, as (biologically active) INPs were mainly present in
supermicron aerosol particles that are not suggested to undergo strong
enrichment during ocean–atmosphere transfer. In addition, the number of CCN
at the supersaturation of 0.30 % was about 2.5 times higher during dust
periods compared to marine periods. Lipids, sugar-like compounds, UV-absorbing (UV: ultraviolet) humic-like substances and low-molecular-weight neutral components
were important organic compounds in the seawater, and highly surface-active
lipids were enriched within the SML. The selective enrichment of specific
organic compounds in the SML needs to be studied in further detail and
implemented in an OM source function for emission modeling to better
understand transfer patterns, the mechanisms of marine OM transformation in the
atmosphere and the role of additional sources.
In summary, when looking at particulate mass, we see oceanic compounds
transferred to the atmospheric aerosol and to the cloud level, while from a
perspective of particle number concentrations, sea spray aerosol (i.e.,
primary marine aerosol) contributions to both CCN and INPs are rather
limited.
Introduction and motivation
The ocean covers around 71 % of the Earth's surface and acts as a source
and sink for atmospheric gases and particles. However, the complex
interactions between the marine boundary layer (MBL) and the ocean surface
are still largely unexplored (Cochran et al., 2017; de Leeuw et al., 2011;
Gantt and Meskhidze, 2013; Law et al., 2013). In particular, the role of
marine organic matter (OM) with its sources and contribution to marine
aerosol particles is still elusive. This includes, for example, how this particle
fraction might lead to a variety of effects such as impacting health through
the generation of reactive oxygen species, OM composition increasing or
decreasing the absorption of solar radiation and therefore radiative
properties, and impacting marine ecosystems via atmospheric deposition (e.g.,
Abbatt et al., 2019; Brooks and Thornton, 2018; Burrows et al., 2013; Gantt
and Meskhidze, 2013; Pagnone et al., 2019; Patel and Rastogi, 2020).
Furthermore, knowledge of the properties of marine organic aerosol particles
and their ability to act as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs) is not fully understood. The fraction of marine
CCN made up of sea spray aerosol is still debated and suggested to comprise
about 30 % on a global scale (excluding the high southern latitudes)
(Quinn et al., 2017). Important pieces of information about marine CCN
are still missing (e.g., Bertram et al., 2018). Ocean-derived INPs were
proposed to play a dominating role in determining INP concentrations in
near-surface air over remote areas such as the Southern Ocean; however,
their source strength in other oceanic regions and knowledge about
which physicochemical properties determine the INP efficiency are still
largely unknown (Burrows et al., 2013; McCluskey et al., 2018a, b). In recent years, it was clearly demonstrated that marine
aerosol particles contain a significant organic mass fraction derived from
primary and secondary processes (Middlebrook et al., 1998; Prather et al., 2013; Putaud et al., 2000; van Pinxteren et al., 2015, 2017). Although it is known that the main OM groups show similarities to
oceanic composition and comprise carbohydrates, proteins, lipids, and
humic-like and refractory organic matter, a large fraction of OM in the
marine environment is still unknown at a molecular level, thereby limiting
our ability to constrain interlinked processes (e.g., Gantt and Meskhidze,
2013).
The formation of ocean-derived aerosol particles and their precursors is
influenced by the uppermost layer of the ocean, the sea surface microlayer
(SML), which forms due to different physicochemical properties of air and
water (Engel et al., 2017; Wurl et al., 2017). Recent investigations suggest
that the SML is stable up to wind speeds of >10 m s-1; it
is therefore existent at the global average wind speed of 6.6 m s-1 and is a fixed component influencing the ocean–atmosphere interaction on
global scales (Wurl et al., 2011). The SML is involved in the generation of
sea spray (or primary) particles, including their organic fraction by the
transfer of OM to rising bubbles before they burst out to jet droplets and
film droplets (de Leeuw et al., 2011). A mechanistic and predicable
understanding of these complex and interacting processes is still lacking
(e.g., Engel et al., 2017). Moreover, surface films influence air–sea gas
exchange and may undergo (photo)chemical reactions, leading to the production
of unsaturated and functionalized volatile organic compounds (VOCs) acting
as precursors for the formation of secondary organic aerosol (SOA) particles
(Brueggemann et al., 2018; Ciuraru et al., 2015). Thus, the dynamics of OM and
especially the surface-active compounds present at the air–water interface may
have global impacts on the air–sea exchange processes necessary to
understand oceanic feedbacks on the atmosphere (e.g., Pereira et al., 2018).
Within the SML, OM is a mixture of different compounds including
polysaccharides, amino acids, proteins, lipids and chromophoric dissolved
organic matter (CDOM) that are either dissolved or particulate (e.g.,
Gašparović et al., 1998a, 2007; Stolle et al., 2019). In addition, the complex microbial community is assumed to
exert a strong control on the concentration and composition of OM
(Cunliffe et al., 2013). In calm conditions, bacteria accumulate in the SML
(Rahlff et al., 2017) and are an integral part of the biofilm-like habitat
forming at the air–sea interface (Stolle et al., 2010; Wurl et al., 2016).
A variety of specific organic compounds such as surface-active substances
(SASs), volatile organic compounds (VOCs) and acidic polysaccharides
aggregating to transparent exopolymer particles (TEPs) strongly influence
the physicochemical properties of OM in the SML. SASs (or surfactants) are
highly enriched in the SML relative to bulk water and contribute to the
formation of surface films (Frka et al., 2009, 2012; Wurl et al., 2009). SASs are excreted by phytoplankton, during zooplankton grazing and
bacterial activities (e.g., Gašparović et al., 1998b). The enrichment
of SASs in the SML occurs predominantly via advective and diffusive transport
at low wind speeds or bubble scavenging at moderate to high wind speeds
(Wurl et al., 2011). When transferred to the atmosphere, OM with surfactant
properties, ubiquitously present in atmospheric aerosol particles, has the
potential to affect the cloud droplet formation ability of these particles
(e.g., Kroflič et al., 2018).
Sticky and gel-like TEPs are secreted by phytoplankton and bacteria and can
form via abiotic processes (Wurl et al., 2009). Depending on their buoyancy
they may contribute to sinking particles (marine snow) or can rise and
accumulate at the sea surface. Due to their sticky nature, TEPs are called
“marine glue”, and as such they contribute to the formation of hydrophobic
films by trapping other particulate and dissolved organic compounds (Wurl et al., 2016). Additionally, TEPs are suspected to play a pivotal role in the
release of marine particles into the air via sea spray and bursting bubbles
(Bigg and Leck, 2008).
Many studies recognize a possible link between marine biological activity
and marine-derived organic aerosol particles (Facchini et al., 2008; O'Dowd et al., 2004; Ovadnevaite et al., 2011) and thus to the SML due to the
linkages outlined before. Yet, the environmental drivers and mechanisms for
OM enrichment are not very clear (Brooks and Thornton, 2018; Gantt and
Meskhidze, 2013), and individual compound studies can only explain a small
part of OM cycling (e.g., van Pinxteren et al., 2017; van Pinxteren and
Herrmann, 2013). The molecular understanding of the occurrence and
processing of OM in all marine compartments is essential for a deeper
understanding and for an evidence-based implementation of organic aerosol
particles and their relations to the oceans in coupled ocean–atmosphere
models. Synergistic measurements in comprehensive interdisciplinary field
campaigns in representative areas of the ocean and also laboratory studies
under controlled conditions are required to explore the biology, physics and
chemistry in all marine compartments (e.g., Quinn et al., 2015).
Accordingly, the project MarParCloud (Marine biological
production, organic aerosol Particles and marine Clouds: a process chain) addresses central aspects of ocean–atmosphere
interactions focusing on marine OM within an interdisciplinary field
campaign at the Cape Verde islands that took place from 13 September
to 13 October 2017. Together with contributions from the Research and
Innovation Staff Exchange EU project MARSU (MARine atmospheric
Science Unravelled: Analytical and mass
spectrometric techniques development and application), synergistic
measurements will deliver an improved understanding of the role of marine
organic matter. MarParCloud focuses on the following main research
questions.
To what extent is seawater a source of OM to aerosol particles (regarding
number, mass, chemical composition, CCN and INP concentration) and in cloud
water?
What are the important chemically defined OM groups (proteins, lipids,
carbohydrates – as sum parameters and on a molecular level) in oceanic surface
films, aerosol particles and cloud water, and how are they linked?
What are the main biological and physical factors responsible for the
occurrence and accumulation of OM in the surface film and in other marine
compartments (aerosol particles, cloud water)?
What functional role do bacteria play in aerosol particles?
Does the SML contribute to the formation of ice nuclei, and at what
temperatures do these nuclei become ice-active? Are these ice nuclei found
in cloud water?
Does the presence of marine OM in the surface ocean drive the concentration
of CCN in the MBL?
How must an emission parameterization for OM (including individual species)
be designed in order to best reflect the concentrations in the aerosol
depending on those in seawater or biological productivity under given
ambient conditions?
The tropics, with high photochemical activity, are of central importance in
several aspects of the climate system. Approximately 75 % of the
tropospheric production and loss of ozone occurs within the tropics, in
particular in the tropical upper troposphere (Horowitz et al., 2003). The
Cape Verde islands are located downwind of the Mauritanian coastal upwelling
region northwest of the islands. In addition, they are in a region of
the Atlantic that is regularly impacted by dust deposition from the African
Sahara (Carpenter et al., 2010). The remote station of CVAO is therefore an
excellent site for process-oriented campaigns embedded into long-term
measurements of atmospheric constituents, which are essential for
understanding atmospheric processes and their impact on climate.
Strategy of the campaign
The present contribution intends to provide an introduction, overview and
first results of the comprehensive MarParCloud field campaign to the
MarParCloud Special Issue. We will describe the oceanic and atmospheric
ambient conditions at the CVAO site that have not been synthesized elsewhere
and are valuable in themselves because of the sparseness of the existing
information at such a remote tropical location. Next, we will describe the
sampling and analytical strategy during MarParCloud, taking into account all
marine compartments, i.e., the seawater (SML and bulk water), ambient aerosol
particles (at ground level and Mt. Verde, elevation: 744 m a.s.l.) and
cloud water. Detailed aerosol investigations were carried out for both the
chemical composition and physical properties at both stations. In
addition, vertical profiles of meteorological parameters were measured at
CVAO using a helikite. These measurements were combined with modeling
studies to determine the MBL height. In conjunction, they are an indicator
for the mixing state within the MBL, providing further confidence for
ground-level-measured aerosol properties being representative for those at
cloud level. The chemical characterization of OM in the aerosol particles as
well as in the surface ocean and cloud water included sum parameters (e.g.,
OM classes like biopolymers and humic-like substances) and molecular
analyses (e.g., lipids, sugars and amino acids). Additionally, to address
direct oceanic transfer (bubble bursting), seawater and aerosol particle
characterizations obtained from a systematic plunging waterfall tank are
presented. Ocean surface mercury (Hg) associated with OM was investigated.
Marine pigments and marine microorganisms were analyzed to investigate their
relation to OM and to algae-produced trace gases. Marine trace gases such as
dimethyl sulfide (DMS), other VOCs and oxygenated (O)VOCs were measured and
discussed. Furthermore, a series of continuous nitrous acid (HONO)
measurements was conducted at the CVAO with the aim of elucidating the
possible contribution of marine surfaces to the production of this acid. To
explore whether marine air masses exhibit significant potential to form
SOA, an oxidation flow reactor (OFR) was deployed at the CVAO. Finally,
modeling studies to describe the vertical transport of selected marine
organic compounds from the ocean to the atmosphere up to cloud, level taking
into account advection and wind conditions, will be applied. From the obtained
results of organic compound measurements, a new source function for the
oceanic emission of OM will be developed. The measurements, first
interpretations and conclusions aggregated here will provide a basis for
upcoming detailed analysis.
Experimental designGeneral CVAO site and meteorology
The Cape Verde archipelago islands are situated in the eastern tropical
North Atlantic (ETNA). The archipelago experiences strong northeast trade
winds that divide the islands into two groups, the Barlavento (windward) and
Sotavento (leeward) islands. The northwestern Barlavento islands of São
Vicente and Santo Antão, as well as São Nicolau, are rocky and hilly,
making them favorable for the formation of orographic clouds.
The CVAO is part of a bilateral initiative between Germany and the UK to
conduct long-term studies in the tropical northeast Atlantic Ocean
(16∘51.49′ N, -24∘52.02′ E). The station is located directly at the shoreline at the northeastern tip
of the island of São Vicente at 10 m a.s.l.
The air temperature varies between 20 and 30 ∘C with a mean of
23.6 ∘C. The relative humidity is on average 79 % and
precipitation is very low (Carpenter et al., 2010). Due to the trade winds,
this site is free from local island pollution and provides reference
conditions for studies of ocean–atmosphere interactions. However, it also
lies within the Saharan dust outflow corridor to the Atlantic Ocean and
experiences strong seasonal dust outbreaks with peaks between late November
and February (Fomba et al., 2014; Patey et al., 2015; Schepanski et al., 2009). Air mass inflow to this region can vary frequently within a day,
leading to strong inter-day temporal variation in the aerosol mass and
chemical composition (Fomba et al., 2014; Patey et al., 2015).
Despite the predominant NE trade winds, air masses from the USA and
from Europe are partly observed. However, during autumn, marine air masses
are mainly present with few periods of dust outbreaks because at these times
the dust is transported at higher altitudes in the Saharan air layer (SAL)
over the Atlantic to the Americas (Fomba et al., 2014). During autumn, there
is no significant transport of the dust at lower altitudes, and only
intermittent effects of turbulence in the SAL lead to occasional dust
deposition and sedimentation from the SAL to lower altitudes and at ground
level. Furthermore, during autumn the mountain site (Mt. Verde) is often
covered with clouds as surface temperatures drop after typically very hot
summer months. Due to the frequent cloud coverage and less dust influence in
autumn, the MarParCloud campaign was scheduled from 13 September to
13 October 2017.
CVAO equipment during MarParCloud
The setup of the CVAO station is explained in detail in Carpenter et al. (2010) and Fomba et al. (2014). During the MarParCloud campaign, the 30 m
high tower was equipped with several aerosol particle samplers, including
high-volume PM1, PM10 (Digitel, Riemer, Germany) and total
suspended particulate (TSP; Sierra Anderson, USA) samplers, low-volume TSP
(home-built) and PM1 (Comde-Derenda, Germany) samplers, and a
size-resolved aerosol particle Berner impactor (five stages). The sampling
times were usually set to 24 h (more details in the Supplement). Online aerosol
instruments included a cloud condensation nuclei counter (CCNC; Droplet
Measurement Technologies, Boulder, USA) (Roberts and Nenes, 2005) to measure
the cloud condensation nuclei number concentration (NCCN). A TROPOS-type
scanning mobility particle sizer (SMPS) (Wiedensohler et al., 2012) and an
APS (aerodynamic particle sizer; model 3321, TSI Inc., Paul, MN, USA) with
a PM10 inlet were used to measure the size range from 10 nm to 10 µm. The particles hygroscopicity (expressed as κ; Petters and
Kreidenweis, 2007) was derived from combined NCCN and particle number
size distribution (PNSD) measurements from the SMPS and APS. Vertical
profiles of meteorological parameters were measured using a 16 m3
helikite (Allsopp Helikites Ltd, Hampshire, UK), a combination of a kite and
a tethered balloon. Additional equipment at the CVAO station on the ground
included a plunging waterfall tank, a long-path absorption photometer
(LOPAP) and a Gothenburg potential aerosol mass reactor (Go : PAM) chamber.
Further details on the measurements are listed and explained in the Supplement, and
all instruments can be found in Table S1.
Mt. Verde
Mt. Verde was a twin site for aerosol particle measurements and the only
site with cloud water sampling during the MarParCloud campaign. It is the
highest point of São Vicente island (744 m), situated in the
northeast of the island (16∘86.95′ N,
-24∘93.38′ E) and northwest of the CVAO. Mt. Verde
also experiences direct trade winds from the ocean with no significant
influence of anthropogenic activities from the island. Mt. Verde was in
clouds during roughly 58 % of the campaign. However, the
duration of the cloud coverage varied between 2 and 18 h, with longer
periods of cloud coverage observed in the nights when surface temperatures
dropped.
During the campaign, Mt. Verde was, for the first time, equipped with
similar collectors as operated at the CVAO, namely a high-volume Digitel
sampler for PM1 and PM10 bulk aerosol particles, a low-volume
TSP sampler, and a five-stage Berner impactor for size-resolved aerosol
particle sampling. Bulk cloud water was collected using six (four plastic and two stainless steel) compact Caltech active strand cloud water collectors
(CASCC2) (Demoz et al., 1996). The six samplers were run in parallel for a
sampling time between 2.5 and 13 h collecting between 78 and 544 mL of cloud
water per sampler in an acid-precleaned plastic bottle. It needs to be
pointed out that the aerosol particle samplers run continuously and aerosol
particles were also sampled during cloud events. The cloud droplets were
efficiently removed due to the preconditioning of the aerosol particles
sampled with the Berner impactor (more information in the Supplement) and due to the
size cut the PM1 sampler. However, for aerosol particles sampled with
the PM10 sampler, small cloud droplets can be collected as well. In
addition, the particles sampled with the low-volume TSP sampler can be
influenced by cloud droplets to some extent. The cloud liquid water content
was measured continuously by a particle volume monitor (PVM-100, Gerber
Scientific, USA), which was mounted on a support at the same height as the
cloud water samplers. The same suite of online aerosol instruments as
employed at the CVAO (SMPS, APS, CCNC) was installed at the mountainside.
All instruments employed at the Mt. Verde site are listed in Table S2.
Oceanographic setting and seawater sampling site
The ETNA around Cape Verde is characterized by a so-called oxygen minimum
zone (OMZ) at a water depth of approximately 450 m and by sluggish water
velocities (Brandt et al., 2015). The region is bounded by a highly
productive eastern boundary upwelling system (EBUS) along the African coast,
by the Cape Verde Frontal Zone (CVFZ) on its western side and by zonal
current bands towards the Equator (Stramma et al., 2005). Upper water masses
towards the archipelago are dominated by North Atlantic Central Water
(NACW) masses with enhanced salinity, whereas the South Atlantic Central Water (SACW) mass
is the dominating upper-layer water mass in the EBUS region (Pastor et al., 2008). Filaments and eddies generated in the EBUS region
propagate westwards into the open ocean and usually dissipate before
reaching the archipelago. However, observations from the Cape Verde Ocean
Observatory (CVOO) 60 nmi northeast of São Vicente island
(17∘35.00′ N, -24∘17.00′ E; http://cvoo.geomar.de, last access: 10 April 2020) also revealed the
occurrence of water masses originating from the EBUS region that were
advected by stable mesoscale eddies (Fiedler et al., 2016; Karstensen et al., 2015).
For the MarParCloud campaign, the water samples were taken at Baía das
Gatas, a beach that is situated upwind of the CVAO about 4 km northwest in
front of the station. The beach provided shallow access to the ocean that
allowed the employment of fishing boats for manual SML and bulk water
sampling as well as the other equipment. For SML sampling, the glass plate
technique as one typical SML sampling strategy was applied (Cunliffe and
Wurl, 2014). A glass plate with a sampling area of 2000 cm2 was
vertically immersed into the water and then slowly drawn upwards with a
withdrawal rate between 5 and 10 cm s-1. The surface film adheres to
the surface of the glass and is removed using framed Teflon wipers
(Stolle et al., 2010; van Pinxteren et al., 2012). Bulk
seawater was collected from a depth of 1 m using a specially designed device
consisting of a glass bottle mounted on a telescopic rod used to monitor
sampling depth. The bottle was opened underwater at the intended sampling
depth with a specifically conceived seal-opener.
In addition, the MarParCat, a remotely controllable catamaran, was applied
for SML sampling using the same principle as manual sampling (glass plate).
The MarParCat sampled bulk water in a depth of 70 cm. A more detailed
description of the MarParCat can be found in the Supplement. Using the two techniques,
manual sampling and the MarParCat, between 1 and 6 L of SML was
sampled at each sampling event. For the sampling of the SML, great care was
taken that all parts in contact with the sample (glass plate,
bottles, catamaran tubing) underwent an intense cleaning with 10 % HCl to
avoid contamination and carryover problems.
The sampling sites with the different setup and equipment are illustrated
in Fig. 1. All obtained SML and bulk water samples and their standard
parameters are listed in Table S3.
Illustration of the different sampling sites during the campaign.
Ambient conditionsAtmospheric conditions during the campaignMarine and dust influences
During autumn, marine background air masses are mainly observed at the CVAO,
interrupted by a few periods of dust outbreaks (Carpenter et al., 2010;
Fomba et al., 2014). A 5-year average dust record showed low
concentrations with average values of 25 and 17 µg m-3 during September and October, respectively (Fomba et al., 2014).
The dust concentrations during the campaign were generally <30µg m-3; however, strong temporal variation of mineral dust
markers was observed (Table 1). According to Fomba et al. (2013, 2014), a
classification into marine conditions (dust <5µg m-3, typically Fe <50 ng m-3), low dust
(dust <20µg m-3) and moderate dust (dust
<60µg m-3) conditions was used to describe
the dust influence during this period. Following this classification, one
purely marine period was defined from 22 to 24 September,
which was also evident from the course of the back trajectories (Fig. S1).
For the other periods, the air masses were classified as mixed with marine
and low or moderate dust influences as listed in Table 1. Based on a
three-modal parameterization method that regarded the number concentrations
in different aerosol particle modes, a similar but much finer classification
of the aerosol particles was obtained as discussed in Gong et al. (2020a).
Classification of the air masses according to dust concentrations
from the impactor samples after the calculation of dust concentrations
according to the Fomba et al. (2014) sample and under the consideration of backward
trajectories (Fig. 2).
The classification of air masses was complemented by air mass backward
trajectory analyses; 96 h back trajectories were calculated on an hourly
basis within the sampling intervals using the HYSPLIT model (HYbrid
Single-Particle Lagrangian Integrated Trajectory;
http://www.arl.noaa.gov/ready/hysplit4.html, last access: 26 July 2019) published by the
National Oceanic and Atmospheric Administration (NOAA) in the ensemble mode
at an arrival height of 500 m ± 200 m (van Pinxteren et al., 2010).
The back trajectories for the individual days of the entire campaign, based
on the sampling interval for aerosol particle sampling, were calculated and
are listed in Fig. S1. Air parcel residence times over different sectors
are plotted in Fig. 2. The comparison of dust concentration and the
residence time of the back trajectories revealed that in some cases low dust
contributions were observed although the air masses traveled almost
completely over the ocean (e.g., the first days of October). In such cases,
the entrainment of dust from higher altitudes might explain this finding. The
related transport of Saharan dust to the Atlantic during the measurement
period can be seen in a visualization based on satellite observations
(https://svs.gsfc.nasa.gov/12772, last access: 1 October 2019). For specific days with a low MBL height, it might be more
precise to employ back trajectories that start at a lower height and
therefore exclude entrainment effects from the free troposphere for the
characterization of CVAO data. Similarly, for investigating long-lived
components, it might be helpful to analyze longer trajectory integration
times (e.g., 10 d instead of 4 d). However, the longer the back
trajectories, the higher the level of uncertainty. Regarding aerosol
analysis, it is important to notice that dust influences are generally more
pronounced on supermicron particles than on submicron particles (e.g.,
Fomba et al., 2013; Müller et al., 2009, 2010),
meaning that bigger particles may be affected by dust sources, whereas
smaller particles may have stronger oceanic and anthropogenic as well as
long-range transport influences. Consequently, the
classification presented herein represents a first general characterization of air mass
origins. Depending on the sampling periods of other specific analyses,
slight variations may be observed, and this will be indicated in the specific
analysis and manuscripts.
The residence time of the air masses calculated from 96 h (4 d)
back trajectories in ensemble mode.
Meteorological conditions
Air temperature, wind direction and wind speed measured between 15 September
and 6 October (17.5 m a.s.l.) are shown in Fig. 3
together with the mixing ratios of the trace gases ozone, ethane, ethene,
acetone, methanol and DMS. During this period the air temperature ranged
from 25.6 ∘C (06:00 UTC) to 28.3 ∘C (14:00 UTC) with an
average diurnal variation of 0.6 ∘C. The wind direction was
northeasterly (30 to 60∘), except for a period between
19 and 20 September and again on 21 September when
northerly air and lower wind speeds prevailed. The meteorological
conditions observed during the campaign were typical for this site (e.g.,
Carpenter et al., 2010; Fomba et al., 2014). The concentrations of the
different trace gases will be more thoroughly discussed in Sect. 5.3.
Time series of air temperature, wind direction, wind speed,
ethene, dimethyl sulfide, methanol, acetone, ethane and ozone.
Measured and modeled marine boundary layer (MBL) height
The characterization of the MBL is important for the interpretation of both ground-based and vertically resolved measurements because
the MBL mixing state allows us to elucidate the possible connections between
ground-based processes (e.g., aerosol formation) and the higher (e.g.,
mountain and cloud level) altitudes. The Cape Verde islands typically exhibit a
strong inversion layer with a sharp increase in the potential temperature
and a sharp decrease in the humidity (Carpenter et al., 2010).
Vertical measurements of meteorological parameters were carried out at
CVAO with a 16 m3 helikite. The measurements demonstrate
that a helikite is a reliable and useful instrument that can be deployed
under prevailing wind conditions such as at this measurement site. A total of 19 profiles on 10 different days could be obtained, and Fig. 4 shows an
exemplary profile from 17 September. During the campaign, the wind
speed varied between 2 and 14 m s-1, and the MBL height was found to be
between about 600 and 1100 m (compare to Fig. 5). Based on the measured
vertical profiles, the MBL was found to often be well mixed. However, there
are indications for a decoupled boundary layer in a few cases that will be
further analyzed.
The measured temperature and humidity profiles at the CVAO on
17 September using a 16 m3 helikite. From the
measurements the boundary layer height was determined (here: ∼850 m). This figure was adapted from Fig. S3 in Gong et al. (2020a).
Time series and vertical profiles of the MBL height simulated with
COSMO–MUSCAT on the second inner nest with a grid spacing of 0.875 km (N2 domain) and measured with the helikite.
As it was not possible to obtain information on the MBL height for the
entire campaign from online measurements, the MBL height was also simulated
using the bulk Richardson number. The simulations showed that the MBL height
was situated where the bulk Richardson number exceeded the critical value of
0.25. Figure 5 shows that the simulated MBL height was always lower
compared to the measured one during the campaign and also compared to
previous measurements reported in the literature. Based on long-term
measurements, Carpenter et al. (2010) observed an MBL height of 713±213 m at Cape Verde. In the present study a simulated MBL height of
452±184 m was found but covering solely a period over 1
month. The differences might be caused by the grid structure of the applied
model (more details in the Supplement). The vertical resolution of 100 to 200 m
might lead to a misplacement of the exact position of the MBL height.
Moreover, the model calculations were constructed to identify the lowest
inversion layer. Therefore, the modeled MBL height might represent a low,
weak internal layer within the MBL and not the actual MBL. These issues will
be analyzed in further studies.
Cloud conditions
The Cape Verde islands are dominated by a marine tropical climate, and as
mentioned above, marine air is constantly supplied from a northeasterly
direction, which also transports marine boundary layer clouds towards the
islands. Average wind profiles derived from European Centre for
Medium-Range Weather Forecasts (ECWMF) model simulations are shown in Fig.
6a. On the basis of the wind profiles, different cloud scenes have been
selected and quantified (Derrien and Le Gleau, 2005) using geostationary
Meteosat SEVIRI data with a spatial resolution of 3 km (Schmetz et al., 2002); these are shown in Fig. 6b–f. The island São Vicente is located in
the middle of each picture. The first scene at 10:00 UTC on 19 September
was characterized by low wind speeds throughout the atmospheric
column (Fig. 6b). In this calm situation, a compact patch of low-level
clouds was located northwest of the Cape Verde islands. The cloud field was
rather spatially homogeneous, i.e., marine stratocumulus, which transitioned
to more broken cumulus clouds towards the island. Southeastwards of the
islands, high-level ice clouds dominated and possibly mask lower-level
clouds. For the second cloud scene at 10:00 UTC on 22 September (Fig. 6c), wind speed was higher at more than 12 m s-1 in the boundary
layer. Similarly, coverage of low-level to very low-level clouds was rather high
in the region around Cape Verde. A compact stratocumulus cloud field
approached the islands from the northeasterly direction. The clouds that had
formed over the ocean dissolved when the flow traversed the islands.
Pronounced lee effects appeared downstream of the islands. Cloud scene three
at 10:00 UTC on 27 September was again during a calm phase with a wind
speed of only a few meters per second (Fig. 6d). The scene was dominated by
fractional clouds (with a significant part of the spatial variability close
to or below the sensor resolution). These clouds formed locally and grew.
The advection of clouds towards the islands was limited. The last two cloud scenes
(at 10:00 UTC on 1 October in Fig. 6e and at 10:00 UTC on 11 October
in Fig. 6f) were shaped by higher boundary layer winds and
changing wind directions in higher atmospheric levels. The scene in Fig. 6e
shows a complex mixture of low-level cloud fields and higher-level cirrus
patches. The scene in Fig. 6f was again dominated by low-level to very low-level
clouds. The eastern part of the islands was embedded in a rather homogeneous
stratocumulus field. A transition of the spatial structure of the cloud
field happened in the center of the domain, with more cumuliform clouds and
cloud clumps west of the Cape Verde islands. Overall, the majority of
low-level clouds over the islands were formed over the ocean, and
ocean-derived aerosol particles, e.g., sea salt and marine biogenic
compounds, might be expected to have some influence on cloud formation.
Infrequent instances of locally formed clouds influenced by the orography of
the islands could be also identified in the satellite data The different
cloud scenes reflect typical situations observed in conditions with either
weaker or stronger winds. The average in-cloud time of an air parcel might
depend on cloud type and cloud cover, which in turn impacts in-cloud chemical
processes (e.g., Lelieveld and Crutzen, 1991), such as the formation of
methane-sulfonic acid and other organic acids (Hoffmann et al., 2016; Chen et al., 2018). Future studies will relate the chemical composition of the
aerosol particles and cloud water to the cloud scenes and their respective
oxidation capacity. However, the rather coarse horizontal resolution of the
satellite sensor and missing information about the time-resolved vertical
profiles of thermodynamics and cloud condensate limit a further detailed
characterization of these low-level cloud fields and their formation
processes. A synergistic combination with ground-based in situ and remote
sensing measurements would be highly beneficial for future investigations to
elucidate how cloud chemistry might be different for the varying cloud
scenes depending on horizontal cloud patterns and vertical cloud structures.
(a) ECMWF wind forecasts and (b–f) cloud scenery derived from
Meteosat SEVIRI observations for the Cape Verde islands region using a
state-of-the-art cloud classification algorithm (the cloud retrieval
software of the Satellite Application Facility, with support for Nowcasting and
Very Short-Range Forecasting version 2016. (a) Average horizontal winds have
been derived from a 2.5×2.5∘ (250 km × 25 km) domain centered on the
Cape Verde islands and are plotted for each pressure level from 1000 to 250 hPa against time using arrows. The arrow colors refer to the pressure
level. Gray vertical lines mark the times of the subsequently shown cloud
scenes. (b–f) Different cloud scenes observed with Meteosat SEVIRI for a
domain of size 1500 km × 1000 km centered on the Cape Verde islands. The
shadings refer to different cloud types derived with the cloud
classification algorithm of the NWC-SAF v2016.
Biological seawater conditionsPigment and bacteria concentration in seawater
To characterize the biological conditions at CVAO, a variety of pigments
including chlorophyll a (chl a) were measured in the samples of Cape Verde
bulk water (data in Table S4 and illustrated in Sect. 5.4.1). Chl a is the
most prominently used tracer for biomass in seawater; however, information on
phytoplankton composition can only be determined by also determining marker
pigments. Therefore, each time when a water sample was taken, several
liters of bulk water were also collected for pigment analysis (more details in
the Supplement). Chl a concentrations varied between 0.11 and 0.6 µg L-1 and are more thoroughly discussed together with the
pigment composition in Sect. 5.4.1. Moreover, as organisms other than phytoplankton
can contribute to the OM pool, bacterial abundance was analyzed in
the SML and bulk water samples, and these data are reported in Sect. 5.7.3.
Wave glider fluorescence measurements
Roughly at the same time as the MarParCloud field campaign took place, an
unmanned surface vehicle (SV2 wave glider, Liquid Robotics Inc.) equipped
with a biogeochemical sensor package, a conductivity–temperature–depth
sensor (CTD) and a weather station was operated in the vicinity of the
sampling location. The wave glider carried out continuous measurements of
surface water properties (water intake depth: 0.3 m) along a route near the
coast (Fig. 7a), and on 5 October it was sent on a transect from
close to the sampling location towards the open ocean in order to measure
lateral gradients in oceanographic surface conditions.
The glider measurements delivered information on the spatial resolution of
several parameters. Fluorescence measurements, which can be seen as a proxy
for chl a concentration in surface waters and hence for biological production,
indicated some enhanced production leeward of the islands and also at one
location upwind of the island of Santa Luzia next to São Vicente. In the
vicinity of the MarParCloud sampling site the glider observed a slight
enhancement in fluorescence when compared to open-ocean waters. This is in
agreement with the measured pigment concentration. The overall pattern of
slightly enhanced biological activity was also confirmed by the MODIS Terra
satellite fluorescence measurements (Fig. 7b). However, both in situ glider
and sample data as well as remote sensing data did not show any particularly
strong coastal bloom events and thus indicate that the MarParCloud sampling
site represented the open-ocean regime well during the sampling period.
(a) The mission track of an SV2 wave glider as color-coded
fluorescence data derived from a Wet LABS FLNTURT sensor installed on the
vehicle (data in arbitrary units) (b). Chlorophyll a surface ocean
concentrations derived from the MODIS Terra satellite (mean concentration
for October 2017). Please note that logarithmic values are shown.
Measurements and selected resultsVertical resolution measurementsPhysical aerosol characterization
Based on aerosol particles measured during the campaign, air masses could be
classified into different types depending on differences in PNSDs. Marine-type and dust-type air masses could be clearly distinguished, even if the
measured dust concentrations were only low to medium according to the
annual mean at the CVAO (Fomba et al., 2013, 2014). The median of PNSDs
during marine conditions is illustrated in Fig. 8 and showed three modes,
i.e., Aitken, accumulation and coarse mode. There was a minimum between the
Aitken and accumulation mode of PNSDs (Hoppel minimum; see Hoppel et al., 1986) at roughly 70 nm. PNSDs measured in marine-type air masses
featured the lowest Aitken-, accumulation- and coarse-mode particle number
concentrations, with median values of 189, 143 and 7 cm-3,
respectively. The PNSDs present during times with dust influences featured a
single mode in the submicron size range (Fig. 8), and no visible Hoppel
minimum was found. The dust-type air masses featured the highest total
particle number concentration (994 cm-3) and a median coarse-mode
particle number concentration of 44 cm-3.
(a) The median of PNSDs of marine type (blue) and dust type (black),
with a linear and (b) logarithmic scaling on the y axis, measured from
21 September 03:30:00 to 21 September 20:00:00 (UTC) and from
28 September 09:30:00 to 30 September 18:30:00 (UTC). Panel (b) includes the aerosol size modes that fit the method also used in Modini et al., 2015. The error bar indicates the range between the 25th and 75th
percentiles. This figure was adapted from Fig. 5 in Gong et al. (2020a).
NCCN values at different supersaturations were compared during dust and marine
periods, as shown in Fig. 9. During dust periods, the aerosol particles
show a great enhancement in Aitken-, accumulation- and coarse-mode number
concentrations such that overall NCCN increases distinctly. NCCN at
a supersaturation of 0.30 % (proxy for the supersaturation encountered in
clouds present during the campaign) during the strongest observed dust
periods is about 2.5 times higher than that during marine periods. The
fraction of sea spray aerosol, i.e., primary aerosol originating from the
ocean, was determined based on three-modal fits from which the particle
number concentrations in the different modes were determined (Modini et al., 2015; Wex et al., 2016; Quinn et al., 2017). The SSA mode in this study
coved a size range from ∼30 nm to 10 µm with a peak at
∼600 nm (Fig. 8b). More details on the method and
calculations are given in Gong et al. (2020a). During marine periods, SSA
accounted for about 3.7 % of CCN number concentrations at 0.30 %
supersaturation and for 1.1 % to 4.4 % of Ntotal (total particle
number concentration). The hygroscopicity parameter kappa (κ)
averaged 0.28, suggesting the presence of OM in the particles (see Gong et al., 2020a). Particle sizes for which κ was determined (i.e., the
critical diameters determined during CCN analysis) were roughly 50 to 130 nm. The low value determined for κ is in line with the fact that
sodium chloride from sea salt was below the detection limit in the size-segregated chemical analysis for particles in this size range (Fig. 11),
while insoluble EC and WSOM made up 30 % of the main constituents at CVAO
on average.
NCCN as a function of supersaturation during dust (black line)
and marine (blue line) periods. The shading shows the 25th to 75th
percentiles.
(a) The median of PNSDs for marine-type particles during cloud
events and non-cloud events at CVAO and MV; (b) scatter plots of NCCN at CVAO against those at MV at a supersaturation of ∼0.30 %. Slope and R2 are given. This figure was adapted from
Fig. 9 in Gong et al. (2020a).
A thorough statistical analysis of NCCN and particle hygroscopicity
concerning different aerosol types is reported in Gong et al. (2020a).
Figure 10a shows the median of marine-type PNSDs for cloud-free conditions
and cloud events at CVAO and Mt. Verde. Figure 10b shows the scatter plot of
NCCN at CVAO versus those on Mt. Verde. For cloud-free conditions, all
data points are close to the 1:1 line, indicating that NCCN is similar at
the CVAO and Mt. Verde. However, during cloud events, larger particles,
mainly accumulation- and coarse-mode particles, were activated to cloud
droplets and were consequently removed by the inlet. Therefore, during
these times, NCCN at the CVAO was larger than the respective values
measured on Mt. Verde. Altogether, these measurements suggested that, for
cloud-free conditions, the aerosol particles measured at ground level (CVAO)
represent the aerosol particles at the cloud level (Mt. Verde).
Chemical composition of aerosol particles and cloud water
Between 2 and 9 October, size-resolved aerosol particles at
the CVAO and Mt. Verde were collected simultaneously. The relative
contribution of their main chemical constituents (inorganic ions,
water-soluble organic matter (WSOM), and elemental carbon) at both sites is
shown in Fig. 11. Sulfate, ammonium and WSOM dominated the submicron
particles, and the chemical composition aligned well with the κ value
from the hygroscopicity measurements (Gong et al., 2020a). The supermicron
particles were mainly composed of sodium and chloride at both stations.
These findings agree well with previous studies at the CVAO (Fomba et al., 2014; van Pinxteren et al., 2017). From the chemical composition no
indication for anthropogenic influences was found as concentrations of
elemental carbon and submicron potassium were low (see Table S5). However,
according to the dust concentrations (Table 2) and the air mass origins
(Fig. S1), as well as the PNSD (Gong et al., 2020a), the air masses during
this period experienced low dust influences that were not visible
from the main chemical constituents studied here. These findings warrant
more detailed chemical investigations (like size-resolved dust
measurements), a distinguishing between mass-based and number-based analysis, and detailed source investigations that are currently ongoing. The
absolute concentrations of the aerosol constituents were lower at Mt.
Verde compared to the CVAO site (Table S5); they were reduced by factor of 7 (supermicron particle) and by a factor of 4 (submicron
particles). This decrease in the aerosol mass concentrations and the
differences in chemical composition between the ground-based aerosol
particles and the ones at Mt. Verde could be due to cloud effects as
described in the previous section. Different types of clouds consistently
formed and disappeared during the sampling period of the aerosol particles
at Mt. Verde (more details about the frequency of the cloud events are
available in the Supplement and in Gong et al., 2020a) and potentially affected the
aerosol chemical composition. These effects will be more thoroughly examined
in further analysis.
(a) Percentage aerosol composition at the CVAO (mean value of five blocks) and (b) at Mt. Verde (mean value of six blocks) between 2 and 9 October. Aerosol particles were samples in five
different size stages from 0.05 to 0.14 (stage 1), 0.14 to 0.42
(stage 2), 0.42 to 1.2 (stage 3), 1.2 to 3.5 (stage 4) and
3.5 to 10 µm (stage 5).
A first insight into the cloud water composition of a connected cloud water
sampling event from 5–6 October is presented in
Fig. 12. Sea salt, sulfate and nitrate compounds dominated the chemical
composition, making up more than 90 % of the mass of the investigated
chemical constituents. These compounds were also observed in the coarse
fraction of the aerosol particles, suggesting that the coarse-mode particles
served as efficient CCN and were efficiently transferred to the cloud
water. To emphasize, these chemical analyses are based on mass, but the
control of the cloud droplet number concentration comes from CCN number
concentrations, including all particles with sizes roughly above 100 nm.
As larger particles contribute more to the total mass, chemical bulk
measurements give no information about a direct influence of sea spray
particles on cloud droplet concentrations, but they can show that the chemical
composition is consistent with an (expected) oceanic influence on cloud
water. No strong variations were found for the main cloud water constituents
over the sampling period reported here. However, the WSOM contributed with
a maximal 10 % to the cloud water composition and with higher contributions
at the beginning and at the end of the sampling event, which warrants
further analysis. The measured pH values of the cloud water samples ranged
between 6.3 and 6.6 and agreed with previous literature data for marine
clouds (Herrmann et al., 2015). In summary, cloud water chemical composition
seemed to be dominated by coarse-mode aerosol particle composition, and the
presence of inorganic marine tracers (sodium, methane-sulfonic acid) shows
that material from the ocean is transported to the atmosphere where it can
become immersed in cloud droplets. More detailed investigations on the
chemical composition, including a comparison of constituents from submicron
aerosol particles and the SML with the cloud water composition, are planned.
Cloud water composition for one connected sampling event between
5 October 7:45 (start; local time, UTC-1) and 6 October 08:45
(start; local time, UTC-1).
Lipid biomarkers in aerosol particles
Lipids from terrestrial sources such as plant waxes, soils and biomass
burning have frequently been observed in the remote marine troposphere
(Kawamura et al., 2003; Simoneit et al., 1977) and are common in marine
deep-sea sediments. Within MarParCloud, marine-derived lipids were
characterized in aerosol particles using lipid biomarkers in conjunction
with compound-specific stable carbon isotopes. Bulk aerosol filters sampled
at the CVAO and PM10 filters sampled at Mt. Verde (not reported
here) were extracted and the lipids were separated into functional groups
for molecular and compound-specific carbon isotope analysis. The content of
identifiable lipids was highly variable and ranged from 4 to 140 ng m3. These concentrations are in the typical range for
marine aerosol particles (Mochida et al., 2002; Simoneit et al., 2004), but
somewhat lower than previously reported for the tropical northeast Atlantic
(Marty et al., 1979), and 1 to 2 orders of magnitude lower than reported
from urban and terrestrial rural sites (Simoneit, 2004). They mainly comprised
the homologue series of n-alkanoic acids, n-alkanols and n-alkanes. Among
these the c16:0 acids and the c18:0 acids were by far the dominant compounds,
each contributing 20 % to 40 % to the total observed lipids. This result
aligns well with the findings of Cochran et al. (2016) from sea spray tank
studies that connected the transfer of lipid-like compounds to their
physicochemical properties such as solubility and surface activity. Among
the terpenoids, dehydroabietic acid, 7-oxo-dehydroabietic acid and friedelin
were present in some samples in remarkable amounts. Other terpenoid
biomarkers, in particular phytosterols, were rarely detectable. The total
identifiable lipid content was inversely related to dust concentration, as
shown for the fatty acids (Fig. 13) with generally higher lipid
concentrations in primary marine air masses. This is consistent with
previous studies reporting low lipid yields in Saharan dust samples and
higher yields in dust from the more vegetated savannahs and dry tropics
(Simoneit et al., 1977). The first measurements of typical stable carbon isotope
ratios of the lipid fractions were (-28.1±2.5) ‰
for the fatty acids and (-27.7±0.7) ‰ for the
n-alkanes, suggesting a mixture of terrestrial C3 and C4, as well as marine
sources. In a separate contribution the lipid fraction of the aerosol
particles in conjunction with its typical stable carbon isotope ratios will
be further resolved.
Straight-chain unsaturated fatty acid (Σ; C12 to C33)
concentrations on the PM10 aerosol particles versus atmospheric dust
concentrations.
Trace gas measurements:
dimethyl sulfide, ozone, (oxygenated) volatile organic compounds and
nitrous acid
Trace gases such as dimethyl sulfide (DMS), volatile organic compounds
(VOCs) and oxygenated (O)VOCs were measured during the campaign, and the
results are presented together with the meteorological data in Fig. 3. The
atmospheric mixing ratios of DMS during this period ranged between 68
and 460 ppt with a mean of 132±57 ppt (1σ). These levels were
higher than the annual average mixing ratio for 2015 of 57±56 ppt;
however, this may be due to seasonably high and variable DMS levels observed
during summer and autumn at Cape Verde (observed mean mixing ratios were 86
and 107 ppt in September and October 2015). High DMS concentrations on
19–20 September occurred when air originated
predominantly from the Mauritanian upwelling region (Fig. S1) and on
26 and 27 September. These elevated concentrations will be
linked to the phytoplankton composition reported in Sect. 5.4.1 to elucidate
associations, for example, between DMS and coccoliths (individual plates of
calcium carbonate formed by coccolithophore phytoplankton) as observed by Marandino et al. (2008). Ethene showed similar variability as DMS, with coincident peaks
(>300 ppt DMS and >40 ppt ethene) on 20, 26 and 27 September, consistent with an oceanic source for
ethene. Ethene can be emitted from phytoplankton (e.g., McKay et al., 1996),
and therefore it is possible that it originated from the same biologically
active regions as DMS. In the North Atlantic atmosphere, alkenes such as
ethene emitted locally have been shown to exhibit diurnal behavior with a
maximum at solar noon, suggesting photochemical production in seawater
(Lewis et al., 2005). There was only weak evidence of diurnal behavior at
Cape Verde (data not shown), possibly because of the very short atmospheric
lifetime of ethene (8 h assuming [OH] =4×106 molecules cm-3; Vaughan et al., 2012) in this tropical environment, which would
mask photochemical production. Mean acetone and methanol mixing ratios were
782 ppt (566–1034 ppt) and 664 ppt (551–780 ppt),
respectively. These are similar to previous measurements at Cape Verde and
in the remote Atlantic at this time of year (Lewis et al., 2005; Read et al., 2012). Methanol and acetone showed similar broad-scale features,
indicating common sources. The highest monthly methanol and acetone
concentrations have often been observed in September at Cape Verde, likely
as a result of increased biogenic emissions from vegetation or plant matter
decay in the Sahel region of Africa (Read et al., 2012). In addition to
biogenic sources, (O)VOCs are anthropogenically produced from fossil fuels
and solvent usage in addition to having a secondary source from the
oxidation of precursors such as methane. Carpenter et al. (2010) showed
that air masses originating from North America (determined via 10 d back
trajectories) could impact (O)VOCs at the CVAO.
The average ozone mixing ratio during the campaign was 28.7 ppb (19.4–37.8 ppb). Lower ozone concentrations on 27–28 September
were associated with an influence from southern hemispheric air. Ozone showed
daily photochemical loss, as expected in these very low-NOx conditions, on
most days with an average daily (from 09:00 to 17:00 UTC) loss of 4 ppbv.
It was previously shown that the photochemical loss of O3 at Cape Verde
and over the remote ocean is attributable to halogen oxides (29 % at Cape
Verde) and ozone photolysis (54 %) (e.g., Read et al., 2008).
Finally, a series of continuous measurements of nitrous acid (HONO) was
conducted, aiming to evaluate the possible contribution of marine surfaces
to the production of HONO. The measurements indicated that HONO
concentrations exhibited diurnal variations peaking at noontime. The
concentrations during daytime (08:00 to 17:00 local time) and nighttime
(17:30 to 07:00 local time) periods were around 20 and 5 ppt on average,
respectively. The fact that the observed data showed higher values during
the day compared to the nighttime was quite surprising since HONO is
expected to be photolyzed during the daytime. If confirmed, the measurements
conducted here may indicate that there is an important HONO source in the
area of interest. Altogether, for the trace gases, a variety of conditions
were observed in this 3-week period with an influence from ocean–atmosphere
exchange and also potential impacts of long-range transport.
Organic matter and related compounds in seawaterDissolved organic carbon and pigments
Dissolved organic carbon (DOC) comprises a complex mixture of different
compound groups and is diverse in its composition. For a first overview, DOC
as a sum parameter was analyzed in all SML and bulk water samples (data in
Table S4). The DOC concentration varied between 1.8 and 3.2 mg L1 in the
SML and 0.9 and 2.8 mg L-1 in the bulk water; concentrations were in general
agreement with previous studies at this location (e.g., van Pinxteren et al., 2017). A slight enrichment in the SML with an enrichment factor (EF) of 1.66 (±0.65) was found; i.e., SML concentrations contain roughly
70 % more DOC that the corresponding bulk water. The concentrations of DOC
in the bulk water together with the temporal evolution of biological
indicators (pigments and the total bacterial cell numbers) and atmospheric
dust concentrations are presented in Fig. 14.
Temporal evolution of DOC concentrations in the bulk water samples
along the campaign together with the main pigment (chl a,
zeaxanthin and fucoxanthin) concentrations and total cell numbers measured
in the bulk water and dust concentrations in the atmosphere (yellow
background area).
Phytoplankton biomass expressed in chl a was very low with 0.11 µg L-1 at the beginning of the campaign. Throughout the campaign two
slight increases in biomass occurred but were always followed by a biomass
depression. The biomass increase occurred towards the end of the study,
when pre-bloom conditions were reached with values up to 0.6 µg L-1. These are above the typical chl a concentration in this area. In
contrast, the abundance of chlorophyll degradation products such as pheophorbide
a and pheophythin a decreased over time. The low concentrations of the
chlorophyll degradation products suggested that only moderate grazing took
place, and the pigment-containing organisms were fresh and in a healthy
state. The most prominent pigment throughout the campaign was zeaxanthin,
suggesting cyanobacteria as the dominant group in this region. This is in good
agreement with the generally low biomass in the waters of the Cape Verde
region and in line with previous studies reporting the dominance of
cyanobacteria during the spring and summer seasons (Franklin et al., 2009;
Hepach et al., 2014; Zindler et al., 2012). However, once the biomass
increased, cyanobacteria were repressed by diatoms as indicated by the relative increase in
fucoxanthin. The prymnesiophyte and haptophyte marker 19-hexanoyloxyfucoxanthin and the pelagophyte and
haptophyte marker 19-butanoyloxyfucoxanthin were present and also increased when
cyanobacteria decreased. In contrast, dinoflagellates and chlorophytes were background communities as indicated by
their respective markers peridinin and chlorophyll b. Still, chlorophytes were much more
abundant than dinoflagellates. In summary, the pigment composition indicated the presence
of cyanobacteria, haptophytes and diatoms with a change in dominating taxa (from cyanobacteria to diatoms). The increasing
concentration of chl a and fucoxanthin implied that a bloom started to
develop within the campaign dominated by diatoms. The increasing concentrations
could also be related to changing water masses; however, since the
oceanographic setting was relatively stable, the increasing chl a
concentrations suggest that a local bloom had developed, that might be
related to the low but permanent presence of atmospheric dust input, which
needs further verification. In the course of further data analysis of the
campaign, the phytoplankton groups will be related to the abundance of, e.g.,
DMS (produced by haptophytes) or isoprene, which have been reported to be produced by
diatoms or cyanobacteria (Bonsang et al., 2010), as well as to other VOCs. First analyses show
that the DOC concentrations were not directly linked to the increasing
chl a concentrations; however, their relation to single pigments, to the
microbial abundance, to the background dust concentrations, and finally to
wind speed and solar radiation will be further resolved to elucidate
potential biological and meteorological controls on the concentration and
enrichment of DOC.
DOC concentrations: manual glass plate vs. MarParCat sampling
For several dates, both SML sampling devices (glass plate and catamaran)
were applied in parallel to compare the efficiency of different sampling
approaches: manual glass plate and catamaran sampling (Fig. 15). As
mentioned above both techniques used the same principle, i.e., the collection
of the SML on a glass plate and its removal with a Teflon wiper. The
deviation between the two techniques concerning DOC measurements was below
25 % in 17 out of 26 comparisons and therefore within the range of
variability of these measurements. However, in roughly 30 % of all cases
the concentration differences between the manual glass plate and catamaran were
larger than 25 %. The discrepancy for the bulk water results could be
related to the slightly different bulk water sampling depths using the
MarParCat bulk water sampling system (70 cm) and the manual sampling with
telescopic rods (100 cm). Although the upper meters of the ocean are
assumed to be well mixed, recent studies indicate that small-scale
variabilities can already be observed within the first 100 cm of the ocean
(Robinson et al., 2019a).
(a) Concentrations of DOC in the SML and (b) and in the bulk water
sampled for paired glass plate (GP) and the MarParCat (cat) sampling events.
The variations within the SML measurements could be due to the patchiness of
the SML that has been tackled in previous studies (e.g., Mustaffa et al., 2017, 2018). Small-scale patchiness was recently reported as a common
feature of the SML. The concentrations and compositions probably undergo
more rapid changes due to high physical and biological fluctuations.
Mustaffa et al. (2017) have recently shown that the enrichment of
fluorescence dissolved matter (a part of DOC) showed short-timescale
variability, changing by 6 % within 10 min intervals. The processes
leading to the enrichment of OM in the SML are probably much more complex
than previously assumed (Mustaffa et al., 2018). In addition, the changes in
DOC concentrations between the glass plate and the catamaran could result
from the small variations of the sampling location as the catamaran was
typically 15 to 30 m away from the boat where the manual glass plate
sampling was carried out.
Given the highly complex matrix of seawater and especially the SML, the two
devices applied were in quite good agreement considering DOC measurements.
However, this is not necessarily the case for single parameters like
specific organic compounds and INP concentrations. Low-concentrated constituents in particular might be more affected by small changes in the
sampling procedure, and this remains to be evaluated for the various compound
classes.
Surfactants and lipids in seawater
Due to their physicochemical properties, surfactants (SASs) are enriched in
the SML relative to the bulk water and form surface films (Frka et al., 2009, 2012; Wurl et al., 2009). During the present campaign,
the SASs in the dissolved fraction of the SML samples ranged from 0.037 to
0.125 mg of Triton-X-100 equivalent per liter with a mean of 0.073±0.031 mg of Triton-X-100 equivalent per liter (n=7). For bulk water, the dissolved
SASs ranged from 0.020 to 0.068 mg of Triton-X-100 equivalent per liter (mean 0.051±0.019 mg of Triton-X-100 equivalent per liter, n=12). The SAS enrichment showed EFs from
1.01 to 3.12 (mean EF =1.76±0.74) (Fig. 16) and was slightly
higher than that for the DOC (mean EF =1.66±0.65), indicating some
higher surfactant activity of the overall DOM in the SML with respect to the
bulk DOM. An accumulation of the total dissolved lipids (DLs) in the SML was
observed as well (mean EF =1.27±0.12). Significant correlation
was observed between the SAS and DL concentrations in the SML (r=0.845,
n=7, p<0.05), while no correlation was detected for the bulk
water samples. Total DL concentrations ranged from 82.7 to 148 µg L-1 (mean 108±20.6µg L-1, n=8) and from 66.5 to
156 µg L-1 (mean 96.9±21.7µg L-1, n=17) in
the SML and the bulk water, respectively. In comparison to the bulk water,
the SML samples were enriched with lipid degradation products, e.g., free
fatty acids and long-chain alcohols (DegLip; mean EF =1.50±0.32) (Fig. 16), pointing to
their accumulation from the bulk and/or enhanced OM degradation within the
SML. DegLips are strong surface-active compounds (known as dry surfactants),
which play an important role in surface film establishment (Garrett, 1965).
The overall surfactant activity of the SML is the result of the competitive
adsorption of highly surface-active lipids and other less surface-active
macromolecular compounds (polysaccharides, proteins, humic material)
(Ćosović and Vojvodić, 1998) dominantly present in seawater. The
presence of even low amounts of lipids results in their significant
contribution to the overall surface-active character of the SML complex
organic mixture (Frka et al., 2012). The observed biotic and/or abiotic
lipid degradation processes within the SML will be further resolved by
combining surfactant and lipid results with detailed pigment
characterization and microbial measurements. The same OM classes of the
ambient aerosol particles will be investigated and compared with the
seawater results. This will help to tackle the question of to what extent the
seawater exhibits a source of OM on aerosol particles and which important
aerosol precursors are formed or converted in surface films.
Average enrichment (EF) of surfactants (SASs) and dissolved lipid
classes indicating organic matter degradation (DegLip).
Seawater untargeted metabolomics
For a further OM characterization of SML and bulk seawater, an ambient
MS-based metabolomics method using direct analysis in real-time quadrupole
time-of-flight mass spectrometry (DART-QTOF-MS) coupled to multivariate
statistical analysis was designed (Zabalegui et al., 2019). A strength of a
DART ionization source is that it is less affected by high salt levels than
an electrospray ionization source (Kaylor et al., 2014), allowing for the
analysis of seawater samples without observing salt deposition at the mass
spectrometer inlet, or having additional limitations such as low ionization
efficiency due to ion suppression (Tang et al., 2004). Based on these
advantages, paired SML–bulk water samples were analyzed without the need for
desalinization by means of a transmission-mode (TM) DART-QTOF-MS-based
analytical method that was optimized to detect lipophilic compounds
(Zabalegui et al., 2019). An untargeted metabolomics approach, referred to as
seaomics, was implemented for sample analysis. SML samples were successfully
discriminated from ULW samples based on a panel of ionic species extracted
using chemometric tools. The coupling of the DART ion source to
high-resolution instrumentation allowed for generating elemental formulae for
unknown species, and tandem MS capability contributed to the identification
process. Tentative identification of discriminant species and the analysis
of relative compound abundance changes among sample classes (SML and bulk
water) suggested that fatty alcohols, halogenated compounds and oxygenated
boron-containing organic compounds may be involved in water–air transfer
processes and in photochemical reactions at the water–air interface of the
ocean (Zabalegui et al., 2019). These identifications (e.g., fatty alcohols)
agree well with the abundance of lipids in the respective samples. In this
context, TM-DART-HR-MS appears to be an attractive strategy to investigate
the seawater OM composition without requiring a desalinization step.
Ocean surface mercury associated with organic matter
Several elements are known to accumulate in the SML. In the case of Hg, the
air–sea exchange plays an important role in its global biogeochemical cycle,
and hence the processing of Hg in the SML is of particular interest. Once
deposited from the atmosphere to the ocean surface via dry and wet
deposition, divalent mercury (HgII) can be transported to the
deeper ocean by absorbing on sinking OM particles, followed by methylation.
On the other hand, HgII complexed by DOM in the ocean surface can be
photoreduced to Hg0, which evades into the gas phase. In both
processes, OM, dissolved or particulate, is the dominant factor influencing
the complexation and adsorption of Hg. To explore the Hg behavior with OM,
the concentrations of total and dissolved Hg as well as methylmercury
(MeHg) were determined in the SML and in the bulk water using US EPA
methods 1631 and 1630, as described in Li et al. (2018). Figure 17 shows the
concentrations of Hg and MeHg associated with DOC and POC in the SML and
bulk water. The total Hg concentrations were 3.6 and 4.6 ng L-1 in the
SML but 3.1 and 1.3 ng L-1 in the bulk water on 26 and
27 September, respectively, which were significantly enriched compared to data
reported for the deep North Atlantic (0.18±0.06 ng L-1)
(Bowman et al., 2015). Atmospheric deposition and more OM-adsorbing Hg are
believed to result in high total Hg at the ocean surface. The dissolved Hg
concentrations were enriched by 1.7 and 2.7 times in the SML relative to
bulk water, consistent with the enrichments of DOC by a factor of 1.4 and
1.9 on 26 and 27 September, respectively. Particulate Hg in
the SML accounted for only 6 % of the total Hg concentration on 26 September
but 55 % on 27 September, in contrast to their similar
fractions of ∼35 % in the bulk water on both days.
According to the back trajectories (Fig. S1) a stronger contribution of
African continental sources (e.g., dust) was observed on 27 September
that might be linked to the higher concentrations of particulate Hg in
the SML on this day. The water–particle partition coefficients (logKd)
for Hg in the SML (6.8 L kg-1) and bulk water (7.0 L kg-1) were
similar regarding POC as the sorbent but one unit higher than the reported
logKd values in seawater (4.9–6.1 L kg-1) (Batrakova et al., 2014). MeHg made up lower proportions of the total Hg concentrations in the
SML (2.0 %) than bulk water (3.4 % and 4.2 %), probably due to
the photodegradation or evaporation of MeHg at the surface water (Blum et al., 2013). From the first results, it seems that the SML is the major
compartment where Hg associated with OM is enriched, while MeHg is more
likely concentrated in deeper water. The limited data underline the
importance of the SML in Hg enrichment dependent on OM, which needs further
studies to understand the air–sea exchange of Hg.
Concentrations of Hg, MeHg, DOC and POC in the sea surface
microlayer (SML) and bulk water sampled on 26 and 27 September 2017.
Ocean–atmosphere transfer of organic matter and related compoundsDissolved organic matter classes
To investigate the complexity of dissolved organic matter (DOM) compound
groups, liquid chromatography, organic carbon detection, organic nitrogen
detection and UV absorbance detection (LC-OCD-OND-UVD; Huber et al., 2011;
more details in the Supplement) were combined and applied to identify five different DOM classes.
These classes include (i) biopolymers that are likely hydrophobic, high-molecular-weight (≫20000 g mol-1), largely non-UV-absorbing extracellular polymers; (ii) “humic substances” (with higher
molecular weight at ∼1000 g mol-1, UV-absorbing); (iii) “building blocks” that are lower-molecular-weight (300–500 g mol-1), UV-absorbing humics; (iv) low-molecular-weight “neutrals” (350 g mol-1,
hydrophilic or amphophilic, non-UV-absorbing); and (v) low-molecular-weight acids
(350 g mol-1). These measurements were performed from a first set of
samples from all the ambient marine compartments. That comprised three SML
samples and the respective bulk water, three aerosol particle filter samples
(PM10) from the CVAO and two from Mt. Verde, and finally four cloud
water samples collected during the campaign. The DOM concentrations were
derived from the sum of the individual compound groups (µg L-1), and the EFs for DOM varied from 0.83 to 1.46, which agrees very
well with the DOC measurements described in Sect. 5.4.1. A clear compound
group that drove this change has not been identified so far. Figure 18 shows
the relative composition of the measured DOM groups in the distinct marine
compartments as an average of the single measurements (concentrations are
listed in Table S6). In the SML and in the bulk water, the low-molecular-weight neutral (LMWN) compounds generally dominated the overall DOM pool (37 %
to 51 %). Humic-like substances, building blocks and biopolymeric
substances contributed 22 % to 32 %, 16 % to 23 % and 6 % to 12 %,
respectively. Interestingly, low-molecular-weight acids (LMWAs) were
predominantly observed in the SML (2 % to 8 %) with only one bulk water time
point showing any traces of LMWA. This finding agreed well with the presence
of free amino acids (FAAs) in the SML; e.g., the sample with the highest LMWA
concentration showed the highest FAA concentration (more details in Triesch et al., 2020). Further interconnections between the DOM fractions and single
organic markers and groups (e.g., sugars, lipids and surfactants; see Sect. 5.4.3) are subject to ongoing work. In contrast, aerosol particles were
dominated by building blocks (46 % to 66 %) and LMWN (34 % to 51 %) compound
groups, with a minor contribution of LMWA (> 6 %).
Interestingly, higher-molecular-weight compounds of humic-like substances
and biopolymers were not observed. Cloud water samples had a variable
contribution of substances in the DOM pool, with humic substances and
building blocks generally dominating (27 % to 63 % and 16 % to 29 %,
respectively) and lower contributions of biopolymers (2 % to 4 %) and LMW
acids and neutrals (1 % to 20 % and 18 % to 34 %) observed. The first
measurements indicate that the composition of the cloud waters is more
consistent with the SML and bulk water and different from the aerosol
particle's composition. This observation suggests a two-stage
process whereby selective aerosolization mobilizes lower-molecular-weight humics
(building blocks) into the aerosol particle phase, which may aggregate in
cloud waters to form larger humic substances. These preliminary observations
need to be further studied with a larger set of samples and could relate to
different solubilities of the diverse OM groups in water, the
interaction between DOM and particulate OM (POM), including TEP formation,
or an indication of the different OM sources and transfer pathways. In
addition, the chemical conditions, like pH value or redox, could
preferentially preserve or mobilize DOM fractions within the different types
of marine waters. In summary, all investigated compartments showed a
dominance of LMW neutrals and building blocks, which suggests a link between
the seawater, aerosol particles and cloud water at this location and
possible transfer processes. Furthermore, the presence of humic-like
substances, biopolymers and partly LMWA in the seawater and cloud water,
but not in the aerosol particles, suggests an additional source or formation
pathway of these compounds. For a comprehensive picture, however, additional
samples need to be analyzed and interpreted in future work. It is worth
noting that the results presented here are the first for such a diverse set
of marine samples and demonstrate the potential usefulness of identifying
changes in the flux of DOM between marine compartments.
DOM classes measured in all compartments. The data represent mean
values of three SML samples and the respective bulk water, three aerosol
particle samples (PM10) from the CVAO and two aerosol samples
(PM10) from Mt. Verde, and four cloud water samples, all collected
during 26–27 September as well as 1–2 and 8–9 October 2017.
A more comprehensive set of samples was analyzed for FAAs on a molecular level
as important organic-nitrogen-containing compounds (Triesch et al., 2020).
The FAAs, likely resulting from the ocean, were strongly enriched in
submicron aerosol particles (EFaer(FAA)=102–104) and to
a lesser extent enriched in supermicron aerosol particles (EFaer(FAA)=101). The cloud water contained the FAAs in significantly
higher concentrations compared to their respective seawater concentrations,
and they were enriched by a factor of 4×103 compared to
the SML. These high concentrations cannot currently be explained, and
possible sources such as the biogenic formation or enzymatic degradation of
proteins, selective enrichment processes, or pH-dependent chemical reactions
are subject to future work. The presence of high concentrations of FAAs in
submicron aerosol particles and in cloud water, together with the presence of
inorganic marine tracers (sodium, methane-sulfonic acid), points to an
influence of oceanic sources on the local clouds (Triesch et al., 2020).
Transparent exopolymer particles: field and tank measurements
As part of the OM pool, gel particles, such as positive buoyant transparent
exopolymer particles (TEPs), formed by the aggregation of precursor material
released by plankton and bacteria accumulate at the sea surface. The
coastal water in Cape Verde was shown to be oligotrophic with low chl a
abundance during the campaign (more details in Sect. 4.2.1). Based on
previous work (Wurl et al., 2011) it is expected that surfactant enrichment,
which is closely linked to TEP enrichment, in the SML would be higher in
oligotrophic waters but have a lower absolute concentration. This
complements the findings achieved here, which showed low TEP abundance in
these nearshore waters; the abundance in the bulk water ranged from 37 to
144 µg of xanthan gum equivalent per liter and 99 to 337 µg of xanthan gum equivalent per liter in the SML. However, while the SML layer was relatively thin
(∼125µm) there was positive enrichment of TEPs in the
SML, with an average EF of 2.0±0.8 (Fig. 19a). The enrichment factor
for TEPs was furthermore very similar to surfactant enrichment (Sect. 5.4.3).
(a) Total TEP abundance in the SML and the bulk water as well as the
enrichment factor (SML/ULW) of TEPs for field samples taken in the nearshore
water of Cape Verde, (b) together with a tank experiment including >3 h
bubbling of water collected from nearshore Cape Verde.
In addition to the field samples, a tank experiment was run simultaneously
using the same source of water. Breaking waves were produced via a waterfall
system (details in the Supplement), and samples were collected from the SML and bulk
water after a wave simulation time of 3 h. TEP abundance in the tank
experiment matched the field samples at the beginning but quickly increased
to 1670 µg of xanthan gum equivalent per liter in the SML, with an EF of 13.2 after the first
day of bubbling (Fig. 19b). The enrichment of TEPs in the SML during the tank
experiment had a cyclical increase and decrease pattern. Interestingly, in
the field samples, even on days with moderate wind speeds (>5 m s-1) and the occasional presence of white caps, TEP abundance or enrichment
did not increase, but it did increase substantially due to the waves in the
tank experiment. This suggests that the simulated waves are very effective
in enriching TEPs in the SML, and TEPs were more prone to transport or
formation by bubbling than by other physical forces, confirming
bubble-induced TEP enrichment in recent artificial setups (Robinson et al., 2019b). Besides the detailed investigations of TEPs in seawater, first
analyses show a clear abundance of TEPs in the aerosol particles and in cloud
water. Interestingly, a major part of TEPs seems to be located in the
submicron aerosol particles (Fig. 20). Submicron aerosol particles
represent the longest-living aerosol particle fraction; they have a high
probability to reach cloud level and to contribute to cloud formation and
the occurrence of TEPs in cloud water, which strongly underlines the possible
vertical transport of these ocean-derived compounds.
Microscopy image of TEPs in TSP aerosol particles sampled at the
CVAO between 29 and 30 September with a flow rate of 8 L min-1.
Bacterial abundance in distinct marine samples: field and tank
measurements
The OM concentration and composition are closely linked with biological and
especially microbial processes within the water column. Throughout the
sampling period, the temporal variability of bacterial abundance in the SML and
bulk water was studied (data listed in Table S4). Mean absolute cell numbers
were 1.3±0.2×106 cells mL-1 and 1.2±0.1×106 cells mL-1 for SML and bulk water, respectively (Fig. 21a; all
data listed in Table S4). While comparable SML data are lacking for this
oceanic province, our data are in range with previous reports for surface
water of subtropical regions (Zäncker et al., 2018). A strong day-to-day
variability of absolute cell numbers was partly observed (e.g., the decline
between 25 and 26 September), but all these changes were found
in both the SML and bulk water (Fig. 21a). This indicates that the upper
water column of the investigated area experienced strong changes, e.g., by
inflow of different water masses and/or altered meteorological forcing. As
for the absolute abundance, the enrichment of bacterial cells in the SML was
also changing throughout the sampling period, with EFs ranging from 0.88 to
1.21 (Fig. 21b). A detailed investigation of physical factors (e.g., wind
speed, solar radiation) driving OM concentration and bacterial abundance in
the SML and bulk water will be performed to explain the short-term
variability observed. Further ongoing investigations aim to determine the
bacterial community composition by 16S sequencing approaches. The resulting
comparison of water and aerosol particle samples will help to better
understand the specificity of the respective communities and to gain
insights into the metabolic potential of abundant bacterial taxa in aerosol
particles. During the tank experiment, cell numbers ranged between 0.6 and
2.0×106 cells mL-1 (Fig. 21c); the only exception was observed
on 3 October, when cell numbers in the SML reached 4.9×106 cells mL-1. Both in the SML and bulk water, bacterial cell numbers
decreased during the experiment, which may be attributed to limiting
substrate supply in the closed system. Interestingly, SML cell numbers
always exceeded those from the bulk water (Fig. 21d), although the SML was
permanently disturbed by bursting bubbles throughout the entire experiment.
This seems to be in line with the high TEP concentrations observed for the
SML in the tank (Sect. 5.7.2). A recent study showed that bubbles are very
effective transport vectors for bacteria into the SML, even within minutes
after disruption (Robinson et al., 2019a). The decline of SML bacterial cell
numbers (both absolute and relative) during the experiment may be partly
caused by permanent bacterial export into the air due to bubble bursting.
Although this conclusion remains speculative as cell abundances of air
samples are not available for our study, previous studies have shown that
the aerosolization of cells may be quite substantial (Rastelli et al., 2017).
Bacterial abundance in cloud water samples taken at Mt. Verde during the
MarParCloud campaign ranged between 0.4 and 1.5×105 cells mL-1
(Fig. 21a). Although few samples are available, these numbers agree well
with previous reports (e.g., Hu et al., 2018).
Bacterial abundance of SML and ULW from (a) field and (c) tank
water samples as well as from cloud water samples (diamonds, a) taken during
the campaign. Additionally, enrichment factors (i.e., SML versus
ULW) are presented (b, d). In panel (a), please note the different power
values between SML, ULW (106 cells mL-1) and cloud water samples
(104 cells mL-1).
Ice-nucleating particles
The properties of ice-nucleating particles (INPs) in the SML and in bulk
seawater (airborne in the marine boundary layer), as well as the contribution
of sea spray aerosol particles to the INP population in clouds, were examined
during the campaign. The numbers of INPs (NINP) at -12, -15 and -18 ∘C in the PM10 samples from the CVAO varied from 0.000318
to 0.0232, 0.00580 to 0.0533 and 0.0279 to 0.100 per liter (for standard temperature and pressure), respectively. INP measurements in the ocean water showed that the enrichment and
depletion of INPs in the SML compared to the bulk seawater occurred, and
enrichment factors varied from 0.36 to 11.40 and 0.36 to 7.11 at -15 and
-20 ∘C, respectively (details in Gong et al., 2020b). NINP values
in PM1 were generally lower than those in PM10, and, furthermore,
NINP in PM1 at CVAO did not show elevated NINP at warm
temperatures, in contrast to NINP in PM10. These elevated
concentrations in PM10 decreased upon heating the samples, clearly
pointing to a biogenic origin of these INPs. Therefore, ice-active particles
in general and biologically active INPs in particular were mainly present in
the supermicron particles, and particles in this size range are not
suggested to undergo strong enrichment of OM during oceanic transfer via
bubble bursting (Quinn et al., 2015, and references therein). NINP (per
volume of water) of the cloud water was roughly similar to or slightly above
that of the SML (Fig. 22), while concentrations of sea salt were clearly
lower in cloud water compared to ocean water. Assuming sea salt and the INPs
to be similarly distributed in both sea and cloud water (i.e., assuming that
INPs would not be enriched or altered during the production of supermicron
sea spray particles), NINP is at least 4 orders of magnitude higher
than what would be expected if all airborne INPs originated from sea
spray. These first measurements indicate that other sources besides the
ocean, such as mineral dust or other long-range-transported particles,
contributed to the local INP concentration (details in Gong et al., 2020b).
NINP of SML seawater (n=9) and cloud water (n=13) as a
function of temperature.
The SML potential to form secondary organic aerosol particles
To explore if marine air masses exhibit significant potential to form SOA,
a Gothenburg potential aerosol mass reactor (Go : PAM) was used that relies
on providing a highly oxidizing medium to reproduce atmospheric oxidation on
timescales ranging from a day to several days in much shorter timescales
(i.e., a few minutes). During the campaign, outdoor air and gases produced
from a photochemical reactor were flowed through the Go : PAM (Watne et al., 2018) and exposed to high concentrations of OH radicals formed via the
photolysis of ozone and subsequent reaction with water vapor (Zabalegui et al., 2019, and references therein). The aerosol particles produced at the outlet of
the OFR were monitored by means of an SMPS, i.e., only size distribution and
number concentration were monitored. A subset of the collected SML samples
were investigated within the Go : PAM and showed that particles were formed
when these samples were exposed to actinic irradiation. These particles
most likely resulted from the reaction of ozone with gaseous products that
were released from the SML as shown recently (Ciuraru et al., 2015), and the
results obtained here are explained in more detail in a separate paper by
Zabalegui et al. (2019). Zabalegui et al. (2019) also pointed out the
clear need to have concentrated SML samples (achieved here by centrifugation
of the authentic samples) as for prerequisite of aerosol formation, which
points toward a specific “organic-rich” chemistry. Outdoor air masses
were also investigated for their secondary mass production potential. During
the campaign, northeast wind dominated; i.e., predominantly clean marine air
masses were collected. Those did not show any distinct diurnal difference
for their secondary aerosol formation potential. However, a significant
decrease in secondary organic mass was observed on 30 September,
which will be analyzed in more detail.
The way to advanced modelingModeling of cloud formation and vertical transfer of ocean-derived
compounds
Besides the assessment of cloud types (Sect. 4.1.4), modeling approaches are planned to be
applied to simulate the occurrence and formation of
clouds at the Mt. Verde site, including advection, wind, effective transport
and vertical transport. This will allow us to model chemical multiphase
processes under the given physical conditions. Furthermore, the potential
vertical transfer of ocean-derived compounds to the cloud level will be
modeled. To this end, the meteorological model data by the Consortium for
Small-scale Modelling-Multiscale Chemistry Aerosol Transport Model (COSMO)
(Baldauf et al., 2011) will be used to define a vertical meteorological data
field. COSMO is a compressible and non-hydrostatic meteorological model and
the current weather forecast model of the German Weather Service. The
numerical calculation of the weather forecast is achieved by using
information on the underlying orography and land use, as well as boundary
data for all meteorological fields. The needed boundary and initial fields
will be derived from reanalysis data and/or input parameters from
coarse-resolved weather model data. The first simulations show that clouds
frequently occurred at heights of 700 to 800 m (Fig. 23), in strong
agreement with the observations. This demonstrates that clouds at Mt. Verde
can form solely due to the local meteorological conditions and not
necessarily due to orographic effects. Accordingly, the combination of
ground-based aerosol measurements and in-cloud measurements at the top
of Mt. Verde will be applied to examine important chemical transformations
of marine aerosol particles during horizontal and vertical transport within
the MBL. From the measurements presented here, a transfer of ocean-derived
compounds to the cloud level is very likely. To link and understand both
measurement sites in terms of important multiphase chemical pathways, more
detailed modeling studies regarding the multiphase chemistry within the MBL,
combined with the impact of horizontal and vertical transport on the
aerosol and cloud droplet composition, will be performed by using different
model approaches (more details in the Supplement). In general, both projected model
studies will focus on (i) determining the oxidation pathways of key marine
organics and (ii) the evolution of aerosol and cloud droplet acidity by
chemical aging of the sea spray aerosol. The model results will finally be
linked to the measurements and compared with the measured aerosol particle
concentration and composition as well as the in-cloud measurements at the top of Mt. Verde.
Modeled 2D vertical wind field on 5 October after 12 h
of simulation time. The model domain spans 222 km of length and 1.5 km of height.
The black contour lines represent the simulated cloud liquid water content
(with a minimum of 0.01 g m-3 and a maximum of 0.5 g m-3). The
more dense the lines, the higher the simulated liquid water content of the
clouds.
Development of a new organic matter emission source function
The link of ocean biota to marine-derived organic aerosol particles has
been recognized (e.g., O'Dowd et al., 2004). However, the usage of a single
parameter like chl a as an indicator for biological processes and its
implementation in oceanic emission parameterizations is insufficient as it
does not reflect pelagic community structure and associated ecosystem
functions. It is strongly suggested to incorporate process-based models for
marine biota and OM rather than relying on simple parameterizations
(Burrows et al., 2014). A major challenge is the high level of complexity of
the OM in marine aerosol particles as well as in the bulk water and the SML
as potential sources. Within MarParCloud modeling, a new source function
for the oceanic emission of OM will be developed as a combination of the sea
spray source function of Salter et al. (2015) and a new scheme for the
enrichment of OM within the emitted sea spray droplets. This new scheme will
be based on the Langmuir adsorption of organic species at the bubble films.
The oceanic emissions will be parameterized following Burrows et al. (2014), whereby the OM is partitioned into several classes based on its
physicochemical properties. The measured concentration of the species in the
ocean surface water and the SML (e.g., lipids, carbohydrates and proteins)
will be included in the parameterization scheme. Finally, size-class-resolved enrichment functions of the organic species groups within the jet
droplets will be included in the new scheme. The new emission scheme will be
implemented to the aerosol chemical transport model MUSCAT (Multi-Scale
Chemistry Aerosol Transport). MUSCAT is able to treat the atmospheric transport
and chemical transformation of different traces gases as well as particle
properties. In addition to advection and turbulent diffusion, sedimentation as well as
dry and wet deposition through the transport processes are considered, too.
MUSCAT is coupled with COSMO, which provides MUSCAT with all needed
meteorological fields (Wolke et al., 2004). The multiscale model system
COSMO–MUSCAT will be used further to validate the emission scheme of OM via
small-scale and mesoscale simulations.
Summary and conclusion
Within MarParCloud and with substantial contributions from MARSU, an
interdisciplinary campaign in the remote tropical ocean took place in autumn 2017. This paper delivers a description of the measurement objectives,
including the first results, and provides an overview for upcoming detailed
investigations.
Typical for the measurement site, the wind direction was almost constant
from the northeasterly sector (30–60∘). The analysis of the
air masses and dust measurements showed that dust input was generally low;
however, partly moderate dust influences were observed. Based on very
similar particle number size distributions at the ground and mountain sites,
it was found that the MBL was generally well mixed, with a few exceptions, and
the MBL height ranged from 600 to 1100 m. Differences in the PNSDs arose
from the dust influences. The chemical composition of the aerosol particles
and the cloud water indicated that the coarse-mode particles served as
efficient CCN. Furthermore, lipid biomarkers were present in the aerosol
particles in typical concentrations of marine background conditions and
anticorrelated with dust concentrations.
From the satellite cloud observations and supporting modeling studies, it
was suggested that the majority of low-level clouds observed over the
islands formed over the ocean and could form solely due to the local
meteorological conditions. Therefore, ocean-derived aerosol particles, e.g.,
sea salt and marine biogenic compounds, might be expected to have some
influence on cloud formation. The presence of compounds of marine origin in
cloud water samples (e.g., sodium, methane-sulfonic acid, FAAs, TEPs, distinct
DOM classes) at Mt. Verde supported an ocean–cloud link. The transfer of
ocean-derived compounds, e.g., TEPs, from the ocean to the atmosphere was
confirmed in controlled tank measurements. The DOM composition of the cloud
waters was consistent with the SML and bulk water composition and partly
different from the aerosol particle composition. However,
based on the findings that (biologically active) INPs were mainly present in
supermicron aerosol particles that are not suggested to undergo strong
enrichment during ocean–atmosphere transfer as well as the INP abundance in
seawater and in cloud water, other non-marine sources most likely
significantly contributed to the local INP concentration.
The bulk water and SML analysis comprised a wide spectrum of biological and
chemical constituents and consistently showed enrichment in the SML.
Especially for the complex OM characterization, some of the methods
presented here have been used for the first time for such diverse sets of
marine samples (e.g., DOM fractioning, metabolome studies with DART-HR-MS).
Chl a concentrations were typical for oligotrophic regions such as Cape
Verde. The pigment composition indicated the presence of cyanobacteria,
haptophytes and diatoms with a temporal change in dominating groups (from
cyanobacteria to diatoms) suggesting the start of the diatom bloom. Possible
linkages to the background dust input will be resolved. Concentrations and
SML enrichment of DOC were comparable to previous campaigns at the same
location. For the DOC as a sum parameter, the two applied sampling techniques
(manual and catamaran glass plate) provided very similar results. However,
whether this is also true for the various compound classes remains to be
evaluated. Lipids established an important organic compound group in the SML,
and a selective enrichment of surface-active lipid classes within the SML
was found. Observed enrichments also indicated biotic and/or abiotic
lipid degradation processing within the SML. The temporal variability of
bacterial abundance was studied and provided the first colocated SML and cloud
water measurements for this particular oceanic province. Whether the strong
day-to-day variability of absolute cell numbers in the SML and bulk water
was derived from changing water bodies or altered meteorological forcing
needs to be further elucidated. Regarding mercury species, results indicate
that the SML is the major compartment where (dissolved plus particulate) Hg
was enriched, while MeHg was more likely concentrated in the bulk water,
underlining the importance of the SML in Hg enrichment dependent on OM.
For the trace gases, a variety of conditions were observed, showing
influences from the ocean and the long-range transport of pollutants. High
amounts of sunlight and high humidity in this tropical region are key in
ensuring that primary and secondary pollutants (e.g., ethene and ozone) are
removed effectively; however, additional processes need to be regarded.
Measurements within the marine boundary layer and at the ocean–atmosphere
interface, such as those shown here, are essential to understand the various
roles of these short-lived trace gases with respect to atmospheric
variability and wider climatic changes. The Cape Verde islands are likely a
source region for HONO, and the potential of the SML to form secondary
particles needs to be further elucidated.
This paper shows the proof of concept of the connection between organic
matter emission from the ocean to the atmosphere and up to the cloud level.
We clearly see a link between the ocean and the atmosphere as (i) the
particles measured at the surface are well mixed within the marine boundary
layer up to cloud level, and (ii) ocean-derived compounds can be found in the
(submicron) aerosol particles at mountain height and in the cloud water. The
organic measurements will be implemented in a new source function for the
oceanic emission of OM. From the perspective of particle number
concentrations, the SSA (i.e., primary marine aerosol) contributions to both
CCN and INPs are, however, rather limited. Furthermore, CCN and INP
populations are much lower during clean marine periods than during dust
periods. These findings underline the fact that further in-depth studies
differentiating between submicron and supermicron particles as well as
between aerosol number and aerosol mass are strongly required. A clear
description of any potential transfer patterns and the quantification of
additional important sources must await the complete analysis of all the
samples collected. The main current objective is to finalize all
measurements and interconnect the meteorological, physical, biological and
chemical parameters also to be implemented as key variables in model runs.
Finally, we aim to achieve a comprehensive picture of the seawater and
atmospheric conditions for the period of the campaign to elucidate the
abundance, cycling and transfer mechanisms of organic matter between the
marine environmental compartments.
List of acronymsAPSAerodynamic particle sizerCCNCloud condensation nucleiCCNCCloud condensation nuclei counterCDOMChromophoric dissolved organic matterchl aChlorophyll aCOSMOConsortium for Small-scale Modelling-Multiscale Chemistry Aerosol Transport ModelCTDConductivity–temperature–depth sensorCVAOCape Verde atmospheric observatoryCVFZCape Verde frontal zoneCVOOCape Verde ocean observatoryDART-QTOF-MSDirect analysis in real-time quadrupole time-of-flight mass spectrometryDegLipLipid degradation productsDLDissolved lipidsDMSDimethyl sulfideDOCDissolved organic carbonDOMDissolved organic matterECWMFEuropean Centre for Medium-Range Weather ForecastsEBUSEastern boundary upwelling systemEFEnrichment factor (analyte concentration in the SML with respect to the analyte concentration in the bulk water)ETNAEastern tropical North AtlanticFAAsFree amino acidsGo : PAMGothenburg potential aerosol mass reactorHONONitrous acidHYSPLITHybrid single-particle lagrangian integrated trajectoryINP(s)Ice-nucleating particle(s)LOPAPLong-path absorption photometerLMWAsLow-molecular-weight acidsLMWNsLow-molecular-weight neutralsMarParCatCatamaran with glass plates for SML samplingMarParCloudMarine biological production, organic aerosol Particles and marine Clouds: a process chainMARSUMARine atmospheric Science UnravelledMBLMarine boundary layerMeHgMethylmercury (MeHg)Mt. VerdeHighest point of São Vicente island (744 m)MUSCATMulti-Scale Chemistry Aerosol TransportNACWNorth Atlantic Central Water massesNCCNCloud condensation nuclei number concentrationNINPNumbers of INPsOHHydroxyl radicalOFROxidation flow reactorOMOrganic matterOMZOxygen minimum zone(O)VOCs(Oxygenated) volatile organic compoundsPM1Particulate matter (aerosol particles) smaller than 1 µmPM10Particulate matter (aerosol particles) smaller than 10 µmPNSDsParticle number size distributionsPOMParticulate organic matterPVMParticle volume monitorSACWSouth Atlantic Central Water massSALSaharan air layerSASsSurface-active substances and/or surfactantsSMLSea surface microlayerSOASecondary organic aerosolSSASea spray aerosolSMPSScanning mobility particle sizerTEPsTransparent exopolymer particlesTSPTotal suspended particulateTMTransmission modeWSOMWater-soluble organic matterData availability
The data are available through the World Data Centre PANGAEA under the following links: https://doi.pangaea.de/10.1594/PANGAEA.910693 (van Pinxteren, 2020a; Table 2) and https://doi.pangaea.de/10.1594/PANGAEA.910692 (van Pinxteren, 2020b; Table 1).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-6921-2020-supplement.
Author contributions
MvP, KWF, NT and HH organized and coordinated the MarParCloud campaign.
MvP, KWF, NT, CS, EB, XG, JV, HW, TBR, MR, CL, BG, TL, LW, JL and HC
participated in the campaign. All authors were involved in the analysis,
data evaluation and discussion of the results. MvP and HH wrote the
paper with contributions from all coauthors. All coauthors proofread
and commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Marine organic matter: from biological production in the ocean to organic aerosol particles and marine clouds (ACP/OS inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We acknowledge the CVAO site manager, Luis Neves, and the Atmospheric Measurement Facility at the National Centre for Atmospheric Science (AMF, NCAS). The authors acknowledge Thomas Conrath, Tobias Spranger and Pit Strehl for their support during fieldwork. Kerstin Lerche from the Helmholtz-Zentrum für Umweltforschung GmbH – UFZ (Magdeburg) is acknowledged for the pigment measurements. The authors thank Susanne Fuchs, Anett Dietze, Sontje Krupka, René Rabe and Anke Rödger for providing additional data and filter samples, as well as Elisa Berdalet for the discussion about the pigment concentrations. Kay Weinhold, Thomas Müller and Alfred Wiedensohler are acknowledged for their data support. We thank Johannes Lampel for providing the photograph in Fig. 1. In addition, the use of SEVIRI data and NWCSAF processing software, distributed by EUMETSAT and obtained from the TROPOS satellite archive, is acknowledged. María Eugenia Monge is a research staff member from CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina). Ryan Pereira thanks Juliane Bischoff and Sara Trojahn for technical support. We also thank the Monaco Explorations program as well as captain and crew of MV YERSIN for supporting the wave glider deployment. Finally, the authors thank the anonymous reviewers for their valuable comments and suggestions.
Financial support
This work was funded by the Leibniz Association SAW under the project “Marine biological production, organic aerosol particles and marine clouds: a Process Chain (MarParCloud)” (SAW-2016-TROPOS-2) and within the Research and Innovation Staff Exchange EU project MARSU (grant no. 69089). We received support from the European Regional Development fund by the European Union under contract no. 100188826. Jianmin Chen received funding from the Ministry of Science and Technology of China (grant no. 2016YFC0202700) and the National Natural Science Foundation of China (grant nos. 91843301, 91743202, 21527814). Sanja Frka and Blaženka Gašparović received full support from the Croatian Science Foundation under the Croatian Science Foundation project IP-2018-01-3105. Erik H. Hoffmann received financial support from the PhD scholarship program of the German Federal Environmental Foundation (Deutsche Bundesstiftung Umwelt, DBU, AZ: 2016/424). Sebastian Zeppenfeld received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project 268020496−TRR 172) within the Transregional Collaborative Research Center “ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3”.
Review statement
This paper was edited by Paul Zieger and reviewed by two anonymous referees.
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