School
of Ocean and Earth Science and Technology, Hawaii Institute of Geophysics and
Planetology, University of Hawaii, Honolulu, HI 96822.
Scanning lidar measurements were
carried out during the Shoreline
Environmental Aerosol Study (SEAS) experiment (April 19-30, 2000) to
characterize the aerosol scattering fields in the coastal marine boundary layer
at Bellows Beach on the Southeast side of Oahu. The sea salt was found to be
well mixed throughout the mixed layer although the depth of the trade wind
mixed layer was found to vary significantly over short time scales. As
expected, the frequency distribution of aerosol scatter had a lognormal distribution
with the exception of regions downwind of breaking waves where the frequency
distribution was bimodal. A spatial statistical study revealed that the island
blocking effects cause low-level clouds to develop as they approach the island
with enhanced drizzle near the coastline reaching all the way to the surface.
The spray from waves breaking on an outer reef was found to be intermittent and
to be contained to heights of 20 m (on average) for the average wind speed of 7
m/s. Sea salt concentrations and fluxes from the breaking waves were estimated
from the lidar measurements and found to be within the range of values reported
by other investigators.
Small-scale aerosol
variability in the coastal marine boundary layer can have significant effects
on atmospheric visibility and extinction of light. For instance, under calm dry
conditions (few clouds) one can clearly see
Different spatial and
temporal scale processes affect optical extinction in the coastal marine
boundary layer. Synoptic scale processes (~1000 km) such as large-scale wind
fields, upper level divergence and air mass modification affect background salt
spray concentrations (Woodcock, 1953), atmospheric stability (Albrecht, 1984),
and vertical mixing in the boundary layer
(Stull, 1988). Cloud processes, which occur at micro- to meso-scale
dimensions (1-10 km), modulate the background drizzle and aerosol extinction
fields in the coastal marine boundary layer (Takahashi, 1976; Albrecht, 1989).
Mountain or island blocking affects the aerosol extinction fields at
intermediate scales. Finally, small-scale (10-100 m) processes such as breaking
waves and coastal topography play an important role in the small-scale aerosol
concentrations near the coastline.
Figure
1 Image of
Scanning lidar
measurements can provide detailed spatial maps of aerosol scattering fields in
the coastal marine boundary layer in near real time (Hooper and Martin, 1999).
From 1998 to 2002, scanning lidar measurements have been carried out on
Figure
2. Seven-day backward trajectories for low-level air arriving at
During
Our lidar system consists of a coaxial design employing a 30-cm Schmidt-Cassegrain telescope directly coupled to an EMI-9863 photomultiplier tube (PMT). The PMT output is digitized at 60 MHz (~2.5 m resolution) with a 12 bit Gage digitizer. During the SEAS experiment a 50-Hz Nd:YAG laser (50 mJ/pulse, at 532 nm) was used. The laser beam diameter is expanded to a diameter of 4 cm. The direction of the lidar is controlled by two motorized scanning mirrors (DFM scanner) for azimuth and zenith motion. A fast diode is used to measure the laser pulse energy and correct for pulse to pulse energy variations. Further details of system and software are given in Sharma et al., (1998; 2001) and Lienert et al. (1999).
The
lidar equation (Reagan, 1995; Porter et al., 2000) can be written as
(1)
where n(r) is the
number of photons from distance r, C is the lidar calibration,
β(r) is the backscattering coefficient (m-1 sr –1)
at range r, and T(r)2
is the two-way transmission given by , where σe(r)
is the extinction coefficient (m-1) at range r. The backscattering coefficient is given by , where Pm(r) and Pa(r)
are the molecular and aerosol phase functions at 180° scattering angle (at
range r), and σm(r) and σa(r)
are molecular and aerosol scattering coefficients (m-1) at range r.
For the clean marine atmosphere over
We
have used a constrained forward stepping approach (CFS) (Porter et
al., 2000; Porter et al., 2001) to convert our
lidar measurements into aerosol scattering coefficients. This process is carried out
by calculating the aerosol scatter at each distance in a forward stepping manner and
integrating the transmission out to each new range. It is well known (Klett,
1981; Hughes et al., 1985) that a simple forward stepping approach such as this
one will develop large errors, at larger ranges, if the aerosol phase function
or lidar calibration being employed is wrong. This error is caused by the accumulated error in
the transmission term in Eq.1. The CFS approach consists of collecting
horizontal lidar measurements in horizontally homogeneous air, then manually
adjusting either the aerosol phase function or the lidar calibration factor
until the derived aerosol scattering coefficients (derived from the lidar data)
are constant with range. Modeling studies show that if the aerosol scattering
coefficient values derived from the forward stepping approach are constant with
range, then the derived aerosol scattering coefficients are also correct
(Porter et al., 2000; Porter et al., 2001). Furthermore, as mentioned above,
errors in the assumed aerosol phase function can be offset by an error in lidar
calibration resulting in correct aerosol scattering coefficients (and vice-versa). In order
to implement this approach for real lidar data, ideally we should have
conditions with horizontally homogeneous aerosol. Perfectly homogeneous
conditions rarely occur in the atmosphere. On the other hand, conditions do
often exist over the open ocean where the aerosol scattering values vary about
a mean value. Field experiments carried out at Bellows Beach have shown that as
long as the aerosol scattering values do not vary systematically over the open
ocean (past the coastal reef), then the CFS approach gave aerosol scattering
coefficient values that agreed with nephelometer and sun photometer
measurements within the expected error values (Porter et al., 2000). The small-scale changes in the open ocean
aerosol scattering (which typically have no trend with range) do add error to
the process. Our modeling studies (Porter et al., 2000) showed that the accuracy
of the derived aerosol scattering coefficients improve when the range of the
horizontal lidar measurements increase. For the SEAS experiment, the usable
scanning lidar range of our old detector system was ~3 km. Based on Fig. 8 in Porter et al. (2000), this
is expected to result in aerosol scattering errors less than 35% in the mixed
layer for the aerosol scattering coefficient values experienced during the SEAS
experiment. If many lidar scans are averaged, then the small-scale variations
are smoothed out and the lidar calibration can be improved somewhat. This
approach has been applied to lidar data collected during the SEAS experiment
and is expected to reduce the error in the lidar calibration from 35% to 25%.
Comparisons between our lidar derived aerosol scattering coefficients for the mixed
layer during the SEAS experiment and those obtained from measured size
distributions and by nephelometry are discussed in Clarke et al. (this issue)
and are within the expected range of uncertainty. Since mid-2001 we have been
using a custom logarithmic
amplifier (Lienert et al., 2002), which increases the range of our
scanning lidar to 10 km, allowing more accurate retrievals of the aerosol
scattering coefficient.
Above the mixed layer
the aerosol size distribution and therefore the aerosol phase function (which
describes the probability of a photon being scattered at a particular angle) is
likely to change so that larger errors are expected in the retrieved aerosol
scattering coefficients. However, our data and other vertical salt mass
measurements (Woodcock, 1953) show the sea salt aerosol concentration is
expected to be 2-5 times lower there. When clouds occur in the lidar
measurements the particle phase function may not be valid, and multiple
scattering can also become significant. These effects are not modeled here so
it is assumed that the scattering values derived in and past clouds are less
reliable.
While the error in the
aerosol scattering (for the mixed layer) is expected to be less than 25%, some
additional error will occur in heavy spray from waves breaking over the reef.
In these dense salt spray conditions the aerosol size distribution is larger,
resulting in a different aerosol phase function. Porter et al., (2000)
investigated the aerosol phase function for a wide range of sea salt size
distributions. They found that the lidar aerosol phase function increased and
then decreased as the sea salt size distribution increased (up to hurricane
force conditions) but that for a wide range of common conditions the aerosol
phase function ranged from 0.6 to 0.65.
In
processing the lidar data in this paper, we have used a value of 0.6 and
therefore expect that the error in the aerosol phase function is at most 0.05
(8.3%). For a short distance in dense salt spray plumes, the use of this
incorrect phase function will result in an additional error of at most 8.3% (or
a total of 33%). But if the optical depth through dense salt spray plumes is
large, the error in the calculated transmission will accumulate, resulting in
larger errors with distance. Most of our vertical scanning lidar data only
passes a short distance (10-20 m) through the low level spray from breaking
waves. One exception occurs when the laser is pointing horizontally over the
reef. In this case the path length through the heavy spray can be large and
error factors up to two can occur for measurements beyond the reef at low
height (below 20 m height). This error is occasionally seen in individual scans
and cases where only heavy wave spray was sought (see discussion on Fig. 14
below). This effect is not present in the overall averages discussed below
(Fig. 10) because the spray from the outer reef was not continuous.
The CFS approach
described above works equally well in deriving aerosol extinction values for
absorbing and non-absorbing aerosols (Porter et al., 2001). Aerosol absorption
is typically small in clean marine aerosol conditions and this was the case for
the SEAS experiment even during the case of enhanced accumulation mode aerosol
near the end of the experiment (Masonis et al.,
Clarke et al., this issue). Due to the small aerosol absorption during SEAS,
the scattering and extinction coefficients are essentially identical for the
conditions studied in this paper.
Large-scale open ocean
processes such as wind speeds, and atmospheric stability affect background
oceanic aerosol extinction conditions. The increase in sea salt concentration
with wind speed has been documented by many investigators (Woodcock, 1953; Gathman et al., 1983; and Porter and Clarke, 1997) and
models exist which describe this relationship (Zeisse,
1999). The vertical distribution of sea salt aerosol over Hawaii has been
measured using filter and kite measurements (Blanchard et al., 1984; Daniels,
1989), and it has been shown that on average larger sea salt aerosol
concentrations occur near the surface with a strong gradient in the lowest 30
m. Above 30m height, much weaker gradients exist with nearly constant
concentrations up to cloud base where the top of the mixed layer often occurs.
Above the mixed layer up to the top of the boundary layer (trade wind
inversion) the salt aerosol concentrations often decrease significantly
depending on the strength of the lid at the top of the mixed layer. Above the
trade wind inversion the free troposphere air typically has very low aerosol
scattering values with the exception of episodic Asian dust or pollution events
over
Figure 3. Vertical
distribution of virtual potential temperature and relative humidity measured at
Bellows Beach (
To investigate the boundary layer vertical
structure during the SEAS campaign, we released a radiosonde to measure
the vertical profile of temperature and humidity. The radiosonde was released
from a small boat ~2 km upwind of the lidar site to obtain open ocean
conditions. Figure 3 shows the relative humidity, and virtual potential
temperature derived from the sounding taken on
Figure 4. Vertical
distribution of aerosol scattering coefficients derived from the scanning lidar
at the same time as the sounding data in Fig. 3.
Figure 4 shows the aerosol scattering coefficients obtained from the scanning lidar at the time of the sounding. It can be seen that the sea salt (the cause of the aerosol scattering) is well mixed up to approximately 0.55 km, which is consistent with the mixed layer seen in Fig. 3. Between 0.5 and 2 km several layered cloud and aerosol fields exist which are consistent with aged trade wind clouds evaporating in a stable stratified layer. The depth of the mixed layer and the strength of the lid (at the top of the mixed layer) affect mixing processes in the boundary layer. If the mixed layer is deeper, then the salt aerosol will be mixed throughout a deeper layer. A clear example of this mixed layer lid effect on salt vertical distributions is shown in Fig. 5 where the mixed layer is evident up to ~400 m.
Figure 5 An example
of the aerosol scattering coefficient derived from the scanning lidar. In this
case, island blocking is causing an increase in the mixed layer near the
island.
The height of the mixed layer increases near the island (right side of the image). This island blocking effect is discussed below. Current meso-scale models such as MM5 (see http://www.mmm.ucar.edu/mm5/), which are initialized from large scale models, do not capture the trade wind inversion much less the depth and strength of the mixed layer (Duane Stevens, personal communication). Layer models (Albrecht, 1984) introduce a mixed layer and show its evolution but require further validation (Bruce Albrecht, personal communication). One feature, which is not currently modeled, is the rapid variability in the height (depth) of the mixed layer, which we observed during the experiment.
Figure 6. Time
series of height of the mixed layer (circles) and the top and bottom of the
trade wind inversion (stars) as observed in the Lihue,
Figure 6 shows how the height of the trade wind inversion and mixed layer varied during the SEAS experiment as observed in the lidar data sets (2-10 minute intervals). Here the depth of the mixed layer was determined manually from the lidar data as a sudden decrease in the aerosol extinction coefficient. Cases where the top of the mixed layer could not be clearly defined (clouds or other factors) were omitted. The lidar mixed layer heights ranged from 300–1300 meters.
In
order to provide an independent measure of the mixed layer throughout the SEAS
experiment, soundings collected at Lihue,
Figure 7. Feature
preserving average of virtual potential temperature (at Lihue,
In order to obtain a typical virtual potential temperature profile for the SEAS time period, an average of the Lihue soundings was carried out using a “feature-preserving” average (Atkinson, 1971). Only soundings that were not contaminated by clouds were used. The “feature preserving” average (Fig. 7) shows the height of the mixed layer at about 800 m and the trade wind inversion is at about 1800 m during the SEAS experiment. Substantial variability in the low level stability (both dry adiabatic an moist adiabatic processes) existed in this short period, which means that on some occasions the low level thermals were capped at the mixed layer whereas in other more convective periods the vertical motions should be able to penetrate past the mixed layer and produce trade wind clouds.
Figure 8a.
Frequency distribution of aerosol scattering coefficient occuring at 75 to 100
m altitude and at 1600 to 1700 m range. This calculation includes all the lidar
data collected during the SEAS experiment.
Various statistical properties of the aerosol
scatter were calculated from the scanning lidar data. Box
sizes of 25 m high by 25 m long were used as a compromise
between good spatial resolution and reasonable counting statistics. Figure 8a shows the frequency distribution of
aerosol scattering coefficients in one such box from 75 to
100 m high and 1600 to 1625 m from the shore. This is sufficiently high that
spray from breaking waves does not affect it and is therefore representative of
background trade wind aerosol scattering properties. From Fig. 8a it can be
seen that a lognormal distribution fits the aerosol scattering distribution reasonably well, as was also found by Gerber (1984).
Figure 8b.
Frequency distribution of aerosol scattering coefficient occuring near the
surface (0-25 m high) and at 1600 to 1700 m range (near the breaking waves over
the reef). This calculation includes all the lidar data collected during the
SEAS experiment.
Figure 8b shows a similar set of
values for a box located near the
surface just downwind from the breaking waves on the reef. Its bimodal nature is clearly evident as the sources
of aerosol are both the background and the breaking waves. Hooper and Martin
(1999) found a somewhat similar bimodal statistical behavior from breaking
waves off
Figure 9. Vertical
distribution of average aerosol scattering coefficients (standard deviation
bars are also shown) derived from the scanning lidar.
Figure
9 shows the vertical distribution of average aerosol scattering coefficient
(for one day) over the reef (1.8 to 1.85 km from the shore). Here we have
calculated the average of the logarithm of the aerosol scattering coefficient
for each box and then plotted the resulting aerosol scattering coefficient.
This is equivalent to the geometric mean of the scattering and has the property
that it minimizes the effect of occasional large values resulting in a value
that is somewhat smaller than an arithmetic mean. The lognormal standard
deviations are shown as error bars at each height. It can be seen that the
aerosol scattering coefficient is roughly constant up to 600 m then decreases
above the mixed layer (similar to the case shown in Figs. 4 and 5).
When the trade winds
encounter the
Figure 10a
Figure 10b
Figure 10. Panels A
and B show the spatial distribution of the average and standard deviation of
the log of aerosol scattering coefficient derived from the scanning lidar data
at
Figure
10a shows the spatial distribution of the lognormal average aerosol scattering coefficients
calculated from four days of vertical scans. Individual values were
calculated for boxes, which were 10 m high by 25 m wide. Figure 10b shows the lognormal standard deviation for the
same data as shown in Fig. 10a. In interpreting these Figs. several
cautions are required. The scattering
values derived in clouds (and on the far side of clouds) are expected to be
uncertain due to multiple scattering (which we do not account for) and
different particle phase functions. Below cloud base (typically at 500 m) the
averages do not suffer from this uncertainty. A separate problem is seen in
Fig. 10b from 0 to 500 m height and at a range from 2.25 to 3.5 km where the
standard deviation increases. This is likely due to degraded signal-to-noise in
our scanning lidar data. The subsequent use of a custom logarithmic amplifier
(Lienert et al., 2002) has shown that most of this noise was caused by poor
digitization resolution and not from PMT noise. When the signal is small (i.e.,
at longer ranges) then the digitization error (±1 count) of our 12-bit
digitizer can be a significant fraction of the total counts, which is then
amplified by the transmission term (in eq. 1). The
standard deviation values in this region (Fig. 10b) are nearly as large as the
averages (Fig. 10a). The 4-day average shown in Fig. 10a appears to have
removed most this noise because small box-to-box variations are not evident.
Figure
10a and 10b show that the cloud layer (region of largest standard deviation)
was mainly centered near 0.6 km (0.45 to 0.75 km) height and that cloud
development is occurring near the coastline. During the SEAS experiment the
trade winds had an average value of 7 m/s (Clarke et al., 2002) and never
dropped below 5 m/s (for a 30-min. average). Therefore it is unlikely that
increased clouds near the coast are due to diurnal land heating effects.
Woodcock (Blanchard et al., 1984) analyzed 262 days between 1968 and 1969 and
found no significant diurnal change in wind speeds over
Figure 10a shows an
increase in the aerosol scattering coefficient in the mixed layer near the
shore. While some of this may be attributed to enhanced cloud development near
the shore, Fig. 10b shows that the larger standard deviations, which are due to
passing clouds, are concentrated between 0.45 to 0.75 km height and that their mean thickness is
increasing near the shore (at ranges of less than 1 km). Below 0.45 km height the enhanced scattering
seen near the shore in Fig. 10a could be related to two processes, which are both consistent with the cloud
development seen in Fig. 10b near the coastline. As air piles up against the
island it can cause a net increase in rising motion, which would cause the air
to rise and cool increasing the relative humidity. The hygroscopic salt
aerosols would then swell due to the uptake of water at higher relative humidities. On the other hand such a process could not
explain the observed increase near the surface (below 100 m height) where the
relative humidity would increase little. In this example, we are not
considering the spray from the breaking waves, which is confined to below 30 m (on
average) at wind speeds of 7 m/s (see Fig. 7). A second mechanism which might
explain the increased in aerosol scattering near the coastline is an increase
in drizzle and virga falling from the trade wind clouds as they near the
island. Cloud physics models have shown that clean marine conditions, with low cloud
condensation nuclei concentrations (CCN), can be very efficient at producing
rain (Takahashi, 1976; Porter, 1988). Interestingly, Alfred Woodcock (personal
communication) noticed small trade wind cumulus near
Figure 11. Vertical scan of lidar
measurements during a trade wind shower episode during the SEAS experiment.
Figure 11 shows a vertical lidar scan with an example of one of the stronger trade wind showers, which occurred during the SEAS experiment.
Figure
12.Horizontal lidar scan obtained on
Figure 12 shows a horizontal lidar scan with an example where drizzle from an isolated trade wind cloud affected the optical scattering properties near the surface. The horizontal lidar scans collected during this period showed numerous similar patches moving towards shore, which were associated with isolated trade wind showers. The blue areas in Fig. 12 are unusually clean and are possible due to cloud related downdrafts bring aerosol free air to the surface. Incorporating these small-scale cloud drizzle effects will be a challenge for future modelers.
One interesting feature,
which appears in Fig. 11, is the fact that the shower is falling vertically
over the lidar while it is tilted out over the ocean. This illustrates the
artificial effects introduced by the relatively slow scanning speed of the
lidar scanner during the SEAS experiment (~1 degree/sec) combined with lateral movement of
the rain with the wind.
The use of a new logarithmic amplifier is now allowing faster lidar scans to
minimize this effect.
Spray from breaking
waves has a large effect on small-scale aerosol variability in coastal regions
at low altitude. This is evident in Figs. 10a and 10b near the surface at distances of 1.5-2 km.
Fig. 13a
Fig. 13b
Figure
13. Panels A and B show the log normal average aerosol scattering coefficient
and standard deviation obtained from vertical lidar scans over the reef area.
Figures 13a
and 13b show the same data expanded near the surface to illustrate the salt
spray plumes observed over
the reef during the SEAS experiment. The salt plumes were confined to
below ~40 m for the typical trade wind conditions experienced during the SEAS
experiment ( 7 m/s wind speeds). This is consistent
with past kite measurements of sea salt mass at
Breaking waves were not
always present on the reef and approximately 50% of the time no reef plumes are observed
in the scanning lidar data. Movies made of the salt plumes show looping
properties possibly consistent with random turbulent mixing. During light wind
periods (~ 1.8 m/s) (not during the SEAS experiment) we have observed these
spray plumes develop very large salt concentrations over the reef area and can rise higher (up to 400 m) due to reduced wind dilution
effect (Sharma et al., 2001). Similar wind dilution effects have been reported
at higher wind speeds when observed by nephelometers
along the
Figure
14. Panels A, B and C show the aerosol scattering coefficients obtained from an
individual lidar horizontal scan, an average of many horizontal scans, and the
standard deviation calculated from the lidar scans. Salt spray from the outer
reef is seen moving towards the shore (collected on
On
7.
Sea Salt Fluxes
In order to estimate the fluxes of salt mass from the breaking waves, we must first convert our derived aerosol scattering coefficient values into salt mass concentrations. This requires an estimate of the mass scattering coefficient for sea salt aerosols. On the basis of the Woodcock (1953) measurements, Porter and Clarke (1997) give a range of salt size distributions for different wind speeds up to hurricane force winds. As expected, the size distributions tend to shift to larger sizes with increasing wind speed. Using Mie theory calculations and the sea salt models presented in Porter and Clarke (1997) results in aerosol mass scattering efficiencies (at 75% relative humidity) of 5, 3, 1.5, 1.3, 0.6, and 0.27 m2/g for sea salt mass concentrations of 0.69, 3.13, 12.96, 26.5, 136, and 711 μg/m3. Here the salt hygroscopic uptake of water was modeled following Tang et al., (1997).
As a relative test of
our ability to convert the lidar derived aerosol scattering coefficients into sea
salt aerosol mass concentrations, we now compare our derived values with those
reported by Blanchard et al., (1984). During the SEAS experiment the average
background aerosol scattering coefficient derived from the lidar was ~4.7 x 10-5
m-1 (see Fig. 10a). Under typical clean marine conditions near
In order to estimate the
salt mass flux from the waves breaking on the reef, we assume a larger aerosol
size distribution (compared to the background sea salt size distribution at 7
m/s) with a peak in the wet area size distribution near 7 μm
diameter (Clarke et al., this issue; Shifrin and Zolotov, this issue).
This is similar to the size distributions reported in Porter and Clarke
(1997) for wind speeds of 14-17 m/s. Based on that size distribution; the sea
salt mass scattering for spray from breaking waves was calculated to be 1.3 m2/g at 75% relative humidity. In order to
estimate the aerosol scattering coefficient from the sea spray we take the
average of the near surface (0-10 and 10-20 m height) aerosol scatter seen in
Fig. 5 (5.6 m-1) and subtract the background values (4.7 m-1)
to obtain a value of 0.9 m-1, which is maintained by the breaking
waves. Dividing by the assumed aerosol
mass scattering efficiency for spray from breaking waves (1.3
m2/g at 75% relative humidity) results in an average salt
concentration of 9.0 μg/m3, which is maintained by reef spray.
Multiplying by the average wind speed during SEAS (7 m/s) we obtain a salt flux
rate of 63 μg m-2 sec-1
from waves breaking on the outer reef.
Although wind and wave conditions vary at each site, it is useful to
compare these salt flux rates with those obtained at other sites. Petelski and Chomka (1996)
reported salt mass flux rates from 3.2 to 384 μg
m-2 sec-1 for heights between 2 and 5 m for various
offshore and onshore wind conditions in the
During the SEAS experiment, the lidar data
showed that salt spray from waves breaking on the outer reef was episodic and
maybe half of the time had no spray at all. On the other hand, the waves
breaking right on
Figure 15. Vertical distribution of lognormal average and lognormal
standard deviation over the waves breaking on the beach.
Figure 15 shows the
vertical distribution of aerosol scatter measured over the beach on
In order to obtain the salt flux rate from the waves breaking on Bellows Beach we take the average aerosol scattering coefficient over a height from 0 to 5 m (2.8x10-4 m-1) and subtract the background value (7.0x10-5 m-1), which results in an aerosol scattering coefficient of 2.1x10-4 m-1 caused by the breaking waves. Dividing this value by an assumed aerosol mass scattering efficiency of 1.3 m2/g (at 75% relative humidity) results in a salt mass concentration of 161 μg/m3 maintained by the beach spray. Assuming a wind speed of 4.7 m/s (measured at the time of the beach lidar measurements) results in an average salt mass flux of 760 μg m-2 sec-1 from the waves breaking on Bellows Beach shore which is within the values reported by de Leeuw et al., (2000) (562-1034 μg m-2 sec-1 ) for breaking waves at Scripps Pier, California. Therefore, our estimated salt concentrations over the breaking waves at Bellows Beach were lower than those measured over breaking waves at Scripps Pier (Jensen et al., 2001) but the salt flux rates were comparable (de Leeuw et al., 2000) between the two sites, which is consistent with the larger winds at Bellows Beach compared to the Scripps Pier.
Based on lidar and
sounding data, we found that the top of the mixed layer (often called a
transition layer) frequently forms a lid on the large sea salt concentrations
found in the trade wind mixed layer. It was also found that the height of the
mixed layer was surprisingly variable (from 400 to 800 m) considering the
typical trade wind conditions experienced during the SEAS experiment. The
maintenance of the mixed layer lid effect requires cooler air be distributed
throughout the mixed layer and warmer air be distributed throughout the cloud
layer. One obvious explanation is that latent heat released in clouds warms the
cloud layer relative to the mixed layer. But this requires some drizzle or rain
to occur since eventual cloud evaporation in the cloud layer would absorb the
latent heat released in that layer. Cloud drizzle/virga could also cool the
mixed layer through evaporative cooling (Albrech,
1989). A second mechanism is possible. When clouds penetrate (or are in contact
with) the dry free troposphere air above the mixed layer they experience strong
evaporative cooling which can result in downdrafts. If these cool downdrafts
reach down to below the mixed layer they could cool the mixed layer. Alternatively,
if the evaporative cooling is not strong, then the air brought into the cloud
layer from these downdrafts may actually warm the cloud layer (the potential
temperature in the free troposphere is large), which would also support the
maintenance of the mixed layer. Models that explain the presence of a mixed
layer have mainly focused on explaining the transition of a
stratocumulus-topped boundary layer to a trade wind broken cumulus (Albrecht,
1984). Bretherton and Wyatt (1997) emphasized the importance
of surface latent heat fluxes and cloud top evaporative cooling in the
decoupling of the boundary layer. The backward trajectories (Fig. 2) and wind
speeds for the SEAS experiment were fairly constant, so it is expected that the
surface fluxes would also be similar and that the mixed layer should also be
fairly constant. But our data show that the height of the mixed layer is quite
variable (Fig. 4). Two possibilities are
that drizzle and cloud detrainment (which affect the mixed layer) are
focused in regions with more active cloud activity and that small cloud scale
processes should be a dominant feature in controlling the trade wind structure
near Hawaii. We are not aware of other studies that show such large and
frequent variability in the mixed layer height. Further studies are needed to
better understand and model this variability.
The statistical
calculation of the scanning lidar data showed a significant island blocking
effect. As the trade winds approach
Modeling the various processes responsible for the coastal optical properties observed during the SEAS experiment remains a challenge. While meso-scale models have advanced dramatically, to our knowledge none are run at sufficient resolution (below 25 m) to resolve individual aerosol sources such spray from waves breaking on reefs or the shoreline. We have shown that cloud drizzle also plays an important role in coastal optical properties and that island blocking contributes to this effect. Correctly modeling this effect would require knowledge of aerosol concentrations as well as real time cloud activity. Modeling the small-scale aerosol scatter in the coastal marine boundary layer will also require a statistical approach, as it is difficult to model instantaneously processes that are random or semi-random. One approach to obtain instantaneous estimates of aerosol scatter could be to collect real time measurements with a small portable scanning lidar and make short-term forecasts. This is the approach the National Weather Service has followed in trying to forecast local rainstorms, which also have semi-random characteristics.
Acknowledgements.
The work carried out here was funded by an ONR Grant # N00014-96-1-0317. We
wish to thank Steve Ackleson and Ron Ferek for their support and interest. This is SOEST
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