Top-down and bottom-up aerosol–cloud shortwave radiative flux
closures were conducted at the Mace Head Atmospheric Research Station in
Galway, Ireland, in August 2015. This study is part of the BACCHUS (Impact of
Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a
Holistic UnderStanding) European collaborative project, with the goal of
understanding key processes affecting aerosol–cloud shortwave radiative flux
closures to improve future climate predictions and develop sustainable
policies for Europe. Instrument platforms include ground-based unmanned
aerial vehicles (UAVs) The regulatory term for UAV is remotely
piloted aircraft (RPA).
One of the greatest challenges in studying cloud effects on climate are that the clouds are literally out of reach. Many ground-based measurement sites have a long historical record that are useful for identifying climatological trends; however, it is difficult to quantify such trends in cloud microphysical and radiative properties at these stations based solely on remote-sensing techniques such as radar and lidar. In situ aerosol measurements at the surface are often used to estimate cloud properties aloft, but the simulations used to estimate above surface conditions require many idealized assumptions such as a well-mixed boundary layer (BL) and adiabatic parcel lifting. Satellites have the advantage to infer cloud properties over a much larger area than ground-based observations; however, they can only see the uppermost cloud layer, and satellites need in situ observations to improve their retrievals. In this study, we combine ground-based and airborne measurements with satellite observations to determine cloud radiative properties and compare these results to an aerosol–cloud parcel model (ACPM) to identify sources of uncertainty in aerosol–cloud interactions.
The atmospheric research station at Mace Head has been a research platform for studying trace gases, aerosols and meteorological variables since 1958 (O'Connor et al., 2008). The station is uniquely exposed to a variety of air masses, such as clean marine air and polluted European air. Over the long history of observations and numerous field campaigns held at the Mace Head Atmospheric Research Station, few airborne field experiments have been conducted. During the PARFORCE campaign in September 1998, aerosol and trace gas measurements were made to map coastal aerosol formation (O'Dowd et al., 2001). During the second PARFORCE campaign in June 1999, measurements of sea spray plumes were made on an aircraft installed with a lidar (Kunz et al., 2002). In the NAMBLEX campaign in August 2002, flights were conducted to measure aerosol chemical and physical properties in the vicinity of Mace Head (Heard et al., 2006; Norton et al., 2006; Coe et al., 2006). None of the research flights thus far have studied aerosol–cloud interactions and cloud radiative properties at Mace Head.
For ground-based observations, it is often assumed that measured species are
well mixed throughout the boundary layer. Often this assumption is valid, and
many observational studies have shown that models which use ground-based
measurements can accurately simulate cloud droplet number concentrations
(CDNCs; Russell and Seinfeld, 1998; Conant et al., 2004; Fountoukis et
al., 2007), making bottom-up closure a viable method for predicting cloud
properties. Closure is defined here as the agreement between observations
and model simulations of CDNC and cloud-top shortwave radiative flux (
Satellite measurements of microphysical properties, such as CDNC, have the potential to be independent of ground-based measurements and therefore be reliable for studying decoupled clouds. Satellite estimates of CDNC have only become possible recently due to the increased resolution in measurements (Rosenfeld et al., 2012, 2014, 2016; Painemal and Zuidema, 2011). Therefore, current measurements still require ground-based validation until the method is further developed.
The focus of this manuscript is on the top-down closure between satellite retrievals and airborne measurements of cloud microphysical properties, as well as traditional bottom-up closure coupling below and in-cloud measurements of cloud condensation nuclei (CCN), updraft and cloud microphysical properties. In situ measurements of CDNC are not available, so bottom-up closure is expressed in terms of cloud-top shortwave radiative flux rather than CDNC, and top-down closure of satellite CDNC is compared to ACPM-simulated CDNC. The Methods section describes how observations were collected, as well as the methods for estimating CDNC with satellite measurements and calculating shortwave radiative flux with the ACPM. The Results/discussion section summarizes the bottom-up and top-down closure for coupled and decoupled clouds and quantifies the differences in cloud shortwave radiative flux for cases that were affected by cloud-top entrainment.
The August 2015 campaign at the Mace Head Atmospheric Research Station (Galway, Ireland;
53.33
Time series for the month of August 2015 at Mace Head,
Ireland, of ground-based CCN concentrations
At the Mace Head research site, aerosol instruments are located in the
laboratory at about 100 m from the coastline. They are connected to the
laminar flow community air sampling system, which is constructed from a 100 mm diameter stainless-steel pipe with the main inlet at 10 m above ground
level, so that samples are not impacted by immediate coastal aerosol
production mechanisms, such as wave breaking and biological activity
(Norton et al., 2006; O'Dowd et al., 2004, 2014; Coe et al., 2006; Rinaldi et
al., 2009). The performance of this inlet is described
in Kleefeld et al. (2002). Back trajectories during the period
of the experiment show that the origin of air masses is predominantly from
the North Atlantic; therefore, the air masses sampled at Mace Head generally
represent clean open-ocean marine aerosol. Mace Head contains a variety of
aerosol sampling instrumentation, spanning particle diameter range of 0.02–20
UAV research flights conducted at Mace Head, Ireland, and
measured parameters in 2015. Flight start and end times are in UTC. NASA's Suomi
National Polar-orbiting Partnership satellite overpasses occurred at
approximately 13:00 UTC. Measurements include relative humidity (RH),
temperature (
The UAV operations were conducted directly on the coast about 200 m
from the Mace Head Atmospheric Research Station. UAVs were used to collect vertical
profiles of standard meteorological variables, temperature (IST, Model
P1K0.161.6W.Y.010), pressure (Bs rep Gmbh, Model 15PSI-A-HGRADE-SMINI) and
relative humidity (IST, P14 Rapid-W), as well as aerosol size distributions
with an optical particle counter (OPC, Met One Model 212-2), cloud droplet
extinction (Harrison and Nicoll, 2014) updraft velocity at cloud
base with a five-hole probe. A list of the various UAV flights and their
instrumentation is given in Table 1. Measurement errors for the relative
humidity and temperature sensors are
The OPC measured aerosol number size distributions in eight size bins
between 0.3 and 10
OPC concentrations with particle diameters (
In-cloud extinction was measured in situ using a miniature optical cloud
droplet sensor developed at the University of Reading (Harrison and
Nicoll, 2014). The sensor operates by a backscatter principle using
modulated LED light which is backscattered into a central photodiode.
Comparison of the sensor with a Cloud Droplet Probe (Droplet Measurement Technologies) demonstrates good
agreement for cloud droplet diameters > 5
Finally, a five-hole probe for measuring three-dimensional wind vectors was mounted on a third UAV. The 3-D wind vectors are determined by subtracting the UAV motion given by an inertial measurement unit (IMU) from the total measured flow obtained by differential pressures in the five-hole probe (Wildmann et al., 2014; Lenschow and Spyers-Duran, 1989; Calmer et al., 2017). UAV five-hole probe measurements were collected along 6 km long straight and level legs at cloud base. Normalized cloud radar vertical velocity distributions are compared to vertical wind distributions obtained from the UAV in Fig. 3. The positive updraft velocities in Fig. 3 are used to initialize the ACPM to produce simulated cloud droplet size distributions throughout the depth of the cloud. The droplet distributions for each updraft velocity are averaged and weighted by the probability distribution of the measured positive velocities. Differences in results when using the cloud radar updrafts versus the UAV five-hole probe updrafts (Fig. 3) are discussed in Sect. 3.1.2.
Research flights with the UAV were conducted in conjunction with satellite
overpasses to compare retrieved CDNC and maximum supersaturation (
Normalized observed vertical velocity distributions measured by the cloud radar and UAV for each case presented in Table 2.
Suomi NPP satellite RGB composite image for 21 August 2015
To obtain CDNC, cloud droplet effective radius profiles were extracted from
the Suomi NPP satellite. Figure 4 shows an image
from the Suomi Visible Infrared Imaging Radiometer Suite on 21 August
overlapped on a map of western Ireland. The vertical profile in Fig. 4
shows satellite-retrieved and ACPM-simulated effective radius. To estimate
the CDNC, the satellite effective radius (Fig. 4) is first converted to
mean volume radius (
A detailed description of the ACPM is presented in Russell and Seinfeld (1998) and Russell et al. (1999). The ACPM is based on a fixed-sectional approach to represent the (dry) particle size domain, with internally mixed chemical components. Aerosols are generally internally mixed at Mace Head because there are no immediate strong sources of pollution. The model employs a dual-moment (number and mass) algorithm to calculate particle growth from one size section to the next for non-evaporating compounds (namely, all components other than water) using an accommodation coefficient of 1.0 (Raatikainen et al., 2013). The dual-moment method is based on Tzivion et al. (1987) to allow accurate accounting of both aerosol number and mass, and it incorporates independent calculations of the change in particle number and mass for all processes other than growth. The model includes a dynamic scheme for activation of particles to cloud droplets. Liquid water is treated in a moving-section representation, similar to the approach of Jacobson et al. (1994), to account for evaporation and condensation of water in conditions of varying humidity. In subsaturated conditions, aerosol particles below the cloud base are considered to be in local equilibrium with water vapor pressure (i.e., relatively humidity < 100 %).
Coagulation, scavenging and deposition of the aerosol were included in the
model, but their effects are negligible given the relatively short
simulations used here (< 2 h) and low marine total aerosol particle
concentrations (< 500 cm
The simulated cloud droplet size distribution is used to calculate the
shortwave cloud extinction. Cloud extinction is proportional to the total
droplet surface area (Hansen and Travis, 1974; Stephens,
1978) and is calculated from
UAV observations of cloud heights and temperatures and
cloud property estimates based on ground measurements. Ground-based Hoppel
minimum diameter (
Finally, the shortwave radiative flux (RF) is calculated as RF
For this study, closure is defined as the agreement between observations and
model simulations of CDNC and cloud-top shortwave radiative flux. In situ
measurements of clouds were made by UAVs on 13 days during the campaign. Of
these, a subset of six are chosen here for further analysis, which includes
comparison with satellite CDNC as well as simulation of cloud properties
with the ACPM (Table 2). The remaining days with UAV measurements did not
contain sufficient cloud measurements for analysis. A satellite overpass
occurred on each of the 6 days; however only 4 of the days contained
clouds that were thick enough to analyze with the satellite. The 10 August
case experienced a light drizzle, so ACPM simulations were not conducted
for this case; however analysis with satellite imagery was still conducted.
On 5 August, two cloud layers were examined, for a total of seven case studies
shown in Table 2. Aerosols were occasionally influenced by anthropogenic
sources; however, the cases shown consist of aerosol of marine origin with
concentrations under 1000 cm
SMPS and APS derived size distributions used for each case study in Table 2. The 5 August size distribution is used for both the coupled and decoupled case. Individual distributions (grey) are from the indicated time ranges in the figure. The time ranges are in UTC. Average distributions are shown in red.
Comparison of simulated CDNC from ACPM with both Hoppel
minimum diameter (
The columns in Table 2 represent the different cases for both clouds that
were (a) coupled with and (b) decoupled from the surface BL (“C” and
“D”, respectively). The first row in Table 2 includes the state
of atmospheric mixing, the date, the type of cloud present and the
abbreviation used for each case. The top portion of Table 2 consists of in situ airborne
measurements; the bottom portion presents ACPM simulation results and their
relation to in situ cloud extinction and satellite-retrieved observations. The
ground-based in situ measurements in Table 2 include the Hoppel minimum
diameter The Hoppel minimum diameter is the diameter with the
lowest aerosol concentration between Aitken mode and accumulation mode.
Figure 7 displays vertical profiles of meteorological parameters, as well as
OPC aerosol number concentration (
Vertical profiles of temperature, virtual potential
temperature (
Vertical profiles of measured and simulated cloud
extinction from flights D05Sc, C11Sc and C21Cu (
Figure 8a, c and e present the observed and simulated adiabatic cloud
extinction profile for three of the case studies (C11Sc, D05Sc and
C21Cu) C/D – coupled/decoupled; xx – date in August 2015; Sc/Cu – stratocumulus/cumulus cloud.
All ACPM simulation results, including those in Table 2, use the cloud radar
updraft velocity as input and not the five-hole probe updraft velocity because
five-hole probe updraft velocities are not available for all cases.
Nonetheless, the differences in ACPM-simulated shortwave radiative flux
between using the five-hole probe and cloud radar updraft velocities (Fig. 3)
is less than 3 W m
UAV vertical profile of OPC aerosol number
concentrations (
The integrated effect of
The satellite and simulated CDNC and
Based on the ground-based and UAV measurements, ACPM simulations overestimate cloud shortwave radiative flux significantly for three cases (C11Sc, D05Sc, D06Cu). Section 3.1.2 identified that clouds in decoupled layers (D05Sc, D06Cu) have smaller radiative effects than predicted based on ground-based observations as aerosol (and CCN) number concentrations in the decoupled layer are often smaller than in the surface mixed layer. In this section, cloud-top entrainment is also shown to influence the radiative properties of two sub-adiabatic stratocumulus clouds, C11Sc and D05Sc.
The UAV observations show that both C11Sc and D05Sc have sub-adiabatic lapse rate measurements, compared to simulated moist-adiabatic lapse rates within the cloud (Table 2). The difference between the observed and simulated lapse rates therefore suggests a source of heating in the cloud. The sub-adiabatic lapse rate is attributed to cloud-top entrainment by downward mixing of warmer air at cloud top. The D06Cu case has a slightly sub-adiabatic observed lapse rate (Table 2); however the difference with respect to an adiabatic lapse rate is within instrument error. For this reason, cloud-top entrainment is not explored for this case, though it may contribute to the error.
Further evidence of cloud-top entrainment is shown through conserved
variable mixing diagram analysis. In previous studies, a conserved variable
mixing diagram analysis was used to show lateral or cloud-top entrainment by
showing linear relationships between observations of conserved variables
(Paluch, 1979; Neggers et al., 2002; Burnet and Brenguier, 2007). Paluch (1979) first observed a linear relationship of conservative
properties (total water content,
To apply the cloud-top mixing, a fraction of air at cloud base and a
fraction of air above cloud top are mixed, conserving
Results of the application of entrainment fraction and the measured lapse rate entrainment parameterization for two clouds with observed cloud-top entrainment.
Conservative variables, water vapor content
(
Figure 11 shows the relative humidity and
UAV vertical profiles of relative humidity
Figure 12 shows the sensitivity of the simulated cloud extinction profile,
for the 11 August case, based on measurement uncertainties related to the
entrained
Table 3 shows
Sensitivity of simulated cloud extinction based on
variability of entrained-air potential temperature (
The inclusion of inhomogeneous mixing entrainment improved the ACPM accuracy
for both C11Sc and D05Sc using the measured lapse rate and entrainment
fraction methods (Fig. 8, Table 3). After accounting for inhomogeneous
entrainment,
This work presents measurements conducted in August 2015 at the Mace Head Atmospheric Research Station in Ireland, from multiple platforms including ground-based, airborne and satellite measurements. As part of the BACCHUS (Impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding) European collaborative project, the goal of this study is to understand key processes affecting aerosol–cloud shortwave radiative flux interactions. Seven cases including cumulus and stratocumulus clouds were investigated to quantify aerosol–cloud interactions using ground-based and airborne measurements (bottom-up closure), as well as cloud microphysical and radiative properties using airborne measurements and satellite retrievals (top-down closure). An aerosol–cloud parcel model was used to link the ground-based, airborne and satellite observations, and to quantify uncertainties related to aerosols, cloud microphysical properties and resulting cloud optical properties.
ACPM simulations represent bottom-up and top-down closures within
uncertainties related to satellite retrievals for conditions with a coupled
boundary layer and adiabatic cloud development. For these conditions, the
difference in shortwave radiative flux between simulations and in situ observed
parameters is no greater than 20 W m
For the cases with cloud-top entrainment, D05Sc and the coupled
stratocumulus case on 11 August (C11Sc), liquid water content is one of the
major factors in overestimating cloud-top shortwave radiative flux with the
ACPM. For these cases, the measured in-cloud lapse rates are lower than
adiabatic lapse rates, suggesting a source of heat due to entrainment of
warmer, drier air from above the cloud. Furthermore, linear relationships
between conservative variables (simulated total water vapor,
Based on airborne observations with UAVs, decoupling of the boundary layer occurred on 4 of the 13 flight days (two decoupled cloud cases were not discussed due to the lack of in-cloud measurements). However, cloud drop entrainment was only observed on 2 of those days, limited by the ability to make in situ measurements. These measurements occurred during the summer, so additional measurements are needed to look at seasonal trends. These cases illustrate the need for in situ observations to quantify entrainment mixing and cloud-base CCN concentrations particularly when the mixing state of the atmosphere is not known. Using ground-based observations to model clouds in decoupled boundary layers and not including cloud-top entrainment are shown to cause significant differences between observations and simulation radiative forcing and, therefore, should be included in large-scale modeling studies to accurately predict future climate forcing.
UAV measurements were coordinated with 13 days of satellite overpasses, and cloud microphysical properties were retrieved for four of the cases. When accounting for entrainment, the differences between simulated and satellite-retrieved CDNC are within the expected 30 % accuracy of the satellite retrievals (Rosenfeld et al., 2016). However, in situ measurements are necessary to refine satellite retrievals to allow cloud properties to be studied on larger spatial scales.
All data are available by contacting the corresponding author or through the
following link:
The authors declare that they have no conflict of interest.
This article is part of the special issue “BACCHUS – Impact of Biogenic versus Anthropogenic emissions on Clouds and Climate: towards a Holistic UnderStanding (ACP/AMT/GMD inter-journal SI)”. It is not associated with a conference.
The research leading to these results received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) project BACCHUS under grant agreement no. 603445. EU H2020 project ACTRIS-2 under the grant agreement no. 654109 is also acknowledged for supporting the Mace Head Atmospheric Research Station. Keri Nicoll acknowledges a NERC Independent Research Fellowship (NE/L011514/1). Darius Ceburnis acknowledges the Irish EPA (2012-CCRP-FS.12). Jana Preissler acknowledges the Irish EPA (2015-CCRP-FS.24). Radiance Calmer acknowledges financial support from Météo France. Kevin J. Sanchez acknowledges the Chateaubriand Fellowship. We thank École Nationale de l'Aviation Civile (ENAC) for assistance with construction and operation of the UAVs. The authors also acknowledge Kirsten Fossum for the collection of SMPS data. We applied a sequence-defines-credit approach for the sequence of authorship. Edited by: Hailong Wang Reviewed by: four anonymous referees