ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-3119-2018Aerosol–cloud interactions in mixed-phase convective clouds – Part 1: Aerosol perturbationsAerosol–cloud interactions in mixed-phase convective cloudsMiltenbergerAnnette K.a.miltenberger@leeds.ac.ukhttps://orcid.org/0000-0003-3320-4272FieldPaul R.HillAdrian A.RosenbergPhilhttps://orcid.org/0000-0002-6920-0559ShipwayBen J.WilkinsonJonathan M.https://orcid.org/0000-0002-6906-4999ScovellRobertBlythAlan M.https://orcid.org/0000-0001-7115-2587Institute of Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UKMet Office, Exeter, UKNational Centre for Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, UKAnnette K. Miltenberger (a.miltenberger@leeds.ac.uk)5March20181853119314523August20175September201716December201722January2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/3119/2018/acp-18-3119-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/3119/2018/acp-18-3119-2018.pdf
Changes induced by perturbed aerosol conditions in moderately deep mixed-phase convective clouds
(cloud top height ∼ 5 km)
developing along sea-breeze convergence
lines are investigated with high-resolution numerical model simulations. The
simulations utilise the newly developed Cloud–AeroSol Interacting Microphysics (CASIM) module for
the Unified Model (UM), which allows for the representation of the two-way interaction between cloud and
aerosol fields. Simulations are evaluated against observations collected during the COnvective
Precipitation Experiment (COPE) field campaign over the southwestern peninsula of the UK in 2013.
The simulations compare favourably with observed thermodynamic profiles, cloud base cloud droplet
number concentrations (CDNC), cloud depth, and radar reflectivity statistics. Including the
modification of aerosol fields by cloud microphysical processes improves the correspondence with
observed CDNC values and spatial variability, but reduces the agreement with observations for
average cloud size and cloud top height.
Accumulated precipitation is suppressed for higher-aerosol conditions before clouds become organised
along the sea-breeze convergence lines. Changes in precipitation are smaller in simulations with
aerosol processing. The precipitation suppression is due to less efficient precipitation production
by warm-phase microphysics, consistent with parcel model predictions.
In contrast, after convective cells organise along the sea-breeze convergence zone, accumulated
precipitation increases with aerosol concentrations. Condensate production increases with the
aerosol concentrations due to higher vertical velocities in the convective cores and higher cloud
top heights. However, for the highest-aerosol scenarios, no further increase in the condensate
production occurs, as clouds grow into an upper-level stable layer. In these cases, the reduced
precipitation efficiency (PE) dominates the precipitation response and no further precipitation
enhancement occurs. Previous studies of deep convective clouds have related larger vertical
velocities under high-aerosol conditions to enhanced latent heating from freezing.
In the presented simulations changes in latent heating above the 0∘C are negligible, but latent
heating from condensation increases with aerosol concentrations. It is hypothesised that this
increase is related to changes in the cloud field structure reducing the mixing of environmental air
into the convective core.
The precipitation response of the deeper mixed-phase clouds along well-established convergence lines
can be the opposite of predictions from parcel models. This occurs when clouds interact with a
pre-existing thermodynamic environment and cloud field structural changes occur that are not
captured by simple parcel model approaches.
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Introduction
Aerosol-induced changes to the climate system, in particular the radiation budget,
are thought to be important for understanding changes between present-day and pre-industrial
radiative fluxes . A large and poorly constrained aspect is the impact of
aerosols on clouds and precipitation formation . Numerous studies have tried to
isolate the aerosol effect on clouds and precipitation using observational data or investigated the
aerosol effect in numerical models of varying complexity. Recent reviews by ,
, , and provide a good overview.
Aerosols are thought to impact clouds through a well-established link between the number of aerosols
available and the number of cloud droplets that form under a specific supersaturation. This initial
change in cloud droplet number should subsequently impact radiative and cloud
microphysical processes that are directly dependent on the number and size of the hydrometeors.
These impacts can have further ramifications by altering the precipitation formation in clouds,
cloud geometry, cloud lifetime, anvil properties, thermodynamic properties of the environment, and
the spatial pattern of energy and moisture transport. In the atmosphere a multitude of other
processes, such as interactions with other clouds, aerosol properties and spatial distribution,
radiation, larger-scale dynamics, and surface fluxes, can further complicate the picture. While the
first link in this chain, the relation between aerosol concentration and cloud droplet number at
cloud base, is uncontroversial and can be confirmed with observational data
e.g., the subsequent impacts on the temporal and spatial evolution of the
cloud field are more controversial and difficult to observe. Perhaps unsurprisingly, given the
complexity, highly non-linear nature, and our partial quantitative understanding of many relevant
processes, different studies do not necessarily agree on the amplitude or sign of
aerosol-induced changes to clouds e.g.. Some attempts
have been made to systematically assess the impact of specific model parameters
e.g. or to stratify responses according to crucial meteorological parameters
e.g.. expressed changes in
precipitation as the result of modified condensate production (ΔG) and modified
evaporation losses of condensate (ΔL). With this approach they are able to classify
aerosol-induced precipitation changes documented in various observational and
modelling studies. According to their analysis the balance between ΔG and
ΔL is dependent on the cloud regime and environmental conditions. For example,
ΔL dominates in stratocumulus and ΔG in deep tropical clouds,
while deep convective clouds transition from ΔL- to ΔG-dominated
with increasing environmental relative humidity.
The increased shortwave reflectance e.g. and decreased efficiency of
collision–coalescence due to greater cloud droplet number concentrations (CDNC)
e.g. is thought to dominate aerosol–cloud interactions in shallow warm-phase
clouds. The reduced collision–coalescence delays or suppresses precipitation formation and extends
the cloud lifetime e.g.. In contrast, it has been hypothesised
that precipitation can be enhanced through feedbacks on the cloud dynamics in deep convective clouds
with partially or completely glaciated cloud tops .
The proposed mechanism for this so-called convective invigoration is that a slower growth of cloud
droplets into precipitation-sized particles in the warm-phase part of the cloud enhances the
transport of cloud condensate into the mixed-phase region. The subsequent freezing of the additional
condensate increases the latent heat release enhancing in-cloud buoyancy and vertical velocities.
This leads to a larger condensate content, higher cloud tops, larger anvils, and a longer cloud
lifetime. These changes, together with accompanying modifications of the precipitation production
pathways (e.g. bulk microphysics: , and ; bin microphysics:
, and ), are hypothesised to enhance precipitation for high aerosol concentrations
when compared to lower aerosol concentrations. The conceptual idea of convective invigoration has
been developed using simulations of individual clouds under idealised conditions
e.g.. Several studies have highlighted that this may
not apply to less idealised or very polluted conditions due to a number of factors,
such as an increased importance of evaporation and stronger downdrafts (e.g. , using
a bin microphysics scheme) or a weakening of the updraft core by an increased
water loading (e.g. bulk microphysics: ; bin microphysics: ).
Simulated aerosol-induced changes in cloud properties and precipitation are also
subject to systematic differences between and biases in different modelling studies, e.g. in
parameterisations of sub-grid-scale processes, the formulation of the dynamical core, or the spatial
resolution (e.g. bulk microphysics: , , , , and ; bin
microphysics: , , , and ). In particular, the
complexity of the employed cloud microphysical scheme can impact predicted aerosol-induced changes
e.g.. While most regional models represent the hydrometeor size
distributions with typically one to three moments of the distribution (so-called bulk microphysics),
in idealised studies a more sophisticated representation of the size distributions can be used
(so-called bin microphysics) e.g.. Also, demonstrated that
the sign and amplitude of the precipitation signal is dependent on the choice of parameters in the
cloud microphysics parameterisation (parametric uncertainty), which are either not known or have
spatio-temporal variability not represented in the model formulation.
Precipitation enhancement and/or associated changes in the cloud structure are not always predicted
consistently for different cases even within the same modelling framework. This illustrates that
different environmental conditions and interactions between different clouds (direct or indirect via
modification of the environment) can influence, impede, or allow for precipitation enhancement (e.g.
bulk microphysics: , , , and ; bin microphysics:
, , , and ). In addition, aerosol–cloud interactions can modify the
thermodynamic and aerosol environment (e.g. bulk microphysics: , and ; bin
microphysics: ) and impact storm-scale (e.g. , using bulk
microphysics) or even large-scale dynamics (e.g. , using bulk microphysics). These
changes to the cloud environment are often found to modify the aerosol impact on the entire cloud
system.
As a consequence of the interaction of many non-linear processes, aerosol-induced
changes in precipitation are typically less apparent and more sensitive to the particular modelling
framework than changes in other cloud properties that are more directly related to hydrometeor
number (e.g. radiative fluxes). For example, showed, in simulations of
convective precipitation over Germany during three summer seasons (using bulk microphysics), that
aerosol-induced modifications to cloud radiative fluxes were significant, while
changes in average surface precipitation are not.
Aerosol–cloud interactions are thought to be important for quantitative precipitation forecasts and
radiative forcing estimates, but there are uncertainties and deficiencies of aerosol effects in
numerical models. Therefore, it is important to test any model-derived hypothesis with observational
data. A number of observational studies have tried to identify aerosol signals in the properties of
deep convective systems including systematic changes in cloud top height, cloud fraction, or
precipitation e.g.. These studies are based on
satellite data that provide a relatively large temporal and spatial sample.
However, studies based on satellite data necessarily rely on correlations between bulk parameters
such as aerosol optical depth and cloud top height. This approach raises the question of causality,
coincidence, and co-variability e.g.. The need to better understand and
incorporate the existence of co-variability between aerosol and meteorological fields in analysis
methods has recently been highlighted by . In this context, it is important to
consider how similar, in a meteorological sense, different instances must be for a meaningful
analysis and whether the analysis of a sufficiently large sample provides a robust
cloud aerosol signal. From a modelling standpoint, one approach to address questions related to
co-variability is the use of an ensemble forecasting system (see Part 2, Miltenberger et al., 2018).
In this study, we use a convection-permitting numerical weather prediction model (the Unified Model, UM)
with a multi-moment bulk microphysics scheme to investigate the aerosol–cloud interactions for an
observed case of mixed-phase convective clouds forming along a sea-breeze convergence zone.
Sea-breeze convergence zones provide a predictable location for convective
initiation, which aids the comparison to observations and also provides a good basis for planning
observational campaigns. Convective clouds and precipitation are associated with sea-breeze systems
at many coastal regions on the globe, e.g. the southwest peninsula of the UK
e.g., the Salento peninsula in Italy e.g., the Hainan
Island in China e.g., coastal Cameroon e.g., and many
others e.g.. In this first part of the study, we evaluate the performance of a
newly developed cloud microphysics scheme against observational data and investigate the impact of
aerosol perturbations on the cloud properties and precipitation formation. In the second part of the
study, the aerosol-induced changes are compared to variations in cloud field
properties due to perturbations in the meteorological initial conditions. With this analysis, we address
questions related to the detectability of aerosol-induced changes and their
robustness to small changes in meteorological initial conditions.
The study focuses on a case from the COPE (COnvective Precipitation Experiment)
campaign, which took place in July and August 2013 over the southwestern peninsula of the UK
. The selected case (3 August 2013) has been
previously analysed from an observational viewpoint with a focus on cloud glaciation
and aerosol concentrations, composition, and sources
. Isolated shallow cumulus clouds were scattered across most of the
southwestern UK in the early morning. After about 11:00 UTC clouds organised along sea-breeze
convergence lines, which were located roughly along the major axis of the peninsula. The cloud
organisation proceeded with the development of larger and on average deeper clouds and cloud
clusters. New isolated cells generally formed close to the southwestern tip of the
peninsula and subsequently developed or merged into larger cloud clusters as they moved
northeastwards. This band-like cloud feature remained intact until about 18:00 UTC.
This first part of the study focuses on the comparison of the model simulations to observational
data and the physical mechanism of aerosol-induced changes. It is structured as
follows: Sect. describes the model set-up, the microphysics module, and the
observational data. In Sect. the modelled cloud field is compared to
observations. The impact of aerosol processing on the spatial distribution and evolution of the
aerosol field is described in Sect. .
Aerosol-induced changes to the cloud field are described in
Sect. and the mechanisms responsible for these changes are discussed in
Sect. . The results are summarised in Sect. .
Data and methodsModel set-up
The Unified Model (UM version 10.3) is used for the simulations presented in
this study. The UM is developed by the Met Office for operational forecasting
over the UK and a range of different geographical locations (e.g. New Zealand
and Australia). A global model run (UM version 8.5, GA6 configuration, N512
resolution, ) starting from the Met Office operational
analysis for 18:00 UTC 2 August 2013 provides the initial and boundary
conditions for a regional simulation (UM version 10.3, GA6 configuration)
with a grid spacing of 1 km (500 by 500 grid points) over the southwestern
peninsula of the UK (Supplement Fig. S1a). Simulations with a grid spacing of
250 m (900 by 600 grid points) are nested within the 1 km simulation.
Different resolutions of the inner nest (500m and
1km) have been tested. The simulation with a grid spacing of
250m agrees best with the observed precipitation rate and radar
reflectivity distribution (Supplement Fig. S2). A stretched vertical
coordinate system is used with 120 vertical levels between the surface and
40 km altitude. The model-level spacing is about 40m in the
boundary layer and 500m at 5km altitude. The
nested simulations are started at 00:00 UTC 3 August 2013 and run for 24 h.
Only results from the highest resolution nest (grid spacing Δx=250 m) will be discussed in this article.
Moisture conservation in the regional model domain is enforced using the scheme by
and . Conservation of moisture is an important physical
constraint and impacts the precipitation response to aerosol perturbations (not shown). Mass
conservation is also a requirement for the condensate budget analysis conducted in
Sect. .
The regional simulations are run without a convection parameterisation. Sub-grid-scale variability
of relative humidity is not considered for droplet activation and condensation. Boundary layer
processes, including surface fluxes of moisture and heat, are parameterised with the blended
boundary layer scheme . Sub-grid-scale turbulent processes are represented
with a 3-D Smagorinsky-type turbulence scheme . Radar
reflectivity has been calculated from the model fields assuming Rayleigh scattering only and
neglecting extinction. Phase mixtures of hydrometeors, i.e. partly liquid particles, are not
considered.
We replaced the operational microphysics with the newly developed Cloud–AeroSol Interacting
Microphysics (CASIM) module, which is described in more detail in Sect. . The CASIM
module provides options for one- or two-way coupling between aerosol and cloud properties.
Simulations are performed in both modes.
Parameters of the aerosol size distribution in the boundary layer prescribed in the initial
and lateral boundary conditions.
N (cm-3)m (kgm-3)σ (1)Aitken mode8605.86×10-102.2Accumulation mode1503.84×10-91.7Coarse mode0.231.07×10-81.5Insoluble aerosol16.74.26×10-101.5
Parameters used for the representation of the different hydrometeor types (x:
cloud, rain, ice, snow, and graupel). The size distributions of all hydrometeors are described as
gamma distributions with a fixed curvature μx. The relation between particle mass
Dx and particle diameter mx is described by mx=cx⋅Dxdx; the relation between Dx and the terminal fall velocity vx by
vx=axDxbxρ0ρ0.5.
Aerosol initial and boundary conditions are prescribed based on aerosol size
distributions derived from aircraft observations (see Sect. ). A
profile of aerosol mass and number densities has been derived by combining
data from a below cloud-base flight leg carried out in the morning and
various cloud-free flight segments at higher altitude. In the boundary layer
and free troposphere a vertically uniform mass mixing ratio and number
concentration are used for each aerosol mode (Fig. S1b, c and
Table ). A linear transition between the two
concentrations is assumed in a 500 m vertical slice centred at the mean
boundary layer top (z=1.15 km). A total of 10% of the observed
accumulation-mode aerosol is considered to be insoluble and to act as
ice-nucleating particles in the model. No surface sources of aerosol have
been included. Neglecting surface sources is not expected to have a large
impact on the simulations because (i) the chosen aerosol profiles are based
on observational data over the peninsula and therefore are representative of
the environment in which the clouds form and (ii) the residence time of air
in the model domain is only several hours (based on an average flow velocity
of 7.5ms-1 and a domain length of 225km).
According to the National Atmospheric Emissions Inventory data for 2014
, the average PM2.5 (PM1) emission flux over the model domain
is 5.30×10-12kgm-2s-1 (2.75×10-12kgm-2s-1). Assuming the emitted aerosol is evenly
distributed over the boundary layer and with a mean flow velocity of
7.5ms-1, the resulting change in aerosol mass mixing ratio
is 1.75×10-10kgkg-1 (9.1×10-11kgkg-1). This corresponds to about 1%
(0.5%) of the total aerosol mass or 5%
(2%) of the accumulation-mode aerosol mass in the boundary
conditions. In addition, the aerosol replenishment by advection from the
boundary of the domain that maintains the initial profile is sufficient to
avoid a very strong depletion by scavenging inside the domain (see
Sect. ). Therefore, we have ignored local aerosol
emissions.
For the perturbed aerosol simulations, the aircraft-derived aerosol profiles are multiplied by
factors of 10 and 0.1 at all altitudes, while conserving the mean diameter of each mode. These
simulations are referred to as “high-aerosol” and “low-aerosol” runs, respectively. For
additional tests on the thermodynamic limitations of the precipitation response, simulations with
aerosol concentrations increased by a factor of 30 were conducted (“very high aerosol”). The simulation with
the unperturbed aircraft-derived profile is named the “standard-aerosol” run.
CASIM microphysics and aerosol processing
Cloud microphysical processes and their interaction with the aerosol environment are represented by
the newly developed CASIM module . The CASIM module is
a double-moment, five hydrometeor classes microphysics scheme. The hydrometeor size distribution for
each category is described by a gamma distribution, two moments of which (the mass
and number mixing ratios), are prognostic variables. In addition, fixed densities, diameter–mass
relations, and diameter–fall-speed relations are assumed for each hydrometeor category
(Table ). The simulated precipitation rate and reflectivity distributions are
particularly sensitive to the assumed graupel density and diameter–fall-speed relation (not shown).
We have chosen to use the diameter–fall-speed relation for medium-density graupel from
with a graupel density of 250 kgm-3, since this results in the
closest agreement between modelled and observed reflectivity and surface precipitation rates (not
shown). Represented transfer rates between the different hydrometeor categories and water vapour
include droplet activation , condensation (using
saturation adjustment), primary ice formation from cloud droplets , freezing
of rain drops , secondary ice formation from rime splintering in the
Hallett–Mossop temperature zone, vapour deposition, evaporation, sublimation, collision–coalescence
between all hydrometeor categories, and sedimentation of all hydrometeor categories except cloud
droplets.
Aerosols are represented by three soluble modes and one insoluble mode that are described by a
log-normal distribution with a prescribed width (Table ). The aerosol mass
mixing ratio and number mixing ratio are prognostic variables. The chemical and
physical particle properties (density, solubility, etc.) are prescribed for each mode separately.
The aerosol fields are initialised from a spatially homogeneous aerosol profile, which is also used
for the lateral boundary conditions throughout the simulations. The aerosol fields are subject to
advection.
For the aerosol–cloud interaction, two different modes are available within CASIM: (i) one-way
coupling of aerosols and cloud properties (passive mode) and (ii) two-way interaction between
aerosols and clouds (processing mode). In the passive mode, aerosol fields are considered in the
droplet activation and primary ice nucleation, but the aerosol fields are not modified by cloud
microphysical processes. We note that, while aerosols are not scavenged, this does not lead to an
infinite supply of droplets. In the passive mode, the activation scheme will only activate
additional droplets if the current population is lower than that expected by activation for the
current grid-cell conditions. In the processing mode, aerosol fields are modified consistently with
the cloud microphysical processes. The interstitial aerosols are depleted by nucleation scavenging
(droplet activation and primary ice nucleation). Impaction scavenging is currently not represented.
Previous work suggests that in-cloud scavenging is the dominant wet aerosol removal processes and
that impaction scavenging is only important below cloud base e.g.. Therefore, omitting impaction scavenging should have no major implications for the present
study. Droplet activation takes into account the different soluble aerosol modes according to
, while a small constant fraction of the insoluble aerosol mode is activated
in water-supersaturated conditions. For the ice nucleation all insoluble aerosols, i.e. both
interstitial and CCN (cloud condensation nuclei) activated insoluble aerosol, are used for the computation of ice crystal number
concentrations to be consistent with the formulation in . Additional tracers for
the soluble and insoluble aerosol mass as well as insoluble aerosol number in liquid and frozen
hydrometeors are included. These tracers are subject to sedimentation fluxes of the respective
hydrometeors and advection. During evaporation and sublimation aerosols are released into the
interstitial aerosol modes according to their diagnosed effective radius and the effective radius of
the interstitial aerosol modes. One soluble aerosol particle is released for each evaporating
hydrometeor. Therefore, hydrometeor collision–coalescence results in fewer, but larger, interstitial
aerosols if the hydrometeors subsequently evaporate. For insoluble aerosols, the activated number
and mass are tracked. The number of insoluble aerosols released upon evaporation or sublimation of
the hydrometeor is identical to the number of insoluble aerosol particles in the hydrometeor.
Therefore, the number of insoluble aerosols is retained and is not impacted by collision–coalescence
processes.
Column maximum radar reflectivity (shading) over the COPE domain at (a) 12:00 UTC and (b)
14:00 UTC from the model simulation with passive aerosol and the standard-aerosol profile. The grey
contour lines indicate convergence of 2×10-6s-1 at 250m above
ground.
Comparison of (a) the time series of domain-mean surface precipitation rate and (b) the
normalised distribution of column maximum radar reflectivity from model simulations with the
standard-aerosol profile (blue) and radar observations (red). The distribution includes only grid
points with column maximum reflectivity larger than 0dBZ. The solid line shows results
from the simulation with passive aerosols and the dashed line from the simulation with aerosol
processing. Simulated precipitation rates and radar reflectivity have been coarse-grained to the
spatial resolution of the radar observations (1 km horizontal and 500 m vertical).
Simulations were conducted with passive and processing aerosol treatments. The impact on the model
performance and the simulated hydrometeor and aerosol fields are discussed in
Sects. and , respectively.
Observational data from COPE
For the evaluation of the model simulations, we make use of the observational data gathered from
various platforms during the COPE campaign. The details of the experiment design are outlined in
. presented an overview of the campaign results.
This study utilises radiosonde data and observations made with the Facility for Airborne Atmospheric
Measurements (FAAM) BAe-146 research aircraft. Radiosondes were launched at roughly 2-hourly
intervals from Davidstow (50.64∘N, 4.61∘W) between 08:00
and 15:00 UTC. These provide profiles of air temperature, dew-point temperature, and wind vectors.
The aerosol initial conditions were derived from data collected by the FAAM BAe-146 aircraft. Three
instruments with overlapping size ranges were utilised: a scanning mobility particle sizer (SMPS)
for measurements from 0.01 to 0.3 µm diameter with a 30s scan time, a
wing-mounted passive cavity aerosol spectrometer probe (PCASP) for measurements
from 0.1 to 3 µm, and a wing-mounted cloud droplet probe (CDP) for
measurements from 2 µm and above. The PCASP and CDP were calibrated using the methods
of . The data were split into out-of-cloud straight-and-level
legs spanning an integer number of SMPS scans. For each of these legs, a three-mode log-normal
distribution was fitted to the data. No refractive index corrections were made to account for the
potential different compositions of the aerosol. However, as we use just a single average profile
for each of the boundary layer and the free troposphere, any refractive index correction is much
smaller than the variability in the measurements.
Cloud droplet number was also provided by the CDP. The sensitive sample area of this instrument was
calibrated using a droplet generator and found to be approximately twice the nominal sample area in
the manufacturer's specification. Vertical wind measurements were provided by a five-port turbulence
probe on the aircraft nose combined with Pitot tube airspeed measurements and GPS–inertial-navigation-unit
aircraft altitude information .
In addition to the campaign-specific data, we use a 3-D radar composite provided by the Met Office
. The composite data used here have a horizontal resolution of
1km, a vertical resolution of 500m over the study area, and a temporal
resolution of 10min. In addition, we use the Radarnet IV rainfall retrieval
, which is also based on the operational radar network. The
horizontal resolution is 1km and the temporal resolution 5min.
Evaluation of model simulations with the standard-aerosol profileRadar reflectivity and surface precipitation
The model simulations capture the general evolution of the cloud band and the major structural
features as described in the introduction (Fig. , Supplement Figs. S3 and S4): the first
larger clouds (maximum dimension of areas with column maximum reflectivity larger than
25dBZ exceeding 10km) appear after 11:00 UTC and are organised along a line
roughly along the axis of the peninsula. In the subsequent hours, clouds cluster along the
convergence line with cells remaining more isolated and smaller over the western half of the
peninsula and larger clusters developing further east. While the majority of clouds develop along
the convergence lines, some more isolated clouds develop in other parts of the domain. A double line
feature appears in some model simulations, but is not as well defined as in the observational radar
data. In agreement with observations, the modelled cloud line slowly assumes a more northeasterly
orientation throughout the day. The line of convective clouds starts to dissipate at around 17:00 UTC,
i.e. slightly earlier than in the observations.
The domain-average surface precipitation rates from the operational radar network
and the model simulations with the standard-aerosol profile are
compared in Fig. a. The radar data show some
precipitation from isolated convective cells before 11:00 UTC. The simulated domain-average
precipitation during this initial time period is much lower, but the average cell precipitation rate
is comparable (excluding grid points with no precipitation, Fig. S5a). The simulated cells are
less numerous and remain smaller (Fig. S5c, d). The lack of development of larger cells is
consistent with the previously described tendency of high-resolution models to
produce too many too-small cells e.g.. In contrast to the weakly
forced convection in the morning, the organisation into larger cells later in the simulation is
supported by the establishment of sea-breeze convergence lines (Fig. S5b). Domain-mean
precipitation rapidly increases between 11:00 and 13:00 UTC in the radar observations and between
11:00 and 14:00 UTC in the simulations. During the afternoon, both data sets show consistently high
surface precipitation rates until about 17:00 UTC (model) and 18:00 UTC (observations). The cessation of
precipitation is linked to the dissolution of the convergence lines (Fig. S5b). While the model
captures the main evolution of the precipitation linked with the convergence lines, the peak
domain-mean precipitation rate occurs about an hour later than in the radar data. The model underestimates
the domain-mean surface precipitation relative to the radar-derived precipitation irrespective of
the chosen aerosol treatment. In contrast to domain-average precipitation, cell average
precipitation is overestimated (Fig. S6a). This indicates that too few instances with surface
precipitation are simulated. In particular, instances of weak precipitation
(<4mmh-1), both over all data points and over raining data points only, are
underestimated (Fig. S6a, b). High precipitation rates are overestimated by a
factor of 2 relative to the radar-derived estimate (Fig. S6a, b).
Quantitative estimates of precipitation rates from radar reflectivity can exhibit biases due to
assumptions of hydrometeor properties and the representation of sub-cloud evaporation in the
algorithm used to derive rain rate from radar observations. In addition, beam blocking can
significantly affect low-level radar reflectivity. We therefore compare diagnosed radar reflectivity
from the simulations with a 3-D radar composite (Fig. b and Fig. S6c,
d). The distribution of column maximum reflectivity, also known as composite reflectivity, for
cloudy grid points is shown in Fig. b. The observed and modelled
distributions are in good agreement: the most frequent column maximum radar reflectivity is about
5 dBZ too low in both simulations and the peak reflectivity is overestimated by about 5 dBZ in the
passive-aerosol simulation. The occurrence of low reflectivity values (<10dBZ for
passive aerosol, <5dBZ for aerosol processing) is underestimated
(Fig. b). The frequency distribution computed over all grid points, i.e.
taking into account the fraction of the domain covered by clouds, indicates an
underestimation of column maximum reflectivity for values smaller than 40dBZ in the
simulation with passive aerosols (Fig. S6d). In contrast, all reflectivity values occur with
larger frequency in the observations than for the simulations with aerosol processing. This again
indicates too few occurrences of cloudy grid points in the model (Fig. S6d).
Cloud base cloud droplet number density as a function of vertical velocities from aircraft
data (red symbols) and model data (grey shading). Panel (a) shows the simulation with passive
aerosols and panel (b) the simulation with aerosol processing. The simulations in both panels use
the standard-aerosol profile. Aircraft observations include data collected during low-level flight
legs close to cloud base (z¯=1160m, 12:00 to 12:50 UTC). CDP measurements
are used for the cloud droplet number concentrations and Airborne Integrated Meteorological
Measurement System (AIMMS)-20 measurements for the vertical
velocity. Cloud base cloud droplet number density and vertical velocity in the model is retrieved
from the lowest model level with a cloud droplet mass larger than 1mgkg-1 from the
entire domain between 12:00 and 13:00 UTC.
Surface precipitation is more closely linked to cloud base reflectivity than to column maximum
reflectivity. The distribution of in-cloud low-level radar reflectivity (at 750m) from
the passive-aerosol simulation indicates an underestimation in the frequency of reflectivity values
between 20 and 30 dBZ (Fig. S6c). Above 35dBZ the modelled frequency of
occurrences is overestimated in the simulation with passive aerosols and almost identical to the
observed distribution in the simulation with aerosol processing. In the simulation with aerosol
processing, the reflectivity in this range is also underestimated, but the occurrence of
reflectivity smaller than 10dBZ is overestimated.
The overall agreement between the observed and the modelled
radar reflectivity distributions is better than that seen between the radar-derived rain rate and
model rain rate. However, both variables indicate an underestimation of the occurrence of cloudy
points (in space and time). The better agreement with radar reflectivity may suggest potential
problems with the radar-derived surface precipitation for medium to low precipitation rates, e.g.
due to the missing representation of sub-cloud evaporation in the retrieval of surface rain rates
from radar data e.g.. Another possibility is issues with the
diagnosed reflectivity from the model simulations, which does not account for extinction,
non-spherical drops, or contributions from Mie scattering e.g..
The 3-D radar composite also provides information on the cloud structure. Here we compare the largest
altitude at which the radar reflectivity exceeds 18dBZ in each column. The
18dBZ contour is often used in radar data sets to determine the “echo top”
e.g.. The mean height of the 18dBZ contour
increases from about 2km in the morning to 3km in the afternoon in the
observations and the model simulations (Fig. S7a). This indicates a general deepening of
convective cells in correspondence to larger convergence as the sea-breeze lines establish (Fig. S5b).
The modelled mean height of the 18dBZ contour agrees within 200–500 m with
the one derived from the 3-D radar composites (Fig. S7a). The maximum height of the
18dBZ contour in the observations only shows a small increase from about
5km to about 5.5–6 km between 10:00 and 13:00 UTC, while in the model
the maximum height increases from 3.5 to 5–5.5 km (Fig. S7b). The
larger maximum heights in the radar observations are mainly due to higher-level ice clouds
that are not present in the model simulations. The modelled and radar-derived
domain-average heights of contours between 5 and 25 dBZ differ by a maximum of
500m (not shown). The model tends to underestimate (overestimate) the mean altitudes
for lower (higher) reflectivity values. Given the vertical resolution of the radar data set
(500m) and the model-level spacing (200m at 5km), this is a
reasonable agreement.
Aircraft observations of hydrometeor number concentrations
The fraction of aerosol activated to cloud droplets is important for the aerosol effect on clouds.
We therefore compare the cloud base cloud droplet number concentration measured by the CDP
on-board the BAe-146 with the modelled cloud base droplet number concentration
(Fig. ). The aircraft data are taken from several flight legs
close to cloud base (within 500m) sampling multiple cells along the convergence lines
between 12:00 and 12:50 UTC (red dots). In the model all clouds in the domain are sampled within
the same time period (grey shading). Cloud base in the model is defined as the lowest vertical level
in each column with a cloud droplet mass larger than 1mgkg-1. CDNC at cloud base
generally increases with the vertical velocity in the simulations and the observations, as expected.
Sensitivity experiments with the aerosol size distribution used in the model suggest that a
multi-mode representation of aerosols is required to match the observed relation over the range of
cloud base updraft velocities (up to 7ms-1) (not shown). CDNC values in the
observations reach about 375cm-3, which is most closely matched by the simulation
with aerosol processing (Fig. b). The simulation assuming passive aerosols
over-predicts maximum CDNC values by about 30% (Fig. a).
Aerosol fields from the simulation with aerosol processing at 14:00 UTC. (a) The colour
shading shows the column maximum reflectivity and the black line indicates the location of the cross
sections plotted in the other panels: (b) number density of Aitken-mode aerosol, (c) accumulation-mode aerosol, and (d) coarse-mode aerosol. The white contour lines in panels (b, c, d) indicate
areas with hydrometeor mixing ratios larger than 1mgkg-1.
Combining aircraft data from cloud penetrations at various altitudes throughout the day provides
some information of the CDNC variation with height above cloud base. The observational data in
general suggest a decrease in the maximum observed CDNC with altitude above cloud base
. In the simulation with passive aerosol, mean CDNC decreases slowly with
height. However, CDNC values remain comparable to the cloud base values at all levels within the
clouds (Fig. S8a). In the simulation with aerosol processing, CDNC decreases more rapidly above
cloud base and the spread is significantly larger than for the passive-aerosol simulations (Fig. S8b),
i.e. a behaviour more compatible with observational data. However, a direct comparison
to the model results is not possible, since the location of the aircraft observations relative to
updraft cores is not known.
Radiosonde data
The overall structure of the thermodynamic profiles in the model is similar to the 2-hourly
radiosonde data at Davidstow (Fig. S9). The radiosonde data were compared to the
thermodynamic profile at the grid column closest to the release location of the radiosonde. The
temperature agrees within ±1K and the dew-point temperature within
±5K (±10K) below (above) a stable layer located between
5 and 6km altitude. As discussed later, the stable layer at
5–6 km is an important feature of the thermodynamic profile for the
aerosol-induced changes. This stable layer is located at the same altitude in
the simulations and the observations (Fig. S9). Other
parameters, such as the height of the 0∘C level and the lifting condensation
level, are similar in the model and the observational data throughout the day with maximum
deviations of 100 and 250m, respectively (Fig. S10).
The model simulations using the standard-aerosol profiles compare favourably with radar and aircraft
observations (air and dew-point temperature profiles differences smaller than ±1
and ±5K, respectively; 0∘C level, lifting condensation level,
and height of the 18dBZ contour within ±250m; cloud base CDNC
differences smaller than 30%; domain-average precipitation and precipitation rates
larger than 4mmh-1 within a factor of 2; radar reflectivity within
±5dBZ for reflectivity larger than 5dBZ). This suggests that the model
is adequately representing the processes important for this case, providing confidence that changes
predicted by aerosol perturbation experiments will be physically meaningful.
Modification of the aerosol environment by aerosol processing
Cloud microphysical processes can alter the aerosol fields by
changing the aerosol size distribution, the aerosol chemistry, or by redistributing or removing
aerosols. While these feedbacks are often not represented in numerical weather
prediction models, they can be represented with the CASIM module, except for
changes to the aerosol chemistry. In this section we discuss the changes in aerosol and hydrometeor
number density resulting from the representation of the two-way coupling between aerosol and cloud
microphysics. We only discuss simulations with the standard-aerosol profile.
In the passive-aerosol run, the aerosol fields are only subject to advection and are not modified by
cloud microphysical processes. Therefore, only minor changes in the aerosol concentrations relative
to the prescribed profile occur (Fig. S11: compare profile at upstream, i.e. western, boundary
with rest of domain). Hovmöller diagrams of aerosol loading (vertically integrated and
latitudinally averaged aerosol concentrations) confirm this picture for the entire duration of the
simulation (Fig. S12a, c, e). Small decreases and increases occur in regions of divergent and
convergent flow, which are likely related to gravity waves excited by the sea-land
contrast (5.5∘W) and orography (4.5 and
4.0∘W).
In the aerosol-processing run, aerosol fields are modified according to microphysical processes. The
additional tendency terms for the aerosol fields include (i) depletion of interstitial aerosol
number and mass during cloud droplet activation and ice nucleation and (ii) increases in
interstitial aerosol number and mass during evaporation and sublimation. Collision–coalescence
reduces the number of aerosol particles released during evaporation compared to the number
originally activated. Hence, for example, aerosol activated from the Aitken-mode population may be
released back into the interstitial aerosol population in the accumulation mode. Changes in the
aerosol field relative to the passive-aerosol simulation or to a good approximation of the upstream
boundary can be directly attributed to either of these processes. Cross sections of the aerosol
concentrations along the convective line are shown in Fig. . The Aitken-mode aerosol is depleted within cloud, below cloud, in areas of evaporated clouds (e.g. around
5.25∘W), and in the outflow at higher levels. The reduction occurs in regions
where activation is expected and in downstream areas. The accumulation-mode aerosol is also reduced
inside clouds where activation is expected. The reduction in aerosol in these regions is due to
nucleation scavenging because this is the only process that can consume interstitial aerosol in the
model. Below cloud base and in areas of evaporated clouds, accumulation-mode aerosols are enhanced
compared to the upstream profile. Similar increases in the number concentration occur at the lateral
boundaries of clouds. These are regions where evaporation of hydrometeors takes place.
Therefore, increases in accumulation-mode number concentration can be explained by evaporation of larger cloud
droplets or rain drops. The coarse aerosol mode behaves very similarly to the accumulation-mode
aerosol, but increases compared to upstream conditions are more widespread and have a larger
amplitude.
Time evolution of (a) number of cells, (b) average cell size, (c) cloud fraction, and (d)
domain-average cloud top height. Cloudy areas are defined as having a water path
larger than 1gm-2. Cells are defined as coherent areas with a column maximum radar
reflectivity larger than 25dBZ. Different line colours indicate the different aerosol
initial conditions. Solid lines correspond to simulations with passive aerosols and dashed lines to
simulations with aerosol processing.
The impact of aerosol processing seen in the cross section is representative of the modifications
that the model imparts to aerosol fields during the entire simulation (Hovmöller diagrams of
aerosol loading: Fig. S12b, d, f). Aitken-mode aerosols are depleted at the outflow (eastern)
boundary relative to the values at the inflow (western) boundary, while accumulation-mode and coarse-mode aerosol increases. In the time interval between 09:00 and 20:00 UTC, the
Aitken-mode number concentration reduces on average by 7 %, the accumulation-mode number concentration increases by
15 %, and the coarse-mode number concentration increases by a factor of 10 across the study area
(4.61 to 3.5∘W). These estimates discount any clouds
present at the downstream boundaries and take only cloud-free areas into account. The predicted
changes in the coarse-mode aerosol concentrations are large enough that they eventually could be
identified in future aircraft campaigns by flying long north–south-oriented runs up- and downstream
of the convective line. Such observations would provide valuable information for the evaluation of
the model representation of aerosol–cloud interactions.
Aerosol processing reduces the total number of aerosol available at cloud base due to the
modification of the aerosol size distribution by collision–coalescence
(Fig. ). This impacts the mean (maximum) cloud base droplet number
concentration, which is reduced by 50% (10%) compared to the simulations
with passive aerosols. The reduction of aerosol number concentration at cloud base
is particularly relevant for clouds developing along the eastern part of the convergence line, where
aerosol concentrations in the boundary layer have been modified by previous clouds
(Fig. ). In addition to the impact on the overall number concentration,
aerosol processing changes the relative contributions from each aerosol mode
(Fig. ). Therefore, aerosol processing impacts the relation between CDNC
and vertical velocity at cloud base as discussed in Sect.
(Fig. ). Further differences occur in the vertical distribution of CDNC within
the cloud (Fig. S8). With aerosol processing, a larger variability of CDNC values occurs and CDNC
decreases more strongly towards cloud top. Activation in the model occurs where CCN concentrations
predicted by the parameterisation (based on vertical velocity and aerosol
concentration) exceed the CDNC present in the grid box. The maximum CDNC in the passive-aerosol
simulation is almost constant with altitude. We interpret this as a result of activation at higher
altitudes enabled by the high interstitial aerosol concentrations in the cloud. In contrast, maximum
CDNC values decrease with altitude in the aerosol-processing run. In this simulation, nucleation
scavenging is taken into account and the therefore reduced interstitial aerosol concentrations
impede any activation above cloud base. This argumentation assumes no major differences in the
vertical velocity, which is justified by the rather small differences of in-cloud kinetic energy
discussed later (Sect. ).
Impact of the aerosol environment on the cloud field and surface precipitation
Cloud properties and precipitation are influenced by the aerosol available during cloud formation.
To investigate the sensitivity of the studied cloud field to different aerosol concentrations, we
conducted simulations with enhanced and reduced aerosol concentrations in the initial and lateral
boundary conditions (see Sect. ). The impact of these perturbations on the cloud
field and precipitation is described in this section, while the physical mechanisms responsible for
the changes are discussed in Sect. .
Cloud field structure and cloud geometry
Cloud fields from simulations with higher aerosol concentrations are more organised with larger,
less widespread, and more densely packed cells (qualitative: Fig. S13). The changes to the cloud
field structure can be quantified by comparing the number of cells and the mean cell size
(Fig. a, b). A cell is defined as a coherent area with a column maximum
radar reflectivity larger than 25dBZ. The number of cells decreases and the mean cell
size increases with larger aerosol concentrations throughout the simulation. The cell number changes
in all development stages of the convective line, while the change in cell size is particularly
evident in the afternoon (after about 13:00 UTC). Aerosol-induced changes in cell
number and size are smaller for aerosol concentrations enhanced above the standard-aerosol profile
compared to reduced aerosol concentrations. The transition to a more structured cloud field in the
high-aerosol environment is accompanied by a small reduction in cloud fraction
(Fig. c). The cloud fraction is defined as the proportion of the domain
covered by clouds with a condensed water path larger than 1gm-2.
Changes in cell number and size change in opposition such that aerosol-induced
changes in cloud fraction are very small and occur mainly between 13:00 and 16:00 UTC.
The average cloud top height rises in high-aerosol environments (Fig. d).
Cloud top height is defined as the altitude of the highest grid box with a condensate content larger
than 1mgkg-1 in each column. Differences in mean cloud top height are small in the
development phase of the convective line but amount to about 100–200 m in the
afternoon. The increase in mean cloud top height is due to a reduced occurrence
frequency of cloud tops between 3 and 4 km and an increased occurrence
frequency of cloud tops above 4.5km (Fig. S14a). Cloud base height variations
between simulations with different aerosol profiles are much smaller (mean height
±50m, Fig. S14b). Aerosol-induced changes in cloud top height
are larger in the aerosol processing than the passive-aerosol simulations. The maximum cloud top
height is restricted by a stable layer extending from about 5–6 km, which is evident
in the thermodynamic profiles from radiosondes and model simulations (Fig. S9).
In the simulations with passive aerosols, the cloud tops reach the stable layer under standard
aerosol concentrations, while they only reach this altitude under enhanced aerosol concentrations in
simulations with aerosol processing. Cloud deepening from the standard to the high-aerosol
scenario is not limited by the thermodynamic profile in the aerosol-processing case, while it is in
simulations with passive aerosols. For the simulations with passive aerosol, a similar asymmetry in
the response to increases and decreases in the aerosol concentration is also evident in other
variables (Sects. and ). We hypothesise that this asymmetry is
controlled by thermodynamic constraints on the cloud top height. This hypothesis is further
discussed in Sect. .
Surface precipitation
The overall precipitation response to changes in the aerosol profile can be divided
into two different periods (Fig. b). An early
period (09:00–12:00 UTC) during which the precipitation decreases with increasing
aerosols and a later period (12:00–20:00 UTC) during which precipitation increases
with increasing aerosols in most cases. For the high and very high-aerosol scenario, accumulated
precipitation decreases in the later period relative to the simulation with the
standard-aerosol profile. The continuous decrease in precipitation with aerosol concentration in the
first period agrees with parcel model results indicating a less efficient
collision–coalescence in the presence of more CDNC e.g.. However,
contrary to the precipitation suppression idea, the accumulated precipitation increases with
enhanced aerosol concentrations in the afternoon. The underlying physical processes are discussed in
Sect. . The transition from precipitation suppression to enhancement coincides
roughly with the transition from isolated and unorganised convective clouds to on average larger
and deeper cloud clusters forming along the converging sea-breeze fronts (Figs. S3, S4).
The accumulated surface precipitation depends on the chosen aerosol representation
(Fig. a). In simulations with aerosol processing, the accumulated precipitation is
about 20% smaller than in simulations with passive aerosols.
Including aerosol processing in the simulations results in a larger aerosol-induced
change in accumulated precipitation with a maximum change of 14% (standard- to high-aerosol scenario, aerosol processing) compared to 4%
(low- to standard-aerosol scenario, passive aerosol). The transition from precipitation suppression to enhancement occurs
with aerosol processing and passive aerosol.
(a) Time evolution of accumulated precipitation since 09:00 UTC. (b) Change in accumulated
precipitation relative to simulations with the standard-aerosol scenario. Different line colours
indicate the different aerosol initial conditions. Solid lines correspond to simulations with
passive aerosols and dashed lines to simulations with aerosol processing.
The precipitation rate distribution is also influenced by the aerosol concentrations (Fig. S15).
In the passive-aerosol simulations, medium rain rates (∼ 1–20 mm h-1) are
more frequent and small rain rates less frequent with increasing aerosol concentrations. High
precipitation rates (>30mmh-1) are less frequent both in the high and low
scenario than in the standard-aerosol case. If aerosol processing is included, then changes in low
and medium rain rates are much smaller and the probability of high rain rates
(>30mmh-1) increases continuously with the aerosol concentration.
Condensed water content
Under enhanced-aerosol conditions, precipitation formation is thought to be suppressed due to a less
efficient conversion of condensed water to precipitation-sized hydrometeors. Consistent with this
idea, the domain-integrated condensed water path increases with the aerosol
concentration in the time period of main convective activity, i.e. between 12:00 and 20:00 UTC (Fig. S16a).
This increase is larger and extends to the entire simulated time period if only
hydrometeors with small sedimentation velocities (cloud droplets, ice, and snow) are taken into
account (Fig. S16b).
The box and whisker plots show the distribution of the condensed water path
(cloud and ice categories) in columns with certain precipitation rates for simulations with (a)
passive aerosol and (b) aerosol processing. Different colours correspond to the different aerosol
profiles. Values are only shown for precipitation bins with more than 100 data points. The median
water path is shown by the horizontal line in the box. The boxes cover the
interquartile range (25th to 75th percentile) and whiskers represent the
10th and 90th percentile, respectively.
Parcel model considerations suggest that a higher condensed water content is required to obtain the
same precipitation rate for higher cloud droplet number concentrations. To investigate whether this
also holds for the relation of condensate loading and precipitation in a more complex model, the
distribution of condensed water path (in the ice and cloud droplet categories) in
columns with a specific precipitation rate is displayed in Fig. . While there is
no clear trend for precipitation rates up to 16mmh-1, the median condensed
water path increases strongly with aerosol concentrations for larger precipitation
rates. For higher percentiles (75th and 90th percentile) an increasing
condensed water path is also evident for small precipitation
rates. A trend to a larger median condensed water path occurs
also for low precipitation rates if only the first part of the simulation until
12:00 UTC is considered (Fig. S17c, d). In contrast, the distribution of condensed
water paths for small precipitation rates does not depend on the aerosol scenario
in the afternoon (Fig. S17e, f). The same trends occur in the simulations with aerosol processing
and with passive aerosol, but the condensed water path dependency of surface
precipitation is less pronounced in the aerosol-processing simulations. The behaviour of the total
condensed water path (including all hydrometeor categories) is very similar to
changes in the condensed water path including the cloud and ice category only (Fig. S17a, b).
Physical mechanism of aerosol-induced changesDecomposition of precipitation response into changes in condensate generation and loss
Changes in precipitation can be contextualised and interpreted by investigating the condensate
budget of the considered clouds e.g.. The change in
precipitation at the surface ΔP is thereby considered as the result of changes in
condensate gain ΔG (condensation and deposition of water vapour) and condensate
loss ΔL (evaporation and sublimation). If changes in condensate gain are larger
than changes in the loss terms, then surface precipitation increases and
vice-versa. Furthermore, the ratio between ΔG and ΔL combined with
the precipitation efficiency (PE) of the control simulation indicates whether changes in the condensate
gain, i.e. changes in uplift, are driving surface precipitation responses or whether changes in
precipitation efficiency, i.e. more or less efficient conversion of condensate to precipitation, are
involved as well (see Appendix A). The analysis of the condensate budget provides insight into the
influences of cloud microphysics, cloud dynamics, and the cloud environment on precipitation
formation, because the condensate budget is intrinsically linked to latent heating, condensate
distribution within the cloud, and different timescales of cloud microphysics and dynamics
e.g..
The condensate budget approach requires (i) reasonable mass conservation properties of the
underlying numerical model, (ii) no change in storage of condensate in the considered domain, and
(iii) no change in advection of condensate out of the considered domain. The first requirement is
ensured in our simulation by using the and approach to
enforce moisture conservation in the regional model domain. Very little condensate is present in the
domain at the start and end of the analysis period (09:00–20:00 UTC, Fig. S16a). Therefore, the
second requirement is fulfilled to a very good approximation. The domain used in our simulation does
not cover the entire length of the convergence lines and therefore condensate is advected out of the
domain. To investigate the impact of the advection on the validity of the condensate budget analysis
(requirement iii), we diagnose the advected condensate amount at the domain edge from meteorological
fields at 10min resolution. Figure a shows that the inclusion of the
advective terms has a small impact on the changes in gain and loss terms (compare open and filled
symbols). This suggests that changes in advective terms are small compared to changes in other terms
of the condensate budget for our simulations. Advective terms will therefore be ignored for the rest
of the analysis.
(a) Scatterplot of change in condensate gain ΔG and loss ΔL
relative to the simulation with the standard-aerosol profile. Points falling above the
one-to-one line (solid black line) portray a decrease in surface precipitation, while points below
it portray a surface precipitation increase. For points in the area between the solid black line and
the dashed lines (dark blue: passive aerosol, light blue: aerosol processing) the change in
condensate generation dominates over the change in PE (Appendix A). The
impact of advection of condensed water out of the domain is illustrated by the open symbols. For
these points the advective flux is discounted as loss. (b) Same as (a), but separating contribution
of condensation and evaporation (filled symbols) from contribution of deposition and sublimation
(open symbols).
The condensate budget terms are calculated by integrating condensation, evaporation, deposition, and
sublimation rates over the model domain and the time period of convective activity, i.e. between
09:00 and 20:00 UTC. Changes in the condensate gain ΔG and
loss ΔL relative to simulations with the standard-aerosol profiles are shown in
Fig. a. In this plot all points above the one-to-one line correspond to simulations
for which the condensate gain changes less than the condensate loss. Surface precipitation in the
corresponding simulations is smaller than in the reference case, i.e. the standard-aerosol scenario.
Points below the one-to-one line portray simulations with enhanced precipitation. The concept of
this plot is discussed in more detail in the appendix and Fig. . The condensate
gain increases with the aerosol concentrations for all simulations. However, the absolute value of
ΔG decreases towards high-aerosol scenarios for the passive-aerosol simulations. In
the highest-aerosol scenarios, cloud tops are located close to an upper-level stable layer as
discussed in Sect. , which imposes a limit on further cloud deepening and the
condensate gain. For simulations with aerosol processing, the absolute value of ΔG
is smaller (larger) for the low (very high) aerosol scenario compared to the simulation with passive
aerosol. Cloud tops in the aerosol-processing simulations are on average lower for a given aerosol
scenario and therefore cloud deepening is not yet limited by the upper-level stable layer.
Precipitation efficiency (PE) of the different simulations.
PE is defined as the ratio of domain-integrated precipitation to domain-integrated condensate gain.
The condensate loss also becomes larger with increasing aerosol concentrations. However,
ΔL does not simply scale with ΔG, suggesting that changes
in precipitation efficiency are important for the precipitation
response as well. PE is defined here as the ratio of domain-integrated time-accumulated surface precipitation to the condensate gain. PE quantifies the
efficiency of cloud microphysical processes in converting condensate to surface precipitation. The
precipitation efficiency for the different simulations is listed in Table . In the
simulations with passive aerosol, PE shows little change from the low to
the standard aerosol number concentrations but decreases by about 2% up to the very
high-aerosol scenario. In contrast, in the simulations with aerosol processing,
PE increases from the low to the high aerosol number concentration by
about 4%. For a further increase in the aerosol number concentration, PE
increases more slowly. Cloud base precipitation efficiency, i.e. discounting sub-cloud evaporation
of rain, is overall about 10% larger but behaves in a similar way (not shown). These
tendencies in PE may be related to changes in the graupel production
rate. While the graupel mass mixing ratio increases with aerosol number concentrations in the
aerosol-processing simulations, it decreases in the passive-aerosol simulations (Fig. S19b, d,
f). Since graupel production rates depend strongly on the number and mean size of cloud droplets, it
can be speculated that this difference between the aerosol-processing and passive-aerosol
simulations is due to the different vertical variations in cloud droplet number density (see
Sect. ). The lower cloud tops in the aerosol-processing simulations
may also play a role in the lower graupel production rate.
Change in surface precipitation expected from the simulated change in
PE (left column) and the change in condensate generation (middle
column). The last column gives the total relative change in precipitation, as predicted by the
simulations. All changes are relative to the simulation with the standard-aerosol profile.
To investigate the relative importance of ΔG and ΔPE for the
surface precipitation, the precipitation response is decomposed into the relative
contributions according to Eq. (A2) (Table ). The decomposition is graphically
represented in Fig. a by the blue dashed lines. For simulations outside the area
between the one-to-one line and the blue dashed line ΔPE is more important than
ΔG for the precipitation response (see Appendix A). While only the precipitation
response in the simulation with low aerosol number concentration and passive aerosol is dominated by
ΔG, ΔG and ΔPE are both important for most other
simulations. In the aerosol-processing simulations, ΔG and ΔPE
contribute to an increase in precipitation in enhanced-aerosol scenarios. In contrast,
ΔPE partly compensates for ΔG in the simulations with passive aerosol.
While ΔG causes a precipitation increase for low-aerosol scenarios in the absence
of large changes in PE, larger ΔPE and smaller ΔG cause a
precipitation decreases in high-aerosol scenarios.
In a first step to investigate the physical processes responsible for the changes in condensate gain
and loss, the condensate budget is further split according to the phase of the involved
hydrometeors, i.e. into condensation/evaporation and deposition/sublimation. The changes in these
four terms, again relative to the simulation with the standard-aerosol profile, are shown in
Fig. b. Absolute changes in condensation and evaporation (filled symbols) are
generally larger than changes in deposition and sublimation (open symbols). The only exception is
the simulation with aerosol processing and a high aerosol concentration, for which both are of
similar magnitude. The changes in terms involving liquid or solid hydrometeors have in general the
same sign; i.e. if condensation increases then deposition does so as well. ΔL is
very small for solid-phase hydrometeors suggesting (i) that the contribution of solid-phase
hydrometeors to total precipitation increases with aerosol and (ii) that detrainment and subsequent
sublimation of solid-phase hydrometeors does not significantly increase with aerosol concentrations
for simulations with passive aerosol.
From the analysis of the condensate budget so far, we can conclude that the
aerosol-induced changes in the condensation and evaporation are larger than
changes in sublimation and deposition. For most cases, changes in the total condensate gain are of
similar importance to changes in PE. Changes in the loss terms (and
therefore the precipitation efficiency) are important for the simulations with (very) high aerosol
concentrations and for the difference between simulations with passive aerosol and aerosol
processing. The mechanisms driving changes in condensate budget terms are investigated in more
detail in the next section.
Changes in (a) condensate gain and (b) updraft area for updraft regions with different
column maximum in-cloud vertical velocities. In (a) dark colours represent changes in condensation
and lighter colours changes in deposition relative to the simulation with the standard-aerosol
profile. Solid lines correspond to simulations with passive aerosol and dashed lines to simulations
with aerosol processing.
Average profiles of (a, b) kinetic energy from vertical velocity, (c, d) latent heat
release, and (e, f) condensate content. The left panels shows the average over all columns with a
column maximum in-cloud vertical velocity of wmax=0–3 m s-1 and the right
panels for those with wmax>3 m s-1. The grey horizontal line indicates the
location of the 0∘C level.
Aerosol impact on convective core and stratiform regions
For a closer analysis of the changes in condensate gain and loss, the cloud field is decomposed into
regions with different updraft strength. Cloudy columns are stratified according to the column
maximum in-cloud vertical velocity at each grid point (wmax). In-cloud grid points
have a minimum condensate content of 1mgkg-1. ΔG for the
conditionally sampled areas of the domain is shown in Fig. a. Condensation
changes are dominated by regions with large wmax, while smaller changes in opposite
sign occur in weaker-updraft regions. In contrast, weak-updraft regions contribute most to changes
in deposition. These modifications go along with changes in the vertical extent and area covered by
the updraft regions (Fig. b, Fig. S18a). Updraft cores deepen by about
100–150 m for each factor of 10 increase in aerosol number concentration, while the
areal extent increases by about 25%. These changes in the updraft geometry contribute
to the differences in column-integrated condensate generation between simulations. However, they do
not fully explain them, since the volume-averaged condensation rate also increases with aerosol
concentration (Fig. S18b). These changes are consistent between simulations with and without
aerosol processing.
Different responses to aerosol perturbations are expected in the convective core regions and the
more stratiform regions of the cells. Based on the change in sign of ΔG between
regions with a column maximum velocity wmax=3 m s-1 and
wmax=4ms-1, we define updraft regions by
wmax>3ms-1 and more stratiform regions by wmax=[0,3]ms-1. Average profiles of kinetic energy, latent heating rates, and total condensed
water for the two regions are shown in Fig. .
In the convective core regions, the kinetic energy and the latent heat release increases with
increasing aerosol concentrations (Fig. b, d). Both of these variables peak in
the warm-phase part of the cloud. The peak in kinetic energy occurs about 1km above the
peak in latent heat release. The maximum in both variables shifts to higher altitudes with
increasing aerosol concentrations. Latent heat release above the 0∘C level
increases slightly for higher-aerosol scenarios, but the changes are very small compared to those
below the 0∘C level. The generally small changes above the 0∘C level indicate that the precipitation enhancement is mainly a result of changes in the
warm-phase part of the cloud, as suggested by the analysis in Sect. . While
energy released from phase transitions below 2km altitude increases with increasing
aerosol number concentrations (Fig. f), the vertical velocity
is almost unaltered (Fig. b) as is the cloud base temperature (ΔT<0.1∘C, not shown). Therefore, we hypothesise that the higher condensation rates are
due to less dry air being mixed into the high-updraft regions for increasing aerosol conditions. The
reduced impact of mixing with drier air would be consistent with on average larger cells and an
increasing stratiform area (Figs. b, b). The stronger
latent heating from convection as well as the weaker mixing with low kinetic energy air masses
(due to a wider updraft region) contributes to the higher vertical velocities aloft. The larger
vertical motion promotes the upward transport of condensate. The condensate mass in the lower parts
of the cloud decreases with increasing aerosol, while it increases above the 0∘C
level (Fig. f). The altitude of the maximum condensate loading also shifts to
higher altitudes for higher aerosol loadings. The higher condensate amounts towards cloud top are
also supported by slower conversion rates of cloud condensate into rain with increasing aerosol
concentration (Fig. S19). In contrast, condensate mass increases with decreasing aerosol
number concentrations in the lower parts of the clouds. This is mainly a result of
the sedimentation of rain, which is more efficiently produced in low-aerosol conditions. No further
increase in latent heating, kinetic energy, or condensate content occurs for an increase beyond the
high-aerosol scenario.
The panels in the upper row show ΔG in relation to ΔL for
the time period between (a) 09:00–12:00 UTC and (b) 12:00–21:00 UTC. The panels in the lower row show
the difference in (c) accumulated condensate generation and (d) condensate loss relative to the
simulation with standard-aerosol profile. Solid lines represent simulations with passive aerosols
and dashed lines those with aerosol processing.
Schematic summary of aerosol-induced changes in the investigated clouds
for a scenario in which cloud tops are not limited by an upper-level stable layer (a, c) and one in
which they are (b, d). The cloud evolution in a low-aerosol environment is illustrated in (a, b) and
in a high-aerosol environment in (c, d). The intensity of the shading indicates the condensate mass
mixing ratio. Small dots represent cloud droplets and larger ones rain drops. Different stages
during the cloud evolution are depicted from left to right and the lower section of each panel shows
the time series of accumulated precipitation from each cloud (cyan: baseline aerosol, dark
blue: enhanced aerosol). The orange arrows indicate vertical velocity in the convective core
region.
In the stratiform region, the kinetic energy in the lower parts of the clouds is not affected by
modified aerosol concentrations, while it increases with increasing aerosol concentrations higher up
(Fig. a). With increasing aerosol concentrations, the latent heat release close
to cloud base slightly increases. In contrast, the latent heating becomes more negative in the upper
part of the clouds (Fig. c). The condensed water content also shows a small
increase close to cloud base, little change up to 2km, and a strong increase aloft
(Fig. e). The changes in the upper parts of the clouds are most likely due to a
larger horizontal transport of condensed water into the stratiform regions of the clouds. This may
be caused by a stronger divergence in the upper parts of the clouds in direct consequence of a
higher vertical flux in the convective core region. Also, it is expected that the higher condensate
content in the upper parts of the clouds enables lateral mixing to broaden the cloud. In contrast,
for low condensate conditions lateral mixing most likely leads to the evaporation of the cloud. The
hypothesis of a broadening of the clouds by larger transport into the stratiform regions is
consistent with the overall larger cloud size in the scenarios with higher aerosol concentrations
(Fig. b). Furthermore, reduced lateral mixing would result in a weaker
impact of entrainment of dry air into the convective core regions supporting the formation of wider
and deeper regions as discussed before. The increase in condensate loading in the stratiform region
is particularly pronounced for simulations in which vertical growth of the clouds is prohibited by
the stable layer aloft (high-aerosol scenario with passive aerosols). The enhanced export of
condensate to the stratiform region together with a less efficient rain and graupel production (Fig. S19) likely contributes to a reduction in PE. The reduced
PE impedes a further increase in surface precipitation.
The discussed changes in cloud dynamics and microphysics are consistent between simulations with
passive aerosol and aerosol processing. The difference between the passive-aerosol and
aerosol-processing simulations can be understood based on the same physical mechanism when taking into
account the overall lower CDNC and lower cloud tops for the same aerosol scenario.
Transition from precipitation suppression to enhancement
The discussion in the previous two sections focused on the precipitation enhancement with increasing
aerosol concentrations during the period of main convective activity (12:00–20:00 UTC). However, in
the earlier period with scattered convection, precipitation is suppressed by higher aerosol
concentrations (Sect. ). In the morning, the average cloud depth is smaller and
cloud top height shows only little sensitivity to aerosol perturbations
(Fig. d, Fig. S20a). The clouds are predominantly warm phase with very
small amounts of ice-phase particles close to cloud top. Consistent with the small change in cloud
top height, the condensate gain displays only minor changes. Changes in the condensate loss are more
significant and, in contrast to the clouds developing later, dominate the condensate budget
(Fig. ). Changes in mean cell size are much smaller in magnitude than later
on in the day (Fig. b). The lack of ice-phase species and the lower wind
speeds at cloud top (not shown) limit the lateral transport of condensate into the area surrounding
the updraft core and thereby prevent cells from growing larger. Accordingly, the main control on the
precipitation formation is the efficiency of cloud microphysical processes in producing
precipitation-sized hydrometeors (Fig. a). It is known from parcel model
studies that the efficiency of the cloud condensate to precipitation conversion process rapidly
increases with decreasing CDNC and hence aerosol concentrations.
In contrast, as clouds organise along the sea-breeze fronts and become on average deeper later in
the day (Fig. a, Fig. S20b), changes in the condensate gain become
more important. Maximum in-cloud vertical velocities often exceed 3ms-1, a larger
mixed-phase region develops, and the cloud depth and cell width is more sensitive to the aerosol
scenario. The change in surface precipitation in the early period is mainly driven
by weak-updraft regions (Fig. S21a). After the transition to organised convection, regions with
larger updraft velocities contribute to the precipitation response. Also, the precipitation response
in the weak-updraft regions changes sign. The shift from precipitation decrease to increase in the
weak-updraft regions is likely associated with a large increase in the condensed
water path in the later period (Fig. S21c to f). We
hypothesise that this is due to the added impact of lateral transport from the
convective core regions into the more stratiform regions (Fig. e and
Sect. ).
While the sign of the precipitation change is consistent for all vertical velocity
regions in the aerosol-processing runs, for simulations with passive aerosol the medium-updraft
regions (wmax=2–4ms-1) show a precipitation change opposite to the weak
and strong-updraft regions (Fig. S21b). This is particularly important for the high and very
high-aerosol scenario, for which the total precipitation response is dominated by the medium-updraft
regions. Importantly, the precipitation from the convective core region does not increase further
for high or very high-aerosol scenarios. As discussed earlier (see
Sect. ), we hypothesise that the thermodynamic limits on cloud deepening
is responsible for this behaviour.
The main driver for the transition from precipitation suppression to precipitation enhancement
therefore is the larger forcing of the convective clouds by the sea-breeze convergence zone which
influences the cloud depth and horizontal structure of the cloud field.
The aerosol-induced changes in cloud structure, life cycle, and precipitation
formation for the phase of organised convection are summarised in Fig. . The
right (left) column corresponds to clouds for which the vertical development is (not) limited by a
stable layer aloft. The upper panels depict the control scenario, while the lower panels show the
cloud evolution under increased aerosol conditions.
Discussion and conclusions
Aerosol–cloud interactions are investigated for mixed-phase convective clouds developing along a
sea-breeze convergence zone over the southwestern peninsula of the UK.
High-resolution (Δx=250m) simulations with the Unified
Model have been conducted with a newly developed cloud microphysics scheme (CASIM), which can
represent the modification of the aerosol environment by cloud microphysical processes. Evaluation
of the model simulations with observations from the COPE campaign suggests a good model performance
in terms of the thermodynamic, cloud, cloud microphysical, and radar reflectivity
structures. The good agreement with low-level radar reflectivity but larger difference in surface
precipitation rate may point either to issues with the assumptions used for the
reflectivity diagnostics in the model or potential issues with the radar-retrieved surface
precipitation rates.
A novel aspect of CASIM is the representation of modifications to the aerosol environment by cloud
microphysical processes in a numerical weather prediction framework. Including this feedback has a
largely positive impact on the model performance in terms of cloud base cloud droplet number density
and radar reflectivity but leads to a stronger underestimation of domain-average surface
precipitation and average cell sizes. The most important impacts of including aerosol processing for
cloud properties and aerosol-induced changes thereof are as follows:
Aerosol processing reduces cloud base CDNC, results in a more rapid decrease in CDNC with
altitude, and increases the spread of CDNC values at each altitude.
In the period with unorganised convection, aerosol processing reduces the
amplitude of the precipitation suppression with increasing aerosol compared to simulations with
passive aerosol.
Precipitation changes in the second period with organised convection are
larger when aerosol processing is included. The larger signal is due to on
average lower cloud tops with a larger potential for cloud deepening and a
larger PE for high-aerosol scenarios. For passive aerosols,
small reductions in PE occur for increasing aerosol.
The two-way interaction between clouds and aerosols is an important feedback mechanism, which may
impact the magnitude of aerosol-induced changes in clouds and is one source for
co-variability between cloud and aerosol fields. The modification of the aerosol
size distribution and number density by cloud microphysics has been studied in laboratory
experiments e.g. and documented in aircraft campaigns e.g..
Its importance has been demonstrated in several modelling studies for orographic clouds
e.g. and stratocumulus clouds e.g.. The
impact of aerosol–cloud co-variability is particularly important on larger spatial and temporal
scales, which typically cannot be represented in very high-resolution simulations
with detailed bin microphysics. Hence, one-way (aerosol impact on cloud field) or two-way coupling
(aerosol impact on cloud field and vice versa) between aerosol and cloud fields has been implemented
in some numerical weather prediction models with bulk microphysics schemes (e.g.
COSMO-ART, Consortium for Small-scale Modeling model coupled to the Aerosols and Reactive
Trace gases model, ; COSMO-MUSCAT, COSMO model coupled to the
Multi-Scale Chemistry Aerosol Transport model, ; or
WRF-Chem, Weather Research and Forecasting model coupled with Chemistry, ).
Notwithstanding the recent development of these modelling systems, only a limited number of
studies on the sensitivity of cloud–aerosol interactions are available. The published studies
predominantly focus on aerosol processing in stratocumulus clouds. In this work we have shown the
importance of aerosol processing in mixed-phase convection along sea-breeze fronts.
Perturbations to the aerosol initial and boundary conditions (modifications by factors of 0.1, 10, and
30) cause changes in the cloud microphysical properties, geometry, and precipitation production in
the case analysed here. These changes are summarised in Fig. . Key aspects are listed as follows:
Aerosol perturbations modify the cell number and sizes but have little impact on the domain
cloud fraction.
Changes in the cloud field structure and presumably associated changes in lateral mixing are
important for the response to aerosol perturbations.
Precipitation suppression under high-aerosol conditions transitions to precipitation
enhancement when the clouds organise and on average grow deeper.
Changes in precipitation are mainly a result of modified condensate gain in the warm-phase
part of the clouds. Changes in PE support the precipitation enhancement
for simulations with aerosol processing but act in the opposite direction for simulations with
passive aerosol.
The enhanced condensate gain is due to changes in the convective core region, where vertical
velocities and latent heat release from condensation increase.
The enhanced condensate gain is not translated into a precipitation enhancement when clouds
grow into an upper-level stable layer limiting cloud depth.
The change in the sign of the precipitation response from shallower unorganised to deeper and more
organised convection is in line with previous results from individual simulations (summarised for
example by ). Different precipitation responses for convective and for more
stratiform precipitation have also been documented in the large domain simulations of tropical
convection by . Previous studies on aerosol-induced changes in
precipitation formation in deep convective clouds mainly focussed on changes in latent heating in
the mixed-phase part of the clouds e.g.. In contrast, our
simulations suggest that the precipitation response is mainly driven by changes in latent heating
below the 0∘C level. The idealised studies of
and have also found changes in the warm-phase section of the
clouds to dominate over latent heating changes in the mixed-phase part. The increase in warm-phase
latent heating in and is due to a more efficient vapour
deposition on the more numerous cloud droplets in polluted conditions. This mechanism cannot be
represented in our modelling system, as we use a saturation adjustment scheme. In contrast, we
hypothesise that the changes in latent heating rates from condensation are related to changes in the
horizontal cloud field structure. Interestingly, showed with a simplified
modelling system that temperature perturbations in the warm-phase section of convective clouds have
a larger potential to increase the updraft strength compared to temperature perturbations in the
mixed-phase section. Changes in horizontal cloud structure have received less attention in previous
studies compared to the more frequently analysed changes in cloud top height
e.g.. Most previous studies used either small domain,
high-resolution simulations unable to represent large changes in cloud field
structure or larger domain but coarser resolution simulations lacking a representation of updraft
dynamics. In the present study the spatial resolution is high enough to at least partly resolve
updraft dynamics and the domain is large enough to represent cloud–cloud interactions as well as to
allow for changes in cloud field structure. Our simulations indicate small changes in cloud fraction
but major changes in cell number and area. This supports the hypothesised importance of changes in
cloud field structure and related compensating mechanisms as suggested for example by
.
Despite the fairly high resolution of the presented simulations, there are some issues regarding the
representation of lateral mixing in the model simulations. Numerical weather prediction models have
known issues with reproducing observed cell size distributions. Also, modelled cell size
distributions often do not converge in simulations with increasing spatial resolution
e.g.. These problems have been at least partly attributed
to the representation of lateral mixing and parameter settings therein . Future studies should investigate the sensitivity of the
aerosol-induced changes in cloud field structure to the representation of lateral
mixing and test whether similar changes occur in models resolving lateral mixing (LES, large eddy simulations).
Other caveats for the presented simulations arise from choices in the microphysical
parameterisations. Firstly, the CASIM microphysical module uses the assumption of saturation
adjustment. found that the representation of supersaturation can lead to
significant differences in the magnitude of aerosol-induced changes in latent
heating in the mixed-phase part of clouds. Changes in latent heating were found to be much smaller
if saturation adjustment was used. Secondly, the representation of mixed-phase cloud microphysics in
models has a number of uncertainties of parametric and structural nature. These include, but are not
limited to, the representation of primary and secondary ice formation, drop freezing, rimed particle
density, and diameter–fall-speed relations e.g.. Most
of these uncertainties in the microphysics are expected to influence the precipitation efficiency.
However, given that changes in condensate generation play an important role in the studied clouds,
it can be speculated that these changes may have an impact on the overall precipitation but not on
the mechanism of the precipitation response.
In the second part of this study, we will investigate how the aerosol-induced
changes in cloud structure and precipitation compare to uncertainties in meteorological initial
conditions. We will also assess whether the aerosol-induced changes are consistent
in sign and amplitude across the initial condition ensemble. This will provide insight into the
detectability of aerosol–cloud interactions in observational data and the demands on observational
data to enable a detection of aerosol-induced changes.
Model data are stored on the tape archive provided by the JASMIN
(http://www.jasmin.ac.uk/, last access: March 2018) service. Data access to Met Office data via JASMIN is described at
http://www.ceda.ac.uk/blog/access-to-the-met-office-mass-archive-on-jasmin-goes-live/
(last access: March 2018).
Condensate budget analysis
The surface precipitation P equals the difference between
condensate generation G (condensation and deposition) and condensate loss L
(evaporation and sublimation) in a mass conserving system with no change in condensate storage:
P=G-L.
The condensate generation is mainly determined by the cloud dynamics, i.e.
uplift in saturated conditions, and to a smaller extend the efficiency with
which the generated supersaturation is depleted by transfer to the condensed
phase. The condensate loss is determined by the efficiency of microphysical
processes in converting condensate to surface precipitation and the timescale
available for this conversion, i.e. the residence time of any infinitely
small air parcels in (super-)saturated conditions. Accordingly the change in
precipitation between two different cases is the result of changes in the
generation and loss terms. If the changes in loss are larger than those in
the generation term, precipitation will decrease and vice-versa. A convenient
way to display this analysis is therefore a plot of ΔG against
ΔL (Fig. ).
Exemplary ΔG versus ΔL diagram as explained in
Appendix A. In simulations falling into the yellow (blue) shaded area less (more) precipitation is formed
than in the reference. The change in precipitation is dominated by ΔG for
simulations in the green shaded area, while precipitation changes in the rest of the phase space are
dominated by changes in PE. For example the green
shaded area illustrates an assumed PE of 0.2 for the reference
simulation. For other values of PE, the area is bounded by the black
solid line and the respective green dashed lines.
This analysis can be extended to address the question of whether a specific
change in surface precipitation is dominated by a change in the generation
term or a change in the conversion efficiency. For this purpose, the
precipitation efficiency is used, which is defined as the ratio of surface
precipitation to condensate generation. The change in surface precipitation
can be decomposed according to
ΔP=Pctr-Pper=GctrPEctr-GperPEper=GctrPEctr-GperPEper-GperPEctr+GperPEctr=PEctrΔG+GperΔPE.
The terms with subscript “ctr” refer to the simulation with the
control aerosol scenario and those with subscript “per” to the
simulation with a perturbed aerosol scenario. The first term on the right
side of the equation quantifies the contribution of a change in generation
and the second term those of an altered PE. The conditions for
which the change in condensate generation dominate are
accordingly
|PEctrΔG|>|GperΔPE|=|GperPctrGctr-Pper|=|GperPctrGctr-Pper|=|-GperGctrLctr+Lper|=|-ΔL-ΔG(1-PEctr)|.
These conditions are met by the following combinations of ΔG and ΔL:
1.ΔG>0&ΔL<ΔG&ΔL>ΔG(1-2⋅PEctr),2.ΔG<0&ΔL>ΔG&ΔL<ΔG(1-2⋅PEctr).
The respective areas in the ΔG-ΔL are illustrated in Fig. .
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-3119-2018-supplement.
All authors contributed to the development of the concepts and ideas presented
in this paper. BJS developed the CASIM microphysics code. AAH, JMW, PRF, and
AKM contributed to the further development of the CASIM code. AKM, PR, and
PRF helped set up the model runs. RS provided the 3-D radar composite. PR
compiled and analysed the aircraft data set. AMB provided expertise on the
observational data sets and the observational campaign. AKM performed the
model simulations and model analysis and wrote the majority of the
manuscript, along with input and comments from all co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the COPE research team for collecting observational data and in
particular John Taylor from the University of Manchester for discussion
related to the cloud droplet data. Further, we acknowledge the use of the
MONSooN system, a collaborative facility supplied under the Joint Weather and
Climate Research Programme, a strategic partnership between the Met Office
and the Natural Environment Research Council. Further we acknowledge JASMIN
storage facilities (10.1109/BigData.2013.6691556), FAAM, NCAS AMF (Atmospheric Measurement Facility), CEDA, BADC,
and the Radarnet at the Met Office team for providing data. The University of
Leeds is acknowledged for providing funds for this study. We thank two
anonymous reviewers for their valuable feedback. Edited by: Johannes Quaas Reviewed by: two
anonymous referees