Introduction
Atmospheric aerosol particles are highly relevant in terms of weather,
climate and human health. They modify the formation of clouds and
precipitation, alter the global radiation budget by scattering and
absorption,
and can have adverse effects on the human respiratory system. Globally
accelerated industrialization and urbanization are linked with increased
emissions of anthropogenic pollutants, in particular in developing and newly
industrializing countries.
Southern West Africa (SWA) is densely populated and affected by land use
changes and global climate change. More than half of the global population
growth between now and 2050 will occur in Africa. For Nigeria, which had a
population of 182 million in 2015 (rank 7), a population increase to 399
million (rank 3) is expected for 2050 . Based on these
projections, show that African anthropogenic emissions
will significantly increase from 2005 to 2030 if no emission regulations are
implemented. The atmospheric composition over SWA is marked by a
superposition of local emissions and emissions from remote areas affecting
SWA through long-range transport in particular biomass burning
pollutants;. Emissions of mineral dust, sea salt, biomass
burning pollutants, biogenic volatile organic compounds (BVOCs) and
anthropogenic emissions from cities and industries with the special case of
gas flaring from oil industries play a role. emphasize
the complexity of these anthropogenic emissions resulting i.a. from
transportation, wood and waste burning, or charcoal production.
estimate CO, NOx and volatile organic
compound emissions of the SWA megacity Lagos from aircraft measurements to be
1.44, 0.03 and 0.37 Mt yr-1, respectively.
highlight the relevance of emissions from domestic fires, with
significantly increased NH3 concentrations as well as traffic and
industry.
(a) Modeling domain SWA (red
rectangle, 2.5 km grid mesh size) together with its coarse domain (blue,
5 km grid mesh size). (b) Map of the research area SWA. The color
shading denotes the topography (meters above sea level, a.s.l.). Topographic features
are named in bold, coastal cities are shown as blue dots and the three
DACCIWA supersites as red dots. The modeling domain SWA is again denoted with
a red rectangle.
We know very little about possible impacts on regional meteorology,
which is partly because of shortcomings in adequate observations. To overcome these
shortcomings, the project Dynamics–Aerosol–Chemistry–Cloud
Interactions in West Africa DACCIWA; follows a
combined observational and modeling effort for SWA. A comprehensive field
campaign took place in June–July 2016 including extensive ground-based
and airborne measurements .
With respect to clouds, SWA is characterized by frequent nocturnal low-level
stratus (NLLS) and stratocumulus e.g. that have a significant influence on the radiation
budget e.g.. How sensitive the cloud radiative properties
are to high aerosol loadings has not been quantified. The modeling study of
focuses on the impacts of aerosol–monsoon interactions on
variability over the northern Indian Himalaya foothills during the summer of
2008. They highlight the fact that the aerosol direct effect (ADE), i.a. mineral dust
transport and radiative-heating-induced dynamical feedback processes, have
major impacts on the large-scale monsoon circulation. The ADE leads to an
increased north–south temperature gradient, a northward displacement of
monsoon precipitation and an advanced monsoon onset over the Himalaya
foothills. The mineral dust leads to an increase in atmospheric stability via
the aerosol semi-direct effect, whereupon the aerosol indirect effect (AIE)
may further enhance ADE by the convective-cloud invigoration mechanism
. underline the need to consider
aerosol–monsoon interactions even in short-term numerical forecasting of
monsoon circulation and precipitation.
In a modeling study of marine warm-cloud regimes, show
that an increase in the amount of cloud condensation nuclei promotes and
accelerates the stratus-to-cumulus transition (SCT) due to an increase in
evaporation and entrainment at the stratus top and deeper penetrating cumuli
within the stratus that lead to a dissolution of the surrounding stratus via
entrainment and the subsequent subsidence of cold air. Furthermore, the study
indicates a domain-wide reduction of clouds with moderate precipitation but a
localized precipitation intensification via the convective-cloud invigoration
mechanism. The interaction between AIE, the land surface characteristics and
tropical sea breeze convection over Cameroon was analyzed by
for boreal summer conditions. The study reveals a weakening
of the sea breeze front with increasing aerosol due to a reduction in
surface shortwave radiation and therefore surface heating linked with less
precipitation. and emphasize the need to
analyze AIE with a dependence on cloud regimes with fine-scale models to
explicitly resolve the interacting processes rather than using global models
with parameterizations. This is supported by the study of
, which reveals that the West African monsoon (WAM)
representation in the UK Met Office Unified Model shows fundamental
differences between realizations with explicit and parameterized moist
convection. A comprehensive overview of the current state of research on the
AIE is presented in .
This study focuses on the assessment of the aerosol impact on clouds and
atmospheric dynamics over SWA using a 2-day process study. The following
research questions encompass the focus of this study: what are the dominating
aerosol impacts on meteorological characteristics over SWA and which spatial
and temporal scales do they exhibit? Do we see changes in radiation and
precipitation? To what extent do altered cloud radiative properties play a
role?
Schematic view of SWA atmospheric
dynamics via a meridional–vertical transect (m a.s.l.) through the Gulf of
Guinea (blue shading) and adjacent land (brown shading). (a) During
nighttime the NLLJ leads to a wind maximum at about 300 m a.g.l. as
emphasized by the black arrows. Over land, NLLS forms at the level of the
NLLJ axis. The NLLS establishment occurs around 22:00 UTC over Kumasi and
01:00 UTC over Savè . (b) The maximum
spatial coverage of NLLS is reached in the morning hours around 09:00 to
10:00 UTC. After sunrise (05:30 UTC) a lifting of the cloud base height can
be observed. (c) During late morning or early afternoon the NLLS
deck breaks up to cumuliform clouds around 11:00 UTC over Kumasi and
12:00 UTC over Savé;. (d) During daytime
(05:30–18:00 UTC) the momentum of the onshore monsoon flow (bold red arrow)
is mixed vertically over land due to atmospheric turbulence from solar
heating (eddies). The balance between the monsoon flow and the turbulence
leads to a frontal structure inland from the coast (black dashed line).
This study is structured as follows: Sect. describes
the model system COSMO-ART employed in this study together with the
observational data used for evaluation. In Sect. Atlantic inflow (AI) and
stratus-to-cumulus transition (SCT) as prevailing meteorological
characteristics in SWA are introduced. The results comprise an evaluation of
the modeled cloud properties with aircraft observations
(Sect. ),
the COSMO-ART representation of AI (Sect. ) and the aerosol impact thereon
(Sect. ). The study concludes with a
summary and evaluation of the findings (Sect. ).
Methods and data
Model framework and setup
For this study, the regional-scale model framework COSMO-ART
Consortium for Small-scale Modeling – Aerosols and Reactive Trace
gases; is used. COSMO-ART is based on the operational weather
forecast model COSMO of the German Weather
Service (DWD). The ART extensions allow for an online treatment of
aerosol dynamics and atmospheric chemistry. The model application of this
study is accompanied by significant further developments of the emission
parameterizations regarding mineral dust and BVOCs
. Furthermore, a parameterization for trace gas emissions
from gas flaring from the oil industry was developed to reproduce the specific
pollution conditions of the research area . The model
domain comprises Ivory Coast, Ghana, Togo, Benin and the Gulf of Guinea (red
rectangles in Fig. ). The model setup is summarized in
Appendix .
The simulations using the setup denoted in Table
are the result of a nesting into a 5 km COSMO-ART simulation (blue rectangle
in Fig. a) using the ICON operational forecasts
(approximately 13 km grid mesh size) as meteorological boundary conditions.
These cover the time period 25 June to 3 July to allow for an
aerosol–chemistry spin-up. The meteorological state is initialized every day
at 00:00 UTC.
To assess the sensitivity of the ADE and AIE on the meteorological
conditions, two factors FADE and FAIE were introduced
in COSMO-ART, which allow for the scaling of the total aerosol mass and number
densities, respectively, by simultaneously preserving the underlying aerosol
distribution. All aerosol modes are changed uniformly by the factors. Note
that the aerosol scaling only comes into consideration when deriving the
aerosol optical properties (with respect to ADE) and the aerosol activation
(with respect AIE). All aerosol dynamic processes remain unaffected by the
scaling. Within a simulation the constraint
FADE = FAIE is used to allow for physically
consistent results. Table summarizes the
realizations used in this study. FADE=FAIE=1.0 is used
as the reference case, whereas the factor variations 0.1, 0.25, 0.5, 2.0 and
4.0 are applied to assess the aerosol sensitivity.
Overview of the COSMO-ART realizations
capturing the variation in the aerosol amount with respect to the aerosol
direct effect (ADE) and aerosol indirect effect (AIE). The realization
abbreviations include the prefix AE (aerosol effect) and the corresponding
factor.
Abbreviation
Description of simulation
AE0.1
FAIE=0.1 and FADE=0.1
AE0.25
FAIE=0.25 and FADE=0.25 (clean case)
AE0.5
FAIE=0.5 and FADE=0.5
AE1.0
FAIE=1.0 and FADE=1.0 (reference case)
AE2.0
FAIE=2.0 and FADE=2.0
AE4.0
FAIE=4.0 and FADE=4.0 (polluted case)
The terms clean, reference and polluted should be
seen in a relative sense as part of this experimental setup. They do not
imply general evaluation of the SWA aerosol conditions. The period 2–3 July
was selected due to the intense and persistent NLLS at the Savè supersite
during that time . Furthermore, 3 July is the center of
the monsoon post-onset phase and it is expected that the undisturbed
monsoon conditions favor and support the process studies. Since the
meteorological conditions show less variation from day to day, it is assumed
that, even with a focus on a very short time period, insight can be achieved
that can be generalized at least qualitatively to the length of the
post-onset phase (22 June–20 July).
Observational data
Within the DACCIWA project, an extensive field campaign took place in
June–July 2016 in SWA (Fig. b) .
The time period was selected to capture the onset of the WAM and a period
characterized by increased cloudiness. The DACCIWA ground-based measurement
campaign encompassed the time period from 13 June to 31 July 2016, including
the three supersites Kumasi (Ghana), Savè (Benin) and Ife (Nigeria; red
dots in Fig. b). A complete overview of the DACCIWA
ground-based measurement campaign, their supersites, instrumentation and a
first insight into the available data are presented in .
The DACCIWA airborne measurement campaign captured the time period from 27
June to 17 July 2016 . The focus of this study is on
Ivory Coast and therefore fewer observational data from the DACCIWA campaign
are available for evaluation. However, a substantial evaluation with respect
to meteorology and air pollution is realized with COSMO-ART over SWA with
respect to other time periods and by focusing on the eastern part of the
research area, which is not presented in this study but can be found in
. For this study, observations of liquid cloud properties
from the CDP-100 (cloud droplet probe, data revision 3) of the British
Antarctic Survey (BAS) Twin Otter aircraft on 3 July 2016 are used for a
comparison with COSMO-ART. The CDP-100 is a wing-mounted canister instrument
including a forward-scatter optical system to measure the cloud droplet
spectrum between 2 and 50 µm with a frequency of 1 Hz. The aerosol
number density is evaluated using observations of the ATR42 SAFIRE (Service
des Avions Français Instrumentés pour la Recherche en Environnement)
for 3 July 2016. Additionally, the comparison of the modeled net downward
shortwave and longwave radiation as well as the sensible and latent heat flux
with Savè supersite observations is presented in
Fig. of
Appendix . COSMO-ART reasonably
reproduces the fluxes with lower fluxes with increasing aerosol as expected.
Evaluation of modeled cloud and aerosol properties with aircraft observations
To evaluate the modeled cloud properties, observations of the research
aircraft British Antarctic Survey (BAS) Twin Otter on 3 July 2016
between 10:47 and 14:06 UTC (flight number TO-02) are used, capturing the
Lomé-Savè area. The following figures show the flight path and altitude
(Fig. ) as well as the observed and modeled
cloud droplet number concentration (CDNC; Fig. a, b) and
effective radii (Fig. c, d). The aircraft position at
10:45–11:30, 11:30–12:30, 12:30–13:30 and 13:30–14:06 UTC is shown in
blue, gray, red and black, respectively, for the flight track
(Fig. a) and the altitude
(Fig. b). For a more robust statistical
comparison of the observed and modeled cloud location, the comparison with
COSMO-ART is not realized along the flight track but by using cubes
spanned horizontally by the rectangles around the flight track sections
for 11:00–14:00 UTC (according to the hourly output of COSMO-ART) and
vertically by the lowest 2.3 km a.g.l. in accordance with the Twin Otter maximum
flight altitude during this flight.
The observed and modeled CDNC and effective radii are compared via box plots
(Fig. ) for the flight track sections on 2 July between
11:00 and 14:00 UTC. The box plot colors follow the definition in
Fig. . For 11:00 UTC, the observations are
omitted since the Twin Otter did not penetrate clouds during that time. The
modeled CDNC values (Fig. b) are generally higher than the
observed ones (Fig. b) but both stay below a median of
400 cm-3. The model shows a general trend of increasing median CDNC
with time. This is expected during the SCT, since cumulus clouds tend to have
a higher CDNC than stratus. provided a database of
observed cloud properties of low-level stratiform clouds. For example, for the
Madeira Islands, they identified CDNC around 50 cm-3 for nocturnal
stratiform clouds and around 300 cm-3 for cumulus and stratocumulus.
The smaller CDNC at 14:00 UTC is likely related to a reduced number of
observations in clouds due to the approach of the Twin Otter at Lomé. In
addition to the uncertainty in the modeled aerosol number and number of
activated particles, the limited number of cloud penetrations of the Twin
Otter can also contribute to the deviations. The Twin Otter did not fly
continuously in clouds but performed descents and ascents (see
Fig. b). The modeled increase in CDNC with time
in Fig. b is related to a slight decrease in the effective
radii in Fig. d. Generally, the observed and modeled
median effective radii are around 6 µm and thus in good agreement.
The research aircraft ATR42 SAFIRE also obtained aerosol properties in the
Lomé–Savè area on 3 July 2016 (08:32–13:16 UTC). The flight track and
altitude are presented in Fig. , showing similar flight
patterns compared to the Twin Otter (Fig. ).
Flight track of the ATR42 SAFIRE on 3 July
2016 between 08:32 and 13:13 UTC in the (a) horizontal and
(b) vertical dimension (m a.g.l.). In (a) the
topography (m a.s.l.) is added. The flight track in (a) and (b) is separated
into hourly time steps for subsequent collocation with hourly model data
from COSMO-ART, highlighted in pink (08:32–09:30 UTC), blue
(09:30–10:30 UTC), gray (10:30–11:30 UTC), red (11:30–12:30 UTC) and
black (12:30–13:13 UTC). Furthermore, the arrows in
(a) indicate the flight direction with the takeoff at Lomé, the
flight to Savè and the return to Lomé airport. Note the
meridional compression of the map in (a).
By assuming dry aerosol, Fig. shows the comparison between
COSMO-ART and the spectrometer scanning mobility particle sizer (SMPS) to
evaluate the aerosol number density in the size range 0.02–0.5 µm.
Aerosol number density (AND; cm-3) in the
size interval 0.02 to 0.5 µm as measured by the spectrometer
scanning mobility particle sizer (SMPS) onboard the ATR42 (black) and
modeled with COSMO-ART (solid blue, reference case). The horizontal dashed
blue line shows the COSMO-ART AND divided by 2. The vertical blue dashed
lines indicate the COSMO-ART model output hours, which are compared to
observations.
Figure reveals that the modeled aerosol number density
shows a similar temporal evolution compared to the observations but has a
constant bias, overestimating the observed aerosol number density by a factor
of about 2 (indicated by the blue dashed line). Therefore, in the subsequent
study it has to be considered that the reference case shows higher
aerosol concentrations compared to the current state in SWA as quantified by
the aircraft measurements. Overall, the evaluation reveals that COSMO-ART is
capable of reproducing the aerosol situation on 3 July 2016 over SWA, which is
the basis for further sensitivity studies.
Model representation of the Atlantic inflow (AI)
All the realizations in Table exhibit the AI
phenomenon. Following , the AI front position can be
estimated by the location at which a specific isentrope of virtual potential
temperature θv,s crosses a
specific height hs. For Mauritania, used
θv,s=310 K at the surface pressure level. For this study,
reasonable results are achieved by using the potential temperature
θs=302 K and the height hs=250 m a.g.l.. These values are
selected empirically and are related to the COSMO-ART results of this study.
They do not claim general applicability. However, in contrast to the
definition in , here it seems more appropriate to use a
level elevated from the ground to identify the front, since the frontal
gradients are most prominent at the height of the NLLJ axis (about
250 m a.g.l.), whereas the frontal passage is hard to detect in surface
observations (N. Kalthoff, personal communication).
Figure shows the location of the AI front between
15:00 and 22:00 UTC for 2 July 2016 (Fig. a) and
3 July 2016 (Fig. b) in the reference case.
Localization of the AI front on
(a) 2 July 2016 and (b) 3 July 2016 between 15:00 and
22:00 UTC for the reference case. The front is detected by the arrival of
the isentropic surface θs=302 K at hs=250 m a.g.l.. The
color of the front denotes the time (UTC, bottom legend). The underlying
shading shows the topography of SWA (m a.s.l., legend on the right). The black
diamonds denote the cities shown in Fig. .
Although the focus is on 2 July, 3 July is added to underline the fact that the AI is
a robust feature occurring frequently over SWA, which is also indicated by
the results of . The θs method for the AI front
location is only an estimation, since the potential temperature is also
altered by surface conditions and diabatic effects. We focus on the front
location for the time period 15:00–22:00 UTC that coincides well with the
wind speed patterns as presented subsequently. With the increasing nighttime
cooling over land after 22:00 UTC, the temperature gradient between the AI
postfrontal and prefrontal air mass diminishes, impeding the localization of
the front. Figure shows an inland propagation of
the AI front with time (coded by the line colors). Generally the front is
parallel to the coast. This is most obvious for the domain west of
2∘ W. In contrast, the Lake Volta area and also the area east of the
Atakora Mountains show higher variability in the frontal location. Lake Volta
is a flat area with fixed surface temperatures in the model and reduced
roughness, likely affecting the frontal propagation. For the following
analysis, the focus is set to Ivory Coast (7.5–3.0∘ W).
The distance between the hourly frontal locations reveals that in the evening
(approximately 15:00–18:00 UTC) the propagation velocity is slow at the
beginning but then increases. At 15:00 UTC the front is located about
100 km inland. Before 15:00 UTC the AI front is not detectable, since the
inland area is subject to warming, which shifts θs in the coastal
direction. However, between 11:00 and 15:00 UTC a horizontal wind
speed gradient develops in an area between the coastline and 100 km inland
with enhanced (reduced) values over the Gulf of Guinea (over land).
Meridional–vertical transects of wind speed and potential temperature for
this time period are provided in Appendix . Interestingly, these transects also emphasize the reduced monsoon flow
further inland with the development of the AI front (compare 6–7∘ N
between Fig. a and b–e), which is also shown
schematically in Fig. d. The estimated frontal
propagation velocity for the reference case on 2 July stagnates around
7 m s-1 after 19:00 UTC. This is of the same order of magnitude as
the findings of of 10 ± 1 m s-1 for
Mauritania.
Meridional–vertical transects (m a.s.l.) of
(a) wind speed (shading, m s-1) and (b) turbulent
kinetic energy (TKE; m-2 s-2 in logarithmic scale) along
5.75∘ W (central Ivory Coast) for 2 July 21:00 UTC with respect to
the reference case. The solid black contours show the potential temperature
for 301, 302 and 303 K, while the bold isentrope (302 K) is used for the
identification of the AI front (vertical dashed line). The horizontal dashed
line shows the NLLJ wind speed maximum (jet axis) in the AI postfrontal
area. The gray shading indicates the topography.
To gain further insight into the general structure of the AI,
Fig. shows the meridional–vertical transects along
5.75∘ W (central Ivory Coast) for the reference case.
Figure a shows the horizontal wind speed as shading and
the isentropes of 301, 302 and 303 K as solid black contours. As described
above, the AI front (vertical dashed line) is identified by using the 302 K
isentrope (bold solid line). Several general characteristics can be concluded
from Fig. a.
The AI front marks the location of the strongest horizontal gradients in wind
speed and potential temperature.
The postfrontal wind speeds are significantly higher than the prefrontal
wind speeds. The postfrontal area reveals a band of high wind speeds below
approximately 900 m a.s.l. with a maximum at around 300 m a.g.l.. This is
typical of the NLLJ with the jet axis highlighted by the horizontal dashed
line. The entire postfrontal area is affected by this low-level wind band or
”blanket” when considering the entire SWA domain.
The prefrontal wind speed is vertically more homogeneous than in the
postfrontal area, indicating that the AI front is also a border between a
predominant well-mixed boundary layer prefrontally and ongoing stabilization
postfrontally.
The postfrontal air mass is characterized by cooler
temperatures than the prefrontal area. Therefore the AI frontal passage is
related to an increase in wind speed and a decrease in temperature.
In
agreement with the findings of the flow patterns are
structurally similar to that of a density current in which fast-moving cold
air and surface friction lead to the formation of an overhanging nose and a
head that can extend to higher altitudes than the tail
. Vertical extensions of the head of about 1 km
are found for atmospheric density currents , which agrees
with the flow in Fig. a.
emphasize that the wind surge behind the nose that propagates
close to the ground leads to strong turbulent mixing. This can also be
observed in this process study when focusing on the vertical transect of TKE
(Fig. b). Generally, the postfrontal area shows higher
TKE values than prefrontally. Especially in the area behind the nose, TKE is
enhanced. The strongest turbulence is not within the jet axis (horizontal
dashed line) but below (near the surface) and above due to shear. The
location of the 302 K isentrope, which is used for the AI front detection,
corresponds well with the layer of increased TKE at the upper border of the
AI. It is expected that the near-surface turbulence favors the vertical
mixing of moisture as indicated e.g. by .
The study of reveals that the AI frequently occurs under
undisturbed monsoon conditions over SWA, reaching Savè around 21:00 UTC.
This agrees well with the latitudinal AI front evolution in this study (not
shown).
Aerosol impacts and mechanisms
First insight in the aerosol impact on AI
After describing the general AI properties in
Sect. , this section
presents the first insight into the aerosol influence on AI. To give an
impression of the aerosol change related to the clean, reference and polluted
case, Fig. in Appendix shows the mean total aerosol number
and mass over Ivory coast in the lowest 2 km a.g.l. as a diurnal cycle from
2 July 15:00 UTC to 3 July 21:00 UTC. Figure
shows the horizontal wind speed difference at 250 m a.g.l. (hs) on
2 July 22:00 UTC between the clean and the reference case together with the
corresponding AI front locations. The wind speed difference exhibits a
filament structure in the zonal direction that covers nearly the entire SWA
domain. Furthermore, it propagates inland with time (not shown).
Wind speed difference at 250 m a.g.l. on
2 July 22:00 UTC (m s-1) between the reference and the clean case. The
black dashed (solid) line shows the AI front for the clean (reference) case.
Especially over Ivory Coast, a coherent pattern can be observed with a spatial
shift between the two AI fronts with that of the clean case (black dashed
line in Fig. ) ahead of the reference case front
(black solid line in Fig. ). This anomaly pattern
results from the fact that postfrontal wind speeds are generally higher
than the prefrontal wind speed, as shown in Fig. a. To
assess the aerosol impact on the vertical structure of the AI,
Fig. shows the meridional–vertical transect of wind
speed and potential temperature for the clean
(Fig. a) and polluted case
(Fig. b) in the same way as presented for the
reference case in Fig. a.
Same as Fig. a but
for the (a) clean and (b) polluted case.
When comparing the results between the clean and polluted cases and by
considering the reference case (Fig. a) as intermediate,
aerosol-specific characteristics can be identified in addition to the general
AI characteristics presented in
Sect. . Whereas the
temperature characteristics over the ocean are similar for the realizations,
the inland temperature decreases with increasing aerosol amount. This is
especially visible in the prefrontal area (Fig. ).
In the polluted case the advective cooling is more effective, since the
daytime inland near-surface air is a priori cooler due to a lower sensible
heat flux from aerosol extinction. The reduced ocean–land temperature
gradient in the polluted case leads to reduced temperature contrasts at the
AI front (compare the 302 K isentrope for the clean case, bold line in
Fig. a, and the polluted case, bold line in
Fig. b). With the change in the ocean–land
temperature gradient, the AI frontal position and the NLLJ strength and
vertical extension are altered. The higher the aerosol amount, the more the AI
front is lagging behind and the weaker the NLLJ. In the polluted case the
vertical extension of the inland NLLJ and its wind speed in the jet axis are
reduced by about 150 m and 2–3 m s-1, respectively. The AI frontal
difference averaged over Ivory Coast at 21:00 UTC is 10 km between the
clean and reference case and 20 km between the clean and the polluted case.
With the decrease in temperature with increasing aerosol amount, the
prefrontal wind speed generally increases (in contrast to the postfrontal
area). This leads, with respect to the polluted case
(Fig. b), to some areas of increased wind speed in
the prefrontal area at a height that is typical of the NLLJ. Generally, the
polluted case is characterized by a blurring of the prefrontal and postfrontal
temperature and wind speed differences.
Temporal evolution of the
inland propagation of the AI front via the
distance from the coast (km) on 2 July 2016 between 15:00 and 22:00 UTC,
spatially averaged over Ivory Coast (7.5–3.0∘ W) for the six
experiments in Table . Dashed lines denote
realizations with aerosol amounts below that of the reference case (black
solid). The gray line shows the frontal propagation of the polluted case by
using θs=301 K instead of 302 K for the front detection.
The temporal evolution of the median coastal distance of the AI front over
Ivory Coast is presented in Fig. for the six
realizations. The polluted case (solid red) denotes a special case in
Fig. . The other realizations with scaling factors
between 0.1 and 2.0 show a systematic behavior. As expected, the front
propagates inland with time. The higher the aerosol amount (dashed blue to
solid green) the slower the inland propagation. This leads to a median
spatial difference of about 27 km between factor 0.1 (dashed blue) and 2.0
(solid green) at 22:00 UTC.
Furthermore, Fig. reveals two regimes, one before
and one after 17:00 UTC. For the latter the frontal propagation diverges
according to the aerosol amount as described above. Before 17:00 UTC an
opposite behavior can be observed leading to the circumstances that with less
aerosol the AI front is closer to the coastline. Therefore the starting point
for the inland propagation at 15:00 UTC is not equal for all realizations
but a reversed order can be observed compared to the situation at 22:00 UTC.
The underlying mechanisms for the occurrence of these two regimes that switch
around 17:00 UTC are assessed in the subsequent section. With a further
increase in the aerosol amount, as realized in the polluted case (solid red
line in Fig. ), the ocean–land temperature
gradient is reduced as shown in Fig. b. The AI front
evolution is therefore less pronounced than for the other realizations. In
the eastern part of Ivory Coast the location of θs persists inland
and does not form a coherent front near the coast. Averaging the frontal
location over Ivory Coast therefore leads to a temporal evolution, which does
not follow the behavior of the other realizations. By reducing the benchmark
of θs from 302 to 301 K, the polluted case also follows the trend of
a weaker frontal propagation with increasing aerosol (gray line in
Fig. ). In this polluted case, with its cooler
lower layers, the 301 K isentrope better represents the frontal location, as
is also visible in Fig. b.
Aerosol–AI impact mechanism
After diagnosing the characteristics of AI and the AI changes with changing
aerosol amounts in Sect.
and , respectively, the
question of the underlying feedback mechanism arises. The stationarity of the
AI front near the coast in the early afternoon is related to the balance
between the onshore-directed monsoon flow over the ocean and the turbulence
over land e.g.. Therefore the change in turbulence
can alter the balance and lead to differences in the AI front propagation
(turbulence mechanism). With increased aerosol amounts, a near-surface
cooling (relative to the realizations with less aerosol) is expected during
daytime (as observed in Fig. b) either due to the
ADE via scattering and absorption on aerosol particles or due to AIE via
an increased albedo with reduced cloud droplet effective radii (Twomey
effect). A reduced surface heating with increased aerosol amounts might lead
to an increase in surface pressure. By considering the fact that the
sea surface temperature (SST) is fixed in COSMO-ART and that the surface
temperature over the ocean will therefore not be subject to substantial
changes, a reduction in the land–sea pressure gradient can be expected, which
could affect the AI front propagation (pressure gradient mechanism). In order
to further elaborate this, Fig. shows
the spatial distribution of total cloud water
(Fig. a) and precipitation
(Fig. b) for the reference case on
2 July 15:00 UTC. The red line denotes the AI front. Clouds and
precipitation occur primarily in the AI postfrontal area over Ivory Coast
due to convergence and vertical lifting, upstream of mountain areas due to
topographic lifting (especially at the Mampong Range and the Atakora
Mountains) and via localized convection (primarily in the AI prefrontal area
over Ivory Coast).
(a) Total cloud water
(kg m-2) and (b) precipitation (mm) on 2 July 15:00 UTC for
the reference case. The red line denotes the AI front.
Surface meteorological quantities over
SWA on 2 July 15:00 UTC as differences between the reference and the clean
case (AE1.0–AE0.25), (a, c, e) including cloudy and cloud-free
areas and (b, d, f) over areas that are simultaneously cloud free in
the clean and reference case. (a, b) Surface net downward shortwave
radiation difference (W m-2), (c, d) 2 m temperature
difference (K) and (e, f) sea level pressure difference (hPa). The
black solid lines denote the location of the reference case AI front.
To shed light on potential effects from the turbulence mechanism and the
pressure gradient mechanism, Fig. shows the
differences in surface net downward radiation
(Fig. a), 2 m temperature
(Fig. b) and sea level pressure
(Fig. c) between the reference and the clean case.
In addition, Fig. b, d and f present the same
variables but for the areas that are cloud free in both realizations to
exclude effects from displaced clouds and to highlight the ADE in a
cloud-free environment.
The differences in surface meteorological quantities presented in
Fig. reveal a clear signal. The following values
in brackets indicate the median and the 99th or 1st percentile of the surface
quantities considering the cloud-free inland area. With increasing aerosol,
more downward shortwave radiation is scattered and absorbed, leading to an
average decrease in surface net downward shortwave radiation (-37 and
-185 W m-2; Fig. b). The decrease in
incoming shortwave radiation leads to a decrease in 2 m temperature (-0.5
and 2.5 K; Fig. d). The temperature decrease
furthermore leads to a domain-wide inland surface pressure increase (+0.16
and +0.45 hPa; Fig. f). Omitting the negative
pressure anomaly over Lake Volta due to the fixed SST, slightly higher
domain-wide pressure anomalies are found (+0.17 and +0.45 hPa;
Fig. f).
To prove the hypothesis that the surface pressure difference is caused by the
temperature difference, the surface pressure over Ivory Coast at 15:00 UTC
(over land and in cloud-free areas) is estimated from the pressure and
temperature at 850 hPa using the barometric equation. This approach yields a
spatially averaged value of +0.12 hPa that broadly agrees with the modeled
value of +0.17 hPa (compare Fig. f). It can be
concluded that the pressure changes are dominated by changes in low-level
temperature. In the cloudy areas around the SWA mountains a higher pressure
difference can be observed (Fig. e), indicating
that cloud radiative effects are also contributing. To assess whether the
reduction of incoming shortwave radiation due to clouds is related to a
change in the cloud water content and therefore the optical thickness of the
clouds or due to the Twomey effect with a change in cloud droplet number
concentration (CDNC) and effective radius, Fig. exhibits the
empirical cumulative distribution function (ECDF) with respect to the
COSMO-ART realizations of the CDNC (Fig. a), cloud droplet
effective radius (Fig. b), cloud water (Fig. c)
and precipitation (Fig. d). This figure corresponds to the
cloud and precipitation patterns presented in
Fig. for 2 July 15:00 UTC.
Empirical cumulative distribution function (ECDF) of
(a) CDNC (cm-3), (b) cloud droplet effective radius
(µm), (c) cloud water (g kg-1) and
(d) precipitation (mm) for the six experiments in
Table considering the full vertical column over the
inland area of SWA on 2 July 15:00 UTC. The circles and dots highlight the
median values. Dashed lines and circles relate to realizations with less
aerosol than the reference case and solid lines and dots refer to simulations
with aerosol amounts greater than or equal to the reference case.
A strong susceptibility of the CDNC and effective radii towards a change in
the aerosol amount can be observed (Fig. ). The factor
variation from 0.1 to 4.0 leads to an increase in the median CDNC by 1
order of magnitude from 100 to 1000 cm-3 (Fig. a) and a
reduction in the median effective radius from 9 to about 3.5 µm.
When considering the green and red curves in Fig. , which are
related to an aerosol change symmetrically around the reference case (black),
the effect on the CDNC and effective radius is nonlinear
e.g.. An aerosol increase (solid green and red lines)
has significantly stronger impacts than the aerosol decrease (dashed green
and red lines).
In contrast to these remarkable changes, the effect on cloud water and
precipitation (Fig. c, d, respectively) is insignificant.
Except for the polluted case (solid red lines) all realizations show similar
ECDFs, indicating that the aerosol increase neither leads to a cloud water
increase due to precipitation suppression or due to enhanced water vapor
condensation on the aerosol particles nor a cloud water decrease via enhanced
evaporation. The polluted case shows a tendency of precipitation decrease
(increase) for the weak (strong) precipitating areas related to an increase
(a decrease) in cloud water. This effect of greater local rainfall amounts is
in agreement with the findings of , likely via the
convective-cloud invigoration mechanism. However, the deviations from the
other realizations are small. Figure reveals that the aerosol
impact on radiation via the Twomey effect is very likely dominating the
cloud–radiation interaction, whereas the cloud optical thickness impact (via
a change in the amount of cloud water) is of minor importance. The weak
precipitation response to the changing aerosol amount underlines the finding
that radiation and its variation are the key players in the observed
changes over SWA due to the ADE in and outside of clouds and the Twomey
effect. There is ongoing work within DACCIWA with respect to large eddy
simulations (LES) of aerosol–atmosphere interactions. It will be of interest
to see whether the COSMO-ART results are consistent with the outcomes of
these studies on smaller scales.
The turbulence and pressure gradient mechanisms are counteracting. With
respect to the turbulence mechanism, a reduced heating weakens the turbulence
in the PBL. Therefore the AI balance between the monsoon flow and the inland
turbulence is shifted to the monsoon flow, favoring an inland propagation.
Regarding the pressure gradient, a reduced heating decreases the land–sea
pressure gradient, shifting the AI balance in the opposite direction and
suppressing the inland propagation. When going back to the temporal evolution
of the coastal distance of the AI front in Fig. ,
both mechanisms are evident and cause the two observed regimes before and
after 17:00 UTC. The first regime includes the stationary phase of the AI
front near the coast. With the decrease in incoming solar radiation with
increasing aerosol, the turbulence decreases and therefore the stationary
front location shifts inland. Unfortunately, the AI front detection via the
θs method fails for the time period earlier than 15:00 UTC.
Therefore the total difference in the stationary AI front location with
changing aerosol cannot be assessed. Nevertheless, it is interesting that the
location of the AI front during its stationary phase over Ivory Coast could
be used as a proxy for the aerosol burden in that area (under otherwise
identical conditions).
For the time period after 17:00 UTC when turbulence has decreased
sufficiently, the pressure gradient mechanism dominates because the AI front
in the clean case, although lagging behind at 15:00 UTC, is 11 km ahead
of the reference case at 22:00 UTC (Fig. ).
Temporal evolution of the differences in
surface sensible heat flux (red, W m-2), surface latent heat flux
(green, W m-2) and surface pressure (blue, hPa) between the reference
and the clean case (dashed line) and between the polluted and the reference
case (solid line) for the time period 2 July 15:00–21:00 UTC spatially
averaged for the AI prefrontal area over Ivory Coast as defined by the
θs method. The sensible heat flux is defined positive downward.
Cloud water (g kg-1, shading)
along the Lomé–Savè (L and S, respectively) vertical transect (m a.s.l.) for the temporal
evolution on 3 July (a, b) 10:00 UTC and (c, d)
11:00 UTC considering (a, c) the clean case and (b, d)
the reference case. The red shading reflects instability
(dθ / dz<0) to highlight the evolution of the CBL. The black
solid (red solid) line denotes the height of the CBL in the reference case
(polluted case), which is simultaneously added to the panels on the left- and right-hand side.
Figure summarizes the counteracting components
of turbulence and pressure difference that govern the inland propagation of the
AI front by comparing the temporal evolution of the differences between the
reference and clean case (dashed lines) and the polluted and reference case
(solid lines) in surface sensible heat flux (red, positive
downward), surface latent heat flux (green, positive downward) and surface
pressure (blue) spatially averaged for the AI prefrontal area over Ivory
Coast.
The temporal evolution clearly shows that the sensible and latent heat flux
differences (and the absolute value itself) decrease strongly with time in
contrast to the pressure differences. After sunset (e.g. 18:24 UTC at
Kumasi) the sensible and latent heat flux is negligible but the pressure
differences continue. In fact, the altered land–sea pressure gradient is
maintained until the AI front and the subsequent cool air mass have passed the
area and compensates for the differences (not shown). It is expected that the
high moisture in the monsoon layer prevents it from cooling significantly and
reducing the differences that developed during daytime. The factor increase of
4 from 1.0 to 4.0 reduces (increases) the sensible heat flux (sea level
pressure) more than the increase from 0.25 to 1.0, in agreement with the
findings of the sensitivity of CDNC and effective radius in
Fig. .
The monsoon flow over SWA is driven by the temperature gradient between the
cool SSTs over the eastern equatorial Atlantic Ocean that are fixed in the
model and the Saharan heat low that is not part of the modeling domain. With
this location of the modeling domain, changes in the aerosol amount can serve
as an amplifier for the monsoon flow that is able to increase or decrease the
temperature gradients and thereby the AI front characteristics. In agreement with this,
show that the sea breeze front over Cameroon weakens with
enhanced aerosol number concentration. Longwave cooling is not significantly
reduced, likely due to water vapor saturation in the monsoon layer (not
shown). In contrast, the coherent differences in 2 m temperature and
pressure, which were observed at 15:00 UTC
(Fig. ), also persist during nighttime. The
daytime heating of the land, stronger in the clean case and weaker in the
polluted case, persists during night and exceeds potential effects from
longwave cooling. The differences between the realizations are finally
equalized by the passage of the AI front and postfrontal air mass.
Aerosol–SCT impact mechanism
In addition to the aerosol impact on AI, impacts on the SCT can also be
observed. Figure shows the vertical transect of
modeled cloud water between Lomé and Savè
(Fig. b) regarding the clean case (left) and the
reference case (right) for 2 July 10:00 UTC (top) and 11:00 UTC (bottom).
The red shaded area below the cloud layer denotes the development of the
convective boundary layer (CBL) identified by dθ / dz<0. The
black (red) solid line shows the top of this unstable layer regarding the
reference (polluted) case to allow for comparison between the three
realizations.
Spatial average
(8∘ W–3.5∘ E, 5–10∘ N) of (a) total
cloud cover (%), (b) total cloud water (kg m-2) and
(c) cloud base height (m a.g.l.) for the temporal evolution between
2 July 21:00 UTC and 3 July 19:00 UTC. The cloud cover is detected by
nonzero values of total cloud water. A value of 60 % denotes that
60 % of the domain is covered by clouds. For the spatial average of total
cloud water, values below 10 g m-2 were omitted. The cloud base height
is detected via the lowest height above ground level with a nonzero cloud water value. The
blue dashed, black solid and red solid lines denote the clean, reference and
polluted case, respectively.
After sunrise the CBL starts to evolve. Via the same mechanism as described
in Sect. , less shortwave radiation reaches
the ground with increased amounts of aerosol and therefore the surface
sensible and latent heat fluxes also decrease. This leads to a decelerated daytime
CBL development and with that to a reduction of the cloud base height
(Fig. , left). To underline the fact that this effect is
visible not only in the Lomé–Savè transect but also for the entire SWA region,
Fig. shows the temporal evolution of the
spatial average of total cloud cover
(Fig. a), total cloud water
(Fig. b) and the cloud base height
(Fig. c) over SWA for the clean (blue
dashed), reference (black solid) and polluted case (red solid).
Between 21:00 UTC and sunrise (05:30 UTC) the cloud cover
increases (Fig. a) due to clouds that are
advected onshore or develop inland. This is linked with a reduction in the
mean cloud base (Fig. c). Between 01:00
and 07:00 UTC the clean case shows lower cloud base values than the
reference and polluted cases. A detailed analysis reveals that this deviation
is not related to NLLS but to mid-level clouds over the Lake Volta basin and
in the northwestern part of the domain (not shown). After sunrise it is
assumed that the NLLS intensifies via the vertical mixing of moisture in the
developing convective PBL. With respect to the spatial average in
Fig. c this leads to a reduction in mean
cloud base height. The maximum cloud cover
(Fig. a) is related to the minimum cloud
base (Fig. c), underlining the dominance
of NLLS. After reaching the cloud cover maximum, the SCT continues, which is
related to a lifting of the cloud base and a decrease in cloud cover. For
this SCT a clear temporal shift of about 1 h can be observed between the
clean and the reference case and 2 h between the clean and the polluted
case. The realizations with increased aerosol amounts react slower to the
insolation after sunrise, reach the NLLS maximum coverage later and start
later with the SCT as observed for the Lomé–Savè transect in
Fig. . After 15:00 UTC this finally leads to a
cloud cover that is increased compared to the clean case
(Fig. a), implying an additional reduction
in surface shortwave radiation that can be used for further cooling the
surface and decelerating the AI front. The cloud water
(Fig. b) shows a similar temporal shift
with increasing aerosol amounts as for the cloud cover and cloud base. The
weakening of the SCT with a higher aerosol burden leads to reduced amounts of
cloud water after 13:00 UTC (Fig. b),
likely due to reduced convective activity. However, during nighttime, the
polluted case uniformly shows higher cloud water values than the clean and
reference cases.
Figure in Appendix
shows the cloud analysis
restricted to clouds below 1500 m a.g.l. to assess the sensitivity of the
spatial averaging towards the considered vertical column. The cloud cover
(Fig. a) shows a similar temporal
evolution as presented in Fig. a. The
cloud water and cloud base temporal evolution in the lowest 1500 m a.g.l.
(Fig. b, c) show less variation between
the realizations compared to Fig. b and c.
However, the temporal shift in the onset of the SCT is obvious in both
figures. As expected, the initiation of the cloud base increase via the SCT
occurs earlier when considering only the clouds below 1500 m a.g.l. in the
averaging (compare
Fig. c with Fig. c).
The aerosol feedback process study simulations presented in
Sect. and
revealed several mechanisms relevant for SWA affecting the
location and propagation of the AI front and the temporal evolution of the
SCT. In the following section a proposal for a conceptual model will be
presented.
Conceptual model of aerosol–atmosphere interactions in SWA
This section aims to synthesize the findings that have been obtained with
this aerosol feedback process study. We showed that AI affects the entire SWA
domain through the course of the day via cold air advection, the NLLJ that
can be found in the AI postfrontal area, and convergence-induced convection
and precipitation. Two distinct meteorological responses to changes in the
amount of aerosol via ADE and the Twomey effect were identified: a
spatial shift of the Atlantic inflow (AI) and
a temporal shift of the stratus-to-cumulus transition (SCT).
Scheme of the aerosol-related atmospheric feedbacks summarizing the
findings of the process study simulations on 2–3 July 2016. The main loop is
labeled AI (Atlantic inflow) and the additional loop SCT (stratus-to-cumulus
transition). The small arrows in the upward and downward direction denote whether
a quantity reacts with a decrease (downward) or increase (upward) to the
increase in aerosol mass and number (blue) as the initial perturbation. The
red arrow shows the linkage between AI and SCT via the decrease in shortwave
radiation and surface temperature and a potential pathway for a negative
feedback of SCT on AI.
Figure shows a conceptual scheme that combines both
responses. The bigger loop is related to the first response (AI) and the
smaller loop to the second (SCT).
Following the AI loop in Fig. , the increase in the
amount of aerosol (number and mass) by a factor of 4 (0.25 to 1.0) is the
initial perturbation of the system. The subsequent numbers in parenthesis are
related to the median value over Ivory Coast (cloud-free inland areas)
on 2 July 15:00 UTC to provide guiding values for the denoted changes.
Via ADE the aerosol increase leads to a decrease in surface net downward
shortwave radiation (-37 W m-2) and surface temperature (-0.5 K).
Previous studies showed that until the early afternoon, the AI front is
stationary near the coast due to the balance between the monsoon flow from
the sea and the sensible heat flux (turbulence) over land. With the afternoon
decrease in sensible heat flux, the AI front propagates inland. This study
showed that the decreased surface heating leads to a positive pressure
anomaly over land (+0.16 hPa) and with that to a reduced land–sea pressure
gradient. The latter is more persistent than the sensible heat flux that
vanishes around sunset (compare Fig. ). The reduced
pressure gradient leads to a reduced AI frontal velocity and therefore to a
southward shift in the case of increased aerosol (11 km on 2 July
22:00 UTC). The postfrontal area is characterized by stronger wind speeds
in the lowest 1000 m a.g.l. with the maximum around 250 m a.g.l. that is
characteristic of the NLLJ. Therefore an AI frontal shift leads to a shift in
the NLLJ inland propagation. Since the AI frontal propagation is linked to
convergence-induced convection and convective precipitation, a
meridional shift of the AI-related precipitation is also observed. These effects
are primarily related to the afternoon but the AI frontal and NLLJ shift also
leads to a shift in the inland propagation of coastal NLLS with a similar
spatial magnitude as observed for the AI front (not shown).
The AI loop denoted in Fig. includes a further
mechanism related to the counteracting effects of the monsoon flow over the
ocean and the sensible heat flux over land in the stationary phase of the AI
front. With increasing aerosol the inland sensible heat flux decreases, which
relocates the front farther from the coast. Therefore with increased aerosol
the AI frontal inland propagation starts farther from the coast but is slower
than in the low aerosol case due to
the reduced land–sea pressure gradient as soon as the turbulence has declined after sunset.
The SCT loop is coupled to the AI loop via the decrease in surface shortwave
radiation and temperature. This study pointed out that the deficit in surface
heating due to ADE and cloud brightening via the Twomey effect lead to a
decrease in sensible heat flux and therefore to a delayed development of the
CBL. The lower CBL height leads to a lower cloud base and therefore to a
later SCT and breakup of the closed cloud layer to scattered cumuli (compare
Fig. a). Both loops are initialized after
sunrise with the input in shortwave radiation. The SCT loop implies a
positive cloud cover anomaly after 15:00 UTC with increasing aerosol. Sunset
is around 18:30 UTC. Although the AI front already starts penetrating inland
around 14:00–15:00 UTC, approximately a 3.5 h period is available for an
additional surface cooling from the later cloud-layer breakup. This is a
pathway for a further deficit in surface shortwave radiation and surface
heating that could further weaken the AI loop as emphasized by the red arrow
in the opposite direction in Fig. . However, the latter
coupling between the two loops is only hypothesized. A future study needs to
assess the significance of the contribution to inland surface pressure
increase that
comes from the deficit in shortwave heating via the later cloud-layer breakup.
The mechanisms described in Fig. raise questions
about the possibility to generalize these results. The AI feature is very
likely a regular phenomenon under undisturbed monsoon conditions as confirmed
by previous studies that focus on longer time periods. Within this process
study the AI frontal shift was obvious for both days in the evening. However,
the results presented above are related to Ivory Coast, which shows a more
coherent AI frontal pattern than the eastern part of the domain, likely
related to topographic features. This conceptual picture reveals radiation as
a key player governing the feedbacks either via ADE or via a change in cloud
albedo (Twomey effect). The AIE assessment within the process study reveals
the known mechanisms, in particular the increase (decrease) in the CDNC
(effective radius) with an increase in the aerosol number concentration.
However, the AI-related clouds and precipitation reveal, aside from a
meridional shift, no statistically significant difference. Although, the
possibility for substantial effects from AIE cannot be excluded, a conceptual
view as presented for the radiative effects has to be left for subsequent
studies.
Conclusions
This study focused on southern West Africa (SWA) to assess the implications
of aerosols on clouds and atmospheric dynamics using a process study with the
regional model COSMO-ART on 2–3 July 2016, a time period in the
well-established West African monsoon (WAM) without impacts of mesoscale
convective systems. The results revealed an elongated front over SWA that
develops during daytime between the monsoon flow over the ocean and the
turbulence over land being stationary near the coast around noon and
propagating inland in the evening. This phenomenon has been identified for
several African coastal regions and was conceptually separated from the
classical land–sea breeze. Based on we used the term
Atlantic inflow (AI). The AI postfrontal area is characterized by a
distinct decrease in temperature and increase in wind speed and relative
humidity,
emphasizing that the nocturnal low-level jet (NLLJ) in SWA is a widespread phenomenon related to AI.
By changing the aerosol number and mass in COSMO-ART, the aerosol direct effect
(ADE) and indirect effect (AIE) was quantified, indicating a considerable
sensitivity of the AI frontal location to changes in the aerosol amount.
With increasing aerosol the AI front shows reduced propagation velocities
over Ivory Coast, leading to frontal displacements of 10–30 km.
modeled a similar behavior for the sea breeze over
Cameroon. Longwave cooling influences the AI prefrontal area but even after
sunset the positive temperature anomaly from daytime solar heating persists
and dominates. Effects on SST are not considered in this study. In cases
considering the impact of reduced incoming solar radiation on the SST with
increased aerosol, stronger land–sea temperature gradients are expected.
Therefore, the estimations of this study with fixed SST denote the upper
limit of the magnitude of the effects. However, this model setup in numerical
weather prediction mode is less appropriate to study effects on SST. Global
models on a longer timescale are more suitable to provide added value to
this question.
In addition to the effect on AI, the decrease in near-surface heating leads
to a delayed stratus-to-cumulus transition (SCT) via a later onset of the
convective boundary layer. We synergized this subtle aerosol–atmosphere
feedback in a new conceptual model combining the AI and SCT loops
(Fig. ). Furthermore, we hypothesize that the
additional radiation deficit due to the later SCT leads to a further
weakening of AI.
The results exhibit radiation as the key player governing the aerosol
affects on SWA atmospheric dynamics during boreal summer via ADE and the
Twomey effect. In contrast, effects on precipitation are small.
identified AIE as relevant for the SCT over tropical
oceans with an accelerated transition with increasing aerosol. This study
identified ADE and the Twomey effect as predominant for the SCT over tropical
land areas with a decelerated transition with increasing aerosol. The
importance of ADE in monsoon-related processes has also been shown by
for the Indian monsoon. For northern India, they reveal that
the ADE dominates large-scale aerosol–monsoon interactions. A detailed
literature study suggests that in the current aerosol research, ADE and
cloud–radiation interactions are underrepresented. Especially with respect to
monsoon regimes, special focus should be on ADE. Whether the AI frontal
displacement is detectable in long-term observations is left for subsequent
studies. A potential strategy is the analysis of the AI front around noon via
remote sensing cloud observations from past to present by assuming a positive
trend in the aerosol burden. It is expected that the daytime AI front
location has shifted landwards from the past to current conditions, but
other phenomena (e.g. decadal SST variations) also have the potential to affect
the front location.