Aerosol–cloud interactions (ACIs) are considered to be the most uncertain
driver of present-day radiative forcing due to human activities. The
nonlinearity of cloud-state changes to aerosol perturbations make it
challenging to attribute causality in observed relationships of aerosol
radiative forcing. Using correlations to infer causality can be challenging
when meteorological variability also drives both aerosol and cloud changes
independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide “opportunistic experiments” (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.
Introduction
Numerous studies have attempted to quantify the different aerosol effects on
warm liquid clouds. Increases in aerosol loading increase cloud drop number
and decrease cloud drop size the so-called Twomey
effect;. However, microphysically driven adjustments in cloud
properties like areal coverage and cloud water path that result from increased
drop number remain uncertain. A reduction in precipitation due to smaller
cloud droplets can moisten the atmosphere and enhance cloudiness the
so-called lifetime effect;. At the same time, a larger
number of smaller cloud droplets can also enhance the cloud-top evaporation
and dry air entrainment as well as reduce the sedimentation
of cloud droplets , thereby leading to feedbacks which
can decrease cloudiness. There is ample evidence of aerosol-driven
precipitation suppression in stratocumulus , but the effects
of this process on liquid water path and cloud fraction remain
uncertain. Cloud adjustments may therefore either compound or counteract the
cloud albedo (i.e., reflectance) change due to the “Twomey effect” of higher
cloud droplet number and smaller droplet size. and
confirmed these effects and their complexities using
multiple lines of evidence. While mixed and ice phase clouds are critical to
Earth's radiation budget, and change in response to changing aerosol
concentrations, we choose to focus on warm liquid clouds due to the wealth of
existing knowledge and relative simplicity of this system.
A difficulty in understanding these aerosol–cloud interactions is that while
it is easy to control experiments in model simulations (where aerosol
populations are perturbed in a controlled manner in the same environment),
this is not possible in the real world. Comparison of two different clouds
with different aerosol populations requires understanding how the
co-variability in aerosols and “meteorology” (defined as the temperature,
specific humidity, turbulence, vertical motion, etc) affects cloud
microphysical properties (liquid water path, drop number/size) and ultimately
cloud radiative effects.
Schematic showing examples of the aerosol effect on boundary layer liquid clouds from some prominent natural laboratories found over the globe. Figure was adapted from .
However, when emissions perturb aerosols in “controlled” (fixed or defined)
conditions with minimal changes to meteorology, it is possible to use
observations to understand ACI processes and to quantify the magnitude of
anthropogenic aerosol radiative effects. These opportunistic experiments are
defined as injections of aerosols into an environmental regime
(“laboratory”) where the unperturbed state is to some extent known. While
these are sometimes called “natural laboratories”, some are natural
(e.g., volcanoes) while some are human-caused (e.g., industrial plumes and ship
tracks). In this review, we will use the term “opportunistic” to apply to
both. A laboratory refers to a regime (e.g., ship or volcanic emissions)
and type of emission, while “experiment” refers to a particular case (e.g., a
ship track or a shipping corridor).
The first type of experiment dates back to the 1960s when ship tracks,
curvilinear cloud features that can be traced back to the movements of
individual ships, were identified in Television Infrared Observation Satellite
(TIROS) imagery of marine stratus decks . Since then
several similar opportunistic experiments have been explored, such as aerosols
emitted from industrial sources, volcanoes, and biomass burning
plumes. Opportunistic experiments also include anthropogenic aerosol changes
due to particular events, such as emission changes due to the 2008 Beijing
Olympics, the “Great Recession” of 2007–2009, or even the COVID-19
pandemic. Finally, weekly cycles or long-term decadal trends have been used to
understand aerosol radiative forcing and cloud modification on
local to regional scales .
This review will analyze different types of opportunistic experiments and how
they can be used to test hypotheses about ACI and to quantify their effects to
better constrain total anthropogenic aerosol forcing for warm boundary layer
clouds. Some examples of mixed-phase, ice cloud, and convective cloud
opportunistic experiments are discussed, but due to their episodic nature,
heterogeneity of convection, and difficulty of detection, we primarily focus
on warm cloud physics. Other examples such as aircraft contrails, dust events
(e.g., Saharan air outbreaks), and cloud seeding to intentionally affect
precipitation are also beyond the scope of this work.
The review is organized around two main types of opportunistic experiments
covering different spatial scales. The first type is based on relatively small-scale perturbations in which “in-plume and out-of-plume” comparisons are
possible (Sect. 2.1, 2.2, and to some extent 2.3 and 2.4). These cases
provide opportunities to determine the unperturbed case (out-of-plume
conditions), and thus, directly evaluating the response of clouds to aerosol
perturbation under similar meteorological conditions. The second type of
opportunistic experiments cover events with much larger spatial scales or
comparing situations that are very distant in time (Sect. 2.5, 2.6, 2.7, and
2.8). In these cases the relevance to the climate scale is easier to establish,
but it is much more difficult to determine the unperturbed/reference
conditions. Then we provide a linked summary database of different experiments
that have been used in previous studies (Sect. 3). Section 4 brings together
the different experiment types to synthesize qualitative and quantitative
aerosol effects across methods and experiment types. In Sect. 4 we also
examine the factors controlling the cloud response to aerosol perturbations
and the challenges of using small-scale perturbations to constrain ACI across
spatiotemporal scales. Section 5 provides a synthesis of these findings and
their conclusions.
Ship tracks across the Bay of Biscay are shown in true color imagery from MODIS on the Aqua satellite on 27 January 2003 at 13:40 UTC.
Overview of opportunistic experiments
Figure highlights several key laboratories of significant
interest and their influence on clouds and potentially climate. A wealth of
papers describing cloud microphysical properties and their changes associated
with each laboratory is described in Table S1 in the Supplement, and datasets
generated for many of these papers are listed in Table S2. The following subsections provide a brief description of some of
the primary characteristics of each laboratory and their strengths and
limitations for teasing out process-level understanding of aerosol–cloud
interactions.
Polluted cloud tracks across spatial and temporal scales. Snapshot
MODIS daytime near-infrared composite satellite images are shown in panels (a–c): the polluted clouds are shown in bright greyish colors and unpolluted clouds in yellowish-brownish colors. Night lights are overlaid in white. (a) Two localized aerosol sources induce ship-track-like polluted cloud lines in Newfoundland, Canada, on 17 December 2014. The near-surface wind is blowing from the northeast based on MERRA reanalysis. (b) Emissions from Moscow, Russia, induce a more than 100 km wide polluted cloud area on 11 October 2016. Near the surface wind is blowing from the east based on MERRA reanalysis. (c) Many aerosol sources in the Great Lakes region, USA, induce a more than 500 km wide polluted cloud area on 3 January 2016. Near the surface wind is blowing from the northwest based on MERRA reanalysis. In panel (d) AVHRR cloud droplet effective radius data averaged over the years 1982 to 2015 are shown for the larger Moscow region. In the long-term average data, the cloud droplet effective radius decreases by 1 to 1.5 µm in the Moscow region compared to the nearby less polluted clouds. In panel (d) brownish colors represent larger droplets, and white colors represent smaller droplets.
Figure shows an example of ship tracks off the coast of Portugal from the MODerate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite. Generally, as the spatial domain of the aerosol perturbation increases (e.g., from individual ship tracks to shipping corridors to the entire globe) different methodologies are required to compute a counterfactual “background” (or unperturbed) cloud state. For example, in some experiments like ship, fire, and volcano tracks the counterfactual can easily be established by selecting unpolluted clouds in nearby locations in the same cloud regime. Establishing the observed counterfactual in opportunistic experiments involving large smoke plumes, volcanic eruptions, and shipping corridors is not as straightforward. The difficulty of attribution changes how effectively each opportunistic experiment can be studied and what kinds of conclusions can be drawn. Some prominent examples of natural laboratories and their associated opportunistic experiments (Fig. ) are described below.
Shipping emissions
For decades, ships burning high-sulfur-content fuels have plied the world's
oceans, emitting aerosol and aerosol-precursor gases in regions with
relatively low levels of natural aerosol . The
world's major shipping routes have elevated concentrations of SO2
emissions according to the Emissions Database for Global Atmospheric Research
(EDGAR), version 5.0 , shown in Fig. S1 in the
Supplement. Below we discuss some of the key opportunistic experiments: ship
tracks, shipping corridors, and the role of policy change in ship emissions
and associated radiative effects.
Ship tracks
Ship tracks themselves have been studied since the mid-1960s, as soon as they
were first identified in TIROS-VII imagery . The TIROS
series was NASA's first experiment with systematic satellite remote sensing of
the Earth system. Multiple hypotheses, such as that the tracks were aircraft
contrails or even secret missile tests, were considered before they were
correctly identified as resulting from ships traveling through conditions of
shallow, cloudy marine boundary layers (MBLs) with low background aerosol
levels . In the late 1980s,
satellite and aircraft measurements
confirmed the qualitative effects of ships on cloud properties hypothesized
earlier. The apparent increase in liquid water content and decrease in
drizzle-sized droplets in the ship tracks sampled by as
well as cloud reductions along the edges of ship tracks from local-scale
circulations served as a partial inspiration for the
modeling work generally credited with establishing the cloud adjustment
(“drizzle suppression”) hypothesis .
More systematic measurements of ship tracks were taken during the Monterey
Area Ship Track experiment (MAST) campaign in the mid-1990s
. An analysis
of 131 ship tracks studied in MAST showed that the tracks tended to form in
shallow boundary layers (300–750 m) and last for 7 h on
average, with many lasting longer than 12 h. Cloud
condensation nuclei (CCN) emitted from the ships directly and potentially
coated by sulfate (from the SO2 co-emitted with carbonaceous particles
in fuel burning) were found to be responsible for influencing cloud
properties, rather than any effects from sea salt produced in ships' wakes or
from the temperature or moisture perturbations associated with fuel burning
. Several subsequent campaigns continued studying
ship impacts on clouds, including the Marine Stratus/Stratocumulus Experiments
(MASE) I and II , the Eastern Pacific Emitted Aerosol
Experiment (E-PEACE) , and the Nucleation in California
Experiment (NiCE) . The majority of these campaigns
were conducted using the Center for Interdisciplinary Remotely-Piloted
Aircraft Studies (CIRPAS) Twin Otter aircraft, which would fly directly to
ships and then conduct zigzag or racetrack patterns behind ships to
characterize both the clean and perturbed boundary layer
. Relevant payload instruments included
those measuring droplet size distributions, composition of both cloud water
and droplet residual particles to chemically confirm evidence of ship
influence, and aerosol size distributions and composition below cloud
base. The data show clear evidence of clouds perturbed by ship plumes based on
sharp enhancements in Nd.
Some MAST observations did appear to support the lifetime effect hypothesis of
, such as the finding that drizzle was generally reduced
in ship tracks . However, a weak anti-correlation was
observed between liquid water content and cloud droplet number concentration
(Nd) within a sample of 69 ship tracks . Later
satellite analyses of ship tracks also cast doubt on a unidirectional lifetime
effect by demonstrating that decreased liquid water path (LWP) within ship
tracks was a frequent occurrence . In approximately
30 % of cases, this decrease in LWP is enough to offset the
brightening from the Twomey effect entirely and actually darken the ship
tracks . While darkened ship tracks can occur in satellite
imagery (Fig. S2), ship tracks (particularly those forming
in typical closed-cell stratocumulus) sometimes lack a sufficient
signal-to-noise ratio in the near-infrared reflectance between the polluted and
surrounding unpolluted clouds, which can be a significant issue for estimating
radiative forcing when the signal is relatively small (in contrast to the
background clouds being highly noisy). Systematic studies of ship tracks from
across many ocean basins suggest that ship emissions have a varied influence
on LWP, with large increases occurring under clean conditions and decreases
under more polluted conditions . However, the overall
effect of LWP changes from all ship tracks has been estimated to be small
compared to the relative changes in droplet number concentration on average
. In particular, LWP tends to increase when clouds are
drizzling (as inferred from CloudSat observations) and are topped by a
relatively moisture-free troposphere but decrease in non-precipitating and drier
cases .
Many of the satellite observational studies use passive satellite imagers such
as AVHRR (Advanced Very High Resolution Radiometer) and MODIS. The spatial
resolutions are typically 1 km, making them very useful for detection
and attribution, but they only provide imagery once per day from each
platform. Geostationary satellites are an ideal tool for investigating
time-dependent processes in response to aerosol
because of their ability to
take snapshots throughout the day, but their observations are often difficult
to utilize due to uncertainty in their calibration, limited spectral coverage,
and impractical data volumes. With the new high-resolution Advanced Baseline
Imager (ABI) on the GOES and Himawari platforms, local-scale cloud retrievals
can be performed within cloud fields perturbed by point-source emissions as
the response evolves .
Other studies have investigated potential increases in cloud top height from
in situ aircraft measurements and lidar retrievals from
satellite data as well as differences in responses
between closed-cell and open-cell mesoscale convective organization
. Ship emissions can increase cloud fraction (CF)
even without changing cell structure . Furthermore,
transitions from open-cellular to closed-cellular convection induced by ship
emissions provide evidence of a cloud fraction enhancement occurring over
several days following the evolution of several dozen ship tracks
. These older, more diffuse ship tracks do not typically
retain their original track-like characteristics, thereby making them
difficult to detect without geostationary satellite observations and are thus
underrepresented in nearly all ship track studies.
Instantaneous satellite observations from polar-orbiting satellites can be
used to infer time-dependent processes. used MODIS
imagery to study ship track evolution, assuming that the response of clouds to
the ship emissions as a function of time is related to the distance from the
head of the ship track. This novel methodology allows the use of higher
resolution, relative to most of the older generation geostationary satellites,
to investigate aerosol–cloud interaction from satellites.
used geostationary satellite observations to relate long-lived extensive
overcast stratocumulus deck to air pollution originating in western
Europe. Complementing their satellite analysis with an aerosol transport model
and in situ observations of CO concentration, they explicitly showed that a
closed-cell cloud deck was associated with polluted continental outflow from
western Europe.
Ship track studies, thus, have been very helpful in formulating and testing
many hypotheses about aerosol–cloud interaction mechanisms. However,
observational studies aiming to quantify the effects from shipping at
climatically relevant temporal and spatial scales have tended to find
negligible or undetectable effects
, at least until
recently. analyzed 1 year of manually detected ship
tracks within low-cloud-dominated satellite scenes and calculated a negligible
global radiative forcing of -0.0004 to -0.0006 Wm-2. However,
a very large percentage of ship tracks likely go undetected, as there are on
the order of 100 000 ships in the global fleet , and yet
studies of ship tracks tend to identify only hundreds to thousands of tracks
per year
. The
under-identification of ship tracks is likely to be especially pronounced in
more complex cloud scenes . This lack of detection may
suggest that either our observing systems are not sensitive enough or
methodologies are not sophisticated enough to capture the many weak ship track
signatures. New automated methods for identifying ship tracks using machine
learning or by following air mass trajectories to
interpolate between observed ship track segments hold
promise for identifying a substantially larger number of ship tracks than has
previously been possible. An outstanding question is whether these weak tracks
are frequent enough to have a noticeable effect on shortwave reflection to
space. However, because the tracks are weak, a large number of cases would be needed
to contribute significantly to the radiation budget. In a global modeling
study, showed significant radiative effects
(0.3 Wm-2) from the net emissions of global shipping. Contrasting
their results to the satellite observations suggests that the integrated
radiative effect from easily detected and isolated ship tracks make up a small
contribution to the total aerosol indirect radiative effect from shipping.
performed numerical modeling experiments of ship emissions
and found that boundary layer decoupling is an important process that affects
the vertical transport of ship emissions. used large-eddy simulation (LES) to
simulate a particular observed ship-track case and demonstrated a good
agreement with observations. LES sensitivity studies demonstrated the role of
the alignment between the track and the winds in the boundary layer and of the
ambient aerosol concentration in determining the magnitude of the response
. used LES to study how emitted aerosols
are transported within the marine boundary layer and how they impact cloud
microphysical processes, and development. They also demonstrated that the
amount of cloud brightening strongly depends on meteorology, background
aerosol conditions, and the effect of secondary circulations (discussed in
Sect. ). further used LES in a
Lagrangian setup, in which clouds are simulated along a realistic observed
trajectory and are driven by meteorological conditions taken from
reanalysis. They showed that closed cells, which formed within a polluted air
mass, would have broken up sooner in a cleaner atmosphere. While aerosol was
the main factor determining the consistent delayed cloud breakup by
suppressing precipitation onset, the breakup time was also significantly
modulated by LWP changes driven by diurnal cycle and large-scale meteorology.
Shipping corridors
In order to evaluate shipping effects more holistically, several studies have
attempted to circumvent issues involving detection and identification of
individual ship tracks by analyzing entire shipping corridors
instead. evaluated satellite-derived cloud properties
upstream and downstream of three tropical and subtropical shipping corridors
in which low-level winds typically blow perpendicular to the corridors, under
the hypothesis that observations upstream of the corridor would represent
unpolluted clouds and those downstream would show the effect of shipping
pollution. No statistically significant impacts from shipping could be
detected. However, lacked the control conditions against
which to contrast the changes due to shipping. A follow-up analysis applying
this same methodology to climate model output confirmed that natural sources
of meteorological variability and gradients in cloud properties obscure the
effects of shipping . found substantial
increases in climatological Nd and cloud reflectance within a shipping
corridor in the southeast Atlantic Ocean, the primary difference from the
earlier work being that low-level winds, parallel with the shipping corridor,
keep the ship emissions relatively concentrated. They employed a method in
which the cloud and aerosol properties within the corridor that would be
expected to exist in the absence of shipping emissions (the “counterfactual”
situation) were estimated via a universal kriging algorithm trained on nearby
presumably non-shipping-affected values. The difference between the
counterfactual and the observed or reanalysis cloud and aerosol
properties (“factual”) was taken as the effect of shipping emissions.
Figure shows a comparison of the results from
for Nd with output from the Community Earth System
Model version 2 (CESM2) analyzed in a similar manner (see Sect. S1 in the
Supplement for full details). In contrast to the MODIS/Aqua observations
(Fig. g), CESM2 does not show a clear, statistically significant
enhancement in Nd coincident with the major southeast Atlantic shipping
corridor (Fig. h). A similar analysis performed for surface
sulfate mass concentration (Fig. S3) shows that there is a
perturbation coincident with the shipping corridor as expected, albeit weaker
and more diffuse than that inferred from the Modern-Era Retrospective analysis
for Research and Applications, Version 2
MERRA-2;. However, one must recognize the uncertainty
in sulfate evolution from SO2, including primary sulfate fractions and
size distributions that could contribute to these differences. Comparing the
control run of CESM2 (with normal shipping emissions included,
Fig. c) with an experimental run with shipping emissions set to
zero (Fig. f) shows that shipping emissions cause a broad
increase in Nd over the southeast Atlantic with some hint of a particular
enhancement within the heavily trafficked corridor
(Fig. i). Similarly, Fig. S3 shows that the greatest enhancement
in sulfate from shipping emissions occurs within the corridor but that there
is a sizable effect throughout the entire region as well as also found
by. Thus, comparisons of the observational and
reanalysis-based results of with climate model data may
not be straightforward, and detailed cloud
processes prove challenging to resolve. In part, this may be due to the much larger heterogeneity in the
model mean cloud properties compared to the observations, in terms of both
the overall spread in values and their smoothness in space. Climate model
studies focused on comparing output to observed corridor perturbations may
need to restrict emission reductions to the region of interest only, as
opposed to reducing emissions worldwide, due to the non-negligible
contributions from longer-range transport.
Comparison of shipping corridor results for cloud droplet number concentration with CESM2 output. Factual (“Ship”) fields for (a) MODIS/Aqua and (b, c) CESM2 control (“ctrl”), counterfactual (“NoShip”) fields obtained by kriging for (d) MODIS/Aqua and (e) CESM2 ctrl and (f) results from CESM2 with zero shipping emissions (“0ship”), and the (g, h) factual–counterfactual or (i) ctrl–0ship differences. For panels (g, h), white dots indicate significance at 95 % confidence, whereas black dots indicate values that are not statistically significant.
Another potential caveat to consider is that the presence of black carbon may
lead to cloud burn-off and affect cloud properties in this shipping corridor
off the coast of South Africa , although attempts to quantify
this effect suggest its magnitude may be insignificant
. Also, ship emissions may also be important for
mixed-phase cloud properties, although studies have suggested that the effect
on cloud brightness is more muted than in warm clouds
. LES has also been shown to be useful for
studying the response of mixed-phase clouds to ship emissions
, and the commonalities to and differences between the
response of mixed-phase and warm clouds have been demonstrated. Shipping may
even affect deep convective clouds: lightning appears to be enhanced over
major shipping corridors in the northeastern Indian Ocean and the South China
Sea, which has been hypothesized to be due to convective invigoration from
shipping-related aerosol perturbations in a well-defined shipping lane flanked
by lower background aerosol concentrations
. However, the low aerosol
baseline with more lightning in the shipping lane is also consistent with a
signature of rainfall scavenging and does not imply causality.
Global response and policy change
Cloud sensitivity to ship emissions on a larger, more climate relevant, scale
is estimated using general circulation models (GCMs). For example, used a GCM to study the impact of particulate matter from ship emissions on aerosols, clouds, and the radiation budget under different emission inventories. They demonstrated that emissions from ships increased the area mean Nd of low marine clouds by up to 30 % depending on the geographic region, while the change in liquid water content was small. In addition, the Re values were shown to decrease, leading to an increase in cloud optical thickness of up to 5 %–10 %, again, depending on the geographical region. used a GCM to show that the cloud response to ship emissions depended on the natural dimethyl sulfide (DMS) emissions, which determine the background aerosol concentration and cloud sensitivity. In addition, they estimated the global net cloud radiative effect of ship emissions to be -0.153 Wm-2.
Another example of an opportunistic experiment recently manifested itself
temporally through a policy change. On 1 January 2020, the International
Maritime Organisation (IMO) of the United Nations mandated that for all ships,
the maximum allowed sulfur content relative to mass of fuel needs to be
reduced from 3.5 % to 0.5 %, hence reducing the amount of sulfur
compounds emitted into the atmosphere. This was largely accomplished by
burning lower sulfur fuel oil (the strategy employed thus far by
ca. 90 % of the global fleet as of 2021) or installing scrubbers on
ship exhaust (ca. 10 % of ships). In 2015, similar policy changes
were carried out but only surrounding the US and European nearshore coastal
regions called sulfur emission control areas (SECAs) near the US and European
coasts (where only 0.1 % of sulfur in the fuel is
allowed). Interestingly, the relative frequency of occurrence of ship tracks
within the Californian SECA was found to drop by 73 % following this emission control area policy change.
The primary goal of the Atmospheric Composition and Radiative forcing changes
due to UN International Ship Emissions regulations (ACRUISE) project is to
determine how this international regulation affects aerosols, clouds, and
climate. A series of flights as part of the UK ACRUISE project using a wide
range of instrumentation on the Facility for Airborne Atmospheric Measurements
(FAAM) aircraft were conducted in summer 2019 through the English Channel and
off the west coasts of Portugal and the UK. The main goals of the flights,
supported by large-eddy simulations and satellite cloud detection, were to
quantify ship emission rates and study cloud properties in ship
tracks. Analyses to date that compare 2019 observations
outside and inside of the Sulfur Emission Control Areas (SECA) indicate much
lower emissions of sulfur dioxide gas, particulate sulfate, and aerosol
particles large and/or hygroscopic enough to act as CCN within the SECA
despite higher shipping traffic density (taken to be a proxy for the open
ocean after 2020). Post-regulation flights took place during the summer of
2021 primarily off the west coast of France (instead of Portugal as was the
case in the 2019 flights) and will be used to verify the anticipated changes
in emissions and cloud sensitivities. Long-term ground observations of sulfate
aerosol (and sulfur isotopes) at the Penlee Point Atmospheric Observatory in
the southwestern UK and at the ARM-ENA site at the Azores is being examined within
ACRUISE to quantify the impact of shipping regulation on the aerosol sulfur
burden. Finally, both global modeling and satellite cloud detection (aided by
machine learning) in conjunction with air mass trajectory analyses will be used
to estimate the total radiative effect of ship emissions. In contrast to
earlier studies, ACRUISE aims to quantify the impacts of ship emissions not
only in the near field (e.g., ship tracks, which only occur for a very small
fraction of the time), but also in the far field, where diffuse emissions are
expected to affect the background aerosol concentrations. The extent to which
the 2020 policy change has influenced the global occurrence of ship tracks or
climate at large is a current research question under investigation in ACRUISE
and, at least for 2020–2021, may be obscured by COVID-19-related effects
on both decreased shipping traffic and enforcement efforts.
Overall, ship emissions provide a useful laboratory to study process-level
physics of ACI in ship tracks as well as for quantifying the radiative effects
on shallow marine cloud systems more broadly over entire shipping corridors
and even the globe. Unique changes in policy and regulations directly
influence ship emissions, and these changes are currently creating an
interesting experiment to examine, but it may take several years for a clear
signal to emerge from the radical emissions changes in 2020 due to the COVID
pandemic (see Sect. ).
Industrial sources
Industrial aerosol sources are responsible for a large part of the global
anthropogenic aerosol forcing . This means that cloud
responses to emissions originating from a subset of strong industrial sources
(e.g., smelters) may serve as an analogue for global anthropogenic impacts
. Industrial perturbations cover a variety of spatial scales
(Fig. ): from isolated factories with a single chimney
inducing a narrow ship-track-like perturbation to
continental-scale industrial perturbations
. While cloud responses to emissions originating
from localized isolated sources provide the highest signal-to-noise ratio and
are highly informative for process-level understanding ,
analysis of continental-scale perturbations is probably more relevant to
global forcing estimates . Industrial sources often emit
constantly, although emissions can change over time. As an example, copper and
nickel production facilities in Norilsk, Russia, emit more than 1 Mt of
SO2 each year, i.e., more than 1 % of global anthropogenic
SO2 emissions . Such strong localized emissions
induce a high contrast between clouds affected by the emissions of the Norilsk
smelters and nearby less polluted clouds . The opening and
closing of large factories and implementation of desulfurization devices can
lead to rapid changes in emissions , providing additional
insight into aerosol impacts on clouds. On the downside, since industrial
sources are most often clustered into larger industrial regions, and therefore
create a polluted background, it can be difficult to observe the impact of
individual sources.
One of the first discussions of the potential for aerosol–cloud–climate
interactions in the literature involves the effect of pollution from an
industrialized port in southeastern Australia . An early
confirmation of the Twomey effect of increasing Nd from pollution came from
flights through plumes emitted by the Centralia coal plant in Washington State
. Figure S1 shows sulfur dioxide emissions from the power
industry and combustion for manufacturing sectors from EDGAR for 2015. The
large concentration of pollution sources in rapidly industrializing regions
like southern and eastern Asia is apparent. Given the number of sources in
these regions, it may be hard to use individual power plants or industrial
sites as opportunistic experiments. In other more remote locations like
Australia and Canada, however, there appear to be more frequently isolated but
large sources. studied continental clouds influenced by
industrial pollution in Russia, Kazakhstan, Canada, and Australia. Stratiform
clouds over land responded to isolated pollution sources in much the same
manner as marine stratiform clouds did. An expanded analysis, focusing on the
Norilsk pollution hotspot in Russia but including some data from the United
States, Europe, and eastern Asia in addition to that used in
confirmed that competing LWP adjustments in varying conditions average out to
a small offset of the Twomey effect . It is noteworthy
that this result, where the focus is more in continental areas, contrasts with
more significant Twomey effect offsets in the shipping lane study of
.
Like ship emissions, industrial sources provide unique opportunities to study
ACI but with the added advantage of having more information with regards to
the source and characteristics of the emitted aerosol. While these cloud
systems are commonly found over land areas and are less of a direct analog for
anthropogenic forcing over the oceans, there is also the potential to have
greater coverage of ground-based observations, and a greater range of particle
types and background conditions, to aid in quantifying ACI. In addition,
industrial sources have fixed locations and often emit continuously, enabling
analysis of cloud perturbations for various cloud types and meteorological
conditions characteristic to the specific location.
Volcanoes
Large explosive volcanic eruptions have long been studied for their ability to
affect the climate by injecting aerosols into the stratosphere, blocking
sunlight and causing a temporary cooling . It has now been
recognized that passive degassing and weakly explosive or effusive eruptions,
in which volcanic emissions remain at relatively low altitudes, can also
produce a cooling effect via their indirect effects on clouds
. Ship-track-like perturbations have
been observed downwind of volcanoes at Hawai'i, South Sandwich Islands, Kuril
Islands, and Vanuatu Islands
and also show increased Nd, decreased drop effective radius Re,
increased cloud brightness, and variable effects on LWP in larger-scale
eruptions .
Satellite measurements between 1978 and 2014 estimate an average SO2
flux of 23±2 Tg yr-1 into the troposphere from passive
(non-eruptive) degassing . The average SO2 emission rate from explosive and effusive eruptions is 3 Tg yr-1, of which about 1 Tg yr-1 is injected into the upper troposphere and stratosphere . Modeling studies indicate that passive degassing and weakly explosive or effusive eruptions elevate the tropospheric background level of sulfur and can induce a significant radiative forcing . The Kīlauea volcano on the island of Hawai'i is an effusive volcano that erupted continuously from 1983 to 2018, with large SO2 emissions in 2008 and 2018. Kīlauea induces significant perturbations in Re downwind of the island of Hawai'i . The eruptions resulted in a 3 standard deviation increase in Nd in the downstream wake of the
plume. also found higher cloud top heights in the
Kīlauea plume relative to adjacent clouds unaffected by the plume. Finally, Kīlauea emits continuous SO2 for long periods of time (months) and thus has the advantage of perturbing clouds over a longer timescale and region and may be more relevant (compared to ship tracks, which
are shorter lived) to the climate scale .
The 2014–2015 Holuhraun eruption in Iceland lasted 6 months (31 August 2014
to 28 February 2015) and emitted a total of around 11 Tg of
SO2 into the lowermost troposphere . Daily
SO2 emission rates averaged 0.06 Tg d-1, which dwarfs other such eruptions in recent history. Space-based, multi-angle imaging of the eruption on 11 September 2014 shows sulfate particles growing in size downwind, and at about 350 km from the volcano, at an approximate plume age of 10–12 h, the particles merge into cloud at the same elevation . These observations offer a constraint on the timescale of downwind particle processing such as aggregation, deposition, and/or new particle formation under different atmospheric static stability, relative humidity, and wind shear conditions at plume altitude, and notably in this case, particle hydration and likely activation.
Another analysis of the 2014–2015 Holuhraun fissure eruption in Iceland
revealed that global climate models can represent the decrease in
Re observed in satellite retrievals. show
that the increases in LWP are far from uniform across models (e.g.,
HadGEM-UKCA averages to a zero LWP adjustment with significant regional
increases and decreases, while other models show a wide
variation). estimated that emissions from the Holuhraun
eruption in Iceland resulted in a regional radiative forcing of
-0.21 Wm-2, 80 % of which was attributed to ACI. Had
this level of emissions occurred in summer rather than in autumn, the
radiative forcing would have been much larger (-0.61 Wm-2,
94 % of which is attributable to ACI) . During
summer the radiative effects are larger due to a greater solar flux and a
higher burden of sulfates from gas-phase oxidation.
The last major volcanic eruption globally occurred at Mount Pinatubo in
1991. Satellite and modeling capabilities to observe and model such events
have greatly improved since, and a future major eruption would offer a unique
natural experiment for further ACI studies. The eruption of Pinatubo and the
associated suite of measurements proved a catalyst for improving our knowledge
and understanding and modeling of stratospheric aerosol. Even after 25 years,
studies into Pinatubo show no sign of abating, indicating the longevity of such
important natural analogues to the science community. In much the same way,
opportunistic experiments found in large degassing events such as those that
occurred in Iceland and Hawaii provide a similarly compelling case study for
aerosol–cloud interactions. Volcanoes thus serve as another useful laboratory
to study ACI because they can emit significantly more aerosols and
SO2 than typical ships or industrial plants (see Sect. 4.3), but
their episodic nature and uncertain emissions can make interpretation and
quantification of ACI relationships challenging.
Fires and biomass burning
Agricultural burning as a promising natural laboratory for studying
aerosol–cloud interactions was proposed as early as the 1960s, as there
appeared to be a decrease in precipitation following an intensification of
burning associated with sugar cane production in northeastern Australia
. Biomass burning events around the globe have
been recognized as promising targets for studying aerosol–cloud interactions
and wildfire-driven
thunderstorms, for example, that can manifest as pyrocumulonimbus clouds
through intensive and widespread surface burning
.
Biomass burning can emit black carbon into the atmosphere and influence cloud
properties in a myriad of ways. Black carbon can strengthen the effective
radiative forcing by aerosol–cloud interactions by reducing entrainment when
it resides above the cloud but burn off the cloud when it resides in the
cloud layer . Furthermore, methods that relate cloud
properties to above-cloud rather than below-cloud aerosol concentrations
likely misrepresent aerosol microphysical effects on clouds
. At high smoke concentrations, clouds move from an
aerosol-limited to an updraft-limited regime in which cloud sensitivity to
further aerosol increases is limited . Meanwhile some of the
lowest aerosol concentrations observed at Ascension Island (farther from the
source of the biomass burning aerosols) are likely due to in-cloud scavenging
. Surprisingly, smoke from subequatorial Africa
influences clouds north of the Equator in southern West Africa as well
. In addition, revealed contrasting
responses of lightning to aerosol optical depth (AOD) for smoke and dust
aerosols in Africa. Lightning frequency increases with AOD (for
AOD < 0.3) but then decreases for dust and remains flat for smoke with
further AOD increase. However, this result does not imply causality, and
meteorological co-variability may confound the AOD–lightning relationship
.
Recent fire seasons in California in 2020 and in Australia in 2019/20
generated many large-scale smoke plumes (example in
Fig. ). These strong fire seasons have the potential to
induce large-scale anomalies in cloud properties. The NiCE campaign and
subsequently the 2016 Fog and Stratocumulus Evolution Experiment (FASE)
included numerous flights and quantified the impacts of biomass burning plumes
on stratocumulus clouds including both when the plumes were above
and in/below clouds . Analysis
of cloud anomalies compared to long-term climatology is challenging in the
case of fires, as it is difficult to separate the aerosol effect from the
influence of weather anomalies that favor the occurrence of the extreme fire
season in the first place.
Smoke plume and clouds at the US west coast on 9 September 2020 as seen by NOAA-20 Visible Infrared Imaging Radiometer Suite.
Some individual wildfire plumes were analyzed in the studies of
and . Another opportunistic experiment is
the smoke–cloud system that develops seasonally over the southeast Atlantic
stratocumulus deck , where it is obvious that
the smoke can be traced to the effects of agricultural burning over the
continent rather than processes occurring over the ocean. The regional-scale
perturbation lasts in some form for 4 or 5 months each year. The aerosol
contribution from the smoke clearly overwhelms other aerosol sources in the
free troposphere and on occasion dominates the marine boundary layer aerosol
population. However, meteorological influences (e.g., the atmosphere stability
profile) still play a major role in any observed cloud properties
. Finally, the large vertical and horizontal
extents of smoke plumes make disentangling aerosol radiative effects caused by
enhanced solar absorption over both the continent and ocean a challenge.
Hemispheric differences
The Southern Hemisphere (SH), in particular the remote Southern Ocean (SO), is
thought to be our closest present-day (PD) analog to the pre-industrial (PI)
aerosol state . Hemispheric differences in
aerosols and clouds may thus provide a potential natural
laboratory. Re is smaller in the Northern Hemisphere (NH)
, and Nd is larger
compared to the SH. However, high values of Nd can be found in pristine
conditions over the ocean when clouds are coupled to a surface under
conditions of high wind . The hemispheric contrast
between cloud properties in the more pristine SH and the more polluted NH is a
unique form of natural laboratory for estimating the bulk effect of natural
and anthropogenic aerosol emissions on our climate. Several studies have
employed this method to understand the PI environment, estimate the change in
climate due to industrialization, and improve the accuracy of our future
climate predictions by constraining radiative forcing by aerosol–cloud
interactions (RFaci) and thus reducing uncertainty in effective radiative
forcing by aerosol–cloud interactions (ERFaci)
. used the hemispheric difference in
Re to evaluate the robustness of RFaci simulated in several
global climate models (GCMs) after prescribing a relationship between sulfate
mass and Nd. found that a chemical transport model driven
by reanalysis meteorology was able to produce a difference in Nd between
the NH and SH that is consistent with hemispheric contrasts in satellite
retrievals of Re and cloud optical depth. When comparing to
satellite studies, found that the hemispheric Nd
contrast is overestimated by a collection of CMIP5 and
development GCM simulations , as well as a perturbed
parameter ensemble (PPE) exploring parametric uncertainty
. This bias was shown to be a result of models producing
uniformly too little SH Nd, and thus too little inferred PI Nd, while
also producing increasingly too much NH Nd with increasing
RFaci. Application of the Nd contrast to the PPE was able to constrain
RFaci by eliminating overly negative RFaci values (see example in
Fig. ), producing an RFaci range consistent with independent
analysis methods e.g., and further substantiating the
usefulness of the hemispheric contrast methodology.
Example of using the hemispheric contrast in Nd (ΔNd (NH-SH)) to constrain radiative forcing associated with aerosol–cloud interactions (RFaci). A smaller hemispheric Nd contrast has a smaller RFaci magnitude and thus less cooling. Curves (solid line is linear fit, dashed lines are 95 % prediction bands) are based on perturbed parameter ensemble (PPE) results described in and approximately shaded by RFaci (blue for more aerosol cooling, orange for less). The inset shows zonal Nd of individual PPE members colored by RFaci approximately corresponding to shading along the best-fit line (dotted lines). The gray bar shows 95 % confidence on the inter-annual range of ΔNd (NH-SH) from MODIS satellite estimates between 2003–2015 , yielding an observational constraint on RFaci between -1.2 and -0.6 Wm-2.
Long-term trends
Long-term trends in aerosol driven by economic growth and/or policy-driven
reductions in pollution may also arguably serve as natural laboratories with
the benefit that the long timescales minimize the effect of weather noise on
results. For instance, the decrease in cloud reflectance between the 1980s and
1990s has been called the “Gorbachev” effect as it is related to the economic
restructuring of eastern Europe following political changes that caused
decreased emissions of aerosols and their precursors . The
co-incident upward trend in surface solar radiation caused
by both ACI and clear-sky aerosol radiative interactions (ARIs) was found
useful as an emergent constraint on simulated total aerosol effective
radiative forcing (ERF) in the CMIP5 multi-model ensemble
. In other regions, there are large discrepancies between
surface radiation trends and model results .
Figure shows that, according to the CMIP6 emissions
database , aerosol-generating SO2 emissions from
the continental US increased steadily from 1850 to around 1910, when they
stabilized and then later dropped fairly rapidly from just after 1960 until
the end of the record in 2014. The latter decrease is associated with the
various federal Clean Air and Air Pollution acts, the first of which was
introduced in 1955, and is also supported by OMI observations of atmospheric
SO2 concentrations for the period after 2003. The
SO2 emission changes are mirrored by Nd changes in the
ensemble mean CMIP6 UK Earth System climate model
UKESM1; for a region in the North Atlantic that is
downwind of the US. The model Nd and trend match those from
MODIS very well over the 2003–2014 period, giving confidence in the CMIP6
emissions and the ability of this model to accurately translate emissions into
changes in cloud properties, which involves several stages. However,
and show that this model does
exhibit biases in Nd and its trends in other regions.
Time series of the annual mean CMIP6 emission rate for anthropogenic SO2(a) for the continental US region (26–50∘ N, 70–100∘ W; land-only grid points), the cloud droplet number concentration (Nd, b), and the all-sky (i.e., including both cloudy and clear parts of grid boxes) liquid water path (LWP, c) for a region in the Atlantic Ocean downwind of the US (26–42∘ N, 56–80∘ W; ocean-only grid points). Lines are shown for the UKESM1 model ensemble mean, the MODIS satellite instrument using the collection 5.1 product, and the MAC microwave satellite LWP dataset. The blue shading denotes ±2 times the intermodel standard deviation across the ensemble. The error bar plotted at the year 2016 for the Nd and LWP plots shows the ±2σ range of the annual average values from the pre-industrial control run along with the time mean (blue dot). The inset figure in the Nd plot shows a closeup of the time period for which observations are available using the same axes.
It is tempting to relate these changes in Nd to observed and
simulated trends in cloud fraction, LWP, and shortwave fluxes. For example,
suggest that the negative upwelling shortwave top-of-atmosphere flux trend in UKESM1 for the wider North Atlantic region is too
strong compared to CERES, with the model also displaying a positive bias in
upwelling shortwave top of atmosphere fluxes coincident with a cloud fraction
that is too high compared to CALIPSO see also. The
overly strong trend may be interpreted as an overly strong cloud response to
aerosol. However, natural multi-decadal variations in the sea surface
temperature in the North Atlantic (which are not necessarily captured by
models) could also lead to cloud trends unrelated to aerosols
. Figure provides a demonstration
of this through the time series of the all-sky LWP (i.e., including the zero
LWP values in the clear parts of grid boxes and hence showing the combined
effect of both cloud thickness and cloud area fraction changes) from the CMIP6
UKESM1 model for the same region downwind of the US where large negative
Nd trends over the 1960–2014 period were described above. A
negative 1971–2014 LWP trend of -0.1±0.03gm-2yr-1
(significant to >99.9%) is apparent in the mean of the 16-member
ensemble. However, the magnitude of the LWP change over this period is much
smaller than the inter-ensemble spread in LWP for a given year (shading), and
there is a large range of trends across the ensemble when computed using
individual members (-0.21 to -0.02 gm-2yr-1). This
implies that, to the extent that we can trust the model, for the same forcing
a wide range of trends is equally plausible due to natural variability and
that it would therefore be difficult to attribute an observed trend to a
forcing (e.g., the aerosol forcing). This is supported by the observed LWP
time series from the MAC (Multisensor Advanced Climatology) microwave
satellite LWP dataset ; however the dataset is also very
noisy, and the 1988–2014 trend is not statistically significant.
Furthermore, climate models predict that greenhouse-gas-driven cloud changes
(and by extension temperature-driven changes, i.e., cloud feedbacks) are very
likely to have occurred over the historical period in addition to aerosol-driven changes and natural variations
. Thus, any observed cloud changes
include natural variability, aerosol–cloud interactions, cloud feedbacks (due
to surface temperature change), and cloud adjustments to the forcing
(CO2, aerosols, etc.) evolution. This makes it difficult to infer
cloud–aerosol adjustments from long-term trends since it requires knowledge of
the non-aerosol-driven changes. The agreement over the satellite era between
the modeled CMIP5 cloud fraction trends and those from observations as
demonstrated in gives some confidence in the ability of
the models to represent changes in clouds in response to the different balance
of forcings, but the uncertainty does not allow an easy quantification of the
forcing. Further uncertainty comes from the possibility that spurious observed
trends can be introduced due to several issues in satellite data such as
instrument and platform changes, orbital drift, calibration issues, and other
unidentified stability problems, in addition to differences in retrieval
algorithms .
Rapid changes in anthropogenic emissions have occurred over east and south
Asia (especially China) over the last few decades. China's aerosol loading
increased most strongly during the rapid industrial growth of the 1970s to 1990s,
followed by gentle increases from 2000–2010 and finally a decrease thereafter
as a result of increased political attention and action on air pollution
. Accompanying these trends were changes in surface
radiation, temperature, and precipitation, some of which were attributed to
the influences of ARI and ACI, at least to some extent
. Yet, different types of aerosols were
identified to play rather different roles, which helps explain the opposite
decadal trends in severe thunderstorms in central China (where absorbing
aerosols dominate) and southeast China where hygroscopic aerosols
dominate;.
Increases in Nd over the East China Sea were observed from the
1980s to the 2000s . Co-incident with this is a
decreasing trend in cloud fraction in the same region
, which may hint at a reduction in cloudiness with
increasing Nd and decreased surface incoming solar radiation,
although the trend could also be due to other drivers.
observed a stabilization of Nd over China in the 2000s followed
by a decreasing trend in the 2010s. In this more recent period (2006–2015),
document an increase in LWP and cloud fraction that, if
caused by the decrease in aerosol, would imply a reduction in both quantities
with increasing aerosol.
More generally, demonstrated that in the CMIP6
multi-model ensemble, aerosol optical depth (AOD) and Nd trends
compared favorably to trends derived from MODIS over four different regions
with different behaviors of anthropogenic aerosol sources. In contrast, CMIP5
model trends were erroneous, e.g., over northwestern North America, but also
over China. Both CMIP5 and CMIP6 models generally showed trends in LWP and
cloud fraction that were inconsistent with the pattern derived from MODIS,
although the observed trends were rarely statistically significant.
A MODIS analysis examining negative long-term AOD and aerosol index, possibly
a better measure of finer-mode aerosol and hence
possibly CCN , found that Nd also decreased
while LWP was relatively unaffected in 15 years of MODIS data off the eastern
coasts of the United States and China and western coast of Europe
. This is in line with the other opportunistic experiments
that also indicated small LWP adjustments. However, as discussed above,
extreme caution is required when interpreting trends in cloud properties as
being caused by aerosol forcing even when there are strong concurrent aerosol
trends. Ways forward may involve using climate models or machine learning to
identify situations when cloud trends are likely to be caused by aerosol
rather than other factors and focusing on those for the quantification of
cloud–aerosol adjustments. Other approaches include stratifying vast amounts
of satellite data into small bins in meteorological variables and examining
aerosol–cloud relationships within bins to control for co-varying meteorology
e.g.,.
Overall, long-term trends are useful for correlating observed changes in
clouds and radiative effects to aerosols but are likely not suited for process
understanding of ACI unless new analysis techniques can overcome the
abovementioned issues.
Weekly cycle
A 7 d cycle is not a common naturally occurring phenomenon, and the
regional variation in weekdays with maxima and minima in anthropogenic
reactive gases offers clear evidence of an anthropogenic signal
. Weekend effects have been directly tied to the study of
ACI in particular. Weekend declines and weekday peaks in pollution have also
been observed in satellite Nd and reconstructed in climate
models in Europe . There is a clear weekly cycle in AOD with
minima on Mondays and a co-incident cycle in Nd
(Fig. ). However, trends in any other quantity (including
LWP) are unclear or ambiguous. Higher weekday aerosol levels in the United
States have been argued (controversially) to be linked to the invigoration of
storms . Similarly,
lower weekend levels of absorbing aerosol have been hypothesized to suppress
thunderstorm activity in central China whereas higher weekday levels of more
hygroscopic aerosol in southeast China have been hypothesized to invigorate
storms in that region . However, the occurrence of a single
maximum and minimum each, among just seven instances, is rather likely, so
that attribution using model evidence is required to corroborate
conclusions
.
The 7 d cycles in geophysical quantities do not typically arise by natural
variability, and if they can be identified with certainty this laboratory may
provide a clear pathway to attributing an aerosol influence on clouds.
Weekly cycle of (a) AOD, (b)Nd, and (c) LWP, in percent deviation from the temporal average, as an average over continental Europe (35 to 70∘ N, 10∘ W to 30∘ E, land only) from MODIS Collection 6 retrievals , where Nd and LWP are computed assuming adiabatic clouds . In an update to , the period from 2003 to 2020 is used for Terra (10:30 LT, upward-pointing blue triangles, dashed line) and Aqua (13:30 LT, downward-pointing orange triangles, plain line).
Particular events
Effects on aerosols from short-term events at the regional or global scale
may also provide a natural laboratory if the perturbations are large or abrupt
enough. These events range in scale from a single holiday to sudden global
economic changes (see below). Recurring holidays and days of rest have been
investigated around the world
. Traffic and
firework effects have sizable impacts on gaseous and particulate pollutant
concentrations during the extended Chinese Lunar New Year celebrations
.
Emission events in China
There have been a number of special events held in China during which air
quality experienced drastic changes over relatively short periods, such as the
2008 Olympics and Paralympics in Beijing
, 2010 World Expo in Shanghai
, 2014 Youth Olympic Games in Nanjing ,
2014 Asia-Pacific Economic Cooperation meeting , 2015 China
Victory Day parade , and 2016 G20 Summit
. These have provided unique opportunities to investigate the
impact of human activities on air quality, weather, and climate. Perhaps the
most famous example of an abrupt, ephemeral change in the environment clearly
associated with human decisions is the massive effort to reduce air pollution
surrounding the 2008 Beijing Olympic Games. used a neural
network to account for potential meteorological confounders of an aerosol
effect from the Olympics-related cleanup. Although they were able to detect a
decrease in satellite-retrieved aerosol loading around Beijing during the
Summer Olympics, its magnitude was relatively small compared to meteorological
variability. Cloud-seeding efforts using silver iodide were carried out ahead
of the 2008 Olympics opening ceremony in an attempt to create a downpour but
keep the stadium dry, although the efficacy of weather modification above
natural variability remains difficult to verify . The
annual Chinese New Year Spring Festival holiday is another major, yet more
regular, occasion when the vast majority of the population stops working for 2
to 4 weeks, as hundreds of millions of migrant workers return to their
hometowns in the countryside. This event results in localized changes to
anthropogenic emissions, gaseous pollutants, and fine particulate matter
(PM2.5) . As shown recently by
, sharp reductions were observed during the 2019 festival in
virtually all precursor gases (e.g., SO2, NO2), except ozone
(O3), and aerosol particle (organic, sulfate, nitrate, BC, etc.)
number and mass concentration at all sizes, while the meteorology remained
relatively stable prior to and during the festival
(Fig. ). However, even small changes in meteorology can have large
implications for cloud radiative properties . There are
relatively few studies concerning the impact of these events on meteorological
variability , partially due to the short periods and thus
limited data samples.
Time series of meteorology and aerosol precursor gases and species before (polluted) and during (background) the 2019 Chinese Spring Festival (16 January to 17 February 2019) in Beijing, China. (a) Ambient
temperature (T) and relative humidity (RH), (b) wind direction (WD) and speed (WS), (c) volume mixing ratios of trace gases [O3, SO2, NO2, and Ox (O3+NO2)], (d) the aerosol particle number size distribution measured by the SMPS, and (e) mass concentrations of aerosol chemical species in PM2.5 measured by the ACSM and the AE-33. adapted from .
COVID pandemic
The global COVID-19 pandemic that emerged and spread around the world in early
2020 created unprecedented socioeconomic changes. The resulting changes in
economic activity have been linked with sharp and sudden declines in certain
forms of air pollution such as nitrogen oxides in China, Europe, South Korea,
and the United States . However, the effects of
the shutdowns on other pollutants like ozone and aerosol particles have proven
to be less straightforward
. Carbon
dioxide emissions declined modestly due to shutdown measures worldwide
. Strong declines in NO2 have been
observed in locations including eastern Asia, Europe, the Indian subcontinent,
and North America . Estimates of
changes in PM2.5 from ground stations in China range from no or
small changes to reductions of a third to half
; follow-up work that accounted for long-term trends by
showed that PM2.5 was decreased by 17.13 µm.
found a substantial decline in NO2 over China
during the February 2020 shutdowns but no clear changes in AOD or
Re and thus suggested that the February 2020 shutdown effect on
regional climate was negligible. In China, the reduction in emissions during
the pandemic may have been offset by the shallowing of the planetary boundary
layer (PBL) caused primarily by anomalous meteorology . As a
consequence, the occurrence of a very serious widespread pollution episode in
the midst of the pandemic due largely to the accumulation of pollutants in the
shallow PBL posed a special challenge to the evaluation of the influences of
the pandemic-related reductions. used a multivariate linear
regression method to estimate that there would have been a substantial
reduction in AOD and aerosol direct radiative effect over China had February
and March 2020 not been as humid as they were. However, ,
using a gradient boosted regression tree machine learning method, did not find
an unequivocal AOD decrease even after controlling for daily meteorology.
Ensembles of simulations with two different Earth system models using
emissions reductions from mobility data to represent the
COVID-19 response show a robust decrease in AOD and increase in
Re over China in February 2020 but at a level likely too small
for observational methods to detect due to substantial
natural variability in clouds as noted above. Nonetheless, the two models
produce a sizable global mean forcing from reduced aerosol–radiation and
aerosol–cloud interactions (up to +0.3Wm-2 in May 2020), although much of that effect is from later shutdown measures outside of China and would probably not be distinguishable from noise in actual observations. An assessment of the relative magnitude of the effects of pandemic-induced changes to greenhouse gases, air pollution, and aerosols with a climate model emulator found that ERFaci dominates the response relative to greenhouse gas and ozone changes. The net radiative effect is projected to be a negligible global warming for the next 2 years, followed by slight relative cooling from lowered CO2 emissions . Unless a prolonged global depression develops in response to the economic shock of COVID-19 and the curtailment of many normal business activities, it seems unlikely that large climate-relevant effects will be detectable in observations .
The aviation sector saw some of the most significant changes during the
COVID-19 pandemic, with reductions in air traffic of up to 70 %. , , and
have used these changes to try to quantify radiative
forcing from contrail reductions. Contrails are linear cirrus clouds formed
from aviation exhaust in ice supersaturated regions in the upper troposphere,
and like other cirrus clouds, their net affect is to warm the planet (positive
ERF). found improved correspondence in radiative fields
between observations and simulations when contrails were included, and
found corresponding changes in cirrus at a global scale in
regions with large changes in air traffic, implying a global radiative forcing
of 0.061 Wm-2. found similar sign changes
using a global model, which has an overall contrail radiative forcing from all
aviation in 2020 of 0.050 Wm-2, helping to validate results of
recent assessments of aviation radiative impacts .
Overall, particular events like the experiments discussed here provide
potential opportunities to quantify ACI processes and response to changes in
the patterns of anthropogenic emissions. They work best if the emissions
changes are known and if sufficient observations are available before and/or
after the event to establish a good baseline. One challenge with particular
events, especially one-off events that are not repeated, is that
meteorological effects may be difficult to disentangle from emissions-related
changes and cannot be mitigated by averaging over multiple realizations (as
can be done with repeating holidays or within a climate model
ensemble). Moreover, care must be taken in selecting a baseline for
comparison, as other factors such as long-term policy-driven emission trends
or unrelated holiday or weekday effects may have influenced the “no event”
counterfactual. Nonetheless, because the emissions perturbations are
independent of meteorology and reasonably knowable, these events still hold
promise for improving our understanding of causality in aerosol–cloud
interactions as long as meteorological and other source variability can be
addressed.
Databases for experiments of opportunity
Over the decades, a growing number of databases have contributed to the
increasing knowledge on this topic. Table S2 lists several cited databases
that are either publicly available or downloadable through private means. Many
of these databases are tagged to specific peer-review
publications. catalog the emission rates of SO2
from several hundred passive degassing volcanoes using a combination of
satellite retrievals and ground-based measurements. In addition, opportunistic
experiments resulting from prominent industrial sites such as Norilsk
, persistent and weakly explosive volcanic eruptions,
(e.g., South Sandwich Islands' volcanoes and Ambrym), and significant fire
“outbreak” seasons have been logged from satellite imagery in
and . Ship track databases identified
from MODIS satellite imagery are available for the tracks: (a) off the
California coast during summer months of 2002–2004
; (b) off the California,
Chilean, and Namibian coasts from 2007–2010 collocated to CloudSat and
CALIPSO ;
(c) off the California coast for studying recent shipping emission regulations
; and (d) globally through the use of machine learning
.
Change in (a) cloud droplet concentration ΔNd, (b) sensitivity of cloud droplet effective radius change to Nd, and (c) sensitivity of liquid water path change (LWP) to Nd averaged over numerous studies involving experiments of opportunity. The number of studies going into each category are as follows: volcano tracks Satellite (6), industry tracks Satellite (9), fire tracks Satellite (1), ship tracks Satellite (9), ship tracks LES (6), ship tracks CRM (4), ship tracks in situ (18), shipping corridor Sc Satellite (2), shipping corridor Cu Satellite (2), effusive volcanic eruption Sat. (1), and global shipping Model (3). For a complete listing see Table S1. Error bars represent 1 standard deviation of reported values for each category representing diversity of the mean amongst studies.
These databases and emission estimates have already facilitated fruitful
intercomparisons of observations and models e.g., for GCMs see AeroCom
ACI experiment, and for LES intercomparison see, with the
synthesized values used to construct the statistics in Figs.
and S4 (as described further in the results section). These figures were
constructed from published estimates of Nd, Re, and
LWP for numerous opportunistic experiments derived from satellite and in situ
observations, large-eddy simulations, cloud resolving, and global model
simulations. Figure contains satellite retrievals of volcano,
industry, and fire tracks mostly from and ship
tracks. Shipping corridor perturbation results are from ,
effusive volcanic eruption is from , and the global
shipping model is from and . LES
and cloud resolving model
simulation results for ship tracks are also
included in Fig. . An exact breakdown of each study used in the
figure is provided in Table S1. This list is weighted more to observational
studies partly due to their high occurrence in the literature. Thus,
publications were sorted by the type of opportunistic experiment and data used
in order to provide a comprehensive reference for the expected cloud
responses. The expansion and synergistic use of these databases are key to
providing constraints on aerosol radiative forcing and cloud perturbations in
atmospheric modeling. Finally, while some sources like volcanoes or
industrial sites are well documented from public sources, some key data like
ship movements are proprietary and unavailable for most researchers.
Controlling factors
This section lays out prominent “experimental conditions” that studies
typically endeavor to hold fixed in a natural laboratory as a means to compare
different opportunistic experiments and systematic frameworks to one
another. We have compiled a list of peer-reviewed articles that quantify cloud
properties and their responses in many opportunistic experiments. An
opportunistic experiment means an aerosol perturbation that affects the
radiative properties of a cloud scene as a result of a chain of processes:
After emission there is nucleation, condensation, and coagulation for the
aerosol to reach CCN sizes. In addition, aerosol is diluted while being
transported to the cloud. Upon entering the cloud, aerosol particles act as
CCN and increase Nd. This leads to a distribution of available
condensate to more but smaller droplets and increases their overall surface area
and thus cloud albedo. In addition, the microphysical perturbation also
affects processes that control the evolution of the macroscopic
characteristics of the cloud, in particular precipitation formation,
entrainment, local circulations, LWP, cloud fraction, and cloud depth. This
discussion can be formalized by the following relationship
,
ΔαΔlnNa=ΔlnNdΔlnNa(ΔαΔlnNdLWP,CF+ΔαΔlnLWPNd,CF1×ΔlnLWPΔlnNd+ΔαΔlnCFNd,LWPΔlnCFΔlnNd),
where Na is the aerosol number concentration, and Δα denotes the change in scene albedo in response to an aerosol
perturbation ΔNa. Here, single-directional difference
quotients ((ΔY/ΔX)|Z≈∂Y/∂X)
are represented as a linear relationship; however, they depend upon
meteorological conditions and the background aerosol conditions
. The first term on the right-hand side is the Twomey
effect which represents the change in cloud albedo at constant LWP and
CF while the second and third terms on the right-hand side are
LWP and CF adjustments. The sign of the LWP change can reverse
too, and therefore the joint PDF approach employed by
is a useful methodology for quantifying nonlinear behavior. For warm clouds,
the expression simplifies to
ΔαΔlnNd=ϕatmCFαc(1-αc)32×1+52ΔlnLWPΔlnNd+3(αc-αclr)αc(1-αc)ΔlnCFΔlnNd,
where ϕatm is the transfer function that relates a change in
top-of-atmosphere albedo to a change in cloud albedo, which typically takes a
value of 0.7 ; αc is the cloudy-sky
albedo; and αclr is the clear-sky albedo. The complete
derivation is described in the Supplement (Sect. S2). Table S1 shows the
quantitative values of these cloud properties across diverse laboratories that
are used to construct the statistics shown in Figs. and S4 (but
using fractional changes instead). Re, cloud optical thickness,
LWP, and Nd are included where provided in the peer-reviewed
publications. The extent to which each of these effects influences the overall
cloud albedo is strongly dependent on the specific circumstances: (1) cloud
susceptibility, (2) thermodynamic phase, (3) aerosol and precursor emission
rate, (4) dilution, (5) methodology and observing system, (6) meteorology, and
(7) representativeness. Qualitatively, aerosols increase Nd and
can increase or decrease LWP and CF. Ultimately cloud and scene-averaged albedo impacts radiation as shown in Fig. . Isolated
volcanoes and ship tracks exhibit the largest fractional changes in
Nd while the changes in clouds from shipping corridors and
global-scale average cloud perturbations exhibit weaker responses by
comparison. Differences in the cloud responses are influenced by numerous
controlling factors that give rise to the diversity shown in
Fig. . Each factor is discussed below.
Cloud susceptibility
The background cloud state (namely, Nd) to a large extent
determines the specific sensitivities of scene albedo and cloud processes to
aerosol perturbations. showed that cloud albedo
sensitivity to a change in Nd is largest at low Nd
and cloud albedo of 0.5 (Eq. S4), where the background Nd and
α set the strength of the cloud albedo susceptibility as shown by the
division by Nd and confirmed in many field campaigns
. While Nd
changes at constant LWP can occur (i.e., the LWP in the polluted clouds is the
same as the unpolluted clouds on either side of the track) in ship tracks, it
is a relatively rare occurrence (roughly 10 %) in satellite-derived
ship track databases . In a majority of
ship tracks, the LWP actually decreases, and in roughly 30 % of tracks
the decreases are so large that the cloud albedo becomes dimmer in the
polluted clouds . Similar behavior has been observed in
volcano, industry, and fire tracks . Lower cloud albedo has
also been identified in ship tracks from in situ measurements
. This effect is generally attributed to the background
meteorology (Sect. ). Nevertheless, cloud
susceptibility is a useful construct and could be even more useful with an
improved understanding of the relationship between meteorological controlling
factors and the terms in Eq. (), as well as a better understanding
of the timescales for LWP adjustments .
Thermodynamic phase
Decreases in cloud albedo are shown to occur more frequently in polluted
clouds when they contain ice particles . Cloud albedo
in this context averages the cloud albedo retrievals from liquid and ice
clouds in satellite imagery over the polluted section of an opportunistic
experiment. Higher concentrations of ice in polluted ship track clouds have
been identified from several hundred cases using the Cloud-Aerosol Lidar with
Orthogonal Polarization (CALIOP). The higher occurrence of ice phase
retrievals was hypothesized to be caused by an increase in contact or
immersion freezing by the plumes of oceangoing vessels that have higher
concentrations of solid species such as calcium, ash oxides of vanadium,
nickel, sodium, iron oxides, and other heavy metals that
may serve as effective ice-nucleating particles. The cloud albedo effect is
weaker in mixed-phase clouds presumably due to enhanced precipitation
occurring by greater amounts of ice particle production causing total water
path to decrease via glaciation indirect effects . The
cloud albedo effect may also be weaker because colder and deeper clouds with
larger Nd values are less susceptible than thinner shallower warm
boundary layer clouds. As many of the cloud perturbations from volcanic
aerosols occur at higher latitudes, careful screening of warm boundary layer
clouds must be performed for comparison with other laboratories in warmer
regions and deeper investigation into glaciation indirect effects. As the
distribution of super-cooled liquid clouds may increase with increasing global
mean temperature and more shipping activity is expected
across the Arctic in the future as sea ice extent declines further, the study
of glaciation indirect effects will be pivotal for understanding the radiative
effects of climate change.
Aerosol emission strength
Cloud perturbations are strongly influenced by the strength of the emissions
of gases and particles into the atmosphere, but emission rates are highly
variable across laboratories. Passively degassing volcanoes typically emit
several orders of magnitude more SO2 than an oceangoing vessel. While
estimates range from about 5000 td-1 at Kīlauea compared to
250 td-1 at Mt. Michael in the South Sandwich Islands, both have
been shown to produce bright volcano tracks . Like
volcanoes, ship emissions also exhibit a wide diversity in emission
rates. Measurements from demonstrate that diesel-powered
ships burning low-grade marine fuel oil emitted 4–7 times more SO2
than gas turbine engines. Brighter, more reflective ship tracks have also been
shown to result from ships with higher SOx emissions
or in locations where tracks intersect but with rapidly
diminishing returns related to weaker cloud susceptibility as Nd
increases.
Dilution
Aerosol plumes from ship stacks can overwhelm the ambient CCN by several
orders of magnitude at local scales ranging from tens to hundreds of
kilometers. Over time the emissions disperse and dilute over broader
scales. Due to dilution, the aerosol concentration that reaches the cloud will
generally be significantly smaller than at the source. For individual ships
the typical area affected is approximately 2500 km2 from
250 kg of SO2 emissions over a 7 h period
. examined dilution in ship tracks as a
function of time and found that Re increases at a rate of
0.5–1 µm per hour along the polluted portions of ship tracks. This
translates to about a 2 µm increase over a distance of
100 km at typical container ship speeds of 24 knots
(45 kmh-1). and found
that the change in the width of the ship track over time depends on the
background concentration of Nd.
Dilution over larger scales may result in weaker cloud responses. The
Holuhraun fissure eruption emitted about 120 kt of SO2 per day
(at its peak in 2014–2015), an equivalent of 4 times the 28 EU member states
emission rates . This event led to decreases
in Re across most of the Norwegian sea
. Nevertheless, the volcanic emissions from Holuhruan
have a much weaker effect on cloud droplet size and liquid water path
(Fig. S4) when compared to isolated volcano track studies
e.g.,. The localized sampling of
these highly polluted clouds within volcano or ship tracks and their
surrounding cleaner clouds provides a significantly greater contrast in cloud
properties compared to studies of aggregated emissions over larger areas
(e.g., Holuhruan eruption or shipping corridors in the , and
analyses). The smaller responses at these larger-scale
perturbations shown in Fig. S4 may be the result of dilution. Interestingly,
when the fractional Nd increase is
normalized by MERRA-2 sulfate perturbation, the change in cloud droplet size
and liquid water path is similar to other studies
.
Cloud perturbations near the emission sources are likely caused by primary
aerosols (e.g., sulfate aerosols formed inside of the smoke stack or
black/organic carbon from combustion). Farther away from emission sources or
on a larger scale, secondary aerosols (e.g., sulfate aerosols formed from
atmospheric transformation of SO2) probably become more important (and
contribute towards the “background” aerosol state), while a larger fraction
of larger-sized primary aerosols may be lost due to wet/dry
deposition. Finally, weaker but more widespread effects due to greater
dilution could also lead to greater overall reflection of sunlight since the
Twomey effect is sublinear and the polluted clouds may deepen and cover a
larger region. Overall, the extent to which the magnitudes of cloud responses
across these studies are influenced by dilution and whether the responses can
be normalized by some other means for a comparative study remain open
research questions.
Methodology and observing system
Methodology and observing systems (in situ, satellite, and modeling) and
spatiotemporal scale have been shown to influence ACI metrics
. The biases in coarse-scale models are
likely related to parameterized physics and unresolved/missing processes (such
as entrainment feedbacks). If there is a mismatch in the spatiotemporal scale
of the perturbation in relation to the scale of the observing system, then
attributing aerosol–cloud interactions in a diluted manner (space or time) is
also likely to induce biases
. The latter
may be more of an issue for ship and industrial tracks than larger-scale
volcanic eruptions. In the case of volcanoes, biases would be reduced to
issues of representativeness of local measurements in an inhomogeneous field
rather than scale mismatches for a more complete discussion of
representation error see. Scale mismatches will also need to
be considered if the scaling of results from plumes of differing degrees of
dilution is to be attempted (see Sect. above).
demonstrated that a regional model running with a
2 km grid spacing was able to capture the structure of an observed
ship track. They demonstrated that the ship emissions generated a doubling of
the cloud optical thickness, an increase in Nd by 300 %,
and decrease in Re by about 40 %. In addition,
studied the dependency of the clouds' response to ship
emissions on the model resolution. They used a regional model at a range of
resolutions, ranging from a GCM scale (Dx=50km) to the convection-resolving scale (Dx=1km), to assess the impact of emission dilution
and mixing of aerosols in the atmosphere. They demonstrated that both
processes contributed almost equally to the simulated increase in the
shortwave cloud radiative effect at coarser (50 km) horizontal
resolution. The contrast in the aerosol radiative effect across model
resolutions suggests more closure studies are needed to resolve this gap.
Cloud sensitivity to ship emissions on a larger, more climate relevant, scale
is estimated using GCMs. For example, used a GCM to study
the impact of particulate matter from ship emissions on aerosols, clouds, and
the radiation budget under different emission inventories. They demonstrated
that emissions from ships increased the area mean Nd of low
marine clouds by up to 30 % depending on the geographic region, while
the change in liquid water content was small. In addition, the
Re values were shown to decrease, leading to an increase in cloud
optical thickness of up to 5 %–10 %, again, depending on the
geographical region. used a GCM to show that the cloud
response to ship emissions depended on the natural dimethyl sulfide (DMS)
emissions, which determine the background aerosol concentration. In addition,
they estimated the global net cloud radiative effect of ship emissions to be
-0.153 Wm-2. GCMs were also used to study the effect of the
2014–2015 Holuhraun effusive eruption (referred to as the Nornahraun eruption
in the paper, which was the unofficial name at the time) on the climate system
by . They estimated that emissions from the Holuhraun
eruption in Iceland resulted in a regional radiative forcing of
-0.21 Wm-2, 80 % of which was attributed to ACI. These
GCM simulations demonstrated that had this level of emissions occurred in
summer rather than in autumn, the radiative forcing would have been much
larger (-0.61 Wm-2, 94 % of which attributable to
ACI). During summer the radiative effects are larger due to a greater solar
flux and a higher burden of sulfates from gas-phase oxidation.
Cloud water response depends on the meteorological conditions. Dependence on above-cloud relative humidity (RH, a), cloud top height (CTH, b), and background cloud droplet number concentration (Nd, c) is shown independently for ocean-based ship and volcano tracks blue line represents data from and land-based industry and fire tracks green line represents data from.
Uncertainties that can influence the estimate of satellite-retrieved ERFaci
are the humidification of aerosols and enhanced reflectance due to scattering
off the edges of clouds typically leading to larger estimates of the Twomey
effect and adjustments in CF. Furthermore, invalid assumptions on
adiabaticity for non-plane-parallel clouds where 1D radiative transfer is used
on 3D clouds can typically result in uncertainties in retrieved
Nd typically larger than 70 %. One should keep in mind that satellite studies of LWP
adjustment suffer from uncertainties that enter into satellite-retrieved
values of dlnLWPdlnNd
via the retrieval uncertainties in Nd. Further uncertainty then follows by different choices
made during the quality checks applied to Nd retrievals. This is
exemplified by inconsistent estimates of dlnLWPdlnNd in the subtropical stratocumulus
regions . These estimates
stem from the same retrievals. Yet, different choices made across the three
studies in how to address the uncertainty in Nd lead to a
considerable variability in both magnitude and sign of dlnLWPdlnNd. Such uncertainties in satellite
retrievals and differing methods of filtering clouds are also possible
explanations for the observation that the LWP adjustments observed in
are comparable to those of the ship track work of
and for similar background values of
Re and Nd.
Finally, more attention should be paid to potential changes in the width of
the droplet size distribution (DSD) , which cloud chamber
experiments suggest could be quite important
. If both the width and center of the
droplet size distribution are of first-order importance, it may be more useful
to think about primary indirect effects (traditional Twomey effect plus
narrowing) and secondary indirect effects (adjustments to the DSD shift)
rather than adjustments being due to the Twomey/first indirect effect of a
larger number (zeroth moment of the DSD) and smaller effective radius (ratio
of third and second moments) alone. Some evidence of a modification of the DSD
width may be responsible for creating negative biases in the LWP retrievals
within the first 100 km of ship tracks where LWP changes are expected
to be zero as there would not have been enough time to modify the clouds
. showed the contrasting
role of DSD width. When spectral broadening is associated with increasing
Nd (because of competition for water vapor in the relatively
polluted, condensation-dominated regime), albedo susceptibility is diminished,
whereas when broadening is associated with a reduction in Nd
(the cleaner, coalescence-dominated regime), susceptibility is
enhanced. Polarimeter measurements can provide an estimate of the DSD width
(e.g., POLDER, over a limited spatial scale) and would be useful additions
(e.g., the upcoming NASA Atmosphere Observing System mission or airborne
polarimetry) to the observational toolbox.
Meteorology
The meteorological and aerosol background conditions determine the cloud
regime and the processes that dominate cloud
evolution. Figure shows the dependence of cloud water
response on the environmental conditions for the ocean-based and land-based
polluted cloud tracks. The depth of the PBL and free tropospheric humidity
have been identified as playing significant roles in the strength of the
aerosol–cloud metrics shown in Fig. S4. As the humidity in the free
troposphere (above the cloud tops) becomes drier, polluted clouds with smaller
droplets evaporate more efficiently , causing
liquid water paths and cloud albedo to decrease
(Fig. ). Also, the sign of LWP adjustments
dlnLWPdlnN is positive when cloud
evolution is dominated by precipitation suppression and negative when
dominated by evaporation and entrainment. However, precipitation suppression also
leads to greater turbulent kinetic energy and more entrainment, and the LWP
increase by drizzle suppression may only be active when
precipitation reaches the surface
. Figure shows clear cloud water
response dependence on baseline Nd: more pristine clouds are
more likely to be precipitating, and thus cloud water is more likely to
increase.
Representativeness
Four other important challenges for applying lessons learned from natural
laboratories and experiments to the study of aerosol–cloud interactions more
broadly pertain to representativeness in terms of perturbation concentration,
timescale, sampling, and environment.
Perturbation concentration
Concentrated aerosol plumes surrounded by “clean” air behave fundamentally
differently than the same amount of aerosol spread out more evenly. Models of
isolated ship-track-like plumes show that such concentrated aerosol
perturbations can create a secondary circulation transverse to the track. The
circulation results in moisture convergence into the track and a positive LWP
adjustment and cloud-free downdrafts alongside the track
. The extent of cloud horizontal clearing along
the edges of ship tracks has been shown to buffer the net cloud albedo effect
in some ship tracks . These non-local effects may lead to the
overall scene albedo change for an isolated perturbation to differ
systematically from what would be obtained by a more uniform increase.
Timescales
More recently, , hereafter G21, have argued that ship
track studies underestimate climatological liquid water path decreases from
aerosol injections into non-precipitating clouds because
evaporation–entrainment adjustments take place on timescales of ∼20h. G21 argue that clearly visible ship tracks only persist for
∼6–7 h and are on average sampled within 3 h of forming
and thus do not last long enough to develop substantially negative liquid
water adjustments. The results from show a more negative
liquid water path adjustment which G21 explain as resulting from a longer
effective lifetime of ship tracks in the corridor methodology of ≥9h. Thus, the analysis of G21 suggests that short timescale
adjustments observed in ship track studies may be unrepresentative of the
climatological response to greater aerosol/cloud droplet
number. used mixed layer modeling to show that clouds can
thin on short timescales (e.g., when the lifted condensation level rises more
quickly than the inversion height) and thicken on longer ones. The adjustment
timescale of G21 in fact falls in between these two timescales of an
individual stratocumulus cloud system because when quantifying adjustments it
compares perturbed and unperturbed systems, each of which have a different
equilibration time. The added complexity here is that cloud adjustments seem
to vary with time after emissions, and thus near-source impacts are not
sufficient for estimating global impacts. The lifetime of industry tracks has
not been well quantified. It is unclear whether industry tracks live longer
than ship tracks and whether these opportunistic experiments are more
representative of the climatological cloud responses . All of
these studies point to the need to account for, and quantify, the timescales
of emissions and cloud adjustments for both local and climatically relevant
conditions.
Sampling and over-representation
Although and have made great strides in
extending the study of ship-track-like perturbations to deeper continental
boundary layers, it remains true that the special cases of shallow well-mixed
marine boundary layer with low background aerosol concentrations are
over-represented in the natural experiment literature due to the formation of
clearly discernible tracks in such environments . However,
real but less easily detectable effects may exist in other conditions
, and different integrated aerosol–cloud responses are
expected between shallow well-mixed marine boundary layers, deeper decoupled
marine boundary layers, and continental boundary layers
. The shipping corridor approach of
partially addressed this concern by capturing all shipping effects over a
defined region from the “top down” rather than building up statistics of
clearly detected cases from the “bottom up”. Improved approaches for the
detection of pollution tracks via machine learning and
trajectory analysis from known point sources , taken by
the ACRUISE project, also provide the opportunity to better sample a more
diverse set of regimes via natural experiment methods.
Environmental representativeness
Altogether, the challenges raised above point to the necessity of coupling
insights from both modeling and observations even for the seemingly
straightforward case of natural experiments like clearly visible ship tracks,
in order to extrapolate from the specific situations in which natural
experiments can be studied to aerosol–cloud interactions more broadly. The
spatial extrapolation of opportunistic experiments requires a good
understanding of the dependence of cloud response not only to cloud regime
(stratocumulus, shallow cumulus, etc) and dominant microscopic processes
(rain- or entrainment-dominated; warm, ice, or mixed phase) but also to
external cloud-controlling factors like above-cloud humidity and the typical
persistence time of the perturbation. Climate model intercomparisons in
specific geographic and meteorological natural experiment settings
could help to overcome the limited representativeness
of natural experiments.
Summary
Experiments of opportunity have been looked upon by some as akin to a
“Rosetta Stone” connecting the effects of changing aerosol over the ocean
and cloud albedo effects on climate . It could be argued that
ship, volcano, and industrial pollution tracks are the most striking examples
of aerosol–cloud interactions in the climate system. A wealth of field
campaigns, satellite observations, and modeling studies related to these
opportunistic experiments provide incontrovertible evidence that changes in
aerosol concentration can lead to significant changes in the microphysics and
macrophysics of clouds for the same meteorological conditions. Over the
decades, several well-known field campaigns have made a concerted effort to
pin down controlling factors that lead to large uncertainty in cloud responses
and aerosol indirect radiative forcing as a whole.
Natural laboratories are excellent for process-level understanding of
aerosol–cloud interactions. One key result from the Monterey Area Ship Track
(MAST) experiment revealed that the cloud condensation nuclei from individual
ships are solely responsible for the reflectance perturbations in ship tracks
as opposed to the hypotheses involving heat and moisture from the exhaust or
sea salt produced in the wake of a ship . While this
connection between the aerosol and cloud microphysics is understood,
macrophysical responses (such as cloud liquid water path, geometrical
thickness, precipitation, and fractional coverage) exhibit more diversity and
are poorly understood. Several hypotheses have emerged to explain the
bidirectional response in macrophysical responses, and a greater understanding
has emerged in recent decades. The dryness of free-tropospheric air can lead
to greater evaporation in polluted clouds, thereby decreasing liquid water path
. The evolution of the clouds and duration over which they
have been influenced by aerosols can affect precipitation, circulation, and
liquid water path .
It remains unclear how representative opportunistic experiments are for
understanding of the global response of clouds to anthropogenic
aerosols. Typically only shallow clouds are within reach of the emissions from
underlying ships or industrial sources, and the albedo cloud susceptibility
typically becomes weaker as the PBL deepens . Furthermore,
enhanced lightning in shipping lanes may suggest deep convective clouds are
also influenced by shipping aerosol . Thus, it is unclear
how reliable extrapolations of these opportunistic experiments are to the
global scale. The timescale of cloud perturbations is one key aspect of these
extrapolations for quantifying global aerosol radiative forcing
. Furthermore, satellite observations typically focus on
the “hits” where tracks are observed instead of the “misses” where
aerosols may influence clouds but not produce an evident track. The extent to
which deeper clouds respond to dilute plumes and radiative forcing remains
largely unanswered. It has been estimated that the global coverage of ship
tracks is only 0.002 %. In order to accurately
determine the global ERFaci a new framework may be needed to track individual
plumes through to cloud responses when “tracks” are not directly observed by
our current and planned observing systems.
This review paper collates the results from experiments of opportunity in over
50 publications. These experiments can provide useful observational
constraints on ERFaci through the quantification of key terms
represented in Eq. (). Figures and S4 show good
agreement of the increases in cloud droplet number concentration
(Fig. a) and decreases in cloud droplet effective radius
associated with most opportunistic experiments (Fig. b). The
larger-scale assessments of corridors (satellite) or global shipping
(simulations) have smaller drop number perturbations, perhaps indicating
dilution effects. There is less agreement on the sign on the LWP response,
with uncertainties typically spanning a wide range of negative and positive
values (Fig. c). Observations of tracks see decreases in LWP,
while models tend to show increases, and corridor observations are mixed. This
analysis provides a hint that different adjustment processes dominate on
different space and timescales. This approach which combines opportunistic
experiments may offer a useful framework for future studies, as it is
essential to pin down LWP and CF adjustments for more accurate
estimates of ERFaci. The range of uncertainty in these ACI metrics denotes the
important roles of several cloud controlling factors. Two field campaigns,
ACTIVATE and ACRUISE, have recently shifted the focus from individual
plume-scale cloud interactions to larger regional- and global-scale
perturbations to better characterize dilution and nonlinear cloud responses
as they relate to emission strength. Furthermore, a better understanding of
aerosol's invigoration of convective and ice clouds alongside the temporal
evolution as the clouds evolve and change in accordance with meteorology is
essential to understand the albedo responses as they relate to macrophysical
cloud property changes. Coordinated model experiments, such as AeroCom have
been instrumental in pinpointing deficiencies in atmospheric models and their
diversity of simulated effective aerosol radiative forcing
.
Finally, opportunistic experiments may assist in understanding large-scale
sulfate injection or marine cloud brightening for geoengineering. They might
be used to better understand potential geoengineering pathways in similar or
analogous environments where the environmental impacts can be quantified
. Many natural laboratories cause
low-cloud perturbations and may well serve as useful analogs for developing
climate intervention strategies, so understanding them is critical for future
and past aerosol radiative forcing.
Code and data availability
Code and data used to analyze the intercomparison of opportunistic experiments are archived via 10.5281/zenodo.5839101.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-641-2022-supplement.
Author contributions
MWC and AG are co-leads and wrote the manuscript with contributions in writing, figures, and data from the following co-authors: JC, GD, MSD, AD, GF, FG, TG, DPG, EG, RK, ZL, PM, FM, ILM, DTM, GM, JM, SP, AnP, AdP, JQ, DR, AnS, RS, ArS, PS, VT, DW, RW, MY, and TY.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank the editors and staff of Atmospheric Chemistry and Physics as well as the two anonymous referees for their time and feedback during the writing of this paper.
Financial support
Matthew W. Christensen and Philip Stier were partly supported by European Research Council Project constRaining the EffeCts of Aerosols on Precipitation under the European Union's Horizon 2020 research and innovation program grant 724602 and from the FORCeS project under the European Union's Horizon 2020 research program with grant agreement 821205. Matthew W. Christensen, Po-Lun Ma, and Johannes Mülmenstädt were supported by the “Enabling Aerosol-cloud interactions at GLobal convection-permitting scalES (EAGLES)” project (74358), funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, Earth System Model Development program. The Pacific Northwest National Laboratory is operated for the US Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830. Mingxi Yang, Matthew W. Christensen, Duncan Watson-Parris, and Philip Stier were supported by the Natural Environment Research Council (UK) project ACRUISE (grant number: NE/S005390/1). Velle Toll acknowledges support from the Estonian Research Council grant PSG202. Michael Diamond was supported in part by NASA headquarters under the NASA Earth and Space Science Fellowship Program, grant number NNX-80NSSC17K0404, and in part by the CIRES Visiting Fellows Program that is funded by the National Oceanic and Atmospheric Administration (NOAA) Cooperative Agreement with CIRES, grant number NA17OAR4320101. Armin Sorooshian was supported by ONR grant N00014-21-1-2115 and NASA grant 80NSSC19K0442 in support of ACTIVATE, a NASA Earth Venture Suborbital-3 (EVS-3) investigation funded by NASA's Earth Science Division and managed through the Earth System Science Pathfinder Program Office. Anja Schmidt acknowledges funding from NERC grants NE/S00436X/1 (V-PLUS), NE/T006897/1 (ADVANCE), and NE/P013406/1 (A-CURE). Zhanqing Li is funded by the US National Science Foundation (AGS1837811) and NASA (80NSSC20K0131). Isabel L. McCoy was supported by the NOAA Climate and Global Change Postdoctoral Fellowship Program, administered by UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS) under award NA18NWS4620043B. Anna Possner is funded by the Federal Ministry of Education and Research (BMBF) under the “Make our Planet Great Again – German Research Initiative”, grant number 57429624, implemented by the German Academic Exchange Service (DAAD). Edward Gryspeerdt was supported by a Royal Society University Research Fellowship (URF/R1/191602). Franziska Glassmeier acknowledges support from The Branco Weiss Fellowship – Society in Science, administered by ETH Zürich, and from a Veni grant of the Dutch Research Council (NWO). Johannes Quaas acknowledges support from the EU Horizon 2020 projects ACACIA (GA 875036) and FORCES (GA 821205). Robert Wood acknowledges support from the US National Oceanographic and Atmospheric Administration (NOAA award NA20OAR4320271). Graham Feingold acknowledges funding from a NOAA Earth's Radiation Budget grant, NOAA CPO Climate & CI #03-01-07-001. The National Center for Atmospheric Research is funded by the US National Science Foundation. Adam Povey is funded as part of the Natural Environment Research Council's support of the National Centre for Earth Observation, contract number PR140015. Guy Dagan was supported by the Israeli Science Foundation Grant 1419/21.
Review statement
This paper was edited by Martina Krämer and reviewed by two anonymous referees.
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