Aerosol indirect effects have potentially large impacts
on the Arctic Ocean surface energy budget, but model estimates of
regional-scale aerosol indirect effects are highly uncertain and poorly
validated by observations. Here we demonstrate a new way to quantitatively
estimate aerosol indirect effects on a regional scale from remote sensing
observations. In this study, we focus on nighttime, optically thin,
predominantly liquid clouds. The method is based on differences in cloud
physical and microphysical characteristics in carefully selected clean,
average, and aerosol-impacted conditions. The cloud subset of focus covers
just
Aerosol indirect effects on clouds are among the biggest uncertainties in climate models (Boucher et al., 2013). It is particularly important to reduce these uncertainties in the Arctic, where warming is occurring at a faster rate than in other locations (Serreze et al., 2009), and where local aerosol indirect effects can be large (Garrett et al., 2004; Garrett and Zhao, 2006; Lubin and Vogelmann, 2006; Zhao and Garrett, 2015). Understanding aerosol indirect effects is also important because aerosol emissions within and in the vicinity of the Arctic are changing, and perhaps more importantly, the major aerosol removal processes and transport pathways to the Arctic may be changing as well (Jiao and Flanner, 2016).
Unfortunately, accurate observation-based estimates of regional mean forcings are very difficult to obtain at most locations around the planet due to a variety of confounding factors and errors. These include (1) a reliance on proxies for cloud condensation nuclei (CCN) and ice-nucleating particles (INPs); (2) meteorological co-variability and other synoptic-scale surface and atmospheric factors, including the aerosol spatial distribution; (3) the complexity of cloud responses to aerosol type and amount (Fan et al., 2016); (4) spatial and temporal limitations of the datasets; and (5) an insufficient understanding of cloud characteristics even in the absence of anthropogenic aerosols (Ghan et al., 2016; Wilcox et al., 2015). Knowledge of this last factor is difficult to obtain because pristine conditions are rare at most locations globally (Hamilton et al., 2014). To quantify mean regional aerosol indirect effects using observations, one would need datasets that cover the large spatial and temporal scales required to include the full range of natural heterogeneity, plus a way to correctly identify clean background conditions. As a result, current estimates of regional indirect aerosol impacts on the surface radiation rely predominantly upon models that still cannot accurately represent many relevant Arctic processes (e.g., Morrison et al., 2012; Ovchinnikov et al., 2014).
In some ways, isolating aerosol indirect effects over the Arctic Ocean can be even more challenging than in other regions. Sampling conditions at the ground are harsh, there is low thermal and visible contrast between sea ice and clouds, and observations are limited by the frequent presence of multilayer clouds. The very cold temperatures that characterize the Arctic affect chemical reactions and physical processes (e.g., the development of frost flowers, diamond dust, and blowing snow), making comparisons with lower-latitude systems more challenging. However, the Arctic Ocean is ideal for the study of indirect effects in other ways. For example, the surface and meteorological conditions over sea ice are highly homogenous compared to many other regions of the world. Moreover, pristine conditions still occur in this region with relatively high frequency, despite periodic episodes of combustion-derived aerosol transport from lower latitudes. Present-day observations in clean background conditions are among our best proxies for preindustrial conditions (Hamilton et al., 2014), and a better understanding of preindustrial conditions is, in turn, key to the ability to determine present-day indirect aerosol impacts on a regional scale (e.g., Gettleman, 2015; Ghan et al., 2016; Ghan, 2013; Carslaw et al., 2013; Wilcox et al., 2015; Kiehl et al., 2000).
Here we present a method for identifying spatially distributed properties in a subtype of Arctic Ocean clean background clouds using a combination of the CALIPSO and CloudSat active remote sensing instruments and an atmospheric transport model. We use the difference between average cloud characteristics gathered across the Arctic Ocean and average clean background clouds over the same region to estimate the maximum regional indirect aerosol impacts on the surface. This calculation provides an estimate of the actual regional impact of aerosol indirect effects on the surface, including aerosol and meteorological co-variability after stochastic meteorological effects have been taken into account. We also examine differences between the cloud characteristics under various aerosol conditions to assess cloud formation mechanisms in the presence of aerosol.
One goal of this work is to illustrate one way that regional-scale aerosol
indirect effects on the surface can be obtained quantitatively from
observational data. In the past, such estimates have primarily been supplied
only by models. We focus on the subset of Arctic Ocean clouds where aerosol
impacts can be identified with the greatest certainty: optically thin (cloud
optical depth, COD
To describe aerosol impacts on Arctic Ocean clouds with high confidence
using CALIPSO and CloudSat data, it was vital that we be able to accurately identify
clean background cases. We selected a specific group of clouds
where non-background aerosol (hereafter simply referred to as “aerosol”)
conditions and cloud properties could be ascertained with the greatest
confidence. The main Arctic Ocean cloud subset of focus consists of clouds
that are optically thin (COD
Criteria used for cloud and air mass classification.
Aerosol vertical distribution, cloud-top height, cloud-base height, cloud
optical depth, and initial approximate cloud phase were obtained from the
polar-orbiting CALIPSO satellite lidar v. 3.01 level 2, 5 km aerosol profile
and cloud layer products products (CALIPSO Science Team, 2015a, b) at 532 nm. These data have a vertical resolution of
30 m within layer (up to 8 km), where most predominantly liquid Arctic Ocean
clouds were found. Before averaging, along-track cloud profile data were
collected at a horizontal resolution of
Because our samples were taken at night, Moderate Resolution Imaging
Spectroradiometer (MODIS) optical depths were not available. Instead, the
CALIPSO product was used to measure CODs, as it offers substantially higher
data availability in the optical thickness range of interest (COD
Because it can be difficult to accurately separate Arctic aerosol from diamond dust and thin ice clouds using backscatter data (M. Vaughan, personal communication; 2016; Grenier and Blanchet, 2010), we focused on CALIPSO liquid-containing clouds. To gain greater confidence in the aerosol classification within the MOONLiT subset, ice clouds were not allowed in those profiles. Note that CALIOP cloud phase indicates only whether the cloud predominantly contained liquid or ice; there is no mixed-phase designation. At a later step, CloudSat data were used to further refine cloud-phase information.
CALIPSO data were obtained over the Arctic Ocean between 60 and 82
The clean background cloud subset met the criteria above, but no
aerosol features were permitted above or below cloud, even when air masses
had been horizontally averaged across 80 km resolution in the CALIPSO
aerosol detection algorithm, which is the resolution that detects weak
aerosol layers with highest confidence. Given these constraints, the
backscatter aerosol detection limit for clean background clouds is as
low as possible and should have only negligible variations based on
detector noise and background molecular scattering and O
CloudSat cloud profiling radar data are collected at a vertical resolution
of 240 m. CloudSat has a wider swath than CALIPSO (1.
The geographical distribution of ONLi and MOONLiT cloud profiles,
where
Average reflectivity between the CALIPSO-determined cloud top and base was
obtained from the CloudSat 2B-GEOPROF version R04 dataset. Cloud phase and
precipitation occurrence were acquired from 2B-CLDCLASS-lidar version R04
estimates (Wang, 2013). In this product, cloud phase is
determined from a combination of CALIPSO water layer detection and
integrated backscattering coefficient, temperature, CloudSat reflectivity,
and an assumed temperature-dependent reflectivity threshold for ice
particles (Zhang et al., 2010). This phase classification is
uncertain for clouds with reflectivities of
Estimated mean liquid cloud droplet effective radii (
We present some limited CloudSat-derived
The locations of combustion aerosol plumes were modeled using BC from the FLEXPART model (Stohl et al., 1998, 2005). The FLEXPART model has been used extensively to study pollution and smoke transport in the Arctic, and is well-validated for this purpose (Damoah et al., 2004; Eckhardt et al., 2015; Forster et al., 2001; Paris et al., 2009; Sodemann et al., 2011; Stohl et al., 2002, 2003, 2015). We chose BC as a combustion aerosol tracer because it represents aerosol removal better than a gaseous tracer like carbon monoxide and because FLEXPART can largely capture the Arctic BC seasonal cycle (Eckhardt et al., 2015) that is driven by a combination of seasonal changes in emissions, atmospheric transport patterns, and removal processes. In some cases, wildfires can emit large amounts of light-absorbing organic carbon aerosols (or brown carbon) without emitting large amounts of BC (e.g., Chakrabarty et al., 2016). In these cases, FLEXPART BC may not represent smoke aerosols well.
For this study, as in Eckhardt et al. (2015),
FLEXPART was driven with meteorological analysis data from the European
Centre for Medium-Range Weather Forecasts (ECMWF) at a resolution of
1
Aircraft out-of-cloud BC data were obtained from NASA's Arctic
Research of the Composition of the Troposphere from Aircraft and Satellites
(ARCTAS) campaign
(Fuelberg
et al., 2010; Jacob et al., 2010; Kondo et al., 2011). The aircraft data
with the highest aerosol particle concentrations were clustered between
50 and 60
An aircraft-derived, 180
Several other supplemental datasets were used for cloud environmental
context. ETOPO1 Bedrock GMT4 data (Amante and Eakins, 2009)
were used to identify cloud profiles over the Arctic Ocean region.
NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration,
version 2 data (Meier et al., 2013; Peng et
al., 2013) were used to approximate the fractional sea ice cover over ocean
in the specific month and at the specific location of each profile. A sample was classified
as being primarily over sea ice or open ocean when the sea ice fraction at
the given location and in the given month was
Lastly, integrated surface longwave (4–30
To accurately characterize clean background conditions, it is necessary to detect combustion-related aerosol layers with confidence. For CALIPSO, dilute aerosols are least likely to be detected below cloud due to signal attenuation inside the cloud (Di Pierro et al., 2013), but CALIOP can sometimes miss dilute aerosol layers even in clear air above clouds (Di Pierro et al., 2013; Kacenelenbogen et al., 2014; Rogers et al., 2014; Sheridan et al., 2012; Winker et al., 2013). Most previous works focused either on daytime samples, which have comparatively low signal-to-noise ratios, or on extinction data, which are more uncertain because they assume a prescribed lidar ratio. To begin quantifying the false negative rate relevant to this study, we used two independent methods to estimate the fraction of the time when nighttime Arctic CALIPSO data would not detect above-cloud aerosols when actually present.
First, we estimated the fraction of air masses containing various observed
concentrations of aerosol tracers that would be detected at the reported
theoretical 80 km resolution nighttime backscatter detection limit from
Winker et al. (2009). This analysis is based
on co-located aircraft backscatter, particle number, and BC data from the
ARCTAS aircraft campaign (Fig. 2a). The results suggest that CALIOP would
miss
As an independent consistency check, we next determined the frequency at
which aerosols were detected by both FLEXPART and CALIOP. To do so, we
compared the fraction of observed clear-sky (no-cloud) CALIOP profiles that
were expected to contain aerosols at different simulated FLEXPART aerosol
concentrations for January 2008 (Fig. 2b). These results suggested that
CALIOP may not have detected up to
Based on CALIPSO criteria alone, the estimates above suggest that aerosol
detection uncertainties may be higher than desirable, particularly below
cloud. We address this issue in two ways. First, we apply the criteria for
determining clean background cloud that depend not only on aerosol-free
CALIPSO profiles but also on modeled above- and below-cloud BC
concentrations of
Based on CALIPSO Arctic profiles under non-cloudy conditions, we
compare
In order to have greater confidence in quantifying the regional-scale
aerosol indirect effects, this study is limited to ONLi clouds and their
MOONLiT cloud subset. It is important to emphasize that the ONLi cloud group
is not representative of all Arctic clouds. During our study period, ONLi
clouds were present in only 5.3 % of all total comparable nighttime cloudy
profiles over the Arctic Ocean (comparable clouds defined as having a
satisfactory in-cloud CAD score of 70–100 and with cloud bases
Moreover, the cloud-selection criteria imposed by our methods may induce
some uncertainties in the analysis. For example, due to the low COD
constraint, it is possible that some fraction of the cloud subset influenced
by aerosols may be selected from a different group of cloud types than some
fraction of the clean background cloud subset. As an illustration, in a
subarctic aircraft case
study presented in Zamora et al. (2016) (see Appendix A for further details), cumulus clean background clouds with
an observed cloud thickness of
Similarly, any aerosol-driven phase changes that shifted clouds between predominantly ice- and liquid-containing clouds (e.g., Girard et al., 2013) could have eliminated samples from or added samples to our study, also potentially adding some bias to our results. These uncertainties are difficult to quantify but are likely to be much smaller than the error that would be introduced by expanding the dataset to include other non-ONLi cloud subsets that would be characterized with greater uncertainty.
In our study, sampled clouds were thin by definition and were thus unlikely to occur under very turbulent conditions. The range in turbulence covered in the sample set was also likely limited during polar night due to the lower variability in external heating and generally high static stability of the Arctic atmosphere. Nonetheless, we expect that clouds over the open ocean are impacted more by thermodynamic coupling with the surface (Shupe et al., 2013) than over sea ice, where surface-based inversions occur more frequently (Ganeshan and Wu, 2015). In this study, we stratify clouds into these two regimes to distinguish the effects of systematic differences in atmospheric stability and large-scale atmospheric and surface forcing between the two systems (Curry et al., 1996; Jaiser et al., 2012; Taylor et al., 2015).
The weighted-average gridded maps of features below individual
cloud points from Fig. 1b for
Median (interquartile range) and sample number (no.) of Arctic Ocean ONLi
cloud properties as classified by the criteria in Table 1, separated by
reflectivity above and below detection limit (DL,
ONLi clouds were more likely to overlay another cloud layer over open ocean
than over sea ice, as demonstrated by the average height of the next
below-cloud feature (Fig. 3b, Table 2). A similar result was also observed
previously at the SHEBA ship-based observatory
(Intrieri et al., 2002) and for general cloud aggregates over the Arctic (Li et al., 2015). There are
also differences between shallow and higher clouds. Shallow clouds are
defined here as having cloud bases
It is possible that some of the differences between shallow and high ONLi clouds are due to differences in cloud formation mechanisms. For example, previous studies suggest that shallow liquid-containing Arctic clouds might form from the advection of warm, moist air over a cool surface, whereas higher liquid-containing clouds might form from a longwave radiative flux divergence (Smith and Kao, 1995) or partial dissolution of a higher-level stratus cloud (Herman and Goody, 1976). One previous model sensitivity study linked shallow liquid-containing clouds in a 3-day Arctic multilayer cloud system with surface turbulent heat fluxes, and overlying liquid-containing clouds with large-scale advection and maintenance by radiative cooling (Luo et al., 2008). Because of these differences, shallow ONLi clouds were characterized separately in later analysis in order to better understand the influence of confounding meteorological factors on the results.
The different probabilities of cloud-layering occurrence over sea ice vs.
open ocean and in cloud properties over different heights complicates
comparisons between the two regimes. However, comparing only single-layer
clouds with bases above 1.1 km, the median cloud-base height of open-ocean
clouds is
Over the open ocean, clouds were also warmer than over sea ice, and a higher
fraction of ONLi clouds were observed with very-low-layer mean reflectivity
(
Following Table 2, median characteristics of all (grey) and
aerosol-influenced (orange) ONLi clouds over sea ice and open ocean where
significant differences from clean conditions (blue) are observed. Data are
not shown for cases without significant differences between clean and
non-clean clouds. Bars denote the interquartile range of the data. Green
shading indicates that significant differences were only observed in
non-shallow (
Because reflectivity was fairly low within the thin, predominantly liquid
cloud profiles that fit our criteria, and temperatures were generally
between
A comparison of CALIPSO ONLi cloud thickness (km) with CloudSat
reflectivity (dBZ), as separated by sea ice and open-ocean regimes, and by
clouds found in conditions labeled as clean background conditions, all conditions, and
aerosol-impacted conditions. To better show changes in the two parameters,
plots have been divided into four quadrants (above (grey and blue) and below
(orange and black) the CloudSat reflectivity detection limit of
We expect that the greater uniformity in surface and meteorological
conditions over sea ice will increase the likelihood of being able to isolate
aerosol impacts from meteorological noise, compared to the situation over the
open ocean. Cloud characteristics were indeed fairly uniform over sea ice. We
observed only minor differences in cloud-base height between ONLi clouds
present in clean background conditions and all ONLi clouds (Table 2, Fig. 4).
Above 1.1 km, the cloud-base temperatures in clean background conditions
were not significantly different from those in all air mass conditions. Below
1.1 km, clean background clouds appear to be found in slightly warmer
conditions (by
Clean background clouds were significantly more likely to be precipitating than other clouds in both height bins (Table 2). This observation falls in line with aerosol-driven reductions in snowfall that have been predicted and observed previously, inside and outside of the Arctic (Albrecht, 1989; Borys et al., 2000, 2003; Girard et al., 2005; Lance et al., 2011; Lohmann et al., 2003; Mauritsen et al., 2011; Morrison et al., 2008). These observed reductions in precipitation are inconsistent with the glaciation indirect effect, in which ice formation would be expected to increase due to higher concentrations of combustion-related INPs (Lohmann and Feichter, 2005). The presence of aerosols is also correlated with a significant reduction in radar reflectivity, generally associated with smaller particles on theoretical grounds (Fig. 5, Table 2). Correspondingly, there is also a significantly higher probability that clean background clouds detected by CALIPSO would also be detected by CloudSat than all clouds or aerosol-impacted clouds (Table 2, Fig. 4).
The
There are differences between cloud thicknesses in clean background air and other air masses that suggest the potential for meteorological co-variability in the samples. Clean ONLi clouds are optically and geometrically thicker than the other cloud groups (Table 2, Fig. 4). Lower moisture associated with continental airflow that carries the aerosol might explain this difference (Lohmann and Feichter, 2005), if recent surface contact with warmer, mostly midlatitude regions did not enhance moisture. However, in two related remote sensing studies where Arctic clouds were tightly binned within related meteorological groups, COD differences still appeared, and thus the authors attributed these differences to aerosol-driven changes in the liquid water path (LWP) (Coopman et al., 2016; Tietze et al., 2011).
We also observed a small but significant increase in the portion of detected
liquid-phase clouds within sea ice clouds above 1.1 km (Tables 2 and S1).
The trend in phase was not significant in MOONLiT cases (Table S3), and as
with
It is difficult to say whether the aerosol-related impacts on precipitation and radar reflectivity observed here are simply indicative of Twomey effects on liquid droplets, or whether some more complex mixed-phase and/or meteorological dynamics are also involved. One previous aircraft-based study offered some evidence to suggest that Twomey effects on droplet size may reduce the efficiency of secondary ice formation in the Arctic, particularly for thin clouds (Jackson et al., 2012), which would be consistent with the greater fraction of clouds estimated as liquid phase in non-background clouds. However, low sample number and surface and meteorological variability make this mechanism difficult to conclusively demonstrate on a larger scale. Laboratory studies indicate that smaller droplets may also lower the probability of critical ice embryo formation (Pruppacher and Klett, 2010).
The deactivation effect, whereby sulfates reduce ice-nucleating particle
efficiency
(Du
et al., 2011; Girard et al., 2005, 2013; Lohmann, 2017), could also be
consistent with our observations. Some limited in situ data support the
occurrence of this mechanism
(Jouan et al., 2012), but remote
sensing data are contradictory
(Grenier et al., 2009; Grenier and
Blanchet, 2010), perhaps in part because of high uncertainties in
below-cloud aerosols and a focus on ice-phase clouds, where it is more
difficult for CALIPSO to accurately separate aerosols from ice particles.
Other possible mechanisms that could explain the observed aerosol-related
impacts on cloud properties are that polluted air might contain fewer INPs than clean background air
(Borys, 1989) and/or that riming efficiency could
be reduced (Lohmann and Feichter, 2005). If the very-low-
Whereas cloud properties over sea ice were relatively tightly constrained, there was a much larger range in cloud properties over the open ocean (Table 2) that may in part reflect the greater variability in and higher magnitudes of surface turbulent heat and moisture fluxes over open ocean (e.g., Morrison et al., 2008; Strunin et al., 1997; Taylor et al., 2015). Variability reduced our ability to compare clouds within this regime, as did the uneven vertical distribution of aerosols. CALIPSO-detected aerosols in the Arctic are most frequently found at altitudes below 2 km (Devasthale et al., 2011b; Di Pierro et al., 2013; Kafle and Coulter, 2013; Winker et al., 2013). Over the open ocean, the median ONLi cloud base was above this level (2.1 km), and the median cloud base in the clean background cloud subset was even higher (2.6 km). The greater likelihood of clean background clouds being found at higher altitudes than non-background clouds likely induces a categorical bias in the cloud properties shown in Table 2 and Fig. 4.
To better understand any meteorological bias induced by aerosol height
differences between clean vs. non-clean clouds, but still retain a sample
size from our 2-year dataset that is as informative as possible, we separated
clouds found over open ocean into three cloud-base-height bins (Table S2) and
summarized the resulting information in Table 2 and Fig. 4. As over sea ice,
the first bin includes clouds with base heights between 0.2 and 1.1 km. This
range encompasses the lower quartile range of all open-ocean clouds,
isolating the shallow clouds that were observed to have different
characteristics from the higher clouds in clean background conditions
(Sect. 3.3). This range also happens to coincide with the lower quartile
range of sea ice clouds so that these two bins are more or less comparable to
each other with respect to cloud-base height. The second bin covers
1.2–3.2 km (the interquartile range of open-ocean clouds). The last bin
includes clouds with bases
There are some significant differences between clouds with and without
aerosol influence in non-shallow ONLi clouds with bases above 1.1 km.
Similarly to clouds over sea ice, radar reflectivity is reduced with higher
aerosol influence, and the fraction of low-
The reflectivity and
In contrast to clouds found at higher levels, there were not many
significant differences associated with aerosol influence in shallow ONLi
clouds with bases below 1.1 km. Moreover, some of the differences that were
significant were small enough to not be very meaningful (e.g., a 20 m
reduction in mean cloud-base height with a corresponding 0 m difference in
median cloud-base height for clean clouds compared to all ONLi clouds). This
observation suggests that dynamics might be overwhelming any aerosol changes
to cloud microphysics in this regime, although our sample size for CloudSat-derived parameters was reduced by only assessing those clouds that were
Over our 2-year time period, we identified tens of thousands of
predominantly liquid ONLi clouds over the Arctic Ocean (Table 2). The
sample size and regional spread of the data are large enough that we make
the assumption that the cloud characteristics provided in Table 2
approximate the net nighttime cloud characteristics that exist for this
cloud subset after exposure to the full spectrum of environmental conditions
in each regime (sea ice and open ocean). We calculated the maximum regional radiative impact
of clean background ONLi clouds on the nighttime surface, based on the
regional frequency of occurrence of observable ONLi clouds in nighttime
profiles over the entire (cloudy or clear) Arctic Ocean during our time
period (2.52 and 4.84 % over sea ice and open ocean, respectively;
3.23 % over the full Arctic Ocean domain). Table 2 clean background cloud
characteristics were used to calculate longwave flux changes to the surface
compared to clear air, assuming cloud homogeneity and a single cloud layer,
estimated at 56.05–58.44 W m
The estimated maximum regional radiative impact of clean background ONLi
clouds during polar night was between 1.41–1.47 W m
We also estimated the maximum regional surface indirect radiative effect of
aerosols on ONLi clouds over sea ice. To do so, we subtracted the maximum
regional surface radiative impacts of the clean background cloud subset from
the impacts expected of all observed ONLi clouds. Radiative calculations were
not made for aerosol-driven effects on ONLi clouds over the open ocean due to
the lack of significant differences in most relevant parameters and the
altitude-based bias in the full open-ocean dataset. As with background
clouds, aerosol indirect radiative effect estimates were made using the
median cloud-base and cloud-top heights, the median COD, and the
To be clear, in estimating mean aerosol indirect effects in this section, we did not isolate absolute or local indirect aerosol effects from the confounding effects of meteorology and meteorological co-variability. Instead, we estimated the present-day impact of combustion-derived aerosols on the regional indirect effect that ultimately influences the present-day surface radiation (which includes any meteorological co-variability present during these 2 years). This study was limited to only 2 years of data; future studies with more data might provide a better representation of the full range in aerosol and meteorological conditions the Arctic experiences over longer timescales.
As a final note, in this study we did not account for any aerosol-driven changes in cloud fraction. Aerosol-driven changes in cloud fraction may have occurred given the reduced precipitation in increasingly aerosol-impacted conditions over sea ice (Table 2, Fig. 4). If aerosols do increase cloud fraction, this effect could be the most important indirect impact that aerosols have on the Arctic's surface radiation budget because the presence of a cloud where there otherwise would not be one has more of a local impact on surface radiation than does a change to a cloud that is already present (Feingold et al., 2016; Sedlar and Devasthale, 2012; Shupe and Intrieri, 2004). Addressing these issues will require further study with additional types of data.
Aerosol indirect effects have uncertain, but potentially large, impacts on
the Arctic Ocean surface energy budget. As a step toward reducing
uncertainty in present-day aerosol regional indirect effects on the surface,
here we have better constrained the characteristics of a small subset of
clean, average, and aerosol-impacted clouds for which we have relatively
strong constraints on cloud properties and the associated aerosol
environment. We focused on optically thin (COD
Within the ONLi cloud subset, we observed clear differences between clouds
over open ocean and over sea ice, consistent with different surface and
meteorological conditions in these two regimes. For example, when the
surface is open ocean compared to sea ice, ONLi clouds are much more likely
to overlay another cloud and to be present in the liquid phase. A greater
frequency of multilayer clouds over the open ocean might affect the retreat
of sea ice, and in turn, how this changes the impact of clouds on surface
radiation of the Arctic Ocean. However, further study is needed to expand
this observation beyond just conditions that contain ONLi clouds. There were
also noticeable differences between shallow ONLi clouds (cloud bases
Except in shallow, open-ocean clouds, we observed that ONLi clouds are
susceptible to aerosols. Consistent with other studies, the presence of
aerosols exceeding background levels in clouds over sea ice is associated
with reductions in
We find no evidence to suggest that the glaciation indirect effect is
important within the ONLi cloud subset. Beyond that, we have no strong
support for aerosol impacts on mixed-phase cloud dynamics, although we see
some evidence to suggest that large liquid particles need be
present for ice formation in non-shallow ONLi clouds. These findings are in
line with and expand upon previous aircraft observations
(Jackson et al., 2012), although the
deactivation effect could also explain the results. Aerosols were associated
with higher fractions of liquid-phase clouds than in clean background cases
in both sea ice ONLi clouds
Although we limited this study to carefully describing average and clean background clouds within only a subset of remotely sensed Arctic Ocean clouds, we were able to provide the first observation-based estimate of regional-scale aerosol indirect effects on the surface for such clouds, demonstrating one way in which remote sensing observations can be used to quantitatively assess aerosol–cloud interactions on a regional scale in other conditions and at other locations as well. Given that so far only models have been able to estimate regional aerosol indirect effects on the surface energy balance, this study lays an important foundation for improving the quantification of aerosol indirect effects. The trade-off for selecting a small subset of clouds in this study is the low representativeness of ONLi clouds. To constrain observation-based aerosol impacts and nucleation processes on a larger scale for the Arctic Ocean, optically thick and ice-containing clouds must also be included. Expanding this study to a longer time period would help better incorporate the natural variability in Arctic meteorology and aerosols that might not be represented during this 2-year period. Including daylight or summertime air masses would also be useful; midsummer air masses tend to be cleaner than wintertime Arctic air masses and have a higher fraction of liquid-containing clouds (Van Tricht et al., 2016). Moreover, it would enable the use of MODIS data to examine cloud phase (e.g., via the DARDAR data product; Delanoë and Hogan, 2010) and droplet distribution.
For access to the CALIPSO, ETOPO, NSIDC, and MERRA-2 data, see CALIPSO Science Team (2015a, b),
Amante et al. (2009), Meier et al. (2009), and GMAO (2015), respectively. CloudSat data were obtained from
In Zamora et al. (2016),
the case study CODs were not presented. Here, we calculated the relevant
CODs from the following relationship:
The authors declare that they have no conflict of interest.
We recognize and thank the efforts and funding from the large number of people and agencies involved in making the following datasets available, including the NASA Langley Research Center Atmospheric Science Data Center, which provided the CALIPSO data; the CloudSat Data Processing Center run by the Cooperative Institute for Research in the Atmosphere (CIRA); and the NASA ARCTAS program and its members. Specifically we would like to thank Y. Kondo and B. Anderson for making their ARCTAS data publically available. We also thank G. de Boer, J. Creamean, G. Feingold, K.B. Huebert, J. Limbacher, S. Platnick, A. Solomon, H. Telg, M. Vaughan, D.L. Wu, Y. Yang, H. Yu, and T.L. Yuan for helpful discussions. The research of Lauren Zamora was supported by the NASA ACMAP program, via an appointment to the NASA Postdoctoral Program at the NASA Goddard Space Flight Center, administered by the Universities Space Research Association. The work of Ralph Kahn is supported in part by NASA's Climate and Radiation Research and Analysis Program under H. Maring and NASA's Atmospheric Composition Program under R. Eckman. The NILU team was supported by funding from NordForsk in the framework of eSTICC (eScience Tools for Investigating Climate Change at High Northern Latitudes).Edited by: A. Perring Reviewed by: two anonymous referees