Next Article in Journal
Pollution Characteristics of Water-Soluble Inorganic Ions in PM2.5 from a Mountainous City in Southwest China
Next Article in Special Issue
On the Trend in Below-Cloud Solar Irradiance in The Netherlands versus That in Aerosol Sulfate Concentration
Previous Article in Journal
Projecting Changes in Rainfall Extremes for the Huai River Basin in the Context of 1.5 °C and 2 °C Global Warming
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Atmosphere
Volume 13
Issue 10
10.3390/atmos13101710
atmosphere-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impact of Cloud Condensation Nuclei Reduction on Cloud Characteristics and Solar Radiation during COVID-19 Lockdown 2020 in Moscow

by
Julia Shuvalova
1,2,*,
Natalia Chubarova
1 and
Marina Shatunova
1,2
1
Department of Meteorology and Climatology, Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia
2
Laboratory of Detailed Numerical Weather Forecasts, Hydrometeorological Research Center of the Russian Federation, 123242 Moscow, Russia
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(10), 1710; https://doi.org/10.3390/atmos13101710
Submission received: 18 August 2022 / Revised: 28 September 2022 / Accepted: 13 October 2022 / Published: 18 October 2022
(This article belongs to the Special Issue Aerosols: Direct, Semi-direct, and Indirect Radiative Effects)
Browse Figures
Review Reports Versions Notes

Abstract

:
We used MODIS observations to retrieve number concentration of cloud droplets (Nd) at cloud lower boundary during spring 2018–2020 for the Moscow region. Looking through the similar synoptic situations of the northern clear air advection, we obtained Nd within the limits of 200–300 cm−3. During the lockdown period, with similar northern advection conditions, the reduction of Nd on 40–50 cm−3 (or 14–16%), with the increase in droplet effective radius by 8 ± 1% and cloud optical thickness reduction by 5 ± 2%, was observed in contrast to the values in typical conditions in 2018–2019. We used these values for setting up corresponding parameters in numerical simulations with the COSMO-Ru model. According to the numerical experiments, we showed that the observed reduction in cloud droplet concentration by 50 cm−3 provides a 5–9 W/m2 (or 9–11%) increase in global irradiance at ground in overcast cloud conditions with LWP = 200–400 g/m2.

1. Introduction

Spring 2020 is remembered due to the unique situation of the COVID-19 pandemic lockdown [1]. Reductions in air pollution as an effect of activity restrictions were noted in China [2], India [3], the USA [4], and many European cities [5,6,7]. In Moscow, the lockdown period continued from the end of March until 9 June 2020. Restrictive measures superposed atmosphere circulation features and led to a significant reduction in emissions of pollutants, concentrations of small gas species and particulate matter (PM2.5, PM10) [8,9,10].
Variations in aerosol concentration, which can act as cloud condensation nuclei (CCN), have a significant impact on cloud microphysics, its optical and radiative properties and, hence, on the Earth energy budget [11,12,13], providing so called indirect or aerosol–cloud interaction (ACI) effects. However, still this phenomenon is characterized by large uncertainties [13]. Recent studies show an influence of aerosol content reduction during the lockdown period on cloud parameters: significant correlations between changes in PM2.5 concentrations and thunderstorm cloud activity [14,15], impacting on cloud lower boundary height [16]. The decrease in aerosol concentration over the Indo-Gangetic Plane may lead to a weakening of the Aerosol Invigoration Effect [17]. On the other hand, the effects due to a decrease in aerosol concentration were not revealed for China [18]. In addition, aircraft data and numerical simulations show insignificance of the aerosol indirect effect due to its high variability for Europe [19]. Thereby, the role of aerosol concentration reduction in aerosol–cloud interaction is defined by regional and meteorological conditions.
Relation between cloud droplet number concentration (Nd) and number concentration of cloud condensation nuclei (NCCN) is widely used in retrieval methods and simulations. Usually, NCCN is assumed equal to Nd at cloud base, precisely within the first tens of meters above the cloud base. NCCN can also be related to aerosol optical thickness (AOT) [20] or hydrophilic aerosol concentration Naer [21]. However, this approach is criticized because of the incomplete correspondence of the recovered Nd to the integral AOT values and because AOT and Naer do not fully represent aerosol physical properties. The uncertainty of NCCN retrievals increases for the unpolluted atmosphere when the optical effect of the aerosol is small [22].
Another solution is the application of the methods based on physical relationships of Nd with other cloud characteristics, such as liquid water content or liquid water path, droplet size, cloud optical thickness and radar reflectivity [23,24,25,26,27,28].
In this paper, we estimated the possible effect of Nd reduction during the spring lockdown in Moscow on cloud characteristics and global irradiance. Nd retrieval methods based on satellite measurements are described in Section 2. The results for the lockdown period in comparison with the previous year’s data are presented in Section 3.1. In Section 3.2 and Section 3.3, we discuss an effect of variations in Nd on cloud characteristics and radiation according to observations and simulations.

2. Materials and Methods

We evaluated the aerosol indirect effects based on observations and simulations of COSMO-Ru numerical weather prediction model. A brief flowchart of the research is shown in Figure 1. Further, the applied observation data, methods of Nd retrievals, the structure of numerical experiments, analysis and comparisons of results are presented in detail.
We used MODIS dataset (Collection 6.1 Level 2) [29] to retrieve Nd values, which are available at LAADS DAAC (https://ladsweb.modaps.eosdis.nasa.gov, accessed on 1 February 2021). The main information about the MODIS data used in this study is shown in Table 1. Cloud microphysical and optical parameters are calculated by MODIS bispectral algorithms using observations at wavelength within water absorption lines and out of them [30]. MODIS cloud water path is determined proportionally to the retrieved MODIS cloud optical thickness and MODIS effective radius of cloud droplets [31].
We calculate Nd values using the two following methods. The first one (hereafter, method 1) [35] defines the relationship of Nd with liquid cloud optical thickness (COTliq) and droplet effective radius (Reff) as follows:
N d = k 1 COT liq 0.5 R eff − 2.5 ,
where k1 is equal to 1.37 × 10−5 m−0.5.
According to [27] (hereafter, method 2), Nd is connected with liquid cloud optical thickness and liquid water path (LWP):
N d = k 2 COT liq 3 LWP − 2.5
where k2 is a constant equal to 157.216 kg5/2m−8.
The above expressions are valid for adiabatic liquid low-level cloud (cloud phase infrared and cloud multi-layer flag (see Table 1)), where the cloud droplet number concentration is assumed to be vertically constant and the liquid water content linearly depends on the vertical extent of the cloud and the coefficient of wet adiabatic condensation. The cloud condensation nuclei number concentration is assumed equal to the cloud droplet number concentration. A detailed description of this approach can be found in [36].
There are other more complicated approaches considered: inhomogeneity in solar radiation within the observation cell [37], cloud adiabaticity based on the analysis of satellite measurements at different wavelengths [38] and additional data filtering [22]. However, the comparisons with aircraft observations [39] showed that there was no significant accuracy improvement if these more sophisticated techniques were used.
Cloud features may depend not only on the presence of aerosols as condensation nuclei, but also on the synoptic processes [40,41], which can be described by specification of the air mass water vapor path. We used near-infrared (NIR) spectral MODIS data with 1 km horizontal resolution to evaluate water vapor path (see Table 1), since the observations in NIR spectrum have less air temperature sensitivity compared to infrared-range (IR) data [42]. Water vapor path was analyzed only for cloudless pixels (95% clear sky probability) [43].
We selected the days during springtime in 2018–2020, which corresponded to the restrictions of the applied methods described in [44]. Given the uncertainty in the retrieved Nd increases in the presence of mixed and multilayer clouds, we chose the cases of one- and two-layer liquid clouds. To decrease observation errors of solar radiation at ground and at the top of the atmosphere we examined data when solar height was higher than 25° and radiometer zenith angle was less than 50°. Zinner and Mayer [45] showed that in the case of broken cloudiness, the underestimation of the retrieval cloud characteristics reaches 20%, while for a continuous overcast cloud cover error is about ±5%. The dependence of Nd retrieval accuracy on cloud amount [46] limits the sample to situations with overcast cloudy conditions with cloud amount more than 90%.
Since MODIS cloud data have 5 km horizontal resolution, while the other data have 1 km resolution (see Table 1), we compared Nd retrievals for both spatial resolution options. We also used additional filtering, excluding data, when cloud optical thickness (COT) was less than 5 (COTliq < 5) and effective droplet radius (Reff) was less than 4 μm (Reff < 4 μm) [47].
A significant effect of a change in the concentrations of pollutants over Moscow was noted not only for the entire lockdown period, but also for the periods of the different type air mass advections [8].
Here, we used periods with quasi-homogeneous meteorological conditions (QHMC) in April 2018–May 2020 in the Moscow region, which were defined [8,48] taking into account observations on atmospheric pressure, the predominant winds and day-to-day variations in meteorological parameters together with conditions for the intensity of pollution dispersion (IPD) [49]. Amid the QHMC periods, we chose ones with northern advection to eliminate overestimated lockdown effects and to reduce the influence of large-scale processes on the variability in Nd [50].
We identified 4 days for numerical simulations among ones with northern advection when all above-mentioned conditions were satisfied: 22 April 2018, 31 May 2018, 8 and 22 March 2020. These days were characterized by the presence of stratiform or stratocumulus optically thick clouds over Moscow. This condition was applicable to ensure a better ‘Nd-NCCN’ agreement [51]. The days with precipitation [24], the presence of smoke aerosol and high wind speed (>10 m/s) were also excluded from the sample. Temperature conditions were typical for northern air advection within a range of 2.4–10 °C. Water vapor path (WVP) median values varied from 8.5 up to 12.9 kg/m2 that corresponds to the Arctic air moisture content, taking into account the intra-annual variation and air mass transformation during advection [52,53].
The northern advection air masses coming to Moscow region originate from Northern Atlantic, Arctic Ocean, the northern parts of Siberia and European part of Russia. There are not any major emission sources of PM2.5 and PM10 in these areas according to EMEP (https://www.ceip.at/, accessed on 15 March 2021) and TNO-MACC II (https://eccad.aeris-data.fr, accessed on 15 March 2021). Usual annual anthropogenic emissions of PM2.5 and PM10 based on EMEP database (flux in µg/(m2s), year 2009) and TNO-MACC II database (emission in Tg per year, year 2017) are shown in Figure 2.
HYSPLIT (https://aeronet.gsfc.nasa.gov/, accessed on 15 March 2021) calculations (trajectories in Figure 2) showed that in the lower troposphere the trajectories did not pass through large industrial cities. Thus, remote sources of emissions did not affect the obtained Nd differences between 2018–2019 and 2020.
As a result, we obtained three samples. The first sample contains all available data subject to limitations on satellite observations. The second sample (NA cases) was obtained from the first one by selecting cases of northern advection and the third sample (NWP cases) was obtained from the second one with the use of more stringent selection conditions.
A summary of the observed cloud parameters and retrieved Nd for the three samples is presented in Table 2. NWP cases account for 15% of the second sample (NA cases) and 9% of the all cases. Cloudiness in NWP cases is characterized by larger values of liquid water path and optical thickness.
The results of the standard MODIS bispectral algorithm were used with a base wavelength of 2.1 μm. Their measurement errors generally correspond to the estimates of the observation uncertainties in the updated version of Collection 6. The uncertainties in the retrieved Reff are about 10% and for COTliq are up to 8% for the liquid clouds. Measurement uncertainties for each pixel provided by the developers are on average 6–7% for the droplet effective radius and 6–7% for the optical thickness of liquid clouds. These errors include instrument calibration, atmospheric corrections and errors in the atmospheric model used to reconstruct cloud characteristics [29].
We obtained global solar irradiance data at ground from satellite CERES retrievals (SSF FM2-FM3) [54] and from ground-based measurements from the Meteorological Observatory of the Moscow State University (MO MSU) (http://www.momsu.ru, accessed on 1 August 2022). The latter dataset included global irradiance data from Kipp&Zonen CNR4 and direct radiation data from M-3 actinometer. Kipp&Zonen CNR4 global irradiance measurement error is less than 5% (https://www.kippzonen.com, accessed on 1 August 2022) and the uncertainty in direct radiation observations by M-3 actinometer is less than 4%. Ground-based observations were averaged over one-hour interval.
CERES data were synchronized with 1 km resolution MODIS data within 20 min time window and within a radius of up to 1 km. In this work, we used CERES retrieved radiative flux data using Model B [55], which is based on Langley Parameterized Shortwave Algorithm. Studies of the shortwave downward radiation and the radiation balance at the Earth’s surface under cloudy conditions showed a good agreement between the CERES data and ground-based measurements at different points on the planet [56,57]. The deviation in CERES shortwave radiation relative to observations at continental stations in cloudy conditions is on average 2–4% [54].
We carried out numerical experiments with the COSMO model with 1.1 km grid spacing [58] over the Moscow region for NWP cases. The region of simulations is shown in Figure 3.
The simulations were performed with a 24 h lead time from 00 UTC. Two-moment microphysical scheme [59] was applied. In the COSMO model, radiative transfer scheme is represented by the delta two-stream method [60] with the CLOUDRAD cloud–radiative interaction scheme [61]. The CLOUDRAD scheme takes into account the indirect effects of aerosol through the concentration of cloud condensation nuclei. CLOUDRAD works with several options for initial data to obtain NCCN. In this work, the NCCN was set constant, which made it possible to conduct experiments based on space-averaged observational data and to estimate sensitivity to this characteristic. The NCCN in the CLOUDRAD is assumed equal to the number concentration of cloud droplets in the planetary boundary layer with the exponential decrease above. Thereby, the number concentration of cloud condensation nuclei was equal to the number concentration of cloud droplets both for measurement data and for numerical experiments.

3. Results

3.1. Number Concentrations of Cloud Droplets

First, we calculated Nd by the two mentioned retrieval methods for all types of air masses over Moscow for the springs in 2018–2020 to obtain a general picture for the spring period (Figure 4a). Statistics of the retrieved data are presented in Table 2.
On average, the lockdown spring of 2020 is not distinguished by particularly low concentrations compared to other years. This is because Nd depends more on the synoptic conditions and on the prevalence of specific situations during the period. The median of daily Nd was 184 cm−3, 247 cm−3 and 194 cm−3 for 2018, 2019 and 2020, respectively. Nd values in 2018 were the lowest among the presented years due to the colder spring conditions. The average spring air temperature at 2 m was 6 °C in 2018, 8 °C in 2019 and 7 °C in 2020. During the lockdown period in April–May 2020, during several periods, rather low air temperatures were also observed due to the predominance of northern air advection [8]. In addition, due to the lockdown itself in the Moscow region, the anthropogenic emissions from enterprises decreased by 20% [62]. Traffic load in the city and surrounding territories also decreased significantly, leading to a decrease in the concentration of aerosol precursor gases up to 50–60% [8]. According to [9], surface concentrations of particulate matter (PM10) in the atmosphere of Moscow in April 2020 were 17% lower on highways and 28% lower in residential areas than the average for recent years. Road transport made a substantial contribution to the level of the final decrease in concentrations of suspended aerosol in the atmosphere [63]. A significant reduction in anthropogenic emissions in Moscow affected the content of potential condensation nuclei. In particular, during the northern advection, the average concentration of PM10 during the lockdown period was 12 ± 0.4 μg/m3, while before and after the lockdown, it was 17.3 ± 0.8 μg/m3 [8].
In general, the Nd range varied from several tens to thousands of particles per cubic centimeter. For all cases of spring periods in 2018–2020, Nd median value was 246 cm−3 according to method 1 and 280 cm−3 according to method 2 (Table 2). These results are generally consistent with the results of other similar studies. The droplet concentration over the European part of Russia, on average, over the spring periods of 10 years, was about 300 cm−3 according to MODIS data and about 200 cm−3 according to CALIPSO data [64]. The Nd range over Moscow also corresponds to the values typical for continental cloudiness. According to airborne measurements of continental clouds in the central regions of [65,66,67] the USA, Nd was about 300–400 cm−3 with maximum values up to 1000 cm−3. The high variability in Nd and the dependence on synoptic conditions are noticeable. This complicates the analysis of the Nd effects, so we will further consider only the cases of northern advection. Figure 4b shows the frequency distribution densities of Nd for the NA cases in the spring periods of 2018–2019 and 2020 according to these two methods. For 2018–2019, the median Nd was 272 ± 3 cm−3 according to method 1 and 310 ± 3 cm−3 according to method 2, while in 2020, it was 232 ± 2 cm−3 and 263 ± 3 cm−3, respectively. Contrary to the sample with all cases, the sample with northern advection is characterized by a noticeable shift in the distribution to lower concentrations by 40–50 cm−3 (14–16%) in 2020 relative to 2018–2019.
The Nd distribution for the NWP cases (Figure 4c) is narrower and values higher than 300 cm−3 are not presented practically for the days in 2020 compared to 2018–2019. The Nd median values were 144 ± 2 cm−3 (method 1) and 161 ± 3 cm−3 (method 2) in 2020, while in 2018–2019 Nd median values were 240 ± 4 cm−3 (method 1) and 272 ± 4 cm−3 (method 2), which is 80–110 cm−3 (39–41%) larger than in 2020. The difference in retrieved Nd between the two methods is about 14%, which can be attributed to the droplet effective radius influence [68].
Thus, the NA and NWP samples for 2020 are characterized by lower Nd compared to those observed in 2018–2019, which can be explained by the influence of the lockdown on decreasing the level of air pollution.
Spatial distribution during the lockdown period is quite homogeneous. Contrarily, in 2018–2019, the Nd values varied greatly and reached 2700 cm−3 in some locations. The example of Nd spatial distribution retrieved by method 2 for the days of the NWP sample is presented in Figure 5.
The median value of WVP and cloud characteristics are also shown below the maps for a particular day there. White areas on the maps indicate missing data. The low data availability on 8 May 2020 is associated with the presence of ice clouds over part of the territory, which prevented the Nd retrieval over the entire region. Spatial variability in Nd can be associated with the influence of the city itself. We considered the differences between Nd values in the central part of the city and surrounding territory within the Moscow Ring Road. The excess of cloud droplet concentrations over the city center relatively to the surrounding area was small: on average, it was 9 ± 19 cm−3 (5 ± 7%) according to method 1 and 19 ± 23 cm−3 (9 ± 8%) according to method 2. The reasons for the relatively uniform field may be explained firstly by low level of the aerosol pollution, observed in Moscow, like in many other European cities [69,70] and by the absence of the sulfate hygroscopic aerosol emissions [62]. The vehicle emissions in the city have a major influence on condensation nuclei formation.
We also analyzed the data with coarser grid spacing of 5 km (Table 2). Decreasing the observational resolution leads to a decrease of the average Nd values by 66 ± 13 cm−3 (23%) according to method 1 and by 52 ± 12 cm−3 (16%) according to method 2. The decrease in Nd values is due to the increase in LWP values by 3% and Reff values by 10% along with a COT decrease by 3% in 5 km gridded data. However, using 5 km data with an additional condition for continuous cloud cover allows one to take into account subgrid inhomogeneity of cloudiness, which increases the horizontal component of radiative transfer and affects the retrieved cloud characteristics [47]. According to coarser resolution data, Nd median values for NWP cases in 2018–2019 were 212 ± 12 cm−3 and 254 ± 15 cm−3, depending on retrieval method, while in 2020, they were 129 ± 8 cm−3 and 151 ± 10 cm−3, respectively (Table 2). Thus, taking into account the cloud amount, Nd median value is slightly reduced; however, the tendency towards the Nd decrease during the lockdown period persists.
Based on the retrieved values of Nd from the MODIS data, for our numerical experiments, we chose Nd equal to 100, 150, 200, 250 and 300 cm−3, since these values represent typical cloud droplet number concentration over the Moscow region for the cases of northern air mass advection during the lockdown period and before it.

3.2. Observed and Simulated First Aerosol Indirect Effect

The changes in the cloud droplet number concentration were accompanied by the changes in other cloud parameters as well as by variations in global shortwave irradiance. In these experiments, we deal with the first indirect aerosol effect in observations and simulations; however, this effect may provide further changes in cloud properties. Correlations of liquid water path with droplet effective radius and liquid cloud optical thickness are presented in Figure 6a,b for two samples—NA and NWP—correspondingly for 2020 (shown in red) and 2018–2019 (shown in blue). LWP data are according to the MODIS observations with 1 km grid spacing. We used the intervals of liquid water path according to the evaluation of cloud–aerosol observations for 10 years, described in [71].
According to the MODIS retrievals, the decrease in cloud droplet number concentration during the lockdown period in 2020, together with the growth of droplet effective radius, leads to the COT reduction compared to the values observed in 2018–2019 (averaged difference is shown in plots). For the NA cases, this reduction is about 1.3 ± 1.0 (5 ± 2%) due to the Nd decrease by 43 ± 28 cm−3 (12 ± 7%) and the Reff increase by 0.8 ± 0.1 μm (8 ± 1%). For the NWP cases, the changes are larger: the COT decreases by 4.0 ± 1.9 (18 ± 4%), the Nd decreases by 132 ± 42 (45 ± 9%) cm−3 and Reff increases by 2.3 ± 0.4 μm (25 ± 5%).
The same relationships but according to the simulation are presented in Figure 6c,d. We set the Nd difference of 50 or 100 cm−3, thus, simulating the changes due to lockdown observed in the NA and NWP cases and taking the maximum simulated Reff along the vertical profile. The latter setting provides model Reff values closer to the satellite data estimates, where the measured effective radius corresponds to the top of the cloud. The maximum value also better reflects the effect of change in NCCN due to the peculiarities of the CLOUDRAD scheme, when using space-constant NCCN [61]. In Table 3, we summarize the observed and simulated features of the obtained results.
The indirect effects estimated in our work are comparable with the results obtained in other studies. Christensen [72] showed a COT increase by 28% and a decrease in the effective radius by 10% under conditions of polluted clouds. According to research by Liu [73] for China, a decrease in the Aerosol Index is accompanied by an increase in Reff by 20–30% and a decrease in COT by 25%.
The effects of lockdown on cloud characteristics over the Moscow region are significant for all cases of northern advection. They are not very large but quite noticeable. Based on satellite data for the same type of synoptic conditions of northern advection, we obtained a slight (about 14–16%) decrease in the Nd in the spring of 2020 compared to Nd in 2018–2019 with the increase in droplet effective radius by 8 ± 1% and cloud optical thickness reduction by 5 ± 2% (see Table 3). As mentioned above, this may be attributed to the decrease in industrial anthropogenic emissions up to 20% [62], as well as to the significant decrease in transport emissions, which, in turn, affected the concentration of aerosol and precursor gases [8,9]. The structure of anthropogenic emissions changed during the lockdown period. As shown in many studies for Moscow and other cities around the world, a decrease in transport activity is a main factor in emission reduction [74,75]. It can be assumed that a less pronounced decrease in the Nd compared with the decrease in surface PM10 concentrations of about 30–40% [8,74] can be explained by the location of the major-changed aerosol sources of emission near the ground and characteristics of planetary boundary layer over Moscow. The study of [76] noticed the complex structure of aerosol profile and high dependence of aerosol on the structure of the planetary boundary layer.

3.3. Global Irradiance at Ground

For evaluating the radiative effects of cloud–aerosol interaction and the effects of the lockdown conditions, we compared the CERES data with ground-based observations at the MO MSU. The measurements at the MO MSU were averaged over a 15 min window relative to the time of the satellite observations. All synchronous observations (73 cases in total) for April–May of 2018–2020 are presented in Figure 7a. Color indicates the distance between mean satellite point and ground-based point over the MO MSU. One can see that the CERES data are generally consistent with ground-based measurements, with a correlation coefficient of 0.84. The total CERES global irradiance is lower than the observations at the MO MSU by only 11 ± 27 W/m2 (2%). However, the difference between satellite and ground-based observations slightly increases with the distance between the points. Thus, at a distance of up to 3 km, the standard deviation of the differences is 87 W/m2, and at a distance of up to 5 km, it increases to 119 W/m2. Thus, the CERES data within 0–2 km distance are in good agreement with ground-based measurements and can be used in further analysis.
In order to analyze the joint cloud and radiative characteristics, we combined the MODIS measurements with the CERES data in a 20 min time window for those pixels, which were located within 1 km distance. Taking into account the spatial resolution of the CERES data, the number of cases in the samples was significantly decreased (all cases—1428 pixels, NA—847, NWP—105).
Mean values with confidence intervals for Nd, Reff and COT and WVP of the reduced samples are presented in Figure 6e,f. For all the cases of 2018–2019, WVP was about 14.6 ± 1.2 kg/m2, while in 2020, due to the predominant northern advection during the period, it was 11.0 ± 1.4 kg/m2. For the similar synoptic conditions observed during the northern advection in 2018–2019 and in 2020, WVP was 11.4 ± 1.3 kg/m2 (in 2018–2019) and 9.5 ± 1.2 kg/m2 (in 2020). These values are consistent with data for the transformed Arctic air masses [77]. Note that WVP is used only as an indicator of NA advections and the WVP difference itself did not affect the liquid water path of the NA sample (not shown).
The growth of the effective droplet radius with a decrease in the droplet number concentration for the reduced samples was equal to 1 µm and 4 µm for NA and NWP cases, respectively, in spring 2020 compared to previous years (see Table 3). At the same time, LWP values for the NA and NWP samples in 2020 were, on average, higher by 0.015 kg/m2 and 0.036 kg/m2, respectively, compared to 2018–2019 (not shown). Taking into account the combination of these two factors, the optical thickness of the NA and NWP clouds of the cases in 2018–2019 and 2020 differ little (Figure 6f). For the NWP sample, the cloud optical thickness in 2020 is, on average, lower than that in 2018–2019. However, with a significant decrease in the number of cases, the variability in COT increased.
CERES global irradiance data were adjusted to a solar height of 40° to exclude the influence of its small differences in the samples (Figure 7b). On average, global irradiance was higher by 14 W/m2 in 2020 compared to that in 2018–2019 for the NA sample. At the same time, the difference was negligible for the NWP sample, because the lower droplet concentrations effects were compensated by the higher values of WVP and LWP for these days in 2020, which demonstrated possible effects of warmer and more humid air mass advection for these particular cases (see Figure 6e). We considered global irradiance observations at the MO MSU for the overcast conditions (Figure 7b). Ground-based observations as well as CERES satellite data show an increase of about 20–40 W/m2 in global irradiance in 2020 compared to the previous two years for the northern advection cases.
We also simulated the global irradiance by setting the NCCN in a range of 100–300 cm−3 with a step of 50 cm−3 for evaluating the sensitivity of solar global irradiance to CCN; whereas COSMO model underestimated LWP compare to observations [78,79], here, we consider the sensitivity of the global irradiance to the CCN changes rather than determine the absolute cloud–aerosol radiative effect. Average LWP underestimation relative to MODIS data was 168 ± 2 g/m2 (42 ± 1%) in our experiments with NCCN of 300 cm−3. This led to an overestimation of the global radiation on 112 ± 15 W/m2 (38 ± 5%).
From the experimental results, we obtained that the LWP (main factor in COT and, hence, Q modification) decreases by 4–6% (3–6 g/m2) with an NCCN decrease of 50–100 cm−3. For evaluating the direct relations of the global irradiance with NCCN, we simulated their dependence for overcast conditions and adjusted results to the Sun’s height of 40°. We also averaged the simulated global irradiance over the liquid water path within the interval of LWP = 200–-400 g/m2 to avoid the influence of the LWP variation. Thus, we considered the clear effect of NCCN on global irradiance through the size of cloud droplets without influence of the solar height and liquid water path. One can see a quasi-linear dependence between global irradiance and CCN (Figure 7c) with a 5–9 W/m2 (or 9–11%) decrease for every 50 cm−3 CCN increase.
Evaluated here, the COSMO-Ru simulated aerosol–cloud interaction effect on the global irradiance at the ground corresponds to the assessment of cloud–radiation interaction scheme in the COSMO model. According to the idealized numerical experiments of warm stratus, the global irradiance increases by about 20% when the CCN decreases from 200 to 100 cm−3 [61] (Figure 6b). We obtained the growth of global irradiance by 9% in similar conditions. Taking into account the differences in physical settings in the model, the Sun’s elevation and cloud geometrical depth results are comparable.

4. Discussion and Conclusions

We presented the analysis of cloud condensation nuclei (CCN) number concentrations and first indirect aerosol effect over Moscow in the spring period during the COVID-19 lockdown and during the springs in 2018–2019 according to the observations and numerical experiments. Based on MODIS retrieval, the average cloud number concentration over Moscow was about 200–300 cm−3 in April and May 2018–2020. However, during the lockdown in spring 2020, for the periods of northern air advection, we detected a reduction in droplet number concentration of about 14–16% (40–50 cm−3) compared to the values in the same conditions in 2018–2019. The reduction in Nd was accomplished by an increase in the effective radius of droplets by an average of 8% and a decrease in the optical thickness of clouds by an average of 5% according to MODIS observations. A decrease in cloud condensation nuclei number concentration in the spring of 2020 was found in both datasets with a resolution of 1 km and 5 km. The satellite-based NCCN retrievals have a wide range of uncertainties due to the cloud heterogeneity, which provides a complex three-dimensional structure of radiative transfer along the cloud edges, etc. [47]. We tried to reduce the regular errors of NCCN retrievals, but even with careful evaluation, the uncertainty in retrieved NCCN is high [38,47]. Another method for qualitative NCCN assessment can be achieved using alternative sources of NCCN data (for example, observations of cloud radar) [24].
The decrease in CCN number concentration by about 14–16% is lower than the received reduction in PM10 near the ground by about 30–40% in Moscow [8]. We attribute this discrepancy to complex processes in the planetary boundary layer of the urban area, the influence of atmospheric stratification. However, the effect of decreasing the pollution during the lockdown period should be more pronounced at ground level, located closer to the emission source, which is mainly transport in our case [62]. We plan to add the analysis of vertical aerosol structure in Moscow and its influence on CCN.
We showed good agreement (2%, 11 ± 27 W/m2) of CERES global irradiance retrievals with ground-based observations over the Meteorological Observatory of Moscow State University with r = 0.84 in cloudy conditions, which provides a tool for reliable spatial comparisons of model and CERES satellite data over the whole area.
Using the cloud microphysical retrievals from satellite observations, we carried out numerical experiments with the COSMO model. In the process of comparing the observed and simulated cloud characteristics and radiative fluxes, we obtained a significant underestimation of liquid water path and cloud optical thickness and overestimation of global irradiance at ground. To eliminate the possible impact of model errors on the simulated aerosol indirect effects, we considered only relative changes. According to COSMO model simulations, we show that due to a NCCN decrease by 50 cm−3, the liquid water path decreases, on average, by 4%. Sensitivity of solar irradiance at ground to the changes in NCNN comprises a 5–9 W/m2 (or 9–11%) increase to the reduction in cloud droplet concentration by NCCN = 50 cm−3 in overcast cloud conditions with LWP = 200–400 g/m2 at a solar height of 40°. These estimates are in agreement with the idealized numerical experiments of warm stratus shown in [61].

Author Contributions

J.S. and N.C. analyzed the observation data. J.S. and M.S. designed, carried out and evaluated numerical experiments. All the authors wrote, edited and finalized manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Government of Russia (grant number 075-15-2021-574) and fulfilled at the Lomonosov Moscow State University under the Development Program of the MSU Interdisciplinary Scientific and Educational School “Future Planet and Global Environmental Change”. The implementation and sensitivity studies of two-moment microphysics scheme in COSMO-Ru was applied within Roshydromet Research Work number AAAA-A20-120021490079-3.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The study of CLOUDRAD scheme is provided within CAIIR priority project (http://cosmo-model.org/content/tasks/priorityProjects/caiir, accessed on 11 August 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Marinello, S.; Butturi, M.A.; Gamberini, R. How changes in human activities during the lockdown impacted air quality parameters: A review. Environ. Prog. Sustain. Energy 2021, 40, e13672. [Google Scholar] [CrossRef] [PubMed]
  2. Bao, R.; Zhang, A. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci. Total Environ. 2020, 731, 139052. [Google Scholar] [CrossRef] [PubMed]
  3. Singh, V.; Singh, S.; Biswal, A.; Kesarkar, A.P.; Mor, S.; Ravindra, K. Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India. Environ. Pollut. 2020, 266, 115368. [Google Scholar] [CrossRef]
  4. Liu, Q.; Harris, J.T.; Chiu, L.S.; Sun, D.; Houser, P.R.; Yu, M.; Yang, C. Spatiotemporal impacts of COVID-19 on air pollution in California, USA. Sci. Total Environ. 2021, 750, 141592. [Google Scholar] [CrossRef] [PubMed]
  5. Briz-Redón, Á.; Belenguer-Sapiña, C.; Serrano-Aroca, Á. Changes in air pollution during COVID-19 lockdown in Spain: A multi-city study. J. Environ. Sci. 2021, 101, 16–26. [Google Scholar] [CrossRef]
  6. Cameletti, M. The effect of Corona Virus lockdown on air pollution: Evidence from the city of Brescia in Lombardia Region (Italy). Atmos. Environ. 2020, 239, 117794. [Google Scholar] [CrossRef]
  7. Collivignarelli, M.C.; De Rose, C.; Abba, A.; Baldi, M.; Bertanza, G.; Pedrazzani, R.; Sorlini, S.; Miino, M.C. Analysis of lockdown for CoViD-19 impact on NO2 in London, Milan and Paris: What lesson can be learnt? Process Saf. Environ. Prot. 2021, 146, 952–960. [Google Scholar] [CrossRef]
  8. Chubarova, N.E.; Androsova, E.E.; Lezina, E.A. The dynamics of the atmospheric pollutants during the COVID-19 pandemic 2020 and their relationship with meteorological conditions in Moscow. Geogr. Environ. Sustain. 2021, 14, 168–182. [Google Scholar] [CrossRef]
  9. Ginzburg, A.S.; Semenov, V.A.; Semutnikova, E.G.; Aleshina, M.A.; Zakharova, P.V.; Lezina, E.A. Impact of COVID-19 Lockdown on Air Quality in Moscow. Dokl. Earth Sci. 2020, 495, 862–866. [Google Scholar] [CrossRef]
  10. Yushin, N.; Chaligava, O.; Zinicovscaia, I.; Vergel, K.; Grozdov, D. Mosses as Bioindicators of Heavy Metal Air Pollution in the Lockdown Period Adopted to Cope with the COVID-19 Pandemic. Atmosphere 2020, 11, 1194. [Google Scholar] [CrossRef]
  11. Twomey, S. The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci. 1977, 34, 1149–1152. [Google Scholar] [CrossRef]
  12. Albrecht, B.A. Aerosols, cloud microphysics, and fractional cloudiness. Science 1989, 245, 1227–1230. [Google Scholar] [CrossRef] [PubMed]
  13. Arias, P.A.; Bellouin, N.; Coppola, E.; Jones, R.G.; Krinner, G.; Marotzke, J.; Naik, V.; Palmer, M.D.; Plattner, G.-K.; Rogelj, J.; et al. Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2021; pp. 33–144. [Google Scholar] [CrossRef]
  14. Chowdhuri, I.; Pal, S.C.; Saha, A.; Chakrabortty, R.; Ghosh, M.; Roy, P. Significant decrease of lightning activities during COVID-19 lockdown period over Kolkata megacity in India. Sci. Total Environ. 2020, 747, 141321. [Google Scholar] [CrossRef] [PubMed]
  15. Pérez-Invernón, F.J.; Huntrieser, H.; Gordillo-Vázquez, F.; Soler, S. Influence of the COVID-19 lockdown on lightning activity in the Po Valley. Geophys. Res. Lett. 2021, 263, 105808. [Google Scholar] [CrossRef]
  16. Latha, R.; Murthy, B.S.; Sandeepan, B.S.; Bhanage, V.; Rathod, A.; Tiwari, A.; Beig, G.; Singh, S. Propagation of cloud base to higher levels during Covid-19-Lockdown. Sci. Total Environ. 2021, 759, 144299. [Google Scholar] [CrossRef]
  17. Khatri, P.; Hayasaka, T.; Holben, R.; Tripathi, S.N.; Misra, P.; Patra, P.K.; Hayashida, S.; Dumka, U.C. Aerosol loading and radiation budget perturbations in densely populated and highly polluted Indo-Gangetic Plain by COVID-19: Influences on cloud properties and air temperature. Geophys. Res. Lett. 2021, 48, e2021GL093796. [Google Scholar] [CrossRef]
  18. Diamond, M.S.; Wood, R. Limited regional aerosol and cloud microphysical changes despite unprecedented decline in nitrogen oxide pollution during the February 2020 COVID-19 shutdown in China. Geophys. Res. Lett. 2020, 47, e2020GL088913. [Google Scholar] [CrossRef]
  19. Reifenberg, S.F.; Martin, A.; Kohl, M.; Hamryszczak, Z.; Tadic, I.; Röder, L.; Crowley, D.J.; Fischer, H.; Kaiser, K.; Schneider, J.; et al. Impact of reduced emissions on direct and indirect aerosol radiative forcing during COVID–19 lockdown in Europe. Atmos. Chem. Phys. Discuss. 2021, 1–23. [Google Scholar] [CrossRef]
  20. Shinozuka, Y.; Clarke, A.D.; Nenes, A.; Jefferson, A.; Wood, R.; McNaughton, C.S.; Ström, J.; Tunved, P.; Redemann, J.; Thornhill, K.L.; et al. The relationship between cloud condensation nuclei (CCN) concentration and light extinction of dried particles: Indications of underlying aerosol processes and implications for satellite-based CCN estimates. Atmos. Chem. Phys. 2015, 15, 7585–7604. [Google Scholar] [CrossRef]
  21. McCoy, D.T.; Bender, F.M.; Mohrmann, J.K.C.; Hartmann, D.L.; Wood, R.; Grosvenor, D.P. The global aerosol-cloud first indirect effect estimated using MODIS, MERRA, and AeroCom. J. Geophys. Res. Atmos. 2017, 122, 1779–1796. [Google Scholar] [CrossRef] [Green Version]
  22. Zhu, Y.; Rosenfeld, D.; Li, Z. Under What Conditions Can We Trust Retrieved Cloud Drop Concentrations in Broken Marine Stratocumulus? J. Geophys. Res. Atmos. 2018, 123, 8754–8767. [Google Scholar] [CrossRef] [Green Version]
  23. Brandau, C.L.; Russchenberg, H.W.J.; Knap, W.H. Evaluation of ground-based remotely sensed liquid water cloud properties using shortwave radiation measurements. Atmos. Res. 2010, 96, 366–377. [Google Scholar] [CrossRef]
  24. Merk, D.; Deneke, H.; Pospichal, B.; Seifert, P. Investigation of the adiabatic assumption for estimating cloud micro-and macrophysical properties from satellite and ground observations. Atmos. Chem. Phys. 2016, 16, 933–952. [Google Scholar] [CrossRef] [Green Version]
  25. Rosenfeld, D.; Zheng, Y.; Hashimshoni, E.; Pöhlker, M.; Jefferson, A.; Pöhlker, C.; Yu, X.; Zhu, Y.; Liu, G.; Yue, Z.; et al. Satellite retrieval of cloud condensation nuclei concentrations by using clouds as CCN chambers. Proc. Natl. Acad. Sci. USA 2016, 113, 5828–5834. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Boers, R.; Acarreta, J.R.; Gras, J.L. Satellite monitoring of the first indirect aerosol effect: Retrieval of the droplet concentration of water clouds. J. Geophys. Res. Atmos. 2006, 111, D22. [Google Scholar] [CrossRef] [Green Version]
  27. McComiskey, A.; Feingold, G.; Frisch, A.S.; Turner, D.D.; Miller, M.A.; Chiu, J.C.; Min, Q.; Ogern, J.A. An assessment of aerosol-cloud interactions in marine stratus clouds based on surface remote sensing. J. Geophys. Res. Atmos. 2009, 114, D09203. [Google Scholar] [CrossRef] [Green Version]
  28. Grandey, B.S.; Stier, P. A critical look at spatial scale choices in satellite-based aerosol indirect effect studies. Atmos. Chem. Phys. 2010, 10, 11459–11470. [Google Scholar] [CrossRef]
  29. Platnick, S.; Meyer, K.G.; King, M.D.; Wind, G.; Amarasinghe, N.; Marchant, B.; Arnold, G.T.; Zhang, Z.; Hubanks, P.A.; Holz, R.E.; et al. The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE Trans. Geosci. Remote Sens. 2016, 55, 502–525. [Google Scholar] [CrossRef]
  30. King, M.D.; Tsay, S.C.; Platnick, S.E.; Wang, M.; Liou, K.N. Cloud Retrieval Algorithms for MODIS: Optical Thickness, Effective Particle Radius, and Thermodynamic Phase. MODIS Algorithm Theoretical Basis Document. 1997. Available online: https://cimss.ssec.wisc.edu/dbs/China2011/Day2/Lectures/MOD06OD_Algorithm_Theoretical_Basis_Document.pdf (accessed on 11 August 2022).
  31. Platnick, S.; King, M.D.; Ackerman, S.A.; Menzel, W.P.; Baum, B.A.; Riédi, J.C.; Frey, R.A. The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens. 2003, 41, 459–473. [Google Scholar] [CrossRef]
  32. Platnick, S.; King, M.D.; Arnold, G.T.; McGill, M.; Ackerman, S.A.; Baum, B.A.; Yang, P. Multilayer Cloud Detection in the MODIS Collection 5 Cloud Product. Hyperspectral Imaging Sound. Environ. 2007, JWA15. [Google Scholar] [CrossRef]
  33. Marchant, B.; Platnick, S.; Meyer, K.; Arnold, G.T.; Riedi, J. MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP. Atmos. Meas. Tech. 2016, 9, 1587–1599. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Gao, B.C.; Kaufman, Y.J. Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared channels. J. Geophys. Res.Atmos. 2003, 108, D13. [Google Scholar] [CrossRef]
  35. Quaas, J.; Boucher, O.; Lohmann, U. Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data. Atmos. Chem. Phys. 2006, 6, 947–955. [Google Scholar] [CrossRef] [Green Version]
  36. Brenguier, J.L.; Pawlowska, H.; Schüller, L.; Preusker, R.; Fischer, J.; Fouquart, Y. Radiative properties of boundary layer clouds: Droplet effective radius versus number concentration. J. Atmos. Sci. 2000, 57, 803–821. [Google Scholar] [CrossRef]
  37. Liang, L.; Di Girolamo, L.; Platnick, S. View-angle consistency in reflectance, optical thickness and spherical albedo of marine water clouds over the northeastern Pacific through MISR-MODIS fusion. Geophys. Res. Lett. 2009, 36, L09811. [Google Scholar] [CrossRef] [Green Version]
  38. Bennartz, R.; Rausch, J. Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations. Atmos. Chem. Phys. 2017, 17, 9815–9836. [Google Scholar] [CrossRef] [Green Version]
  39. Gryspeerdt, E.; McCoy, D.T.; Crosbie, E.; Moore, R.H.; Nott, G.J.; Ziemba, L. The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data. Atmos. Meas. Tech. 2022, 15, 3875–3892. [Google Scholar] [CrossRef]
  40. Kourtidis, K.; Stathopoulos, S.; Georgoulias, A.K.; Alexandri, G.; Rapsomanikis, S. A study of the impact of synoptic weather conditions and water vapor on aerosol–cloud relationships over major urban clusters of China. Atmos. Chem. Phys. 2015, 15, 10955–10964. [Google Scholar] [CrossRef] [Green Version]
  41. Liu, Q.; Duan, S.; He, Q.; Chen, Y.; Zhang, H.; Cheng, N.; Li, J. The variability of warm cloud droplet radius induced by aerosols and water vapor in Shanghai from MODIS observations. Atmos. Res. 2021, 253, 105470. [Google Scholar] [CrossRef]
  42. Bai, J.; Lou, Y.; Zhang, W.; Zhou, Y.; Zhang, Z.; Shi, C. Assessment and calibration of MODIS precipitable water vapor products based on GPS network over China. Atmos. Res. 2021, 254, 105504. [Google Scholar] [CrossRef]
  43. Khaniani, A.S.; Nikraftar, Z.; Zakeri, S. Evaluation of MODIS Near-IR water vapor product over Iran using ground-based GPS measurements. Atmos. Res. 2020, 231, 104657. [Google Scholar] [CrossRef]
  44. Quaas, J.; Arola, A.; Cairns, B.; Christensen, M.; Deneke, H.; Ekman, A.M.; Feingold, G.; Fridlind, A.; Gryspeerdt, E.; Hasekamp, O.; et al. Constraining the Twomey effect from satellite observations: Issues and prespectives. Atmos. Chem. Phys. 2020, 20, 15079–15099. [Google Scholar] [CrossRef]
  45. Zinner, T.; Mayer, B. Remote sensing of stratocumulus clouds: Uncertainties and biases due to inhomogeneity. J. Geophys. Res. Atmos. 2006, 111, D14209. [Google Scholar] [CrossRef] [Green Version]
  46. Painemal, D.; Chang, F.L.; Ferrare, R.; Burton, S.; Li, Z.; Clayton, M. Reducing uncertainties in satellite estimates of aerosol–cloud interactions over the subtropical ocean by integrating vertically resolved aerosol observations. Atmos. Chem. Phys. 2020, 20, 7167–7177. [Google Scholar] [CrossRef]
  47. Grosvenor, D.P.; Sourdeval, O.; Zuidema, P.; Ackerman, A.; Alexandrov, M.D.; Bennartz, R.; Boers, R.; Cairns, B.; Chiu, J.C.; Christensen, M.; et al. Remote sensing of droplet number concentration in warm clouds: A review of the current state of knowledge and perspectives. Rev. Geophys. 2018, 56, 409–453. [Google Scholar] [CrossRef]
  48. Chubarova, N.Y.; Zhdanova, Y.Y.; Androsova, Y.Y.; Kirsanov, A.A.; Shatunova, M.V.; Khlestova, Y.O.; Volpert, E.V.; Poliukhov, A.A.; Eremina, I.D.; Vlasov, D.V.; et al. The Aerosol Pollution of Cities and Its Effects on the Weather Forecast, Regional Climate and Geochemical Processes; MAKS Press Ltd.: Moscow, Russia, 2020. (In Russian) [Google Scholar]
  49. Kuznetsova, I.N.; Shalygina, I.Y.; Nakhaev, M.I.; Glazkova, A.A.; Zakharova, P.V.; Lezina, Y.A.; Zvyagintsev, A.M. Unfavorable for air quality meteorological factors. Proc. Hydrometcentre Russ. 2014, 351, 154–172. (In Russian) [Google Scholar]
  50. George, R.C.; Wood, R. Subseasonal variability of low cloud radiative properties over the southeast Pacific Ocean. Atmos. Chem. Phys. 2010, 10, 4047–4063. [Google Scholar] [CrossRef] [Green Version]
  51. Reutter, P.; Su, H.; Trentmann, J.; Simmel, M.; Rose, D.; Gunthe, S.S.; Wernli, H.; Andreae, M.O.; Pöschl, U. Aerosol-and updraft-limited regimes of cloud droplet formation: Influence of particle number, size and hygroscopicity on the activation of cloud condensation nuclei (CCN). Atmos. Chem. Phys. 2009, 9, 7067–7080. [Google Scholar] [CrossRef] [Green Version]
  52. Rinke, A.; Segger, B.; Crewell, S.; Maturilli, M.; Naakka, T.; Nygard, T.; Vihma, T.; Alshawaf, F.; Dick, G.; Wickert, J.; et al. Trends of vertically integrated water vapor over the Arctic during 1979–2016: Consistent moistening all over? J. Clim. 2019, 32, 6097–6116. [Google Scholar] [CrossRef]
  53. Crewell, S.; Ebell, K.; Konjari, P.; Mech, M.; Nomokonova, T.; Radovan, A.; Strack, D.; Triana-Gomez, A.M.; Noöl, S.; Scarlat, R.; et al. A systematic assessment of water vapor products in the Arctic: From instantaneous measurements to monthly means. Atmos. Meas. Tech. 2021, 14, 4829–4856. [Google Scholar] [CrossRef]
  54. Kratz, D.P.; Gupta, S.K.; Wilber, A.C.; Sothcott, V.E. Validation of the CERES Adition-4A Surface-Only Flux Algorithms. J. Appl. Meteorol. Climatol. 2020, 59, 281–295. [Google Scholar] [CrossRef]
  55. Gupta, S.K.; Kratz, D.P.; Stackhouse, P.W.; Wilber, A.C. The Langley Parameterized Shortwave Algorithm (LPSA) for Surface Radiation Budget Studies; NASA Technology Paper TP-2001-211272; Langley Research Center: Hampton, VA, USA, 2001; pp. 1–31.
  56. Yan, H.; Huang, J.; Minnis, P.; Wang, T.; Bi, J. Comparison of CERES surface radiation fluxes with surface observations over Loess Plateau. Remote Sens. Environ. 2011, 115, 1489–1500. [Google Scholar] [CrossRef] [Green Version]
  57. Jia, A.; Jiang, B.; Liang, S.; Zhang, X.; Ma, H. Validation and spatiotemporal analysis of CERES surface net radiation product. Remote Sens. 2016, 8, 90. [Google Scholar] [CrossRef] [Green Version]
  58. Rivin, G.S.; Rozinkina, I.A.; Vil’fand, R.M.; Kiktev, D.B.; Tudrii, K.O.; Blinov, D.V.; Varentsov, M.I.; Zakharchenko, D.I.; Samsonov, T.E.; Repina, I.A.; et al. Development of the High-resolution Operational System for Numerical Prediction of Weather and Severe Weather Events for the Moscow Region. Russ. Meteorol. Hydrol. 2020, 45, 455–465. [Google Scholar] [CrossRef]
  59. Seifert, A.; Beheng, K.D. A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description. Meteorol. Atmos. Phys. 2006, 92, 45–66. [Google Scholar] [CrossRef]
  60. Ritter, B.; Geleyn, J.F. A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Weather Rev. 1992, 120, 303–325. [Google Scholar] [CrossRef]
  61. Muskatel, H.; Blahak, U.; Khain, P.; Levi, Y.; Fu, Q. Parametrizations of Liquid and Ice Clouds’ Optical Properties in Operational Numerical Weather Prediction Models. Atmosphere 2021, 12, 89. [Google Scholar] [CrossRef]
  62. Semutnikova, E.G.; Kudryashova, Y.S.; Zhevachevsky, I.M.; Alekseev, D.A.; Zakharova, P.V.; Kravtcova, E.S.; Zhalnina, O.N.; Lyzhnikov, O.G.; Emelyanov, P.V.; Sidorenko, S.N.; et al. The ecologization of urban economy and transport system in Moscow. In The Report on the State of Moscow Environment in 2020; Kulbachevsky, A.O., Ed.; Department of Nature Management and Environmental Protection of the City of Moscow: Moscow, Russia, 2021; pp. 162–206. (In Russian) [Google Scholar]
  63. Guevara, M.; Jorba, O.; Soret, A.; Petetin, H.; Bowdalo, D.; Serradell, K.; Tena, C.; van der Gon, H.D.; Kuenen, J.; Peuch, V.-H.; et al. Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns. Atmos. Chem. Phys. 2021, 21, 773–779. [Google Scholar] [CrossRef]
  64. Li, J.; Jian, B.; Huang, J.; Hu, Y.; Zhao, C.; Kawamoto, K.; Liao, S.; Wu, M. Long-term variation of cloud droplet number concentrations from space-based Lidar. Remote Sens. Environ. 2018, 213, 144–161. [Google Scholar] [CrossRef]
  65. Austin, P.H.; Baker, M.B.; Blyth, A.M.; Jensen, J.B. Small-scale variability in warm continental cumulus clouds. J. Atmos. Sci. 1985, 42, 1123–1138. [Google Scholar] [CrossRef]
  66. Paluch, I.R.; Knight, C.A. Mixing and the evolution of cloud droplet size spectra in a vigorous continental cumulus. J. Atmos. Sci. 1984, 41, 1801–1815. [Google Scholar] [CrossRef]
  67. Sassen, K.; Mace, G.G.; Wang, Z.; Poellot, M.R.; Sekelsky, S.M.; McIntosh, R.E. Continental Stratus Clouds: A case Study Using Coordinated Remote Sensing and Aircraft Measurements. J. Atmos. Sci. 1999, 56, 2345–2358. [Google Scholar] [CrossRef]
  68. Rausch, J.; Meyer, K.; Bennartz, R.; Platnick, S. Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS collections 5.1 and 6 over global oceans. Atmos. Meas. Tech. 2017, 10, 2105–2116. [Google Scholar] [CrossRef] [Green Version]
  69. Putaud, J.P.; Cavalli, F.; Martins dos Santos, S.; Dell’Acqua, A. Long-term trends in aerosol optical characteristics in the Po Valley, Italy. Atmos. Chem. Phys. 2014, 14, 9129–9136. [Google Scholar] [CrossRef] [Green Version]
  70. Guerreiro, C.B.; Foltescu, V.; De Leeuw, F. Air quality status and trends in Europe. Atmos. Environ. 2014, 98, 376–384. [Google Scholar] [CrossRef] [Green Version]
  71. Yan, H.; Wang, T. Ten years of aerosol effects on single-layer overcast clouds over the US Southern Great Plains and the China Loess Plateau. Adv. Meteorol. 2020, 2020, 6719160. [Google Scholar] [CrossRef]
  72. Christensen, M.W.; Jones, W.K.; Stier, P. Aerosols enhance cloud lifetime and brightness along the stratus-to-cumulus transition. Proc. Natl. Acad. Sci. USA 2020, 117, 17591–17598. [Google Scholar] [CrossRef]
  73. Liu, J.; Li, Z. First surface-based estimation of the aerosol indirect effect over a site in southeastern China. Adv. Atmos. Sci. 2018, 35, 169–181. [Google Scholar] [CrossRef]
  74. Gubanova, D.P.; Elansky, N.F.; Skorokhod, A.I.; Kuderina, T.M.; Iordansky, M.A.; Sadovskaya, N.V.; Anikin, P.P. Physical and chemical properties of atmospheric aerosols in Moscow and its suburb for climate assessments. IOP Conf. Ser. Earth Environ. Sci. 2020, 606, 012019. [Google Scholar] [CrossRef]
  75. Sokhi, R.S.; Singh, V.; Querol, X.; Finardi, S.; Targino, A.C.; Andrade, M.d.F.; Pavlovic, R.; Garland, R.M.; Massague, J.; Kong, S.; et al. A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions. Environ. Int. 2021, 157, 106818. [Google Scholar] [CrossRef]
  76. Su, T.; Li, Z.; Li, C.; Li, J.; Han, W.; Shen, C.; Tan, W.; Wei, J.; Guo, J. The significant impact of aerosol vertical structure on lower atmosphere stability and its critical role in aerosol-planetary boundary layer (PBL) interactions. Atmos. Chem. Phys. 2020, 20, 3713–3724. [Google Scholar] [CrossRef] [Green Version]
  77. Nomokonova, T.; Ebell, K.; Löhnert, U.; Maturilli, M.; Ritter, C. The influence of water vapor anomalies on clouds and their radiative effect at Ny-Ålesund. Atmos. Chem. Phys. 2020, 20, 5157–5173. [Google Scholar] [CrossRef]
  78. Dipu, S.; Quaas, J.; Wolke, R.; Stoll, J.; Mühlbauer, A.; Sourdeval, O.; Salzmann, M.; Heinold, B.; Tegen, I. Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT (5.0) and evaluation using satellite data. Geosci. Model Dev. 2017, 10, 2231–2246. [Google Scholar] [CrossRef] [Green Version]
  79. Köhler, F.; Görsdorf, U. Towards 3D prediction of supercooled liquid water for aircraft icing: Modifications of the microphysics in COSMO-EU. Meteorol. Z. 2014, 23, 253–262. [Google Scholar] [CrossRef]
Atmosphere 13 01710 g001 550
Figure 1. A flowchart of research aerosol indirect effects over Moscow using observations and numerical experiments.
Figure 1. A flowchart of research aerosol indirect effects over Moscow using observations and numerical experiments.
Atmosphere 13 01710 g001
Atmosphere 13 01710 g002 550
Figure 2. Annual anthropogenic emissions of PM2.5 and PM10 over Europe from EMEP database for 2009 ((a,b), in μg/(m2s)) and TNO-MACC II database for 2017 ((c,d), in Tg per year). The lines are the backward trajectories from HYSPLIT model at 500 m above ground for NWP cases (12 UTC of each day).
Figure 2. Annual anthropogenic emissions of PM2.5 and PM10 over Europe from EMEP database for 2009 ((a,b), in μg/(m2s)) and TNO-MACC II database for 2017 ((c,d), in Tg per year). The lines are the backward trajectories from HYSPLIT model at 500 m above ground for NWP cases (12 UTC of each day).
Atmosphere 13 01710 g002
Atmosphere 13 01710 g003 550
Figure 3. The area of numerical experiments. The borders of Russian regions are shown in white. The Moscow metropolis is located in the center. The figure was created using Google Earth Pro.
Figure 3. The area of numerical experiments. The borders of Russian regions are shown in white. The Moscow metropolis is located in the center. The figure was created using Google Earth Pro.
Atmosphere 13 01710 g003
Atmosphere 13 01710 g004 550
Figure 4. Frequency distribution of Nd by the two methods in April–May 2020 and 2018–2019 for three samples: (a) all cases, (b) northern advection cases, (c) NWP cases.
Figure 4. Frequency distribution of Nd by the two methods in April–May 2020 and 2018–2019 for three samples: (a) all cases, (b) northern advection cases, (c) NWP cases.
Atmosphere 13 01710 g004
Atmosphere 13 01710 g005 550
Figure 5. Spatial distribution of the droplet number concentration retrieved by method 2 for NWP sample. Each subfigure (a–g) describes an observational case.
Figure 5. Spatial distribution of the droplet number concentration retrieved by method 2 for NWP sample. Each subfigure (a–g) describes an observational case.
Atmosphere 13 01710 g005
Atmosphere 13 01710 g006 550
Figure 6. Lockdown 2020 effect on cloud characteristics for different samples: MODIS droplet effective radius and cloud optical thickness for NA (a) and NWP (b) samples; simulated maximum effective radius in the cloud profile and cloud optical thickness for NA (c) and NWP (d) samples; reduced samples (coupled MODIS and CERES data) for water vapor path and droplet number concentration (e); droplet effective radius and cloud optical thickness (f). Data for 2020 are marked by red, for 2018–2019, by blue colors.
Figure 6. Lockdown 2020 effect on cloud characteristics for different samples: MODIS droplet effective radius and cloud optical thickness for NA (a) and NWP (b) samples; simulated maximum effective radius in the cloud profile and cloud optical thickness for NA (c) and NWP (d) samples; reduced samples (coupled MODIS and CERES data) for water vapor path and droplet number concentration (e); droplet effective radius and cloud optical thickness (f). Data for 2020 are marked by red, for 2018–2019, by blue colors.
Atmosphere 13 01710 g006
Atmosphere 13 01710 g007 550
Figure 7. Observed and simulated global irradiance at ground: (a) CERES and MO MSU synchronous data (color indicates the distance between observations points); (b) CERES data over Moscow region (the same samples as for Figure 6e–f) and ground-based MO MSU global irradiance for three samples (data for 2020 is marked by red, for 2018–2019—by blue colors); (c) simulated global irradiance in dependence on Nccn for LWP = 200–400 gm−2 for overcast condition. Solar height is equal 40º.
Figure 7. Observed and simulated global irradiance at ground: (a) CERES and MO MSU synchronous data (color indicates the distance between observations points); (b) CERES data over Moscow region (the same samples as for Figure 6e–f) and ground-based MO MSU global irradiance for three samples (data for 2020 is marked by red, for 2018–2019—by blue colors); (c) simulated global irradiance in dependence on Nccn for LWP = 200–400 gm−2 for overcast condition. Solar height is equal 40º.
Atmosphere 13 01710 g007
Table
Table 1. A brief description of the MODIS data used in this study.
Table 1. A brief description of the MODIS data used in this study.
CharacteristicData
Resolution, km		Method of
Observation
Cloud optical thickness1[30]
Liquid water path1[31]
Droplets effective radius1[30]
Cloud multi-layer flag1[32]
Cloud phase infrared1[31,33]
Water vapor path1[34]
Cloud fraction5[31]
Table
Table 2. Median and interquartile range (shown through a slash “/”) of the observed and retrieved cloud characteristics for the samples. Errors were provided with the data by the developers.
Table 2. Median and interquartile range (shown through a slash “/”) of the observed and retrieved cloud characteristics for the samples. Errors were provided with the data by the developers.
CharacteristicsData Grid
Step, km		All CasesOnly Northern
Advection Cases
(NA Cases)		Selected Northern
Advection Cases for Numerical
Experiments (NWP Cases)
Number of daysAll: 116All: 66All: 4
2018: 262018: 112018: 22/04, 31/05
2019: 422019: 16
2020: 482020: 392020: 08/05, 22/05
Effective radius of cloud droplets (Reff), μm 19/49/411/4
Reff error, %7/26/16/1
Liquid water path
(LWP), g/m2		106/138120/150151/175
LWP error, %16/616/615/4
Liquid cloud optical
thickness (COTliq)		19/2121/2223/22
COTliq error, %7/57/57/4
Number of points33806518715228809
Number of points519705119791371
Nd according to method 1, cm−3
2018–2020
2018–2019
2020		1246/274
252/270
232/281		250/286
272/292
232/278		188/159
240/188
144/114
2018–2020
2018–2019
2020		5213/250
220/244
201/256		223/254
243/256
208/259		167/133
212/132
129/112
Nd according to method 2, cm−3
2018–2020
2018–2019
2020		1280/322
288/318
264/331		285/336
310/345
263/327		212/185
272/220
161/130
2018–2020
2018–2019
2020		5259/309
267/303
245/321		272/327
300/331
251/321		194/153
254/169
151/123
Table
Table 3. Observed and simulated changes in cloud microphysical and optical characteristics in spring 2020 compared to the values in springs 2018–2019.
Table 3. Observed and simulated changes in cloud microphysical and optical characteristics in spring 2020 compared to the values in springs 2018–2019.
DataSampleΔNd, cm−3ΔReff, μmΔCOTNumber of Points
MODISNA−43 ± 28 (−12 ± 7%)0.8 ± 0.1 (8 ± 1%)−1.3 ± 1.0
(−5 ± 2%)		84,866
NWP−132 ± 42 (−45 ± 9%)2.3 ± 0.4 (25 ± 5%)−4.0 ± 1.9 (18 ± 4%)15,068
COSMO-RuNWP (variant 1)−500.5 ± 0.2 (6 ± 2%)−1.8 ± 1.7
(−4 ± 2%)		2,668,076
NWP (variant 2)−1001.1 ± 0.5 (15 ± 5%)−4.0 ± 3.7
(−9 ± 5%)		2,684,304
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Shuvalova, J.; Chubarova, N.; Shatunova, M. Impact of Cloud Condensation Nuclei Reduction on Cloud Characteristics and Solar Radiation during COVID-19 Lockdown 2020 in Moscow. Atmosphere 2022, 13, 1710. https://doi.org/10.3390/atmos13101710

AMA Style

Shuvalova J, Chubarova N, Shatunova M. Impact of Cloud Condensation Nuclei Reduction on Cloud Characteristics and Solar Radiation during COVID-19 Lockdown 2020 in Moscow. Atmosphere. 2022; 13(10):1710. https://doi.org/10.3390/atmos13101710

Chicago/Turabian Style

Shuvalova, Julia, Natalia Chubarova, and Marina Shatunova. 2022. "Impact of Cloud Condensation Nuclei Reduction on Cloud Characteristics and Solar Radiation during COVID-19 Lockdown 2020 in Moscow" Atmosphere 13, no. 10: 1710. https://doi.org/10.3390/atmos13101710

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop