Open AccessArticle

Optical–Physical Characteristics of Low Clouds and Aerosols in South America Based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation

Bo Su

^1,2,3,

Dekai Lin

¹,

Ziji Liu

¹,

Qingyan Wu

¹,

Wenkai Song

¹ and

Miao Zhang

^1,2,3,*

Academy of Remote Sensing Technology and Application, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, China

Key Laboratory of Natural Disaster and Remote Sensing of Henan Province, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, China

Engineering Research Center of Environmental Laser Remote Sensing Technology and Application of Henan Province, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, China

Author to whom correspondence should be addressed.

Atmosphere 2024, 15(12), 1513; https://doi.org/10.3390/atmos15121513 (registering DOI)

Submission received: 19 November 2024 / Revised: 7 December 2024 / Accepted: 13 December 2024 / Published: 17 December 2024

(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)

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Abstract

Clouds and aerosols, as important factors in the Earth’s climate system, have significant impacts on the atmospheric environment and global climate. This study investigated the optical and physical properties of clouds and aerosols over South America from 2006 to 2021 using CALIPSO Level 2 products. South America was divided into four regions: A (Western Andean Mountains), B (Northern Orinoco and Amazon plains), C (Southern La Plata Plains), and D (Eastern Brazilian Highlands). Seasonal variations in the optical properties of low clouds and their interactions with the lowest-layer aerosols were analyzed and compared. The results indicate that Region C had the highest OP_lc (probability of low clouds) and AOD_lc (AOD (Aerosol Optical Depth) of low clouds, likely due to its flat terrain and westerly influences. Both AOD_lc and OP_lc were higher in September–November compared to other seasons. DR_lc (depolarization ratio of low clouds) values were higher in Regions C and D, particularly in September–February, possibly due to topographic effects and more precipitation and higher humidity during this period. The elevated CR_lc (color ratio of low clouds) in Region A may be attributed to the Andes blocking warm, moist air, leading to increased precipitation and cloud particle content. HL_lc (top height of low clouds) and BL_lc (base altitude of low clouds) were positively correlated with geographic elevation, while T_lc (thickness of low clouds) was greater at night, potentially due to enhanced atmospheric stability. Furthermore, strong correlations among certain parameters suggested significant interactions between aerosols and clouds.

Keywords:

low clouds; aerosol; optical property; vertical distribution; CALIPSO satellite

1. Introduction

In the atmospheric system, clouds and aerosols are key components that significantly influence the Earth’s climate. Through complex interaction mechanisms, they profoundly impact the global energy balance, precipitation patterns, and climate change [1,2,3]. Aerosols exert a substantial effect on the Earth’s energy balance by directly scattering and absorbing solar radiation and indirectly altering the optical properties and lifecycle of clouds. Low clouds (cloud base height, CBH < 2.5 km), as a significant component of the cloud system, have extensive coverage and a considerable influence on the radiative budget of the Earth–atmosphere system [4,5,6,7]. Therefore, the vertical distribution of low clouds and aerosols, along with their interactions, is crucial for understanding atmospheric physical processes and climate system feedbacks and formulating appropriate environmental policies [2,8,9,10]. In recent years, this area has attracted increasing attention; however, due to the complexity of the underlying mechanisms and the significant influence of regional factors, further research is needed to fully elucidate the physicochemical and optical properties, vertical distribution, and interactions between low clouds and aerosols.

The study of aerosols and clouds primarily relies on ground-based observations and satellite observations, with the latter having been proven to be a reliable tool for characterizing clouds or precipitation within cyclones [11,12,13,14]. To further advance research in this field, NASA (National Aeronautics and Space Administration) and CNES (Centre National d’Études Spatiales) jointly initiated the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) mission, which was launched from Vandenberg Air Force Base on 28 April 2006, alongside the CloudSat satellite [15,16,17]. The CALIPSO satellite is equipped with CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), which is the first lidar to provide long-term atmospheric measurements from Earth’s orbit. This lidar is used to detect the vertical distribution of aerosols and clouds in the atmosphere, as well as their optical and microphysical properties. CALIOP offers unique advantages, such as long-term, continuous, high-precision monitoring and the ability to operate day and night. It can also observe the multi-layered structures of thin clouds and aerosols [15,18,19,20]. With advancements in active optical remote sensing, the use of the CALIPSO lidar allows for precise measurements of atmospheric scattering layers, providing high-resolution vertical profiles, and offers significant advantages in determining the multi-layered structure of clouds and studying thin clouds [21,22].

South America, characterized by its geographical diversity and climatic complexity, is selected as a key study area for investigating the optical and physical properties of aerosols and low clouds. This region, featuring the Amazon rainforest, the Andes Mountains, and extensive plains, provides an ideal setting for studying aerosol–cloud interactions due to its unique natural environment and diverse aerosol sources. The Amazon rainforest, the world’s largest tropical rainforest and a critical carbon sink, significantly influences the global climate. South America also hosts multiple aerosol sources, including natural (forest fires, vegetation emissions) and anthropogenic (industrial emissions, agricultural activities), which offer valuable data for research [23,24]. Previous studies utilized long-term ground-based observation station data and satellite remote sensing data to reveal the temporal trends in aerosols and their long-term impacts on the climate system. In particular, research in key regions, such as the Amazon rainforest, elucidated the specific effects of aerosols on local ecosystems and the water cycle [25,26,27,28,29,30]. However, there were several limitations. Observation stations were unevenly distributed across South America, with insufficient data available in certain areas, especially remote regions. Additionally, most studies focused on specific seasons or extreme events, and there was a lack of research on the annual cycle of aerosol variations, making it difficult to fully understand their long-term climatic impacts [31,32,33]. Notable research includes Daniel et al.’s reliable estimates of cloud condensation nuclei (CCN) aerosols over the South Atlantic using MODIS data [34], Breón et al.’s investigation into aerosol–cloud droplet interactions [35], and Jonathan et al.’s analysis of continental aerosol environments, including smoke, dust, and pollution, in South America, Central Africa, and Southeast Asia using CloudSat and CALIPSO satellite data [36]. However, there are currently limited studies that specifically investigate the vertical-distribution relationship between low clouds and aerosol layers both during the day and at night over South America. Therefore, this study aims to employ Level 2 cloud and aerosol data from CALIOP to analyze the vertical distribution of aerosol and cloud optical properties across South America from 2006 to 2021, covering both diurnal and nocturnal periods. Our objective is to achieve a deeper understanding of the characteristics of low cloud layers in South America, including their seasonal and regional variations, as well as their interactions with aerosols. This research seeks to provide a more comprehensive understanding of the atmospheric environment over South America, offering new scientific insights into the region’s climate change and ecosystems. The methodology is presented in Section 2, while the results and discussion are detailed in Section 3. A concise conclusion is offered in Section 4.

2. Methodology

2.1. Study Area

Figure 1 shows a topographic map of South America, which spans from 80° W to 40° W in longitude and from approximately 10° N to 60° S in latitude. It is bordered by the Atlantic Ocean to the east, the Pacific Ocean to the west, and the Caribbean Sea to the north. The continent is separated from North America by the Panama Canal. South America is home to the world’s largest tropical rainforest, the Amazon Rainforest, and the world’s largest plateau, the Brazilian Highlands, as well as the world’s longest mountain range, the Andes. The majority of South America has a tropical rainforest and savanna climate, with a strip of tropical desert and Mediterranean climates along the western coast. The south-eastern part of the continent experiences a subtropical monsoon and humid subtropical climate, with annual precipitation ranging from a few dozen millimeters to 5000 mm [37]. Geographically, South America can be divided into three longitudinal zones: the western mountainous region (the Andes), the central plains (Orinoco, Amazon, and La Plata basins), and the eastern highlands (Guiana, Brazilian, and Patagonian plateaus).

2.2. Materials and Methods

CALIPSO provides high-resolution and sensitive measurements of aerosols and clouds on a global scale, which enhances our ability to measure and understand the role of aerosols and clouds in the climate system [15,38]. Orbiting at an altitude of 705 km in a Sun-synchronous orbit, CALIPSO completes approximately 16 orbits around the Earth per day, providing near-global coverage from 82° N to 82° S. The satellite is equipped with CALIOP, as well as two passive sensors operating in the visible and thermal infrared spectral regions. CALIOP features a 1064 nm channel, a 532 nm horizontally polarized channel, and a 532 nm vertically polarized channel [39,40].

This study utilized the Level 2 (L2) dataset, which is closely related to the optical and physical properties of aerosols and clouds; specifically, the 532 nm wavelength data with a horizontal resolution of 5 km were utilized. The parameters used were the SAOD_a, AOD_la, PAOD_la, B_la, H_la, T_la, DR_la, CR_la, SAOD_c, D_c, D_lc, OP_lc, AOD_lc, PAOD_lc, B_lc, H_lc, DR_lc, and CR_lc; see Appendix A Table A1 for details.

Among these parameters, SAOD_a, AOD_la, B_la, H_la, DR_la, CR_la, SAOD_c, AOD_lc, B_lc, H_lc, DR_lc, and CR_lc can be directly obtained from the CALIPSO L2 dataset, whereas T_la, PAOD_la, T_lc, and PAOD_lc are derived indirectly using the following formulas:

T_{l a} = H_{l a} - B_{l a},

(1)

P A O D_{l a} = A O D_{l a} / S A O D_{a},

(2)

T_{l c} = H_{l c} - B_{l c},

(3)

P A O D_{l c} = A O D_{l c} / S A O D_{c} .

(4)

PAOD_lc is derived by dividing the number of low cloud samples by the total number of cloud samples within a 1°×1° grid, as obtained from CALIPSO:

O P_{l c} = D_{l c} / D_{C} .

(5)

Simultaneously, based on the topography of South America, we divided the continent into four regions: the western Andean mountain range (A), the northern Orinoco and Amazon plain areas (B), the southern La Plata Plain (C), and the eastern Brazilian Plateau (D). The seasonal variations in low clouds in these four regions from 2006 to 2021 were calculated. For this study, the seasons were defined as SON (September to November), DJF (December to February), MAM (March to May), and JJA (June to August).

In this experiment, our workflow was primarily divided into the following steps:

(1): Data acquisition and quality control. Data were obtained from reliable sources, such as NASA’s Langley Atmospheric Research Center (LARC). Quality control of the data was necessary when using these parameters. The data were considered reliable when the parameters met the criteria listed in Appendix A Table A2. Additionally, we filtered the data according to the definition of low clouds (CBH < 2.5 km), retaining only the data where the first-layer base was below 2.5 km. Only the data that satisfied all the conditions mentioned in the table were retained, ensuring high reliability for further processing and analysis.
(2): Data preprocessing. Data were categorized according to geographical regions (e.g., different sub-regions of South America). Temporal and spatial matching was performed to ensure that cloud and aerosol data fell within the same time window and spatial extent.
(3): Temporal and spatial variation analysis. The primary optical and physical parameters of low clouds were thoroughly examined, and their spatiotemporal variation trends were analyzed. Specifically, seasonal statistical analyses of low cloud parameters were conducted in four regions of South America, covering both daytime and nighttime data.
(4): Statistical analysis. Correlation analysis was conducted to explore the relationships between different parameters of low clouds and between low clouds and the underlying aerosols.
(5): Result interpretation. The optical and physical characteristics of clouds and aerosols and their changes were interpreted in the context of South America’s geographical, meteorological, and environmental background. The influence of aerosols on cloud physical properties and radiative effects, as well as their impacts on regional climate and weather systems, was discussed.

3. Results and Discussion

3.1. Seasonal Variations in Optical Properties of Low Clouds over South America

A seasonal analysis of the optical and physical characteristics of low clouds over South America was conducted, with an examination of their vertical distribution patterns under both daytime and nighttime conditions (Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7, Table A3 and Table A4). OP_lc refers to the likelihood of low clouds appearing under cloudy conditions (Figure 2, Figure 3 and Figure 4), with the calculation method detailed in Equation (5). In the central Andes, where the geographical elevation exceeded 2.5 km, low clouds did not exist, resulting in mostly zero OP_lc values. In the Amazon Basin (Region B), close to the equator, active convection and lower altitude contributed to a higher abundance of clouds, yet the OP_lc values were comparatively lower. Conversely, along the eastern coast of the Pacific Ocean between 15° S and 30° S, OP_lc values were mostly close to 1, indicating that almost all observed clouds were low-level clouds. These low clouds predominantly accumulated over the sea, extending across the eastern edges of mid-latitude and subtropical oceans, which were also high-incidence areas for stratocumulus [40,41]. Additionally, in the La Plata Plain (Region C), slightly higher OP_lc values occurred, possibly due to the flat terrain and its location within the path of frontal activities influenced by the westerlies, where stable precipitation conditions and cyclonic activity facilitated the development of low clouds [42,43].

AOD_lc values in four South American regions, as depicted in Figure 2, Figure 3 and Figure 4 and Table A3 and Table A4, generally peaked in June–August and reached their nadir in September–February, which was consistent with some previous research conclusions [31,33]. This trend was likely due to the increased atmospheric stability during JJA, which restricted the vertical mixing and dispersion of pollutants, leading to an increase in aerosol concentrations within low clouds. Conversely, during September–February, the atmospheric stratification became more unstable, promoting the vertical diffusion of pollutants. Simultaneously, the increased precipitation reduced aerosol concentrations through wet deposition [44]. Spatially, along the Pacific coast, high AOD concentrations over the ocean suggested a significant presence of cloud droplets. This phenomenon was attributed to warm continental air flowing over the cold sea surface, creating a strong temperature inversion that stabilized the lower atmosphere, preventing cloud droplets from rising and increasing AOD_lc values. The region also experienced substantial seasonal AOD_lc variations, possibly due to the shifting of wind and pressure belts. On land, Region C in central South America showed the highest AOD_lc values (daytime: 2.41–5.81, nighttime: 3.61–6.43), likely due to its unique geography, climate, flat terrain, and moist atmospheric conditions. Region D, on a plateau (daytime: 1.75–3.37, nighttime: 2.90–4.01), had the next highest AOD_lc values, while Region B, a plain (daytime: 1.37–2.34, nighttime: 2.12–3.15), exhibited the lowest values. Region B, near the equator, was characterized by abundant precipitation, active convection, and fewer low clouds, primarily consisting of mid- and high-level clouds [23]. Across all regions, nighttime AOD_lc values were higher than daytime values, possibly because of radiative cooling at night, which destabilized the atmospheric stratification and prevented near-surface aerosols from dispersing, thereby increasing their concentration [44,45].

For PAOD_lc (Figure 2, Figure 3 and Figure 4 and Table A3 and Table A4), the values in June–August exceeded those in other seasons, likely due to higher atmospheric stability, which inhibited the vertical dispersion of pollutants and led to aerosol accumulation at lower altitudes [46]. Spatially, PAOD_lc values over the sea were greater than those over land, which is attributable to increased evaporation from the sea surface, providing abundant water vapor and promoting the formation of low clouds. In Region C and along the eastern edge of the subtropical ocean, PAOD_lc values were slightly elevated (daytime: 0.59–0.77, nighttime: 0.64–0.77), possibly due to the frequent occurrence of stratocumulus clouds. Conversely, Region B, located in the Amazon Plain, exhibited the lowest PAOD_lc values (daytime: 0.56–0.70, nighttime: 0.51–0.61), a result of its high humidity, frequent rainfall, and relatively low levels of human activity [10].

The DR_lc value reflected the irregular characteristics of cloud particles in the lowest cloud layer, with smaller values indicating that the cloud particles were closer to spherical [38]. The results showed (Figure 2, Figure 3 and Figure 4 and Table A3 and Table A4) that over South America, the DR_lc value exhibited a seasonal upward trend; it was higher in September–February (daytime: 0.30–0.60, nighttime: 0.11–0.33) than in March–August (daytime: 0.26–0.42, nighttime: 0.13–0.28). This was likely due to the combined effects of increased atmospheric humidity, enhanced biological activity, frequent precipitation, and strengthened vertical mixing during September–February in South America, which led to higher DR values [47]. Spatially, the seasonal average values of DR_lc in Region C (daytime: 0.49, nighttime: 0.29) and Region D (daytime: 0.44, nighttime: 0.21) were higher than those in Region A (daytime: 0.30, nighttime: 0.19) and Region B (daytime: 0.29, nighttime: 0.13). This could be attributed to the orographic steering of airflows by the Andes Mountains and the topographical variations of the Brazilian Highlands, which promoted aerosol accumulation in these regions. Additionally, during the dry season, the Pampas and the Brazilian Highlands experienced increased dust emissions from the soil, while the Amazon Basin’s vegetation cover protected the soil, reducing dust release [23,43]. The value of DR_lc during the day was higher than at night, which was due to the enhanced light scattering by aerosols under strong solar radiation. Photochemical reactions altered the properties of aerosols, leading to an increase in the depolarization ratio. Furthermore, the daytime rise in the atmospheric boundary layer and the increase in the mixing layer height facilitated a more widespread distribution of aerosols, promoting the mixing and interaction of particles of different sizes, thereby affecting their optical properties.

Figure 5, Figure 6 and Figure 7 and Table A3 and Table A4 demonstrated the seasonal variations in CR_lc values during the day and at night. The results indicated that the seasonal changes in Regions A and B were not significant. In contrast, Regions C (daytime: 0.99, nighttime: 1.05) and D (daytime: 0.94, nighttime: 1.07) exhibited higher CR_lc values in June–August, with the lowest values occurring in December–February (C: daytime: 0.70, nighttime: 0.92; D: daytime: 0.85, nighttime: 1.00). This may have been due to the fact that during DJF, strong convective activities and frequent precipitation processes reduced the concentration of aerosols, leading to a decrease in the effective radius of cloud droplets. Spatially, Region A showed higher CR_lc values, which could be attributed to its location in the Andes Mountains, where the mountains blocked warm, moist air currents from the Pacific Ocean, resulting in more precipitation and an increased water vapor content, causing an increase in the size of cloud particles in the low-cloud layer [48]. The CR_lc value in Region B was also notably high, possibly due to the presence of the Amazon rainforest, where forest fires led to an increase in airborne particulate matter, enhancing the number of particles embedded in the moisture of the low clouds and thereby increasing the cloud particle diameter [49]. Simultaneously, the studies by Regina et al. also showed that during the burning season (typically the dry season), the aerosol concentrations in northern South America (such as the Amazon Basin) significantly increased [31]. This was mainly due to the increase in biomass-burning activities, leading to substantial emissions of particulate matter and organic carbon into the atmosphere [25,30].

Based on the definitions of HL_lc and BL_lc, there existed a certain positive correlation between the HL_lc or BL_lc values and geographical elevation. Figure 5, Figure 6 and Figure 7 and Table A3 and Table A4 show that among all regions, Region A had the highest average values across seasons, followed by Region D, while Regions B and C exhibited the lowest values. This is basically consistent with the results of Liu et al. [50,51]. This might have been attributed to the impact of altitude, as topography forced air to rise, thereby increasing the base and top height of the lower cloud layers [52]. Additionally, the west coast of South America, being adjacent to the Andes Mountains, experienced conditions where moist sea breezes were blocked by the mountains and forced to ascend, cooling and condensing into clouds. This process resulted in a relatively high cloud base and top heights. In contrast, other coastal areas, under the moderating influence of the ocean, maintained relatively humid and stable air, which was conducive to sustaining cloud layers within a higher altitude range [53,54]. The seasonal variations in HL_lc and BL_lc values over South America were not particularly significant.

The seasonal variation in TL_lc values was not particularly pronounced. However, the average nighttime TL_lc values for each season in Region B (SON: 1.28, DJF: 1.12, MAM: 1.13, JJA: 1.19,) were significantly higher than those in other regions. This might have been due to the influence of temperature and water vapor. Region B, being a typical tropical rainforest area, was consistently under the control of the tropical low-pressure belt, leading to strong atmospheric vertical convection, high temperatures, and heavy rainfall throughout the year. The vegetation in the local rainforests could also retain a certain amount of water, maintaining high levels of atmospheric moisture, which contributed to thicker cloud layers [55]. Additionally, deforestation anda forest fires might have released a large amount of organic matter into the atmosphere, increasing the aerosol content and thus the thickness of the clouds [48,56,57]. The higher nighttime values, compared to daytime values, could be attributed to the increased atmospheric stability at night, the lowering of the boundary layer, and the less favorable conditions for atmospheric dispersion at night compared to during the day. These factors collectively facilitated the accumulation of aerosols in the lower cloud layers at night [44].

3.2. Autocorrelation Analysis of Low-Cloud Optical Properties

To better understand the spatiotemporal distribution characteristics of low clouds over South America, we analyzed the seasonal correlations among some important parameters of low clouds (Figure 8, Figure 9, Figure 10, Figure 11, Figure 12 and Figure 13). As shown, a strong positive correlation existed between PAOD_lc and AOD_lc (Figure 8). This indicated that the more aerosols that are contained within the low clouds, the stronger their ability to scatter and absorb light, directly leading to an increase in AOD values and, consequently, an increase in the percentage. This positive correlation is widely recognized in atmospheric science, reflecting the significant role of aerosols in the formation and development of clouds, and it is commonly used to assess atmospheric pollution conditions and the impact of clouds on the radiative balance. The correlation was higher at night than during the day, which might have been due to the more stable atmosphere and the lack of solar radiation at night, resulting in a higher concentration of aerosol particles in the low clouds and thus an increase in PAOD_lc. There was also a strong positive correlation between T_lc and H_lc (Figure 9). This result suggests that, over South America, thicker low clouds tended to reach higher altitudes. This may indicate that more water vapor was lifted to higher elevations, experiencing more significant cooling effects, thereby promoting the rise of the cloud top height.

Figure 10 demonstrated a positive correlation between B_lc and H_lc over South America, indicating that the overall vertical extension of the cloud layer might vary in tandem with its base starting height. This means that as the cloud base height increased, so did the cloud top height, which could be related to specific atmospheric conditions, such as the stability, humidity, and updraft intensity. In a stable atmospheric environment, the cloud layer was able to develop more fully, leading to a simultaneous rise in both the cloud base and cloud top. Additionally, the correlation for March–August was higher than that for September–February, possibly due to the more stable atmospheric environment, with the boundary layer characteristics being more conducive to the formation of regular cloud patterns, and less weather interference during March–August, all of which promoted the consistent development of low-cloud structures and thus enhanced their association.

DR_lc exhibited a strong correlation with B_lc, AOD_lc, and PAOD_lc within the range of 0 to 0.35 (Figure 11, Figure 12 and Figure 13). Low clouds, which were predominantly composed of water droplets, had a relatively low altitude. As the cloud base height increased, the cloud droplets tended to become more ellipsoidal or even transformed into ice crystals, leading to a continuous increase in the depolarization ratio. It is noteworthy that, once the depolarization ratio exceeded 0.4, it became largely independent of the aforementioned parameters, possibly due to the fact that, at depolarization ratios greater than 0.4, the clouds consisted of ice crystals rather than water droplets [55]. Over South America, DR_lc and B_lc were directly proportional (Figure 11), reflecting the relationship between the cloud base height and the distribution of aerosol particles. Aerosol particles served as condensation nuclei for cloud droplets, facilitating the formation of clouds. When there were more aerosol particles, the cloud base height tended to be lower; conversely, fewer aerosol particles resulted in a higher cloud base height. The depolarization ratio, being indicative of the distribution of aerosol particles in the atmosphere, thus showed a direct proportionality with the cloud base height of low clouds. A strong positive correlation was observed between DR_lc and AOD_lc over the four regions (Figure 12), suggesting that an increase in aerosols not only influenced cloud formation and microphysical processes but also potentially altered the distribution of cloud droplets or the scattering properties within the cloud, thereby affecting the depolarization ratio. Similarly, a positive correlation existed between DR_lc and PAOD_lc (Figure 13), indicating that an increase in AOD was accompanied by an increase in DR_lc. This further suggests that the presence of aerosols indeed affected the microphysical processes and radiative properties of clouds, consequently impacting the depolarization ratio.

3.3. Correlation of Properties Between Low Clouds and the Lowest Aerosol Layer

Figure 14 describes the relationship between H_lc and B_lc across four regions in South America. The results indicate a positive correlation between these two parameters, which varied with the time of day and season. Among the examined areas, Region A exhibited the strongest correlation between H_lc and B_lc. This strong correlation can possibly be attributed to the unique valley winds and temperature inversions associated with the Andes Mountains, which might have led to changes in local atmospheric stability. Such changes influenced the vertical development of clouds and the mixing layer height of aerosols, thereby enhancing the correlation [58]. Additionally, it can be observed from Figure 14 that the relative positioning between the lowest aerosol layer and the cloud base was not fixed, but rather varied depending on the season, geographical location, and time of day [34]. This variability suggests that the relationship between these elements depended on multiple factors, such as the type, concentration, and origin of aerosols, as well as local meteorological conditions and topographical features.

Figure 15 showed a strong positive correlation between H_lc and H_la, with the correlation being stronger during the day than at night. This was possibly due to the increased stability of the near-surface atmosphere, caused by surface cooling at night, which might have kept the cloud base and aerosol layer base relatively stable, while the cloud top and aerosol layer top rose in tandem with daytime heating. The correlation in Region A was higher than in other regions, likely because the mountainous terrain promoted air ascent and cooling, not only facilitating cloud formation but also potentially influencing the vertical distribution of aerosols. During the evolution of clouds, the condensation and growth of cloud droplets were influenced by aerosol particles (acting as condensation nuclei), and the changes in the vertical distribution of these aerosol particles were further coupled with variations in the cloud top height.

Figure 16 shows a positive correlation between T_lc and T_la, with the correlation being more pronounced at night. The thicker the low clouds, the thicker the lowest aerosol layer. During the night, due to the decrease in temperature and the increase in atmospheric stability, aerosol particles in the lower atmosphere were likely to become more concentrated. These aerosol particles served as condensation nuclei for cloud droplet formation, promoting the formation and growth of cloud droplets [34]. Therefore, an increase in the thickness of the aerosol layer may have facilitated the formation and growth of low clouds, leading to an increase in the thickness of the low clouds.

The relationship between PAOD_lc and PAOD_la essentially reflected the interaction between aerosol loading in the atmosphere and the optical properties of cloud layers. AOD, a measure of the scattering and absorbing capacity of aerosols in the atmosphere, saw an increase in PAOD_lc, which typically indicates a higher concentration of aerosol particles within or beneath the cloud layer. These aerosols acted as cloud condensation nuclei, influencing the formation and size distribution of cloud droplets, thereby potentially altering the optical properties of clouds, such as their reflectance and transmittance [42,50]. As shown in Figure 17, there was primarily a negative correlation between the two during the day, while at night, the correlation was mostly positive. The negative correlation suggested that an increase in the aerosol concentration might lead to a brighter appearance of the cloud layer (i.e., a decrease in the AOD value) because more aerosols acting as condensation nuclei promoted the formation of more cloud droplets. Similarly, active cloud processes may have ‘washed out’ aerosols beneath the clouds, reducing the near-surface aerosol concentration, which is consistent with the negative correlation. At night, due to radiative cooling from the ground, the near-surface atmospheric layer tended to stabilize, forming an inversion layer with an increasing temperature with height. This stable boundary layer structure limited vertical mixing, allowing aerosols to accumulate more easily in the near-surface layer, leading to an increase in PAOD_la. Simultaneously, the weakening of turbulence within the cloud layer made it less likely for aerosols to be diluted by dispersion, resulting in a relatively higher PAOD_lc, thus showing a positive correlation. The correlation during the day was slightly weaker compared to that at night. This diurnal difference might be related to factors, such as the solar radiation intensity, boundary layer dynamics, and temperature variations. Enhanced solar radiation during the day led to a more active atmospheric boundary layer, potentially making the distribution of aerosols more complex. Compared to the conditions at night, this dynamic change might have weakened the direct relationship between AOD and low clouds.

4. Conclusions

The optical and physical characteristics of aerosols and vertical cloud layers in South America were studied using CALIOP Level 2 data from 2006 to 2021. The analysis focused on the low clouds and aerosols in four regions: the western mountainous area, the central plains (further divided into northern and southern subregions), and the eastern plateau. The study included the seasonal variations in OP_lc, AOD_lc, PAOD_lc, DR_lc, CR_lc, B_lc, H_lc, and T_lc both during the day and at night, along with the autocorrelation of low cloud properties and the correlation between low clouds and the lowest aerosol layer. The main conclusions are as follows:

(1): The highest AOD_lc values were observed in the La Plata Plain, while the lowest AOD_lc values were found in the Orinoco and Amazon plain areas for South America. AOD_lc and PAOD_lc were generally higher in June–August, which may be attributed to the higher water vapor content in the air during winter in most regions of South America, leading to an increase in AOD_lc. Additionally, a strong positive correlation was observed between PAOD_lc and AOD_lc.
(2): The seasonal average values of DR_lc were higher in September–February. In terms of the spatial distribution, the La Plata Plain and Brazilian Plateau had higher values than the Andes, the Orinoco Basin, and the Amazon Plain, which may be attributed to topographical variations that facilitated the accumulation of aerosols in these regions. Additionally, during the dry season, the soils in the La Plata Plain and the Brazilian Plateau were more susceptible to dust generation, leading to the increased release of mineral dust. In contrast, the vegetation cover in the Amazon Plain protected the soil, reducing the release of dust. Furthermore, the DR values of low clouds were higher during the day than at night.
(3): CR_lc values were higher in the Andes, Orinoco, and Amazon plains. Orographic lifting over the Andes Mountains caused water vapor to condense into clouds, increasing their thickness and density and affecting the color ratio. The humid conditions in the Orinoco Basin and Amazon Plain favored the formation and maintenance of low clouds, which typically exhibited higher color ratios under high humidity. The DR_lc values over South America, within the range of 0 to 0.35, showed a strong correlation with B_lc, AOD_lc and PAOD_lc. This indicates that the presence of aerosols indeed likely influenced the depolarization ratio by affecting the microphysical processes and radiative properties of clouds.
(4): HL_lc and BL_lc were positively correlated, and there was a certain positive correlation with the geographical elevation. The nighttime TL_lc values in the Orinoco Basin and Amazon Plain were significantly higher than those in other regions. This may have been influenced by a combination of factors, including the temperature, water vapor, and atmospheric conditions, which facilitated the accumulation of aerosols in the low-cloud layer at night in this region. Additionally, there was a positive correlation between the base height and top height of low clouds.
(5): In South America, seasonal mean values of B_lc and B_la, H_lc and H_la, T_lc and T_la, and PAOD_lc and PAOD_la showed some correlation. All these indicate that there is a close interaction between low clouds and low-level aerosols, which is worthy of further investigation.

Our research results contribute to the understanding of aerosol sources in different regions and their impact on ecosystems. They can also be used to improve the parameterization schemes of aerosols and low clouds in climate models. Specifically, data on the characteristics of aerosols and low clouds in different regions can help to more accurately simulate the climate system of South America, thereby enhancing the prediction accuracy. However, it also has certain limitations, as the research primarily focused on CALIPSO satellite data, lacking validation from other satellite and ground-based observation stations. In the future work, we will enhance data integration by incorporating additional data sources, such as ground-based measurements and satellite data from other sensors (e.g., MODIS, MISR), to provide a more comprehensive understanding of cloud and aerosol properties. This will aid in validating and improving the accuracy of CALIPSO data. Moreover, case studies of specific events, such as drought or floods, will be conducted to investigate their short-term and long-term impacts on cloud and aerosol characteristics.

Author Contributions

B.S.; methodology, B.S. and M.Z.; software, D.L. and Z.L.; validation, Q.W.; formal analysis, B.S.; investigation, B.S. and Z.L.; resources, B.S.; data curation, B.S.; writing—original draft preparation, B.S.; writing—review and editing, W.S.; visualization, B.S.; supervision, Q.W.; project administration, B.S.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported in part by the Sponsored by Program for Science & Technology Innovation Talents in Universities of Henan Province of China (No. 24HASTIT018), in part by the Natural Science Foundation of Henan Province of China (No. 242300421369), in part by the Program of Undergraduate Universities Young Backbone Teacher Training of Henan Province of China (No. 2024GGJS104), in part by the Nanyang Normal University Scientific Research Project (No. 2025ZX027), and in part by Henan Province Science and Technology R&D Projects (222102320340).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We thank the editors for assisting in the linguistic refinement of this manuscript. And we also thank NASA for providing datasets (https://subset.larc.nasa.gov/calipso/login.php, accessed on 1 March 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. An abbreviated list of aerosol and cloud optical–physical parameters used in this paper.

Type	Parameter	Abbreviation
Aerosol	Sum of AOD of all aerosol layers	SAOD_a
	AOD of the lowest aerosol layer	AOD_la
	Percentage of AOD in the lowest aerosol layer	PAOD_la
	Base height of the lowest aerosol layer	B_la
	Top height of the lowest aerosol layer	H_la
	Thickness of the lowest aerosol layer	T_la
	Depolarization ratio of the lowest aerosol layer	DR_la
	Color ratio of the lowest aerosol layer	CR_la
Cloud	Sum of AOD of all cloud layers	SAOD_c
	Satellite sampling dataset for clouds	D_c
	Satellite sampling data for low clouds	D_lc
	Probability of occurrence of low clouds	OP_lc
	AOD of low clouds	AOD_lc
	Percentage of AOD for low clouds	PAOD_lc
	Base altitude of low clouds	B_lc
	Top height of low clouds	H_lc
	Thickness of low clouds	T_lc
	Depolarization ratio of low clouds	DR_lc
	Color ratio of low clouds	CR_lc

Table A2. Parametric data filtering range.

Parameter	Elevation Selection Range (km)
B_lc, B_la	0~2.5
DR_lc, DR_la	0~5
CR_lc, CR_la	0~10
AOD_lc, AOD_la	0~20
SAOD_c, SAOD_a	0~30
H_lc, H_la	0~30

Appendix B

Table A3. Seasonal mean and standard deviation values of eight variables during the day over South America (four regions: A, B, C, and D).

Region	Season	Day
Region	Season	SAOD_c	AOD_lc	PAOD_lc	B_lc	H_lc	T_lc	DR_lc	CR_lc
A	MAM	4.60 ± 4.23	3.81 ± 4.31	0.74 ± 0.33	1.46 ± 0.66	2.25 ± 0.82	0.79 ± 0.61	0.27 ± 0.39	1.15 ± 0.97
	JJA	4.66 ± 4.33	3.92 ± 4.42	0.76 ± 0.32	1.38 ± 0.66	2.16 ± 0.89	0.79 ± 0.61	0.27 ± 0.37	1.17 ± 0.92
	SON	4.40 ± 4.15	3.58 ± 4.23	0.72 ± 0.34	1.47 ± 0.66	2.25 ± 0.85	0.78 ± 0.62	0.33 ± 0.48	1.13 ± 1.00
	DJF	4.38 ± 4.21	3.55 ± 4.29	0.72 ± 0.33	1.53 ± 0.65	2.32 ± 0.81	0.79 ± 0.63	0.33 ± 0.49	1.15 ± 1.09
B	MAM	3.28 ± 3.67	2.32 ± 3.48	0.64 ± 0.34	1.23 ± 0.72	1.93 ± 0.80	0.70 ± 0.65	0.26 ± 0.44	1.08 ± 1.03
	JJA	3.07 ± 3.96	2.34 ± 3.83	0.70 ± 0.34	1.19 ± 0.67	1.84 ± 0.76	0.65 ± 0.64	0.27 ± 0.46	1.15 ± 1.07
	SON	2.41 ± 3.08	1.37 ± 2.62	0.56 ± 0.34	1.11 ± 0.70	1.87 ± 0.73	0.76 ± 0.73	0.32 ± 0.59	1.12 ± 1.24
	DJF	3.35 ± 3.48	2.31 ± 3.29	0.62 ± 0.34	1.31 ± 0.72	1.99 ± 0.82	0.69 ± 0.63	0.30 ± 0.52	1.08 ± 1.05
C	SON	5.29 ± 4.75	4.41 ± 4.92	0.73 ± 0.33	1.25 ± 0.66	1.82 ± 0.78	0.57 ± 0.51	0.41 ± 0.61	0.92 ± 0.85
	DJF	6.64 ± 5.06	5.81 ± 5.33	0.77 ± 0.31	1.17 ± 0.70	1.72 ± 0.80	0.55 ± 0.44	0.42 ± 0.58	0.99 ± 0.84
	MAM	4.89 ± 4.55	3.89 ± 4.71	0.69 ± 0.34	1.28 ± 0.70	1.87 ± 0.82	0.60 ± 0.62	0.52 ± 0.76	0.91 ± 0.97
	JJA	3.68 ± 3.79	2.41 ± 3.66	0.59 ± 0.34	1.24 ± 0.70	1.80 ± 0.81	0.55 ± 0.61	0.60 ± 0.83	0.70 ± 0.88
D	SON	3.56 ± 3.90	2.61 ± 3.83	0.65 ± 0.34	1.44 ± 0.65	2.11 ± 0.74	0.67 ± 0.65	0.39 ± 0.63	0.96 ± 1.01
	DJF	4.01 ± 4.28	3.37 ± 4.32	0.76 ± 0.32	1.49 ± 0.64	2.16 ± 0.71	0.67 ± 0.60	0.40 ± 0.65	0.94 ± 0.87
	MAM	3.40 ± 3.83	2.36 ± 3.70	0.63 ± 0.34	1.34 ± 0.69	2.13 ± 0.77	0.79 ± 0.78	0.46 ± 0.73	0.90 ± 1.09
	JJA	3.13 ± 3.25	1.75 ± 2.84	0.54 ± 0.34	1.34 ± 0.67	2.02 ± 0.78	0.69 ± 0.71	0.52 ± 0.78	0.85 ± 1.10

Table A4. Seasonal mean and standard deviation values of eight variables during the night over South America (four regions: A, B, C, and D).

Region	Season	Night
Region	Season	SAOD_c	AOD_lc	PAOD_lc	B_lc	H_lc	T_lc	DR_lc	CR_lc
A	MAM	5.66 ± 4.36	4.46 ± 4.53	0.70 ± 0.36	1.30 ± 0.73	2.39 ± 1.08	1.09 ± 0.87	0.18 ± 0.21	1.14 ± 1.03
	JJA	5.76 ± 4.44	4.61 ± 4.69	0.71 ± 0.36	1.24 ± 0.72	2.32 ± 1.12	1.08 ± 0.88	0.19 ± 0.20	1.18 ± 1.04
	SON	5.43 ± 4.30	4.17 ± 4.45	0.68 ± 0.37	1.20 ± 0.73	2.32 ± 1.19	1.12 ± 0.97	0.19 ± 0.25	1.20 ± 1.13
	DJF	5.46 ± 4.21	4.24 ± 4.37	0.69 ± 0.36	1.30 ± 0.72	2.40 ± 1.05	1.10 ± 0.82	0.19 ± 0.25	1.18 ± 1.12
B	MAM	4.77 ± 3.84	3.09 ± 3.64	0.58 ± 0.35	0.93 ± 0.78	2.06 ± 1.24	1.13 ± 1.14	0.14 ± 0.17	1.04 ± 1.15
	JJA	4.58 ± 3.96	3.08 ± 3.82	0.61 ± 0.36	0.92 ± 0.78	2.11 ± 1.22	1.19 ± 1.14	0.13 ± 0.19	1.05 ± 1.16
	SON	3.87 ± 3.61	2.12 ± 3.06	0.51 ± 0.35	0.78 ± 0.73	2.06 ± 1.40	1.28 ± 1.37	0.11 ± 0.17	0.96 ± 1.20
	DJF	4.79 ± 3.66	3.15 ± 3.52	0.59 ± 0.36	1.02 ± 0.80	2.14 ± 1.14	1.12 ± 1.03	0.14 ± 0.18	1.03 ± 1.09
C	SON	6.73 ± 4.74	5.75 ± 4.99	0.77 ± 0.32	1.10 ± 0.77	1.88 ± 0.93	0.78 ± 0.56	0.27 ± 0.43	1.06 ± 0.89
	DJF	7.35 ± 5.02	6.43 ± 5.33	0.79 ± 0.32	1.13 ± 0.80	1.94 ± 0.96	0.80 ± 0.56	0.28 ± 0.44	1.05 ± 0.91
	MAM	5.93 ± 4.76	4.83 ± 4.95	0.72 ± 0.34	1.03 ± 0.79	1.94 ± 1.06	0.91 ± 0.80	0.29 ± 0.53	1.02 ± 1.06
	JJA	4.96 ± 4.39	3.61 ± 4.38	0.64 ± 0.36	1.05 ± 0.77	1.81 ± 0.97	0.76 ± 0.70	0.33 ± 0.63	0.92 ± 1.05
D	SON	4.73 ± 4.19	3.49 ± 4.17	0.65 ± 0.36	1.08 ± 0.68	1.96 ± 0.91	0.88 ± 0.73	0.21 ± 0.39	1.05 ± 1.03
	DJF	4.82 ± 4.26	4.01 ± 4.34	0.77 ± 0.34	1.06 ± 0.66	1.95 ± 0.85	0.89 ± 0.64	0.21 ± 0.42	1.07 ± 0.99
	MAM	4.52 ± 4.51	3.50 ± 4.55	0.69 ± 0.35	0.88 ± 0.62	1.85 ± 0.96	0.97 ± 0.83	0.20 ± 0.40	0.97 ± 1.10
	JJA	4.44 ± 4.01	2.90 ± 3.84	0.58 ± 0.36	1.03 ± 0.68	1.91 ± 0.96	0.88 ± 0.80	0.23 ± 0.46	1.00 ± 1.11

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Figure 1. The geographical location and zoning of South America. The color bar represents the altitude (elevation). Divided into four regions: A, B, C, and D.

Figure 2. Seasonal spatial distribution of probability of occurrence of low clouds (OP_lc), AOD of low clouds (AOD_lc), percentage of AOD for low clouds (PAOD_lc), and depolarization ratio of low clouds (DR_lc) over South America during the day.

Figure 3. Seasonal spatial distributions of OP_lc, AOD_lc, PAOD_lc, and DR_lc over South America at night.

Figure 4. Seasonal variation of AOD_lc, PAOD_lc, and DR_lc over South America during the day and at night ((a) AOD_lc daytime; (b) PAOD_lc daytime; (c) DR_lc daytime; (d) AOD_lc nighttime; (e) PAOD_lc nighttime; (f) DR_lc nighttime).

Figure 5. Seasonal spatial distributions of the probability of color ratio of low clouds (CR_lc), base altitude of low clouds (B_lc), top height of low clouds (H_lc), and thickness of low clouds (T_lc) over South America during the day.

Figure 6. Seasonal spatial distributions of CR_lc, B_lc, H_lc, and T_lc over South America at night.

Figure 7. Seasonal variation in CR_lc, B_lc, H_lc, and T_lc over South America during the day and at night ((a) CR_lc daytime; (b) B_lc daytime; (c) H_lc daytime; (d) T_lc nighttime; (e) CR_lc nighttime; (f) B_lc nighttime; (g) H_lc nighttime; (h) T_lc nighttime).

Figure 8. Correlation of PAOD_lc and AOD_lc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 9. Correlation of Tlc and Hlc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 10. Correlation of B_lc and H_lc over South America from 2006 to 2021: (a) MMA daytime; (b) winter daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 11. Correlation of B_lc and DR_lc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 12. Correlation of AODR_lc and DR_lc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 13. Correlation of PAODR_lc and DR_lc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 14. Correlation of B_la and B_lc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 15. Correlation of H_la and H_lc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 16. Correlation of T_la and T_lc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

Figure 17. Correlation of PAOD_la and PAOD_lc over South America from 2006 to 2021: (a) MMA daytime; (b) JJA daytime; (c) SON daytime; (d) DJF nighttime; (e) MMA nighttime; (f) JJA nighttime; (g) SON nighttime; (h) DJF nighttime.

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MDPI and ACS Style

Su, B.; Lin, D.; Liu, Z.; Wu, Q.; Song, W.; Zhang, M. Optical–Physical Characteristics of Low Clouds and Aerosols in South America Based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation. Atmosphere 2024, 15, 1513. https://doi.org/10.3390/atmos15121513

AMA Style

Su B, Lin D, Liu Z, Wu Q, Song W, Zhang M. Optical–Physical Characteristics of Low Clouds and Aerosols in South America Based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation. Atmosphere. 2024; 15(12):1513. https://doi.org/10.3390/atmos15121513

Chicago/Turabian Style

Su, Bo, Dekai Lin, Ziji Liu, Qingyan Wu, Wenkai Song, and Miao Zhang. 2024. "Optical–Physical Characteristics of Low Clouds and Aerosols in South America Based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation" Atmosphere 15, no. 12: 1513. https://doi.org/10.3390/atmos15121513

APA Style

Su, B., Lin, D., Liu, Z., Wu, Q., Song, W., & Zhang, M. (2024). Optical–Physical Characteristics of Low Clouds and Aerosols in South America Based on Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation. Atmosphere, 15(12), 1513. https://doi.org/10.3390/atmos15121513

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