Optical Properties of Near-Surface Cloud Layers and Their Interactions with Aerosol Layers: A Case Study of Australia Based on CALIPSO

Abstract

This study utilized Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite level-2 data with high-confidence cloud–aerosol discrimination (|CAD| > 70) to investigate the optical properties, vertical distributions, seasonal variations, and aerosol interactions of near-surface cloud layers (cloud base height < 2.5 km) over Australia from 2006 to 2021. This definition encompasses both traditional low clouds and part of mid-level clouds that extend into the lower troposphere, enabling a comprehensive view of cloud systems that interact most directly with boundary-layer aerosols. The results showed that the optical depth of low clouds (CODL) exhibited significant spatial heterogeneity, with higher values in central and eastern regions (often exceeding 6.0) and lower values in western plateau regions (typically 4.0–5.0). CODL values demonstrated clear seasonal patterns with spring peaks across all regions, contrasting with traditional summer-maximum expectations. Pronounced diurnal variations were observed, with nighttime CODL showing systematic enhancement effects (up to 19.29 maximum values compared to daytime 11.43), primarily attributed to surface radiative cooling processes. Cloud base heights (CBL) exhibited counterintuitive nighttime increases (41% on average), reflecting fundamental differences in cloud formation mechanisms between day and night. The geometric thickness of low clouds (CTL) showed significant diurnal contrasts, decreasing by nearly 50% at night due to enhanced atmospheric stability. Cloud layer number (CN) displayed systematic nighttime reductions (18% decrease), indicating dominance of single stratiform cloud systems during nighttime. Regional analysis revealed that the central plains consistently exhibited higher CODL values, while eastern mountains showed elevated cloud heights due to orographic effects. Correlation analysis between cloud and aerosol layer properties revealed moderate but statistically significant relationships (|R| = 0.4–0.6), with the strongest correlations appearing between cloud layer heights and aerosol layer heights. However, these correlations represent only partial influences among multiple factors controlling cloud development, suggesting measurable but modest aerosol effects on cloud properties. This study provides comprehensive observational evidence for cloud optical property variations and aerosol–cloud interactions over Australia, contributing to an improved understanding of Southern Hemisphere cloud systems and their climatic implications.

1. Introduction

Clouds and aerosols play significant roles in the global climate system. Clouds can directly influence the climate by regulating the Earth’s radiation balance [1,2,3]. Aerosols can directly scatter and absorb solar radiation or indirectly affect the land–atmosphere radiation effect by serving as cloud condensation nuclei [4,5,6]. Cloud–aerosol interactions significantly impact climate change and remain one of the most challenging issues in climate prediction [7,8,9,10,11,12,13]. Therefore, investigating the optical properties of clouds and their interactions with aerosols holds considerable scientific significance.

In recent years, substantial progress has been achieved internationally in studying cloud optical properties and cloud–aerosol interactions [14,15]. Ecuyer et al. explored the vertical structural variations of clouds on a global scale using multi-source satellite datasets [16]. Rosenfeld et al. examined the influence of aerosol–cloud interactions on climate models [4]. Pan et al. investigated the optical properties of clouds over East Asia using CALIPSO data [17]; Fan et al. demonstrated that aerosols can significantly influence precipitation processes by altering cloud microphysical properties utilizing multi-source satellite data [18]. Yeom et al. further noted that such impacts were particularly evident in semi-arid regions [19]. The advent of the CALIPSO active remote sensing has provided new opportunities to study cloud–aerosol interactions, which can offer more accurate information on the vertical distribution of clouds and aerosols [20,21,22,23,24,25]. However, most of these studies focused on Northern Hemisphere regions; research on cloud–aerosol interactions in the Southern Hemisphere remained limited, especially over Australia. As the largest landmass in the Southern Hemisphere, Australia features unique geographical and climatic characteristics, making it an ideal region to study cloud–aerosol interactions [26].

Australia spans tropical and temperate climatic zones, encompassing vast deserts and humid coastal areas [27]. These complex underlying surface conditions significantly influenced cloud formation and development [28]. Additionally, frequent wildfires in Australia have led to high aerosol loads, which can provide a unique natural laboratory for investigating aerosol–cloud interactions [29]. Moreover, the characteristic variations of clouds and aerosols in this region can directly impact the climate system of the Southern Hemisphere [30]. In-depth studies of cloud–aerosol interactions over Australia were crucial for understanding global climate change [1,8,31]. Our research team analyzed seasonal and regional differences in aerosol layers’ optical properties based on CALIPSO [32].

McCoy et al. used high-resolution global models and satellite data to reveal the relationship between storm cloud properties and aerosol concentrations, and found that pollution aerosols can increase cloud albedo and enhance short-wave radiation output in mid-latitude regions of the Southern Hemisphere (including Australia) [33]. Hernandez-Jaramillo et al. utilized a new airborne research facility and found that there was a significant difference between the effects of anthropogenic aerosol (such as industrial emissions) and natural aerosol (such as wildfire smoke) on cloud microphysical processes in eastern Australia [34]. The study of Leung et al. showed that the seasonal change of aerosol gradient in Australia triggers the “aerosol breeze” effect, resulting in an increase in cloud cover and precipitation, particularly in the summer wildfire frequent period [35]. However, these studies were often limited to specific regions or time periods, lacking systematic analysis across Australia using long-term data.

This study is the first to address this gap by systematically analyzing the optical properties of low clouds over Australia using long-term CALIPSO satellite observations (2006–2021). The study focused on regional differences, seasonal variations, and mechanisms of low cloud–aerosol interactions. The findings of this study will enhance the understanding of cloud–aerosol interactions in the Southern Hemisphere and can also provide scientific support for climate change assessment, weather forecasting, and environmental protection in Australia.

2. Methodology

2.1. Study Area

The study region spans the Australian continent, bounded by latitudes 45° S to 10° S and longitudes 112° E to 155° E, covering both land and adjacent maritime zones. Australia is surrounded by seas on all sides. The western region consists of low plateaus with elevations ranging from 200 to 500 m, characterized by extensive deserts and semi-deserts [8]. The central region is a plain with an elevation of less than 200 m. It is abundant in herbaceous vegetation and has the world’s largest artesian basin. The eastern region features ancient mountain highlands, with elevations ranging from 800 to 1000 m. Approximately 70% of Australia’s land area is arid or semi-arid, with deserts and semi-deserts covering 3.4 million square kilometers [36]. Strong wind systems carry large amounts of dust across different regions, leading to increased aerosol loads in the central region [37]. To facilitate research on the optical properties of low clouds and cloud–aerosol interactions in Australia, we divided Australia into three study regions based on topography (Figure 1): the western plateau (A), the central plain (B), and the eastern mountains (C).

2.2. Data and Processing

The CALIPSO satellite had unique advantages in obtaining global aerosol and cloud observation data, which can provide highly accurate vertical distribution information of cloud and aerosol layers [23,38,39]. The CALIOP lidar had high temporal and spatial resolution data and was rigorously calibrated to ensure data reliability and accuracy [25]. Additionally, CALIOP level-2 products can provide fundamental and unparalleled parameters of cloud and aerosol layers, which are particularly suitable for analyzing relationships between clouds and aerosol layers.

Understanding the reliability of CALIPSO cloud–aerosol discrimination is crucial for interpreting our results correctly. The CALIOP lidar data on aerosol and cloudiness are not only parallel, but also simultaneous, and in some cases indistinguishable. For the correct separation of cloud and aerosol signals, CALIPSO provides a parameter “cloud–aerosol discrimination (CAD) score”, which is defined in the range from −100 to 100. Formally, CAD from −100 to 0 identifies aerosol, from 0 to 100 identifies cloudiness. Data with |CAD| > 70 refers to the “high confidence” level values, while |CAD| < 70 represents medium to low confidence levels.

To ensure data reliability for our cloud optical property analysis, we applied strict CAD score filtering criteria, utilizing only data with |CAD| > 70 (high confidence level) for cloud identification. This filtering approach significantly improves the reliability of cloud–aerosol discrimination, though it results in a more conservative dataset.

The CAD filtering process inevitably results in some data gaps, particularly evident in the southwestern coastal regions of Australia, where complex meteorological conditions and surface–atmosphere interactions create challenging discrimination scenarios. These gaps appear as Not a Number (NaN) values in Figure 2 and Figure 3, primarily concentrated along the southwestern coastline, where marine boundary layer conditions, sea-land interactions, and frequent low-level clouds create ambiguous backscatter signatures that fall below the high-confidence discrimination threshold.

CALIPSO observation datasets (available at https://subset.larc.nasa.gov/calipso/, accessed on 13 August 2023) were categorized into three levels during data processing. This study utilized level-2 (L2) datasets about cloud–aerosol optical and physical properties, which had a 5 km horizontal resolution. Parameters used are shown in

Table 1. Definitions of variables used in the analysis.

Variable Name	Explanation
CODL	Optical depths of low clouds
CHL	Top heights of low clouds
CBL	Base heights of low clouds
CTL	Geometric thickness of low clouds
CODGOLL	Fraction of low cloud optical depths
CN	Number of cloud layers
CDRL	Depolarization ratio of low clouds
CCRL	Color ratio of low clouds

. CODL, CHL, CBL, CN, CDRL, and CCRL can be directly obtained from level-2 datasets, while CTL and CODGOLL were indirectly calculated as shown in the following formulas:

$${\mathrm{C}\mathrm{T}}_{\mathrm{L}}={\mathrm{C}\mathrm{H}}_{\mathrm{L}}-{\mathrm{C}\mathrm{B}}_{\mathrm{L}}$$

(1)

$${\mathrm{C}\mathrm{O}\mathrm{D}\mathrm{G}\mathrm{O}\mathrm{L}}_{\mathrm{L}}={\displaystyle \frac{{\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{L}}}{{\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{T}}}}\times 100\%$$

(2)

$${\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{T}}={\sum }_1^{\mathrm{N}}{\mathrm{C}\mathrm{O}\mathrm{D}}_{\mathrm{N}}; \mathrm{N}=1,2,\cdots 7,8$$

(3)

where N = 1 represents the low cloud layer, i.e.,:

$${\mathrm{COD}}_{\mathrm{L}}={\mathrm{COD}}_1$$

(4)

To investigate the vertical and optical characteristics of cloud–aerosol interactions over Australia, the analysis focused on CBL below 2.5 km above the surface, referred to here as near-surface cloud layers. This definition, while broader than the traditional International Satellite Cloud Climatology Project (ISCCP) classification (which designates low clouds based on pressure > 680 hPa), allows inclusion of cloud structures in the lower troposphere that are most susceptible to interactions with surface-based aerosol layers. In this study, seasons were defined as spring (September to November), summer (December to February), autumn (March to May), and winter (June to August). Statistical analysis was conducted on seasonal and diurnal characteristics of parameters over three sub-regions from 2006 to 2021. Strict preprocessing and quality control were conducted to ensure data reliability. Physical parameter thresholds were also established to filter out anomalies: CODL (0~20), CHL and CBL (0~15 km), CDRL (0–0.5) [40,41], and CCRL (0–10). The filtered datasets were then resampled to a 1° × 1° grid.

In CALIPSO data processing, understanding the fundamental mechanisms of cloud optical property measurements is essential for correctly interpreting results. CALIOP lidar measurements capture both transparent and opaque cloud layers. For transparent cloud layers, the laser beam can fully penetrate the cloud, providing accurate cloud base heights and complete optical depth measurements. However, for opaque cloud layers, as pointed out by Young et al. (2018), the reported optical depth values represent only the portion from the cloud top to the signal attenuation altitude, suggesting that actual cloud layers may be thicker and have greater optical depths than observed [41]. This measurement characteristic is an inherent property of active lidar remote sensing and has been taken into consideration in our research, particularly when interpreting regional and seasonal variation patterns. Through a statistical analysis of extensive long-term data, the effects of these measurement characteristics on overall trends have been balanced, enabling us to reliably identify the spatial distributions and temporal variation patterns of near-surface cloud layer optical properties over Australia.

It should be noted that due to CALIPSO’s sun-synchronous polar orbit characteristics, ‘daytime’ and ‘nighttime’ observations over Australia do not represent the same geographical locations on the same day, as the satellite revisits the same location approximately once every 16 days. Therefore, the observed day–night differences reflect the combined effects of diurnal atmospheric processes and spatial sampling variations rather than pure temporal diurnal cycles at fixed locations.

To explore the relationship between low cloud layers and aerosol layers in Australia, correlation analyses were conducted by calculating correlation coefficients between low cloud and aerosol layer properties. Regression models were also constructed to assess the degree of aerosol influence on cloud properties. Pearson correlation coefficients were used as the primary analytical tool, which can effectively evaluate the strength of linear relationships and perform well with normally distributed data. This method was robust to outliers and suitable for long-term observational datasets. These statistical approaches enabled comprehensive and reliable assessments of relationships between low cloud and aerosol optical properties over Australia [42].

3. Results and Discussion

3.1. Spatial Distribution Characteristics of Low Cloud Layers’ Optical Properties in Australia

As shown in Figure 2 and Figure 3, CODL values (Figure 2I and Figure 3I) exhibited significant spatial distribution patterns across Australia, with daytime averages of 5.34 and nighttime averages of 5.25. The spatial heterogeneity was primarily influenced by Australia’s complex topography and diverse climatic conditions [1,2]. Central and eastern regions consistently showed higher values, with some areas exceeding 6.0, while western plateau regions maintained relatively lower values, typically ranging from 4.0 to 5.0. This distinctive spatial pattern reflects the fundamental influence of Australia’s geographical features on atmospheric moisture transport and convergence. The central plains, characterized by their open and flat terrain, provide ideal geographical conditions for moisture transport and convergence from different directions, particularly allowing humid airflow from the southern oceans to flow freely and converge in these regions, creating favorable atmospheric environments for cloud development and optical depth enhancement. The eastern mountainous terrain further enhances this process through orographic lifting mechanisms, which not only promote water vapor condensation but also influence cloud microphysical properties by altering local atmospheric stability structures [3].

In contrast, the western plateau regions are constrained by arid continental climate conditions with relatively scarce atmospheric water vapor content. Additionally, these areas are often controlled by subtropical high-pressure systems year-round, where subsiding airflow dominates, leading to systematically lower cloud optical depths. Notably, nighttime CODL values showed enhanced extremes, with maximum values reaching 19.29 compared to daytime maximums of 11.43, indicating significant nighttime intensification effects driven by surface radiative cooling processes. When nightfall occurs, the surface begins intense longwave radiation heat dissipation, causing rapid temperature decreases in near-surface atmospheric layers and dramatically increasing atmospheric stability. This strong, stable stratification not only suppresses vertical turbulent mixing processes but also creates ideal atmospheric environments for stratiform cloud formation and long-term maintenance.

CHL values (Figure 2II and Figure 3II) displayed relatively uniform spatial distribution with daytime averages of 2.20 km and nighttime averages of 2.00 km, yet still exhibited clear topographic dependencies. Eastern mountainous regions showed consistently higher CHL values due to elevated terrain and complex orographic forcing effects, clearly reflecting the fundamental influence of topography on cloud vertical structure. Mountain terrain not only changes cloud formation heights through mechanical lifting but also regulates cloud development processes by influencing local circulation systems. The slight but statistically significant nighttime decrease in CHL can be explained through nighttime atmospheric stable stratification establishment and development processes, where increased atmospheric stability strongly inhibits vertical motion, making conditions unfavorable for upward cloud development.

CBL distribution (Figure 2III and Figure 3III) demonstrated extremely pronounced topographic correlations and regional variations, with daytime averages of 0.95 km and nighttime averages significantly higher at 1.34 km, representing an approximately 41% increase. This counterintuitive nighttime increase reflects fundamental differences in cloud formation mechanisms. Nighttime cloud formation typically follows a top-down development pattern where radiative cooling first achieves water vapor saturation at specific altitude levels in the free atmosphere, with clouds forming at these heights and subsequently extending downward under appropriate conditions. Therefore, nighttime-observed cloud base heights often represent initial development heights under these special formation mechanisms rather than boundary layer-controlled heights typical of daytime conditions.

CTL values (Figure 2IV and Figure 3IV) showed significant diurnal variations, with daytime averages of 1.25 km decreasing to nighttime averages of 0.66 km, representing a nearly 50% reduction. This substantial nighttime decrease directly reflects the strong inhibitive effects of increased atmospheric stability on cloud vertical structure. Stable nighttime boundary layers effectively limit cloud vertical development and expansion by suppressing vertical convective motion and turbulent mixing processes [23,24]. It should be noted that daytime observations are influenced by higher solar background noise, which affects CALIOP’s sensitivity to weak backscatter signals [25,43]. Therefore, the diurnal differences observed in CTL may reflect a combined result of instrumental detection characteristics and actual atmospheric processes.

CODGOLL values (Figure 2V and Figure 3V) exhibited clear latitudinal gradient characteristics with daytime averages of 0.82 increasing to nighttime averages of 0.88, representing approximately 7% increase. The systematic nighttime increase clearly indicates that single-layer low clouds occupy more dominant positions in nighttime cloud systems, consistent with nighttime atmospheric conditions’ regulatory effects on cloud layer structure. Northern regions showed relatively lower values, clearly indicating more prevalent multi-layer cloud phenomena in these areas. This spatial distribution pattern aligns highly with basic characteristics of Australia’s northern tropical monsoon climate, where complex and variable atmospheric stratification and seasonally active convective systems provide ideal atmospheric background conditions for multi-layer cloud structure formation and maintenance.

CN values (Figure 2VI and Figure 3VI) showed complementary patterns with daytime averages of 1.92 decreasing to nighttime averages of 1.57, representing an approximately 18% reduction. This systematic nighttime decrease further confirms the dominant characteristics of single stratiform cloud systems during nighttime. Enhanced nighttime boundary layer stability not only suppresses vertical motion and convective development but also reduces possibilities for independent cloud layer formation at different altitude levels [23,43]. Northern and northeastern regions consistently showed higher CN values, with maximums reaching 5.16 layers, mainly related to Australia’s continental-scale climate zoning and corresponding atmospheric circulation characteristics. Northern regions, controlled by tropical climate systems, exhibit stronger complexity and variability in atmospheric stratification in vertical directions, creating favorable conditions for multi-layer cloud system development. The diurnal patterns in CN reflect complex atmospheric dynamics, where nighttime surface cooling enhances atmospheric stability and reduces vertical mixing, creating conditions favorable for stratified cloud layers. Additionally, as documented in previous CALIPSO validation studies, the instrument’s detection sensitivity is enhanced during nighttime due to reduced solar background noise [25,43], allowing for better identification of thin cloud layers.

CDRL (Figure 2VII and Figure 3VII) and CCRL (Figure 2VIII and Figure 3VIII) spatial distributions showed relatively small but statistically significant regional variations, with averages of 0.17 and 1.07, respectively, during daytime, increasing slightly to 0.19 and 1.15 at nighttime. CDRL values maintained relatively stable ranges of 0.15–0.20 across most Australian regions, clearly indicating that daytime near-surface cloud layers primarily consist of spherical liquid water droplets with spatially consistent non-sphericity degrees. Slight elevations in central regions may relate to unique dust aerosol environments in these areas. CCRL values remained close to 1.0 across Australia with relatively small spatial variations, with numerical stability reflecting consistency characteristics of scattering ratios between 532 nm and 1064 nm wavelengths, conforming to typical optical characteristics of liquid cloud droplets under long-term oceanic air mass influence.

The nighttime increases in both CDRL (approximately 12%) and CCRL (approximately 7%) may reflect systematic changes in cloud droplet characteristics and aerosol–cloud interaction processes. Significant nighttime boundary layer compression concentrates aerosol particles originally distributed over larger vertical spaces into relatively smaller vertical ranges, with this local concentration increase potentially affecting cloud–aerosol interaction efficiency and characteristics.

3.2. Regional and Seasonal Variations of Low Cloud Layers’ Optical Properties in Australia

The seasonal statistical analysis of low cloud layers’ optical properties from 2006 to 2021 in Australia revealed distinct patterns across the three geographical regions, with daytime and nighttime results shown in Figure 4 and Figure 5. These findings are consistent with previous regional cloud studies over East Asia [17] and aerosol research over Australia [30]. CODL (Figure 4 and Figure 5) exhibited clear and consistent seasonal variation patterns across Australia’s three geographical sub-regions, closely related to Southern Hemisphere seasonal succession and Australia’s unique climate system. Spring CODL values reached annual peaks (Region A: 5.00, Region B: 5.67, Region C: 5.53), followed by decreases in summer (Region A: 4.39, Region B: 5.65, Region C: 5.72), with autumn and winter showing intermediate levels.

This spring-peaked seasonal pattern contrasts sharply with traditional summer-peaked expectations, reflecting Australia’s climate system uniqueness and complexity. Spring, as the critical transition period from winter to summer in the Southern Hemisphere, features relatively active and unstable atmospheric circulation systems. Frequent interactions between cold air masses from the Southern Ocean and gradually warming continental air masses create favorable large-scale weather systems for cloud development and optical depth enhancement. Conversely, although summer provides more abundant moisture supply conditions, strong control by subtropical high-pressure systems intensifies atmospheric subsidence, effectively suppressing cloud vertical development processes. Regional analysis reveals that Region B (central plains) consistently exhibited relatively higher CODL values across most seasons, primarily attributed to the unique geographical location and terrain characteristics of the central plains.

Nighttime CODL observations demonstrated systematic enhancement effects compared to daytime values. Region A showed nighttime seasonal averages of 5.53 (13% increase over daytime), Region B showed 6.13 (10% increase), and Region C showed 5.90 (7% increase). This nighttime enhancement phenomenon was most pronounced during spring, with Region B reaching 6.29. The physical mechanism underlying nighttime CODL enhancement primarily stems from complex radiative cooling processes that create extremely favorable thermodynamic conditions for cloud droplet formation and growth, while significantly lowered nighttime boundary layer heights compress atmospheric water vapor into relatively smaller vertical spaces, further promoting cloud layer optical depth enhancement.

CHL (Figure 4 and Figure 5) seasonal variations were relatively moderate and stable, with regional values maintained within relatively narrow ranges of 1.94–2.30 km during daytime and 2.05–2.40 km during nighttime. Despite this stability, systematic inter-regional differences remained clearly visible, with Region C (eastern mountains) consistently showing slightly higher CHL values across all seasons, directly reflecting the fundamental and persistent influence of mountain terrain on cloud vertical structure. Nighttime values showed slight increases compared to daytime levels, though the magnitude of increase was relatively limited and varied across regions.

CBL (Figure 4 and Figure 5) seasonal variations displayed more complex and regionally specific patterns, with nighttime observations showing remarkably significant systematic increases. Region A nighttime CBL seasonal averages reached 1.57 km (54% increase over daytime 1.02 km), Region B reached 1.45 km (69% increase over daytime 0.86 km), and Region C reached 1.65 km (46% increase over daytime 1.13 km). This substantial nighttime CBL elevation phenomenon reflects fundamental differences in nighttime cloud formation mechanisms, where cloud formation follows top-down development patterns rather than daytime boundary layer-controlled processes.

CTL (Figure 4 and Figure 5) seasonal variations presented clear thick spring–winter and thin summer–autumn bimodal distribution characteristics during daytime, while nighttime CTL values remained relatively stable with minimal seasonal and regional variations, consistently maintaining low values across all seasons and regions (Region A: ~0.67 km, Region B: ~0.67 km, Region C: ~0.73 km). This represents an approximately 40–50% reduction compared to daytime levels, directly reflecting the strong inhibitive effects of enhanced atmospheric stability on cloud vertical development during nighttime hours. This substantial nighttime CTL reduction directly reflects strong inhibitive effects of increased atmospheric stability on cloud layer vertical structure development. Spring CTL values reached annual maximums during daytime (Region A: 1.18 km, Region B: 1.09 km, Region C: 1.02 km), followed by winter, while summer and autumn were relatively thin.

CODGOLL (Figure 4 and Figure 5) seasonal variations showed completely opposite trends to CN, revealing intrinsic physical connections between single-layer low cloud proportions and total cloud layer numbers. Nighttime CODGOLL values were universally higher than daytime levels across all regions and seasons, with winter reaching annual peaks (Region A: 0.945, Region B: 0.943, Region C: 0.930), followed by spring, while summer remained relatively low but still notably higher than daytime levels. The systematic nighttime CODGOLL increase reflects the fundamental regulatory effects of nighttime atmospheric conditions on cloud layer vertical structure complexity.

CN (Figure 4 and Figure 5) seasonal variations further confirmed these seasonal regulatory mechanisms of cloud layer structure, with nighttime values showing systematic decreases (nighttime average 1.36 versus daytime 1.67, representing a 19% reduction). Winter CN values reached annual minimums (Region A: 1.23, Region B: 1.28, Region C: 1.30), clearly indicating that winter nighttime conditions were most favorable for single stratiform cloud system formation and long-term maintenance. Summer CN values reached annual peaks during daytime (Region A: 2.49), clearly reflecting multi-layer cloud phenomena resulting from enhanced summer convective activity.

CDRL (Figure 4 and Figure 5) and CCRL (Figure 4 and Figure 5) maintained relatively stable numerical characteristics across seasons, with seasonal variation amplitudes being relatively small but statistically significant. Both parameters showed systematic increases from spring to winter, regardless of day or night observations, with nighttime values consistently higher than daytime levels. CDRL nighttime averages (0.213) showed 23% increases over daytime values (0.173), while CCRL nighttime averages (1.157) showed 4% increases over daytime values (1.109). Region B exhibited the highest CDRL values during daytime across all seasons, likely due to the coexistence of spherical cloud droplets with non-spherical dust aerosols from surrounding desert areas. Region C recorded the highest CCRL values during both daytime and nighttime across all seasons, with winter reaching maximum values (Region A: 1.164, Region B: 1.164, Region C: 1.166), potentially related to more abundant water vapor conditions and more complex cloud physical processes in mountainous regions.

3.3. Correlations Between Low Cloud Layer and Aerosol Layer Optical Properties in Australia

To further understand interactions between aerosols and clouds, this study analyzed correlations between low aerosol layers’ and cloud layers’ optical parameters in Australia, based on our previous research on low aerosol layers in Australia [31]. Correlation analysis matrices (Figure 6 and Figure 7) revealed interactive relationships between Australia’s near-surface cloud layers and aerosol layers. The most notable correlations appeared in spring Region A daytime observations, where CHL and AHL showed moderate positive correlations (R = 0.54), indicating measurable relationships between aerosol layer heights and cloud layer top heights. This positive correlation suggests potential aerosol–cloud interaction mechanisms [4,6]. Aerosol particles may serve as cloud condensation nuclei, influencing cloud droplet formation processes, while also affecting local atmospheric conditions through radiative effects [31]. However, the moderate correlation coefficients indicate that these relationships, while statistically significant, represent only partial influences among multiple factors controlling cloud development.

CTL and AHL exhibited moderate positive correlations (R = 0.53), while CHL and ATL also showed positive correlations (R = 0.52). The positive correlation between CTL and ATL (R = 0.51) suggests that thicker aerosol layers may be associated with enhanced cloud vertical development, though the underlying mechanisms require further investigation.

The negative correlation between CODGOLL and ATL (R = −0.50) indicates that thicker aerosol layers tend to be associated with reduced single-layer low cloud dominance, possibly related to more complex multi-layer cloud structures under certain atmospheric conditions.

Regional analyses show that Region A (western plateau) exhibited relatively consistent cloud–aerosol correlations, potentially related to the region’s stable atmospheric conditions. Seasonal analyses revealed that spring correlations were generally stronger than other seasons, though overall correlation magnitudes remained moderate across all periods.

Nighttime observations showed somewhat weaker correlation patterns compared to daytime, with reduced coefficient intensities reflecting different atmospheric conditions and cloud formation processes during nighttime periods. The persistence of some correlations throughout diurnal cycles suggests consistent but limited aerosol influences on cloud properties.

Values in the correlation matrices showed that most correlation coefficients were relatively small, with

Table 2. Correlation coefficients (R) between low cloud layers’ and aerosol layers’ physical quantities with |R| > 0.4.

	DAYTIME			NIGHTTIME
	Autumn	Spring	Summer	Autumn	Summer	Winter
	A	A	B	B	C	C
CODL_ATL			−0.41	−0.44
CHL_AHL		0.54			0.44	0.41
CHL_ATL		0.52	0.40		0.44
CBL_ATL					0.44
CTL_AHL		0.53
CTL_ATL		0.51
CDRL_AHL		−0.43	−0.43
CDRL_ATL		−0.44	−0.46
CCRL_AHL		−0.41
CCRL_ATL		−0.40
CN_ATL	0.42	0.43
CODGOLL_AHL		−0.50
CODGOLL_ATL		−0.50	−0.40

summarizing cases where |R| > 0.4. The identified correlations between cloud and aerosol layer properties provide evidence for measurable aerosol–cloud interactions over Australia [4,6]. These relationships, with correlation coefficients typically ranging from 0.4 to 0.6, suggest detectable but modest influences of aerosol properties on cloud characteristics. The regression analyses (Figure 8 and Figure 9) illustrate these relationships, though the scatter in the data indicates that aerosol–cloud interactions represent only one of multiple factors influencing cloud development across Australia’s diverse climate zones. The observed correlations, while moderate in magnitude, are consistent with the current understanding of aerosol–cloud interaction complexities [30] and contribute to the broader scientific effort to quantify these relationships using satellite observations [23,24].

4. Conclusions

To explore the distribution and influencing factors of optical and vertical properties of near-surface cloud layers over Australia, we analyzed high-confidence CALIPSO level-2 data (|CAD| > 70) spanning 2006 to 2021. By focusing on cloud layers with base heights below 2.5 km and applying strict cloud–aerosol discrimination criteria, this study offers a comprehensive and reliable view of cloud systems closely interacting with boundary-layer aerosols. This approach captures both conventionally defined low clouds and vertically extended mid-level clouds with low bases, thereby enriching the understanding of aerosol–cloud processes in the lower troposphere:

(1) Spatial Distribution Characteristics: Throughout the observation period, CODL exhibited pronounced spatial heterogeneity with higher values in central and eastern regions (often exceeding 6.0) and lower values in western Plateau regions (typically 4.0–5.0). This pattern reflects Australia’s complex topography and diverse climatic conditions, where central plains provide ideal conditions for moisture convergence while western plateau regions are constrained by the arid continental climate. CHL, CBL, and CTL showed moderate spatial variations with clear topographic dependencies, particularly in eastern mountainous regions where orographic lifting effects influence cloud vertical structure. CODGOLL displayed distinct latitudinal gradients with lower values in northern regions, indicating more prevalent multi-layer cloud phenomena consistent with tropical monsoon climate characteristics.

(2) Seasonal and Diurnal Variations: Seasonal analysis revealed that CODL reached annual peaks during spring across all regions, contrasting with traditional summer-maximum expectations and reflecting Australia’s unique climate system. This spring-peaked pattern results from active atmospheric circulation during the Southern Hemisphere’s winter-summer transition period. Pronounced diurnal variations were observed across all parameters, with nighttime CODL showing systematic enhancement (maximum values reaching 19.29 compared to daytime 11.43). CBL exhibited counterintuitive nighttime increases (average 41% elevation), reflecting top-down cloud formation mechanisms during nighttime radiative cooling. CTL demonstrated significant diurnal contrasts with a nearly 50% nighttime reduction due to enhanced atmospheric stability suppressing vertical cloud development.

(3) Regional Differences and Climate Relationships: The spatial distribution patterns of cloud optical properties show clear relationships with Australia’s distinct geographical and climatic zones. CODL was consistently higher in southern regions, reflecting temperate climate influences and cyclonic systems, particularly during winter and spring periods. Regional differences in CHL and CTL reveal topographic influences on cloud vertical development across different atmospheric stability conditions. The central plains (Region B) consistently exhibited relatively higher CODL values across most seasons, while eastern mountains (Region C) showed elevated cloud heights, demonstrating the persistent influence of geographical features on cloud properties.

(4) Moderate Aerosol–Cloud Interactions: Correlation analysis revealed measurable but modest relationships between cloud and aerosol layer properties. The identified correlations (CHL-AHL, CHL-ATL) provide quantitative evidence for aerosol–cloud interactions, with correlation coefficients typically ranging from 0.4 to 0.6. While statistically significant, these moderate correlations suggest that aerosol influences represent only one of multiple factors affecting cloud vertical structure and development. The relationships show regional and seasonal dependencies, with spring exhibiting stronger correlations than other seasons, though overall magnitudes remain moderate across all periods.

(5) Data Quality and Methodological Implications: The application of high-confidence CAD score filtering (|CAD| > 70) significantly improves the reliability of cloud–aerosol discrimination while creating a more conservative but scientifically robust dataset. The resulting data gaps, particularly in southwestern coastal regions, reflect the challenging nature of cloud–aerosol discrimination in complex marine boundary layer environments. This methodological approach provides a foundation for future studies requiring high-confidence cloud identification in satellite lidar observations.

The comprehensive analysis demonstrates that Australian cloud optical properties exhibit complex spatial, seasonal, and diurnal patterns driven by geographical, climatic, and atmospheric factors. While measurable aerosol–cloud interactions exist, their influence appears moderate compared to other controlling factors, emphasizing the need for continued research to fully understand cloud system dynamics in this climatically diverse region.