Influence of aerosol and meteorological variables on clouds in the summer monsoon and premonsoon season over India

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Parth Sarthi, Prabhat Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6997465/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract This study emphasizes over the influence of aerosol and meteorology (relative humidity (RH) and vertical velocity (ω)) on cloud properties in the summer monsoon (June-Sept) and premonsoon (March-May) season by using traditional statistical method, followed by geographical detector method (GDM), an alternative statistical approach. Results reveal, in the summer monsoon season, strong influence of meteorology on cloud fraction diminish the effect of aerosol, while the meteorology aids to the influence of aerosol on cloud fraction in the premonsoon season. The interplay between aerosol and meteorology leads to non-linear change in the cloud top height in the summer monsoon season, while in the premonsoon season, it leads to formation of taller clouds in higher aerosol environment due to shallow convection. Both aerosol and meteorology have weak influence on cloud particle radius, however, high RH and strong updraft (negative ω) leads to formation of bigger cloud droplet radius in the summer monsoon season. The GDM is used to determine the relative importance of the effects of aerosol and meteorology and the effects of interaction between aerosol and meteorology on the cloud properties. The interaction between aerosol and meteorology have more effect on the cloud properties unless their interaction weakens each other’s effect. Furthermore, the prevailing meteorology in the season can restrain the influence of aerosol on cloud properties. The work provides valuable insight of the association between aerosol and cloud properties at seasonal time-scale and the influence of meteorological covariations on this relationship and improve the understanding of the aerosol-cloud interaction. AOD cloud properties meteorological variables geographical detector method Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction The persistent growth of economy and growing energy demand across South-East and South Asia (like India, etc.) has enhance the aerosol and its precursors in the atmosphere (Dong et al. 2019 ; Banerjee et al. 2021 ). The increasing aerosol in the atmosphere has increased the frequency of occurrence of extreme weather events (such as dust storms, dense fog, severe pollution episodes, etc.) and intermittent monsoonal precipitation (Samset et al. 2019 ; Wang et al. 2022 ). The aerosol has significant role in causing climate change, mainly at regional scale. They have visible effect on air quality, human health and climate; and serving as cloud condensation nuclei (CCN), they indirectly influence the physical properties and lifetime of clouds (Qin et al. 2018 ). Aerosol has been previously considered as a source of uncertainty that significantly affect the earth’s climate and weather system in many ways. Aerosol shows both spatial and temporal variations, which can lead to variation in the optical cloud properties (Alam et al. 2010). In last few decades, the effect of aerosol on cloud properties has gained significant attention and associated with one of the largest uncertainties in climate system. Atmospheric aerosol has significant role in modulating the micro-physical and macro-physical properties of clouds. Aerosols modulate the hydrological cycle and climate through direct, indirect and semi-direct effect (Rosenfeld et al. 2014 ; Fan et al. 2016 ). In direct effect, aerosol can absorb and scatter the solar and terrestrial radiation (Khatri et al. 2021 ; Wang et al. 2014 ) and influence the thermal balance. Aerosols act both as CCN and ice nucleating particle (INP) and perturb the cloud microphysical and radiative properties, cloud lifetime as well as precipitation, a process known as “indirect effect” (Rosenfeld et al. 2008 ; Fan et al. 2018 ). In semi-direct effect, absorbing aerosols such as soot, black carbon (BC) and dust, can suppress the formation of cloud by warming the atmosphere, resulting into thinning of clouds and increase in water vapour evaporation (Ackerman et al. 2000 ; Huang et al. 2006 ) Numerous studies have been performed to improve our knowledge and reduce the uncertainty associated with the impact of aerosols on clouds and precipitation/rainfall (Koren et al. 2010 ; Sarangi et al. 2017 ; Adhikari and Mejia 2021 ; Anwar et al. 2022 ; Raj et al. 2024 ). Satellite-based studies of aerosol-cloud interaction generally seek to correlate aerosol loadings with cloud micro/macro physical properties (Saponaro et al. 2017 ). In terms of effect of aerosol on cloud micro-physical properties, many studies found that aerosol optical depth (AOD) and cloud droplet radius are negatively correlated (Feingold et al. 2001 ; Feingold et al. 2013 ; Costantino and Breon 2013). However, some studies also reported that cloud droplet radius and AOD are positively correlated especially over land and it is referred as Anti-Twomey effect (Feingold et al. 2001 ; Grandey and Stier 2010 ; Liu et al. 2017 ). For different AOD regime, different behaviours of cloud droplet radius as a function of AOD are also observed by Tang et al. ( 2014 ) and Wang et al. ( 2015 ). In terms of influence of aerosol on cloud macro-physical properties, Yan and Liu ( 2009 ) found that cloud fraction (CF) is positively correlated with AOD in summer. Quaas et al. ( 2010 ) also pointed out that CF and AOD are positively correlated. Aerosol can invigorate the cloud development and negatively correlated with the cloud top pressure (CTP) and cloud top temperature (CTT), resulting to increase in monsoonal rainfall (Sarangi et al. 2017 ). Meteorology can affect the association of aerosol with clouds (Koren et al. 2010 ; Su et al. 2010 ; Stathopoulos et al. 2017 ). Estimation of relationship between aerosol and cloud independent of large-scale meteorology is a major challenge. For example, large-scale convergence could concentrate aerosol as well as increase cloudiness which produces an apparent correlation between aerosol and cloud without any physical interaction (Mauger and Norris 2007 ). A study by Loeb and Monalo-Smith (2005) has reported that both cloud fraction and AOD are correlated with RH and wind speed. Such effect of meteorology on the relationship between aerosol and cloud is important, but it is difficult to isolate from broad observation, like those from satellite (Tang et al. 2014 ). Although the relationship of aerosol with clouds and effect of meteorological covariations on this relationship have received considerable attention, yet it remains highly uncertain in the climate system and effects of meteorology aid more to the uncertainty. This study investigates the relationship between aerosol on cloud as well as influence of meteorological covariations on this relationship in the summer monsoon (monsoon season is used instead of summer monsoon season in later part of this section) and premonsoon season. The study also seeks to determine the relative importance of the effects of aerosol and meteorology, and the effects of the interaction of aerosol and meteorology on cloud. In this paper, Introduction is discussed in section 1 . The study area, dataset and methodology are discussed in section 2 . The section 3 contains the results and discussion. Conclusions are placed in section 4 . 2 Study region, data and methodology 2.1 Study region The study is carried out over the Gangetic plain (GP) of India bounded within 21º N to 31º N latitude and 76º E to 91º E longitude. The region is bounded by Chota Nagpur Plateau in the south and Himalayas in the north. The Gangetic plain is a densely populated region in India and experiences high aerosol loading in both summer monsoon and premonsoon seasons. The aerosol loading in monsoon is comparatively higher (dust transportation through the westerly wind plus local emissions) than that of premonsoon season. Moreover, high relative humidity prevails in the monsoon season due to transport of moisture by the south-westerly wind (generated due to thermal gradient between land and ocean) from the ocean to the GP and strong convection also prevails. While in premonsoon season, low relative humidity along with downdraft condition prevails (Supplementary Figure S2). The varying aerosol loading and meteorology across seasons renders this region an ideal natural laboratory to investigate the influence of aerosol on cloud as well as meteorological covariations. The study region (blue boundary) is shown in Fig. 1 . 2.2 Data This study uses Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua (MYD08_D3) level 3 daily product of AOD and cloud properties (CF, cloud optical thickness (COT), CTP, CTT, liquid cloud effective particle radius (R el ) and ice cloud effective particle radius (R ei )). MODIS instrument measures the 36 spectral bands from visible to infrared wavelength (0.4 µm to 14.4 µm). The Aqua overpass the equator at 1330 local time in sun-synchronous orbit. The combined Dark Target and Deep Blue algorithm is used to retrieve the aerosol optical properties. The MODIS level 3 AOD and cloud properties datasets have been extensively used to study the interaction of aerosol with clouds and precipitation over different parts of the world (Koren et al. 2010 ; Patil et al. 2017 ; Ng et al. 2017 , Raj et al. 2024 ) and to validate the CMIP6 model simulated dataset (Raj et al. 2025 ). The meteorological variables (RH and ω) used are retrieved from ERA5, the most recent reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5, a successor of ERA-interim, is a fifth-generation reanalysis product by ECMWF. ERA5 provides hourly global reanalysis data at spatial resolution of 0.25º with 137 vertical level from surface up to a height of 80 km. The datasets used in the study are only for monsoon and premonsoon season for the time period of 2003-21. The datasets used are given in Supplementary Table S1 . 2.3 Methodology In the study, AOD is used as proxy for aerosol loading in the atmosphere. Only days with AOD < 1 are selected to reduce the potential source of aerosol retrieval errors such as cloud contamination and aerosol humidification due to high RH (Sarangi et al. 2017 ; Adhikari and Mejia 2021 ; Raj et al. 2024 ). Earlier studies by Koren et al. ( 2010 ), Sarangi et al. ( 2017 ) and Adhikari and Mejia ( 2021 ) have observed that meteorological variables (RH and ω) are well correlated with cloud properties. These meteorological variables (RH and ω) are crucial for cloud development. The RH and ω at 9 pressure levels (1000 hPa, 900 hPa, 850 hPa, 700 hPa, 600 hPa, 500 hPa, 400 hPa, 300 hPa and 200 hPa) are used. The mean of hourly RH and ω between 07:00 and 09:00 UTC provided by ERA5 are used to cover ± 1 h of MODIS Aqua overpass. 2.3.1 Statistical method 2.3.1.1 Pearson correlation Pearson bivariate correlation analysis is performed to determine the degree of linear relationship of AOD and cloud properties. The correlation coefficient takes the value between − 1 and 1, where 1, 0, -1 indicate perfect correlation, no correlation, and perfect negative correlation, respectively. The statistical significance of Pearson correlation is analysed by using two-tailed distribution student-t test. The Pearson correlation coefficient ( r ) can be computed using Eq. (1): \(\:r=\:\frac{\sum\:({x}_{i}-\stackrel{̿}{x})({y}_{i}-\stackrel{̿}{y})}{\sqrt{\sum\:{({x}_{i}-\stackrel{̿}{x})}^{2}\sum\:{({y}_{i}-\stackrel{̿}{y})}^{2}}}\) ……………… Eq. (1) where x i and y i are values of x and y variables, and \(\:\stackrel{̿}{x}\) and \(\:\stackrel{̿}{y}\) are the mean of x and y . 2.3.1.2 Geographical detector method The Geographical detector method (GDM), proposed by Wang et al. ( 2010 ), is used to compute the influence of driving factors (AOD, RH and ω) on cloud properties (CF, CTP, R el and R ei ). In general, the GDM assume that the independent variable has important influence on the dependent variable (Wang et al. 2016 ; Wang and Hu 2017; Liu et al. 2024 ). The GDM does not follow the linear hypothesis to analyse the driving factors behind the spatial stratified heterogeneity. The GDM has four packages (factor detector, interaction detector, ecological detector and risk detector). The factor detector and interaction detector package are used to reveal the driving factors ( x ) responsible for the change in cloud properties ( y ). Here the non-spatial GDM is used to compute the extent to which driving factor ( x ) can influence the dependent counterpart ( y ). The core requirement of GDM is that the continuous variables should be converted into categorical strata. Here, Jenks natural break classification method (Jenks 1967 ) is used to categorized the x into strata h . The basic idea of this classification method is that it aims to minimize the variance within the class and maximize the variance between the classes. The power of determinant q of x on y (also considered as power of influencing factor) can be computed using Eq. (2): \(\:q=1-\:\frac{{\sum\:}_{h=1}^{L}{N}_{h}{\sigma\:}_{h}^{2}}{N{\sigma\:}^{2}}\) ……………….. Eq. (2) where h (1, 2, 3, …., L) denotes the stratum of factor ( x ), N is the total number of samples in the dataset (here, N is total area in case of spatial GDM), N h is the number of samples in stratum h , and \(\:{\sigma\:}_{h}^{2}\) and \(\:{\sigma\:}^{2}\) are the variance of samples in the stratum h and total variance in the dataset, respectively. The value of q ranges from 0 to 1 where 0 indicates that the driving factor ( x ) has no influence on y and q close to 1indicates strong influence of driving factor ( x ) on y . For instance, q = 0.75 indicates 75% of the variance of y can be explained by x . In this study the driving factor (AOD and meteorological variables) are categorized into 5 classes using Jenks natural breaks classification method. The factor detector can quantify the extent of the influence of driving factor ( x ) on dependent counterpart ( y ) using the value of q . The interaction detector can quantify whether two driving factors x 1 and x 2 taken together weakens or enhances one another influence on dependent counterpart ( y ) or whether they independently influencing y . The q value of factor x 1 and x 2 obtained from Eq. (1) can be written as q ( x 1) and q ( x 2). When two factor x 1 and x 2 are interacting, it can be written as x 1 ∩ x 2, where ∩ denotes the interaction. Then, the q value of x 1 ∩ x 2 can be computed and written as q ( x 1 ∩ x 2). By comparing the q value of interaction of two factor with the q value of each of the two individual factor, five types of interaction are considered (Liu et al. 2024 ) which are given in Table 1 . Table 1 Types of interaction of two factors. Description Types of interaction q ( x 1 ∩ x 2) < Min[ q ( x 1), q ( x 2)] Weakened, nonlinear Min[ q ( x 1), q ( x 2)] < q ( x 1 ∩ x 2) Max[ q ( x 1), q ( x 2)] Enhanced, bilinear q ( x 1 ∩ x 2) = q ( x 1) + q ( x 2) Independent q ( x 1 ∩ x 2) > q ( x 1) + q ( x 2) Enhanced, nonlinear 3 Results and discussion 3.1 Spatial distribution of AOD and CF Figure 1 depicts the spatial distribution of mean AOD and CF in the monsoon and premonsoon for the period of 2003-21. It has been observed that the GP region experiences high AOD (0.68 ± 0.29) in the monsoon season. The prevalence of higher aerosol loading in the monsoon season over GP is due to local anthropogenic emission and transported dust aerosol through the south-westerly wind from the desert and arid regions of Southwest Asia and Thar desert (Raj et al. 2024 ). Additionally, deeper boundary layer and hygroscopically growth of aerosol due to high relative humidity in the atmosphere are other reasons for higher aerosol loading in the monsoon season. Dey and Girolamo ( 2010 ) has reported that mineral dust is the prime component of aerosol in the monsoon season. In the premonsoon season, moderate level of AOD (0.55 ± 0.19) is observed and it can be attributed to anthropogenic emission and possibly due to aerosol transport from the Indian landmass. The CF is very high in the monsoon season as compared to premonsoon season when CF ranges between low to moderate. Generally, high CF in the monsoon season is associated with increased moisture content (RH is an indicator of moisture content in Supplementary Figure S2) which is crucial for the formation of clouds. The spatial average mean of CF in the monsoon and premonsoon season is 0.84 ± 0.19 and 0.34 ± 0.2, respectively. 3.2 Influence of AOD on cloud properties Among several factors which have ability to modulate the cloud properties, the species and availability of cloud active aerosols are determining factors of cloud formation (Lohmann and Feichter 2005 ). Thus, any change in the characteristics and abundance of aerosols has a direct impact on the cloud properties (Kumar and Tiwari 2023 ). In Fig. 2 , the cloud properties values are averaged over the bins (50 bins are produced using Jenks natural break classification method) of AOD, from 0 to 1. The strength of the influence of AOD on cloud properties has been quantified by the slope (exponent of AOD in power law equation in Fig. 2 represents the slope value) of linear regression on log-log scale between AOD and cloud properties. In the monsoon and premonsoon season, R el decreases with the increase of AOD (Fig. 2 e). The decrease in R el with increasing AOD is termed as “aerosol first indirect effect” or “Twomey effect” which says more aerosol leads to more numerous and smaller liquid cloud particles for a constant liquid water path. Although weak, the influence of AOD on R el in the monsoon season is 2-fold higher that of the premonsoon season. The statistically significant correlation coefficient (-0.59) further supports the association. Here, it is important to note that correlation between AOD and cloud properties does not imply causation. However, it may define a link between AOD and cloud properties at climatological relevant scale (Bender et al., 2019 ). Studies by several researchers have also found similar influence of AOD on R el over different regions (Costantino and Bréon 2013 ; Liu et al. 2024 ; Raj et al. 2025 ). Figure 2 f shows that the influence of AOD on R ei is markedly different across the seasons. The R ei decreases with the increasing AOD in the monsoon season which is similar to the Twomey effect in case of cloud droplet radius. While in the premonsoon season, R ei increases with the increasing AOD which is known as Anti-Twomey effect. In the monsoon season, the moisture content (as RH is an indicator of moisture content) in the atmosphere remains high (Raj et al. 2024 ), it supports the homogeneous nucleation of liquid droplets over heterogeneous nucleation which, in turn, more aerosol could lead to more numerous but smaller ice particles. Under dry condition which prevails in the premonsoon season, early onset of heterogeneous nucleation could possibly prevent the homogeneous nucleation of liquid droplets (DeMott et al. 2010 ; Zhao et al. 2018 ). Therefore, more aerosol could lead to more fraction of ice particles produced by heterogeneous nucleation process which comprises fewer and larger ice particle (Zhao et al. 2018 ). Overall, the low influence on R el due to increasing aerosol over land can be attributed to the low hygroscopic nature of aerosol and thus less susceptible to act as an active CCN (Liu et al. 2024 ). The decrease in CF with the increasing AOD, although it is very weak, could be attributed to the elevated level of absorbing aerosol over Indo-Gangetic plain (IGP) (Srivastava et al., 2012) which absorbs the incoming solar radiation, resulting to aerosol induced heating inside the cloud (Rao and Dey, 2020 , Sandhya et al., 2021). As a result, the decrease in fraction of cloud is observed at higher aerosol loading. Another probable reason is that more aerosols (and hence more CCN) are expected to result in more numerous and smaller cloud droplet in high moisture laden environment. The smaller cloud droplets are more susceptible to faster evaporation which could possibly enhanced the entrainment of drier air surrounding the cloud and decreases the fraction of cloud (Quinn and Bates, 2014 ). While in the premonsoon season, CF increases with the increasing AOD and the strength of the effect of AOD on CF is very high, as indicated by high slope of 1 and statistically significant correlation coefficient of 0.97 (Fig. 3 a). The aerosol humification (Grandey et al., 2013 ), cloud contamination due to retrieval of AOD (Zhang et al., 2005 ) and impact of meteorological covariations (Engström and Ekman, 2010 ; Gryspeerdt et al., 2014 ; Kant et al., 2019 ) can be possible reasons for a large fraction of relationship between AOD and CF. Total relationship between AOD and CF due to the effect of aerosol in observational studies is limited to less than 70% (Mauger and Norris 2007 ; Enstrom and Ekman 2010) and less than 50% (Gryspeerdt et al. 2014 ). Figure 2 b shows the decrease in COT with the increasing AOD in the monsoon season, while increase in COT with AOD in the premonsoon season. The latter is in good agreement with “aerosol first indirect effect”. The magnitude of influence of AOD on COT is weak, as indicated by low slope of 0.1 and low correlation coefficient of 0.31 in the premonsoon season while it is very weak in the monsoon season which is evidence from the very low slope of -0.03 and low correlation coefficient of -0.21. Mixed BC and dust aerosol contributes dominantly to the absorption (Kedia et al. 2014 ) of solar radiation in the monsoon and premonsoon season over the GP. The BC and dust aerosol can supress the formation of cloud by warming the atmosphere which is known as “aerosol semi-direct effect”, which results to thinning of cloud in the monsoon season. The aerosol semi-direct effect plus cloud absorption effect may dominate over the aerosol indirect effect (Sechrist and Jacobson 2017 ) which likely explains the observed decrease in COT with the rising AOD in the monsoon season. The influence of AOD on both CTP and CTT in the monsoon and premonsoon season is shown in Figs. 3 c, d. Here, CTP is used as proxy for the cloud top height (CTH). The increase in CTP represents the decrease in CTH. In the monsoon season, CTP initially increases with the increasing AOD up to 0.4, after which CTP begins to decrease as AOD continues to increase. The nonlinear relationship of AOD and CTP is statistically significant, as evidence by low slope of -0.19 and strong correlation coefficient of -0.80 for the initial increase in CTP with rising AOD, and moderate slope of 0.41 and strong correlation coefficient of 0.83 for the decrease in CTP with AOD. Moreover, CTT increases with the increasing AOD up to 0.4, and after that CTT begins to decrease as AOD continues to increase in the monsoon season. For AOD greater than 0.4, the agreement between AOD and both CTP and CTT indicates that higher clouds with colder tops are formed in highly aerosol loaded conditions. This happens because of more aerosols lead to more numerous and smaller cloud droplets which are less efficient to become precipitating particles and delay the precipitation. Therefore, more cloud water could be lifted above the freezing level which results to release of latent heat due to freezing of cloud droplets near the cloud top can enhance buoyancy and invigorate the convection (Rosenfeld et al. 2008 ; Zhang et al. 2022 ). As a result, more deeper clouds are formed in highly aerosol loaded condition in the monsoon season. Adhikari and Mejia ( 2021 ) and Sarangi et al. ( 2017 ) have also suggested deepening or invigoration of cloud due to increase in aerosol loading in the monsoon season over the Indian region, which agrees with our findings. In highly aerosol loaded conditions, taller clouds due to aerosol induced invigoration of mixed-phase cloud over the Amazon region (Andreae et al. 2004 ), tropics (Niu and Li 2012 ) and Atlantic (Koren et al. 2010 ) are also observed. In the premonsoon season, the decrease in CTP and increase in CTT with increasing AOD indicates that the aerosol induced invigoration may not be the possible reason for the increase in cloud top height. It is likely due to the cloud mediated relationship of aerosol and cloud top height (Gryspeerdt et al. 2014 ) or more widespread shallow convection. 3.3 Influence of meteorological variables The relationship between AOD and cloud properties can be influenced by dynamical and thermodynamical processes (Wang et al. 2014 ; Liu et al. 2017 ) and prevalent meteorological conditions (Quaas et al. 2010 ; Andersen et al 2017 ; Christensen et al. 2017 ) in the environment. That is, variation in certain meteorological variables can simultaneously influence the aerosol and cloud properties (Zhang et al. 2022 ). This can lead to mistaken perception that aerosol alone is influencing cloud properties. To disentangle the influence of meteorological variables from the aerosol effects, meteorological variables are classified into smaller sub-regimes and their influence on cloud properties are analyzed under both low and high AOD conditions. For this analysis, RH at 700 hPa (for CF) and 300 hPa (for CTP), along with ω at 600 hPa (for CF) and 300 hPa (for CTP) are selected for the monsoon season due to their strong correlation with cloud properties at these pressure levels (Supplementary Figure S1 ). Similarly, for the premonsoon season, RH at 600 hPa (for CF) and 300 hPa (for CTP), along with ω at 400 hPa (for both CF and CTP) are selected. Furthermore, to disentangle the influence of meteorological variables cloud particle radius (R el and R ei ), the atmospheric level of meteorological variables which have strong correlation with CF is selected because it is well known fact that the cloud particles are the key microphysical property that involved in the formation of clouds. The most influential level of meteorological variables is selected based on strong correlation to further disentangle the influence of the regimes of meteorological variables. The CF, CTP, R el , R ei , RH and ω data are divided in two AOD classes: low AOD (mean AOD – 1σ (standard deviation)) and high AOD (mean AOD + 1σ (standard deviation)). Then, CF, CTP, R el and R ei data are further divided into 3 RH regimes: RH < 50%, 50% < RH 70% (Adhikari and Mejia 2021). Similarly, the data are divided into 3 ω regimes: ω > 0 (subsidence), ̶ 0.1 < ω < 0 (weak convection) and ω < ̶ 0.1 (strong convection) (Koren et al. 2010 ; Adhikari and Mejia 2021 ). The influence of RH and ω on cloud properties under both low and high AOD conditions in the monsoon and premonsoon seasons is shown in Figs. 3 and 4 . High RH and stronger updraft are associated with taller clouds and higher fraction of cloud in the monsoon (Fig. 3 ) and premonsoon season (Fig. 4 ), while shallow clouds and low fraction of clouds are found in low RH and downdraft regimes. Similar findings in the monsoon season over foothills of Himalayas have been noted by Adhikari and Mejia ( 2021 ). Over the central India, Sarangi et al. ( 2017 ) has found high cloud fraction in high RH condition at lower atmosphere in the monsoon season. Note here that in the monsoon season, the general relationship between AOD and CF are different between the different meteorological sub-regimes (Fig. 3 a, b) and the full data values without the meteorological sub-regimes observed in Fig. 2 a, while it is significantly not different in the premonsoon season (Fig. 4 a, b). It indicates that the aerosol and meteorology exert opposing influence on CF in the monsoon season, i.e., increasing aerosol reduces the faction of cloud and increase in RH and ω increases the fraction of cloud. While they have near-orthogonal (Koren et al. 2010 ) effect on CF in the premonsoon season. This is the most plausible reason for the robust relationship between aerosol and cloud fraction in the premonsoon season observed in Fig. 2 a. In both premonsoon and monsoon seasons, the general relationship between AOD and CTP are not different between the different meteorological sub-regimes (Fig. 3 c, d) and the full data values without the meteorological sub- regimes observed in Fig. 3 c (except for AOD less than 0.4 in the monsoon season). It suggests that the aerosol and meteorology have similar effect on CTP. The presence of high RH and strong updraft (negative ω) in the atmosphere leads to formation of bigger R el / R ei , while smaller R el / R ei is formed in low RH and downdraft condition in the monsoon season (Fig. 3 ). High moisture content are available to condense onto CCN/ INP, resulting to formation of bigger liquid/ice cloud particle. Simultaneously, strong convection aids in keeping the smaller cloud particles (liquid and ice) aloft in the cloud, allowing them to grow into bigger liquid cloud particle through collision-coalescence process and into bigger ice particle at cloud tops through the deposition of super-cooled liquid onto smaller ice particles, particularly in moist environment which prevails in the monsoon season. It is important to note here that the increase in the size of cloud particles with both RH and ω is more observable only under high aerosol loading environment (except for the increase of R ei with ω under high aerosol loading in Fig. 3 h). In the premonsoon season, strong updraft is associated with bigger cloud particle radius (R el and R ei ) than that of downdraft, similar to findings in the monsoon season (Fig. 4 ). The R el does not show any change with rising of RH (low to high) in high aerosol loaded environment (Fig. 4 e). However, significant increase in R ei is observed with rising RH in high AOD environment. 3.4 Geographical detector method In this section, the power of determinant ( q ) value is computed in order to quantify the strength of the influence of driving factors (AOD, RH and ω) on cloud properties. 3.4.1 Factor detector analysis The Factor detector analysis package of GDM is used to analyze strength of influence of each driving factor (AOD, RH and ω) on cloud properties (CF, CTP, R el and R ei ) in the monsoon and premonsoon season. The q value of factor detector analysis of the monsoon and premonsoon season is shown in Table 2 . As expected, AOD can only explain 12% variance for CF, while RH and ω can explain 74% and 30% variance for CF in the monsoon season. This explains that in the monsoon season, meteorological variables have significantly high influence on CF than that of AOD. However, in the premonsoon season, the explanatory power of AOD increased to 25% for CF, while the explanatory power of RH and ω decreased to 43% and 28%, respectively. Much of the formation of taller clouds at higher aerosol loading in the premonsoon season (as discussed in earlier sections) is due to the influence of RH ( q = 0.31) and ω ( q = 14), not due to invigoration effect of aerosol ( q = 0.005). AOD can explain 9% (very low) variation of R el in the monsoon season, while RH and ω can explain 25% and 17%. In the monsoon season, the influence of both RH and ω on R ei are statistically significant but indicate low explanatory power, both accounting for only 12% of the variance. The explanatory power of influence of each driving factors on R el / R ei is very small in the premonsoon season. Table 2 q values of factor detector analysis during monsoon and premonsoon seasons Monsoon Premonsoon AOD RH ω AOD RH ω CF 0.12 *** 0.74 *** 0.30 *** 0.25 *** 0.43 *** 0.28 *** CTP 0.16 *** 0.52 *** 0.34 *** 0.005 0.31 *** 0.14 *** R el 0.09 *** 0.25 *** 0.17 *** 0.007 0.05 *** 0.02 *** R ei 0.11 *** 0.12 *** 0.12 *** 0.003 0.04 *** 0.03 *** Note that the asterisk (***) denotes q value is significant at 0.001 level. Among the three driving factors, RH (with highest q value) is predominately influencing the CF and CTP in the monsoon and premonsoon season. As observed from the Table 2 , the effect of aerosol (as indicated by higher q value of AOD) on cloud properties is stronger in the monsoon than premonsoon season (except the effect of aerosol on CF in premonsoon season). As stronger updraft condition prevails in the monsoon season, Jones et al. ( 2009 ) and Jia et al. ( 2022 ) noted that stronger aerosol-cloud interaction occurs under higher updraft condition, which agree with our results. 3.4.2 Interaction detector analysis The q values of the effect of two factors (AOD, RH and ω) taken together, influencing the cloud properties in the monsoon and premonsoon season are shown in Figs. 5 and 6 . In the monsoon season, the q value of combined effect of AOD and RH can explain 75% variance of CF, while combined effect of AOD and ω have explanatory power of 27%. Similarly, the combined effect of RH and ω have explanatory power of 75% on CF (Fig. 5 ). Furthermore, in the monsoon season, the q values of the combined effects on CTP show that the explanatory power of AOD together with each of the meteorological variables, i.e. RH and ω, is 53% and 35%, respectively. Moreover, the q value of the combined effect of two meteorological variables, i.e., RH and ω can explain 50% of the variance of CTP. This indicates that the interaction of two factors generally yields higher q values than the individual factors alone, unless their interaction weaken each other’s influence. That is, the q values of combined effect of a pair of driving factors can explain more accurately the variance in the cloud properties. Liu et al. ( 2024 ) also reported that the q values of combined effect of pair of factors taken together on warm cloud properties are higher than the q value of individual factor. In the monsoon season, the q values of combined effect of pair of factors taken together on R el and R ei show that the explanatory power has decreased because their interaction are weakening each other’s influence. The results computed from the similar analysis for the premonsoon season show that the q value of combined effect of pair of factors on cloud properties is also higher than the q value of individual factor (Fig. 6 ). The combined effect of AOD and RH, and of RH and ω, each explains 50% variance of CF which indicates that both combinations can predominantly influence the CF. However, the combination of meteorological variables, i.e., RH and ω can predominantly influence the CTP as their q value of combined effect has highest explanatory power of 0.39. The low q value of combined effect of pair of driving factors (AOD, RH and ω) on cloud particle radius (R el and R ei ) suggest limited interactive influence to the variation of cloud particle radius. Although GDM provides an alternative approach to assess the confounding effects of aerosol and meteorological variables, and their interactions on cloud properties, it may not reliably quantify their absolute contributions (Liu et al. 2024 ). One possible reason is the noise inherent in daily data used here, as continuous data (which has been later converted into categories) are used instead of spatially stratified data in the GDM analysis. The q values (results not shown here) computed using weekly means (i.e., 7-day averages) also yield more or less similar results. 4 Conclusions The influence of the aerosol on cloud remains one of the major uncertainties in the weather and climate system. The uncertainty arises due to the contrasting relationship between aerosol and cloud properties observed over the different regions of world as well as the influence of meteorological covariations on this relationship. In the monsoons season, the rising aerosol led reduction in cloud fraction, and deepening of clouds due to aerosol induced invigoration effect for AOD > 0.4. In the premonsoon season, higher cloud fraction, and taller cloud due to shallow convection are associated with rising aerosol loading. As meteorology varies with the seasons, the effects of meteorological covariations may influence the relationship between aerosol and cloud properties. The aerosol and meteorological variables (RH and ω) have opposing influence on both cloud fraction and cloud particle radius (R el and R ei ) in the monsoon season. However, the aerosol and meteorological variables have near-orthogonal influence on CF and CTP in the premonsoon season and on CTP (for AOD > 0.4) in the monsoon season. This reveals that aerosol and meteorology have similar and opposite influence on the cloud properties (except cloud particle radius in the premonsoon season) in different seasons due to varying meteorology. Furthermore, the GDM is used to analyze the influence of driving factors (AOD, RH and ω) alone on cloud properties and interaction detector analysis (a package of GDM) is used to analyze the combined effect of pair of factors taken together on cloud properties. The GDM further supports the above findings of the influence of aerosol and meteorological variables on the cloud properties. The results from the interaction detector analysis suggest that the explanatory power ( q value) of the combined effects of a pair of factors is always higher (unless they weaken each other’s influence) than that of each factor alone. That is, the combined effects provide more accurate estimate of the influence on cloud properties. There is significant seasonal variation in the relative importance of each factor (except R ei ). The findings of this research help to improve the understanding of the aerosol-cloud interaction and alleviate the uncertainty associated with aerosol-cloud interaction at climatological scale. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The authors wish to thank NASA and ECMWF for maintain open-source dataset used in this study. 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Atmospheric Chemistry and Physics 18:1065–1078 Supplementary Files Supplementaryfile1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revision 20 Aug, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers invited by journal 11 Jul, 2025 Editor assigned by journal 01 Jul, 2025 First submitted to journal 28 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6997465","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484187868,"identity":"5bb55160-6c3d-4f6a-b0b9-ad7d9717a64c","order_by":0,"name":"Vikram Raj","email":"","orcid":"","institution":"Central University of South Bihar School of Earth Biological and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vikram","middleName":"","lastName":"Raj","suffix":""},{"id":484187869,"identity":"09566521-a140-4b40-96ad-791368c0b0df","order_by":1,"name":"P. Parth Sarthi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDCCAyCi4gCE/YB4LWcOMPCA2AlEa2Fsg2hhIEoL3+2zxx7+nHdHzl7s8EOgLXZyug0EtEiey0s35t32zJhHOs0AqCXZ2OwAAS0GZ3jMpBm3HU7skU4AaTmQuI0YLZI/54C0pH8gXosEbwNISw6Rtkie4Us35jl22Jjndk7BgQQDIvzCd4b32MMfNYfl2Genb/7wocJOjqAWBgYeNmR3ElSOoWUUjIJRMApGARYAAAGsR0HjKbaBAAAAAElFTkSuQmCC","orcid":"","institution":"Central University of South Bihar School of Earth Biological and Environmental Sciences","correspondingAuthor":true,"prefix":"","firstName":"P.","middleName":"Parth","lastName":"Sarthi","suffix":""},{"id":484187870,"identity":"8461a8bd-735f-470c-b058-98135aa50bb4","order_by":2,"name":"Prabhat Kumar","email":"","orcid":"","institution":"Central University of South Bihar School of Earth Biological and Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Prabhat","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-06-28 11:28:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6997465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6997465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86781723,"identity":"966dd0bf-748c-4d1e-94f2-4de1ffc801a8","added_by":"auto","created_at":"2025-07-15 13:34:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1004549,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of 19-years (2003-21) composite mean of AOD and cloud fraction (CF) during monsoon and premonsoon season over GP\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6997465/v1/9f45bfd8415f86727c6f4066.png"},{"id":86781724,"identity":"ff0551ee-edce-4219-9765-8f09ac4c7218","added_by":"auto","created_at":"2025-07-15 13:34:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5402679,"visible":true,"origin":"","legend":"\u003cp\u003eVariation of (a) cloud fraction (CF), (b) cloud optical thickness (COT), (c) cloud top pressure (COT), (d) cloud top temperature (CTT), (e) liquid cloud effective particle radius (R\u003csub\u003eel\u003c/sub\u003e) and (f) ice cloud effective particle radius (R\u003csub\u003eei\u003c/sub\u003e) as a function of AOD on log-log scale for monsoon and premonsoon season. Error bar represents the standard error of cloud properties for each bin. The correlation coefficient is represented by R and asterisk (**, *) sign indicates statistically significant at 99%, 95% confidence level\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6997465/v1/854870237ec0d3f3fb1bc8f1.png"},{"id":86781728,"identity":"518aec8e-8d7b-4f99-bf9a-5aee6a0e02bf","added_by":"auto","created_at":"2025-07-15 13:34:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3456049,"visible":true,"origin":"","legend":"\u003cp\u003eBox and whisker plot for (a, b) CF, (c, d) CTP, (e, f) CER-liquid and (g, h) CER-ice for three different RH (left column) and ω (right column) regimes for monsoon (June-September) season. For each sub-regime, orange colour represents for high AOD condition and blue colour represents for low AOD condition. The description about the box and whisker are given on the right side.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6997465/v1/dbeae4445c2d9919919c7163.png"},{"id":86781733,"identity":"9df87625-8bfa-46fe-9c65-7cd91d73505f","added_by":"auto","created_at":"2025-07-15 13:34:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3433136,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 3, but for premonsoon (March-May) season.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6997465/v1/d1f6027d1562a7d5cd623d65.png"},{"id":86781725,"identity":"9c12118b-028a-442f-a703-5c741869c5ff","added_by":"auto","created_at":"2025-07-15 13:34:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1446007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eq \u003c/em\u003evalues of the combined effect of driving factors (AOD, RH and ω) on cloud properties for monsoon season. The (WS) beside the \u003cem\u003eq\u003c/em\u003e value indicates the single weaken and (NS) indicates the nonlinear enhancement of two factors and absence of a label beside the \u003cem\u003eq\u003c/em\u003e value indicates bilinear enhancement of two factors. Additionally, the \u003cem\u003eq\u003c/em\u003e values are colour coded according to colour scale (right side).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6997465/v1/d3b7eef0057566353211f611.png"},{"id":86782841,"identity":"e6eef4e8-965a-48c0-99f1-127c2d96e882","added_by":"auto","created_at":"2025-07-15 13:42:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1448728,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 5 but for premonsoon season.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6997465/v1/7ebd5b056a4540be01d1d17f.png"},{"id":86784734,"identity":"1f4e0d78-fbcb-4836-b50c-c889ad6c887b","added_by":"auto","created_at":"2025-07-15 13:58:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17010088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6997465/v1/490674ea-74f3-43ae-b197-4cd2eb5279b0.pdf"},{"id":86782842,"identity":"f57b7cad-de05-4372-8f52-9264aef6959b","added_by":"auto","created_at":"2025-07-15 13:42:39","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1259460,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6997465/v1/495ccbbd80253931e7616ad3.docx"}],"financialInterests":"","formattedTitle":"Influence of aerosol and meteorological variables on clouds in the summer monsoon and premonsoon season over India","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe persistent growth of economy and growing energy demand across South-East and South Asia (like India, etc.) has enhance the aerosol and its precursors in the atmosphere (Dong et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Banerjee et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The increasing aerosol in the atmosphere has increased the frequency of occurrence of extreme weather events (such as dust storms, dense fog, severe pollution episodes, etc.) and intermittent monsoonal precipitation (Samset et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The aerosol has significant role in causing climate change, mainly at regional scale. They have visible effect on air quality, human health and climate; and serving as cloud condensation nuclei (CCN), they indirectly influence the physical properties and lifetime of clouds (Qin et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Aerosol has been previously considered as a source of uncertainty that significantly affect the earth\u0026rsquo;s climate and weather system in many ways. Aerosol shows both spatial and temporal variations, which can lead to variation in the optical cloud properties (Alam et al. 2010).\u003c/p\u003e\u003cp\u003eIn last few decades, the effect of aerosol on cloud properties has gained significant attention and associated with one of the largest uncertainties in climate system. Atmospheric aerosol has significant role in modulating the micro-physical and macro-physical properties of clouds. Aerosols modulate the hydrological cycle and climate through direct, indirect and semi-direct effect (Rosenfeld et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In direct effect, aerosol can absorb and scatter the solar and terrestrial radiation (Khatri et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and influence the thermal balance. Aerosols act both as CCN and ice nucleating particle (INP) and perturb the cloud microphysical and radiative properties, cloud lifetime as well as precipitation, a process known as \u0026ldquo;indirect effect\u0026rdquo; (Rosenfeld et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In semi-direct effect, absorbing aerosols such as soot, black carbon (BC) and dust, can suppress the formation of cloud by warming the atmosphere, resulting into thinning of clouds and increase in water vapour evaporation (Ackerman et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eNumerous studies have been performed to improve our knowledge and reduce the uncertainty associated with the impact of aerosols on clouds and precipitation/rainfall (Koren et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sarangi et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Adhikari and Mejia \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Anwar et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Raj et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Satellite-based studies of aerosol-cloud interaction generally seek to correlate aerosol loadings with cloud micro/macro physical properties (Saponaro et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In terms of effect of aerosol on cloud micro-physical properties, many studies found that aerosol optical depth (AOD) and cloud droplet radius are negatively correlated (Feingold et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Feingold et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Costantino and Breon 2013). However, some studies also reported that cloud droplet radius and AOD are positively correlated especially over land and it is referred as Anti-Twomey effect (Feingold et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Grandey and Stier \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For different AOD regime, different behaviours of cloud droplet radius as a function of AOD are also observed by Tang et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Wang et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In terms of influence of aerosol on cloud macro-physical properties, Yan and Liu (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found that cloud fraction (CF) is positively correlated with AOD in summer. Quaas et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) also pointed out that CF and AOD are positively correlated. Aerosol can invigorate the cloud development and negatively correlated with the cloud top pressure (CTP) and cloud top temperature (CTT), resulting to increase in monsoonal rainfall (Sarangi et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Meteorology can affect the association of aerosol with clouds (Koren et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Su et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Stathopoulos et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Estimation of relationship between aerosol and cloud independent of large-scale meteorology is a major challenge. For example, large-scale convergence could concentrate aerosol as well as increase cloudiness which produces an apparent correlation between aerosol and cloud without any physical interaction (Mauger and Norris \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A study by Loeb and Monalo-Smith (2005) has reported that both cloud fraction and AOD are correlated with RH and wind speed. Such effect of meteorology on the relationship between aerosol and cloud is important, but it is difficult to isolate from broad observation, like those from satellite (Tang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although the relationship of aerosol with clouds and effect of meteorological covariations on this relationship have received considerable attention, yet it remains highly uncertain in the climate system and effects of meteorology aid more to the uncertainty.\u003c/p\u003e\u003cp\u003eThis study investigates the relationship between aerosol on cloud as well as influence of meteorological covariations on this relationship in the summer monsoon (monsoon season is used instead of summer monsoon season in later part of this section) and premonsoon season. The study also seeks to determine the relative importance of the effects of aerosol and meteorology, and the effects of the interaction of aerosol and meteorology on cloud. In this paper, Introduction is discussed in section \u003cspan refid=\"Sec1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study area, dataset and methodology are discussed in section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3\u003c/span\u003e contains the results and discussion. Conclusions are placed in section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e"},{"header":"2 Study region, data and methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study region\u003c/h2\u003e\u003cp\u003eThe study is carried out over the Gangetic plain (GP) of India bounded within 21\u0026ordm; N to 31\u0026ordm; N latitude and 76\u0026ordm; E to 91\u0026ordm; E longitude. The region is bounded by Chota Nagpur Plateau in the south and Himalayas in the north. The Gangetic plain is a densely populated region in India and experiences high aerosol loading in both summer monsoon and premonsoon seasons. The aerosol loading in monsoon is comparatively higher (dust transportation through the westerly wind plus local emissions) than that of premonsoon season. Moreover, high relative humidity prevails in the monsoon season due to transport of moisture by the south-westerly wind (generated due to thermal gradient between land and ocean) from the ocean to the GP and strong convection also prevails. While in premonsoon season, low relative humidity along with downdraft condition prevails (Supplementary Figure S2). The varying aerosol loading and meteorology across seasons renders this region an ideal natural laboratory to investigate the influence of aerosol on cloud as well as meteorological covariations. The study region (blue boundary) is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data\u003c/h2\u003e\u003cp\u003eThis study uses Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua (MYD08_D3) level 3 daily product of AOD and cloud properties (CF, cloud optical thickness (COT), CTP, CTT, liquid cloud effective particle radius (R\u003csub\u003eel\u003c/sub\u003e) and ice cloud effective particle radius (R\u003csub\u003eei\u003c/sub\u003e)). MODIS instrument measures the 36 spectral bands from visible to infrared wavelength (0.4 \u0026micro;m to 14.4 \u0026micro;m). The Aqua overpass the equator at 1330 local time in sun-synchronous orbit. The combined Dark Target and Deep Blue algorithm is used to retrieve the aerosol optical properties. The MODIS level 3 AOD and cloud properties datasets have been extensively used to study the interaction of aerosol with clouds and precipitation over different parts of the world (Koren et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Patil et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ng et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Raj et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and to validate the CMIP6 model simulated dataset (Raj et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe meteorological variables (RH and ω) used are retrieved from ERA5, the most recent reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5, a successor of ERA-interim, is a fifth-generation reanalysis product by ECMWF. ERA5 provides hourly global reanalysis data at spatial resolution of 0.25\u0026ordm; with 137 vertical level from surface up to a height of 80 km. The datasets used in the study are only for monsoon and premonsoon season for the time period of 2003-21. The datasets used are given in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Methodology\u003c/h2\u003e\u003cp\u003eIn the study, AOD is used as proxy for aerosol loading in the atmosphere. Only days with AOD\u0026thinsp;\u0026lt;\u0026thinsp;1 are selected to reduce the potential source of aerosol retrieval errors such as cloud contamination and aerosol humidification due to high RH (Sarangi et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Adhikari and Mejia \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Raj et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Earlier studies by Koren et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Sarangi et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Adhikari and Mejia (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have observed that meteorological variables (RH and ω) are well correlated with cloud properties. These meteorological variables (RH and ω) are crucial for cloud development. The RH and ω at 9 pressure levels (1000 hPa, 900 hPa, 850 hPa, 700 hPa, 600 hPa, 500 hPa, 400 hPa, 300 hPa and 200 hPa) are used. The mean of hourly RH and ω between 07:00 and 09:00 UTC provided by ERA5 are used to cover\u0026thinsp;\u0026plusmn;\u0026thinsp;1 h of MODIS Aqua overpass.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Statistical method\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section4\"\u003e\u003ch2\u003e2.3.1.1 Pearson correlation\u003c/h2\u003e\u003cp\u003ePearson bivariate correlation analysis is performed to determine the degree of linear relationship of AOD and cloud properties. The correlation coefficient takes the value between \u0026minus;\u0026thinsp;1 and 1, where 1, 0, -1 indicate perfect correlation, no correlation, and perfect negative correlation, respectively. The statistical significance of Pearson correlation is analysed by using two-tailed distribution student-t test. The Pearson correlation coefficient (\u003cem\u003er\u003c/em\u003e) can be computed using Eq.\u0026nbsp;(1):\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r=\\:\\frac{\\sum\\:({x}_{i}-\\stackrel{̿}{x})({y}_{i}-\\stackrel{̿}{y})}{\\sqrt{\\sum\\:{({x}_{i}-\\stackrel{̿}{x})}^{2}\\sum\\:{({y}_{i}-\\stackrel{̿}{y})}^{2}}}\\)\u003c/span\u003e\u003c/span\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; Eq.\u0026nbsp;(1)\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e are values of \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e variables, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̿}{x}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{̿}{y}\\)\u003c/span\u003e\u003c/span\u003e are the mean of \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section4\"\u003e\u003ch2\u003e2.3.1.2 Geographical detector method\u003c/h2\u003e\u003cp\u003eThe Geographical detector method (GDM), proposed by Wang et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), is used to compute the influence of driving factors (AOD, RH and ω) on cloud properties (CF, CTP, R\u003csub\u003eel\u003c/sub\u003e and R\u003csub\u003eei\u003c/sub\u003e). In general, the GDM assume that the independent variable has important influence on the dependent variable (Wang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang and Hu 2017; Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The GDM does not follow the linear hypothesis to analyse the driving factors behind the spatial stratified heterogeneity. The GDM has four packages (factor detector, interaction detector, ecological detector and risk detector). The factor detector and interaction detector package are used to reveal the driving factors (\u003cem\u003ex\u003c/em\u003e) responsible for the change in cloud properties (\u003cem\u003ey\u003c/em\u003e). Here the non-spatial GDM is used to compute the extent to which driving factor (\u003cem\u003ex\u003c/em\u003e) can influence the dependent counterpart (\u003cem\u003ey\u003c/em\u003e). The core requirement of GDM is that the continuous variables should be converted into categorical strata. Here, Jenks natural break classification method (Jenks \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) is used to categorized the \u003cem\u003ex\u003c/em\u003e into strata \u003cem\u003eh\u003c/em\u003e. The basic idea of this classification method is that it aims to minimize the variance within the class and maximize the variance between the classes. The power of determinant \u003cem\u003eq\u003c/em\u003e of \u003cem\u003ex\u003c/em\u003e on \u003cem\u003ey\u003c/em\u003e (also considered as power of influencing factor) can be computed using Eq.\u0026nbsp;(2):\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:q=1-\\:\\frac{{\\sum\\:}_{h=1}^{L}{N}_{h}{\\sigma\\:}_{h}^{2}}{N{\\sigma\\:}^{2}}\\)\u003c/span\u003e\u003c/span\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.. Eq.\u0026nbsp;(2)\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eh\u003c/em\u003e (1, 2, 3, \u0026hellip;., L) denotes the stratum of factor (\u003cem\u003ex\u003c/em\u003e), \u003cem\u003eN\u003c/em\u003e is the total number of samples in the dataset (here, \u003cem\u003eN\u003c/em\u003e is total area in case of spatial GDM), \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e is the number of samples in stratum \u003cem\u003eh\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{h}^{2}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003e are the variance of samples in the stratum \u003cem\u003eh\u003c/em\u003e and total variance in the dataset, respectively. The value of \u003cem\u003eq\u003c/em\u003e ranges from 0 to 1 where 0 indicates that the driving factor (\u003cem\u003ex\u003c/em\u003e) has no influence on \u003cem\u003ey\u003c/em\u003e and \u003cem\u003eq\u003c/em\u003e close to 1indicates strong influence of driving factor (\u003cem\u003ex\u003c/em\u003e) on \u003cem\u003ey\u003c/em\u003e. For instance, \u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75 indicates 75% of the variance of \u003cem\u003ey\u003c/em\u003e can be explained by \u003cem\u003ex\u003c/em\u003e. In this study the driving factor (AOD and meteorological variables) are categorized into 5 classes using Jenks natural breaks classification method.\u003c/p\u003e\u003cp\u003eThe factor detector can quantify the extent of the influence of driving factor (\u003cem\u003ex\u003c/em\u003e) on dependent counterpart (\u003cem\u003ey\u003c/em\u003e) using the value of \u003cem\u003eq\u003c/em\u003e. The interaction detector can quantify whether two driving factors \u003cem\u003ex\u003c/em\u003e1 and \u003cem\u003ex\u003c/em\u003e2 taken together weakens or enhances one another influence on dependent counterpart (\u003cem\u003ey\u003c/em\u003e) or whether they independently influencing \u003cem\u003ey\u003c/em\u003e. The \u003cem\u003eq\u003c/em\u003e value of factor \u003cem\u003ex\u003c/em\u003e1 and \u003cem\u003ex\u003c/em\u003e2 obtained from Eq.\u0026nbsp;(1) can be written as \u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1) and \u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e2). When two factor \u003cem\u003ex\u003c/em\u003e1 and \u003cem\u003ex\u003c/em\u003e2 are interacting, it can be written as \u003cem\u003ex\u003c/em\u003e1 \u0026cap; \u003cem\u003ex\u003c/em\u003e2, where \u0026cap; denotes the interaction. Then, the q value of \u003cem\u003ex\u003c/em\u003e1 \u0026cap; \u003cem\u003ex\u003c/em\u003e2 can be computed and written as \u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1 \u0026cap; \u003cem\u003ex\u003c/em\u003e2). By comparing the \u003cem\u003eq\u003c/em\u003e value of interaction of two factor with the \u003cem\u003eq\u003c/em\u003e value of each of the two individual factor, five types of interaction are considered (Liu et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) which are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTypes of interaction of two factors.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTypes of interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1 \u0026cap; \u003cem\u003ex\u003c/em\u003e2)\u0026thinsp;\u0026lt;\u0026thinsp;Min[\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1), \u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e2)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeakened, nonlinear\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMin[\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1), \u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e2)]\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1 \u0026cap; \u003cem\u003ex\u003c/em\u003e2)\u0026thinsp;\u0026lt;\u0026thinsp;Max[\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1), \u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e2)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeakened, Single\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1 \u0026cap; \u003cem\u003ex\u003c/em\u003e2)\u0026thinsp;\u0026gt;\u0026thinsp;Max[\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1), \u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e2)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnhanced, bilinear\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1 \u0026cap; \u003cem\u003ex\u003c/em\u003e2)\u0026thinsp;=\u0026thinsp;\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1)\u0026thinsp;+\u0026thinsp;\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndependent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1 \u0026cap; \u003cem\u003ex\u003c/em\u003e2) \u0026gt;\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e1)\u0026thinsp;+\u0026thinsp;\u003cem\u003eq\u003c/em\u003e(\u003cem\u003ex\u003c/em\u003e2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnhanced, nonlinear\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Spatial distribution of AOD and CF\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the spatial distribution of mean AOD and CF in the monsoon and premonsoon for the period of 2003-21. It has been observed that the GP region experiences high AOD (0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29) in the monsoon season. The prevalence of higher aerosol loading in the monsoon season over GP is due to local anthropogenic emission and transported dust aerosol through the south-westerly wind from the desert and arid regions of Southwest Asia and Thar desert (Raj et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, deeper boundary layer and hygroscopically growth of aerosol due to high relative humidity in the atmosphere are other reasons for higher aerosol loading in the monsoon season. Dey and Girolamo (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) has reported that mineral dust is the prime component of aerosol in the monsoon season. In the premonsoon season, moderate level of AOD (0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19) is observed and it can be attributed to anthropogenic emission and possibly due to aerosol transport from the Indian landmass.\u003c/p\u003e\n \u003cp\u003eThe CF is very high in the monsoon season as compared to premonsoon season when CF ranges between low to moderate. Generally, high CF in the monsoon season is associated with increased moisture content (RH is an indicator of moisture content in Supplementary Figure S2) which is crucial for the formation of clouds. The spatial average mean of CF in the monsoon and premonsoon season is 0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 and 0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Influence of AOD on cloud properties\u003c/h2\u003e\n \u003cp\u003eAmong several factors which have ability to modulate the cloud properties, the species and availability of cloud active aerosols are determining factors of cloud formation (Lohmann and Feichter \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Thus, any change in the characteristics and abundance of aerosols has a direct impact on the cloud properties (Kumar and Tiwari \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the cloud properties values are averaged over the bins (50 bins are produced using Jenks natural break classification method) of AOD, from 0 to 1. The strength of the influence of AOD on cloud properties has been quantified by the slope (exponent of AOD in power law equation in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e represents the slope value) of linear regression on log-log scale between AOD and cloud properties. In the monsoon and premonsoon season, R\u003csub\u003eel\u003c/sub\u003e decreases with the increase of AOD (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ee). The decrease in R\u003csub\u003eel\u003c/sub\u003e with increasing AOD is termed as \u0026ldquo;aerosol first indirect effect\u0026rdquo; or \u0026ldquo;Twomey effect\u0026rdquo; which says more aerosol leads to more numerous and smaller liquid cloud particles for a constant liquid water path. Although weak, the influence of AOD on R\u003csub\u003eel\u003c/sub\u003e in the monsoon season is 2-fold higher that of the premonsoon season. The statistically significant correlation coefficient (-0.59) further supports the association. Here, it is important to note that correlation between AOD and cloud properties does not imply causation. However, it may define a link between AOD and cloud properties at climatological relevant scale (Bender et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies by several researchers have also found similar influence of AOD on R\u003csub\u003eel\u003c/sub\u003e over different regions (Costantino and Br\u0026eacute;on \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Raj et al. \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef shows that the influence of AOD on R\u003csub\u003eei\u003c/sub\u003e is markedly different across the seasons. The R\u003csub\u003eei\u003c/sub\u003e decreases with the increasing AOD in the monsoon season which is similar to the Twomey effect in case of cloud droplet radius. While in the premonsoon season, R\u003csub\u003eei\u003c/sub\u003e increases with the increasing AOD which is known as Anti-Twomey effect. In the monsoon season, the moisture content (as RH is an indicator of moisture content) in the atmosphere remains high (Raj et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), it supports the homogeneous nucleation of liquid droplets over heterogeneous nucleation which, in turn, more aerosol could lead to more numerous but smaller ice particles. Under dry condition which prevails in the premonsoon season, early onset of heterogeneous nucleation could possibly prevent the homogeneous nucleation of liquid droplets (DeMott et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zhao et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, more aerosol could lead to more fraction of ice particles produced by heterogeneous nucleation process which comprises fewer and larger ice particle (Zhao et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Overall, the low influence on R\u003csub\u003eel\u003c/sub\u003e due to increasing aerosol over land can be attributed to the low hygroscopic nature of aerosol and thus less susceptible to act as an active CCN (Liu et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe decrease in CF with the increasing AOD, although it is very weak, could be attributed to the elevated level of absorbing aerosol over Indo-Gangetic plain (IGP) (Srivastava et al., 2012) which absorbs the incoming solar radiation, resulting to aerosol induced heating inside the cloud (Rao and Dey, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e, Sandhya et al., 2021). As a result, the decrease in fraction of cloud is observed at higher aerosol loading. Another probable reason is that more aerosols (and hence more CCN) are expected to result in more numerous and smaller cloud droplet in high moisture laden environment. The smaller cloud droplets are more susceptible to faster evaporation which could possibly enhanced the entrainment of drier air surrounding the cloud and decreases the fraction of cloud (Quinn and Bates, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). While in the premonsoon season, CF increases with the increasing AOD and the strength of the effect of AOD on CF is very high, as indicated by high slope of 1 and statistically significant correlation coefficient of 0.97 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). The aerosol humification (Grandey et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), cloud contamination due to retrieval of AOD (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) and impact of meteorological covariations (Engstr\u0026ouml;m and Ekman, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Gryspeerdt et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kant et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) can be possible reasons for a large fraction of relationship between AOD and CF. Total relationship between AOD and CF due to the effect of aerosol in observational studies is limited to less than 70% (Mauger and Norris \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Enstrom and Ekman 2010) and less than 50% (Gryspeerdt et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb shows the decrease in COT with the increasing AOD in the monsoon season, while increase in COT with AOD in the premonsoon season. The latter is in good agreement with \u0026ldquo;aerosol first indirect effect\u0026rdquo;. The magnitude of influence of AOD on COT is weak, as indicated by low slope of 0.1 and low correlation coefficient of 0.31 in the premonsoon season while it is very weak in the monsoon season which is evidence from the very low slope of -0.03 and low correlation coefficient of -0.21. Mixed BC and dust aerosol contributes dominantly to the absorption (Kedia et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) of solar radiation in the monsoon and premonsoon season over the GP. The BC and dust aerosol can supress the formation of cloud by warming the atmosphere which is known as \u0026ldquo;aerosol semi-direct effect\u0026rdquo;, which results to thinning of cloud in the monsoon season. The aerosol semi-direct effect plus cloud absorption effect may dominate over the aerosol indirect effect (Sechrist and Jacobson \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) which likely explains the observed decrease in COT with the rising AOD in the monsoon season.\u003c/p\u003e\n \u003cp\u003eThe influence of AOD on both CTP and CTT in the monsoon and premonsoon season is shown in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec, d. Here, CTP is used as proxy for the cloud top height (CTH). The increase in CTP represents the decrease in CTH. In the monsoon season, CTP initially increases with the increasing AOD up to 0.4, after which CTP begins to decrease as AOD continues to increase. The nonlinear relationship of AOD and CTP is statistically significant, as evidence by low slope of -0.19 and strong correlation coefficient of -0.80 for the initial increase in CTP with rising AOD, and moderate slope of 0.41 and strong correlation coefficient of 0.83 for the decrease in CTP with AOD. Moreover, CTT increases with the increasing AOD up to 0.4, and after that CTT begins to decrease as AOD continues to increase in the monsoon season. For AOD greater than 0.4, the agreement between AOD and both CTP and CTT indicates that higher clouds with colder tops are formed in highly aerosol loaded conditions. This happens because of more aerosols lead to more numerous and smaller cloud droplets which are less efficient to become precipitating particles and delay the precipitation. Therefore, more cloud water could be lifted above the freezing level which results to release of latent heat due to freezing of cloud droplets near the cloud top can enhance buoyancy and invigorate the convection (Rosenfeld et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, more deeper clouds are formed in highly aerosol loaded condition in the monsoon season. Adhikari and Mejia (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Sarangi et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) have also suggested deepening or invigoration of cloud due to increase in aerosol loading in the monsoon season over the Indian region, which agrees with our findings. In highly aerosol loaded conditions, taller clouds due to aerosol induced invigoration of mixed-phase cloud over the Amazon region (Andreae et al. \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), tropics (Niu and Li \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Atlantic (Koren et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) are also observed. In the premonsoon season, the decrease in CTP and increase in CTT with increasing AOD indicates that the aerosol induced invigoration may not be the possible reason for the increase in cloud top height. It is likely due to the cloud mediated relationship of aerosol and cloud top height (Gryspeerdt et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) or more widespread shallow convection.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Influence of meteorological variables\u003c/h2\u003e\n \u003cp\u003eThe relationship between AOD and cloud properties can be influenced by dynamical and thermodynamical processes (Wang et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and prevalent meteorological conditions (Quaas et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Andersen et al \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Christensen et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) in the environment. That is, variation in certain meteorological variables can simultaneously influence the aerosol and cloud properties (Zhang et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This can lead to mistaken perception that aerosol alone is influencing cloud properties. To disentangle the influence of meteorological variables from the aerosol effects, meteorological variables are classified into smaller sub-regimes and their influence on cloud properties are analyzed under both low and high AOD conditions. For this analysis, RH at 700 hPa (for CF) and 300 hPa (for CTP), along with \u0026omega; at 600 hPa (for CF) and 300 hPa (for CTP) are selected for the monsoon season due to their strong correlation with cloud properties at these pressure levels (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Similarly, for the premonsoon season, RH at 600 hPa (for CF) and 300 hPa (for CTP), along with \u0026omega; at 400 hPa (for both CF and CTP) are selected. Furthermore, to disentangle the influence of meteorological variables cloud particle radius (R\u003csub\u003eel\u003c/sub\u003e and R\u003csub\u003eei\u003c/sub\u003e), the atmospheric level of meteorological variables which have strong correlation with CF is selected because it is well known fact that the cloud particles are the key microphysical property that involved in the formation of clouds. The most influential level of meteorological variables is selected based on strong correlation to further disentangle the influence of the regimes of meteorological variables. The CF, CTP, R\u003csub\u003eel\u003c/sub\u003e, R\u003csub\u003eei\u003c/sub\u003e, RH and \u0026omega; data are divided in two AOD classes: low AOD (mean AOD \u0026ndash; 1\u0026sigma; (standard deviation)) and high AOD (mean AOD\u0026thinsp;+\u0026thinsp;1\u0026sigma; (standard deviation)). Then, CF, CTP, R\u003csub\u003eel\u003c/sub\u003e and R\u003csub\u003eei\u003c/sub\u003e data are further divided into 3 RH regimes: RH\u0026thinsp;\u0026lt;\u0026thinsp;50%, 50% \u0026lt; RH\u0026thinsp;\u0026lt;\u0026thinsp;70% and RH\u0026thinsp;\u0026gt;\u0026thinsp;70% (Adhikari and Mejia 2021). Similarly, the data are divided into 3 \u0026omega; regimes: \u0026omega;\u0026thinsp;\u0026gt;\u0026thinsp;0 (subsidence), ̶ 0.1\u0026thinsp;\u0026lt;\u0026thinsp;\u0026omega;\u0026thinsp;\u0026lt;\u0026thinsp;0 (weak convection) and \u0026omega; \u0026lt; ̶ 0.1 (strong convection) (Koren et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Adhikari and Mejia \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe influence of RH and \u0026omega; on cloud properties under both low and high AOD conditions in the monsoon and premonsoon seasons is shown in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. High RH and stronger updraft are associated with taller clouds and higher fraction of cloud in the monsoon (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and premonsoon season (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), while shallow clouds and low fraction of clouds are found in low RH and downdraft regimes. Similar findings in the monsoon season over foothills of Himalayas have been noted by Adhikari and Mejia (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Over the central India, Sarangi et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) has found high cloud fraction in high RH condition at lower atmosphere in the monsoon season. Note here that in the monsoon season, the general relationship between AOD and CF are different between the different meteorological sub-regimes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, b) and the full data values without the meteorological sub-regimes observed in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea, while it is significantly not different in the premonsoon season (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, b). It indicates that the aerosol and meteorology exert opposing influence on CF in the monsoon season, i.e., increasing aerosol reduces the faction of cloud and increase in RH and \u0026omega; increases the fraction of cloud. While they have near-orthogonal (Koren et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) effect on CF in the premonsoon season. This is the most plausible reason for the robust relationship between aerosol and cloud fraction in the premonsoon season observed in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea. In both premonsoon and monsoon seasons, the general relationship between AOD and CTP are not different between the different meteorological sub-regimes (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec, d) and the full data values without the meteorological sub- regimes observed in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec (except for AOD less than 0.4 in the monsoon season). It suggests that the aerosol and meteorology have similar effect on CTP.\u003c/p\u003e\n \u003cp\u003eThe presence of high RH and strong updraft (negative \u0026omega;) in the atmosphere leads to formation of bigger R\u003csub\u003eel\u003c/sub\u003e / R\u003csub\u003eei\u003c/sub\u003e, while smaller R\u003csub\u003eel\u003c/sub\u003e / R\u003csub\u003eei\u003c/sub\u003e is formed in low RH and downdraft condition in the monsoon season (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). High moisture content are available to condense onto CCN/ INP, resulting to formation of bigger liquid/ice cloud particle. Simultaneously, strong convection aids in keeping the smaller cloud particles (liquid and ice) aloft in the cloud, allowing them to grow into bigger liquid cloud particle through collision-coalescence process and into bigger ice particle at cloud tops through the deposition of super-cooled liquid onto smaller ice particles, particularly in moist environment which prevails in the monsoon season. It is important to note here that the increase in the size of cloud particles with both RH and \u0026omega; is more observable only under high aerosol loading environment (except for the increase of R\u003csub\u003eei\u003c/sub\u003e with \u0026omega; under high aerosol loading in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eh). In the premonsoon season, strong updraft is associated with bigger cloud particle radius (R\u003csub\u003eel\u003c/sub\u003e and R\u003csub\u003eei\u003c/sub\u003e) than that of downdraft, similar to findings in the monsoon season (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The R\u003csub\u003eel\u003c/sub\u003e does not show any change with rising of RH (low to high) in high aerosol loaded environment (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ee). However, significant increase in R\u003csub\u003eei\u003c/sub\u003e is observed with rising RH in high AOD environment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Geographical detector method\u003c/h2\u003e\n \u003cp\u003eIn this section, the power of determinant (\u003cem\u003eq\u003c/em\u003e) value is computed in order to quantify the strength of the influence of driving factors (AOD, RH and \u0026omega;) on cloud properties.\u003c/p\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1 Factor detector analysis\u003c/h2\u003e\n \u003cp\u003eThe Factor detector analysis package of GDM is used to analyze strength of influence of each\u0026nbsp;driving factor (AOD, RH and \u0026omega;) on cloud properties (CF, CTP, R\u003csub\u003eel\u003c/sub\u003e and R\u003csub\u003eei\u003c/sub\u003e) in the monsoon and premonsoon season. The \u003cem\u003eq\u003c/em\u003e value of factor detector analysis of the monsoon and premonsoon season is shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. As expected, AOD can only explain 12% variance for CF, while RH and \u0026omega; can explain 74% and 30% variance for CF in the monsoon season. This explains that in the monsoon season, meteorological variables have significantly high influence on CF than that of AOD. However, in the premonsoon season, the explanatory power of AOD increased to 25% for CF, while the explanatory power of RH and \u0026omega; decreased to 43% and 28%, respectively. Much of the formation of taller clouds at higher aerosol loading in the premonsoon season (as discussed in earlier sections) is due to the influence of RH (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31) and \u0026omega; (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14), not due to invigoration effect of aerosol (\u003cem\u003eq\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). AOD can explain 9% (very low) variation of R\u003csub\u003eel\u003c/sub\u003e in the monsoon season, while RH and \u0026omega; can explain 25% and 17%. In the monsoon season, the influence of both RH and \u0026omega; on R\u003csub\u003eei\u003c/sub\u003e are statistically significant but indicate low explanatory power, both accounting for only 12% of the variance. The explanatory power of influence of each driving factors on R\u003csub\u003eel\u003c/sub\u003e/ R\u003csub\u003eei\u003c/sub\u003e is very small in the premonsoon season.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eq values of factor detector analysis during monsoon and premonsoon seasons\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMonsoon\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePremonsoon\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026omega;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026omega;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003eel\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csub\u003eei\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote that the asterisk (***) denotes q value is significant at 0.001 level.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAmong the three driving factors, RH (with highest q value) is predominately influencing the CF and CTP in the monsoon and premonsoon season. As observed from the Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the effect of aerosol (as indicated by higher q value of AOD) on cloud properties is stronger in the monsoon than premonsoon season (except the effect of aerosol on CF in premonsoon season). As stronger updraft condition prevails in the monsoon season, Jones et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Jia et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) noted that stronger aerosol-cloud interaction occurs under higher updraft condition, which agree with our results.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2 Interaction detector analysis\u003c/h2\u003e\n \u003cp\u003eThe \u003cem\u003eq\u003c/em\u003e values of the effect of two factors (AOD, RH and \u0026omega;) taken together, influencing the cloud properties in the monsoon and premonsoon season are shown in Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. In the monsoon season, the \u003cem\u003eq\u003c/em\u003e value of combined effect of AOD and RH can explain 75% variance of CF, while combined effect of AOD and \u0026omega; have explanatory power of 27%. Similarly, the combined effect of RH and \u0026omega; have explanatory power of 75% on CF (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, in the monsoon season, the \u003cem\u003eq\u003c/em\u003e values of the combined effects on CTP show that the explanatory power of AOD together with each of the meteorological variables, i.e. RH and \u0026omega;, is 53% and 35%, respectively. Moreover, the \u003cem\u003eq\u003c/em\u003e value of the combined effect of two meteorological variables, i.e., RH and \u0026omega; can explain 50% of the variance of CTP. This indicates that the interaction of two factors generally yields higher \u003cem\u003eq\u003c/em\u003e values than the individual factors alone, unless their interaction weaken each other\u0026rsquo;s influence. That is, the \u003cem\u003eq\u003c/em\u003e values of combined effect of a pair of driving factors can explain more accurately the variance in the cloud properties. Liu et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) also reported that the \u003cem\u003eq\u003c/em\u003e values of combined effect of pair of factors taken together on warm cloud properties are higher than the \u003cem\u003eq\u003c/em\u003e value of individual factor. In the monsoon season, the \u003cem\u003eq\u003c/em\u003e values of combined effect of pair of factors taken together on R\u003csub\u003eel\u003c/sub\u003e and R\u003csub\u003eei\u003c/sub\u003e show that the explanatory power has decreased because their interaction are weakening each other\u0026rsquo;s influence.\u003c/p\u003e\n \u003cp\u003eThe results computed from the similar analysis for the premonsoon season show that the \u003cem\u003eq\u003c/em\u003e value of combined effect of pair of factors on cloud properties is also higher than the \u003cem\u003eq\u003c/em\u003e value of individual factor (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The combined effect of AOD and RH, and of RH and \u0026omega;, each explains 50% variance of CF which indicates that both combinations can predominantly influence the CF. However, the combination of meteorological variables, i.e., RH and \u0026omega; can predominantly influence the CTP as their \u003cem\u003eq\u003c/em\u003e value of combined effect has highest explanatory power of 0.39. The low \u003cem\u003eq\u003c/em\u003e value of combined effect of pair of driving factors (AOD, RH and \u0026omega;) on cloud particle radius (R\u003csub\u003eel\u003c/sub\u003e and R\u003csub\u003eei\u003c/sub\u003e) suggest limited interactive influence to the variation of cloud particle radius. Although GDM provides an alternative approach to assess the confounding effects of aerosol and meteorological variables, and their interactions on cloud properties, it may not reliably quantify their absolute contributions (Liu et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). One possible reason is the noise inherent in daily data used here, as continuous data (which has been later converted into categories) are used instead of spatially stratified data in the GDM analysis. The \u003cem\u003eq\u003c/em\u003e values (results not shown here) computed using weekly means (i.e., 7-day averages) also yield more or less similar results.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe influence of the aerosol on cloud remains one of the major uncertainties in the weather and climate system. The uncertainty arises due to the contrasting relationship between aerosol and cloud properties observed over the different regions of world as well as the influence of meteorological covariations on this relationship. In the monsoons season, the rising aerosol led reduction in cloud fraction, and deepening of clouds due to aerosol induced invigoration effect for AOD\u0026thinsp;\u0026gt;\u0026thinsp;0.4. In the premonsoon season, higher cloud fraction, and taller cloud due to shallow convection are associated with rising aerosol loading. As meteorology varies with the seasons, the effects of meteorological covariations may influence the relationship between aerosol and cloud properties. The aerosol and meteorological variables (RH and ω) have opposing influence on both cloud fraction and cloud particle radius (R\u003csub\u003eel\u003c/sub\u003e and R\u003csub\u003eei\u003c/sub\u003e) in the monsoon season. However, the aerosol and meteorological variables have near-orthogonal influence on CF and CTP in the premonsoon season and on CTP (for AOD\u0026thinsp;\u0026gt;\u0026thinsp;0.4) in the monsoon season. This reveals that aerosol and meteorology have similar and opposite influence on the cloud properties (except cloud particle radius in the premonsoon season) in different seasons due to varying meteorology.\u003c/p\u003e\u003cp\u003eFurthermore, the GDM is used to analyze the influence of driving factors (AOD, RH and ω) alone on cloud properties and interaction detector analysis (a package of GDM) is used to analyze the combined effect of pair of factors taken together on cloud properties. The GDM further supports the above findings of the influence of aerosol and meteorological variables on the cloud properties. The results from the interaction detector analysis suggest that the explanatory power (\u003cem\u003eq\u003c/em\u003e value) of the combined effects of a pair of factors is always higher (unless they weaken each other\u0026rsquo;s influence) than that of each factor alone. That is, the combined effects provide more accurate estimate of the influence on cloud properties. There is significant seasonal variation in the relative importance of each factor (except R\u003csub\u003eei\u003c/sub\u003e). The findings of this research help to improve the understanding of the aerosol-cloud interaction and alleviate the uncertainty associated with aerosol-cloud interaction at climatological scale.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank NASA and ECMWF for maintain open-source dataset used in this study. The computational and graphical analysis have been done using free CDO and python modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVikram Raj:\u003c/strong\u003e Conceptualization, Data curation, Formal analysis, Investigation, visualization, Writing \u0026ndash; original draft. \u003cstrong\u003eP. Parth Sarthi:\u003c/strong\u003e Supervision, Project administration, Writing \u0026ndash; review and editing. \u003cstrong\u003ePrabhat Kumar:\u003c/strong\u003e Data curation, Formal analysis, visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available open and free to public access and can be downloaded from Giovanni at https://giovanni.gsfc.nasa.gov/giovanni/ and https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=download.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAckerman AS, Toon OB, Stevens DE, et al (2000) Reduction of Tropical Cloudiness by Soot. 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Atmospheric Chemistry and Physics 18:1065\u0026ndash;1078\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"AOD, cloud properties, meteorological variables, geographical detector method","lastPublishedDoi":"10.21203/rs.3.rs-6997465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6997465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study emphasizes over the influence of aerosol and meteorology (relative humidity (RH) and vertical velocity (ω)) on cloud properties in the summer monsoon (June-Sept) and premonsoon (March-May) season by using traditional statistical method, followed by geographical detector method (GDM), an alternative statistical approach. Results reveal, in the summer monsoon season, strong influence of meteorology on cloud fraction diminish the effect of aerosol, while the meteorology aids to the influence of aerosol on cloud fraction in the premonsoon season. The interplay between aerosol and meteorology leads to non-linear change in the cloud top height in the summer monsoon season, while in the premonsoon season, it leads to formation of taller clouds in higher aerosol environment due to shallow convection. Both aerosol and meteorology have weak influence on cloud particle radius, however, high RH and strong updraft (negative ω) leads to formation of bigger cloud droplet radius in the summer monsoon season. The GDM is used to determine the relative importance of the effects of aerosol and meteorology and the effects of interaction between aerosol and meteorology on the cloud properties. The interaction between aerosol and meteorology have more effect on the cloud properties unless their interaction weakens each other\u0026rsquo;s effect. Furthermore, the prevailing meteorology in the season can restrain the influence of aerosol on cloud properties. The work provides valuable insight of the association between aerosol and cloud properties at seasonal time-scale and the influence of meteorological covariations on this relationship and improve the understanding of the aerosol-cloud interaction.\u003c/p\u003e","manuscriptTitle":"Influence of aerosol and meteorological variables on clouds in the summer monsoon and premonsoon season over India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 13:34:34","doi":"10.21203/rs.3.rs-6997465/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-08-20T16:32:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-07-11T17:18:24+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-11T17:03:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-01T16:11:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2025-06-28T07:28:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6dbc18c3-43ec-4898-98ca-33f6ffb02bd7","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T20:57:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 13:34:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6997465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6997465","identity":"rs-6997465","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}