Observational estimates of aerosol-cloud interactions (ACI) and cloud adjustments are critical for understanding the role of aerosols and clouds in climate and for testing the ability of models to simulate these susceptibilities. During the past decades, numerous studies have taken advantage of multi-year satellite observations for investigating ACI and cloud rapid adjustments in liquid boundary layer clouds (e.g. Myhre et al., 2007; Quaas et al., 2009; Chen et al., 2014). Although questions remain about the appropriateness of evaluating changes in radiative forcing since pre-industrial times with the use of the current satellite data record (e.g. Mülmenstädt et al., 2024), a more fundamental question to be addressed is whether linear regressions between satellite-derived cloud and aerosol properties capture meaningful physical mechanisms. An encouraging line of evidence is the positive linear correlation observed between satellite aerosol optical depth (AOD) and cloud droplet number concentration (e.g. Quaas et al., 2008), which is generally consistent with airborne observations (e.g. Sorooshian et al., 2019), and in agreement with expectations of the first aerosol indirect effect (that is, increase in cloud droplet concentration with aerosol concentration). This correlation consistency appears to be, in part, attributed to the good skill of satellite retrievals to replicate features observed by ground-based and in-situ platforms, especially over the ocean (e.g. Levy et al., 2013; Painemal et al., 2019; Gryspeerdt et al., 2022). However, fortuitous non-causal aerosol-cloud correlations could impact the interpretation of satellite-based statistics and the way they are used for understanding real physical processes (e.g. Quaas et al., 2020; Rosenfeld et al., 2023). Complexities arise in particular from the use of column-integrated AOD, as its adequacy for representing aerosol concentration or cloud condensation nuclei (CCN) in aerosol activation to cloud droplets have been called into question (Shinozuka et al., 2015; Stier, 2016). This is because AOD (or aerosol index) does not uniquely represent aerosol concentration or CCN concentration, as variations in aerosol composition, particle size distribution, and optical properties can yield the same AOD for different aerosol concentrations. A second limitation is the inability to disentangle the contributions of different aerosol layers to the total AOD, which prevents any meaningful vertical collocation between aerosol and cloud layer (e.g. Jia et al., 2022). These limitations are likely responsible for notable differences between in-situ- and satellite-based aerosol-cloud relationships. For example, the observed logarithmic AOD–Nd relationship from satellites resembles a S-curve: Nd features modest variations with AOD for small AOD values, followed by a rapid linear increase of Nd with AOD, and culminating in Nd values that remain nearly constant for high values of AOD (Gryspeerdt et al., 2016). While it is generally assumed that the insensitivity of Nd to high AOD is likely the result of less aerosol activation in highly polluted environments with substantial CCN availability (e.g. Reutter et al., 2009), the weak Nd–AOD dependency for pristine environments is difficult to interpret without invoking large uncertainties in AOD for regions with small aerosol burden. The weak relationship between Nd and AOD for pristine areas is particularly troubling especially considering the widespread occurrence of regions with low AOD over the ocean, which is precisely where one should expect a substantial occurrence of boundary layer clouds. These results are, again, at odds with multiple field campaigns, which consistently identify linear changes of aerosol concentration with Nd for a wide range of aerosol concentrations (e.g. McFarquhar et al., 2021; Painemal and Zuidema, 2013; Gupta et al., 2022; Zheng et al., 2024).

In addition to limitations in the physical information derived from satellite observations, retrieval artifacts can also impact the interpretation of aerosol-cloud linear regressions. For instance, analysis of passive satellite aerosol and cloud retrievals reveal that biases in AOD can yield underestimations of the Nd-AOD regression of at least 3 % due to aerosol biases in the Level 3 (1°×1°) product (Jia et al., 2022). Moreover, recent studies have warned about biases of aggregating satellite observations without removing pixels more prone to uncertainties (Painemal et al., 2025). To advance in the ACI quantification, Painemal et al. (2020) propose the use of vertically resolved satellite aerosol retrievals, with the objective of isolating the aerosol layer closer in altitude to the cloud layer from the rest of the aerosol column. More specifically, the incorporation of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) based aerosol retrievals to the analysis is advantageous for minimizing sensitivities to 3D radiative effects and cloud contamination. Regrettably, studies that make use of spaceborne lidar observations for ACI studies are surprisingly scarce, and global-scale analyses are lacking. Motivated by the proof-of-concept introduced in Painemal et al. (2020), we expand their study by taking advantage of more than 11 years of collocated daytime CALIPSO aerosol properties, MODerate resolution Imaging Spectroradiometer (MODIS) cloud retrievals, and CloudSat precipitation estimates to quantify ACI over the non-polar ocean. This study makes use of aerosol retrievals derived from a physically-based remote sensing algorithm, and thus, no attempts are made to derive aerosol concentration from the CALIPSO observations because we do not have a way to validate the multiple assumptions and approximations needed to compute concentrations from an elastic backscatter lidar. Our overarching objectives are: (a) to investigate the benefits of using vertically resolved aerosol properties and identify regions where the AOD proxy yield meaningful correlations with Nd, and (b) to compute metrics of ACI and cloud susceptibilities over the non-polar oceans.

In light of the results presented here, it is pertinent to revisit previous ACI assessments based on Aqua MODIS AOD. For this purpose, we take 5 years of daily Collection 6 MODIS Level 3 (L3) Atmosphere Gridded Product (MYD08_D3) and we compute ACI as ACIAOD=dln⁡(Nd)dln⁡(AOD), following the binning methodology used for the CALIOP-MODIS ACI calculations. MODIS L3 ACI map (Fig. 10) features a regional distribution that depart from the MODIS-CALIOP (Fig. 3). This level 3 based analysis partially differs from using pixel-level AOD (Fig. S2) due, likely, to the impact of the data filtering of Sect. 2. An important difference between both ACI estimates is in their magnitude, with MODIS L3 exhibiting values twice as large as those from MODIS-CALIOP. Because the functional relationship between AOD and Nd, and AOD and σext are highly non-linear (Fig. 3b and Painemal et al., 2020), with the disadvantages of using AOD previously discussed, comparing ACIAOD magnitudes does not provide meaningful information. Instead, our focus is on the interpretation of regional changes relative to the global map. In this regard, a key difference is the negligible ACIAOD in the extratropics, especially in the Southern Ocean and north of 40° N. This contrasts with the local maximum observed over the same region for the σextCL-based ACI (Fig. 4). Moreover, except for the region west of Australia, the stratocumulus subtropical regions of the eastern Pacific and Atlantic show modest ACI in the AOD-based calculation, in disagreement with the analysis of Fig. 4 and with in-situ observations (e.g. Kang et al., 2021; Gupta et al., 2022; Sorooshian et al., 2019; Zheng et al., 2024). Figure 10 raises the concern, once again, that AOD-Nd relationship might be misrepresenting ACI and, thus, could misguide modelers about the physical processes that need to be refined, or retained, in models.

An aspect not explored in this study is the relationship between aerosol extinction coefficient and aerosol concentration and how it would impact the ACI calculations. While empirical relationships do show a close linear log-scale relationship between boundary layer aerosol concentration and aerosol extinction coefficient (e.g. Shinozuka, et al., 2015) significant regional variations are expected to affect the extinction-to-CCN conversion. An additional difficulty is accounting for the effect of ambient relative humidity in controlling the aerosol hygroscopicity and optical properties (Gasso et al., 2000), which is also dependent on aerosol size and chemical composition, but cannot be characterized with the needed accuracy using satellite data only. Regarding the potential effect of varying regional ambient relative humidity on aerosol extinction, we note that humidity in the boundary layer over the ocean remains on average bounded to values around 85 %, with modest changes across regions (not shown). That is, the narrow range of spatial variability in relative humidity suggests that the ACI patterns described in our study are not explained by humidity driven swelling.

A similar effort of matching aerosol products derived from CALIOP with satellite cloud retrievals was reported in Alexandri et al. (2024). Their study relies on cloud retrievals over Europe from the geostationary sensor The Spinning Enhanced Visible and InfraRed Imager (SEVIRI), and a CCN estimate derived using CALIOP products. Since the estimation of aerosol concentration from an elastic backscatter lidar involves several assumptions (aerosol model, typing, hygroscopic growth, and other empirical approximations), the way multiple uncertainties propagate into the derived product remains to be determined. To circumvent potentially large uncertainties when applying CCN retrieval algorithm, we recommend that future analyses should be framed in terms of the ambient aerosol extinction coefficient. This approach is adopted in an accompanying paper that documents the assessment of ACI over the eastern Atlantic Ocean in the Department of Energy's Energy Exascale Energy System Model (E3SM) using the dataset analyzed here (Zheng et al., 2025). In spite of our pragmatic choice of solely relying on aerosol extinction coefficient, we conduct an additional analysis of the effect of different aerosol types on ACI. Rather than using the aerosol classification product from CALIPSO, which likely misclassifies marine aerosols near the continents (Toth et al., 2025), we opt for a coarse yet effective aerosol classification that only utilizes the lidar ratio. More specifically, following ground-based and airborne studies (Müller et al., 2007; Burton et al., 2012; Groß et al., 2013), we define clean marine aerosol as having LR<25 sr. In addition, pollution and smoke are identified for LR>55 sr. Samples with lidar ratio within the 25–55 sr range are expected to include dust and/or a mixture of aerosols. The Nd-σextCL relationship features changes for the 3 aerosol types: ACI increases with lidar ratio, with marine aerosol showing ACI (slope) of 0.14, mixed aerosols at 0.19, and polluted/smoke aerosols yielding ACI=0.21 (Fig. 11). While additional analysis will be needed to interpret these results, we note that aerosol size differences should be expected between marine aerosols and pollution/smoke, with the latter generally featuring smaller sizes (Müller et al., 2007). This leads us to speculate that the weaking of the ACI relationship for marine aerosols could be associated with the enhanced contribution of aerosol size over concentration in the aerosol extinction coefficient.

The small values of precipitation/POP susceptibility should be interpreted as the lack of satellite data to show a significant influence of aerosols on the occurrence of precipitation. Being cognizant that the results could be dependent on the precipitation threshold applied in our study (-15 dBZ), we also repeated the precipitation susceptibility estimate using a more stringent definition by classifying precipitating samples as those with Zmax greater than -7 dBZ. The use of a higher precipitation threshold (not shown) did not qualitatively change the susceptibility variability of the map in Fig. 9a, yet the number of grids with statistically insignificant values substantially increased. Given the challenges of quantifying precipitation rate, especially for stratocumulus clouds, the use of airborne observations would be necessary to complement this satellite study with the use of precipitation rate retrievals, which will allow for more direct estimates of precipitation susceptibility.

Similar to Gryspeerdt et al. (2016), we explicitly partition the cloud adjustments into the Nd modulation of LWP and precipitation (POP), and the aerosol-cloud modulation of aerosols (σextCL) on Nd. This method is more physically sound than directly calculating the effect of aerosol via aerosol-LWP and aerosol-precipitation regressions, because it takes into account the control variable (Nd) that mediates changes of aerosol in other cloud properties. Moreover, the partition applied here yields a more stringent condition for evaluating the significance of the cloud susceptibility as the requirement is that two regressions are required to produce meaningful values. Another rapid cloud adjustment commonly simulated by models and monitored with satellite observations is the lifetime effect, generally represented by changes in cloud (area) fraction as a function of aerosol concentration. This cloud fraction susceptibility is not reported in this study because cloud retrievals (Nd and LWP) are filtered using cloud fraction, with a threshold that directly affects the regression between CF and Nd. For example, Painemal et al. (2020) show a dramatic decrease in the CF–Nd slope when Nd values estimated in partially broken scenes are removed from the analysis. With optical retrieval biases sensitive to the type of cloud scene and sub-pixel scale cloud coverage, disentangling the physical signature from systematic biases in the CF–Nd relationship will make it difficult to determine the usability of such analysis using cloud observations from passive sensors.

We computed aerosol-cloud interactions and cloud adjustments over the global ocean by combining aerosol retrievals from CALIOP-S, cloud properties from MODIS (CERES algorithm), and precipitation occurrence from CloudSat. This is the first global assessment, to the best of our knowledge, that relies on vertically-resolved aerosol retrievals that are vertically matched with the location of the cloud layer. Here we expand a previous study (Painemal et al., 2020) by considering most of the A-train record (2006–2017) and including the extratropical ocean. Moreover, we also incorporate estimates of liquid water path and precipitation susceptibilities due to aerosols to the analysis.

We corroborate that aerosol optical depth inadequately represents aerosols for the study of aerosol-cloud interactions in marine low clouds. More specifically, we found that AOD shows a negligible variation with cloud microphysics in the extra-tropics despite a strong correlation between cloud-layer aerosol extinction coefficient and Nd. We also found that the S-shape variations of Nd with AOD reported in numerous studies and reproduced here may not fully represent the physical processes governing cloud variability. This is because the S-shape is not replicated by the cloud-level aerosol extinction coefficient analysis presented here (Sect. 3.1) nor by airborne studies over the ocean. The limitation of using of AOD as an aerosol proxy for ACI is particularly manifested for values of AOD less than 0.1 and greater than 0.25, ranges for which Nd minimally varies with AOD. This finding indicates that the lack of sensitivity of Nd to AOD for typical aerosol loading over the ocean, is not indicative of the microphysical processes. We note that this conclusion is valid for the AOD magnitudes analyzed here, and it does not rule out other microphysical behaviors in more polluted conditions. Indeed, thermodynamically-driven saturation of Nd with aerosol loading over heavily polluted environments, especially over land, has been observed in several field studies (e.g. Ramanathan, et al., 2001). Lastly, it is important to emphasize that challenges in quantifying ACI goes beyond AOD. Indeed, near-surface aerosol retrievals weakly correlate with Nd and the associated linear regressions (Fig. S2) substantially differ from the analysis presented here (Fig. 4b), casting doubt on using near-surface ground-based aerosol observations for the quantifications of ACI and cloud adjustments.

The ACI metrics derived from combining CALIOP-S aerosol extinction coefficient, vertically collocated with the cloud layer, and MODIS products reveal regions with high sensitivity of clouds to changes in their Nd due to aerosols. These areas include the stratocumulus cloud regimes off the west coast of the continents, the Southern Ocean, and the extratropical Atlantic and Pacific Oceans in the Northern Hemisphere. High ACI values in stratocumulus cloud regimes are interpreted as the combined effect of a well-mixed boundary layer with readily available aerosols with a continental origin. However, it is somewhat surprising that the Southern Ocean, arguably the most pristine region on Earth, also witnesses high values of ACI, and relatively high Nd relative to other regions over the remote ocean with similar aerosol concentrations. This feature is possibly explained by enhanced aerosol activation driven by strong boundary layer turbulence in the Southern Ocean, especially in postfrontal conditions (e.g. Lang et al., 2021). In addition, abundant biogenic and sea-spray aerosols over the region (Humphries et al., 2021) can effectively serve as CCN.

In terms of LWP susceptibilities, this is negative in subtropical and extratropical regions, that is, LWP decreases with Nd. This decrease in LWP, also observed in other studies (e.g. Qiu et al., 2024), is generally interpreted as the drying effect of cloud top entrainment, which is enhanced with increasing Nd. In contrast, positive LWP-Nd slopes in the tropical ocean yield a modest LWP susceptibility because ACI is small and insignificant. This analysis also shows that the inverted-V shape in the LWP-Nd relationship is generally the consequence of spatial variability, which becomes more apparent when the calculation spatial domain is excessively large (Goren et al., 2025). Precipitation (POP) susceptibility, on the other hand, is also negative and consistent with the idea that aerosol suppresses precipitation. However, the magnitudes are only significant in narrow coastal areas in the eastern Pacific and southeast Atlantic. This is possibly related to the relatively small rain rates in these stratocumulus clouds, making them more susceptible to changes in their precipitation frequency than regions in the extratropics with more significant rain rates.

With the successful launch of the Earth Clouds, Aerosols and Radiation Explorer mission (EarthCARE; Wehr et al., 2023) in May 2024, the EarthCARE sensors will enable assessing aerosol-cloud interactions with products that will largely expand the capabilities of CALIPSO and CloudSat. For example, the improved sensitivity of the EarthCARE Cloud Profiling radar will enhance the detection of clouds relative to CloudSat, detecting clouds as low as 600 m. In addition, the EarthCARE Atmospheric Lidar (ATLID), being a high spectral resolution lidar, will provide direct observations of aerosol extinction coefficient and refined aerosol typing classification. Because of the sampling and collocation constraints in our study (which includes more than 11 years of A-Train observations), multiple years of EarthCARE observations will be required to replicate the statistical robustness of our analysis. Alternatively, efforts for expanding the lidar-cloud record through the homogenization of CloudSat, CALIPSO, and EarthCARE products will be necessary to corroborate and expand the findings of this study.