Dust and smoke layers over the Atlantic Ocean weaken the underlying low-level cloud-top radiative cooling through different pathways

Dust and smoke layers over the Atlantic Ocean weaken the underlying low-level cloud-top radiative cooling through different pathways | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dust and smoke layers over the Atlantic Ocean weaken the underlying low-level cloud-top radiative cooling through different pathways Satyendra Pandey, Adeyemi Adebiyi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7168935/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2026 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract Aerosol semi-direct effects represent one of the least understood yet important pathways of aerosol interactions. These effects occur when absorbing aerosols rapidly adjust Earth’s radiative budget through modifications of thermodynamic structures that influence cloud cover. Over the Atlantic Ocean, where two primary radiation-absorbing aerosols (smoke and dust) dominate above clouds, the mechanisms by which aerosol-layer properties affect underlying low-level cloud-top radiative cooling — a critical parameter controlling cloudiness—remain unclear. Using ten years of satellite-derived aerosol, cloud, and radiative flux observations, combined with radiative-transfer simulations, we find that dust and smoke layers induce longwave-dominated warming responses that weaken the mean radiative cooling at low-level cloud tops. However, the pathways of this warming response differ, resulting in dust layers impacting cloud-top cooling about ten times more than smoke layers. Whereas dust properties dominate dust-induced warming responses through direct interactions in longwave, smoke-induced warming responses involve enhanced smoke-layer moisture that induces longwave radiation, opposing the impacts of smoke properties at cloud tops. This weakened cloud-top cooling response reduces low-level cloudiness by approximately 1.21% and 0.28% for dust and smoke, respectively. Our findings demonstrate the importance of accounting for longwave-mediated processes beyond traditional shortwave-dominated mechanisms in estimates of aerosol semi-direct effects. Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric chemistry Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Semidirect Effect Dust Smoke Low-level Clouds Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Atmospheric aerosols remain one of the largest sources of uncertainty in present-day climate-change estimates because of the challenges in understanding their complex interactions with several aspects of the Earth system 1 – 4 . Among others, these interactions include those with radiation, clouds, and meteorology. Specifically, aerosols can directly scatter and absorb solar and terrestrial radiation, impacting Earth’s energy balance and climate, in the so-called aerosol-radiation interactions or direct aerosol effect 5 – 10 . Aerosols can also serve as cloud condensation nuclei and ice nuclei, altering cloud properties, such as droplet size, reflectivity, and lifetime, in the so-called aerosol-cloud interactions or indirect aerosol effect 2 , 11 – 19 . Beyond these direct and indirect aerosol effects, absorbing aerosols can also modulate clouds by altering the local meteorology and the temperature and humidity structures of the atmosphere, thereby adjusting the effective radiative effects that are due to aerosol-radiation and aerosol-cloud interactions at the top of the atmosphere, in what is known as the semi-direct effect 20 , 21 . Among the uncertainties associated with aerosol-radiation and aerosol-cloud interactions, aerosol semi-direct effects contribute a substantial fraction, resulting in a low confidence designation by past reports from the Intergovernmental Panel on Climate Change 3 . Despite the contribution to uncertainties in climate-change estimates, and our improved understanding of adjustments to radiative effects in recent years 2 , a detailed understanding of the processes associated with aerosol semi-direct effects, particularly one that accounts for the variabilities in the properties among different absorbing aerosol species, remains largely unclear. The process by which the aerosol semi-direct effect influences the climate depends on factors such as cloud types, the aerosol layer characteristics, including its altitude relative to clouds, and the aerosol absorption properties 22 . While the aerosol semi-direct effect is relevant for all cloud types, as aerosol-driven changes in temperature and moisture profiles can modulate the properties of any cloud type, most past studies seeking a process-level understanding of aerosol semi-direct effects have largely focused on low-level marine clouds 22 . This is due to the widespread presence, persistence, and high albedo of these low-level clouds, which make them critical to Earth’s energy balance 23 . Specifically, these low-level clouds account for about 40 % of ll global cloud occurrences, with single-layer low-level clouds covering about one-fifth of the global ocean 24 . For such low-level clouds, previous studies have shown that absorbing aerosols embedded within the cloud layer can absorb sunlight, warm the cloud layer, and reduce the relative humidity, leading to cloud evaporation and cloud burn-off 20 , 21 , 25 . As cloud cover decreases, more sunlight reaches the Earth’s surface, amplifying warming and resulting in a positive semi-direct effect 25 – 27 . In contrast, when an absorbing aerosol layer is located above the clouds, the solar absorption by the aerosol layer can result in a warming effect that can stabilize the atmosphere and increase cloud cover and thickness, consequently leading to a negative semi-direct effect 26 , 28 – 31 . While the relative altitude of the aerosol layer matters for the sign of aerosol semi-direct effect, the strength of its impacts on low-level clouds and consequently on climate depends on the aerosol absorption properties 32 . Among other aerosol species, black carbon (or generally smoke aerosols) and mineral dust aerosols account for a substantial fraction of solar radiation absorbed by aerosols in the atmosphere 33 – 35 Specifically, these two aerosol species account for a combined 91% (about 60% and 31% for black carbon and dust aerosols, respectively) of the shortwave radiation absorbed in the atmosphere, compared to 11% by organic aerosols 36 . Because of black carbon’s dominant role in shortwave absorption, most previous studies have focused considerable attention on black carbon, and this attention has shaped our general understanding of the processes associated with the aerosol semi-direct effect 37 . In addition, previous studies have applied this dominant narrative about processes associated with black carbon’s semi-direct effect to explain similar effects by other absorbing aerosols, such as mineral dust 27 , 38 – 40 . However, smoke and dust aerosols are uniquely different, and one main distinction is in the spectral absorption; whereas smoke aerosols primarily absorbs shortwave radiation because they are mostly submicron particles (i.e diameter ≤ 1 µm), mineral dust aerosols can absorb both shortwave and longwave radiation, because their size distribution includes larger particles up to about 60 µm in the atmosphere 41 , 42 . Therefore, while some aspects of the processes associated with smoke aerosols may apply to dust semi-direct effects, such as those related to shortwave absorption, others are expected to be different, particularly when the influence of longwave absorption and scattering is considered 42 , 43 . One area where the differences in the process associated with smoke and dust semi-direct effects can be explored is at the low-level cloud top. This is because low-level cloud developments are primarily driven by characteristics, such as cloud-top radiative cooling, entrainment, and lower tropospheric stability, at or near the cloud tops that influence the coupling of cloud-topped boundary layers with the surface 44 . For example, an increase in cloud-top radiative cooling has been shown to destabilize the boundary layer, driving more intense convective circulation that substantially alters the clouds and their radiative properties 45 – 48 . As such, when aerosols are present above these clouds, variations in the properties and characteristics of the aerosol layers can have a substantial impact on the cloud-top radiative cooling and cloud cover, which in turn influence the estimate of aerosol semi-direct effect 49 . For example, previous studies have shown that shortwave absorption within the above-cloud aerosol layer increases the lower-tropospheric stability immediately above the low-level clouds, which indirectly strengthens its cloud-top radiative cooling 29 , 30 . In contrast, previous studies have also shown that enhanced relative humidity within the above-cloud aerosol layer can increase downwelling longwave radiation at the cloud top, thereby reducing low-level cloud-top cooling and cloud cover 49 – 51 . Other aerosol-layer characteristics, such as the aerosol vertical extent and the gap between aerosol layers and cloud layers, have been studied less extensively; however, some studies have shown them to be factors that influence processes at the low-level cloud top, and consequently cloud responses and semi-direct effects 52 , 53 . While all these processes are likely occurring simultaneously when aerosols are present above clouds, it remains unclear which properties or characteristics of the elevated aerosol layer dominate the low-level cloud changes, or whether a dominant process used to explain the relationship with smoke aerosols can be similarly applied to other absorbing aerosols, such as mineral dust. To address this gap, we leverage the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations)-CloudSat combined product to estimate aerosol-induced changes in cloud-top radiative cooling for low-level marine clouds. Specifically, we compare how dust and smoke aerosol-layer properties and characteristics, including aerosol optical depth, geometric thickness, base altitude, and aerosol-layer humidity, influence cloud-top radiative cooling, explore the resulting cloud responses, and discuss how these responses may affect the overall estimate of aerosol semidirect effects. We focus on the Atlantic Ocean, where elevated dust and smoke aerosols above low-level clouds are abundant, specifically over the North and South Atlantic Oceans (see yellow boxes in Fig. 1 ). We find that these elevated absorbing aerosol layers suppress cloud-top cooling through increased induced longwave warming near the low-level cloud top, which is primarily driven by longwave-interacting coarser dust particles over the North Atlantic Ocean, and enhanced smoke-layer humidity over the South Atlantic Ocean. Results We seek to understand how the properties and characteristics of the above-cloud dust and smoke layers over the northeast and southeast Atlantic Ocean, respectively, including their altitude, vertical extent, and the extinction of the aerosol layers, influence the underlying low-level cloud-top radiative cooling, and consequently the aerosol semi-direct effect. To do so, we obtained ten years (2007–2017) of satellite-based aerosol and cloud observations, where the identified aerosol layer is well separated from the top of the underlying single-layer low-level clouds by at least 200 m (see Methods). Such layers are assumed to have minimal microphysical influence on the underlying clouds and primarily exert radiative effects 52 , 54 . Because the period of the year when elevated aerosol layers occur above low-level clouds differs between northern dust and southern smoke aerosols, we used observations of the months when they are at their maximum over each basin, which are from May to August for dust and from July to October for smoke (see Methods). Below, we provide details of our results, presenting the heating rate profiles and discussing how the characteristics and properties of the aerosol layers affect cloud-top radiative cooling. We also present the sensitivity of cloud-top radiative cooling to the isolating influences of the aerosol properties and characteristics, cloud properties, and thermodynamic profiles, including the aerosol-layer humidity profiles. Finally, we discuss the observed cloud response to aerosol-layer characteristics and the potential implications for understanding the semidirect effects of dust and smoke aerosols. Weakening of cloud-top radiative cooling by above-cloud dust and smoke We find that the presence of the above-cloud dust layer significantly weakens the low-level cloud-top radiative cooling over the Northeast Atlantic Ocean, whereas the above-cloud smoke layer has a lesser influence on the low-level cloud-top radiative cooling over the Southeast Atlantic. To understand the difference in how the dust and smoke layers affect cloud-top cooling, we examine the heating or cooling rates due to the aerosol layers by using the difference of all-sky fluxes with and without aerosols (see Method). First, the all-sky heating rates, including the influence of clouds, background thermodynamic profiles, and other atmospheric constituents such as ozone, remain representative of the heating profiles when aerosols are above a cloud layer (Fig. 2 a & e). Specifically, dust and smoke absorb shortwave radiation, contributing to the all-sky warming within the aerosol layers (between 2.5 and 6 km in Fig. 2 a & e), whereas the low-level cloud tops experience strong longwave cooling that dominates the net radiative cooling at the top of the boundary layer (between 0.5 and 2 km in Fig. 2 a & e). Second, when the influence of the aerosol layers is considered explicitly, our result shows that the above-cloud dust layer over the Northeast Atlantic Ocean induces substantially stronger longwave cooling but comparable shortwave warming to one induced by the above-cloud smoke layer over the southeast Atlantic (Fig. 2 b & f, and top parts of Fig. 2 c & g). For example, dust-induced longwave cooling at the peak of the dust layer (approximately 3.84 km) is about − 0.10 ± 0.03 K day − 1 (blue line in Fig. 2 b and blue bar Fig. 2 c), which is about 500% stronger than the smoke-induced longwave cooling at the peak of the smoke layer (approximately 3.35 km), which is largely negligible within the error bound (–0.02 ± 0.01 K day − 1 ; Fig. 2 g solid blue bar). This dust-induced longwave cooling counteracts the dust-induced shortwave warming within the dust layer, and contributes to reducing the net radiative heating, at a peak dust level, of 0.46 ± 0.24 K day − 1 (with about 0.56 ± 0.25 K day − 1 contributed by shortwave; Fig. 2 b & 2 c). Overall, the dust-induced net heating accounts for approximately 51% of the overall domain-mean all-sky net radiative heating with a magnitude of 0.90 ± 0.43 K day − 1 (shortwave: 3.10 ± 0.59 K day − 1 and longwave: − 2.21 ± 0.38 K day − 1 ; Fig. 2 c) at the peak of the dust layer. In contrast to the dust layer, the smoke-induced shortwave warming has negligible opposition from the longwave cooling, accounting for almost all the net heating rate within the smoke layer (Fig. 2 f & 2 g), and about 76% of the domain-mean all-sky net radiative heating with a magnitude of 1.10 ± 0.55 K day⁻¹ (shortwave: 3.71 ± 0.74 K day⁻¹ and longwave: − 2.15 ± 0.37 K day⁻¹; Fig. 2 g). In addition, our result shows that the dust layer induces anomalous longwave warming at or near the low-level cloud top that is mostly absent from the low-level cloud top heating induced by the smoke layer (compare Fig. 2 b and 2 f, and 2 c and 2 g). Typically, because of the high liquid water content, all-sky longwave cooling often dominates the all-sky shortwave warming, resulting in net cooling at the cloud top (see blue hatched bar in Fig. 2 c & g). The maximum all-sky net cooling over the northeast Atlantic region occurs at an altitude of 1.68 km, with a magnitude of − 2.02 ± 1.78 K day⁻¹ (see black lines in Fig. 2 a; shortwave: 3.05 ± 1.0 K day⁻¹, longwave − 5.08 ± 2.6 K day⁻¹), while over the southeast Atlantic, the maximum occurs at 1.43 km with stronger net cooling reaches − 4.21 ± 2.08 K day⁻¹ (Fig. 2 e; shortwave: 2.95 ± 1.06 K day⁻¹; − 7.6 ± 2.8 K day⁻¹). Aerosol-induced changes near the cloud top reveal notable contrasts between dust and smoke, particularly in the longwave spectrum. We find that dust induces a net warming of 0.34 ± 0.18 K day⁻¹ at maximum radiative cooling level 1.68 km, which is due primarily to the dust-induced longwave warming from downwelling radiation, and accounts for approximately 16% reduction in the mean cloud-top cooling at this altitude. While this reduction at the 1.68 km is notable, the maximum level of dust-induced longwave warming in the boundary layer approximately 0.72 km, with 0.30 K day − 1 warming that accounts for 12% of the mean all-sky longwave cooling at that level. In contrast, smoke-induced warming is almost negligible, with only 0.025 ± 0.018 K day⁻¹, resulting in almost no changes in the low-level cloud top cooling. Overall, our result suggests that dust layers have a substantially stronger weakening influence on the mean cloud top radiative cooling than the smoke layer. Although the all-sky mean cloud-top radiative cooling is stronger over the southeast Atlantic (compare hatched blue bars in Fig. 2 c & 2 g) with generally more stratocumulus-dominated low-level cloud regimes than the northeast Atlantic, the modulation of cloud-top radiative cooling is more substantial over the northeast Atlantic 55 . For the same aerosol and environmental conditions, our results thus suggest that overlying dust contributes to higher variabilities of low-level cloud top radiative cooling over the northeast Atlantic region (about an order of magnitude more) than smoke above clouds over the southeast Atlantic region. In addition, we note here that the all-sky longwave cooling within the smoke layer (Fig. 2 d) is primarily due to the presence of moisture, which often co-occurs with the aerosol transport over the southeast Atlantic low-level clouds 50 (see Fig. 2 h, and details in section 2.3 below). Such presence of moisture within the smoke layer can have a similar weakening effect, inducing longwave warming that reduces the mean radiative cooling at cloud top 51 . In contrast to the weakening effect of above-cloud dust and moisture, the shortwave warming induced by dust and smoke layers can also indirectly influence the cloud-top radiative cooling 56 , 57 . For example, previous studies have suggested that aerosol-induced shortwave warming enhances lower tropospheric stability 52 , 58 , which indirectly strengthens cloud-top radiative cooling by building up moisture in the boundary layer 59 , 60 . Ultimately, the above-cloud dust and smoke (and any accompanying moisture) can influence cloud-top radiative cooling through different pathways, with opposing effects in shortwave and longwave spectra, that may also depend on the layer characteristics, such as layer base altitude, and geometrical and optical thickness of the layer. Role of aerosol-layer properties and characteristics in modifying cloud-top radiative cooling Since the impacts of aerosol-layer longwave radiation depend on the characteristics of the layer, we examine how its altitude and geometric thickness affect the underlying low-level clouds through changes to cloud-top radiative cooling. We find that the weakening effect of the low-level cloud-top radiative cooling by the above-cloud aerosols depends sensitively on the aerosol-layer altitude relative to the cloud and the geometric thickness of the aerosol layer. To understand this, we first categorized profiles within 5x5 degree grid-box (see Fig. 1 ) based on groups of aerosol layer geometric thickness (representing the vertical extent) and base altitude (representing the separating distance from cloud top) (see Fig. S2) and require that each category contains a minimum of 30 profiles from at least six grid boxes within each basin (see Method for details). Only eight groups of geometric thickness and base altitude satisfy our stringent requirement for both dust and smoke cases (see Fig. S2), which allows us to estimate cloud-top heating responses as the change in cloud-top heating rate per one kilometer change in one aerosol-layer characteristic (i.e., geometric thickness and base altitude) while keeping the other relatively fixed. We find that aerosol-induced heating within the dust and smoke layers increases as the layer becomes geometrically thicker, primarily due to increased shortwave absorption (Fig. 3 and Fig. S3). Smoke layers exhibit stronger heating for a given geometric thickness than dust layers. This is because smoke particles have a higher mass extinction efficiency per unit mass than dust particles at the peak visible wavelength 61 – 63 , and because the smoke layer is associated with lower longwave cooling (see Fig. S3), resulting in larger overall net heating for smoke than dust. Consequently, for one kilometer increase in the geometrical thickness, the net heating within the smoke layer increases by 2.4 K day − 1 km − 1 , higher than the same increase in the dust layer (Fig. 3 a & b; 4.9±0.7K day − 1 km − 1 for smoke and 2.5±0.1 K day − 1 km − 1 for dust). It is worth noting that the aerosol-induced longwave cooling per unit increase in the geometrical thickness is higher for dust than for the smoke layer, because a geometrically thicker dust layer extends to colder altitudes, allowing dust to absorb and emit more longwave radiation due to its dust-top temperature difference with the surface 64 , 65 . In contrast to the changes in the geometric thickness that are consistent for dust and smoke layers, albeit with different magnitudes, a one-kilometer increase in the base altitude of the aerosol layers affects the net heating differently. Raising the base height by one kilometer leads to enhanced warming within the dust layer, but reduced warming within the smoke layer (Fig. 3 a & b). While the general increases in radiative heating responses are largely due to the corresponding increases in aerosol extinction within the dust and smoke layers – that is, geometrically thicker and high-altitude aerosol layers are often optically thicker (e.g., Fig. S4) – the reduced warming of the smoke layer can be associated with reduced reabsorption of shortwave radiation reflected from the top of the low-level clouds as the smoke base altitude increases 50 . These radiative heating responses within the aerosol layer also influence the aerosol-induced changes at the low-level cloud top, as a function of aerosol-layer geometric thickness and base-layer altitude, with more substantial influence from dust than smoke layers. At the cloud top, the dust-induced heating is mainly driven by longwave effects that can reach up to a warming of 1.1 K day − 1 (see Fig. S3). Dust-induced cloud-top radiative heating increases with the geometrical thickness, resulting in a heating response of approximately 0.46 (± 0.04) K day − 1 km − 1 for each kilometer increase in the dust-layer geometric thickness (Fig. 3 c). Although smaller, the cloud-top heating response also increases by about 0.18 (± 0.03) K day − 1 km − 1 for each kilometer increase in the dust-layer base altitude (Fig. 3 c). In contrast, the cloud-top heating response to smoke is substantially weaker by about an order of magnitude for the same change in dust-layer geometric thickness and base altitude as dust (Fig. 3 d). Specifically, the smoke-induced cloud-top radiative heating response is about 0.01 (± 0.01) K day − 1 km − 1 per unit increase in geometric thickness and a negative ( -0.03 ± 0.02 K day − 1 km − 1 ) response per unit increase in base-layer altitude. Additionally, it is worth noting that an increase in smoke-layer base altitude also corresponds to a reduction in cloud-top heating response, although with small values (Fig. 3 d). Unlike the dust layer, that have a considerable impact on cloud-top, our result suggests that the farther the smoke layer is from the cloud top, the weaker its impact on cloud-top radiative cooling (cf. Figure 2 e). The observed cloud-top heating response to changes in aerosol-layer geometrical thickness and base height may be confounded by variations in aerosol optical depth across the different categories, given the increase in aerosol optical depth with geometrical thickness of the dust and smoke layer (see Fig. S4). Therefore, to compare the cloud-top heating response to aerosol optical depth with the response to geometric thickness and base-layer altitude, we estimate the cloud-top heating response per one standard deviation change in each of the three parameters (Fig. 3 e & f). This approach allows for a comparison while accounting for variability in each parameter. Our results highlight the dominant role of optical depth compared to geometric thickness and layer base altitude. One-standard-deviation increase in dust optical depth leads to a warming of 0.52 ± 0.01 K day-1 at the cloud-top, much larger than the 0.03 ± 0.02 K day-1 warming for the same increase in smoke optical depth (first column of Fig. 3 e & 3 f; note the differences in scale, which is still consistent with one order magnitude difference of impacts between dust and smoke). Other layer properties, such as geometric thickness and base altitude, have a comparatively smaller influence on cloud-top heating when compared to the aerosol optical depth, but with similar orders of magnitude differences between dust and smoke. For dust layers, an increase of one standard deviation in geometric thickness results in a warming of 0.05 ± 0.03 K day¹, and a similar increase in base altitude results in a change of 0.03 ± 0.03 K day¹. For smoke layers, the corresponding changes are smaller: 0.01 ± 0.01 K day¹ for geometric thickness and − 0.03 ± 0.02 K day¹ for base altitude. Notably, an increase in smoke layer base altitude leads to a slight cooling effect, though this effect is very small and shows considerable variability (cf. Figure 3 d & 3 f). Overall, our results indicate that weakening of cloud-top radiative cooling is primarily driven by the increased aerosol optical properties more than increases in aerosol-layer characteristics (such as the geometric thickness and base altitude), and that the dust layer overlying low-level clouds weakens cloud-top cooling by about an order of magnitude more strongly than smoke-induced changes in cloud-top radiative cooling. Role of thermodynamic profile and cloud properties in modifying cloud-top radiative cooling Although variabilities of above-cloud aerosol properties dominate the changes in low-level cloud top radiative cooling, when compared to aerosol-layer characteristics, other environmental factors may also confound these changes in a way that similarly impacts the cloud-top radiative cooling. For example, variations in aerosol-layer humidity correlate with aerosol optical depth in both dust and smoke layers (Fig. S4), which could enhance downwelling longwave radiation, potentially weakening low-level cloud-top radiative cooling 50 , 51 , 66 . Furthermore, changes in cloud properties, such as cloud optical depth, due to initial changes in cloud-top radiative cooling may destabilize the cloud layer, further weakening the cloud-top radiative cooling in a feedback process 67 , 68 . Therefore, to isolate these effects, we investigate the sensitivity of cloud-top radiative cooling to aerosol-layer properties and characteristics (aerosol-layer optical depth, geometric thickness, and base altitude), separate from the influence of variations in cloud properties (defined by the cloud optical depth and cloud top height) and thermodynamic profiles (defined by the temperature and humidity profiles), using a radiative transfer model, called Santa Barbara DISORT (Discrete Ordinate Radiative Transfer) Atmospheric Radiative Transfer (SBDART) 69 . The SBDART-derived cloud-top heating responses are consistent with CALIPSO-CloudSat-derived observations, similarly, showing that the cloud responses to changes in dust properties and characteristics are stronger (by about an order of magnitude more) than the responses to smoke (Fig. S5 and cf. Figure 3 c & d). Given these differences in magnitude, we normalize each parameter by the total heating responses resulting from a 1 km increase in aerosol-layer geometric thickness or base height (see Fig. 4 ) to compare the relative contributions of aerosol properties, cloud properties, and thermodynamic profiles to the cloud-top heating response. The results show that the pathway of cloud-top heating response and the associated weakening of all-sky cloud-top radiative cooling due to the presence of above-cloud aerosol layers differ for dust and smoke aerosols. First, we find that changes in dust properties and characteristics influence the cloud-top heating responses more than changes in thermodynamic profiles over the North Atlantic Ocean. Specifically, when normalized by the total heating response, a 1 km increase in dust layer geometric thickness shows that variations in dust properties across categories have about 67% more influence on the simulated cloud-top heating response than the variations in thermodynamic profiles (Fig. 4 a). In contrast, for a 1 km increase in dust layer base altitude, the contribution from thermodynamic profiles becomes essential, having about 32% more influence on the simulated cloud-top heating response than variations in dust optical properties (Fig. 4 a). Second, for smoke, we find that despite the small overall change in cloud-top heating response (cf. Figure 3 d & Fig. S5), the relative contribution by smoke properties is opposed by the contributions by thermodynamic profiles (Fig. 4 b). Specifically, whereas the relative contributions of the variations in smoke properties indicate cloud-top cooling response, the relative contribution of the variation in thermodynamic profile indicate cloud-top warming response for a 1 km increase in aerosol-layer geometric thickness or base altitude. Finally, for both smoke and dust, we find that the relative contribution of variations in cloud properties to the simulated cloud-top heating response is small compared to the variations in aerosol properties and thermodynamic profiles (compare blue bars in Fig. 4 to other colors). Overall, these results indicate that dust-layer properties and characteristics primarily drive the dust-induced cloud-top heating response, whereas the smoke-induced cloud-top response is more sensitive to the counterbalancing influence of the above-cloud smoke properties and thermodynamic profile. Understanding these different pathways for dust-induced and smoke-induced cloud-top heating response requires a better understanding of the key players that likely influence the changes in aerosol-induced longwave radiation reaching the low-level cloud top (cf. Figures 3 & 4 ). Specifically, due to the coarser size distribution of dust aerosols, they can scatter and absorb more longwave radiation, thereby enhancing downwelling radiation that weakens the low-level cloud-top cooling. In addition, both dust and smoke layers over the North and South Atlantic Oceans are associated with enhanced aerosol-layer humidity that is transported off the continent with the aerosols (see Fig. 2 d & e) and vary with aerosol-layer geometric thickness and base altitude (Fig. S4). This enhanced humidity similarly facilitates downwelling longwave radiation, which strengthens the dust-induced cloud-top heating response but counteracts the smoke-induced cloud-top response. Although changes in above-cloud dust and humidity dominate the cloud-top heating response, the potential influence of cloud properties, such as variability in cloud optical depth and cloud-top heights, on the cloud-heating response is small but non-negligible. While variabilities in cloud properties do not drive significant changes in cloud-top heating response, the overall weakening of mean cloud-top cooling due to above-cloud longwave-mediated dust and smoke layer will still drive mean changes in cloud cover response, which, in turn, will influence the aerosol semidirect effect. Cloud-top Radiative Cooling and Low-level Cloud Cover Similar to the cloud-top heating response (Fig. 3 ), we estimate the response of low-level cloudiness and find that it is generally reduced in response to elevated dust and smoke layers. Specifically, the mean low-level cloud responses to one-standard-deviation increases in dust-layer and smoke-layer properties and characteristics are about − 1.21% and − 0.28% respectively (Fig. 4 c & d). As with the cloud-top heating response, the low-level cloud cover also responds to increases in aerosol-layer aerosol optical depth, geometrical thickness, and base altitude. Specifically, for a one-standard-deviation increase in aerosol-layer aerosol optical depth, geometrical thickness, and base altitude, the low-level cloud cover responds by about − 1.64%, -1.49%, and − 0.49% respectively. For smoke-induced cloud responses, these values are − 0.55%, 0.08, and − 0.37%, respectively. Like the cloud-top heating response (Fig. 3 ), the low-level cloud response is generally stronger for dust layers than for smoke layers. In addition, we also find a substantial relationship between aerosol-induced responses in low-level cloud cover and cloud-top heating (Fig. S6). Specifically, an increase in aerosol-induced longwave warming at the cloud top is negatively correlated with changes in cloud fraction by -0.89 and − 0.66 for dust and smoke, respectively. Overall, our results indicate that a weakening of cloud-top radiative cooling is associated with a reduction in low-level cloudiness and variations in above-cloud aerosol properties and characteristics, and the background thermodynamic structure plays a key role in determining the magnitude of the cloud cover response. Discussion and Summary Our results have shown that elevated absorbing aerosol layers suppress cloud-top cooling through increased aerosol-induced longwave-dominated warming near the low-level cloud top. Our analysis focuses on two climatically important regions over the Atlantic Ocean: the northeast Atlantic Ocean, dominated by above-cloud aerosols from May to July, and the southeast Atlantic Ocean, where smoke is predominant from July to October. We used aerosol and cloud profile information from CALIPSO-CloudSat-derived radiative fluxes along with complementary radiative transfer simulations to assess the impact of these aerosols on low-level cloud-top radiative cooling — a critical parameter that significantly influences cloud development. We find that an elevated dust layer induces a longwave warming perturbation at or near the low-level cloud top, which is mostly absent from the low-level cloud top heating associated with the smoke layer. Specifically, the domain-average low-level cloud-top heating response due to the above dust layer is about an order of magnitude higher than that associated with the smoke layer. This aerosol-induced cloud-top warming response weakens the domain-averaged, all-sky cloud-top radiative cooling by as much as 16% when dust is above the cloud. Furthermore, we find that the magnitude of dust- or smoke-induced weakening response on cloud-top radiative cooling is sensitive to aerosol-layer properties and characteristics, including the geometric thickness, base altitude, and optical depth. Specifically, we find that the weakening response of cloud-top radiative cooling is primarily driven by the increased aerosol optical properties more than increases in aerosol-layer characteristics (such as the geometric thickness and base altitude), with stronger dust-induced changes than smoke-induced changes in cloud-top radiative warming response. Specifically, a one-standard-deviation increase in dust optical depth leads to a pronounced cloud-top warming response of 0.52 ± 0.01 K day¹, nearly an order of magnitude larger than the 0.03 ± 0.02 K day¹ warming response for smoke. These findings underscore the dominant role of dust optical depth in modulating cloud-top radiative effects, highlighting the contrasting radiative sensitivities of dust and smoke layers, driven by differences in their vertical structure and absorptive properties. While aerosol optical depth dominates aerosol-induced changes in cloud-top heating response, this response can be confounded by effects from other factors, such as the thermodynamic profiles and cloud properties. Therefore, we conduct sensitivity experiments using the SBDART that isolate the effects of aerosol-layer properties, thermodynamic profile (including temperature and humidity), and cloud properties (including cloud optical depth and cloud-top heights). We find that aerosol-layer humidity plays a critical role in modulating the aerosol-induced cloud-top radiative heating rates due to changes in aerosol properties. Specifically, because of the enhanced induced downwelling longwave radiation, aerosol-layer humidity similarly weakens the cloud-top radiative cooling; however, its impacts amplify the dust-induced cloud-top warming, whereas it counteracts the cloud-top cooling due to increases in smoke-layer properties. In other words, when smoke properties are increased, it strengthens the low-level cloud-top cooling; however, this response is opposed by the weakening effect of aerosol-layer humidity. These results highlight that dust-layer properties and characteristics primarily drive the dust-induced cloud-top heating response, whereas the smoke-induced cloud-top response is more sensitive to the counterbalancing influence of the above-cloud smoke properties and smoke-layer humidity. Overall, our results suggest that when dust and smoke layers are above the cloud over the North and South Atlantic Oceans, the weakening of cloud-top radiative cooling due to the aerosol properties and characteristics is associated with anomalous decreases in low-level cloudiness. These anomalous reductions in low-level cloudiness and cloud-top radiative cooling have important implications for semidirect effects of smoke and dust aerosols and low-level cloud feedback. Our results of negative cloud responses being closely linked to suppressed cloud-top cooling suggest a weakening response in the aerosol semidirect effects when there is elevated dust-induced and smoke-layer moisture-induced longwave warming at the cloud top. Specifically, whereas previous studies have associated enhanced cloudiness and negative semi-direct effects, due in part to the increased lower tropospheric stability by shortwave-absorbing aerosols over stratocumulus-dominated regions of the northeast and southeast Atlantic 29 , 30 , 70 , 71 , our results suggest that longwave warming that weakens the cloud-top radiative cooling and reduces the cloud cover effectively could reduce the magnitude of the negative aerosol semi-direct effects. In addition, weakened cloud-top radiative cooling and a reduction in cloudiness could induce a potential decoupling of the boundary layer, limiting moisture supply from the ocean surface, which would in turn further weaken the cloud-top radiative cooling and further reduce the cloudiness 51 , 72 – 74 . Such a cloud feedback process could further weaken the aerosol semi-direct effects, reducing their magnitude from negative (cooling effect) to positive (warming effect) as the cloud fraction further reduces. This inference is consistent with previous studies that have shown that the magnitude of smoke’s negative semidirect effect may be overestimated if smoke-layer humidity is not accounted for 30 , 54 , 75 – 77 . Similarly, over dust-dominated regions, semidirect effect estimates may be biased if the longwave effects of dust are not fully considered 28 , 39 , 40 , 78 . Overall, our results indicate that cloud-top heating response induced by dust-layer optical properties and smoke-layer moisture content is critical for assessing cloudiness and consequently aerosol semi-direct effect over low-level cloud-dominated regions. While our study provides insight into the often unaccounted radiative effects of dust and smoke on cloud-top radiative cooling, using retrieved aerosol and cloud parameters from active satellite sensors, derived fluxes, and idealized radiative transfer simulations, there are several main caveats. First, although satellite retrievals from CALIPSO and CloudSat are vertically resolved, their limited spatial and temporal coverage means they may miss the history and associated dynamics of the underlying clouds. Second, retrievals of above-cloud aerosol parameters are challenging and can be affected by inherent uncertainties, which may impact our interpretations. Third, in our methods of categorization, the effects of below-cloud aerosols, such as sea salt, which can influence cloud dynamics and therefore cloud-top radiative cooling, are not fully accounted for, although their role may be limited since cloud properties play a less significant part. Fourth, although we rely on SBDART simulations, the assumed particle properties may not fully capture the variability in refractive index, mixing state, or particle shape. Additionally, the assumed cloud microphysical properties influence the simulated radiative effects, and the sensitivity of our results to these assumptions has not been fully quantified in our radiative transfer calculations. Finally, our study did not consider other pathways by which aerosol-induced changes in radiation could indirectly affect cloud fraction and produce similar cloud cover responses as shown above. For example, absorbing an aerosol layer that limits shortwave radiation at the surface could result in a reduction in surface temperature, weakening surface fluxes, and indirectly leading to a decrease in cloud fraction 79 , 80 . On the other hand, aerosol-induced cooling of the surface and simultaneous aerosol-induced heating aloft could cause substantial adjustments in vertical temperature stratification, which is typically associated with increases in cloud cover 81 , 82 . While such pathways of aerosol interactions are likely important, changes in surface temperature affecting surface fluxes are often delayed and therefore are likely to have little influence on the instantaneous effects favoured by our methodology. Methods To investigate the influence of smoke and dust layers on cloud-top radiative cooling, we selected two regions: the northeastern and southeastern Atlantic Ocean, where low-level clouds frequently coexist with overlying layers of absorbing aerosols (see yellow box Fig. 1 ). The absorbing aerosol over the northeastern Atlantic is dust from North African arid regions 83 – 85 , and the smoke from biomass burning over the southeastern Atlantic is from southern Africa 86 – 88 . We used ten years (2007–2017) of aerosol and cloud observations, including aerosol-type, aerosol-layer, and cloud-layer boundaries (bottom and top), aerosol optical depth at 532nm, and aerosol extinction profiles from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) 89 , and cloud cover, radiative flux, and associated radiative heating data from CloudSat 90 . Details of the CALIPSO retrieval methodology for aerosols and cloud parameters, including aerosol type 91 , 92 , aerosol extinction profiles and optical depth at 532 nm 89 , 93 and aerosol and cloud layer boundaries 94 can be found in the respective algorithm documentation and evaluation studies 95 – 100 . Similarly, details of CloudSat-retrieved cloud cover 101 , 102 and radiative fluxes 90 can be found in the literature (see also Table S1 ). Because the months when aerosol layers are typically found above low-level clouds differ between northern dust and southern smoke aerosols, we used the months when aerosol occurrences are maximum over each basin, which are from May to August for dust and from July to October for smoke (see Fig. S1 ). Identification of dust/smoke layer above low-level clouds We divided our analysis domain over the North and South Atlantic Oceans into 5°×5° grid boxes (see dashed yellow lines in Fig. 1 ) to minimize the effect of large-scale meteorological variability, following previous studies 103 – 105 . We selected profiles containing single-layer low-level clouds within each grid with an overlying aerosol layer using the number of layers and feature classification flags in the CALIOP Merged Layer product (CAL_LID_L2_05km). Over the North Atlantic Ocean, we retained aerosol layers identified as dust in the feature classification flag, and separately, for the South Atlantic Ocean, we selected only flags for smoke aerosols. With cloud and aerosol top and bottom boundaries retrieved in CALIPSO using the SIBYL (Selective Iterated Boundary Location Algorithm) algorithm, with an accuracy of ~ 100 m compared to ground-based lidar 94 , 106 – 108 we used the recently improved data and selected profiles where the aerosol and cloud layers were separated by at least 200 m. This minimum required separation provides an additional level of assurance, ensuring that the aerosols are indeed separated from the cloud layers. Additionally, we discarded profiles where aerosol tops extended beyond 6 km, as they are too far from the cloud top to have significant radiative effects 52 and could be misclassified as cirrus clouds at this altitude 109 . Estimating aerosol-induced radiative heating and corresponding cloud-top heating response To estimate the radiative influence of dust and smoke on low-level clouds, we used radiative fluxes and associated heating rates from the CloudSat-CALIPSO combined product 2B-FLXHR-LIDAR 90 , 110 . These fluxes were derived using a radiative transfer model, namely BugRad, using aerosol and cloud information from both CALIPSO and CloudSat. The BugsRad radiative transfer model computes radiative fluxes for each radar profile from the Cloud Profiling Radar (CPR), with a vertical resolution of 240 m and covering from the surface to the lower stratosphere. It uses cloud liquid and ice water content and particle size information, derived precipitation estimates from CloudSat’s and aerosol profiles information, including layer boundaries and aerosol type, obtained from CALIOP, along with MODIS-derived surface properties and atmospheric profiles from collocated re-analyses, to calculate upwelling and downwelling shortwave (0–4 mum) and longwave (4 > 0) fluxes, as well as heating rates. Necessary aerosol optical properties (such as asymmetry parameter and single-scattering albedo) were used from d’Almeida et al. 111 , and surface properties, such as surface albedo and emissivity, are sourced from the International Geosphere-Biosphere Program (IGBP). Additionally, optical properties for smoke were constrained using ORCALES (ObseRvations of Aerosols above CLouds and their intEractionS) field campaign-based measurement 112 . Background meteorological conditions, including surface pressure, surface temperature, as well as profiles of pressure, temperature, specific humidity, and ozone mixing ratio used from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset and provided in the CloudSat dataset as ECMWF-AUX product 113 . Uncertainties in the computed fluxes were evaluated through sensitivity analyses and comparison with top-of-atmosphere fluxes from CERES, showing biases within acceptable ranges 90 . Shortwave flux errors are primarily attributed to uncertainties in CloudSat’s liquid water content estimates, with smaller contributions from undetected low clouds. For longwave fluxes, uncertainties mainly arise from assumptions about surface skin temperature and lower-tropospheric water vapor 90 . Since our study focuses on oceanic regions, associated uncertainties are expected to be lower. We used both upwelling and downwelling shortwave and longwave radiative fluxes, along with radiative heating rate profiles, collocated with above-cloud aerosol cases. While the dataset provides radiative heating profiles under all-sky conditions (as is shown in Fig. 2 a and 2 d), heating profiles for clear-sky (cloud-free; cloud optical depth set to zero) and no-aerosol (aerosol optical depth set to zero) conditions are not included. However, the flux dataset includes both aerosol and no-aerosol cases under clear-sky and all-sky conditions, allowing us to compute the aerosol and cloud radiative effects in both spectral bands. Accordingly, the net (downwelling minus upwelling) flux difference between all-sky cases with and without aerosols provides a direct estimate of aerosol-induced changes in the radiative flux profiles (see Eq. 1). We then computed the vertical flux divergence at each atmospheric layer from this difference and calculated the resulting aerosol-induced radiative heating profiles. Since profiles were selected only when CALIPSO detected dust over the North Atlantic and smoke over the South Atlantic, the aerosol-induced changes are attributed to these aerosol types in each region (dust in the north, smoke in the south). $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\varDelta\:F={F}_{allsky}-{F}_{allsky,\:no\:aerosol\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)\:$$ Where ∆F is the change in radiative fluxes due to aerosols, \(\:{F}_{allsky}\) is net (downwelling minus upwelling) flux for all-sky conditions, and \(\:{F}_{allsky,no\:aerosol}\) is the same, but computed with no aerosol (i.e., aerosol optical depth equals zero). This is estimated for both shortwave and longwave fluxes for each vertical level given in the profile datasets. Subsequently, we calculate profiles of shortwave and longwave heating rates using flux divergence $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\frac{\:\partial\:T}{\partial\:t}=-\left(\frac{1}{\rho\:{C}_{p}}\right).\left(\frac{{\Delta\:}F}{{\Delta\:}Z}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ Where, \(\:\frac{\partial\:T}{\partial\:t}\) is the heating rate (K day − 1 ), ρ = 1.17 kg m − 3 density of the atmospheric layer, C p is the specific heat capacity of air at constant pressure (1004.67 J kg − 1 K − 1 ), and ∆Z is the change in vertical levels (m). Aggregation of aerosol, cloud, and radiative flux based on the vertical structure of dust/smoke Using the aerosol layer’s top and bottom boundaries, we estimated the geometric thickness and base altitude of the aerosol layer within each 5°×5° grid box separately for the north and south Atlantic Oceans. We categorized profiles based on layer base altitude and geometric thickness and stratified them into four groups of dust base heights at 1, 2, 3, and 4 km. We further divided each base-height group into four subgroups with geometric thicknesses of 0.5, 1.5, 2.5, and 3.5 km. Therefore, within a 5°×5° grid box, a maximum of 16 categories could potentially exist (see Fig. S2). However, we retained only categories that contained at least 30 profiles and were present in a minimum of six grid boxes within each basin. This ensures that our analysis of changes in cloud and aerosols is statistically significant. Additionally, each retained category had to be available for both dust and smoke separately over the north and south Atlantic basins. We do so to allow for comparable results, and as a result, the final number of categories available for analysis was eight (see Fig. S2). Corresponding to these eight categories, we retained complete datasets of aerosol properties from CALIPSO, cloud properties from CloudSat, meteorological parameters from the ECMWF Auxiliary product (included in the CloudSat datasets), and corresponding radiative fluxes from CALIPSO-CloudSat 90 . We further assessed the impact of variations in the geometric thickness and base altitude of dust and smoke layers on cloud-top cooling or warming by analysing the dust- and smoke-induced changes in cloud-top heating across categories. Specifically, to evaluate sensitivity to geometric thickness, we compared changes in cloud-top heating across those categories having the same layer base altitude but different geometric thickness. Conversely, to assess the effect of base altitude, we analyzed the categories with the same geometric thickness but varying layer base altitudes. Therefore, we calculated heating responses to a per-kilometer change in geometric thickness for a fixed layer base, and vice versa, using the following $$\:\:Response\:to\:GT\:\left(k{m}^{-1}\right)=\:{\left.\frac{\partial\:X}{\partial\:GT}\right|}_{LB}and\:Response\:to\:LB\:\left(k{m}^{-1}\right)=\:{\left.\frac{\partial\:X}{\partial\:LB}\right|}_{GT}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ Where X, can be aerosol layer heating and cloud top heating/cooling for all-sky conditions or aerosol-induced changes (see Fig. 2 ), GT is for geometric thickness, and LB is the layer base altitude. Furthermore, to evaluate the influence of optical depth, we divided each category into two groups: low and high optical depth. For both smoke and dust cases, the low optical depth group includes values less than 0.1. For the high optical depth group, we used a range of 0.2–0.3 for smoke and 0.3–0.4 for dust. This approach allows us to assess the influence of geometric thickness and base altitude on independent variation in optical depth across categories. Additionally, within each fixed base-height and thickness category, comparing different optical depth ranges enables us to isolate the effect of optical depth. Together, this categorization helps us quantify the individual impacts of optical depth, geometric thickness, and layer base altitude with minimal overlap between their influences. Effect of background thermodynamic conditions and cloud parameters We used the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model 69 to evaluate the sensitivity of cloud-top radiative cooling to aerosol layer characteristics, cloud properties, and underlying thermodynamic conditions. SBDART accounts for key variables influencing radiative transfer and allows selection of surface types (e.g., water, desert, snow, forest), which affect albedo and surface reflectivity. Model uses prescribed cloud layer, with user-defined properties such as cloud optical depth, cloud-top and base heights, phase (liquid or ice), and effective particle radius, enabling an accurate assessment of radiative fluxes within and across cloud boundaries. It also supports user-defined atmospheric profiles, including temperature, humidity, and pressure across altitudes, crucial for determining radiation absorption and emission by gases and aerosols. The required inputs include temperature, temperature (T, in K) and pressure (p, hPa), and water vapor density ( ρ v , g m − 3 ) for each model level altitude (km). While temperature and pressure profiles are available as auxiliary products in the CALIPSO files, water vapor density is computed from specific humidity (q, in kg/kg), also provided in the CALIPSO L2 files, using the following equation, $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:{\rho\:}_{v}=1000\frac{q.p}{ϵ.Rv.T}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)$$ where ϵ = 0.622 is the ratio of molecular weights of water vapor and dry air, and Rv = 462 J kg⁻¹ K⁻¹ is the specific gas constant for water vapor. We used a six-stream discrete solver to simulate the profiles of shortwave and longwave fluxes, corresponding to eight categories, separately for dust and smoke, to estimate the effect of aerosol layers on cloud-top radiative flux. To do so, we used cloud parameters (optical depth and cloud top and base height), aerosol optical depth, normalized dust/smoke profiles, along with temperature and humidity profiles corresponding to each category defined in Fig. S2, separately for smoke and dust. We simulated fluxes at 0.01 µm spectral resolution over the range of 0.25–40 µm for dust and 0.25–20 µm for smoke, with the latter limited due to negligible interactions in the longwave spectrum. To compute broadband spectral averages, we performed three distinct set of simulations one covering the shortwave band (0.25–4 µm), another spanning the longwave region (4–40 µm for dust and 4–20 µm for smoke), and a third covering the full range (0.25–40 µm for dust and 0.25–20 µm for smoke. We configured the model, separately, for the North (17.49°N, 27.77°W) and South Atlantic (16.78°S, 8.90°E) using parameters representative of CALIPSO-derived dust and smoke layers, respectively, and supplied normalized aerosol extinction profiles based on the average values of eight categories, with no additional aerosol layers prescribed above or below the cloud outside the defined aerosol layer. We calculated the required single-particle optical properties for dust, such as spectrally varying single-scattering albedo, extinction efficiency, and asymmetry parameter based on Mie theory, using the complex refractive index of dust from Ref 114 and particle size distributions typically representative of long-range transported dust 43 , 115 , 116 . Thus, the obtained spectral aerosol properties, extinction efficiency, single-scattering albedo, and asymmetry parameter for 60 wavelengths were used as input to capture dust’s characteristic coarse‐mode behaviour (see Fig. S7). On the other hand, for smoke, we used spectrally varying optical depth, single-scattering albedo, and asymmetry parameter values from Ref 117 , which have extended wavelengths up to 2 µm for above-cloud smoke over the South Atlantic (see their Fig. A1 also plotted in Fig. S7). In addition, we set angstrom exponent values to 1.62, which characterizes the wavelength dependence of aerosol extinction and indicates the dominance of fine mode smoke above south Atlantic cloud decks 117 . In all simulations, we used single-layer low-level clouds, whose layer boundaries were obtained by averaging cloud-layer information for each category (see Table S2), separately for dust and smoke. Cloud properties were prescribed using CALIPSO-derived cloud-top heights and cloud optical thicknesses. For both the North and South Atlantic regions, we set droplet radii to 10 µm at the cloud base and 15 µm at the top for all simulations, allowing SBDART’s built-in logarithmic sliding function to generate realistic vertical profiles consistent with observations of marine low clouds 118 , 119 . In addition, we prescribe cloud optical depth based on the average values of each of the eight categories, separately for dust and smoke cases, using uniform opacity to ensure that the in-cloud vertical structure does not modulate cloud-top cooling 120 , 121 . To isolate the effects of dust and smoke layer properties on cloud-top cooling from the influences of background thermodynamic conditions and cloud characteristics, we first recreated radiative flux profiles for the eight cloud–aerosol categories, separately for dust and smoke. These simulations used aerosol optical depth, normalized aerosol extinction profiles, cloud properties, and the mean temperature and humidity profiles associated with each category (see Fig. S8a and Table S2). For each case, we computed shortwave, longwave, and net radiative fluxes and heating rates both with and without aerosols (by setting aerosol optical depth to zero). This allowed us to derive aerosol-induced heating rate profiles and corresponding changes in cloud-top cooling. From these profiles, we further calculated aerosol-induced cloud-top heating responses to a one-kilometer increase in geometric thickness and aerosol layer base height (using Eq. 3). Since the defined categories differ not only in aerosol properties but also in cloud characteristics and meteorological backgrounds, the resulting aerosol-induced changes in cloud-top cooling could be influenced by any combination of these factors. For example, aerosol absorption and its radiative impact depend not only on aerosol loading and vertical structure but also on temperature, humidity, and underlying cloud properties. A thicker cloud, for instance, may buffer or resist aerosol-induced radiative influence. Consequently, the cloud-top heating responses derived from the initial category-based simulations cannot be attributed solely to aerosol effects. To isolate the influence of individual factors on cloud-top radiative cooling, we conducted three set of sensitivity simulations, in which, we varied one group of parameters at a time, namely aerosol properties (includes layer-base and top height, and optical depth), cloud properties (cloud base and top and cloud optical depth), or thermodynamic conditions (temperature and humidity profile), while keeping the others constant. This allowed us to assess how each factor independently contributes to variations in cloud-top heating. First set of experiments, evaluating aerosol sensitivity, for which we kept the temperature, humidity, and cloud properties fixed across all cases and varied only the aerosol optical depth and extinction profiles for dust and smoke (see Fig. S8b). This enabled us to determine how differences in aerosol properties alone affect aerosol-induced cloud-top heating/cooling responses to a one-kilometer increase in geometric thickness and base layer altitude. For the second set of experiments, the cloud-property sensitivity experiments, our aim remained to quantify aerosol-induced changes in cloud-top cooling. To isolate the influence of cloud structure, we held the aerosol profiles and thermodynamic conditions constant across all simulations and varied only the cloud optical depth, cloud-top height, and cloud-base height according to values from the eight original categories (see Fig. S8c). This allowed us to assess how aerosol-induced cloud-top heating varies when the same aerosol conditions interact with different cloud structures, that is, how sensitive the aerosol effect is to changes in underlying cloud conditions. Similarly, for the third set of simulations, for the thermodynamic sensitivity, we fixed both aerosol and cloud properties and varied only the background temperature and humidity profiles across categories (see Fig. S8d). This enabled us to evaluate how aerosol-induced changes in cloud-top cooling are modulated by the surrounding thermodynamic environment, particularly by variations in humidity that can alter aerosol-induced changes at the low-level cloud top. Since the simulated total responses to a 1 km increase in aerosol layer geometric thickness and base height may slightly differ from those derived using CALIPSO-CloudSat fluxes, due to residue and non-linear interactions between the components, we applied a normalization procedure to quantify the relative contributions of aerosol properties, cloud properties, and background thermodynamic conditions. In addition, these differences could arise partly from assumptions used in our simulations—for example, aerosol layers were prescribed as idealized, clean dust or smoke layers with well-defined vertical structure and specified optical properties. In contrast, the observation-based estimates were derived from radiative fluxes in CALIPSO-CloudSat profiles containing only identified dust or smoke layers. However, the presence of other aerosol types in these profiles cannot be ruled out. Furthermore, the vertical structure of aerosols may extend beyond the selected layer boundaries, which may be undetected/misclassified, introducing additional complexity into our results. Thus, the CALIPSO-CloudSat-derived estimates inherently reflect more complex aerosol mixtures. Therefore, to isolate the relative influence of each contributing factor, we normalized the simulated cloud-top heating responses by the sum of combined responses from aerosol, cloud, and thermodynamic components. This normalization constrains the total response to sum to one, enabling clearer attribution of cloud-top radiative effects to individual components for both geometric thickness and layer base responses. Declarations Competing interests The authors declare no competing interests Author contributions AAA and SKP designed the study. SKP performed the analysis. SKP and AAA interpreted the results and wrote the paper Acknowledgments This study was supported by the U.S. Department of Energy (DOE), Office of Science (award #DE-SC0024281). CALIPSO data used in this study were obtained from the NASA Langley Research Center Atmospheric Science Data Center ( https://asdc.larc.nasa.gov/project/CALIPSO ), and we acknowledge the CALIPSO science team for providing these data. CloudSat-CALIPSO radiative flux data were provided by the CloudSat Data Processing Center at Colorado State University ( https://www.cloudsat.cira.colostate.edu/ ), and we thank the CloudSat team for their data processing and dissemination efforts. We acknowledge the use of the SBDART radiative transfer model, developed at the University of California, Santa Barbara, and made publicly available at https://github.com/paulricchiazzi/SBDART . Data availability Cloud and aerosol layer data used in this study were obtained from the CALIPSO Level 2 data products, including the 5 km aerosol layer product ( https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMAPRO-STANDARD-V4-20 ) and the 5 km aerosol profile product ( https://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMALAYSTANDARD-V4-20 ). CloudSat CALIPSO radiative flux data were accessed from the 2B-FLXHR-LIDAR product ( https://www.cloudsat.cira.colostate.edu/data-products/2b-flxhr-lidar ). All data supporting the findings of this study are available at Zenodo References Bender, F. A. -M. Aerosol Forcing: Still Uncertain, Still Relevant. AGU Advances 1, (2020). Bellouin, N. et al. Bounding Global Aerosol Radiative Forcing of Climate Change. Reviews of Geophysics 58, 1–45 (2020). Boucher, O., et al. Clouds and Aerosols . Climate Change 2013 – The Physical Science Basis vol. 9781107057 https://www.cambridge.org/core/product/identifier/CBO9781107415324A024/type/book_part (2013). Forster, P. et al. The Earth’s Energy Budget, Climate Feedbacks and Climate Sensitivity. in Climate Change 2021 – The Physical Science Basis (eds. Masson-Delmotte, V. et al.) vol. 17 923–1054 (Cambridge University Press, 2023). Yu, Q. & Huang, Y. Distributions and Trends of the Aerosol Direct Radiative Effect in the 21st Century: Aerosol and Environmental Contributions. Journal of Geophysical Research: Atmospheres 128, (2023). Atwater, M. A. Planetary Albedo Changes Due to Aerosols. Science (1979) 170, 64–66 (1970). Charlson, R. J. et al. Climate Forcing by Anthropogenic Aerosols. Science (1979) 255, 423–430 (1992). Haywood, J. & Boucher, O. Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: A review. Reviews of Geophysics 38, 513–543 (2000). Satheesh, S. & Krishnamoorthy, K. Radiative effects of natural aerosols: A review. Atmos Environ 39, 2089–2110 (2005). Yu, H. et al. A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmos Chem Phys 6, 613–666 (2006). Twomey, S. Pollution and the Planetary Albedo. Atmos Environ 8, 1251–1256 (1974). Twomey, S. The Influence of Pollution on the Shortwave Albedo of Clouds. J Atmos Sci 34, 1149–1152 (1977). Albrecht, B. A. Aerosols, Cloud Microphysics, and Fractional Cloudiness. Science (1979) 245, 1227–1230 (1989). Penner, J. E. et al. Aerosols, Their Direct and Indirect Effects . IPCC AR3 vol. 91 (2001). Lohmann, U. A glaciation indirect aerosol effect caused by soot aerosols. Geophys Res Lett 29, 1052 (2002). Lohmann, U. Aerosol effects on clouds and climate. Space Sci Rev 125, 129–137 (2006). Lohmann, U. & Feichter, J. Global indirect aerosol effects: a review. Atmos Chem Phys 5, 715–737 (2005). Rosenfeld, D. et al. Global observations of aerosol-cloud-precipitation- climate interactions. Reviews of Geophysics 52, 1–59 (2014). Rosenfeld, D. et al. Climate effects of aerosol-cloud interactions. Science 343, 379–80 (2014). Hansen, J., Sato, M. & Ruedy, R. Radiative forcing and climate response. Journal of Geophysical Research Atmospheres 102, 6831–6864 (1997). Ackerman, A. S. et al. Reduction of Tropical Cloudiness by Soot. Science (1979) 288, 1042–1047 (2000). Koch, D. & Del Genio, A. D. Black carbon semi-direct effects on cloud cover: Review and synthesis. Atmos Chem Phys 10, 7685–7696 (2010). Hartmann, D. L., Ockert-Bell, M. E. & Michelsen, M. L. The Effect of Cloud Type on Earth’s Energy Balance:Global Analysis. J Clim 5, 1281–1304 (1992). Hahn, C. J. & Warren, S. G. A Gridded Climatology of Clouds over Land (1971–1996) and Ocean (1954–2008) from Surface Observations Worldwide (NDP-026E)*. Preprint at https://doi.org/10.3334/CDIAC/cli.ndp026e (2007). Koren, I., Kaufman, Y. J., Remer, L. A. & Martins, J. V. Measurement of the Effect of Amazon Smoke on Inhibition of Cloud Formation. Science (1979) 303, 1342–1345 (2004). Johnson, B. T., Shine, K. P. & Forster, P. M. The semi-direct aerosol effect: Impact of absorbing aerosols on marine stratocumulus. Quarterly Journal of the Royal Meteorological Society 130, 1407–1422 (2004). Allen, R. J. et al. Observationally constrained aerosol–cloud semi-direct effects. NPJ Clim Atmos Sci 2, 1–12 (2019). Perlwitz, J. & Miller, R. L. Cloud cover increase with increasing aerosol absorptivity: A counterexample to the conventional semidirect aerosol effect. Journal of Geophysical Research Atmospheres 115, (2010). Wilcox, E. M. Stratocumulus cloud thickening beneath layers of absorbing smoke aerosol. Atmos Chem Phys 10, 11769–11777 (2010). Wilcox, E. M. Direct and semi-direct radiative forcing of smoke aerosols over clouds. Atmos Chem Phys 12, 139–149 (2012). Randles, C. A., Colarco, P. R. & Da Silva, A. Direct and semi-direct aerosol effects in the NASA GEOS-5 AGCM: Aerosol-climate interactions due to prognostic versus prescribed aerosols. Journal of Geophysical Research Atmospheres 118, 149–169 (2013). Bond, T. C. et al. Bounding the role of black carbon in the climate system: A scientific assessment. Journal of Geophysical Research: Atmospheres 118, 5380–5552 (2013). Bergstrom, R. W. et al. Spectral absorption properties of atmospheric aerosols. Atmos Chem Phys 7, 5937–5943 (2007). Li, J. et al. Scattering and absorbing aerosols in the climate system. Nat Rev Earth Environ 3, 363–379 (2022). Samset, B. H. et al. Aerosol Absorption: Progress Towards Global and Regional Constraints. Curr Clim Change Rep 4, 65–83 (2018). Sand, M. et al. Aerosol absorption in global models from AeroCom phase III. Atmos Chem Phys 21, 15929–15947 (2021). Cappa, C. et al. Absorbing Aerosols Workshop Report, January 20–21 , 2016. https://www.osti.gov/servlets/purl/1471231 / (2016) doi: 10.2172/1471231 . Santos, D., Costa, M. J., Silva, A. M. & Salgado, R. Modeling Saharan desert dust radiative effects on clouds. Atmos Res 127, 178–194 (2013). Amiri-Farahani, A., Allen, J. R., Neubauer, D. & Lohmann, U. Impact of Saharan dust on North Atlantic marine stratocumulus clouds: Importance of the semidirect effect. Atmos Chem Phys 17, 6305–6322 (2017). Amiri-Farahani, A., Allen, R. J., Li, K. F. & Chu, J. E. The Semidirect Effect of Combined Dust and Sea Salt Aerosols in a Multimodel Analysis. Geophys Res Lett 46, 10512–10521 (2019). Adebiyi, A. et al. A review of coarse mineral dust in the Earth system. Aeolian Res 60, 100849 (2023). Kok, J. F. et al. Mineral dust aerosol impacts on global climate and climate change. Nat Rev Earth Environ 4, 71–86 (2023). Pandey, S. K. & Adebiyi, A. A. Dust semi-direct effects: Dust-induced longwave radiation influences low-level cloud response to free-tropospheric dust over the North Atlantic Ocean. Preprint at https://doi.org/10.5194/egusphere-2025-2607 (2025). Nowak, J. L., Siebert, H., Szodry, K. E. & Malinowski, S. P. Coupled and decoupled stratocumulus-topped boundary layers: Turbulence properties. Atmos Chem Phys 21, 10965–10991 (2021). Zheng, Y., Rosenfeld, D. & Li, Z. Estimating the Decoupling Degree of Subtropical Marine Stratocumulus Decks From Satellite. Geophys Res Lett 45, 12,560 – 12,568 (2018). Zheng, Y., Rosenfeld, D. & Li, Z. The Relationships Between Cloud Top Radiative Cooling Rates, Surface Latent Heat Fluxes, and Cloud-Base Heights in Marine Stratocumulus. Journal of Geophysical Research: Atmospheres 123, 11,678 – 11,690 (2018). Nicholls, S. & Leighton, J. An observational study of the structure of stratiform cloud sheets: Part I. Structure. Quarterly Journal of the Royal Meteorological Society 112, 431–460 (1986). Nicholls, S. & Turton, J. D. An observational study of the structure of stratiform cloud sheets: Part II. Entrainment. Quarterly Journal of the Royal Meteorological Society 112, 461–480 (1986). Eastman, R. & Wood, R. The competing effects of stability and humidity on subtropical stratocumulus entrainment and cloud evolution from a Lagrangian perspective. J Atmos Sci 75, 2563–2578 (2018). Adebiyi, A. A., Zuidema, P. & Abel, S. J. The convolution of dynamics and moisture with the presence of shortwave absorbing aerosols over the southeast Atlantic. J Clim 28, 1997–2024 (2015). Deaconu, L. T. et al. Satellite inference of water vapour and above-cloud aerosol combined effect on radiative budget and cloud-Top processes in the southeastern Atlantic Ocean. Atmos Chem Phys 19, 11613–11634 (2019). Herbert, R. J., Bellouin, N., Highwood, E. J. & Hill, A. A. Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations. Atmos Chem Phys 20, 1317–1340 (2020). Samset, B. H. et al. Climate Impacts From a Removal of Anthropogenic Aerosol Emissions. Geophys Res Lett 45, 1020–1029 (2018). Baró Pérez, A., Devasthale, A., Bender, F. A. M. & Ekman, A. M. L. Impact of smoke and non-smoke aerosols on radiation and low-level clouds over the southeast Atlantic from co-located satellite observations. Atmos Chem Phys 21, 6053–6077 (2021). Wood, R. Stratocumulus Clouds. Mon Weather Rev 140, 2373–2423 (2012). Johnson, B. T. Large-eddy simulations of the semidirect aerosol effect in shallow cumulus regimes. Journal of Geophysical Research D: Atmospheres 110, 1–8 (2005). Johnson, B. T. The Semidirect Aerosol Effect: Comparison of a Single-Column Model with Large Eddy Simulation for Marine Stratocumulus . ; (2005). Doherty, O. M., Riemer, N. & Hameed, S. Saharan mineral dust transport into the Caribbean: Observed atmospheric controls and trends. Journal of Geophysical Research Atmospheres 113, 1–10 (2008). Kopec, M. K., Malinowski, S. P. & Piotrowski, Z. P. Effects of wind shear and radiative cooling on the stratocumulus-topped boundary layer. Quarterly Journal of the Royal Meteorological Society 142, 3222–3233 (2016). Zheng, Y., Rosenfeld, D., Zhu, Y. & Li, Z. Satellite-Based Estimation of Cloud Top Radiative Cooling Rate for Marine Stratocumulus. Geophys Res Lett 46, 4485–4494 (2019). Pittock, A. B. et al. Sources and Properties of Smoke and Dust. in Environmental Consequences of Nuclear War Volume I: Physical and Atmospheric Effects vol. 1 (John Wiley & Sons Ltd, 1986). Osborne, S. R. et al. Physical and optical properties of mineral dust aerosol during the Dust and Biomass-burning Experiment. Journal of Geophysical Research Atmospheres 113, (2008). Caponi, L. et al. Spectral- and size-resolved mass absorption efficiency of mineral dust aerosols in the shortwave spectrum: A simulation chamber study. Atmos Chem Phys 17, 7175–7191 (2017). Liao, H. & Seinfeld, J. H. Radiative forcing by mineral dust aerosols: Sensitivity to key variables. Journal of Geophysical Research Atmospheres 103, 31637–31645 (1998). Shell, K. M. & Somerville, R. C. J. Sensitivity of climate forcing and response to dust optical properties in an idealized model. Journal of Geophysical Research Atmospheres 112, (2007). Christensen, M. W., Carrió, G. G., Stephens, G. L. & Cotton, W. R. Radiative impacts of free-tropospheric clouds on the properties of marine stratocumulus. J Atmos Sci 70, 3102–3118 (2013). Godbole, R. V. On Destabilization of Clouds by Radiative Cooling. Mon Weather Rev 101, 496–500 (1973). Ogura, T., Webb, M. J. & Lock, A. P. Positive Low Cloud Feedback Primarily Caused by Increasing Longwave Radiation From the Sea Surface in Two Versions of a Climate Model. Geophys Res Lett 50, (2023). Ricchiazzi, P., Yang, S., Gautier, C. & Sowle, D. SBDART: A Research and Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth’s Atmosphere. Bull Am Meteorol Soc 79, 2101–2114 (1998). Wood, R. & Bretherton, C. S. On the relationship between stratiform low cloud cover and lower-tropospheric stability. J Clim 19, 6425–6432 (2006). Adebiyi, A. A. & Zuidema, P. Low cloud cover sensitivity to biomass-burning aerosols and meteorology over the Southeast Atlantic. J Clim 31, 4329–4346 (2018). Yamaguchi, T., Feingold, G., Kazil, J. & McComiskey, A. Stratocumulus to cumulus transition in the presence of elevated smoke layers. Geophys Res Lett 42, 10478–10485 (2015). Zhou, X., Ackerman, A. S., Fridlind, A. M., Wood, R. & Kollias, P. Impacts of solar-absorbing aerosol layers on the transition of stratocumulus to trade cumulus clouds. Atmos Chem Phys 17, 12725–12742 (2017). Diamond, M. S. et al. Cloud adjustments from large-scale smoke-circulation interactions strongly modulate the southeastern Atlantic stratocumulus-to-cumulus transition. Atmos Chem Phys 22, 12113–12151 (2022). Sakaeda, N., Wood, R. & Rasch, P. J. Direct and semidirect aerosol effects of southern African biomass burning aerosol. Journal of Geophysical Research Atmospheres 116, 1–19 (2011). Meyer, K., Platnick, S., Oreopoulos, L. & Lee, D. Estimating the direct radiative effect of absorbing aerosols overlying marine boundary layer clouds in the southeast Atlantic using MODIS and CALIOP. 118, 4801–4815 (2013). Mallet, M. et al. Direct and semi-direct radiative forcing of biomass-burning aerosols over the southeast Atlantic (SEA) and its sensitivity to absorbing properties: a regional climate modeling study. Atmos Chem Phys 20, 13191–13216 (2020). DeFlorio, M. J. et al. Semidirect dynamical and radiative effect of North African dust transport on lower tropospheric clouds over the subtropical North Atlantic in CESM 1.0. Journal of Geophysical Research: Atmospheres 119, 8284–8303 (2014). Persad, G. G., Paynter, D. J., Ming, Y. & Ramaswamy, V. Competing atmospheric and surface-driven impacts of absorbing aerosols on the East Asian summertime climate. J Clim 30, 8929–8949 (2017). Feingold, G., Jiang, H. & Harrington, J. Y. On smoke suppression of clouds in Amazonia. Geophys Res Lett 32, 1–4 (2005). Huang, X. et al. Effects of aerosol-radiation interaction on precipitation during biomass-burning season in East China. Atmos Chem Phys 16, 10063–10082 (2016). Liu, L. et al. Impact of biomass burning aerosols on radiation, clouds, and precipitation over the Amazon during the dry season: dependence of aerosol-cloud and aerosol-radiation interactions on aerosol loading. Atmos Chem Phys 1–50 (2020) doi: 10.5194/acp-2020-191 . Kok, J. F. et al. Contribution of the world’s main dust source regions to the global cycle of desert dust. Atmos Chem Phys 21, 8169–8193 (2021). Prospero, J. M., Delany, A. C., Delany, A. C. & Carlson, T. N. The discovery of african dust transport to the western hemisphere and the Saharan air layer: A history. Bull Am Meteorol Soc 102, E1239–E1260 (2021). Adebiyi, A. A. et al. Aerosols over Africa: sources, properties, and distribution. in Aerosols and Precipitation Over Africa 1–64 (Elsevier, 2025). doi: 10.1016/B978-0-44-314050-1.00006-2 . Das, S. et al. Biomass burning aerosol transport and vertical distribution over the South African-Atlantic region. J Geophys Res 122, 6391–6415 (2017). Zuidema, P. et al. Smoke and clouds above the southeast Atlantic: Upcoming field campaigns probe absorbing aerosol’s impact on climate. Bulletin of the American Meteorological Society vol. 97 1131–1135 Preprint at https://doi.org/10.1175/BAMS-D-15-00082.1 (2016). Adebiyi, A. A. & Zuidema, P. The role of the southern African easterly jet in modifying the southeast Atlantic aerosol and cloud environments. Quarterly Journal of the Royal Meteorological Society 142, 1574–1589 (2016). Winker, D. M. et al. The CALIPSO Mission. Bulletin of American Meteorological Society 91, 1211–1230 (2010). Henderson, D. S., L’ecuyer, T., Stephens, G., Partain, P. & Sekiguchi, M. A multisensor perspective on the radiative impacts of clouds and aerosols. J Appl Meteorol Climatol 52, 853–871 (2013). Kim, M. H. et al. The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm. Atmos Meas Tech 11, 6107–6135 (2018). Omar, A. H. et al. The CALIPSO automated aerosol classification and lidar ratio selection algorithm. J Atmos Ocean Technol 26, 1994–2014 (2009). Vaughan, M. A. et al. Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products. in (ed. Singh, U. N.) 16 (2004). doi: 10.1117/12.572024 . Vaughan, M. A. et al. Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements. J Atmos Ocean Technol 26, 2034–2050 (2009). Jethva, H., Torres, O., Remer, L. A. & Bhartia, P. K. A color ratio method for simultaneous retrieval of aerosol and cloud optical thickness of above-cloud absorbing aerosols from passive sensors: Application to MODIS measurements. IEEE Transactions on Geoscience and Remote Sensing 51, 3862–3870 (2013). K. Gatebe, C., Jethva, H., Gautam, R., Poudyal, R. & Varnai, T. A new measurement approach for validating satellite-based above-cloud aerosol optical depth. Atmos Meas Tech 14, 1405–1423 (2021). Kacenelenbogen, M. et al. An evaluation of CALIOP/CALIPSO’s aerosol-above-cloud detection and retrieval capability over North America. J Geophys Res 119, 230–244 (2014). Painemal, D. et al. Novel aerosol extinction coefficients and lidar ratios over the ocean from CALIPSO-CloudSat: Evaluation and global statistics. Atmos Meas Tech 12, 2201–2217 (2019). Rajapakshe, C. et al. Seasonally transported aerosol layers over southeast Atlantic are closer to underlying clouds than previously reported. Geophys Res Lett 44, 5818–5825 (2017). Rogers, R. R. et al. Looking through the haze: Evaluating the CALIPSO level 2 aerosol optical depth using airborne high spectral resolution lidar data. Atmos Meas Tech 7, 4317–4340 (2014). Mace, G. G. & Zhang, Q. The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results. J Geophys Res 119, 9441–9462 (2014). Sassen, K. & Wang, Z. Classifying clouds around the globe with the CloudSat radar: 1-year of results. Geophys Res Lett 35, (2008). Loeb, N. G. & Schuster, G. L. An observational study of the relationship between cloud, aerosol and meteorology in broken low-level cloud conditions. Journal of Geophysical Research Atmospheres 113, 1–9 (2008). Mauger, G. S. & Norris, J. R. Assessing the Impact of Meteorological History on Subtropical Cloud Fraction. J Clim 23, 2926–2940 (2010). Scott, R. C. et al. Observed sensitivity of low-cloud radiative effects to meteorological perturbations over the global oceans. J Clim 33, 7717–7734 (2020). Hunt, W. H. et al. CALIPSO Lidar Description and Performance Assessment. J Atmos Ocean Technol 26, 1214–1228 (2009). Lu, X. et al. Satellite retrieval of cloud base height and geometric thickness of low-level cloud based on CALIPSO. Atmos Chem Phys 21, 11979–12003 (2021). Vaughan, M. A., Winker, D. M. & Hostetler, C. A. SIBYL: a selective iterated boundary location algorithm for finding cloud and aerosol layers in CALIPSO lidar data. 5–8 Preprint at (2003). Tackett, J. L. et al. CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design. Atmos Meas Tech 11, 4129–4152 (2018). L’Ecuyer, T. S., Wood, N. B., Haladay, T., Stephens, G. L. & Stackhouse, P. W. Impact of clouds on atmospheric heating based on the R04 CloudSat fluxes and heating rates data set. J Geophys Res 113, 1–15 (2008). d’Almeida, G. A., Koepke, P. & Shettle, E. P. Atmospheric Aerosols: Global Climatology and Radiative Characteristics . (Hampton, Va., USA SE, 1991). Henderson, D. & L’Ecuyer, T. CloudSat Project A NASA Earth System Science Pathfinder Mission Level 2B Fluxes and Heating Rates with Lidar [2B-FLXHR-LIDAR] Process Description and Interface Control Doc-Ument . (2023) doi: https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2b-flxhr-lidar/2B-FLXHR-LIDAR_PDICD.P2_R05.rev0.pdf . Cronk, H. CloudSat ECMWF-AUX Auxillary Data Product Process Description and Interface Control Document . https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/ecmwf-aux/ECMWF-AUX.PDICD.P1_R05.rev0.pdf (2017) doi: https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/ecmwf-aux/ECMWF-AUX.PDICD.P1_R05.rev0.pdf . Di Biagio, C. et al. Complex refractive indices and single scattering albedo of global dust aerosols in the shortwave spectrum and relationship to iron content and size. Atmos Chem Phys 19, 15503–15531 (2019). Adebiyi, A. A. et al. Dust Constraints from joint Observational-Modelling-experiMental analysis (DustCOMM): Comparison with measurements and model simulations. Atmos Chem Phys 20, 829–863 (2020). Formenti, P. & Di Biagio, C. Large synthesis of in situ field measurements of the size distribution of mineral dust aerosols across their life cycles. Earth Syst Sci Data 16, 4995–5007 (2024). Cochrane, S. P. et al. Empirically derived parameterizations of the direct aerosol radiative effect based on ORACLES aircraft observations. Atmos Meas Tech 14, 567–593 (2021). Taylor, J. P. Measurements of the radiative and microphysical properties of stratocumulus over the South Atlantic and around the British Isles. Atmos Res 34, 27–41 (1994). Pawlowska, H., Brenguier, J.-L. & Schüller, L. Microphysical and radiative properties of stratocumulus. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 24, 927–932 (1999). O’Hirok, W. & Gautier, C. A Three-Dimensional Radiative Transfer Model to Investigate the Solar Radiation within a Cloudy Atmosphere. Part I: Spatial Effects. J Atmos Sci 55, 2162–2179 (1998). Chen, Y., Chong, K. & Fu, Y. Impacts of distribution patterns of cloud optical depth on the calculation of radiative forcing. Atmos Res 218, 70–77 (2019). Additional Declarations There is NO Competing Interest. 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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-7168935","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":491800631,"identity":"2aa092a9-2eab-40d8-a12b-0fe51d46bf8f","order_by":0,"name":"Satyendra Pandey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYLACHgYGOX447wADgwQxWowlG0jVkrjhALFa5Gc3P3vwpuJw4uYbyU83fNxjJ8d3gPngbR48WgzuHDM3nHPmsPG2G2lmN2c8SzaWPMCWbI1Xi0SCmTRv22HZbTdy2G7zHDgAdCGPmTQ+LfIz0r9J8/47zLh5BlDLnwMH6jcc4P+GVwvDjRygLQ2HFTdIALUwHDiQYHCAhw2vFoM7Z8ok5xxLN5Y488zsZs+BZMOZh9mMLefgc9js9m0Sb2qs5fjbk5/d+HHATp7vePPDG2/wOQwSBc1IIsz4lCO01BFSNgpGwSgYBSMZAADvwVTwjB7BcwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0604-2183","institution":"University of California Merced","correspondingAuthor":true,"prefix":"","firstName":"Satyendra","middleName":"","lastName":"Pandey","suffix":""},{"id":491800632,"identity":"13343967-1503-4711-b4e7-71f134f41f51","order_by":1,"name":"Adeyemi Adebiyi","email":"","orcid":"https://orcid.org/0000-0001-7091-6872","institution":"University of California Merced","correspondingAuthor":false,"prefix":"","firstName":"Adeyemi","middleName":"","lastName":"Adebiyi","suffix":""}],"badges":[],"createdAt":"2025-07-20 10:05:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7168935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7168935/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-026-03183-x","type":"published","date":"2026-01-16T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87908175,"identity":"6bd447d9-5277-45a1-8b06-6d51cf02211f","added_by":"auto","created_at":"2025-07-30 09:20:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial pattern of low-level clouds and overlying dust and smoke aerosols.\u003c/strong\u003eLow-level cloud cover (blue shaded contours) is overlaid with dust (solid orange) and smoke (dotted black) optical depths. The yellow boxes mark the analysis domain, divided into a 5°×5° grid to minimize meteorological variability (see Methods). Cloud data are from combined Aqua and Terra MODIS products, while aerosol optical depths are from CALIPSO Level 3 data, averaged over 2007–2017. Dust optical depth is averaged for May to July, and separately, smoke optical depth is for July to October, when both aerosols are maximum above the low-level clouds (see Fig. S1).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7168935/v1/7d67ef7920e5767d14225734.jpg"},{"id":87908177,"identity":"931ea22d-63a0-46d5-9f49-14cf26210797","added_by":"auto","created_at":"2025-07-30 09:20:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":208698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfluence of above-cloud absorbing aerosols on radiative heating.\u003c/strong\u003e \u0026nbsp;Domain mean radiative heating rates and changes in thermodynamic profiles associated with above-cloud dust (top panels; a-d) and smoke (bottom panels: e-h; see yellow Box in Fig. 1 for the domain region). The mean all-sky (a \u0026amp; e) and aerosol-induced (b \u0026amp; f) radiative heating rate profiles over the northeast Atlantic (dust; a \u0026amp;b) and southeast Atlantic (smoke; e \u0026amp;f), respectively. Panels (c) and (g) summarize the maximum heating rates within the aerosol (orange and grey shaded layers in a and e) and cloud layers. Solid bars indicate aerosol-induced heating, and hatched bars denote all-sky heating. Colors represent shortwave (red), longwave (blue), and net (=shortwave+longwave, black) contributions; error bars show the standard deviation. Panels (d) and (h) show mean changes in temperature (ΔT, °C; maroon) and relative humidity (ΔRH, %; cyan), calculated as the difference between profiles when dust (d) or smoke (h) layers overlie low-level clouds and all profiles containing low-level clouds, regardless of whether an overlying aerosol layer is present. The temperature and humidity data are obtained from the auxiliary product provided with CALIPSO Level 2 (L2) profiles.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7168935/v1/e5cd9eaf6883b691c5aa975e.jpg"},{"id":87908180,"identity":"293d04f3-acb5-46bf-ab8f-b387127a082b","added_by":"auto","created_at":"2025-07-30 09:20:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResponse of cloud-top radiative heating to aerosol-layer properties and characteristics.\u003c/strong\u003ePanels a-d show dust/smoke induced radiative heating in response to a 1 km increase in aerosol-layer geometric thickness and base height, within the aerosol layer (a, b) and at the cloud top (c, d), for dust (a, c) and smoke (b, d). Bar colours indicate shortwave (red), longwave (blue), and net (grey) radiative heating. Panels (e) and (f) show variability in aerosol-induced cloud-top heating for a one-standard-deviation (1σ) increase in optical depth, geometric thickness, and layer-base height for dust (e) and smoke (f); note the difference in scale. Circles denote the mean response values. Error bars indicate the standard error for the corresponding parameters in each panel. The 1σ values are 0.25 (optical depth), 0.77 km (geometric thickness), and 0.66 km (layer-base height) for dust, and 0.13, 0.51 km, and 0.64 km, respectively, for smoke.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7168935/v1/a4453f9ad84169c9d9c26cc6.jpg"},{"id":87908179,"identity":"ad3b62f6-bb0f-43c9-a4f2-79b8a35ed395","added_by":"auto","created_at":"2025-07-30 09:20:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124910,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative Influence of aerosols, clouds, and thermodynamics on aerosol-induced radiative changes in cloud-top cooling. \u003c/strong\u003ePanels (a–b) show the normalized cloud-top heating response to variations in aerosol layer characteristics (orange/grey), cloud properties (cyan), and thermodynamic profiles (blue) for dust (a) and smoke (b), computed per one-kilometre increase in aerosol layer geometric thickness and base height, normalized by sum of their individual response (see Fig S5). Panels (c–d) show the changes in cloud fraction associated with one-standard-deviation increases in aerosol optical depth, geometric thickness, and base height. Circles denote mean responses, and error bars indicate the standard error across cases.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7168935/v1/91cc130b1dad12c15eb693a7.jpg"},{"id":102574683,"identity":"1e426b95-1e96-4cbb-b7f3-98d3b88d4248","added_by":"auto","created_at":"2026-02-13 08:05:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1756239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7168935/v1/8e7f7119-b829-41cc-97d9-8f33b915a848.pdf"},{"id":87908178,"identity":"78acbf28-946c-419b-9069-17624b28eb9c","added_by":"auto","created_at":"2025-07-30 09:20:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2162405,"visible":true,"origin":"","legend":"Supplementary_Figures_Tables","description":"","filename":"SuppDstSmkFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7168935/v1/c41d7b152da81aeb42bd7ef5.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Dust and smoke layers over the Atlantic Ocean weaken the underlying low-level cloud-top radiative cooling through different pathways","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtmospheric aerosols remain one of the largest sources of uncertainty in present-day climate-change estimates because of the challenges in understanding their complex interactions with several aspects of the Earth system \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Among others, these interactions include those with radiation, clouds, and meteorology. Specifically, aerosols can directly scatter and absorb solar and terrestrial radiation, impacting Earth\u0026rsquo;s energy balance and climate, in the so-called aerosol-radiation interactions or direct aerosol effect \u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Aerosols can also serve as cloud condensation nuclei and ice nuclei, altering cloud properties, such as droplet size, reflectivity, and lifetime, in the so-called aerosol-cloud interactions or indirect aerosol effect \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Beyond these direct and indirect aerosol effects, absorbing aerosols can also modulate clouds by altering the local meteorology and the temperature and humidity structures of the atmosphere, thereby adjusting the effective radiative effects that are due to aerosol-radiation and aerosol-cloud interactions at the top of the atmosphere, in what is known as the semi-direct effect \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Among the uncertainties associated with aerosol-radiation and aerosol-cloud interactions, aerosol semi-direct effects contribute a substantial fraction, resulting in a low confidence designation by past reports from the Intergovernmental Panel on Climate Change \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Despite the contribution to uncertainties in climate-change estimates, and our improved understanding of adjustments to radiative effects in recent years \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, a detailed understanding of the processes associated with aerosol semi-direct effects, particularly one that accounts for the variabilities in the properties among different absorbing aerosol species, remains largely unclear.\u003c/p\u003e\u003cp\u003eThe process by which the aerosol semi-direct effect influences the climate depends on factors such as cloud types, the aerosol layer characteristics, including its altitude relative to clouds, and the aerosol absorption properties \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. While the aerosol semi-direct effect is relevant for all cloud types, as aerosol-driven changes in temperature and moisture profiles can modulate the properties of any cloud type, most past studies seeking a process-level understanding of aerosol semi-direct effects have largely focused on low-level marine clouds \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This is due to the widespread presence, persistence, and high albedo of these low-level clouds, which make them critical to Earth\u0026rsquo;s energy balance \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Specifically, these low-level clouds account for about 40 % of ll global cloud occurrences, with single-layer low-level clouds covering about one-fifth of the global ocean \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For such low-level clouds, previous studies have shown that absorbing aerosols embedded within the cloud layer can absorb sunlight, warm the cloud layer, and reduce the relative humidity, leading to cloud evaporation and cloud burn-off \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. As cloud cover decreases, more sunlight reaches the Earth\u0026rsquo;s surface, amplifying warming and resulting in a positive semi-direct effect\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In contrast, when an absorbing aerosol layer is located above the clouds, the solar absorption by the aerosol layer can result in a warming effect that can stabilize the atmosphere and increase cloud cover and thickness, consequently leading to a negative semi-direct effect \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. While the relative altitude of the aerosol layer matters for the sign of aerosol semi-direct effect, the strength of its impacts on low-level clouds and consequently on climate depends on the aerosol absorption properties \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAmong other aerosol species, black carbon (or generally smoke aerosols) and mineral dust aerosols account for a substantial fraction of solar radiation absorbed by aerosols in the atmosphere \u003csup\u003e\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Specifically, these two aerosol species account for a combined 91% (about 60% and 31% for black carbon and dust aerosols, respectively) of the shortwave radiation absorbed in the atmosphere, compared to 11% by organic aerosols \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Because of black carbon\u0026rsquo;s dominant role in shortwave absorption, most previous studies have focused considerable attention on black carbon, and this attention has shaped our general understanding of the processes associated with the aerosol semi-direct effect \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In addition, previous studies have applied this dominant narrative about processes associated with black carbon\u0026rsquo;s semi-direct effect to explain similar effects by other absorbing aerosols, such as mineral dust \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, smoke and dust aerosols are uniquely different, and one main distinction is in the spectral absorption; whereas smoke aerosols primarily absorbs shortwave radiation because they are mostly submicron particles (i.e diameter\u0026thinsp;\u0026le;\u0026thinsp;1 \u0026micro;m), mineral dust aerosols can absorb both shortwave and longwave radiation, because their size distribution includes larger particles up to about 60 \u0026micro;m in the atmosphere \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Therefore, while some aspects of the processes associated with smoke aerosols may apply to dust semi-direct effects, such as those related to shortwave absorption, others are expected to be different, particularly when the influence of longwave absorption and scattering is considered \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOne area where the differences in the process associated with smoke and dust semi-direct effects can be explored is at the low-level cloud top. This is because low-level cloud developments are primarily driven by characteristics, such as cloud-top radiative cooling, entrainment, and lower tropospheric stability, at or near the cloud tops that influence the coupling of cloud-topped boundary layers with the surface \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. For example, an increase in cloud-top radiative cooling has been shown to destabilize the boundary layer, driving more intense convective circulation that substantially alters the clouds and their radiative properties \u003csup\u003e\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. As such, when aerosols are present above these clouds, variations in the properties and characteristics of the aerosol layers can have a substantial impact on the cloud-top radiative cooling and cloud cover, which in turn influence the estimate of aerosol semi-direct effect \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. For example, previous studies have shown that shortwave absorption within the above-cloud aerosol layer increases the lower-tropospheric stability immediately above the low-level clouds, which indirectly strengthens its cloud-top radiative cooling \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In contrast, previous studies have also shown that enhanced relative humidity within the above-cloud aerosol layer can increase downwelling longwave radiation at the cloud top, thereby reducing low-level cloud-top cooling and cloud cover \u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Other aerosol-layer characteristics, such as the aerosol vertical extent and the gap between aerosol layers and cloud layers, have been studied less extensively; however, some studies have shown them to be factors that influence processes at the low-level cloud top, and consequently cloud responses and semi-direct effects \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. While all these processes are likely occurring simultaneously when aerosols are present above clouds, it remains unclear which properties or characteristics of the elevated aerosol layer dominate the low-level cloud changes, or whether a dominant process used to explain the relationship with smoke aerosols can be similarly applied to other absorbing aerosols, such as mineral dust.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo address this gap, we leverage the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations)-CloudSat combined product to estimate aerosol-induced changes in cloud-top radiative cooling for low-level marine clouds. Specifically, we compare how dust and smoke aerosol-layer properties and characteristics, including aerosol optical depth, geometric thickness, base altitude, and aerosol-layer humidity, influence cloud-top radiative cooling, explore the resulting cloud responses, and discuss how these responses may affect the overall estimate of aerosol semidirect effects. We focus on the Atlantic Ocean, where elevated dust and smoke aerosols above low-level clouds are abundant, specifically over the North and South Atlantic Oceans (see yellow boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We find that these elevated absorbing aerosol layers suppress cloud-top cooling through increased induced longwave warming near the low-level cloud top, which is primarily driven by longwave-interacting coarser dust particles over the North Atlantic Ocean, and enhanced smoke-layer humidity over the South Atlantic Ocean.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe seek to understand how the properties and characteristics of the above-cloud dust and smoke layers over the northeast and southeast Atlantic Ocean, respectively, including their altitude, vertical extent, and the extinction of the aerosol layers, influence the underlying low-level cloud-top radiative cooling, and consequently the aerosol semi-direct effect. To do so, we obtained ten years (2007–2017) of satellite-based aerosol and cloud observations, where the identified aerosol layer is well separated from the top of the underlying single-layer low-level clouds by at least 200 m (see Methods). Such layers are assumed to have minimal microphysical influence on the underlying clouds and primarily exert radiative effects \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Because the period of the year when elevated aerosol layers occur above low-level clouds differs between northern dust and southern smoke aerosols, we used observations of the months when they are at their maximum over each basin, which are from May to August for dust and from July to October for smoke (see Methods). Below, we provide details of our results, presenting the heating rate profiles and discussing how the characteristics and properties of the aerosol layers affect cloud-top radiative cooling. We also present the sensitivity of cloud-top radiative cooling to the isolating influences of the aerosol properties and characteristics, cloud properties, and thermodynamic profiles, including the aerosol-layer humidity profiles. Finally, we discuss the observed cloud response to aerosol-layer characteristics and the potential implications for understanding the semidirect effects of dust and smoke aerosols.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWeakening of cloud-top radiative cooling by above-cloud dust and smoke\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe find that the presence of the above-cloud dust layer significantly weakens the low-level cloud-top radiative cooling over the Northeast Atlantic Ocean, whereas the above-cloud smoke layer has a lesser influence on the low-level cloud-top radiative cooling over the Southeast Atlantic. To understand the difference in how the dust and smoke layers affect cloud-top cooling, we examine the heating or cooling rates due to the aerosol layers by using the difference of all-sky fluxes with and without aerosols (see Method). First, the all-sky heating rates, including the influence of clouds, background thermodynamic profiles, and other atmospheric constituents such as ozone, remain representative of the heating profiles when aerosols are above a cloud layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026amp; e). Specifically, dust and smoke absorb shortwave radiation, contributing to the all-sky warming within the aerosol layers (between 2.5 and 6 km in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026amp; e), whereas the low-level cloud tops experience strong longwave cooling that dominates the net radiative cooling at the top of the boundary layer (between 0.5 and 2 km in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026amp; e). Second, when the influence of the aerosol layers is considered explicitly, our result shows that the above-cloud dust layer over the Northeast Atlantic Ocean induces substantially stronger longwave cooling but comparable shortwave warming to one induced by the above-cloud smoke layer over the southeast Atlantic (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb \u0026amp; f, and top parts of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec \u0026amp; g). For example, dust-induced longwave cooling at the peak of the dust layer (approximately 3.84 km) is about − 0.10 ± 0.03 K day\u003csup\u003e− 1\u003c/sup\u003e(blue line in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and blue bar Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), which is about 500% stronger than the smoke-induced longwave cooling at the peak of the smoke layer (approximately 3.35 km), which is largely negligible within the error bound (–0.02 ± 0.01 K day\u003csup\u003e− 1\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg solid blue bar). This dust-induced longwave cooling counteracts the dust-induced shortwave warming within the dust layer, and contributes to reducing the net radiative heating, at a peak dust level, of 0.46 ± 0.24 K day\u003csup\u003e− 1\u003c/sup\u003e (with about 0.56 ± 0.25 K day\u003csup\u003e− 1\u003c/sup\u003e contributed by shortwave; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Overall, the dust-induced net heating accounts for approximately 51% of the overall domain-mean all-sky net radiative heating with a magnitude of 0.90 ± 0.43 K day\u003csup\u003e− 1\u003c/sup\u003e (shortwave: 3.10 ± 0.59 K day\u003csup\u003e− 1\u003c/sup\u003e and longwave: − 2.21 ± 0.38 K day\u003csup\u003e− 1\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) at the peak of the dust layer. In contrast to the dust layer, the smoke-induced shortwave warming has negligible opposition from the longwave cooling, accounting for almost all the net heating rate within the smoke layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg), and about 76% of the domain-mean all-sky net radiative heating with a magnitude of 1.10 ± 0.55 K day⁻¹ (shortwave: 3.71 ± 0.74 K day⁻¹ and longwave: − 2.15 ± 0.37 K day⁻¹; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg).\u003c/p\u003e\u003cp\u003eIn addition, our result shows that the dust layer induces anomalous longwave warming at or near the low-level cloud top that is mostly absent from the low-level cloud top heating induced by the smoke layer (compare Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). Typically, because of the high liquid water content, all-sky longwave cooling often dominates the all-sky shortwave warming, resulting in net cooling at the cloud top (see blue hatched bar in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec \u0026amp; g). The maximum all-sky net cooling over the northeast Atlantic region occurs at an altitude of 1.68 km, with a magnitude of − 2.02 ± 1.78 K day⁻¹ (see black lines in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; shortwave: 3.05 ± 1.0 K day⁻¹, longwave − 5.08 ± 2.6 K day⁻¹), while over the southeast Atlantic, the maximum occurs at 1.43 km with stronger net cooling reaches − 4.21 ± 2.08 K day⁻¹ (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee; shortwave: 2.95 ± 1.06 K day⁻¹; − 7.6 ± 2.8 K day⁻¹). Aerosol-induced changes near the cloud top reveal notable contrasts between dust and smoke, particularly in the longwave spectrum. We find that dust induces a net warming of 0.34 ± 0.18 K day⁻¹ at maximum radiative cooling level 1.68 km, which is due primarily to the dust-induced longwave warming from downwelling radiation, and accounts for approximately 16% reduction in the mean cloud-top cooling at this altitude. While this reduction at the 1.68 km is notable, the maximum level of dust-induced longwave warming in the boundary layer approximately 0.72 km, with 0.30 K day\u003csup\u003e− 1\u003c/sup\u003e warming that accounts for 12% of the mean all-sky longwave cooling at that level. In contrast, smoke-induced warming is almost negligible, with only 0.025 ± 0.018 K day⁻¹, resulting in almost no changes in the low-level cloud top cooling.\u003c/p\u003e\u003cp\u003eOverall, our result suggests that dust layers have a substantially stronger weakening influence on the mean cloud top radiative cooling than the smoke layer. Although the all-sky mean cloud-top radiative cooling is stronger over the southeast Atlantic (compare hatched blue bars in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg) with generally more stratocumulus-dominated low-level cloud regimes than the northeast Atlantic, the modulation of cloud-top radiative cooling is more substantial over the northeast Atlantic \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. For the same aerosol and environmental conditions, our results thus suggest that overlying dust contributes to higher variabilities of low-level cloud top radiative cooling over the northeast Atlantic region (about an order of magnitude more) than smoke above clouds over the southeast Atlantic region. In addition, we note here that the all-sky longwave cooling within the smoke layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) is primarily due to the presence of moisture, which often co-occurs with the aerosol transport over the southeast Atlantic low-level clouds\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh, and details in section 2.3 below). Such presence of moisture within the smoke layer can have a similar weakening effect, inducing longwave warming that reduces the mean radiative cooling at cloud top\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In contrast to the weakening effect of above-cloud dust and moisture, the shortwave warming induced by dust and smoke layers can also indirectly influence the cloud-top radiative cooling \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. For example, previous studies have suggested that aerosol-induced shortwave warming enhances lower tropospheric stability \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, which indirectly strengthens cloud-top radiative cooling by building up moisture in the boundary layer \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Ultimately, the above-cloud dust and smoke (and any accompanying moisture) can influence cloud-top radiative cooling through different pathways, with opposing effects in shortwave and longwave spectra, that may also depend on the layer characteristics, such as layer base altitude, and geometrical and optical thickness of the layer.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRole of aerosol-layer properties and characteristics in modifying cloud-top radiative cooling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSince the impacts of aerosol-layer longwave radiation depend on the characteristics of the layer, we examine how its altitude and geometric thickness affect the underlying low-level clouds through changes to cloud-top radiative cooling. We find that the weakening effect of the low-level cloud-top radiative cooling by the above-cloud aerosols depends sensitively on the aerosol-layer altitude relative to the cloud and the geometric thickness of the aerosol layer. To understand this, we first categorized profiles within 5x5 degree grid-box (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) based on groups of aerosol layer geometric thickness (representing the vertical extent) and base altitude (representing the separating distance from cloud top) (see Fig. S2) and require that each category contains a minimum of 30 profiles from at least six grid boxes within each basin (see Method for details). Only eight groups of geometric thickness and base altitude satisfy our stringent requirement for both dust and smoke cases (see Fig. S2), which allows us to estimate cloud-top heating responses as the change in cloud-top heating rate per one kilometer change in one aerosol-layer characteristic (i.e., geometric thickness and base altitude) while keeping the other relatively fixed.\u003c/p\u003e\u003cp\u003eWe find that aerosol-induced heating within the dust and smoke layers increases as the layer becomes geometrically thicker, primarily due to increased shortwave absorption (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. S3). Smoke layers exhibit stronger heating for a given geometric thickness than dust layers. This is because smoke particles have a higher mass extinction efficiency per unit mass than dust particles at the peak visible wavelength \u003csup\u003e\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e–\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, and because the smoke layer is associated with lower longwave cooling (see Fig. S3), resulting in larger overall net heating for smoke than dust. Consequently, for one kilometer increase in the geometrical thickness, the net heating within the smoke layer increases by 2.4 K day\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e, higher than the same increase in the dust layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; b; 4.9±0.7K day\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e for smoke and 2.5±0.1 K day\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e for dust). It is worth noting that the aerosol-induced longwave cooling per unit increase in the geometrical thickness is higher for dust than for the smoke layer, because a geometrically thicker dust layer extends to colder altitudes, allowing dust to absorb and emit more longwave radiation due to its dust-top temperature difference with the surface \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. In contrast to the changes in the geometric thickness that are consistent for dust and smoke layers, albeit with different magnitudes, a one-kilometer increase in the base altitude of the aerosol layers affects the net heating differently. Raising the base height by one kilometer leads to enhanced warming within the dust layer, but reduced warming within the smoke layer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; b). While the general increases in radiative heating responses are largely due to the corresponding increases in aerosol extinction within the dust and smoke layers – that is, geometrically thicker and high-altitude aerosol layers are often optically thicker (e.g., Fig. S4) – the reduced warming of the smoke layer can be associated with reduced reabsorption of shortwave radiation reflected from the top of the low-level clouds as the smoke base altitude increases\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese radiative heating responses within the aerosol layer also influence the aerosol-induced changes at the low-level cloud top, as a function of aerosol-layer geometric thickness and base-layer altitude, with more substantial influence from dust than smoke layers. At the cloud top, the dust-induced heating is mainly driven by longwave effects that can reach up to a warming of 1.1 K day\u003csup\u003e− 1\u003c/sup\u003e (see Fig. S3). Dust-induced cloud-top radiative heating increases with the geometrical thickness, resulting in a heating response of approximately 0.46 (± 0.04) K day\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e for each kilometer increase in the dust-layer geometric thickness (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Although smaller, the cloud-top heating response also increases by about 0.18 (± 0.03) K day\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e for each kilometer increase in the dust-layer base altitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In contrast, the cloud-top heating response to smoke is substantially weaker by about an order of magnitude for the same change in dust-layer geometric thickness and base altitude as dust (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Specifically, the smoke-induced cloud-top radiative heating response is about 0.01 (± 0.01) K day\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e per unit increase in geometric thickness and a negative ( -0.03 ± 0.02 K day\u003csup\u003e− 1\u003c/sup\u003e km\u003csup\u003e− 1\u003c/sup\u003e) response per unit increase in base-layer altitude. Additionally, it is worth noting that an increase in smoke-layer base altitude also corresponds to a reduction in cloud-top heating response, although with small values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Unlike the dust layer, that have a considerable impact on cloud-top, our result suggests that the farther the smoke layer is from the cloud top, the weaker its impact on cloud-top radiative cooling (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe observed cloud-top heating response to changes in aerosol-layer geometrical thickness and base height may be confounded by variations in aerosol optical depth across the different categories, given the increase in aerosol optical depth with geometrical thickness of the dust and smoke layer (see Fig. S4). Therefore, to compare the cloud-top heating response to aerosol optical depth with the response to geometric thickness and base-layer altitude, we estimate the cloud-top heating response per one standard deviation change in each of the three parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee \u0026amp; f). This approach allows for a comparison while accounting for variability in each parameter. Our results highlight the dominant role of optical depth compared to geometric thickness and layer base altitude. One-standard-deviation increase in dust optical depth leads to a warming of 0.52 ± 0.01 K day-1 at the cloud-top, much larger than the 0.03 ± 0.02 K day-1 warming for the same increase in smoke optical depth (first column of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef; note the differences in scale, which is still consistent with one order magnitude difference of impacts between dust and smoke). Other layer properties, such as geometric thickness and base altitude, have a comparatively smaller influence on cloud-top heating when compared to the aerosol optical depth, but with similar orders of magnitude differences between dust and smoke. For dust layers, an increase of one standard deviation in geometric thickness results in a warming of 0.05 ± 0.03 K day¹, and a similar increase in base altitude results in a change of 0.03 ± 0.03 K day¹. For smoke layers, the corresponding changes are smaller: 0.01 ± 0.01 K day¹ for geometric thickness and − 0.03 ± 0.02 K day¹ for base altitude. Notably, an increase in smoke layer base altitude leads to a slight cooling effect, though this effect is very small and shows considerable variability (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed \u0026amp; \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Overall, our results indicate that weakening of cloud-top radiative cooling is primarily driven by the increased aerosol optical properties more than increases in aerosol-layer characteristics (such as the geometric thickness and base altitude), and that the dust layer overlying low-level clouds weakens cloud-top cooling by about an order of magnitude more strongly than smoke-induced changes in cloud-top radiative cooling.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRole of thermodynamic profile and cloud properties in modifying cloud-top radiative cooling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAlthough variabilities of above-cloud aerosol properties dominate the changes in low-level cloud top radiative cooling, when compared to aerosol-layer characteristics, other environmental factors may also confound these changes in a way that similarly impacts the cloud-top radiative cooling. For example, variations in aerosol-layer humidity correlate with aerosol optical depth in both dust and smoke layers (Fig. S4), which could enhance downwelling longwave radiation, potentially weakening low-level cloud-top radiative cooling \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Furthermore, changes in cloud properties, such as cloud optical depth, due to initial changes in cloud-top radiative cooling may destabilize the cloud layer, further weakening the cloud-top radiative cooling in a feedback process \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Therefore, to isolate these effects, we investigate the sensitivity of cloud-top radiative cooling to aerosol-layer properties and characteristics (aerosol-layer optical depth, geometric thickness, and base altitude), separate from the influence of variations in cloud properties (defined by the cloud optical depth and cloud top height) and thermodynamic profiles (defined by the temperature and humidity profiles), using a radiative transfer model, called Santa Barbara DISORT (Discrete Ordinate Radiative Transfer) Atmospheric Radiative Transfer (SBDART)\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. The SBDART-derived cloud-top heating responses are consistent with CALIPSO-CloudSat-derived observations, similarly, showing that the cloud responses to changes in dust properties and characteristics are stronger (by about an order of magnitude more) than the responses to smoke (Fig. S5 and cf. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec \u0026amp; d). Given these differences in magnitude, we normalize each parameter by the total heating responses resulting from a 1 km increase in aerosol-layer geometric thickness or base height (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) to compare the relative contributions of aerosol properties, cloud properties, and thermodynamic profiles to the cloud-top heating response.\u003c/p\u003e\u003cp\u003eThe results show that the pathway of cloud-top heating response and the associated weakening of all-sky cloud-top radiative cooling due to the presence of above-cloud aerosol layers differ for dust and smoke aerosols. First, we find that changes in dust properties and characteristics influence the cloud-top heating responses more than changes in thermodynamic profiles over the North Atlantic Ocean. Specifically, when normalized by the total heating response, a 1 km increase in dust layer geometric thickness shows that variations in dust properties across categories have about 67% more influence on the simulated cloud-top heating response than the variations in thermodynamic profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In contrast, for a 1 km increase in dust layer base altitude, the contribution from thermodynamic profiles becomes essential, having about 32% more influence on the simulated cloud-top heating response than variations in dust optical properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Second, for smoke, we find that despite the small overall change in cloud-top heating response (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed \u0026amp; Fig. S5), the relative contribution by smoke properties is opposed by the contributions by thermodynamic profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Specifically, whereas the relative contributions of the variations in smoke properties indicate cloud-top cooling response, the relative contribution of the variation in thermodynamic profile indicate cloud-top warming response for a 1 km increase in aerosol-layer geometric thickness or base altitude. Finally, for both smoke and dust, we find that the relative contribution of variations in cloud properties to the simulated cloud-top heating response is small compared to the variations in aerosol properties and thermodynamic profiles (compare blue bars in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e to other colors). Overall, these results indicate that dust-layer properties and characteristics primarily drive the dust-induced cloud-top heating response, whereas the smoke-induced cloud-top response is more sensitive to the counterbalancing influence of the above-cloud smoke properties and thermodynamic profile.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnderstanding these different pathways for dust-induced and smoke-induced cloud-top heating response requires a better understanding of the key players that likely influence the changes in aerosol-induced longwave radiation reaching the low-level cloud top (cf. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, due to the coarser size distribution of dust aerosols, they can scatter and absorb more longwave radiation, thereby enhancing downwelling radiation that weakens the low-level cloud-top cooling. In addition, both dust and smoke layers over the North and South Atlantic Oceans are associated with enhanced aerosol-layer humidity that is transported off the continent with the aerosols (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed \u0026amp; e) and vary with aerosol-layer geometric thickness and base altitude (Fig. S4). This enhanced humidity similarly facilitates downwelling longwave radiation, which strengthens the dust-induced cloud-top heating response but counteracts the smoke-induced cloud-top response. Although changes in above-cloud dust and humidity dominate the cloud-top heating response, the potential influence of cloud properties, such as variability in cloud optical depth and cloud-top heights, on the cloud-heating response is small but non-negligible. While variabilities in cloud properties do not drive significant changes in cloud-top heating response, the overall weakening of mean cloud-top cooling due to above-cloud longwave-mediated dust and smoke layer will still drive mean changes in cloud cover response, which, in turn, will influence the aerosol semidirect effect.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCloud-top Radiative Cooling and Low-level Cloud Cover\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSimilar to the cloud-top heating response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), we estimate the response of low-level cloudiness and find that it is generally reduced in response to elevated dust and smoke layers. Specifically, the mean low-level cloud responses to one-standard-deviation increases in dust-layer and smoke-layer properties and characteristics are about − 1.21% and − 0.28% respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec \u0026amp; d). As with the cloud-top heating response, the low-level cloud cover also responds to increases in aerosol-layer aerosol optical depth, geometrical thickness, and base altitude. Specifically, for a one-standard-deviation increase in aerosol-layer aerosol optical depth, geometrical thickness, and base altitude, the low-level cloud cover responds by about − 1.64%, -1.49%, and − 0.49% respectively. For smoke-induced cloud responses, these values are − 0.55%, 0.08, and − 0.37%, respectively. Like the cloud-top heating response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the low-level cloud response is generally stronger for dust layers than for smoke layers. In addition, we also find a substantial relationship between aerosol-induced responses in low-level cloud cover and cloud-top heating (Fig. S6). Specifically, an increase in aerosol-induced longwave warming at the cloud top is negatively correlated with changes in cloud fraction by -0.89 and − 0.66 for dust and smoke, respectively. Overall, our results indicate that a weakening of cloud-top radiative cooling is associated with a reduction in low-level cloudiness and variations in above-cloud aerosol properties and characteristics, and the background thermodynamic structure plays a key role in determining the magnitude of the cloud cover response.\u003c/p\u003e"},{"header":"Discussion and Summary","content":"\u003cp\u003eOur results have shown that elevated absorbing aerosol layers suppress cloud-top cooling through increased aerosol-induced longwave-dominated warming near the low-level cloud top. Our analysis focuses on two climatically important regions over the Atlantic Ocean: the northeast Atlantic Ocean, dominated by above-cloud aerosols from May to July, and the southeast Atlantic Ocean, where smoke is predominant from July to October. We used aerosol and cloud profile information from CALIPSO-CloudSat-derived radiative fluxes along with complementary radiative transfer simulations to assess the impact of these aerosols on low-level cloud-top radiative cooling — a critical parameter that significantly influences cloud development. We find that an elevated dust layer induces a longwave warming perturbation at or near the low-level cloud top, which is mostly absent from the low-level cloud top heating associated with the smoke layer. Specifically, the domain-average low-level cloud-top heating response due to the above dust layer is about an order of magnitude higher than that associated with the smoke layer. This aerosol-induced cloud-top warming response weakens the domain-averaged, all-sky cloud-top radiative cooling by as much as 16% when dust is above the cloud.\u003c/p\u003e\u003cp\u003eFurthermore, we find that the magnitude of dust- or smoke-induced weakening response on cloud-top radiative cooling is sensitive to aerosol-layer properties and characteristics, including the geometric thickness, base altitude, and optical depth. Specifically, we find that the weakening response of cloud-top radiative cooling is primarily driven by the increased aerosol optical properties more than increases in aerosol-layer characteristics (such as the geometric thickness and base altitude), with stronger dust-induced changes than smoke-induced changes in cloud-top radiative warming response. Specifically, a one-standard-deviation increase in dust optical depth leads to a pronounced cloud-top warming response of 0.52 ± 0.01 K day¹, nearly an order of magnitude larger than the 0.03 ± 0.02 K day¹ warming response for smoke. These findings underscore the dominant role of dust optical depth in modulating cloud-top radiative effects, highlighting the contrasting radiative sensitivities of dust and smoke layers, driven by differences in their vertical structure and absorptive properties. While aerosol optical depth dominates aerosol-induced changes in cloud-top heating response, this response can be confounded by effects from other factors, such as the thermodynamic profiles and cloud properties. Therefore, we conduct sensitivity experiments using the SBDART that isolate the effects of aerosol-layer properties, thermodynamic profile (including temperature and humidity), and cloud properties (including cloud optical depth and cloud-top heights). We find that aerosol-layer humidity plays a critical role in modulating the aerosol-induced cloud-top radiative heating rates due to changes in aerosol properties. Specifically, because of the enhanced induced downwelling longwave radiation, aerosol-layer humidity similarly weakens the cloud-top radiative cooling; however, its impacts amplify the dust-induced cloud-top warming, whereas it counteracts the cloud-top cooling due to increases in smoke-layer properties. In other words, when smoke properties are increased, it strengthens the low-level cloud-top cooling; however, this response is opposed by the weakening effect of aerosol-layer humidity. These results highlight that dust-layer properties and characteristics primarily drive the dust-induced cloud-top heating response, whereas the smoke-induced cloud-top response is more sensitive to the counterbalancing influence of the above-cloud smoke properties and smoke-layer humidity. Overall, our results suggest that when dust and smoke layers are above the cloud over the North and South Atlantic Oceans, the weakening of cloud-top radiative cooling due to the aerosol properties and characteristics is associated with anomalous decreases in low-level cloudiness.\u003c/p\u003e\u003cp\u003eThese anomalous reductions in low-level cloudiness and cloud-top radiative cooling have important implications for semidirect effects of smoke and dust aerosols and low-level cloud feedback. Our results of negative cloud responses being closely linked to suppressed cloud-top cooling suggest a weakening response in the aerosol semidirect effects when there is elevated dust-induced and smoke-layer moisture-induced longwave warming at the cloud top. Specifically, whereas previous studies have associated enhanced cloudiness and negative semi-direct effects, due in part to the increased lower tropospheric stability by shortwave-absorbing aerosols over stratocumulus-dominated regions of the northeast and southeast Atlantic \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, our results suggest that longwave warming that weakens the cloud-top radiative cooling and reduces the cloud cover effectively could reduce the magnitude of the negative aerosol semi-direct effects. In addition, weakened cloud-top radiative cooling and a reduction in cloudiness could induce a potential decoupling of the boundary layer, limiting moisture supply from the ocean surface, which would in turn further weaken the cloud-top radiative cooling and further reduce the cloudiness\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e–\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Such a cloud feedback process could further weaken the aerosol semi-direct effects, reducing their magnitude from negative (cooling effect) to positive (warming effect) as the cloud fraction further reduces. This inference is consistent with previous studies that have shown that the magnitude of smoke’s negative semidirect effect may be overestimated if smoke-layer humidity is not accounted for \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e–\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Similarly, over dust-dominated regions, semidirect effect estimates may be biased if the longwave effects of dust are not fully considered \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Overall, our results indicate that cloud-top heating response induced by dust-layer optical properties and smoke-layer moisture content is critical for assessing cloudiness and consequently aerosol semi-direct effect over low-level cloud-dominated regions.\u003c/p\u003e\u003cp\u003eWhile our study provides insight into the often unaccounted radiative effects of dust and smoke on cloud-top radiative cooling, using retrieved aerosol and cloud parameters from active satellite sensors, derived fluxes, and idealized radiative transfer simulations, there are several main caveats. First, although satellite retrievals from CALIPSO and CloudSat are vertically resolved, their limited spatial and temporal coverage means they may miss the history and associated dynamics of the underlying clouds. Second, retrievals of above-cloud aerosol parameters are challenging and can be affected by inherent uncertainties, which may impact our interpretations. Third, in our methods of categorization, the effects of below-cloud aerosols, such as sea salt, which can influence cloud dynamics and therefore cloud-top radiative cooling, are not fully accounted for, although their role may be limited since cloud properties play a less significant part. Fourth, although we rely on SBDART simulations, the assumed particle properties may not fully capture the variability in refractive index, mixing state, or particle shape. Additionally, the assumed cloud microphysical properties influence the simulated radiative effects, and the sensitivity of our results to these assumptions has not been fully quantified in our radiative transfer calculations. Finally, our study did not consider other pathways by which aerosol-induced changes in radiation could indirectly affect cloud fraction and produce similar cloud cover responses as shown above. For example, absorbing an aerosol layer that limits shortwave radiation at the surface could result in a reduction in surface temperature, weakening surface fluxes, and indirectly leading to a decrease in cloud fraction \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e,\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. On the other hand, aerosol-induced cooling of the surface and simultaneous aerosol-induced heating aloft could cause substantial adjustments in vertical temperature stratification, which is typically associated with increases in cloud cover \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. While such pathways of aerosol interactions are likely important, changes in surface temperature affecting surface fluxes are often delayed and therefore are likely to have little influence on the instantaneous effects favoured by our methodology.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eTo investigate the influence of smoke and dust layers on cloud-top radiative cooling, we selected two regions: the northeastern and southeastern Atlantic Ocean, where low-level clouds frequently coexist with overlying layers of absorbing aerosols (see yellow box Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The absorbing aerosol over the northeastern Atlantic is dust from North African arid regions \u003csup\u003e\u003cspan additionalcitationids=\"CR84\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e–\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, and the smoke from biomass burning over the southeastern Atlantic is from southern Africa \u003csup\u003e\u003cspan additionalcitationids=\"CR87\" citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e–\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. We used ten years (2007–2017) of aerosol and cloud observations, including aerosol-type, aerosol-layer, and cloud-layer boundaries (bottom and top), aerosol optical depth at 532nm, and aerosol extinction profiles from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e, and cloud cover, radiative flux, and associated radiative heating data from CloudSat \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Details of the CALIPSO retrieval methodology for aerosols and cloud parameters, including aerosol type \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e,\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, aerosol extinction profiles and optical depth at 532 nm\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e and aerosol and cloud layer boundaries\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e can be found in the respective algorithm documentation and evaluation studies \u003csup\u003e\u003cspan additionalcitationids=\"CR96 CR97 CR98 CR99\" citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e–\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. Similarly, details of CloudSat-retrieved cloud cover\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e,\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e and radiative fluxes\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e can be found in the literature (see also Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Because the months when aerosol layers are typically found above low-level clouds differ between northern dust and southern smoke aerosols, we used the months when aerosol occurrences are maximum over each basin, which are from May to August for dust and from July to October for smoke (see Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of dust/smoke layer above low-level clouds\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe divided our analysis domain over the North and South Atlantic Oceans into 5°×5° grid boxes (see dashed yellow lines in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to minimize the effect of large-scale meteorological variability, following previous studies \u003csup\u003e\u003cspan additionalcitationids=\"CR104\" citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e–\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. We selected profiles containing single-layer low-level clouds within each grid with an overlying aerosol layer using the number of layers and feature classification flags in the CALIOP Merged Layer product (CAL_LID_L2_05km). Over the North Atlantic Ocean, we retained aerosol layers identified as dust in the feature classification flag, and separately, for the South Atlantic Ocean, we selected only flags for smoke aerosols. With cloud and aerosol top and bottom boundaries retrieved in CALIPSO using the SIBYL (Selective Iterated Boundary Location Algorithm) algorithm, with an accuracy of ~ 100 m compared to ground-based lidar\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e,\u003cspan additionalcitationids=\"CR107\" citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e–\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e we used the recently improved data and selected profiles where the aerosol and cloud layers were separated by at least 200 m. This minimum required separation provides an additional level of assurance, ensuring that the aerosols are indeed separated from the cloud layers. Additionally, we discarded profiles where aerosol tops extended beyond 6 km, as they are too far from the cloud top to have significant radiative effects\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e and could be misclassified as cirrus clouds at this altitude \u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEstimating aerosol-induced radiative heating and corresponding cloud-top heating response\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo estimate the radiative influence of dust and smoke on low-level clouds, we used radiative fluxes and associated heating rates from the CloudSat-CALIPSO combined product 2B-FLXHR-LIDAR \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e. These fluxes were derived using a radiative transfer model, namely BugRad, using aerosol and cloud information from both CALIPSO and CloudSat. The BugsRad radiative transfer model computes radiative fluxes for each radar profile from the Cloud Profiling Radar (CPR), with a vertical resolution of 240 m and covering from the surface to the lower stratosphere. It uses cloud liquid and ice water content and particle size information, derived precipitation estimates from CloudSat’s and aerosol profiles information, including layer boundaries and aerosol type, obtained from CALIOP, along with MODIS-derived surface properties and atmospheric profiles from collocated re-analyses, to calculate upwelling and downwelling shortwave (0–4 mum) and longwave (4 \u0026gt; 0) fluxes, as well as heating rates. Necessary aerosol optical properties (such as asymmetry parameter and single-scattering albedo) were used from d’Almeida et al. \u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e, and surface properties, such as surface albedo and emissivity, are sourced from the International Geosphere-Biosphere Program (IGBP). Additionally, optical properties for smoke were constrained using ORCALES (ObseRvations of Aerosols above CLouds and their intEractionS) field campaign-based measurement \u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. Background meteorological conditions, including surface pressure, surface temperature, as well as profiles of pressure, temperature, specific humidity, and ozone mixing ratio used from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset and provided in the CloudSat dataset as ECMWF-AUX product \u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e. Uncertainties in the computed fluxes were evaluated through sensitivity analyses and comparison with top-of-atmosphere fluxes from CERES, showing biases within acceptable ranges \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Shortwave flux errors are primarily attributed to uncertainties in CloudSat’s liquid water content estimates, with smaller contributions from undetected low clouds. For longwave fluxes, uncertainties mainly arise from assumptions about surface skin temperature and lower-tropospheric water vapor \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. Since our study focuses on oceanic regions, associated uncertainties are expected to be lower.\u003c/p\u003e\u003cp\u003eWe used both upwelling and downwelling shortwave and longwave radiative fluxes, along with radiative heating rate profiles, collocated with above-cloud aerosol cases. While the dataset provides radiative heating profiles under all-sky conditions (as is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), heating profiles for clear-sky (cloud-free; cloud optical depth set to zero) and no-aerosol (aerosol optical depth set to zero) conditions are not included. However, the flux dataset includes both aerosol and no-aerosol cases under clear-sky and all-sky conditions, allowing us to compute the aerosol and cloud radiative effects in both spectral bands. Accordingly, the net (downwelling minus upwelling) flux difference between all-sky cases with and without aerosols provides a direct estimate of aerosol-induced changes in the radiative flux profiles (see Eq.\u0026nbsp;1). We then computed the vertical flux divergence at each atmospheric layer from this difference and calculated the resulting aerosol-induced radiative heating profiles. Since profiles were selected only when CALIPSO detected dust over the North Atlantic and smoke over the South Atlantic, the aerosol-induced changes are attributed to these aerosol types in each region (dust in the north, smoke in the south).\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varDelta\\:F={F}_{allsky}-{F}_{allsky,\\:no\\:aerosol\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)\\:$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere ∆F is the change in radiative fluxes due to aerosols, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{allsky}\\)\u003c/span\u003e\u003c/span\u003e is net (downwelling minus upwelling) flux for all-sky conditions, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{allsky,no\\:aerosol}\\)\u003c/span\u003e\u003c/span\u003e is the same, but computed with no aerosol (i.e., aerosol optical depth equals zero). This is estimated for both shortwave and longwave fluxes for each vertical level given in the profile datasets. Subsequently, we calculate profiles of shortwave and longwave heating rates using flux divergence\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\frac{\\:\\partial\\:T}{\\partial\\:t}=-\\left(\\frac{1}{\\rho\\:{C}_{p}}\\right).\\left(\\frac{{\\Delta\\:}F}{{\\Delta\\:}Z}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:T}{\\partial\\:t}\\)\u003c/span\u003e\u003c/span\u003eis the heating rate (K day\u003csup\u003e− 1\u003c/sup\u003e), ρ = 1.17 kg m\u003csup\u003e− 3\u003c/sup\u003e density of the atmospheric layer, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the specific heat capacity of air at constant pressure (1004.67 J kg\u003csup\u003e− 1\u003c/sup\u003e K\u003csup\u003e− 1\u003c/sup\u003e), and ∆Z is the change in vertical levels (m).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAggregation of aerosol, cloud, and radiative flux based on the vertical structure of dust/smoke\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the aerosol layer’s top and bottom boundaries, we estimated the geometric thickness and base altitude of the aerosol layer within each 5°×5° grid box separately for the north and south Atlantic Oceans. We categorized profiles based on layer base altitude and geometric thickness and stratified them into four groups of dust base heights at 1, 2, 3, and 4 km. We further divided each base-height group into four subgroups with geometric thicknesses of 0.5, 1.5, 2.5, and 3.5 km. Therefore, within a 5°×5° grid box, a maximum of 16 categories could potentially exist (see Fig. S2). However, we retained only categories that contained at least 30 profiles and were present in a minimum of six grid boxes within each basin. This ensures that our analysis of changes in cloud and aerosols is statistically significant. Additionally, each retained category had to be available for both dust and smoke separately over the north and south Atlantic basins. We do so to allow for comparable results, and as a result, the final number of categories available for analysis was eight (see Fig. S2). Corresponding to these eight categories, we retained complete datasets of aerosol properties from CALIPSO, cloud properties from CloudSat, meteorological parameters from the ECMWF Auxiliary product (included in the CloudSat datasets), and corresponding radiative fluxes from CALIPSO-CloudSat \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe further assessed the impact of variations in the geometric thickness and base altitude of dust and smoke layers on cloud-top cooling or warming by analysing the dust- and smoke-induced changes in cloud-top heating across categories. Specifically, to evaluate sensitivity to geometric thickness, we compared changes in cloud-top heating across those categories having the same layer base altitude but different geometric thickness. Conversely, to assess the effect of base altitude, we analyzed the categories with the same geometric thickness but varying layer base altitudes. Therefore, we calculated heating responses to a per-kilometer change in geometric thickness for a fixed layer base, and vice versa, using the following\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\:Response\\:to\\:GT\\:\\left(k{m}^{-1}\\right)=\\:{\\left.\\frac{\\partial\\:X}{\\partial\\:GT}\\right|}_{LB}and\\:Response\\:to\\:LB\\:\\left(k{m}^{-1}\\right)=\\:{\\left.\\frac{\\partial\\:X}{\\partial\\:LB}\\right|}_{GT}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhere X, can be aerosol layer heating and cloud top heating/cooling for all-sky conditions or aerosol-induced changes (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), GT is for geometric thickness, and LB is the layer base altitude.\u003c/p\u003e\u003cp\u003eFurthermore, to evaluate the influence of optical depth, we divided each category into two groups: low and high optical depth. For both smoke and dust cases, the low optical depth group includes values less than 0.1. For the high optical depth group, we used a range of 0.2–0.3 for smoke and 0.3–0.4 for dust. This approach allows us to assess the influence of geometric thickness and base altitude on independent variation in optical depth across categories. Additionally, within each fixed base-height and thickness category, comparing different optical depth ranges enables us to isolate the effect of optical depth. Together, this categorization helps us quantify the individual impacts of optical depth, geometric thickness, and layer base altitude with minimal overlap between their influences.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffect of background thermodynamic conditions and cloud parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e to evaluate the sensitivity of cloud-top radiative cooling to aerosol layer characteristics, cloud properties, and underlying thermodynamic conditions. SBDART accounts for key variables influencing radiative transfer and allows selection of surface types (e.g., water, desert, snow, forest), which affect albedo and surface reflectivity. Model uses prescribed cloud layer, with user-defined properties such as cloud optical depth, cloud-top and base heights, phase (liquid or ice), and effective particle radius, enabling an accurate assessment of radiative fluxes within and across cloud boundaries. It also supports user-defined atmospheric profiles, including temperature, humidity, and pressure across altitudes, crucial for determining radiation absorption and emission by gases and aerosols. The required inputs include temperature, temperature (T, in K) and pressure (p, hPa), and water vapor density (\u003cem\u003eρ\u003c/em\u003e\u003csub\u003e\u003cem\u003ev\u003c/em\u003e\u003c/sub\u003e, g m\u003csup\u003e− 3\u003c/sup\u003e) for each model level altitude (km). While temperature and pressure profiles are available as auxiliary products in the CALIPSO files, water vapor density is computed from specific humidity (q, in kg/kg), also provided in the CALIPSO L2 files, using the following equation,\u003c/p\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:{\\rho\\:}_{v}=1000\\frac{q.p}{ϵ.Rv.T}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere ϵ = 0.622 is the ratio of molecular weights of water vapor and dry air, and \u003cem\u003eRv = 462 J kg⁻¹ K⁻¹\u003c/em\u003e is the specific gas constant for water vapor.\u003c/p\u003e\u003cp\u003eWe used a six-stream discrete solver to simulate the profiles of shortwave and longwave fluxes, corresponding to eight categories, separately for dust and smoke, to estimate the effect of aerosol layers on cloud-top radiative flux. To do so, we used cloud parameters (optical depth and cloud top and base height), aerosol optical depth, normalized dust/smoke profiles, along with temperature and humidity profiles corresponding to each category defined in Fig. S2, separately for smoke and dust. We simulated fluxes at 0.01 µm spectral resolution over the range of 0.25–40 µm for dust and 0.25–20 µm for smoke, with the latter limited due to negligible interactions in the longwave spectrum. To compute broadband spectral averages, we performed three distinct set of simulations one covering the shortwave band (0.25–4 µm), another spanning the longwave region (4–40 µm for dust and 4–20 µm for smoke), and a third covering the full range (0.25–40 µm for dust and 0.25–20 µm for smoke. We configured the model, separately, for the North (17.49°N, 27.77°W) and South Atlantic (16.78°S, 8.90°E) using parameters representative of CALIPSO-derived dust and smoke layers, respectively, and supplied normalized aerosol extinction profiles based on the average values of eight categories, with no additional aerosol layers prescribed above or below the cloud outside the defined aerosol layer.\u003c/p\u003e\u003cp\u003eWe calculated the required single-particle optical properties for dust, such as spectrally varying single-scattering albedo, extinction efficiency, and asymmetry parameter based on Mie theory, using the complex refractive index of dust from Ref \u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e and particle size distributions typically representative of long-range transported dust \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e,\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e. Thus, the obtained spectral aerosol properties, extinction efficiency, single-scattering albedo, and asymmetry parameter for 60 wavelengths were used as input to capture dust’s characteristic coarse‐mode behaviour (see Fig. S7). On the other hand, for smoke, we used spectrally varying optical depth, single-scattering albedo, and asymmetry parameter values from Ref\u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e, which have extended wavelengths up to 2 µm for above-cloud smoke over the South Atlantic (see their Fig. A1 also plotted in Fig. S7). In addition, we set angstrom exponent values to 1.62, which characterizes the wavelength dependence of aerosol extinction and indicates the dominance of fine mode smoke above south Atlantic cloud decks \u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e. In all simulations, we used single-layer low-level clouds, whose layer boundaries were obtained by averaging cloud-layer information for each category (see Table S2), separately for dust and smoke. Cloud properties were prescribed using CALIPSO-derived cloud-top heights and cloud optical thicknesses. For both the North and South Atlantic regions, we set droplet radii to 10 µm at the cloud base and 15 µm at the top for all simulations, allowing SBDART’s built-in logarithmic sliding function to generate realistic vertical profiles consistent with observations of marine low clouds \u003csup\u003e\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e,\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u003c/sup\u003e. In addition, we prescribe cloud optical depth based on the average values of each of the eight categories, separately for dust and smoke cases, using uniform opacity to ensure that the in-cloud vertical structure does not modulate cloud-top cooling \u003csup\u003e\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e,\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo isolate the effects of dust and smoke layer properties on cloud-top cooling from the influences of background thermodynamic conditions and cloud characteristics, we first recreated radiative flux profiles for the eight cloud–aerosol categories, separately for dust and smoke. These simulations used aerosol optical depth, normalized aerosol extinction profiles, cloud properties, and the mean temperature and humidity profiles associated with each category (see Fig. S8a and Table S2). For each case, we computed shortwave, longwave, and net radiative fluxes and heating rates both with and without aerosols (by setting aerosol optical depth to zero). This allowed us to derive aerosol-induced heating rate profiles and corresponding changes in cloud-top cooling. From these profiles, we further calculated aerosol-induced cloud-top heating responses to a one-kilometer increase in geometric thickness and aerosol layer base height (using Eq.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eSince the defined categories differ not only in aerosol properties but also in cloud characteristics and meteorological backgrounds, the resulting aerosol-induced changes in cloud-top cooling could be influenced by any combination of these factors. For example, aerosol absorption and its radiative impact depend not only on aerosol loading and vertical structure but also on temperature, humidity, and underlying cloud properties. A thicker cloud, for instance, may buffer or resist aerosol-induced radiative influence. Consequently, the cloud-top heating responses derived from the initial category-based simulations cannot be attributed solely to aerosol effects. To isolate the influence of individual factors on cloud-top radiative cooling, we conducted three set of sensitivity simulations, in which, we varied one group of parameters at a time, namely aerosol properties (includes layer-base and top height, and optical depth), cloud properties (cloud base and top and cloud optical depth), or thermodynamic conditions (temperature and humidity profile), while keeping the others constant. This allowed us to assess how each factor independently contributes to variations in cloud-top heating. First set of experiments, evaluating aerosol sensitivity, for which we kept the temperature, humidity, and cloud properties fixed across all cases and varied only the aerosol optical depth and extinction profiles for dust and smoke (see Fig. S8b). This enabled us to determine how differences in aerosol properties alone affect aerosol-induced cloud-top heating/cooling responses to a one-kilometer increase in geometric thickness and base layer altitude. For the second set of experiments, the cloud-property sensitivity experiments, our aim remained to quantify aerosol-induced changes in cloud-top cooling. To isolate the influence of cloud structure, we held the aerosol profiles and thermodynamic conditions constant across all simulations and varied only the cloud optical depth, cloud-top height, and cloud-base height according to values from the eight original categories (see Fig. S8c). This allowed us to assess how aerosol-induced cloud-top heating varies when the same aerosol conditions interact with different cloud structures, that is, how sensitive the aerosol effect is to changes in underlying cloud conditions. Similarly, for the third set of simulations, for the thermodynamic sensitivity, we fixed both aerosol and cloud properties and varied only the background temperature and humidity profiles across categories (see Fig. S8d). This enabled us to evaluate how aerosol-induced changes in cloud-top cooling are modulated by the surrounding thermodynamic environment, particularly by variations in humidity that can alter aerosol-induced changes at the low-level cloud top.\u003c/p\u003e\u003cp\u003eSince the simulated total responses to a 1 km increase in aerosol layer geometric thickness and base height may slightly differ from those derived using CALIPSO-CloudSat fluxes, due to residue and non-linear interactions between the components, we applied a normalization procedure to quantify the relative contributions of aerosol properties, cloud properties, and background thermodynamic conditions. In addition, these differences could arise partly from assumptions used in our simulations—for example, aerosol layers were prescribed as idealized, clean dust or smoke layers with well-defined vertical structure and specified optical properties. In contrast, the observation-based estimates were derived from radiative fluxes in CALIPSO-CloudSat profiles containing only identified dust or smoke layers. However, the presence of other aerosol types in these profiles cannot be ruled out. Furthermore, the vertical structure of aerosols may extend beyond the selected layer boundaries, which may be undetected/misclassified, introducing additional complexity into our results. Thus, the CALIPSO-CloudSat-derived estimates inherently reflect more complex aerosol mixtures. Therefore, to isolate the relative influence of each contributing factor, we normalized the simulated cloud-top heating responses by the sum of combined responses from aerosol, cloud, and thermodynamic components. This normalization constrains the total response to sum to one, enabling clearer attribution of cloud-top radiative effects to individual components for both geometric thickness and layer base responses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eAAA and SKP designed the study. SKP performed the analysis. SKP and AAA interpreted the results and wrote the paper\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThis study was supported by the U.S. Department of Energy (DOE), Office of Science (award #DE-SC0024281). CALIPSO data used in this study were obtained from the NASA Langley Research Center Atmospheric Science Data Center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asdc.larc.nasa.gov/project/CALIPSO\u003c/span\u003e\u003cspan address=\"https://asdc.larc.nasa.gov/project/CALIPSO\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ), and we acknowledge the CALIPSO science team for providing these data. CloudSat-CALIPSO radiative flux data were provided by the CloudSat Data Processing Center at Colorado State University (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cloudsat.cira.colostate.edu/\u003c/span\u003e\u003cspan address=\"https://www.cloudsat.cira.colostate.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ), and we thank the CloudSat team for their data processing and dissemination efforts. We acknowledge the use of the SBDART radiative transfer model, developed at the University of California, Santa Barbara, and made publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/paulricchiazzi/SBDART\u003c/span\u003e\u003cspan address=\"https://github.com/paulricchiazzi/SBDART\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eCloud and aerosol layer data used in this study were obtained from the CALIPSO Level 2 data products, including the 5 km aerosol layer product (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMAPRO-STANDARD-V4-20\u003c/span\u003e\u003cspan address=\"10.5067/CALIOP/CALIPSO/LID_L2_05KMAPRO-STANDARD-V4-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the 5 km aerosol profile product (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5067/CALIOP/CALIPSO/LID_L2_05KMALAYSTANDARD-V4-20\u003c/span\u003e\u003cspan address=\"10.5067/CALIOP/CALIPSO/LID_L2_05KMALAYSTANDARD-V4-20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). CloudSat CALIPSO radiative flux data were accessed from the 2B-FLXHR-LIDAR product (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cloudsat.cira.colostate.edu/data-products/2b-flxhr-lidar\u003c/span\u003e\u003cspan address=\"https://www.cloudsat.cira.colostate.edu/data-products/2b-flxhr-lidar\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All data supporting the findings of this study are available at Zenodo\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBender, F. A. -M. Aerosol Forcing: Still Uncertain, Still Relevant. \u003cem\u003eAGU Advances\u003c/em\u003e 1, (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBellouin, N. \u003cem\u003eet al.\u003c/em\u003e Bounding Global Aerosol Radiative Forcing of Climate Change. \u003cem\u003eReviews of Geophysics\u003c/em\u003e 58, 1\u0026ndash;45 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoucher, O., \u003cem\u003eet al. Clouds and Aerosols\u003c/em\u003e. \u003cem\u003eClimate Change 2013 \u0026ndash; The Physical Science Basis\u003c/em\u003e vol. 9781107057 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cambridge.org/core/product/identifier/CBO9781107415324A024/type/book_part\u003c/span\u003e\u003cspan address=\"https://www.cambridge.org/core/product/identifier/CBO9781107415324A024/type/book_part\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eForster, P. \u003cem\u003eet al.\u003c/em\u003e The Earth\u0026rsquo;s Energy Budget, Climate Feedbacks and Climate Sensitivity. in \u003cem\u003eClimate Change 2021 \u0026ndash; The Physical Science Basis\u003c/em\u003e (eds. Masson-Delmotte, V. et al.) vol. 17 923\u0026ndash;1054 (Cambridge University Press, 2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu, Q. \u0026amp; Huang, Y. Distributions and Trends of the Aerosol Direct Radiative Effect in the 21st Century: Aerosol and Environmental Contributions. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e 128, (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtwater, M. A. Planetary Albedo Changes Due to Aerosols. \u003cem\u003eScience (1979)\u003c/em\u003e 170, 64\u0026ndash;66 (1970).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharlson, R. J. \u003cem\u003eet al.\u003c/em\u003e Climate Forcing by Anthropogenic Aerosols. \u003cem\u003eScience (1979)\u003c/em\u003e 255, 423\u0026ndash;430 (1992).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaywood, J. \u0026amp; Boucher, O. Estimates of the direct and indirect radiative forcing due to tropospheric aerosols: A review. \u003cem\u003eReviews of Geophysics\u003c/em\u003e 38, 513\u0026ndash;543 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSatheesh, S. \u0026amp; Krishnamoorthy, K. Radiative effects of natural aerosols: A review. \u003cem\u003eAtmos Environ\u003c/em\u003e 39, 2089\u0026ndash;2110 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu, H. \u003cem\u003eet al.\u003c/em\u003e A review of measurement-based assessments of the aerosol direct radiative effect and forcing. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 6, 613\u0026ndash;666 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTwomey, S. Pollution and the Planetary Albedo. \u003cem\u003eAtmos Environ\u003c/em\u003e 8, 1251\u0026ndash;1256 (1974).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTwomey, S. The Influence of Pollution on the Shortwave Albedo of Clouds. \u003cem\u003eJ Atmos Sci\u003c/em\u003e 34, 1149\u0026ndash;1152 (1977).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlbrecht, B. A. Aerosols, Cloud Microphysics, and Fractional Cloudiness. \u003cem\u003eScience (1979)\u003c/em\u003e 245, 1227\u0026ndash;1230 (1989).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePenner, J. E. \u003cem\u003eet al. Aerosols, Their Direct and Indirect Effects\u003c/em\u003e. \u003cem\u003eIPCC AR3\u003c/em\u003e vol. 91 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLohmann, U. A glaciation indirect aerosol effect caused by soot aerosols. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 29, 1052 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLohmann, U. Aerosol effects on clouds and climate. \u003cem\u003eSpace Sci Rev\u003c/em\u003e 125, 129\u0026ndash;137 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLohmann, U. \u0026amp; Feichter, J. Global indirect aerosol effects: a review. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 5, 715\u0026ndash;737 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenfeld, D. \u003cem\u003eet al.\u003c/em\u003e Global observations of aerosol-cloud-precipitation- climate interactions. \u003cem\u003eReviews of Geophysics\u003c/em\u003e 52, 1\u0026ndash;59 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenfeld, D. \u003cem\u003eet al.\u003c/em\u003e Climate effects of aerosol-cloud interactions. \u003cem\u003eScience\u003c/em\u003e 343, 379\u0026ndash;80 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHansen, J., Sato, M. \u0026amp; Ruedy, R. Radiative forcing and climate response. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 102, 6831\u0026ndash;6864 (1997).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAckerman, A. S. \u003cem\u003eet al.\u003c/em\u003e Reduction of Tropical Cloudiness by Soot. \u003cem\u003eScience (1979)\u003c/em\u003e 288, 1042\u0026ndash;1047 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoch, D. \u0026amp; Del Genio, A. D. Black carbon semi-direct effects on cloud cover: Review and synthesis. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 10, 7685\u0026ndash;7696 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartmann, D. L., Ockert-Bell, M. E. \u0026amp; Michelsen, M. L. The Effect of Cloud Type on Earth\u0026rsquo;s Energy Balance:Global Analysis. \u003cem\u003eJ Clim\u003c/em\u003e 5, 1281\u0026ndash;1304 (1992).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHahn, C. J. \u0026amp; Warren, S. G. A Gridded Climatology of Clouds over Land (1971\u0026ndash;1996) and Ocean (1954\u0026ndash;2008) from Surface Observations Worldwide (NDP-026E)*. Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3334/CDIAC/cli.ndp026e\u003c/span\u003e\u003cspan address=\"10.3334/CDIAC/cli.ndp026e\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoren, I., Kaufman, Y. J., Remer, L. A. \u0026amp; Martins, J. V. Measurement of the Effect of Amazon Smoke on Inhibition of Cloud Formation. \u003cem\u003eScience (1979)\u003c/em\u003e 303, 1342\u0026ndash;1345 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson, B. T., Shine, K. P. \u0026amp; Forster, P. M. The semi-direct aerosol effect: Impact of absorbing aerosols on marine stratocumulus. \u003cem\u003eQuarterly Journal of the Royal Meteorological Society\u003c/em\u003e 130, 1407\u0026ndash;1422 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen, R. J. \u003cem\u003eet al.\u003c/em\u003e Observationally constrained aerosol\u0026ndash;cloud semi-direct effects. \u003cem\u003eNPJ Clim Atmos Sci\u003c/em\u003e 2, 1\u0026ndash;12 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerlwitz, J. \u0026amp; Miller, R. L. Cloud cover increase with increasing aerosol absorptivity: A counterexample to the conventional semidirect aerosol effect. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 115, (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilcox, E. M. Stratocumulus cloud thickening beneath layers of absorbing smoke aerosol. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 10, 11769\u0026ndash;11777 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilcox, E. M. Direct and semi-direct radiative forcing of smoke aerosols over clouds. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 12, 139\u0026ndash;149 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRandles, C. A., Colarco, P. R. \u0026amp; Da Silva, A. Direct and semi-direct aerosol effects in the NASA GEOS-5 AGCM: Aerosol-climate interactions due to prognostic versus prescribed aerosols. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 118, 149\u0026ndash;169 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBond, T. C. \u003cem\u003eet al.\u003c/em\u003e Bounding the role of black carbon in the climate system: A scientific assessment. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e 118, 5380\u0026ndash;5552 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBergstrom, R. W. \u003cem\u003eet al.\u003c/em\u003e Spectral absorption properties of atmospheric aerosols. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 7, 5937\u0026ndash;5943 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, J. \u003cem\u003eet al.\u003c/em\u003e Scattering and absorbing aerosols in the climate system. \u003cem\u003eNat Rev Earth Environ\u003c/em\u003e 3, 363\u0026ndash;379 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSamset, B. H. \u003cem\u003eet al.\u003c/em\u003e Aerosol Absorption: Progress Towards Global and Regional Constraints. \u003cem\u003eCurr Clim Change Rep\u003c/em\u003e 4, 65\u0026ndash;83 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSand, M. \u003cem\u003eet al.\u003c/em\u003e Aerosol absorption in global models from AeroCom phase III. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 21, 15929\u0026ndash;15947 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCappa, C. \u003cem\u003eet al. Absorbing Aerosols Workshop Report, January 20\u0026ndash;21\u003c/em\u003e, 2016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.osti.gov/servlets/purl/1471231\u003c/span\u003e\u003cspan address=\"https://www.osti.gov/servlets/purl/1471231\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e/ (2016) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2172/1471231\u003c/span\u003e\u003cspan address=\"10.2172/1471231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantos, D., Costa, M. J., Silva, A. M. \u0026amp; Salgado, R. Modeling Saharan desert dust radiative effects on clouds. \u003cem\u003eAtmos Res\u003c/em\u003e 127, 178\u0026ndash;194 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmiri-Farahani, A., Allen, J. R., Neubauer, D. \u0026amp; Lohmann, U. Impact of Saharan dust on North Atlantic marine stratocumulus clouds: Importance of the semidirect effect. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 17, 6305\u0026ndash;6322 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmiri-Farahani, A., Allen, R. J., Li, K. F. \u0026amp; Chu, J. E. The Semidirect Effect of Combined Dust and Sea Salt Aerosols in a Multimodel Analysis. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 46, 10512\u0026ndash;10521 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebiyi, A. \u003cem\u003eet al.\u003c/em\u003e A review of coarse mineral dust in the Earth system. \u003cem\u003eAeolian Res\u003c/em\u003e 60, 100849 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKok, J. F. \u003cem\u003eet al.\u003c/em\u003e Mineral dust aerosol impacts on global climate and climate change. \u003cem\u003eNat Rev Earth Environ\u003c/em\u003e 4, 71\u0026ndash;86 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePandey, S. K. \u0026amp; Adebiyi, A. A. Dust semi-direct effects: Dust-induced longwave radiation influences low-level cloud response to free-tropospheric dust over the North Atlantic Ocean. Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/egusphere-2025-2607\u003c/span\u003e\u003cspan address=\"10.5194/egusphere-2025-2607\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNowak, J. L., Siebert, H., Szodry, K. E. \u0026amp; Malinowski, S. P. Coupled and decoupled stratocumulus-topped boundary layers: Turbulence properties. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 21, 10965\u0026ndash;10991 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng, Y., Rosenfeld, D. \u0026amp; Li, Z. Estimating the Decoupling Degree of Subtropical Marine Stratocumulus Decks From Satellite. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 45, 12,560\u0026thinsp;\u0026ndash;\u0026thinsp;12,568 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng, Y., Rosenfeld, D. \u0026amp; Li, Z. The Relationships Between Cloud Top Radiative Cooling Rates, Surface Latent Heat Fluxes, and Cloud-Base Heights in Marine Stratocumulus. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e 123, 11,678\u0026thinsp;\u0026ndash;\u0026thinsp;11,690 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNicholls, S. \u0026amp; Leighton, J. An observational study of the structure of stratiform cloud sheets: Part I. Structure. \u003cem\u003eQuarterly Journal of the Royal Meteorological Society\u003c/em\u003e 112, 431\u0026ndash;460 (1986).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNicholls, S. \u0026amp; Turton, J. D. An observational study of the structure of stratiform cloud sheets: Part II. Entrainment. \u003cem\u003eQuarterly Journal of the Royal Meteorological Society\u003c/em\u003e 112, 461\u0026ndash;480 (1986).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEastman, R. \u0026amp; Wood, R. The competing effects of stability and humidity on subtropical stratocumulus entrainment and cloud evolution from a Lagrangian perspective. \u003cem\u003eJ Atmos Sci\u003c/em\u003e 75, 2563\u0026ndash;2578 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebiyi, A. A., Zuidema, P. \u0026amp; Abel, S. J. The convolution of dynamics and moisture with the presence of shortwave absorbing aerosols over the southeast Atlantic. \u003cem\u003eJ Clim\u003c/em\u003e 28, 1997\u0026ndash;2024 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeaconu, L. T. \u003cem\u003eet al.\u003c/em\u003e Satellite inference of water vapour and above-cloud aerosol combined effect on radiative budget and cloud-Top processes in the southeastern Atlantic Ocean. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 19, 11613\u0026ndash;11634 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHerbert, R. J., Bellouin, N., Highwood, E. J. \u0026amp; Hill, A. A. Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 20, 1317\u0026ndash;1340 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSamset, B. H. \u003cem\u003eet al.\u003c/em\u003e Climate Impacts From a Removal of Anthropogenic Aerosol Emissions. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 45, 1020\u0026ndash;1029 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBar\u0026oacute; P\u0026eacute;rez, A., Devasthale, A., Bender, F. A. M. \u0026amp; Ekman, A. M. L. Impact of smoke and non-smoke aerosols on radiation and low-level clouds over the southeast Atlantic from co-located satellite observations. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 21, 6053\u0026ndash;6077 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWood, R. Stratocumulus Clouds. \u003cem\u003eMon Weather Rev\u003c/em\u003e 140, 2373\u0026ndash;2423 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson, B. T. Large-eddy simulations of the semidirect aerosol effect in shallow cumulus regimes. \u003cem\u003eJournal of Geophysical Research D: Atmospheres\u003c/em\u003e 110, 1\u0026ndash;8 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson, B. T. \u003cem\u003eThe Semidirect Aerosol Effect: Comparison of a Single-Column Model with Large Eddy Simulation for Marine Stratocumulus\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.cgd.ucar.edu:80/cms/sccm/sccm.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDoherty, O. M., Riemer, N. \u0026amp; Hameed, S. Saharan mineral dust transport into the Caribbean: Observed atmospheric controls and trends. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 113, 1\u0026ndash;10 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKopec, M. K., Malinowski, S. P. \u0026amp; Piotrowski, Z. P. Effects of wind shear and radiative cooling on the stratocumulus-topped boundary layer. \u003cem\u003eQuarterly Journal of the Royal Meteorological Society\u003c/em\u003e 142, 3222\u0026ndash;3233 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng, Y., Rosenfeld, D., Zhu, Y. \u0026amp; Li, Z. Satellite-Based Estimation of Cloud Top Radiative Cooling Rate for Marine Stratocumulus. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 46, 4485\u0026ndash;4494 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePittock, A. B. \u003cem\u003eet al.\u003c/em\u003e Sources and Properties of Smoke and Dust. in \u003cem\u003eEnvironmental Consequences of Nuclear War Volume I: Physical and Atmospheric Effects\u003c/em\u003e vol. 1 (John Wiley \u0026amp; Sons Ltd, 1986).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsborne, S. R. \u003cem\u003eet al.\u003c/em\u003e Physical and optical properties of mineral dust aerosol during the Dust and Biomass-burning Experiment. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 113, (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaponi, L. \u003cem\u003eet al.\u003c/em\u003e Spectral- and size-resolved mass absorption efficiency of mineral dust aerosols in the shortwave spectrum: A simulation chamber study. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 17, 7175\u0026ndash;7191 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao, H. \u0026amp; Seinfeld, J. H. Radiative forcing by mineral dust aerosols: Sensitivity to key variables. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 103, 31637\u0026ndash;31645 (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShell, K. M. \u0026amp; Somerville, R. C. J. Sensitivity of climate forcing and response to dust optical properties in an idealized model. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 112, (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristensen, M. W., Carri\u0026oacute;, G. G., Stephens, G. L. \u0026amp; Cotton, W. R. Radiative impacts of free-tropospheric clouds on the properties of marine stratocumulus. \u003cem\u003eJ Atmos Sci\u003c/em\u003e 70, 3102\u0026ndash;3118 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGodbole, R. V. On Destabilization of Clouds by Radiative Cooling. \u003cem\u003eMon Weather Rev\u003c/em\u003e 101, 496\u0026ndash;500 (1973).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOgura, T., Webb, M. J. \u0026amp; Lock, A. P. Positive Low Cloud Feedback Primarily Caused by Increasing Longwave Radiation From the Sea Surface in Two Versions of a Climate Model. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 50, (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRicchiazzi, P., Yang, S., Gautier, C. \u0026amp; Sowle, D. SBDART: A Research and Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth\u0026rsquo;s Atmosphere. \u003cem\u003eBull Am Meteorol Soc\u003c/em\u003e 79, 2101\u0026ndash;2114 (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWood, R. \u0026amp; Bretherton, C. S. On the relationship between stratiform low cloud cover and lower-tropospheric stability. \u003cem\u003eJ Clim\u003c/em\u003e 19, 6425\u0026ndash;6432 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebiyi, A. A. \u0026amp; Zuidema, P. Low cloud cover sensitivity to biomass-burning aerosols and meteorology over the Southeast Atlantic. \u003cem\u003eJ Clim\u003c/em\u003e 31, 4329\u0026ndash;4346 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYamaguchi, T., Feingold, G., Kazil, J. \u0026amp; McComiskey, A. Stratocumulus to cumulus transition in the presence of elevated smoke layers. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 42, 10478\u0026ndash;10485 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou, X., Ackerman, A. S., Fridlind, A. M., Wood, R. \u0026amp; Kollias, P. Impacts of solar-absorbing aerosol layers on the transition of stratocumulus to trade cumulus clouds. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 17, 12725\u0026ndash;12742 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiamond, M. S. \u003cem\u003eet al.\u003c/em\u003e Cloud adjustments from large-scale smoke-circulation interactions strongly modulate the southeastern Atlantic stratocumulus-to-cumulus transition. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 22, 12113\u0026ndash;12151 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSakaeda, N., Wood, R. \u0026amp; Rasch, P. J. Direct and semidirect aerosol effects of southern African biomass burning aerosol. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 116, 1\u0026ndash;19 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeyer, K., Platnick, S., Oreopoulos, L. \u0026amp; Lee, D. Estimating the direct radiative effect of absorbing aerosols overlying marine boundary layer clouds in the southeast Atlantic using MODIS and CALIOP. 118, 4801\u0026ndash;4815 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMallet, M. \u003cem\u003eet al.\u003c/em\u003e Direct and semi-direct radiative forcing of biomass-burning aerosols over the southeast Atlantic (SEA) and its sensitivity to absorbing properties: a regional climate modeling study. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 20, 13191\u0026ndash;13216 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeFlorio, M. J. \u003cem\u003eet al.\u003c/em\u003e Semidirect dynamical and radiative effect of North African dust transport on lower tropospheric clouds over the subtropical North Atlantic in CESM 1.0. \u003cem\u003eJournal of Geophysical Research: Atmospheres\u003c/em\u003e 119, 8284\u0026ndash;8303 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePersad, G. G., Paynter, D. J., Ming, Y. \u0026amp; Ramaswamy, V. Competing atmospheric and surface-driven impacts of absorbing aerosols on the East Asian summertime climate. \u003cem\u003eJ Clim\u003c/em\u003e 30, 8929\u0026ndash;8949 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeingold, G., Jiang, H. \u0026amp; Harrington, J. Y. On smoke suppression of clouds in Amazonia. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 32, 1\u0026ndash;4 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, X. \u003cem\u003eet al.\u003c/em\u003e Effects of aerosol-radiation interaction on precipitation during biomass-burning season in East China. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 16, 10063\u0026ndash;10082 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, L. \u003cem\u003eet al.\u003c/em\u003e Impact of biomass burning aerosols on radiation, clouds, and precipitation over the Amazon during the dry season: dependence of aerosol-cloud and aerosol-radiation interactions on aerosol loading. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 1\u0026ndash;50 (2020) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/acp-2020-191\u003c/span\u003e\u003cspan address=\"10.5194/acp-2020-191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKok, J. F. \u003cem\u003eet al.\u003c/em\u003e Contribution of the world\u0026rsquo;s main dust source regions to the global cycle of desert dust. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 21, 8169\u0026ndash;8193 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eProspero, J. M., Delany, A. C., Delany, A. C. \u0026amp; Carlson, T. N. The discovery of african dust transport to the western hemisphere and the Saharan air layer: A history. \u003cem\u003eBull Am Meteorol Soc\u003c/em\u003e 102, E1239\u0026ndash;E1260 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebiyi, A. A. \u003cem\u003eet al.\u003c/em\u003e Aerosols over Africa: sources, properties, and distribution. in \u003cem\u003eAerosols and Precipitation Over Africa\u003c/em\u003e 1\u0026ndash;64 (Elsevier, 2025). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-44-314050-1.00006-2\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-44-314050-1.00006-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDas, S. \u003cem\u003eet al.\u003c/em\u003e Biomass burning aerosol transport and vertical distribution over the South African-Atlantic region. \u003cem\u003eJ Geophys Res\u003c/em\u003e 122, 6391\u0026ndash;6415 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZuidema, P. \u003cem\u003eet al.\u003c/em\u003e Smoke and clouds above the southeast Atlantic: Upcoming field campaigns probe absorbing aerosol\u0026rsquo;s impact on climate. \u003cem\u003eBulletin of the American Meteorological Society\u003c/em\u003e vol. 97 1131\u0026ndash;1135 Preprint at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/BAMS-D-15-00082.1\u003c/span\u003e\u003cspan address=\"10.1175/BAMS-D-15-00082.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebiyi, A. A. \u0026amp; Zuidema, P. The role of the southern African easterly jet in modifying the southeast Atlantic aerosol and cloud environments. \u003cem\u003eQuarterly Journal of the Royal Meteorological Society\u003c/em\u003e 142, 1574\u0026ndash;1589 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWinker, D. M. \u003cem\u003eet al.\u003c/em\u003e The CALIPSO Mission. \u003cem\u003eBulletin of American Meteorological Society\u003c/em\u003e 91, 1211\u0026ndash;1230 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenderson, D. S., L\u0026rsquo;ecuyer, T., Stephens, G., Partain, P. \u0026amp; Sekiguchi, M. A multisensor perspective on the radiative impacts of clouds and aerosols. \u003cem\u003eJ Appl Meteorol Climatol\u003c/em\u003e 52, 853\u0026ndash;871 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim, M. H. \u003cem\u003eet al.\u003c/em\u003e The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm. \u003cem\u003eAtmos Meas Tech\u003c/em\u003e 11, 6107\u0026ndash;6135 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmar, A. H. \u003cem\u003eet al.\u003c/em\u003e The CALIPSO automated aerosol classification and lidar ratio selection algorithm. \u003cem\u003eJ Atmos Ocean Technol\u003c/em\u003e 26, 1994\u0026ndash;2014 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaughan, M. A. \u003cem\u003eet al.\u003c/em\u003e Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products. in (ed. Singh, U. N.) 16 (2004). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1117/12.572024\u003c/span\u003e\u003cspan address=\"10.1117/12.572024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaughan, M. A. \u003cem\u003eet al.\u003c/em\u003e Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements. \u003cem\u003eJ Atmos Ocean Technol\u003c/em\u003e 26, 2034\u0026ndash;2050 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJethva, H., Torres, O., Remer, L. A. \u0026amp; Bhartia, P. K. A color ratio method for simultaneous retrieval of aerosol and cloud optical thickness of above-cloud absorbing aerosols from passive sensors: Application to MODIS measurements. \u003cem\u003eIEEE Transactions on Geoscience and Remote Sensing\u003c/em\u003e 51, 3862\u0026ndash;3870 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK. Gatebe, C., Jethva, H., Gautam, R., Poudyal, R. \u0026amp; Varnai, T. A new measurement approach for validating satellite-based above-cloud aerosol optical depth. \u003cem\u003eAtmos Meas Tech\u003c/em\u003e 14, 1405\u0026ndash;1423 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKacenelenbogen, M. \u003cem\u003eet al.\u003c/em\u003e An evaluation of CALIOP/CALIPSO\u0026rsquo;s aerosol-above-cloud detection and retrieval capability over North America. \u003cem\u003eJ Geophys Res\u003c/em\u003e 119, 230\u0026ndash;244 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePainemal, D. \u003cem\u003eet al.\u003c/em\u003e Novel aerosol extinction coefficients and lidar ratios over the ocean from CALIPSO-CloudSat: Evaluation and global statistics. \u003cem\u003eAtmos Meas Tech\u003c/em\u003e 12, 2201\u0026ndash;2217 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRajapakshe, C. \u003cem\u003eet al.\u003c/em\u003e Seasonally transported aerosol layers over southeast Atlantic are closer to underlying clouds than previously reported. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 44, 5818\u0026ndash;5825 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRogers, R. R. \u003cem\u003eet al.\u003c/em\u003e Looking through the haze: Evaluating the CALIPSO level 2 aerosol optical depth using airborne high spectral resolution lidar data. \u003cem\u003eAtmos Meas Tech\u003c/em\u003e 7, 4317\u0026ndash;4340 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMace, G. G. \u0026amp; Zhang, Q. The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results. \u003cem\u003eJ Geophys Res\u003c/em\u003e 119, 9441\u0026ndash;9462 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSassen, K. \u0026amp; Wang, Z. Classifying clouds around the globe with the CloudSat radar: 1-year of results. \u003cem\u003eGeophys Res Lett\u003c/em\u003e 35, (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoeb, N. G. \u0026amp; Schuster, G. L. An observational study of the relationship between cloud, aerosol and meteorology in broken low-level cloud conditions. \u003cem\u003eJournal of Geophysical Research Atmospheres\u003c/em\u003e 113, 1\u0026ndash;9 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMauger, G. S. \u0026amp; Norris, J. R. Assessing the Impact of Meteorological History on Subtropical Cloud Fraction. \u003cem\u003eJ Clim\u003c/em\u003e 23, 2926\u0026ndash;2940 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScott, R. C. \u003cem\u003eet al.\u003c/em\u003e Observed sensitivity of low-cloud radiative effects to meteorological perturbations over the global oceans. \u003cem\u003eJ Clim\u003c/em\u003e 33, 7717\u0026ndash;7734 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHunt, W. H. \u003cem\u003eet al.\u003c/em\u003e CALIPSO Lidar Description and Performance Assessment. \u003cem\u003eJ Atmos Ocean Technol\u003c/em\u003e 26, 1214\u0026ndash;1228 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu, X. \u003cem\u003eet al.\u003c/em\u003e Satellite retrieval of cloud base height and geometric thickness of low-level cloud based on CALIPSO. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 21, 11979\u0026ndash;12003 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaughan, M. A., Winker, D. M. \u0026amp; Hostetler, C. A. SIBYL: a selective iterated boundary location algorithm for finding cloud and aerosol layers in CALIPSO lidar data. 5\u0026ndash;8 Preprint at (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTackett, J. L. \u003cem\u003eet al.\u003c/em\u003e CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design. \u003cem\u003eAtmos Meas Tech\u003c/em\u003e 11, 4129\u0026ndash;4152 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026rsquo;Ecuyer, T. S., Wood, N. B., Haladay, T., Stephens, G. L. \u0026amp; Stackhouse, P. W. Impact of clouds on atmospheric heating based on the R04 CloudSat fluxes and heating rates data set. \u003cem\u003eJ Geophys Res\u003c/em\u003e 113, 1\u0026ndash;15 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ed\u0026rsquo;Almeida, G. A., Koepke, P. \u0026amp; Shettle, E. P. \u003cem\u003eAtmospheric Aerosols: Global Climatology and Radiative Characteristics\u003c/em\u003e. (Hampton, Va., USA SE, 1991).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenderson, D. \u0026amp; L\u0026rsquo;Ecuyer, T. \u003cem\u003eCloudSat Project A NASA Earth System Science Pathfinder Mission Level 2B Fluxes and Heating Rates with Lidar [2B-FLXHR-LIDAR] Process Description and Interface Control Doc-Ument\u003c/em\u003e. (2023) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2b-flxhr-lidar/2B-FLXHR-LIDAR_PDICD.P2_R05.rev0.pdf\u003c/span\u003e\u003cspan address=\"https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2b-flxhr-lidar/2B-FLXHR-LIDAR_PDICD.P2_R05.rev0.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCronk, H. \u003cem\u003eCloudSat ECMWF-AUX Auxillary Data Product Process Description and Interface Control Document\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/ecmwf-aux/ECMWF-AUX.PDICD.P1_R05.rev0.pdf\u003c/span\u003e\u003cspan address=\"https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/ecmwf-aux/ECMWF-AUX.PDICD.P1_R05.rev0.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/ecmwf-aux/ECMWF-AUX.PDICD.P1_R05.rev0.pdf\u003c/span\u003e\u003cspan address=\"https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/ecmwf-aux/ECMWF-AUX.PDICD.P1_R05.rev0.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDi Biagio, C. \u003cem\u003eet al.\u003c/em\u003e Complex refractive indices and single scattering albedo of global dust aerosols in the shortwave spectrum and relationship to iron content and size. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 19, 15503\u0026ndash;15531 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebiyi, A. A. \u003cem\u003eet al.\u003c/em\u003e Dust Constraints from joint Observational-Modelling-experiMental analysis (DustCOMM): Comparison with measurements and model simulations. \u003cem\u003eAtmos Chem Phys\u003c/em\u003e 20, 829\u0026ndash;863 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFormenti, P. \u0026amp; Di Biagio, C. Large synthesis of in situ field measurements of the size distribution of mineral dust aerosols across their life cycles. \u003cem\u003eEarth Syst Sci Data\u003c/em\u003e 16, 4995\u0026ndash;5007 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCochrane, S. P. \u003cem\u003eet al.\u003c/em\u003e Empirically derived parameterizations of the direct aerosol radiative effect based on ORACLES aircraft observations. \u003cem\u003eAtmos Meas Tech\u003c/em\u003e 14, 567\u0026ndash;593 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor, J. P. Measurements of the radiative and microphysical properties of stratocumulus over the South Atlantic and around the British Isles. \u003cem\u003eAtmos Res\u003c/em\u003e 34, 27\u0026ndash;41 (1994).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePawlowska, H., Brenguier, J.-L. \u0026amp; Sch\u0026uuml;ller, L. Microphysical and radiative properties of stratocumulus. \u003cem\u003ePhysics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere\u003c/em\u003e 24, 927\u0026ndash;932 (1999).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Hirok, W. \u0026amp; Gautier, C. A Three-Dimensional Radiative Transfer Model to Investigate the Solar Radiation within a Cloudy Atmosphere. Part I: Spatial Effects. \u003cem\u003eJ Atmos Sci\u003c/em\u003e 55, 2162\u0026ndash;2179 (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen, Y., Chong, K. \u0026amp; Fu, Y. Impacts of distribution patterns of cloud optical depth on the calculation of radiative forcing. \u003cem\u003eAtmos Res\u003c/em\u003e 218, 70\u0026ndash;77 (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Semidirect Effect, Dust, Smoke, Low-level Clouds","lastPublishedDoi":"10.21203/rs.3.rs-7168935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7168935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAerosol semi-direct effects represent one of the least understood yet important pathways of aerosol interactions. These effects occur when absorbing aerosols rapidly adjust Earth\u0026rsquo;s radiative budget through modifications of thermodynamic structures that influence cloud cover. Over the Atlantic Ocean, where two primary radiation-absorbing aerosols (smoke and dust) dominate above clouds, the mechanisms by which aerosol-layer properties affect underlying low-level cloud-top radiative cooling \u0026mdash; a critical parameter controlling cloudiness\u0026mdash;remain unclear. Using ten years of satellite-derived aerosol, cloud, and radiative flux observations, combined with radiative-transfer simulations, we find that dust and smoke layers induce longwave-dominated warming responses that weaken the mean radiative cooling at low-level cloud tops. However, the pathways of this warming response differ, resulting in dust layers impacting cloud-top cooling about ten times more than smoke layers. Whereas dust properties dominate dust-induced warming responses through direct interactions in longwave, smoke-induced warming responses involve enhanced smoke-layer moisture that induces longwave radiation, opposing the impacts of smoke properties at cloud tops. This weakened cloud-top cooling response reduces low-level cloudiness by approximately 1.21% and 0.28% for dust and smoke, respectively. Our findings demonstrate the importance of accounting for longwave-mediated processes beyond traditional shortwave-dominated mechanisms in estimates of aerosol semi-direct effects.\u003c/p\u003e","manuscriptTitle":"Dust and smoke layers over the Atlantic Ocean weaken the underlying low-level cloud-top radiative cooling through different pathways","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 09:20:00","doi":"10.21203/rs.3.rs-7168935/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fdc55852-217e-4af8-bc9d-f14798bfc0bf","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52209056,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric chemistry"},{"id":52209057,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics"}],"tags":[],"updatedAt":"2026-02-13T08:05:50+00:00","versionOfRecord":{"articleIdentity":"rs-7168935","link":"https://doi.org/10.1038/s43247-026-03183-x","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2026-01-16 05:00:00","publishedOnDateReadable":"January 16th, 2026"},"versionCreatedAt":"2025-07-30 09:20:00","video":"","vorDoi":"10.1038/s43247-026-03183-x","vorDoiUrl":"https://doi.org/10.1038/s43247-026-03183-x","workflowStages":[]},"version":"v1","identity":"rs-7168935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7168935","identity":"rs-7168935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}