Review of Che et al.

The semi-permanent stratocumulus clouds over the South-eastern Atlantic Ocean (SEA) can act as an “air conditioners” to the regional and global climate system. The interaction of aerosols and clouds become important in this region, and can lead to negative radiative effects, partially offsetting the positive radiative forcing of greenhouse gases. A key pathway of aerosols affecting cloud properties is by acting as cloud condensation nuclei (CCN). In this paper, we use the United Kingdom Earth System Model to investigate the sources of CCN (from atmospheric processes and emission sources) in the SEA, and the response of cloud droplet number concentration (CDNC), cloud liquid water path (LWP), and radiative forcing to those sources. Overall, total nucleation (binary nucleation) is the most important source of CCN0.2 % in the marine boundary layer, contributing an annual average of 50 % of CCN0.2 %. In terms of emission sources, anthropogenic emissions (from energy, industry, agriculture, etc.) contribute the most to the annual average CCN0.2 % in the marine boundary layer, followed by BB. In the free troposphere, however, BB becomes the dominant source of CCN0.2 %, accounting for 64 % of the annual average. The contribution of aerosols from different sources to CDNC is consistent with their contribution to CCN0.2 % within the marine boundary layer, with total nucleation being the most important source of CDNC overall. In terms of emissions, anthropogenic sources are also the largest contributors to the annual average of CDNC, closely followed by BB. The contribution of BB to CDNC is more significant than its increase to CCN0.2 %, mainly because BB aerosol also can increase CDNC by enhancing the maximum supersaturation through the radiative effect of shortwave absorption. For an aerosol source that shows an increase in CDNC, it also shows an increase in LWP resulting from a reduction in autoconversion. BB aerosol, due to the absorption effect, can enhance existing temperature inversions and reduce the entrainment of sub-saturated air, leading to a further increase in LWP. As a result, the contribution of BB to LWP is second only to total nucleation. These findings demonstrate that BB is not the dominant source of CCN within the marine boundary layer from an emission source perspective. However, its contribution to clouds increases due to its absorption effect (about the same as anthropogenic sources for CDNC and more than anthropogenic sources for LWP), highlighting the crucial role of its radiative effect on clouds. The results on the radiative effects of aerosols show that BB aerosol exhibits an overall positive RFari (radiative forcing associated with aerosol-radiation interaction), but its net effective radiative forcing remains negative due to its effect on clouds (mainly by absorbing effect). By quantifying aerosol and cloud properties affected by different sources, this paper provides a framework to understand aerosol sources effects on the marine cirrocumulus clouds and radiation in the SEA.

The semi-permanent stratocumulus clouds over the South-eastern Atlantic Ocean (SEA) can act as an “air conditioners” to the regional and global climate system. The interaction of aerosols and clouds become important in this region, and can lead to negative radiative effects, partially offsetting the positive radiative forcing of greenhouse gases. A key pathway of aerosols affecting cloud properties is by acting as cloud condensation nuclei (CCN). In this paper, we use the United Kingdom Earth System Model to investigate the sources of CCN (from atmospheric processes and emission sources) in the SEA, and the response of cloud droplet number concentration (CDNC), cloud liquid water path (LWP), and radiative forcing to those sources. Overall, total nucleation (binary nucleation) is the most important source of CCN0.2 % in the marine boundary layer, contributing an annual average of 50 % of CCN0.2 %. In terms of emission sources, anthropogenic emissions (from energy, industry, agriculture, etc.) contribute the most to the annual average CCN0.2 % in the marine boundary layer, followed by BB. In the free troposphere, however, BB becomes the dominant source of CCN0.2 %, accounting for 64 % of the annual average. The contribution of aerosols from different sources to CDNC is consistent with their contribution to CCN0.2 % within the marine boundary layer, with total nucleation being the most important source of CDNC overall. In terms of emissions, anthropogenic sources are also the largest contributors to the annual average of CDNC, closely followed by BB. The contribution of BB to CDNC is more significant than its increase to CCN0.2 %, mainly because BB aerosol also can increase CDNC by enhancing the maximum supersaturation through the radiative effect of shortwave absorption. For an aerosol source that shows an increase in CDNC, it also shows an increase in LWP resulting from a reduction in autoconversion. BB aerosol, due to the absorption effect, can enhance existing temperature inversions and reduce the entrainment of sub-saturated air, leading to a further increase in LWP. As a result, the contribution of BB to LWP is second only to total nucleation. These findings demonstrate that BB is not the dominant source of CCN within the marine boundary layer from an emission source perspective. However, its contribution to clouds increases due to its absorption effect (about the same as anthropogenic sources for CDNC and more than anthropogenic sources for LWP), highlighting the crucial role of its radiative effect on clouds. The results on the radiative effects of aerosols show that BB aerosol exhibits an overall positive RFari (radiative forcing associated with aerosol-radiation interaction), but its net effective radiative forcing remains negative due to its effect on clouds (mainly by absorbing effect). By quantifying aerosol and cloud properties affected by different sources, this paper provides a framework to understand aerosol sources effects on the marine cirrocumulus clouds and radiation in the SEA.

Observation-based studies have shown that the aerosol cloud lifetime effect or the increase of cloud liquid water path (LWP) with increased aerosol loading may have been overestimated in climate models. Here, we simulate shallow warm clouds on 05/27/2011 at the Southern Great Plains (SGP) measurement site established by Department of Energy's Atmospheric Radiation Measurement (ARM) Program using a single column version of a global climate model (Community Atmosphere Model or CAM) and a cloud resolving model (CRM). The LWP simulated by CAM increases substantially with aerosol loading while that in the CRM does not. The increase of LWP in CAM is caused by a large decrease of the autoconversion rate when cloud droplet number increases. In the CRM, the autoconversion rate is also reduced, but this is offset or even outweighed by the increased evaporation of cloud droplets near cloud top, resulting in an overall decrease in LWP. Our results suggest that climate models need to include the dependence of cloud top growth and the evaporation/condensation process on cloud droplet number concentrations.

Abstract. The response in cloud water content to changes in cloud condensation nuclei remains one of the major uncertainties in determining how aerosols can perturb cloud properties. In this study, we used an ensemble of large eddy simulations of marine stratocumulus clouds to investigate the correlation between cloud liquid water path (LWP) and the amount of cloud condensation nuclei. We compare this correlation directly from the model to the correlation derived using equations which are used to retrieve liquid water path from satellite observations. Our comparison shows that spatial variability in cloud properties and instrumental noise in satellite retrievals of cloud optical depth and cloud effective radii results in bias in the satellite-derived liquid water path. In-depth investigation of high-resolution model data shows that in large part of a cloud, the assumption of adiabaticity does not hold, which results in a similar bias in the LWP–CDNC (cloud droplet number concentration) relationship as seen in satellite data. In addition, our analysis shows a significant positive bias of between 18 % and 40 % in satellite-derived cloud droplet number concentration. However, for the individual ensemble members, the correlation between the cloud condensation nuclei and the mean of the liquid water path was very similar between the methods. This suggests that if cloud cases are carefully chosen for similar meteorological conditions and it is ensured that cloud condensation nuclei concentrations are well-defined, changes in liquid water can be confidently determined using satellite data.

Changes in marine boundary layer cloud (MBLC) radiative properties in response to aerosol perturbations are largely responsible for uncertainties in future climate predictions. In particular, the relationship between the cloud droplet number concentration (Nd, a proxy for aerosol) and the cloud liquid water path (LWP) remains challenging to quantify from observations. In this study, satellite observations from multiple polar-orbiting platforms for 2006–2011 are used in combination with atmospheric reanalysis data in a regional machine learning model to predict changes in LWP in MBLCs in the Southeast Atlantic. The impact of predictor variables on the model output is analysed using Shapley values as a technique of explainable machine learning. Within the machine learning model, precipitation fraction, cloud top height, and Nd are identified as important cloud state predictors for LWP, with dynamical proxies and sea surface temperature (SST) being the most important environmental predictors. A positive nonlinear relationship between LWP and Nd is found, with a weaker sensitivity at high cloud droplet concentrations. This relationship is found to be dependent on other predictors in the model: Nd–LWP sensitivity is higher in precipitating clouds and decreases with increasing SSTs.

Aerosol-cloud interaction studies to date consider aerosol with a substantial fraction of soluble material as the sole source of cloud condensation nuclei (CCN). Emerging evidence suggests that mineral dust can act as good CCN through water adsorption onto the surface of particles. This study provides a first assessment of the contribution of insoluble dust to global CCN and cloud droplet number concentration (CDNC). Simulations are carried out with the NASA Global Modeling Initiative chemical transport model with an online aerosol simulation, considering emissions from fossil fuel, biomass burning, marine, and dust sources. CDNC is calculated online and explicitly considers the competition of soluble and insoluble CCN for water vapor. The predicted annual average contribution of insoluble mineral dust to CCN and CDNC in cloud-forming areas is up to 40 and 23.8%, respectively. Sensitivity tests suggest that uncertainties in dust size distribution and water adsorption parameters modulate the contribution of mineral dust to CDNC by 23 and 56%, respectively. Coating of dust by hygroscopic salts during the atmospheric aging causes a twofold enhancement of the dust contribution to CCN; the aged dust, however, can substantially deplete in-cloud supersaturation during the initial stages of cloud formation and can eventually reduce CDNC. Considering the hydrophilicity from adsorption and hygroscopicity from solute is required to comprehensively capture the dust-warm cloud interactions. The framework presented here addresses this need and can be easily integrated in atmospheric models.

Aerosol-cloud interactions remain one of the largest uncertainties in quantifying anthropogenic impacts on climate, particularly through their influence on cloud liquid water path (LWP), cloud droplet number concentration (CDNC), and cloud susceptibility to aerosol perturbations. This presentation synthesizes insights from satellite observations, large-eddy simulations, global climate models, long-term in-situ observations, and advanced statistical analyses to address biases and uncertainties in these interactions. Satellite-based studies often report a decreasing LWP with increasing CDNC, yet retrieval errors and natural spatial variability can mask positive LWP adjustments, leading to an underestimation of the cooling effects of aerosol-cloud interactions. Large-eddy simulations of marine stratocumulus clouds reveal that assumptions of adiabaticity and spatial variability in cloud properties contribute to biases in satellite-derived LWP-CDNC relationships. However, with careful case selection and well-defined meteorological conditions, satellite-based estimations can be improved. Building on these findings, global climate modeling and machine learning analyses highlight the importance of updraft velocity and aerosol size distributions in shaping the CCN-CDNC relationship. Advanced methods such as Elastic Net Regression isolate these confounding factors, refining susceptibility estimates and enhancing consistency with physical expectations. Further, long-term in-situ observations of aerosols and clouds at high-latitude locations reveal that the susceptibility of CDNC to CCN is significantly higher for low-level stratiform clouds than suggested by global oceanic satellite data. This implies stronger aerosol radiative forcing than current satellite-based estimates assume. Comparisons with Earth system models reveal large inter-model variability in susceptibility, driven by differences in sub-grid scale updraft velocities and aerosol size distributions. Even models with relatively accurate susceptibility values exhibit unrealistic underlying physics, highlighting areas for improvement in model representation. Lastly, combining satellite, reanalysis, and in-situ ACTRIS observations, we evaluate the roles of aerosol size distributions and updrafts in warm cloud formation, bridging gaps between microphysical processes and large-scale variability. This comprehensive approach emphasizes the need for integrating multi-platform observations with advanced modeling and statistical methods to reduce biases and improve the fidelity of aerosol-cloud interaction estimates. These advancements are crucial for more accurately quantifying aerosol radiative forcing and its implications for climate prediction.

Abstract. Airborne aerosol measurements in the central equatorial Pacific during PASE (Pacific Atmospheric Sulfur Experiment) revealed that cloud condensation nuclei (CCN) activated in marine boundary layer (MBL) clouds were strongly influenced by entrainment from the free troposphere (FT). About 65% entered at sizes effective as CCN in MBL clouds, while ~25% entered the MBL too small to activate but subsequently grew via gas to particle conversion. The remaining ~10% were inferred to be sea salt aerosol. FT aerosols at low carbon monoxide (CO) mixing ratios (< 63 ppbv) were mostly volatile at 360 °C with a number mode peak of around 30–40 nm dry diameter and tended to be associated with cloud outflow from distant (3000 km or more) deep convection. Higher CO concentrations were commonly associated with trajectories from South America and the Amazon region (ca. ~10 000 km away) and occurred in layers indicative of combustion sources (biomass burning season) partially scavenged by precipitation. These had number modes near 60–80 nm dry diameter with a large fraction of CCN.2 (those activated at 0.2% supersaturation and representative of MBL clouds) prior to entrainment into the MBL. Flight averaged concentrations of CCN.2 were similar for measurements near the surface, below the inversion and in the FT just above the inversion, confirming that subsidence and entrainment of FT aerosol strongly influenced MBL CCN.2. Concurrent flight-to-flight variations of CCN.2 at all altitudes below 3 km also imply MBL CCN.2 concentrations were in quasi-equilibrium with the FT over a 2–3 day timescale. The observed FT transport over thousands of kilometers indicates teleconnections between MBL CCN and cloud-scavenged sources of both natural and/or residual combustion origin. Nonetheless, in spite of its importance, this source of CCN number is not well represented in most current models and is generally not detectable by satellite because of the low aerosol scattering in such layers as a result of cloud scavenging. In addition, our measurements confirm nucleation in the MBL was not evident during PASE and argue against a localized linear relation in the MBL between dimethyl sulfide (DMS) and CCN suggested by the CLAW hypothesis. However, when the FT is not impacted by long-range transport, sulfate aerosol derived from DMS pumped aloft in the ITCZ (Inter-Tropical Convergence Zone) can provide a source of CCN to the boundary layer via FT teleconnections involving more complex non-linear processes.

<p>Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (N<sub>d</sub>) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between N<sub>d</sub> and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses the adiabatic assumption to derive the N<sub>d</sub> (N<sub>Ad</sub>), we have also considered N<sub>Ad</sub> from the LES model for comparison. The joint histogram analysis shows that the N<sub>Ad</sub>-LWP relationship in the LES model and the satellite is in approximate agreement. In both cases, the peak conditional probability (CP) is confined to lower N<sub>Ad</sub> and LWP, and the corresponding mean LWP shows a weak relation with N<sub>Ad</sub>. In contrast, at higher N<sub>Ad</sub> (> 50 cm<sup>−3</sup> ), the CP shows a larger spread; consequently, the mean LWP increases non-monotonically with increasing N<sub>Ad</sub> in both cases. However, the N<sub>Ad</sub>-LWP relation lacks, in particular, the negative sensitivity at higher N<sub>Ad</sub>. This case over continent thus behaves differently compared to previously-published analysis of Oceanic clouds using satellite retrievals. Additionally, our analysis illustrates a regime dependency (marine and continental) in the N<sub>Ad</sub>-LWP relation from the satellite retrievals. When considering the relationship of the simulated cloud-top N<sub>d</sub>, rather than N<sub>Ad</sub>, with LWP, the result shows a much more nonlinear (positive and negative) relationship and is inconsistent with the satellite retrievals. However, the difference is much less pronounced when the sensitivity (N<sub>d</sub>-LWP) is considered for shallow stratiform (adiabatic) than convective (sub-adiabatic) clouds. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear N<sub>d</sub>-LWP relationship. Hence a regime-based N<sub>d</sub>-LWP analysis is even more relevant when it comes to continental clouds and its comparison to satellite retrievals. </p>

Increased anthropogenic aerosols result in an enhancement in cloud droplet number concentration (Nd), which consequently modifies the cloud and precipitation process. It is unclear how exactly cloud liquid water path (LWP) and cloud fraction respond to aerosol perturbations. A volcanic eruption may help to better understand and quantify the cloud response to external perturbations, with a focus on the short-term cloud adjustments. The goal of the present study is to understand and quantify the response of clouds to a selected volcanic eruption and to thereby advance the fundamental understanding of the cloud response to external forcing. In this study we used the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather prediction setup at a cloud-system-resolving resolution of 2.5 km horizontally, to simulate the region around the Holuhraun volcano for one week (1–7 September 2014). A pair of simulations, with and without the volcanic aerosol plume, allowed us to assess the simulated effective radiative forcing and its mechanisms, as well as its impact on adjustments of LWP and cloud fraction to the perturbations of Nd. In comparison to MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals, a clear enhancement of Nd due to the volcanic aerosol is detected and attributed. In contrast, no changes in either LWP or cloud fraction could be attributed. The on average almost unchanged LWP is a result of some LWP enhancement for thick, and a decrease for thin clouds.

Increased anthropogenic aerosols result in an enhancement in cloud droplet number concentration (Nd), which consequently modifies the cloud and precipitation process. It is unclear how exactly cloud liquid water path (LWP) and cloud fraction respond to aerosol perturbations. A volcanic eruption may help to better understand and quantify the cloud response to external perturbations, with a focus on the short-term cloud adjustments. The goal of the present study is to understand and quantify the response of clouds to a selected volcanic eruption and to thereby advance the fundamental understanding of the cloud response to external forcing. In this study we used the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather prediction setup at a cloud-system-resolving resolution of 2.5 km horizontally, to simulate the region around the Holuhraun volcano for one week (1–7 September 2014). A pair of simulations, with and without the volcanic aerosol plume, allowed us to assess the simulated effective radiative forcing and its mechanisms, as well as its impact on adjustments of LWP and cloud fraction to the perturbations of Nd. In comparison to MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals, a clear enhancement of Nd due to the volcanic aerosol is detected and attributed. In contrast, no changes in either LWP or cloud fraction could be attributed. The on average almost unchanged LWP is a result of some LWP enhancement for thick, and a decrease for thin clouds.

<strong class="journal-contentHeaderColor">Abstract.</strong> Increased anthropogenic aerosols result in an enhancement in cloud droplet number concentration (<span class="inline-formula"><i>N</i>_d</span>), which consequently modifies the cloud and precipitation process. It is unclear how exactly the cloud liquid water path (LWP) and cloud fraction respond to aerosol perturbations. A volcanic eruption may help to better understand and quantify the cloud response to external perturbations, with a focus on the short-term cloud adjustments. The goal of the present study is to understand and quantify the response of clouds to a selected volcanic eruption and to thereby advance the fundamental understanding of the cloud response to external forcing. In this study we used the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather prediction setup at a cloud-system-resolving resolution of 2.5 km horizontally, to simulate the region around the Holuhraun volcano for 1 week (1–7 September 2014). A pair of simulations, with and without the volcanic aerosol plume, allowed us to assess the simulated effective radiative forcing and its mechanisms, as well as its impact on adjustments of LWP and cloud fraction to the perturbations of <span class="inline-formula"><i>N</i>_d</span>. In comparison to MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals, a clear enhancement of <span class="inline-formula"><i>N</i>_d</span> due to the volcanic aerosol is detected and attributed. In contrast, no changes in either LWP or cloud fraction could be attributed. The on average almost unchanged LWP is a result of some LWP enhancement for thick clouds and a decrease for thin clouds.

Understanding the complex relationship between aerosols and clouds is crucial for accurately estimating anthropogenic aerosol radiative forcing and its impact on weather and climate. However, quantifying the relationship between cloud droplet number concentration (CDNC) and cloud condensation nuclei (CCN) concentration remains challenging due to extensive heterogeneities in cloud properties, processes occurring during cloud lifecycle, and observational uncertainties. This study integrates satellite observations with a global climate model and employs advanced statistical techniques to improve estimates of cloud droplet susceptibility to aerosol perturbations, particularly focusing on the CCN‐CDNC slope. A key challenge in determining this relationship is that CDNC is influenced by factors beyond CCN alone, such as atmospheric dynamics and cloud microphysics. These additional factors introduce variability that complicates direct correlation between CCN and CDNC, making it difficult to ascertain their true relationship. In addition, traditional methods like Ordinary Least Squares regression can produce biased slope estimates due to uncertainties in both CCN and CDNC measurements. When applied to CCN‐CDNC data, advanced curve fitting methods, such as bivariate least squares, often yield slopes exceeding 1, deviating from expected physical behavior. Our machine learning analysis identifies updraft velocity, among other key predictors, as a major factor leading to this larger‐than‐one slope between CCN and CDNC. To address this, we utilized Elastic Net Regression to isolate the effect of changes in CCN concentration on CDNC. This method refines the slope estimates by accounting for factors affecting CDNC, resulting in a slope that better captures the susceptibility of CDNC to CCN changes.

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties.There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24% and −35% for particles with dry diameters > 50 and > 120nm, as well as −36% and −34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important.An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter.Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer.In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13% and −22% for updraft velocities 0.3 and 0.6 ms−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

Abstract. The global reduction in shipping fuel sulphur that culminated in 2020 with an ∼ 80 % reduction has created a large-scale natural experiment on the role of aerosol-cloud interaction (ACI) in the climate system. We compare observations from the Atmospheric Radiation Measurement program's Eastern North Atlantic site (ARM-ENA; 39.1° N, 28.0° W) during two June to September periods: 2016–2018 (pre-2020) and 2021–2023 (post-2020). We find a significant (∼ 15 %) decrease in cloud condensation nuclei concentrations post-2020, which resulted in a decrease in cloud droplet number (Nd) and an increase in effective radius (re) of marine boundary layer clouds. However, cloud liquid water path (LWP) increased post-2020. The increase in LWP offset the increase in re, resulting in insignificant changes to optical depth. MODIS and CERES data in the vicinity of ENA during these periods produce similar results also with negligible change in albedo and optical depth. Regional cloud occurrence declined in line with changes in the large-scale meteorology. Our results highlight the complex interplay of factors that modulate cloud feedbacks in the Eastern North Atlantic.