Search Results (11)

In this study, we evaluate aerosol, cloud, and radiation interactions in GFS.V17.p8 (Global Forecast System System Version 17 prototype 8). Two experiments were conducted for the summer of 2020. In the control experiment (EXP CTL), aerosols interact with radiation only, incorporating direct and semi-direct aerosol effects. The sensitivity experiment (EXP ACI) couples aerosols with both radiation and Thompson microphysics, accounting for aerosol indirect effects and fully interactive aerosol–cloud dynamics. Introducing aerosol and cloud interactions results in net cooling at the top of the atmosphere (TOA). Further analysis shows that the EXP ACI produces more liquid water at lower levels and less ice water at higher levels compared to the EXP CTL. The aerosol optical depth (AOD) shows a good linear relationship with cloud droplet number concentration, similar to other climate models, though with larger standard deviations. Including aerosol and cloud interactions generally enhances simulations of the Indian Summer Monsoon, stratocumulus, and diurnal cycles. Additionally, the study evaluates the impacts of aerosols on deep convection and cloud life cycles. Full article

The response of low-level cloud properties to aerosol loading remains ambiguous, particularly due to the confounding influence of meteorological factors and water vapor availability. We utilize long-term data from Ka-band Zenith Radar, Clouds and the Earth’s Radiant Energy System, Modern-Era Retrospective analysis for Research and Applications Version 2, and European Centre for Medium-Range Weather Forecasts Reanalysis v5 to evaluate aerosol’s effects on low-level clouds under the constrains of meteorological conditions and liquid water path (LWP) over the Semi-Arid Climate and Environment Observatory of Lanzhou University during 2014–2019. To better constrain meteorological variability, we apply Principal Component Analysis to derive the first principal component (PC1), which strongly correlates with cloud properties, thereby enabling more accurate assessment of aerosol–cloud interaction (ACI) under constrained meteorological conditions delineated by PC1. Analysis suggests that under favorable meteorological conditions for low-level cloud formation (low PC1) and moderate LWP levels (25–150 g/m²), ACI is characterized by a significantly negative ACI index, with the cloud effective radius (CER) increasing in response to rising aerosol concentrations. When constrained by both PC1 and LWP, the relationship between CER and the aerosol optical depth shows a distinct bifurcation into positive and negative correlations. Different aerosol types show contrasting effects: dust aerosols increase CER under favorable meteorological conditions, whereas sulfate, organic carbon, and black carbon aerosols consistently decrease it, even under high-LWP conditions. Full article

Aerosol–cloud interactions play a crucial role in shaping Earth’s climate and hydrological cycle. Observing these interactions with high precision and accuracy is of the utmost importance for improving climate models and predicting Earth’s climate. Over the past few decades, lidar techniques have emerged as powerful tools for investigating aerosol–cloud interactions due to their ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and temporal resolutions. This review paper provides an overview of recent advancements in the study of ACI using lidar techniques. The paper begins with a description of the different cloud microphysical processes that are affected by the presence of aerosol, and with an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol–cloud interactions. This includes investigations into the role of aerosol particles in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research. By reporting the latest findings and methodologies, this review aims to provide valuable insights for researchers engaged in climate science and atmospheric research. Full article

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models. Full article

Aerosol–cloud–precipitation interactions (ACI) are a known major cause of uncertainties in simulations of the future climate. An improved understanding of the in-cloud processes accompanying ACI could help in advancing their implementation in global climate models. This is especially the case for marine stratocumulus clouds, which constitute the most common cloud type globally. In this work, a dataset composed of satellite observations and reanalysis data is used in explainable machine learning models to analyze the relationship between the cloud droplet number concentration ($N_d$), cloud liquid water path (LWP), and the fraction of precipitating clouds (PF) in five distinct marine stratocumulus regions. This framework makes use of Shapley additive explanation (SHAP) values, allowing to isolate the impact of $N_d$ from other confounding factors, which proved to be very difficult in previous satellite-based studies. All regions display a decrease of PF and an increase in LWP with increasing $N_d$, despite marked inter-regional differences in the distribution of $N_d$. Polluted (high $N_d$) conditions are characterized by an increase of 12 gm⁻² in LWP and a decrease of 0.13 in PF on average when compared to pristine (low $N_d$) conditions. The negative $N_d$–PF relationship is stronger in high LWP conditions, while the positive $N_d$–LWP relationship is amplified in precipitating clouds. These findings indicate that precipitation suppression plays an important role in MSC adjusting to aerosol-driven perturbations in $N_d$. Full article

We investigate the effective radiative forcing (ERF) of anthropogenic aerosols using simulations from seven Earth System Models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The ERF of individual aerosol species (black carbon, organic carbon, sulphates) is quantified along with the all-aerosol ERF and decomposed into its aerosol–radiation interactions (ARI), aerosol–cloud interactions (ACI) and surface albedo (ALB) components, using the method proposed by Ghan in 2013. We find that the total anthropogenic aerosol ERF at the top of the atmosphere (TOA) is negative, mainly due to aerosol–cloud interactions. Sulphates exhibit a strongly negative ERF especially over industrialized regions of the Northern Hemisphere, such as Europe, North America, East and South Asia, while black carbon exerts a positive ERF predominantly over East and South Asia. Full article

Long-term satellite climate data records of aerosol and cloud along with meteorological reanalysis data have been used to study the aerosol effects on deep convective clouds (DCCs) over the global oceans from a climatology perspective. Our focus is on identifying sensitive aerosol regimes and active geolocations of the aerosol effects on DCCs by using statistical analyses on long-term averaged aerosol and cloud variables. We found the aerosol effect tends to manifest relatively easily on the long-term mean values of observed cloud microphysical variables (e.g., cloud particle size and ice water amount) compared to observed cloud macrophysical variables (e.g., cloud cover and cloud top height). An increase of aerosol loading tends to increase DCC particle size and ice water amount in the tropical convergence zones but decrease them in the subtropical subsidence regions. The aerosol effect on the cloud microphysical variables is also likely to manifest over the northwestern Pacific Ocean and central and eastern subtropical Pacific Ocean. The aerosol effect manifested on the microphysical cloud variables may also propagate to cloud cover but weakly to cloud top height since the latter is more susceptible to the influence of cloud dynamical and thermodynamic processes. Our results, based on the long-term averaged operational satellite observation, are valuable for the evaluation and improvement of aerosol-cloud interactions in global climate models. Full article

The satellite-based cloud condensation nuclei (CCN) proxies used to quantify the aerosol-cloud interactions (ACIs) are column integrated and do not guarantee the vertical co-location of aerosols and clouds. This has encouraged the use of height-resolved measurements of spaceborne lidars for ACI studies and led to advancements in lidar-based CCN retrieval algorithms. In this study, we present a comparison between the number concentration of CCN (n_CCN) derived from ground-based in situ and spaceborne lidar cloud-aerosol lidar with orthogonal polarization (CALIOP) measurements. On analysing their monthly time series, we found that about 88% of CALIOP n_CCN estimates remained within a factor of 1.5 of the in situ measurements. Overall, the CALIOP estimates of monthly n_CCN were in good agreement with the in situ measurements with a normalized mean error of 71%, normalized mean bias of 39% and correlation coefficient of 0.68. Based on our comparison results, we point out the necessary measures that should be considered for global n_CCN retrieval. Our results show the competence of CALIOP in compiling a global height- and type-resolved n_CCN dataset for use in ACI studies. Full article

Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number ($AC{I}_{Nd}$) and effective radius ($AC{I}_{reff}$) of the droplets with altitude. Furthermore, $AC{I}_{Nd}$ and $AC{I}_{reff}$ for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum $AC{I}_{Nd}$ values are obtained when the activation of new droplets stops, while $AC{I}_{reff}$ reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of $30\%$ at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of $18\%$ cause an ACI variability of $30\%$ while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining $AC{I}_{reff}$ uncertainty below $16\%$ for the uncertainty of all LARSS input parameters of $10\%$. Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index ($AC{I}_{Rs}$), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the $AC{I}_{reff}$, which depends on the Angstrom exponent. The use of $AC{I}_{Rs}$ to derive $AC{I}_{reff}$ presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources. Full article

Aerosols can interact with other meteorological variables in the air via aerosol–radiation or aerosol–cloud interactions (ARIs/ACIs), thus affecting the concentrations of particle pollutants and ozone. The online-coupled model WRF-Chem was applied to simulate the changes in the PM_2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) and ozone concentrations that are caused by these mechanisms in China by conducting three parallel sensitivity tests. In each case, availabilities of aerosol–radiation interactions and aerosol–cloud interactions were set differently in order to distinguish each pathway. Partial correlation coefficients were also analyzed using statistical tools. As suggested by the results, the ARIs reduced ground air temperature, wind speed, and planetary boundary height while increasing relative humidity in most places. Consequently, the ozone concentration in the corresponding region declined by 4%, with a rise in the local annual mean PM_2.5 concentration by approximately 12 μm/m³. The positive feedback of the PM_2.5 concentration via ACIs was also found in some city clusters across China, despite the overall enhancement value via ACIs being merely around a quarter to half that via ARIs. The change in ozone concentration via ACIs exhibited different trends. The ozone concentration level increased via ACIs, which can be attributed to the drier air in the south and the diminished solar radiation that is received in central and northern China. The correlation coefficient suggests that the suppression in the planetary boundary layer is the most significant factor for the increase in PM_2.5 followed by the rise in moisture required for hygroscopic growth. Ozone showed a significant correlation with NO₂, while oxidation rates and radiation variance were also shown to be vitally important. Full article

Aerosol-cloud interactions (ACI) represent one of the most important sources of uncertainties in climate modelling. In this sense, realistic simulations of ACI are needed for a better understanding of the complex interactions between air pollution and the climate system. This work quantifies the added value of including ACI in an online coupled climate/chemistry model (WRF-Chem, 0.44 ${}^{\circ }$ horizontal resolution, years 2003 to 2010) in order to assess whether there is an improvement in the representation of aerosol optical depth (AOD). Modelling results for each species have been evaluated against the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, and AOD at 675 nm has been compared to AERONET data. Results indicate that the improvements of the monthly biases are around 8% for total AOD550 when including ACI, reaching 20% for the monthly bias in AOD550 coming from dust. Moreover, the temporal representation of AOD550 largely improves (increase in the Pearson time correlation coefficients), ranging from 6% to 20% depending on the chemical species considered. The benefits from this improvement overcome the problems derived from the high computational time required in ACI simulations (eight times higher with respect to simulations not including aerosol-cloud interactions). Full article