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Aerosols have a direct effect on the Earth's radiative budget and can also affect cloud development and lifetime, and the aerosols above clouds (AAC) are particularly associated with high uncertainties in global climate models. Therefore, it is a prerequisite to improve the description and understanding of these situations. During the austral winter, large loadings of biomass burning aerosols originating from fires in the southern African subcontinent are lifted and transported westwards, across the southeastern Atlantic Ocean. The negligible wet scavenging of these absorbing aerosols leads to a near-persistent smoke layer above one of the largest stratocumulus cloud decks on the planet. Therefore, the southeastern Atlantic region is a very important area for studying the impact of above-cloud absorbing aerosols, their radiative forcing and their possible effects on clouds. In this study we aim to analyse and quantify the effect of smoke loadings on cloud properties using a synergy of different remote sensing techniques from A-Train retrievals (methods based on the passive instruments POLDER and MODIS and the operational method of the spaceborne lidar CALIOP), collocated with ERA-Interim re-analysis meteorological profiles. To analyse the possible mechanisms of AAC effects on cloud properties, we developed a high and low aerosol loading approach, which consists in evaluating the change in radiative quantities (i.e. cloud-top cooling, heating rate vertical profiles) and cloud properties with the smoke loading. During this analysis, we account for the variation in the meteorological conditions over our sample area by selecting the months associated with one meteorological regime (June–August). The results show that the region we focus on is primarily under the energetic influence of absorbing aerosols, leading to a significant positive shortwave direct effect at the top of the atmosphere. For larger loads of AACs, clouds are optically thicker, with an increase in liquid water path of 20 g m−2 and lower cloud-top altitudes by 100 m. These results do not contradict the semi-direct effect of above-cloud aerosols, explored in previous studies. Furthermore, we observe a strong covariance between the aerosol and the water vapour loadings, which has to be accounted for. A detailed analysis of the heating rate profiles shows that within the smoke layer, the absorbing aerosols are 90 % responsible for warming the ambient air by approximately 5.7 K d−1. The accompanying water vapour, however, has a longwave effect at distance on the cloud top, reducing its cooling by approximately 4.7 K d−1 (equivalent to 7 %). We infer that this decreased cloud-top cooling in particular, in addition with the higher humidity above the clouds, might modify the cloud-top entrainment rate and its effect, leading to thicker clouds. Therefore, smoke (the combination of aerosol and water vapour) events would have the potential to modify and probably reinforce the underlaying cloud cover.

Black carbon aerosols are the second largest contributor to global warming while also being linked to respiratory and cardiovascular disease. These particles are generally found in smoke plumes originating from biomass burning and fossil fuel combustion. They are also heavily concentrated in smoke plumes originating from oil fires, exhibiting the largest ratio of black carbon to organic carbon. In this study, we identified and analysed oil smoke plumes derived from 30 major industrial events within a 12-year timeframe. To our knowledge, this is the first study of its kind that utilized a synergetic approach based on satellite remote sensing techniques. Satellite data offer access to these events, which, as seen in this study, are mainly located in war-prone or hazardous areas. This study focuses on the use of MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) products regarding these types of aerosol while also highlighting their intrinsic limitations. By using data from both MODIS instruments on board Terra and Aqua satellites, we addressed the temporal evolution of the smoke plume while assessing lidar-specific properties and plume elevation using CALIPSO data. The analysis method in this study was developed to better differentiate between oil smoke aerosols and the local atmospheric scene. We present several aerosol properties in the form of plume-specific averaged values. We believe that MODIS values are a conservative estimation of plume aerosol optical depth (AOD) since MODIS algorithms rely on general aerosol models and various atmospheric conditions within the look-up tables, which do not reflect the highly absorbing nature of these smoke plumes. Based on this study we conclude that the MODIS land algorithms are not yet suited for retrieving aerosol properties for these types of smoke plumes due to the strong absorbing properties of these aerosols. CALIPSO retrievals rely heavily on the type of lidar solutions showing discrepancy between constrained and unconstrained retrievals. Smoke plumes identified within a larger aerosol layer were treated as unconstrained retrievals and resulted in conservative AOD estimates. Conversely, smoke plumes surrounded by clear air were identified as opaque aerosol layers and resulted in higher lidar ratios and AOD values. Measured lidar ratios and particulate depolarization ratios showed values similar to the upper ranges of biomass burning smoke. Results agree with studies that utilized ground-based retrievals, in particular for Ångström exponent (AE) and effective radius (Reff) values. MODIS and CALIPSO retrieval algorithms disagree on AOD ranges, for the most part, due to the extreme light-absorbing nature of these types of aerosols. We believe that these types of studies are a strong indicator for the need of improved aerosol models and retrieval algorithms.

This study presents a comparison between the retrieval of optical properties of aerosol above clouds (AAC) from different techniques developed for the A-Train sensors CALIOP/CALIPSO and POLDER/PARASOL. The main objective is to analyse the consistency between the results derived from the active and the passive measurements. We compare the aerosol optical thickness (AOT) above optically thick clouds (cloud optical thickness (COT) larger than 3) and their Ångström exponent (AE). These parameters are retrieved with the CALIOP operational method, the POLDER operational polarization method and the CALIOP-based depolarization ratio method (DRM) – for which we also propose a calibrated version (denominated DRMSODA, where SODA is the Synergized Optical Depth of Aerosols). We analyse 6 months of data over three distinctive regions characterized by different types of aerosols and clouds. Additionally, for these regions, we select three case studies: a biomass-burning event over the South Atlantic Ocean, a Saharan dust case over the North Atlantic Ocean and a Siberian biomass-burning event over the North Pacific Ocean. Four and a half years of data are studied over the entire globe for distinct situations where aerosol and cloud layers are in contact or vertically separated. Overall, the regional analysis shows a good correlation between the POLDER and the DRMSODA AOTs when the microphysics of aerosols is dominated by fine-mode particles of biomass-burning aerosols from southern Africa (correlation coefficient (R2) of 0.83) or coarse-mode aerosols of Saharan dust (R2 of 0.82). A good correlation between these methods (R2 of 0.68) is also observed in the global treatment, when the aerosol and cloud layers are separated well. The analysis of detached layers also shows a mean difference in AOT of 0.07 at 532 nm between POLDER and DRMSODA at a global scale. The correlation between the retrievals decreases when a complex mixture of aerosols is expected (R2 of 0.37) – as in the East Asia region – and when the aerosol–cloud layers are in contact (R2 of 0.36). The correlation coefficient between the CALIOP operational method and POLDER is found to be low, as the CALIOP method largely underestimates the aerosol loading above clouds by a factor that ranges from 2 to 4. Potential biases on the retrieved AOT as a function of cloud properties are also investigated. For different types of scenes, the retrieval of above-cloud AOT from POLDER and from DRM are compared for different underlying cloud properties (droplet effective radius (reff) and COT retrieved with MODIS). The results reveal that DRM AOT vary with reff. When accounting for reff in the DRM algorithm, the consistency between the methods increases. The sensitivity study shows that an additional polarized signal coming from aerosols located within the cloud could affect the polarization method, which leads to an overestimation of the AOT retrieved with POLDER algorithm. In addition, the aerosols attached to or within the cloud can potentially impact the DRM retrievals through the modification of the cloud droplet chemical composition and its ability to backscatter light. The next step of this work is to combine POLDER and CALIOP to investigate the impacts of aerosols on clouds and climate when these particles are transported above or within clouds.

Simulations of total and polarized cloud reflectance angular signatures such as the ones measured by the multi-angular and polarized radiometer POLDER3/PARASOL are used to evaluate cloud heterogeneity effects on cloud parameter retrievals. Effects on optical thickness, albedo, effective radius and variance of the cloud droplet size distribution and aerosol parameters above cloud are analyzed. Three different clouds that have the same mean optical thicknesses were generated: the first with a flat top, the second with a bumpy top and the last with a fractional cloud cover. At small scale (50 m), for oblique solar incidence, the illumination effects lead to higher total but also polarized reflectances. The polarized reflectances even reach values that cannot be predicted by the 1-D homogeneous cloud assumption. At the POLDER scale (7 km × 7 km), the angular signature is modified by a combination of the plane–parallel bias and the shadowing and illumination effects. In order to quantify effects of cloud heterogeneity on operational products, we ran the POLDER operational algorithms on the simulated reflectances to retrieve the cloud optical thickness and albedo. Results show that the cloud optical thickness is greatly affected: biases can reach up to −70, −50 or +40 % for backward, nadir and forward viewing directions, respectively. Concerning the albedo of the cloudy scenes, the errors are smaller, between −4.7 % for solar incidence angle of 20∘ and up to about +8 % for solar incidence angle of 60∘. We also tested the heterogeneity effects on new algorithms that allow retrieving cloud droplet size distribution and cloud top pressures and also aerosol above clouds. Contrary to the bi-spectral method, the retrieved cloud droplet size parameters are not significantly affected by the cloud heterogeneity, which proves to be a great advantage of using polarized measurements. However, the cloud top pressure obtained from molecular scattering in the forward direction can be biased up to about 60 hPa (around 550 m). Concerning the aerosol optical thickness (AOT) above cloud, the results are different depending on the available angular information. Above the fractional cloud, when only side scattering angles between 100 and 130∘ are available, the AOT is underestimated because of the plane–parallel bias. However, for solar zenith angle of 60∘ it is overestimated because the polarized reflectances are increased in forward directions.

This paper presents the results of SO2 dispersion modeling of emissions from a large combustion plant (LCP), between 1-30 September 2010, using ISC AERMOD View software/ ISCST3 model, specialized in modeling of gas dispersion. As input data, the software uses technical parameters of the pollution source, as emission rate, stack height, gas temperature; meteorological data, such as air temperature, atmospheric pressure, air humidity, wind speed and direction; and topographic data from the SRTM3 global digital elevation model. These simulations are used to observe the physical processes that affect air pollutants as they disperse in the atmosphere, and to observe and study the associated environmental impact. Also, the paper compares the simulation results with imission data from local monitoring stations of National Environmental Protection Agency. These data are measured with the HORIBA APSA 370 ambient sulfur dioxide monitor, based on ultraviolet fluorescence method as its operating principle, and they correspond to sulfur dioxide imission concentrations near the power plant.

Constantly exposed to natural and technological disasters, community is increasingly considered a fundamental part of the emergency management system. The efficiency of an emergency response depends on how well the population is informed and prepared to respond to the demands of the authorities. In order to do so, education and risk communication are essential in preparing the population for an effective emergency response. The paper assesses the information and awareness level of a small Romanian community on emergency situations. This analysis was conducted using the social investigation methodology, namely the questionnaire. The target group consisted of people of different social conditions with the minimum age of 18. The current research intends to highlight the significance of education and to develop an appropriate educational curriculum directed towards comprehension and acceptance of risks, towards prevention knowledge and development of response capacity.

Large computer models are ubiquitous in the Earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core hours to run to completion while generating terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties, and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce ESEm: an open-source tool providing a general workflow for emulating and validating a wide variety of models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well-established, high-performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of ESEm through three case studies using ESEm to reduce parametric uncertainty in a general circulation model and explore precipitation sensitivity in a cloud-resolving model and scenario uncertainty in the CMIP6 multi-model ensemble.

The south-eastern Atlantic Ocean (SEA) is semi-permanently covered by one of the most extensive stratocumulus cloud decks on the planet and experiences about one-third of the global biomass burning emissions from the southern Africa savannah region during the fire season. To get a better understanding of the impact of these biomass burning aerosols on clouds and the radiation balance over the SEA, the latest generation of the UK Earth System Model (UKESM1) is employed. Measurements from the CLARIFY and ORACLES flight campaigns are used to evaluate the model, demonstrating that the model has good skill in reproducing the biomass burning plume. To investigate the underlying mechanisms in detail, the effects of biomass burning aerosols on the clouds are decomposed into radiative effects (via absorption and scattering) and microphysical effects (via perturbation of cloud condensation nuclei – CCN – and cloud microphysical processes). July–August means are used to characterize aerosols, clouds, and the radiation balance during the fire season. Results show that around 65 % of CCN at 0.2 % supersaturation in the SEA can be attributed to biomass burning. The absorption effect of biomass burning aerosols is the most significant on clouds and radiation. Near the continent, it increases the supersaturation diagnosed by the activation scheme, while further from the continent it reduces the altitude of the supersaturation. As a result, the cloud droplet number concentration responds with a similar pattern to the absorption effect of biomass burning aerosols. The microphysical effect, however, decreases the supersaturation and increases the cloud droplet concentration over the ocean, although this change is relatively small. The liquid water path is also significantly increased over the SEA (mainly caused by the absorption effect of biomass burning aerosols) when biomass burning aerosols are above the stratocumulus cloud deck. The microphysical pathways lead to a slight increase in the liquid water path over the ocean. These changes in cloud properties indicate the significant role of biomass burning aerosols for clouds in this region. Among the effects of biomass burning aerosols on the radiation balance, the semi-direct radiative effects (rapid adjustments induced by the radiative effects of biomass burning aerosols) have a dominant cooling impact over the SEA, which offset the warming direct radiative effect (radiative forcing from biomass burning aerosol–radiation interactions) and lead to an overall net cooling radiative effect in the SEA. However, the magnitude and the sign of the semi-direct effects are sensitive to the relative location of biomass burning aerosols and clouds, reflecting the critical task of the accurate modelling of the biomass burning plume and clouds in this region.

Aerosols influence Earth’s climate by interacting with radiation and clouds. Remote sensing techniques aim to enhance our understanding of aerosol forcing using ground-based and satellite retrievals. Despite technological advancements, challenges persist in reducing uncertainties in satellite remote sensing. Our study examines retrieval biases in MODIS sensors on Terra and Aqua satellites compared to AERONET ground-based measurements. We assess their performance and the correlation with the AERONET aerosol optical depth (AOD) using 14 years of data (2010–2023) from 29 AERONET stations across 10 Central–East European countries. The results indicate discrepancies between MODIS Terra and Aqua retrievals: Terra overestimates the AOD at 16 AERONET stations, while Aqua underestimates the AOD at 21 stations. The examination of temporal biases in the AOD using the calculated estimated error (ER) between AERONET and MODIS retrievals reveals a notable seasonality in coincident retrievals. Both sensors show higher positive AOD biases against AERONET in spring and summer compared to fall and winter, with few ER values for Aqua indicating poor agreement with AERONET. Seasonal variations in correlation strength were noted, with significant improvements from winter to summer (from R2 of 0.58 in winter to R2 of 0.76 in summer for MODIS Terra and from R2 of 0.53 in winter to R2 of 0.74 in summer for MODIS Aqua). Over the fourteen-year period, monthly mean aerosol AOD trends indicate a decrease of −0.00027 from AERONET retrievals and negative monthly mean trends of the AOD from collocated MODIS Terra and Aqua retrievals of −0.00023 and −0.00025, respectively. An aerosol classification analysis showed that mixed aerosols comprised over 30% of the total aerosol composition, while polluted aerosols accounted for more than 22%, and continental aerosols contributed between 22% and 24%. The remaining 20% consists of biomass-burning, dust, and marine aerosols. Based on the aerosol classification method, we computed the bias between the AERONET AE and MODIS AE, which showed higher AE values for AERONET retrievals for a mixture of aerosols and biomass burning, while for marine aerosols, the MODIS AE was larger and for dust the results were inconclusive.

This study addresses the challenges of characterizing aerosol optical depth (AOD) dynamics from satellite observations, which are often hindered by data gaps and variability. A long short-term memory (LSTM) network was trained on an extended AOD dataset from Sicily to capture temporal patterns. The trained model was then applied to AOD data from distinct geographical regions: Cluj-Napoca and the central Mediterranean Sea. While the LSTM effectively captured general seasonal trends, it tended to smooth extreme AOD events. To mitigate this, a post-processing algorithm was developed to enhance the representation of AOD peaks and valleys. This enhancement method refines the characterization of historical AOD, providing a more accurate representation of observed atmospheric variability, particularly in capturing high and low AOD episodes. The results demonstrate the efficacy of the hybrid approach in improving the characterization of AOD dynamics across different regions.