Search Results (23)

Accurate projection of future climate trends in arid regions critically depends on reliable precipitation simulations. However, most Coupled Model Intercomparison Project Phase 6 (CMIP6) models exhibit systematic overestimations of precipitation in Northwest China, a bias that undermines the credibility of climate projections for this vulnerable region. This persistent bias likely stems from the omission of key physical processes in traditional models. In this study, we incorporate a dust–ice-cloud interaction scheme into the Community Atmosphere Model version 5 (CAM5) model to investigate its role in regulating precipitation over dust-rich arid regions. This physical mechanism, which is rarely included in conventional models, is particularly relevant for Northwest China where dust aerosols are abundant. Our results show that accounting for dust-induced ice nucleation leads to a significant reduction in total precipitation, especially in the convective component, thereby alleviating the longstanding wet bias in the region. These findings underscore the critical importance of dust–ice-cloud interactions in simulating precipitation in arid environments. To improve the accuracy of future climate projections in Northwest China, climate models must incorporate realistic representations of dust-related microphysical processes. Full article

This study investigates the impact of desert dust on precipitation patterns using multi-model simulations. Dust-based processes of formation/removal of ice nuclei (IN) and cloud condensation nuclei (CCN) are investigated by using both the online access model WRF-CHIMERE and the online integrated model WRF-Chem. Comparisons of model predictions with rainfall measurements (GRISO: Spatial Interpolation Generator from Rainfall Observations) over the Italian peninsula show the models’ ability to reproduce heavy orographic precipitation in alpine regions. To quantify the impact of the mineral dust transport concomitant to the atmospheric river (AR) on cloud formation, a sensitivity study is performed by using the WRF-CHIMERE model (i) by setting dust concentrations to zero and (ii) by modifying the settings of the Thompson Aerosol-Aware microphysics scheme. Statistical comparisons revealed that WRF-CHIMERE outperformed WRF-Chem. It achieved a correlation coefficient of up to 0.77, mean bias (MB) between +3.56 and +5.01 mm/day, and lower RMSE and MAE values (~32 mm and ~22 mm, respectively). Conversely, WRF-Chem displayed a substantial underestimation, with an MB of −25.22 mm/day and higher RMSE and MAE values. Our findings show that, despite general agreement in spatial precipitation patterns, both models significantly underestimated the peak daily rainfall in pre-alpine regions (e.g., 216 mm observed at Malga Valine vs. 130–140 mm simulated, corresponding to a 35–40% underestimation). Although important instantaneous changes in precipitation and temperature were modeled at a local scale, no significant total changes in precipitation or air temperature averaged over the entire domain were observed. These results underline the complexity of aerosol–cloud interactions and the need for improved parameterizations in coupled meteorological models. Full article

Clouds and aerosols, as important factors in the Earth’s climate system, have significant impacts on the atmospheric environment and global climate. This study investigated the optical and physical properties of clouds and aerosols over South America from 2006 to 2021 using CALIPSO Level 2 products. South America was divided into four regions: A (Western Andean Mountains), B (Northern Orinoco and Amazon plains), C (Southern La Plata Plains), and D (Eastern Brazilian Highlands). Seasonal variations in the optical properties of low clouds and their interactions with the lowest-layer aerosols were analyzed and compared. The results indicate that Region C had the highest OP_lc (probability of low clouds) and AOD_lc (AOD (Aerosol Optical Depth) of low clouds, likely due to its flat terrain and westerly influences. Both AOD_lc and OP_lc were higher in September–November compared to other seasons. DR_lc (depolarization ratio of low clouds) values were higher in Regions C and D, particularly in September–February, possibly due to topographic effects and more precipitation and higher humidity during this period. The elevated CR_lc (color ratio of low clouds) in Region A may be attributed to the Andes blocking warm, moist air, leading to increased precipitation and cloud particle content. HL_lc (top height of low clouds) and BL_lc (base altitude of low clouds) were positively correlated with geographic elevation, while T_lc (thickness of low clouds) was greater at night, potentially due to enhanced atmospheric stability. Furthermore, strong correlations among certain parameters suggested significant interactions between aerosols and clouds. Full article

Phenolic compounds (PhCs) are aromatic compounds with benzene rings that have one or more hydroxyl groups. They are found or formed in the atmosphere due to various factors such as combustion processes, industrial emissions, oxidation of volatile organic compounds (VOCs), and other photochemical reactions. Due to properties such as relatively high Henry’s law constants and moderate/high water solubility, PhCs are vulnerable to reactions in atmospheric liquid phase conditions with high relative humidity, fog or cloudy conditions. PhCs can lead to the formation of secondary organic aerosols (SOAs), which can have negative effects on atmospheric conditions and human health. Changes in the optical properties of PhCs impact solar radiation absorption and scattering, potentially influencing climate. Additionally, PhCs may interact with other atmospheric constituents, potentially affecting cloud or fog formation and properties, which in turn can impact climate and precipitation patterns. Therefore, monitoring and controlling the emission of PhCs is essential. This paper discusses the transformation processes of PhCs in the atmosphere, including direct conversion of phenol, nitrate-induced and nitrite-induced reactions, hydroxylation reactions and oxidation processes involving triplet excited state organics, also providing a detailed analysis of the transformation processes. The findings lay a theoretical foundation for the future monitoring and control of atmospheric pollutants. Full article

The Mediterranean, and particularly its Eastern basin, is a crossroad of air masses advected from Europe, Asia and Africa. Anthropogenic emissions from its megacities meet over the Eastern Mediterranean, with natural emissions from the Saharan and Middle East deserts, smoke from frequent forest fires, background marine and pollen particles emitted from ocean and vegetation, respectively. This mixture of natural aerosols and gaseous precursors (Short-Lived Climate Forcers—SLCFs in IPCC has short atmospheric residence times but strongly affects radiation and cloud formation, contributing the largest uncertainty to estimates and interpretations of the changing cloud and precipitation patterns across the basin. The SLCFs’ global forcing is comparable in magnitude to that of the long-lived greenhouse gases; however, the local forcing by SLCFs can far exceed those of the long-lived gases, according to the Intergovernmental Panel on Climate Change (IPCC). Monitoring the spatiotemporal distribution of SLCFs using remote sensing techniques is important for understanding their properties along with aging processes and impacts on radiation, clouds, weather and climate. This article reviews the current state of scientific know-how on the properties and trends of SLCFs in the Eastern Mediterranean along with their regional interactions and impacts, depicted by ground- and space-based remote sensing techniques. 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

Interactions between clouds, aerosol, and precipitation are crucial aspects of weather and climate. The simple Koren–Feingold conceptual model is important for providing deeper insight into the complex aerosol–cloud–precipitation system. Recently, artificial neural networks (ANNs) and physics-informed neural networks (PINNs) have been used to study multiple dynamic systems. However, the Koren–Feingold model for aerosol–cloud–precipitation interactions has not yet been studied with either ANNs or PINNs. It is challenging for pure data-driven models, such as ANNs, to accurately predict and reconstruct time series in a small data regime. The pure data-driven approach results in the ANN becoming a “black box” that limits physical interpretability. We demonstrate how these challenges can be overcome by combining a simple ANN with physical laws into a PINN model (not purely data-driven, good for the small data regime, and interpretable). This paper is the first to use PINNs to learn about the original and modified Koren–Feingold models in a small data regime, including external forcings such as wildfire-induced aerosols or the diurnal cycle of clouds. By adding external forcing, we investigate the effects of environmental phenomena on the aerosol–cloud–precipitation system. In addition to predicting the system’s future, we also use PINN to reconstruct the system’s past: a nontrivial task because of time delay. So far, most research has focused on using PINNs to predict the future of dynamic systems. We demonstrate the PINN’s ability to reconstruct the past with limited data for a dynamic system with nonlinear delayed differential equations, such as the Koren–Feingold model, which remains underexplored in the literature. The main reason that this is possible is that the model is non-diffusive. We also demonstrate for the first time that PINNs have significant advantages over traditional ANNs in predicting the future and reconstructing the past of the original and modified Koren–Feingold models containing external forcings in the small data regime. We also show that the accuracy of the PINN is not sensitive to the value of the regularization factor ($\lambda$), a key parameter for the PINN that controls the weight for the physics loss relative to the data loss, for a broad range (from $\lambda =1\times {10}^3$ to $\lambda =1\times {10}^5$). Full article

We constructed the A-Train co-located aerosol and marine warm cloud data from 2006 to 2010 winter and spring over East Asia and investigated the sensitivities of single-layer warm cloud properties to aerosols under different precipitation statuses and environmental regimes. The near-surface stability (NSS), modulated by cold air on top of a warm surface, and the estimated inversion strength (EIS) controlled by the subsidence are critical environmental parameters affecting the marine warm cloud structure over East Asia and, thus, the aerosols–cloud interactions. Based on our analysis, precipitating clouds revealed higher cloud susceptibility to aerosols as compared to non-precipitating clouds. The cloud liquid water path (LWP) increased with aerosols for precipitating clouds, yet decreased with aerosols for non-precipitating clouds, consistent with previous studies. For precipitating clouds, the cloud LWP and albedo increased more under higher NSS as unstable air promotes more moisture flux from the ocean. Under stronger EIS, the cloud albedo response to aerosols was lower than that under weaker EIS, indicating that stronger subsidence weakens the cloud susceptibility due to more entrainment drying. Our study suggests that the critical environmental factors governing the aerosol–cloud interactions may vary for different oceanic regions, depending on the thermodynamic conditions. Full article

The Arabian Peninsula is a region characterized by diverse climatic conditions due to its location and geomorphological characteristics. Its precipitation patterns are characterized by very low annual amounts with great seasonal and spatial variability. Moreover, extreme events often lead to flooding and pose threat to human life and activities. Towards a better understanding of the spatiotemporal features of precipitation in the region, a thirty-year (1986-2015) climatic analysis has been prepared with the aid of the state-of-the-art numerical modeling system RAMS/ICLAMS. Its two-way interactive nesting capabilities, explicit cloud microphysical schemes with seven categories of hydrometeors and the ability to handle dust aerosols as predictive quantities are significant advantages over an area where dust is a dominant factor. An extended evaluation based on in situ measurements and satellite records revealed a good model behavior. The analysis was performed in three main components; the mean climatic characteristics, the rainfall trends and the extreme cases. The extremes are analyzed under the principles of the extreme value theory, focusing not only on the duration but also on the intensity of the events. The annual and monthly rainfall patterns are investigated and discussed. The spatial distribution of the precipitation trends revealed insignificant percentage differences in the examined period. Furthermore, it was demonstrated that the eastern part and the top half of the western Arabian Peninsula presented the lowest risk associated with extreme events. Apart from the pure scientific interest, the present study provides useful information for different sectors of society and economy, such as civil protection, constructions and reinsurance. Full article

The Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions Experiment Proposal (CACTI) field campaigns provided an unprecedented thirteen-disdrometer dataset in Central Argentina during the Intensive (IOP, 15 November to 15 December 2018) and Extended (EOP, 15 October 2018 to 30 April 2019) Observational Periods. The drop size distribution (DSD) parameters and their variability were analyzed across the region of interest, which was divided into three subregions characterized by the differing proximity to the Sierras de Córdoba (SDC), in order to assess the impact of complex terrain on the DSD parameters. A rigorous quality control of the data was first performed. The frequency distributions of DSD-derived parameters were analyzed, including the normalized intercept parameter (log$N_w$), the mean volume diameter ($D_0$), the mean mass diameter ($D_m$), the shape parameter ($\mu$), the liquid water content (LWC), and the rain rate (R). The region closest to the SDC presented higher values of log$N_w$, lower $D_0$, and higher $\mu$, while the opposite occurred in the farthest region, i.e., the concentration of small drops decreased while the concentration of bigger drops increased with the distance to the east of the SDC. Furthermore, the region closest to the SDC showed a bimodal distribution of $D_0$: the lower values of $D_0$ were associated with higher values of log$N_w$ and were found more frequently during the afternoon, while the higher $D_0$ were associated with lower log$N_w$ and occurred more frequently during the night. The data were analyzed in comparison to the statistical analysis of Dolan et al. 2018 and sorted according to the classification proposed in the cited study. The log$N_w$-$D_0$ and LWC-$D_0$ two-dimensional distributions allowed further discussion around the applicability of other mid-latitude and global precipitation classification schemes (startiform/convection) in the region of interest. Finally, three precipitation case studies were analyzed with supporting polarimetric radar data in order to relate the DSD characteristics to the precipitation type and the microphysical processes involved in each case. Full article

This paper presents the spatiotemporal variability of aerosols, clouds, and precipitation within the major cities in Eritrea and it investigates the relationship between aerosols, clouds, and precipitation concerning the presence of aerosols over the study region. In Eritrea, inadequate water supplies will have both direct and indirect adverse impacts on sustainable development in areas such as health, agriculture, energy, communication, and transport. Besides, there exists a gap in the knowledge on suitable and potential areas for cloud seeding. Further, the inadequate understanding of aerosol-cloud-precipitation (ACP) interactions limits the success of weather modification aimed at improving freshwater sources, storage, and recycling. Spatiotemporal variability of aerosols, clouds, and precipitation involve spatial and time series analysis based on trend and anomaly analysis. To find the relationship between aerosols and clouds, a correlation coefficient is used. The spatiotemporal analysis showed larger variations of aerosols within the last two decades, especially in Assab, indicating that aerosol optical depth (AOD) has increased over the surrounding Red Sea region. Rainfall was significantly low but AOD was significantly high during the 2011 monsoon season. Precipitation was high during 2007 over most parts of Eritrea. The correlation coefficient between AOD and rainfall was negative over Asmara and Nakfa. Cloud effective radius (CER) and cloud optical thickness (COT) exhibited a negative correlation with AOD over Nakfa within the June–July–August (JJA) season. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model that is used to find the path and origin of the air mass of the study region showed that the majority of aerosols made their way to the study region via the westerly and the southwesterly winds. Full article

Korea has occasionally suffered from various kinds of severe hazes such as long-range transported aerosol (LH), yellow sand (YS), and urban haze (UH). We classified haze days into LH, YS, and UH and analyzed the characteristics of its associated meteorological conditions for 2011–2016 using reanalysis data and surface observations. The results show that higher boundary layer height and stronger wind speed were found for the LH and YS hazes relative to those for UH. Intensive analysis on a golden episode of 10–18 January 2013 indicates that the cloud fraction increased along with extended light precipitation at a weaker rate by enhanced aerosol loading for an unprecedented LH event, which in turn brought about a decrease in boundary layer height (BLH) with less irradiance, that is, much stronger stability. Later, the intensified stability after the LH event accumulated and increased domestic aerosols, and eventually resulted in the longer-lasting severe haze. This study suggests that aerosol–meteorology interactions play an important role in both short-term weather and fine particle forecasts, especially on polluted days. Full article

The Greenland Ice Sheet (GrIS) has been a topic of extensive scientific research over the past several decades due to the exponential increase in its melting. The relationship between air pollution and GrIS melting was reviewed based on local emission of air pollutants, atmospheric circulation, natural and anthropogenic forcing, and ground/satellite-based measurements. Among multiple factors responsible for accelerated ice melting, greenhouse gases have long been thought to be the main reason. However, it is suggested that air pollution is another piece of the puzzle for this phenomenon. In particular, black carbon (BC) and other aerosols emitted anthropogenically interact with clouds and ice in the Arctic hemisphere to shorten the cloud lifespan and to change the surface albedo through alteration of the radiative balance. The presence of pollution plumes lowers the extent of super cooling required for cloud freezing by about 4 °C, while shortening the lifespan of clouds (e.g., by altering their free-energy barrier to prompt precipitation). Since the low-level clouds in the Arctic are 2–8 times more sensitive to air pollution (in terms of the radiative/microphysical properties) than other regions in the world, the melting of the GrIS can be stimulated by the reduction in cloud stability induced by air pollution. In this study, we reviewed the possible impact of air pollution on the melting of the GrIS in relation to meteorological processes and emission of light-absorbing impurities. Long-term variation of ground-based AERONET aerosol optical depth in Greenland supports the potential significance of local emission and long-range transport of air pollutants from Arctic circle and continents in the northern hemisphere in rapid GrIS melting trend. Full article

This study focuses on the long-term aerosol and precipitation chemistry measurements from colocated monitoring sites in Southern Florida between 2013 and 2018. A positive matrix factorization (PMF) model identified six potential emission sources impacting the study area. The PMF model solution yielded the following source concentration profiles: (i) combustion; (ii) fresh sea salt; (iii) aged sea salt; (iv) secondary sulfate; (v) shipping emissions; and (vi) dust. Based on these results, concentration-weighted trajectory maps were developed to identify sources contributing to the PMF factors. Monthly mean precipitation pH values ranged from 4.98 to 5.58, being positively related to crustal species and negatively related to SO₄²⁻. Sea salt dominated wet deposition volume-weighted concentrations year-round without much variability in its mass fraction in contrast to stronger seasonal changes in PM_2.5 composition where fresh sea salt was far less influential. The highest mean annual deposition fluxes were attributed to Cl⁻, NO₃⁻, SO₄²⁻, and Na⁺ between April and October. Nitrate is strongly correlated with dust constituents (unlike sea salt) in precipitation samples, indicative of efficient partitioning to dust. Interrelationships between precipitation chemistry and aerosol species based on long-term surface data provide insight into aerosol–cloud–precipitation interactions. Full article

The weather effects of aerosol types were investigated using well-posed aerosol climatology through the aerosol sensitivity test of thermodynamic and hydrometeor fields, and the weather forecast performances in July of 2017. The largest aerosol direct radiative forcing (ADRF) in July was due to dust aerosols at the surface and atmosphere, and sulfate at the top of the atmosphere (TOA), respectively. The ADRF of total aerosols had unilateral tendencies in thermodynamic and hydrometeor fields. The contribution of individual aerosols was linearly additive to those of total aerosols in the heat fluxes, heating rates, humidity, and convective precipitation. However, no such linearity existed in temperature, geopotential height, cloud liquid or ice contents, and large-scale precipitation. Dust was the most influential forcing agent in July among five aerosol types due to the largest light-absorption capacity. Such unilateral tendencies of total aerosols and a part of the linearity of individual aerosols were exerted on the weather systems. The verification of medium-range forecasts showed that aerosols alleviated the overestimation of surface shortwave (SW) downward fluxes, the negative biases of temperature and geopotential heights at TOA and surface, and the underestimation in light and moderate precipitation. In contrast, they enhanced warm biases at the mid-atmosphere and underestimation in heavy precipitations, particularly negative biases in the intertropical convergence zone (ITCZ). Weather forecast scores including current aerosol information were improved in geopotential height (GPH) of the northern hemisphere (NH); however, they got worse in the temperature and the upper atmosphere GPH of the southern hemisphere (SH), which was mostly due to black carbon (BC) aerosols in the tropical regions. The missing mechanisms such as aerosol–cloud interactions, better aerosol spectral optical properties including mixing states and aging, and the near-real-time (NRT) based aerosol loading data are worthwhile to be tried in the near future for fixing the intrinsic underestimation of precipitation in ITCZ and surface radiative fluxes in the desert and biomass burning area. Full article