Despite strong impacts that aerosols have on climate and air quality, significant gaps remain in our knowledge concerning their long‐range transport, especially extreme transport events. With this consideration in mind and by leveraging the “atmospheric river” concept, this work develops an objective global algorithm for detecting aerosol atmospheric rivers (AARs), shows a climatology of AARs, elucidates their contributions to major global aerosol transport pathways, and illustrates how AARs can drive extreme cases of poor air quality conditions. Our methodology separately accounts for dust, carbonaceous (accounting for organic and black carbon separately where appropriate), sea salt, and sulfate aerosols. Findings show there are a number of long‐range regional transport pathways where AARs account for a sizable fraction (40% to >100%) of the total transport in relatively few events (20–40 AAR days/year). This study highlights the role of AARs in establishing source‐receptor relationships that can drive regional air‐quality and extremes.

This study provides an observational assessment of the variations of the total rain volume (TRV) with aerosols through the entire lifetime of mesoscale convective systems (MCSs) over tropics. Using 70,000 MCSs' samples, we show that TRV increases with aerosols from clean to moderately heavy polluted conditions (aerosol optical depth [AOD] ~ 0.0–0.4). TRV decreases when AOD exceeds 0.5. The TRV change with AOD is strongest under favorable meteorological conditions, such as high total precipitable water (45–75 kg/m²), high convective available potential energy (1,200–2,400 J/kg), and intermediate vertical wind shear (9–21 × 10⁻⁴/s). TRV of MCSs increases from 2 to 4 km³ (rain depth ~ 20–40 mm) when AOD < 0.15 or >0.5, to more than 12 km³ (~120 mm) when 0.2 < AOD < 0.4 under above the mentioned optimal meteorological conditions. The basic response of TRV to aerosol concentrations is similar under all the meteorological conditions and during all stages of the MCS lifecycle.

Using satellite measurements from A‐Train constellation and Global Precipitation Measurement mission, we investigate the relationships between the afternoon time shallow convective top height (CTH_afternoon) and the evening time deep convective storm top height (CTH_evening) and rain rate (RR_evening) over the Amazon and Congo regions. We use CloudSat cloud type stratus and stratocumulus as the shallow afternoon clouds. Our results indicate that the afternoon shallow clouds over the Congo region are associated with suppressed and weakened evening time deep convection, whereas shallow clouds over the Amazon region are associated with the growth of the evening time deep convection. Over the Congo region, we find that as CTH_afternoon increases, shallow convective rain rate in the afternoon (RR_afternoon) increases. As a result, the evening time convective available potential energy (CAPE) as well as free tropospheric humidity (RH_700‐300) decrease. Consequently, condensation occurring inside deep convection reduces and CTH_evening as well as RR_evening decrease over there. Over the Amazon region, however, RR_afternoon does not vary significantly with CTH_afternoon. As CTH_afternoon increases, CAPE, RH_700‐300, and condensation occurring inside deep convection increase in the evening. As a result, deep convective CTH_evening and RR_evening increase with CTH_afternoon over the Amazon basin. These dissimilarities in the ambient condition drive the shallow to deep convective evolution differently over these two rainforests. On the other hand, shallow clouds that remain shallow in the evening are associated with less CAPE and RH_700‐300, RR_afternoon, and RR_evening. Although CAPE and RH_700‐300 promote deep convection to a height cloud top height, high vertical wind shear inhibits deep convection.

The Antarctic sea ice cover plays a crucial role in regulating global climate and sea level rise. The recent retreat of the Antarctic Sea Ice Extent and the accelerated melting of ice sheets (which causes sea level rise) raise concerns about the impact of climate change. Understanding the spatial patterns of anomalous melting events in sea ice is crucial for improving climate models and predicting future sea level rise, as sea ice serves as a protective barrier for ice sheets. This paper proposes a two-module framework based on Deep Learning that utilizes satellite imagery to identify and predict non-anomalous and anomalous melting regions in Antarctic sea ice. The first module focuses on identifying non-anomalous and anomalous melting regions in the current day by analyzing the difference between consecutive satellite images over time. The second module then leverages the current day's information and predicts the next day's non-anomalous and anomalous melting regions. This approach aims to improve our ability to monitor and predict critical changes in the Antarctic sea ice cover.

Traditional statistical analyses do not reveal the spatial locations and the temporal occurrences of clusters of anomalous events that are responsible for a significant loss of sea ice extent. To address this problem, we present a novel method named Convolution Matrix Anomaly Detection (CMAD). The onset and progression of clusters of anomalous melting events over the Antarctic Sea ice are studied as loss in sea ice extent, which are essentially negative values, where the traditional convolutional operation of the Convolutional Neural Network (CNN) approach is ineffective. CMAD is based on an inverse max pooling concept in the convolutional operation of CNN to address this gap. CMAD is developed to offer a solution without using a neural network, and unlike a full CNN, it doesn't require any training or testing processes. Satellite images are utilized to establish the loss in the Antarctic region. Our analysis shows that anomalous melting patterns have significantly affected the Weddell and the Ross Sea regions more than any other regions of the Antarctic, consistent with the largest disappearance in sea ice extent over these two regions. These findings bolster the applicability of the inverse max pooling based CMAD in detecting the spatiotemporal evolution of clusters of anomalous melting events over the Antarctic region. The anomalous melting process was first noticed along the outer boundary of the sea ice extent in early September 2022 and gradually engulfed the entire sea ice region by February 2023 -in tandem with the scientific literature. These findings indicate that there is a necessity to delve deeper into the role of the anomalous melting process on sea ice retreat for a better understanding of the sea ice retreat process. The nature of the problem is to detect clusters of contiguous grids of anomalous melting events rather than detecting discrete grid points. CMAD's ability to perform both data clustering and anomaly detection via the pooling operations allows for a more comprehensive analysis of sea ice melt patterns, facilitating the pinpointing of areas with potentially significant melt events. This method has the potential to apply in other fields of study where anomalous events are detected in clusters. The inverse max pooling concept has successfully detected clusters of anomalous events in sea ice and demonstrated the capability to detect anomalies with 87% accuracy in benchmark data. In contrast to well-established conventional methods such as DBSCAN, HDBSCAN, K-Means, Bisecting K-Means, BIRCH, Agglomerative Clustering, OPTICS, and Gaussian Mixtures, when applied to dynamic multidimensional data, CMADBenchmark (which is a variation of CMAD) exhibits superior capabilities in detecting extreme events. The comparative analysis reveals that CMADBenchmark outperforms these traditional approaches, showcasing its heightened sensitivity and efficacy in capturing significant variations within evolving multidimensional datasets over time. This heightens the detection accuracy positions of CMAD as a valuable tool for discerning extreme events in the context of dynamic and changing multidimensional data.

This study investigates the impacts of aerosol atmospheric rivers (AARs) on extreme Particulate Matter 2.5 (PM2.5) levels (PM2.5 > 15mgm-3, as per the WHO) and on aerosol optical depth (AOD) extremes (AOD > 98th percentile) over the US and the globe, respectively, between 1997-2020. Results show that over various regions over the US, extreme PM2.5values are associated with AARs up to 70% of the time. Dust (sulfate) AARs are responsible for extreme PM2.5 levels over the southwestern (northeastern and the east coastal) US. Organic and black carbon AARs are associated with extreme PM2.5 levels over the Midwest region of the US. Globally, AARs are associated with 40-80% of the extreme AOD levels over the US, Sahel, Europe, Middle East, US, South America, East Asia, India, and South Africa. Such associations often lead to the highest or the second highest PM2.5 and AOD levels recorded over those stations between 1997-2020.