Atmospheric River Events Research Articles

Seasonal and Geographical Variations in Fundamental Weather Patterns during Extreme Precipitation as Identified from Omega Equation Forcing

Abstract We investigate the large-scale weather patterns during extreme precipitation (PEx) events over the conterminous United States (CONUS) by applying a version of the quasigeostrophic (QG) omega equation. This work aims to develop a climatology of the weather patterns most related to PEx events during current climate. Extreme events are examined for each of seven regions defined by consistent annual cycles of precipitation and spanning the CONUS. For the CONUS we train several self-organizing maps (SOM) on a pressure–time series of vertical velocity from each of the advective forcing terms in the QG omega equation for each extreme event. The unsupervised learning of the SOM allows us to identify the most descriptive set of nine patterns in vertical velocity associated with precipitation extremes. This method finds multiple frontal- and cyclone-driven patterns while grouping primarily convective events into one pattern. Frontal events include a synoptic pattern consistent with West Coast atmospheric river events as well as pattern groups linked to developing and to mature (“occluded”) frontal cyclones. The primary patterns found during PEx events vary seasonally and geographically. Frontal cyclone patterns are most common during PEx events during summer in the part of the Great Plains and during winter for the Northeast, Southeast, Pacific Northwest, and Southwest. Convection is the most common pattern during summer in all regions. Except in the Southeast, the annual cycles of monthly number of PEx events and average precipitation match well, partially validating our choice of regions to aggregate PEx events.

The June 2022 extreme warm event in central West Antarctica

AbstractThe Antarctic surface mass balance has been shown to be sensitive to the impacts of atmospheric rivers (ARs), which bring anomalous amounts of both moisture and heat from lower latitudes poleward. Therefore, describing the characteristics of ARs and their intensity and frequency in the Antarctic regions by applying detection algorithms became a key method to evaluating their impacts on the surface mass balance and melting events. Several intense AR events have influenced Antarctica during the year 2022, and here we report an event with a peak on 10 June 2022 that was detected at 84°S, having a potential impact on West Antarctica. The extreme warm event originated in the Southern Pacific subtropical region and evolved towards the Southern Ocean, crossing the northern Antarctic Peninsula, before reaching as far as most inland regions in Antarctica, different from other typical ARs that are mostly restricted to the continental coast.

Synoptic circulation patterns of urban floods for the city of Hyderabad

AbstractSynoptic fingerprinting of flood driving rainfall events over the Hyderabad city, India were assessed through an unsupervised machine learning technique—self‐organizing maps (SOM) for the period 1979–2020. Flood dates were identified using nonscientific data sources, and then large‐scale climate variables, that is, integrated vapour transport (IVT) and geopotential height at 500 hPa (Z500) along with rainfall at daily scale were considered. SOM was applied on IVT and results indicated large spatial scale‐ and season‐specific mechanisms in addition to local circulation systems—this highlights SOM's ability to cluster circulation mechanisms as per the relevance. The rainy seasons, that is, monsoon and postmonsoon showed well‐known moisture pathways. While synoptic systems can be easily linked with monsoon and postmonsoon mechanisms, it is interesting to note that the local systems exist even during the monsoon and postmonsoon season in addition to the summer season. Spatial patterns of Z500 corresponding to SOM nodes of IVT showed the presence of low‐pressure systems (LPSs), tropical cyclones and trough systems. Further analysis was performed, and a total of 35 atmospheric rivers (ARs) were identified. Plots of back trajectories for the selected AR events indicated that Arabian Sea is the main source of moisture in the monsoon season followed by Bay of Bengal for postmonsoon and tropical cyclones in summer season events. Additionally, moisture from land is also observed as another source of moisture. Back‐trajectory analysis further indicated AR‐LPS interaction during the monsoon and ARs feeding moisture to LPSs. Overall, well‐known and season‐specific synoptic systems that brought significant rainfall to the study region were identified by the SOM. The proposed framework is adaptable for different locations, and as floods in cities across the globe are on the rise, these findings will have a prominent role in urban flood forecasting and management.

Influence of the freezing level on atmospheric rivers in High Mountain Asia: WRF case studies of orographic precipitation extremes

Atmospheric rivers (ARs) reach High Mountain Asia (HMA) about 10 days per month during the winter and spring, resulting in about 20 mm day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} of precipitation. However, a few events may exceed 100 mm day-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}, providing most of the total winter precipitation and increasing the risk of precipitation-triggered landslides and flooding, particularly when the height of the height of the 0 ∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document}C isotherm, or freezing level is above-average. This study shows that from 1979 to 2015, integrated water vapor transport (IVT) during ARs that reach Western HMA has increased 16% while the freezing level has increased up to 35 m. HMA ARs that have an above-average freezing level result in 10–40% less frozen precipitation compared to ARs with a below-average freezing level. To evaluate the importance of these trends in the characteristics of ARs, we investigate mesoscale processes leading to orographic precipitation using Advanced Weather Research and Forecasting (ARW-WRF) simulations at 6.7 km spatial resolution. We contrast two above- and below- average freezing level AR events with otherwise broadly similar characteristics and show that with a 50–600 m increase in freezing level, the above-average AR resulted in 10–70% less frozen precipitation than the below-average event. This study contributes to a better understanding of climate change-related impacts within HMA’s hydrological cycle and the associated hazards to vulnerable communities living in the region.

Stratosphere‐Troposphere Coupling of Extreme Stratospheric Wave Activity in CMIP6 Models

AbstractExtreme stratospheric wave activity has been suggested to be connected to surface temperature anomalies, but some key processes are not well understood. Using observations, we show that the stratospheric events featuring weaker‐than‐normal wave activity are associated with increased North American (NA) cold extreme risks before and near the event onset, accompanied by less frequent atmospheric river (AR) events on the west coast of the United States. Strong stratospheric wave events, on the other hand, exhibit a tropospheric weather regime transition. They are preceded by NA warm anomalies and increased AR frequency over the west coast, followed by increased risks of NA cold extremes and north‐shifted ARs over the Atlantic. Moreover, these links between the stratosphere and troposphere are attributed to the vertical structure of wave coupling. Weak wave events show a wave structure of westward tilt with increasing altitudes, while strong wave events feature a shift from westward tilt to eastward tilt during their life cycle. This wave phase shift indicates vertical wave coupling and likely regional planetary wave reflection. Further examinations of CMIP6 models show that models with a degraded representation of stratospheric wave structure exhibit biases in the troposphere during strong wave events. Specifically, models with a stratospheric ridge weaker than the reanalysis exhibit a weaker tropospheric signal. Our findings suggest that the vertical coupling of extreme stratospheric wave activity should be evaluated in the model representation of stratosphere‐troposphere coupling.

Atmospheric Rivers and Weather Types in Aotearoa New Zealand: A Two‐Way Story

AbstractHere, we analyze the inter‐relationships between weather types (WTs) and atmospheric rivers (ARs) around Aotearoa New Zealand (ANZ), their respective properties, as well as their combined and separate influence on daily precipitation amounts and extremes. Results show that ARs are often associated with 3–4 WTs, but these WTs change depending on the regions where ARs landfall. The WTs most frequently associated with ARs generally correspond to those favoring anomalously strong westerly wind in the mid‐latitudes, especially for southern regions of ANZ, or northwesterly anomalies favoring moisture export from the lower latitudes, especially for the northern regions. WTs and ARs show strong within‐type and inter‐event diversity. The synoptic patterns of the WTs significantly differ when they are associated with AR occurrences, with atmospheric centers of actions being shifted so that moisture fluxes toward ANZ are enhanced. The location, angle, and persistence of ARs appear strongly driven by the synoptic configurations of the WTs. Although total moisture transport shows weaker WT‐dependency, it appears strongly related to zonal wind speed to the south of ANZ, or the moisture content of the air mass to the north. Finally, WT influence on daily precipitation may completely change depending on their association, or lack thereof, with AR events. WTs traditionally considered as favorable to wet conditions may conceal daily precipitation extremes occurring during AR days, and anomalously dry days or near‐climatological conditions during non‐AR days.

Dominant modes and mechanisms of atmospheric rivers in East Asia

Atmospheric rivers (ARs), the long and narrow conveyors of intense moisture transport, are a unique mover of moisture from the low latitudes to the mid to high latitudes and have a considerable impact on hydrometeorological events (i.e., extreme precipitation). Using the top-hat by reconstruction (THR) algorithm and ERA5 reanalysis data, this study analyzes the climatological characteristics of ARs over East Asia (EA) in summer (June to August) during 1979–2020. According to the four leading modes of EOF analysis, ARs events occur mainly in the Yangtze River valley, the Huaihe River valley, and the coastal region of South China. The sea surface temperature anomalies (SSTAs) related to the first EOF mode (EOF1) show the warming of the central and eastern Pacific and Indian Oceans. The northward movement of the westerly jet stream maintains the western North Pacific subtropical high (WNPSH) and guides water vapor transport to China. EOF2 corresponds to the positive phase of EAP (East Asia-Pacific) pattern. SSTAs related to EOF2 show an El Niño recession-like pattern, which modulates precipitation over southern China through anticyclones over the western North Pacific. SSTAs related to EOF3 show a La Niña-like pattern, which regulates the precipitation in the Yangtze-Huaihe valley through upper-level convective heating on the north side of the WNPSH. For EOF4, the circulation system affecting ARs is dispersed, resulting in smaller scale ARs. ARs of EOF4 are associated with cyclones, mainly showing the characteristics of coastal landing ARs. These results can provide a new forecasting source for EA summer precipitation.

Application of Machine Learning Techniques to Improve Multi-Radar Multi-Sensor (MRMS) Precipitation Estimates in the Western United States

Abstract The Multi-Radar Multi-Sensor (MRMS) system produces a suite of hydrometeorological products that are widely used for applications such as flash flood warning operations, water resource management, and climatological studies. The MRMS radar-based quantitative precipitation estimation (QPE) products have greater challenges in the western United States compared to the eastern two-thirds of the CONUS due to terrain-related blockages and gaps in radar coverage. Further, orographic enhancement of precipitation often occurs, which is highly variable in space and time and difficult to accurately capture with physically based approaches. A deep learning approach was applied in this study to understand the correlations between several interacting variables and to obtain a more accurate precipitation estimation in these scenarios. The model presented here is a convolutional neural network (CNN), which uses spatial information from small grids of several radar variables to predict an estimated precipitation value at the central grid point. Several case analyses are presented along with a yearlong statistical evaluation. The CNN model 24-h QPE shows higher accuracy than the MRMS radar QPE for several cool season atmospheric river events. Areas of consistent improvement from the CNN model are highlighted in the discussion along with areas where the model can be further improved. The initial findings from this work help set the foundation for further exploration of machine learning techniques and products for precipitation estimation as part of the MRMS operational system. Significance Statement This study explores the development and use of a deep learning model to generate precipitation fields in the complex terrain of the western United States. Generally, the model is able to improve on the statistical performance of existing radar-based precipitation estimation methods for several case studies and over a long-term period in 2021. We explore the patterns associated with certain areas of strong performance and suggest potential means of improving areas with weaker performance. These initial results indicate the potential of deep learning to supplement radar-based approaches in areas with observational limitations.

Performance of New Near-Real-Time PERSIANN Product (PDIR-Now) for Atmospheric River Events over the Russian River Basin, California

Abstract Most heavy precipitation events and extreme flooding over the U.S. Pacific coast can be linked to prevalent atmospheric river (AR) conditions. Thus, reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for water management and early warning systems of flooding and landslides over these regions. At the same time, high-quality near-real-time measurements of AR precipitation remain challenging due to the complex topographic features of land surface and meteorological conditions of the region: specifically, orographic features occlude radar measurements while infrared-based algorithms face challenges, differentiating between both cold brightband (BB) precipitation and the warmer nonbrightband (NBB) precipitation. It should be noted that the latter precipitation is characterized by greater orographic enhancement. In this study, we evaluate the performance of a recently developed near-real-time satellite precipitation algorithm: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate-Now (PDIR-Now). This model is primarily dependent on infrared information from geostationary satellites as input; consequently, PDIR-Now has the advantage of short data latency, 15–60-min delay between observation to precipitation product delivery. The performance of PDIR-Now is analyzed with a focus on AR-related events for cases dominated by NBB and BB precipitation over the Russian River basin. In our investigations, we utilize S-band (3-GHz) precipitation profilers with Joss/Parsivel disdrometer measurements at the Middletown and Santa Rosa stations to classify BB and NBB precipitation events. In general, our analysis shows that PDIR-Now is more skillful in retrieving precipitation rates over both BB and NBB events across the topologically complex study area as compared to PERSIANN-Cloud Classification System (CCS). Also, we discuss the performance of well-known operational near-real-time precipitation products from 2017 to 2019. Conventional categorical and volumetric categorical indices, as well as continuous statistical metrics, are used to show the differences between various high-resolution precipitation products such as Multi-Radar Multi-Sensor (MRMS).

Impacts of moisture supply from the subtropical western Pacific on the subtropical high and the atmospheric river during the heavy rain of 2020 in Japan

Our recent study suggested that moisture from the subtropical Western Pacific (WP) contributed the most to an atmospheric river (AR) event and the related heavy rainfall during the heavy rain of 2020 in Japan based on a Lagrangian approach. However, the actual role of moisture from the subtropical WP region in the AR and heavy rain formations remains unclear. To evaluate that, we conducted a set of numerical sensitivity experiments by adjusting the surface moisture supply over the subtropical WP region with factors of 0%, 50%, and 200%. The sensitivity experiments suggest that the reduced surface evaporation over the subtropical WP suppressed the local convective activity and decreased moisture content in the whole troposphere, leading to shallow and weak positive geopotential height anomalies. Although the slightly strengthened WP subtropical High (WPSH) and related anomalous anticyclonic circulation enhanced the southwesterly wind, convective activities along the Meiyu-Baiu front were still weakened due to the largely reduced moisture supply, resulting in another anomalous anticyclonic circulation over Japan but had much stronger and deeper structures. These two anomalous circulations and the reduced moisture modulated the AR over Japan, which eventually caused the weakened rainfalls and the northward migration of the rainband. By contrast, larger surface evaporation enhanced the local convective activities and weakened the dominant WPSH, resulting in the weakening and the southward migration of the AR. Overall, this study confirmed the large contributions of moisture supply from the subtropical WP region to the AR and related precipitation over Japan during the record-breaking Meiyu-Baiu season in 2020 via both dynamic and thermodynamic influences. In addition, it reveals that, although larger evaporation over the WP region would increase the total rainfalls but would not have led to more several rainfalls over certain regions, such as the relatively small Kyushu Island.

Preliminary investigations of ground failures triggered during the mid-November 2021 atmospheric river event along the southwestern British Columbia highway corridors

An unusually intense atmospheric river affected the southern coastal region of British Columbia in mid-November 2021. Flooding and several ground failures such as landslides and riverbank erosion caused significant damage and interrupted the main transportation corridors, including major highways and railroads, severing land connectivity between Metro Vancouver and the rest of Canada. In this short note, we summarize and describe slope and riverbank ground failures in the Lower Mainland and connecting corridors to the interior such as the Fraser Canyon and the Coquihalla Valley, with focus on those that produced damage on highways. Field observations are discussed in the context of potential future similar disasters in the region due to more frequent extreme climatic events associated with climate change.

Process and perturbation in a high elevation alluvial system, Yosemite National Park, USA

High-elevation alluvial river environments are little studied by geomorphologists despite their sensitivity to disturbance and their critical role as the landform supporting montane meadow ecosystems. Here, we establish the fundamental fluvial functioning and legacy factors inherent to the upper Tuolumne River in Tuolumne Meadows (elevation 2620 m asl), Yosemite National Park, USA. Evidence was drawn from structured field reconnaissance, morphological surveys, and sediment analysis; integrated with LiDAR monitoring and assessments of flood frequency, flood inundation, and sediment transport potential. Results indicate a sinuous single-thread channel in dynamic equilibrium with fluvial processes but with reduced rates of meander activity over the past century. Geomorphic processes are dominated by the influence of snowmelt discharge, partly because atmospheric river events rarely influence this high elevation. Process intensity is thus directly related to snowpack depth with monitored bank erosion rates substantially higher following the deep snowpack of 2016–17. Diurnal cycling of flow means that bedload-transport potential scales very well with total annual discharge volume. The legacy of human activity is unique here, with perturbations caused primarily by early-to-mid twentieth century infrastructure development rather than typical patterns of watershed development. Pre-eminent was 1930s instream aggregate extraction for road improvements. In conjunction with naturally very low rates of sediment supply, the resulting pit is still only one-quarter full and could disconnect downstream sediment supplies for several centuries more. The gravel pit appears to explain incision of the upper channel reach and reduced rates of lateral activity farther downstream. Future impacts will be dominated by climate change, including the potential increased influence of atmospheric river events, reduced snowpack depth, and tradeoffs in sediment production and connectivity as headwater glaciers recede. The upper Tuolumne River and its meadow typify the unusual, but not singular, geomorphology of high-elevation alluvial rivers, whose high ecological and social values justify greater attention.

Contribution of Atmospheric Rivers to Antarctic Precipitation.

Atmospheric rivers (ARs) are efficient mechanisms for transporting atmospheric moisture from low latitudes to the Antarctic Ice Sheet (AIS). While AR events occur infrequently, they can lead to extreme precipitation and surface melt events on the AIS. Here we estimate the contribution of ARs to total Antarctic precipitation, by combining precipitation from atmospheric reanalyses and a polar‐specific AR detection algorithm. We show that ARs contribute substantially to Antarctic precipitation, especially in East Antarctica at elevations below 3,000 m. ARs contribute substantially to year‐to‐year variability in Antarctic precipitation. Our results highlight that ARs are an important component for understanding present and future Antarctic mass balance trends and variability.

Identifying Eastern US Atmospheric River Types and Evaluating Historical Trends

AbstractAn atmospheric river (AR) is the primary moisture transport forcing in the Western United States, making ARs the predominant producer of extreme precipitation events in this region. A growing body of evidence suggests similar impacts for the Central and Eastern US This study determines the most prominent types of ARs in the Central and Eastern US study domain through the implementation of a machine learning methodology. Self‐organizing maps (SOMs) are leveraged to determine what “flavors” of ARs exist in the study domain. Four atmospheric river detection criteria are utilized to investigate the variability AR types. Mann‐Kendall trend analyses on AR strength and size are produced to evaluate changes over the study period. The results confirm extratropical cyclones as the most common driver of ARs, however, limited kinematic forcing can also instigate the development of AR events. Results show coastal cyclones and lee‐side cyclones are responsible for producing the strongest ARs. The trend analysis results suggest that ARs associated with Nor'easters and ARs originating in the Gulf of Mexico are exhibiting increasing trends in intensity and/or size. Increasing moisture transport by mature cyclones across the Central and Eastern US have important implications for flooding in highly populated corridors. Areas of concern include the Northeast and Southeast US, while localized enhancement of rainfall is seen along the eastern and southern slopes of the Appalachian Mountains. In addition to the physical findings, this research highlights the importance and sensitivity of statistically significant results to the specific atmospheric river detection criteria that was leveraged.

Leveraging Spatial Patterns in Precipitation Forecasts Using Deep Learning to Support Regional Water Management

AbstractShort‐term precipitation forecasts are critical to regional water management, particularly in the Western U.S. where atmospheric rivers can be predicted reliably days in advance. However, spatial error in these forecasts may reduce their utility when the costs of false positives and negatives differ greatly. Here we investigate whether deep learning methods can leverage spatial patterns in precipitation forecasts to (a) improve the skill of predicting the occurrence of precipitation events at lead times from 1 to 14 days, and (b) balance the tradeoff between the rate of false negatives and false positives by modifying the discrimination threshold of the classifiers. This approach is demonstrated for the Sacramento River Basin, California, using the Global Ensemble Forecast System (GEFS) v2 precipitation fields as input to convolutional neural network (CNN) and multi‐layer perceptron models. Results show that the deep learning models do not significantly improve the overall skill (F1 score) relative to the ensemble mean GEFS forecast with bias‐corrected threshold. However, additional analysis of the CNN models suggests they often correct missed predictions from GEFS by compensating for spatial error at longer lead times. Additionally, the deep learning models provide the ability to adjust the rate of false positives and negatives based on the ratio of costs. Finally, analysis of the network activations (saliency) indicates spatial patterns consistent with physical understanding of atmospheric river events in this region, lending additional confidence in the ability of the method to support water management applications.

Human influence on the 2021 British Columbia floods

A strong atmospheric river made landfall in southwestern British Columbia, Canada on November 14th, 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that when characterised in terms of storm-averaged water vapour transport, the variable typically used to characterise the intensity of atmospheric rivers, westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterised in terms of the associated two-day precipitation, the event is substantially more extreme, approximately a one in fifty to one in a hundred year event, and the probability of events at least this large has been increased by a best estimate of 45% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events in October to December has been increased by human-induced climate change by a best estimate of 120–330%. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.

Future changes in extreme precipitation over the San Francisco Bay Area: Dependence on atmospheric river and extratropical cyclone events

Extreme precipitation poses a major challenge for local governments, including the City and County of San Francisco, California, as flooding can damage and destroy infrastructure and property. As the climate continues to warm, reliable future precipitation projections are needed to provide the best possible information to decision makers. However, future changes in the magnitude of extreme precipitation are uncertain, as current state-of-the-art global climate models are typically run at relatively coarse horizontal resolutions that require the use of convective parameterization and have difficulty simulating observed extreme rainfall rates. Here, we performed ensembles of convection-permitting regional climate model simulations to investigate how five historically impactful extreme precipitation events over the San Francisco Bay Area could change if similar events occurred in future climates. We found that changes in storm-total precipitation depend strongly on storm type. Precipitation associated with an atmospheric river (AR) accompanied by an extratropical cyclone (ETC) is projected to increase at a rate exceeding (by up to 1.5 times) the theoretical Clausius Clapeyron scaling of 6–7% per °C warming. On the other hand, future precipitation changes are weak or negative for events characterized by an AR only, despite increases in precipitable water and integrated vapor transport that are similar to those of the co-occurring AR and ETC events. The differences in the sign of future precipitation change between AR-only events and co-occurring AR and ETC events is instead linked with changes in mid-tropospheric vertical velocity. Given that the majority of observed ARs are associated with an ETC, this research has important implications for future precipitation impacts over the Bay Area, as it indicates that storm-total precipitation associated with the most common type of storm event may increase by up to 26–37% in 2100 relative to historical.

The Effect of a Kona Low on the Eastern Pacific Valentine’s Day (2019) Atmospheric River

Abstract A high-impact atmospheric river (AR) event that made landfall on the U.S. West Coast on Valentine’s Day of 2019 and produced widespread flooding in California is examined. The U.S. Naval Research Laboratory cloud resolving and high-resolution Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) captures the main features impacting the life cycle and structure of the Valentine’s Day AR. Analysis of the model-simulated AR reveals the complex processes leading up to the initial northeastward surge of the water vapor and enhanced near-surface flow associated with this AR. These include the preexistence of a mesoscale cold-core kona low, a mesoscale anticyclone, and a strong low-level convergence in the corridor between the kona low and mesoscale anticyclone where the environment becomes supersaturated in a region of weak vertical wind shear. Model sensitivity experiments show that the eastward progression and magnitude of the AR water vapor surge are strongly sensitive to the magnitude of kona low circulation. Experiments with the kona low circulation amplitude reduced to less than 25% showed that the AR is not able to reach the U.S. West Coast. These results help to identify key new aspects of an important player—the kona low—and its significant contributions to the overall AR characteristics of this particular observed event.

Evolution and origin of global glaciers and their impacts on the environment

Glacier is one of the most important freshwater resources. However, the situation of glaciers is changed nowadays because of global warming. This study aimed at two types of glaciers, and analyzed their evolution, origin and impacts on the environment. As for typical melting glaciers, the Vatnajokull glacier and Urumqi Glacier No.1, Tianshan Mountains gradually melt over time. Their causes are related to atmospheric River events, black carbon pollutants, ice recycling, global warming, and human activities. The impacts of glacier retreat mainly include sea-level rise and can alleviate global warming and cause surface deformation. However, there are some “weird” glaciers because they are expanded in the past few years rather than shrinking. Those glaciers are highly concentrated in the Karakoram, Pamir Mountains, Svalbard, the Canadian Arctic islands, Alaska, and Iceland, which are called surged-type glaciers. Hydrological control, thermal regime, deformable bed hypothesis, and critical mass are reasons for glacier expansion. There is no clear and unified opinion on the advantages or disadvantages of glacier expansion.

Atmospheric rivers fueling the intensification of fog and haze over Indo-Gangetic Plains

Indo-Gangetic Plains (IGP) experiences persistent and widespread rise of fog and haze during the winter season. This has been attributed to the rise in pollution levels and water vapor, but the reason for enhancement in latter is not clear yet. We detect moisture incursion from Arabian Sea, a phenomenon called atmospheric rivers (AR), land-falling intermittently along 12–25° N corridor of the west-coast of India during winter; using satellite and reanalysis data. The total vertically integrated horizontal water vapor transport in AR-landfalls ranging from 0.7 × 108 to 2.2 × 108 kg/s; nearly five-orders of magnitude larger than the average discharge of liquid water from Indus River into Arabian Sea. These AR events are playing prominent role in enhancing water vapor over IGP region by 19 ± 5%; in turn fueling the intensification of fog and haze through aerosol-water vapor interaction. We found that AR events enhanced aerosol optical depths over IGP by about 29 ± 13%. The progression of moist-laden winds in ARs onto Himalayan Mountains contributes to the precipitation that explains the observed rise in the extreme flow of western Himalayan Rivers in winter. We conclude that these ARs likely contribute to the decline of snow albedo as pollution-mixed-ARs encounter Hindukush-Karakoram-Himalayan mountain region.