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Abstract: Atmospheric rivers (ARs) deliver significant and essential precipitation to the western United States (US) with consequential interannual variability. The intensity and frequency of ARs strongly influence reservoir levels, mountain snowpack, and groundwater recharge, which are key drivers of water‐resource availability and natural hazards. Between October 2022 and April 2023, western states experienced exceptionally heavy precipitation from several families of powerful ARs. Using observations of surface‐loading deformation from Global Navigation Satellite Systems, we find that terrestrial water‐storage gains exceeded 100% of normal within vital California watersheds. Independent water‐storage solutions derived from different data‐analysis and inversion methods provide an important measure of precision. The sustained storage increases, which we show are closely associated with ARs at daily‐to‐weekly timescales, alleviated both meteorological and hydrological drought conditions in the region, with a lag in hydrological‐drought improvements. Quantifying water‐storage recovery associated with extreme precipitation after drought advances understanding of an increasingly variable hydrologic cycle.

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 of precipitation. However, a few events may exceed 100 mm day-1, 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 ∘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 HMAs hydrological cycle and the associated hazards to vulnerable communities living in the region.

Atmospheric rivers (ARs) transport vast amounts of moisture from low to high latitude regions. One region particularly impacted by ARs is Interior Alaska (AK). We analyze the impact of ARs on the annual river ice breakup date for 26 locations in AK. We investigate the AR‐driven rise in local air temperatures and explore the relationship between ARs and precipitation, including extremes and interannual variability. We found that AR events lead to an increase in local air temperatures for over 1 week (by ≈1°C ${\approx} \mathrm{1}\hspace*{.5em}{}^{\circ}\mathrm{C}$). ARs account for 40% of total precipitation, explain 47% of precipitation variability, and make up 59% of extreme precipitation events, each year. By estimating the heat transfer between winter precipitation and the river ice surface, we conclude that increased precipitation during the coldest period of the year delays river ice breakup dates, while precipitation occurring close to the breakup date has little impact on breakup timing. Plain Language Summary: Atmospheric rivers (ARs) are large storm systems originating in tropical and mid‐latitude regions capable of depositing large amounts of precipitation in high latitude regions. Using river ice breakup data throughout Interior Alaska (AK) we set out to explore the relationship between ARs and annual river ice breakup timing from 1980 to 2023. We found that daily air temperature increases can last up to 13 days after an AR event. ARs account for 40% of total precipitation, explain 47% of the variability of precipitation, and make up 59% of extreme precipitation events, on average annually. Using the mass and temperature of precipitation accumulated on the river ice surface, we approximated thermal energy exchange between precipitation and the river ice surface. The magnitude of energy exchange was then correlated to river ice breakup timing. We found that greater amounts of precipitation from both AR and non‐AR induced precipitation, occurring relatively close to river ice breakup dates have little correlation to the breakup date. However, increased precipitation during the coldest period of the year (typically late December to early February) is strongly inversely correlated with river ice breakup timing and seems to delay the breakup date. Key Points: Atmospheric rivers (ARs) can lead to over a week‐long persistent increase in dailyair temperatures over Interior Alaska (AK)In AK, ARs account for 40% of annual precipitation, 59% of extreme precipitation and explain 47% of interannual variability of precipitationPrecipitation during the coldest months delays the annual breakup date of river ice, precipitation close to the breakup date has less impact [ABSTRACT FROM AUTHOR]

The Bohai Sea is one of the most polluted sea areas in China. In this study, we used 2184 integrated concentrations of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) in the Bohai Sea of China during spring (March, April, and May), summer (June, July, and August), and autumn (October and November) from 2015 to 2022 to explore the trends and sources of nutrients variations. From 2015 to 2022, DIN showed a downward trend until 2020 and then an upward trend, whereas DIP exhibited a stable trend with a slight decrease. The concentrations of DIN and DIP had similar seasonal pattern which was the highest in autumn (0.292 ± 0.247 mg/L for DIN and 0.013 ± 0.016 mg/L for DIP) but lower in spring (0.267 ± 0.238 mg/L for DIN and 0.006 ± 0.010 mg/L for DIP) and summer (0.263 ± 0.324 mg/L for DIN and 0.008 ± 0.010 mg/L for DIP). Sources of DIN and DIP apportioned by the positive matrix factorization (PMF) model were riverine input, sediment resuspension, sewage discharge, atmospheric deposition, and underground input. During 2015–2022, the largest contributor to DIN was sewage discharge (28.7%) and the largest contributor to DIP was sediment resuspension (44.6%). Seasonally, DIN in spring and autumn was dominated by sewage discharge (45.4% and 27.8%, respectively). Whereas in summer, it was dominated by riverine input (32.4%) and atmospheric deposition (29.7%). DIP was dominated by sediment resuspension during all three seasons (35.8%–52.5%). In addition, the increase in DIN concentrations in 2021 and 2022 were mainly due to the incremental input of river discharge and atmospheric deposition caused by increased precipitation during summer and autumn. [ABSTRACT FROM AUTHOR]

The 18th Workshop on Antarctic Meteorology and Climate was held on 31 May through 2 June 2023. The Antarctic Meteorological Research and Data Center hosted this hybrid workshop at the Pyle Center at the University of Wisconsin-Madison in Madison, WI, USA. Global members of the Antarctic meteorological community gathered to present and discuss topics from scientific research to operational support within Antarctic meteorology and climate. Students and early career professionals chaired various presentations and discussions while all attendees engaged to share results, build collaborative plans, and discuss further developments. The main workshop topics included updates on the current Automatic Weather Station (AWS) networks, challenges, and planning concerning Antarctic forecasting and operational services, Antarctic numerical modeling systems, observational applications and research, and Antarctic community and data sources. Across six sessions, over 25 different presenters elaborated on their work in areas such as field season updates, atmospheric rivers, remote sensing, the Antarctic Mesoscale Prediction System (AMPS), and forecasting challenges due to data scarcity. Workshop discussions resulted in several key outcomes and action items regarding fieldwork impacts, exploration of field techniques, observation coverage, and communication between operations and research scientists. Future workshops will provide opportunities for continued discussion on the evolution of the AWS network and operational needs while providing a venue to promote collaboration and cooperation for Antarctic meteorology and climate activities. [ABSTRACT FROM AUTHOR]

Extreme precipitation events can have substantial impacts on communities and the climate system in the warming Arctic, but their changes are not well understood. In this study, the characteristics of extreme precipitation days in the eastern Canadian Arctic and Greenland are examined in historical (1980–1999) and future (2080–2099, SSP5‐8.5) variable‐resolution Community Earth System Model simulations. Comparisons between the simulations illustrate potential changes in the frequency and intensity of these events in the region. Extreme precipitation is expected to increase broadly across the region. The frequency of the most intense daily precipitation rates is projected to rise, particularly in the northernmost areas. The seasonality of extremes does not shift substantially in the future simulation with most areas receiving the highest accumulations and increases during the summer. However, southeastern Greenland is projected to see decreases in extreme precipitation. Algorithms detecting atmospheric rivers and cyclones are employed to assess how their changes may factor into changes in extreme precipitation. Cyclone frequency remains largely consistent with slight decreases near southeastern Greenland, which may explain decreased extreme precipitation seen in the region. Atmospheric rivers are projected to become more frequent across the domain, largely during the summer. Although the majority of the region's extreme precipitation is associated with cyclones, this suggests that atmospheric rivers become more important in a warming climate. These results provide insight into potential changes in impactful precipitation events in the region and how the processes driving them may change. Plain Language Summary: Extreme precipitation events can have significant impacts on Arctic communities and the climate system. As the climate changes, extreme precipitation may bring about different impacts, but we still don't fully understand how these events are changing as temperatures increase. In this study, we use simulations from a high resolution climate model to examine changes in the occurrence and characteristics of extreme precipitation days in Greenland and Baffin Bay between the historical period (1980–1999) and the future (2080–2099) under a high emissions scenario. We also use tracking algorithms to investigate the connection between these events and the occurrence of extratropical cyclones and atmospheric rivers. Extreme precipitation is shown to increase across the region except for in southeastern Greenland, where minor decreases are expected. This decrease is likely tied to a slight reduction in cyclone occurrence in the area. Most of the region sees the strongest extreme precipitation increases in the summer when atmospheric rivers are also expected to become more common. Historically, almost all extreme precipitation was associated with cyclones, but their role does not appear to change in the future simulation, whereas atmospheric rivers become associated with a higher fraction of extreme precipitation. Key Points: Extreme precipitation increases (and accounts for a higher portion of total annual precipitation) for most subregions by 2100 but decreases in SE GreenlandMost extreme precipitation is associated with cyclones, but the role of cyclones does not appear to change in future projectionsIncreasing AR frequency facilitates increased extreme precipitation in SW Greenland, particularly during the summer [ABSTRACT FROM AUTHOR]

Extreme integrated vapor transport (IVT) is a crucial driving factor of extreme precipitation events (EPEs). This paper presents a complex network‐based characterization of propagations from extreme IVT to EPEs. Specifically, the propagations are tracked from extreme IVT to EPEs by event synchronization; and then the source zones of extreme IVT contributing to EPEs are identified by two‐layer complex network. A case study is devised for North America based on the daily NCEP/NCAR Reanalysis 1 from 1948 to 2021. Overall, eight communities of EPEs are identified: the west coast of United States (US) tend to receive substantial EPEs from the Pacific Ocean; the Gulf of Alaska tends to receive oceanic EPEs propagating inland; western Canada typically experiences large amount of out tendencies and the EPEs tend to accumulate in the Baffin Island and Labrador Peninsula; the southeastern US and the northern Great Plains tend to experience northward propagations from Mexico. Along the west coast of North America, the propagations from extreme IVT to EPEs typically originate from the eastern North Pacific between 160°W and 110°W, and make landfalls in 4 days. These propagations are influenced by anomalous cyclonic circulations developing over the Gulf of Alaska forced by eastward Rossby waves. The coincidence rate of these propagations with atmospheric rivers is, respectively, 85.31% in autumn, 91.35% in winter, 73.94% in spring, and 64.52% in summer. Overall, the observed propagations from extreme IVT to EPEs yield insights into the mechanism of atmospheric moisture transport and the predictability of precipitation. Plain Language Summary: Owing to different synoptic systems such as atmospheric rivers, low‐level jets, and tropical cyclones, there exist profound propagations from extreme integrated vapor transport to extreme precipitation. Focusing on North America, this paper is concentrated on the complex network underlying extreme integrated vapor transport and extreme precipitation events. It is found that the Rocky Mountains play an important part in shaping the propagations from extreme integrated vapor transport to extreme precipitation events. In particular, along the west coast of North America, extreme integrated vapor transport originating from the eastern North Pacific tends to be blocked by the Rocky Mountains, causing rising/cooling and leading to extreme precipitation. Overall, along the Rocky Mountains, there exist eight communities of extreme precipitation. Key Points: There exists a complex network on propagations from extreme integrated vapor transport to extreme precipitation events in North AmericaIn the west coast of North America, the propagations typically originate from eastern North Pacific and make landfalls in 4 daysThe coincidence rate of propagations with atmospheric rivers is, respectively, above 90% and 60% in boreal winter and summer [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory's SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting. [ABSTRACT FROM AUTHOR]

Poleward water vapor transport in the midlatitudes mainly occurs in meandering filaments of intense water vapor transport, spanning thousands of kilometers long and hundreds of kilometers wide and moving eastward. The water vapor filaments are known as atmospheric rivers (ARs). They can cause extreme wind gusts, intense precipitation, and flooding along densely populated coastal regions. Many recent studies about ARs focused on the statistical analyses of ARs, but a process-level understanding of ARs remains elusive. Here we show that ARs are streams of air with enhanced vapor kinetic energy (VKE) and derive a governing equation for Integrated VKE to understand what contributes to the evolution of ARs. We find that ARs grow mainly because of potential energy conversion to kinetic energy, decay largely owing to condensation and turbulence, and the eastward movement is primarily due to horizontal advection of VKE. Our VKE framework complements the integrated vapor transport framework, which is popular for identifying ARs but lacks a prognostic equation for understanding the physical processes. A vapor kinetic energy analysis shows that atmospheric rivers' strong winds result from the conversion of potential energy into kinetic energy, and their eastward movement is primarily driven by the horizontal transport of water vapor and kinetic energy. [ABSTRACT FROM AUTHOR]

Regional patterns of the seasonal weather and atmospheric moisture origins can impact the seasonal activities of extreme precipitation in the eastern United States (eUS). At many locations, tracks of atmospheric moisture can have different influence on timing of extreme precipitation based on the moisture origin source. In this study, we evaluate the contribution of atmospheric rivers (ARs) and their moisture origin sources to the distribution and seasonal effectivity of annual maximum precipitation (AMP) across the eUS during 1950–2021. Our results suggest that AR is a dominant mechanism of AMP in the eUS as it contributes to 75% (31 438 out of 41 976) of total AMP events recorded between 1950 and 2021. The seasonal analysis based on the circular density approach shows that spring, summer, and fall seasons display strong signals of seasonality of AMP-AR events. The spatial patterns of AMP associated with the four major moisture sources (the Pacific Ocean, the Atlantic Ocean, the combined source of the Caribbean Sea and the Gulf of Mexico, and the local source of moisture) reinforce the key role ARs play in transporting water vapor to the eUS from both oceanic and inland originated moisture. The results additionally highlight the importance of moisture subsources (major source subregions) in modulating the seasonality of extreme precipitation in the eUS. [ABSTRACT FROM AUTHOR]

The likely changes to precipitation seasonality with warming are both impactful and not well understood. This work aims to describe areas that experience similar changes to seasonal precipitation irrespective of the original underlying precipitation seasonality. We train a self-organizing map on the difference between the seasonal cycle of precipitation in the past and in a high-warming future climate as represented by the Community Earth System Model, version 2, to create regions with similar changes in precipitation seasonality. This method is applied separately over land and ocean surfaces because of the differing processes leading to precipitation over each. This method indicates that future changes in seasonal precipitation are most varied in the tropics because of a southward shift in the intertropical convergence zone. The seasonal shifts found over midlatitude oceans indicate a poleward shift in atmospheric river activity. We find a correspondence between certain land-based precipitation changes and Köppen climate classification. The seasonality of large-scale and convective precipitation is examined for each region. The relationship between the seasonal changes to precipitation and associated atmospheric processes is discussed. These processes include atmospheric rivers, the intertropical convergence zone, tropical cyclones, and monsoons. [ABSTRACT FROM AUTHOR]

Stable isotopic methods in hydroclimate monitoring are powerful for improving water resources management, but applications are limited, especially in semi‐arid regions where such management is needed most. Here, we show that we can address shortcomings related to the lack of a seasonal signal using stable water isotopic signatures measured in precipitation over the East San Francisco Bay area, California, during two contrasting events sampled at more than 20 locations in the winter of 2023. The observed range in δ18O in the rain samples is similar for both storms. However, the distributions do not overlap—the mean air temperature and δ18O during Winter Storm Olive (February 2023) were 2°C and − 12‰, respectively, while a warm atmospheric river event (March 2023) had a mean temperature of 9°C and δ18O of −6‰, close to the long‐term average δ18O measured in local precipitation. The Winter Storm showed expected trends in δ18O related to geography (i.e., lower with greater distance inland and elevation), while the atmospheric river δ18O pattern was more spatially uniform. We use hydrometric data from a gaged watershed in the study area and isotopic signatures of rain sampled during the two storm events and apply a solute transport model (StorAge selection) with a travel‐time approach to examine predicted watershed responses and potential water tracing applications. In this virtual experiment, we find that event size exerts a strong control on the relative amounts of runoff versus pre‐event water in the stream, while uncertainty in stream hydrograph separation is related to the degree of contrast between precipitation/runoff and pre‐event water. Key to flood prediction, adaptation, and mitigation, especially in coastal urban areas, is knowledge of the contributing water sources and timing of stream flow. The strong contrast in stable isotopes between these two events, close in time and over the same area, illustrates the potential to use stable isotope signatures to track the transport and mixing of events through natural and engineered watersheds that are threatened by climate whiplash events. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are closely associated with extreme precipitation and hydrological events in East Asia. Predicting the impacts of climate change on ARs is crucial for preventing the damage caused by extreme precipitation and ensuring the effective operation of water management facilities. We aimed to conduct future projections (2080–2099) of annual and seasonal changes based on the assessment of East Asian AR and AR-related precipitation, using the Coupled Model Intercomparison Project Phase 6 (CMIP6) Multi-model ensemble (MME). The annual average integrated vapor transport (IVT) in East Asia in 2080–2099 will increase by approximately 32.5% compared to 1995–2014. Meanwhile, the annual average AR frequency (FAR) will increase by approximately 111%. Examination of the water vapor and moist wind components of the IVT revealed that the future increase in the IVT was primarily from increases in water vapor. The increase in IVT is largely responsible for the increase in AR frequency. Changes in AR following global warming have also affected precipitation, increasing the total precipitation for East Asia. An examination of the changes in AR characteristics shows that the frequency of intense AR events will also increase owing to global warming. Increases in the frequency of strong AR events during the East Asian summer monsoon season are projected to occur. Projections regarding the frequency and intensity of AR events vary substantially by region, such as Korean Peninsula, Southern China and Western Japan. [ABSTRACT FROM AUTHOR]

Different types of weather events, including tropical cyclones (TCs), mesoscale convective systems (MCSs), and atmospheric rivers (ARs), significantly impact precipitation patterns in East Asia. This study pioneers the application of deep learning (DL) methods, including convolutional neural network, U‐Net, and Attention U‐Net models, to simulate precipitation associated with these weather events. The spatial permutation method is also used to identify key meteorological variables for accurately generating precipitation in DL models. The DL models trained on all timeslots consistently surpass the performance of state‐of‐the‐art numerical simulations, although their efficacy slightly diminishes during extreme weather events. This outperformance is attributed to the appropriate emphasis on key variables that capture precipitation processes, such as low‐level moisture and mid‐level pressure fields. However, new DL models trained separately for TCs, MCSs, and ARs using clipped precipitation as the output does not exceed the performance of the previous DL models. Among all input features, moisture variables contribute the most to precipitation at low intensity, while the importance of other variables increases for more intense precipitation, although some discrepancies vary across models and event types. The spatial results further reveal the detailed locations of variables that are essential for accurately simulating precipitation related to weather events, such as areas of high specific humidity and strong winds. DL models could also acquire useful information from region remote to the events to improve the simulation. Overall, DL models serve as promising tools for simulating and enhancing our understanding of precipitation patterns associated with various weather events in East Asia. Plain Language Summary: Different types of weather events, including tropical cyclones (TCs), mesoscale convective systems (MCSs), and atmospheric rivers (ARs), produce a lot of precipitation in East Asia. In this study, we first use deep learning (DL) models to simulate precipitation associated with these weather events. We find that these models are consistently better than numerical simulations. However, the performance of DL models slightly diminishes during extreme weather events. The success of the DL models can be attributed to their ability to focus on key variables, such as low‐level moisture and mid‐level pressure fields. We also develop new DL models tailored for TCs, MCSs, and ARs with their associated precipitation as the output. Unexpectedly, these models do not perform better than the previous DL models. Among all the features, moisture variables contribute the most to low‐intensity precipitation, whereas other variables become more important for more intense precipitation. We find the important locations of key variables for accurate simulation, such as areas with high specific humidity and strong winds. DL models can also utilize nonlocal information from weather events to enhance their performance. Therefore, DL models are promising in generating precipitation associated with different weather events in East Asia. Key Points: Deep learning (DL) models surpass numerical models in simulating precipitation associated with different weather events in East AsiaDL models trained for different weather event types exhibit inferior performance compared to DL models trained on all timeslotsDL models effectively utilize information from meteorological variables, particularly moisture, to achieve strong performance [ABSTRACT FROM AUTHOR]

The effect of the combination of marine toxins produced by algal blooms, in conjunction with varying environmental characteristics on organisms in the water column, is a poorly explored research field. Pelagic fish species of commercial importance, such as anchoveta (Engraulis ringens) in central Chile, may be exposed to these combined factors in a climate change scenario. This is observed from documented changes in the length of the upwelling season, frequency of upwelling events, and the increased frequency of atmospheric rivers affecting the southern spawning zone of this species. This study evaluated the integrity of hair cells in neuromasts, mechanosensory organs present in fish larvae, under exposure to different combinations of the algal bloom-produced okadaic acid OA (1 ng mL1), and two temperature (12 and 14°C), and salinity treatments (historically more frequent: 34 PSU- 12°C; expected: 32 PSU - 14°C). Viable hair cells were counted in newly hatched larvae from the Biobıo region, central Chile. Results showed a significant decrease in the average number of viable hair cells per neuromast (from 6.1 ± 1.6 to 4.0 ± 1.2) under lower salinity treatments (32 PSU) compared to normal conditions. Additionally, a seasonal trend was observed with fewer viable cells (from 7.4 ± 1.2 to 4.4 ± 1.1) as the fish species' reproductive period progressed. The combined effect of OA exposure and modifications with the environmental factors also resulted in a significant decrease of up to 70% in the number of viable hair cells in larvae exposed to OA and high temperatures, indicating damage influenced by the toxin along with a synergistic and/or additive role of temperature. These findings reveal how the lipophilic toxin okadaic acid, produced by harmful algal blooms, interacts with abiotic environmental factors affecting coastal ecologically and socio-economically important organisms. This emphasizes the need to consider multiple factors when studying the effects of marine toxins. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are key agents in distributing extratropical precipitation and transporting moisture poleward. Climate models forced by historical anthropogenic forcing suggest an increase in AR activity in the extratropics over the past four decades. However, reanalyses indicate a ~6° to 10° poleward shift of ARs during boreal winter in both hemispheres, featuring a rise along 50°N and 50°S and a decrease along 30°N and 30°S. Our analysis demonstrates that low-frequency sea surface temperature variability in the tropical eastern Pacific exhibits a cooling tendency since 2000 that plays a key role in driving this global AR shift, mostly over extratropical oceans, through a tropical-driven eddy-mean flow feedback. This mechanism also operates on interannual timescales, controlled by the El Niño-Southern Oscillation, and is less pronounced over the Southern Ocean due to weaker eddy activity during austral summer. These highlight the sensitivity of ARs to large-scale circulation changes driven by both internal variability and external forcing in current and upcoming decades. [ABSTRACT FROM AUTHOR]

East Asian atmospheric rivers (ARs) exhibit the most pronounced activity in summer with significant impacts on monsoon rainfall. However, their occurrence mechanisms are yet to be revealed in detail. In this study, we unravel the inherently complex nature of East Asian summer ARs by applying a multiscale index that quantifies the relative importance of high-frequency (HF) and low-frequency (LF) moisture transports in AR development. It is found that both HF and LF processes contribute to shaping the summertime ARs in East Asia, contrasting to the wintertime ARs dominated by HF processes. Stratification of ARs with the multiscale index reveals that HF-dominant ARs are driven by baroclinically deepening extratropical cyclones, analogous to the widely accepted definition of canonical ARs. In contrast, LF-dominant ARs result from an enhanced monsoon southwesterly between a quasi-stationary cyclone and an anticyclone with the latter being the anomalous expansion of the western North Pacific subtropical high. Such a pattern is reminiscent of the classical monsoon rainband. While HF-dominant ARs are transient, LF-dominant ARs are quasi-stationary with a higher potential for prolonged local impacts. The intermediate ARs, constituting a majority of East Asian summer ARs, exhibit synoptic conditions that combine HF- and LF-dominant ARs. Therefore, East Asian summer ARs cannot be explained by a single parent system but should be considered as a continuum of extratropical-cyclone-induced and fluctuating monsoon-flow-induced moisture plumes. This finding would serve as a base for the advanced understanding of hydrological impacts, variability, and projected change of East Asian ARs. Significance Statement: Despite the accumulation of studies on summertime atmospheric rivers (ARs) in East Asia, a comprehensive explanation for their occurrence mechanisms remains elusive. This study disentangles their complicated nature through case-level multiscale analyses. In contrast to wintertime ARs, summertime ARs are shaped by both high- and low-frequency moisture transports. The high-frequency moisture transport is associated with migratory extratropical cyclones which are suppressed but still active in summer, while the low-frequency moisture transport arises from the fluctuation of a quasi-stationary monsoon southwesterly along the periphery of the western North Pacific subtropical high. The varying relative contribution of high- and low-frequency components from one AR to another suggests that East Asian summer ARs represent a continuum of extratropical and monsoonal moisture plumes. [ABSTRACT FROM AUTHOR]

This systematic review, based on an adaptation of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement from 2020, focuses on studies of the atmospheric mechanisms underlying extreme precipitation events in mainland Portugal, as well as observed trends and projections. The 54 selected articles cover the period from 2000 to 2024, in which the most used keywords are "portugal" and "extreme precipitation". Of the 54, 23 analyse trends and climate projections of precipitation events, confirming a decrease in total annual precipitation, especially in autumn and spring, accompanied by an increase in the frequency and intensity of extreme precipitation events in autumn, spring and winter. Several articles (twelve) analyse the relationship between synoptic-scale circulation and heavy precipitation, using an atmospheric circulation types approach. Others (two) establish the link with teleconnection patterns, namely the North Atlantic Oscillation (NAO), and still others (three) explore the role of atmospheric rivers. Additionally, five articles focus on evaluating databases and Numerical Weather Prediction (NWP) models, and nine articles focus on precipitation-related extreme weather events, such as tornadoes, hail and lightning activity. Despite significant advances in the study of extreme precipitation events in Portugal, there is still a lack of studies on hourly or sub-hourly scales, which is critical to understanding mesoscale, short-lived events. Several studies show NWP models still have limitations in simulating extreme precipitation events, especially in complex orography areas. Therefore, a better understanding of such events is fundamental to promoting continuous improvements in operational weather forecasting and contributing to more reliable forecasts of such events in the future. [ABSTRACT FROM AUTHOR]

This study explores the contribution of atmospheric rivers (ARs) to the water budget input of the Nechako River Basin (NRB) in British Columbia (BC), western Canada. The study quantifies the fraction of precipitation, rainfall, snowfall, and snow water equivalent (SWE) associated with ARs at multiple scales and tests for trends using the Mann–Kendall (MK) test. AR‐related totals for 1950–2021 were created by linking AR events to water budget input variables of the ERA5‐Land reanalysis product on a daily scale. Associations with different phases of the El Niño‐Southern Oscillation (ENSO) climate pattern and AR‐related contributions to the NRB are also investigated. Results indicate an increasing fractional contribution of rain in ARs landfalling in the NRB in the last two decades (2000–2019). Moreover, 21% of the total annual precipitation in the NRB is associated with ARs, with decreasing contributions from west to east. October has higher AR‐related total precipitation than other months, while March, May and June are the least affected. ARs contribute disproportionately more to mid‐ and high‐intensity daily precipitation totals, and provide up to 45% and 24% of the seasonal rainfall and snowfall, respectively. AR‐related SWE is relatively higher in autumn due to the increased frequency and intensity of ARs, resulting in a greater fractional contribution of ARs to the snowpack compared to winter. ARs influence snowpack accumulation during fall (18%) and winter (13%) but also increase the risk of natural hazards. The MK test for AR‐related water budget variables on the annual scale identified no significant trends. However, AR‐related snowfall shows decreasing trends in the NRB, more specifically in the Upper Nechako, Lower Nechako and Stellako sub‐basins during the summer. Over the study period, ARs consistently contribute up to one‐fifth of the annual input to the NRB's water budget. This study provides the first quantitative assessment and trend analyses of AR contributions to the water budget input of a reservoir‐regulated watershed in north‐central BC, yielding valuable information for hydropower production, ecological flows, irrigation, domestic and industrial water use. [ABSTRACT FROM AUTHOR]

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Rapid and accurate maps of floods across large domains, with high temporal resolution capturing event peaks, have applications for flood forecasting and resilience, damage assessment, and parametric insurance. Satellite imagery produces incomplete observations spatially and temporally, and hydrodynamic models require tradeoffs between computational efficiency and accuracy. We address these challenges with a novel flood model which predicts surface water area from the U.S. National Water Model using a convolutional neural network (NWM‐CNN). We trained NWM‐CNN on 780 flood events, at a 250 m resolution with an RMSE of 4.58% on held out validation geographies. We demonstrate NWM‐CNN across California during the 2023 atmospheric rivers, comparing predictions against Sentinel‐1 mapped flood observations. We compared historical predictions from 1979 to 2023 to flood damage reports in Sacramento County, California. Results show that NWM‐CNN captures inundation extent better than the Height Above Nearest Drainage (HAND) approach (25%–36% RMSE, respectively). Plain Language Summary: We use machine learning (ML) to map floods quickly and accurately over large areas, which can help with predicting flooded extent, understanding impact, and aiding flood insurance and response. On their own, satellite images don't catch everything because they may be obscured or unavailable at the peak of a flood. Computer models that predict floods require a trade‐off between speed, accuracy, and resolution. Our solution uses ML to learn from the U.S. National Water Model and satellite images from past floods to predict how much of an area will be covered in water. We demonstrate this on floods in California in 2023 caused by atmospheric rivers, and we looked back at floods in Sacramento County from 1979 to 2023. We compared our method to another commonly used model and found ours was more accurate, making it a promising tool for future flood mapping and response planning. Key Points: Convolution neural networks (CNN) are suitable for rapid modeling of surface water dynamics for large‐scale inundation mappingWe deploy a CNN for continuous flood mapping across all of California during the devastating 2023 atmospheric river (AR) eventsInundation extent across Sacramento is more accurately predicted with CNN than the Height Above Nearest Drainage (HAND) [ABSTRACT FROM AUTHOR]

Using the regionally refined mesh (RRM) configuration of the US Department of Energy's Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM), we simulate and evaluate four meteorologically distinct atmospheric river events over California. We test five different RRM configurations, each differing in terms of the areal extent of the refined mesh and the resolution (ranging from 800 m to 3.25 km). We find that SCREAM RRM generally has a good representation of the AR-generated precipitation in CA, even for the control simulation which has a very small 3 km refined patch, and is able to capture the fine-scale regional distributions that are controlled largely by the fine-scale topography of the state. It is found that SCREAM generally has a wet bias over topography, most prominently over the Sierra Nevada mountain range, with a corresponding dry bias on the lee side. We find that refining the resolution beyond 3 km (specifically 1.6 km and 800 m) has virtually no benefit towards reducing systematic precipitation biases but that improvements can be found when increasing the areal extent of the upstream refined mesh. However, these improvements are relatively modest and only realized if the size of the refined mesh is expanded to the scale where employing RRM no longer achieves the substantial cost benefit it was intended for. [ABSTRACT FROM AUTHOR]

We performed a systematic space-time analysis of monsoon seasonal (Jun-Sep) rainfall and extremes (3-day maximum rainfall) over India for the period 1951–2019. Employing Partition Around Medoid (PAM) clustering technique on the seasonal rainfall and extremes, six spatially coherent regions (clusters) were identified that are contiguous in space and consistent with the topography, which are: Central-West India (CW), Northwest and Northern India (NW), Western Ghats (WG), Deccan Plateau (DP), Central-East India including Indo-Gangetic plain (CE) and Northeast India (NE). Integrated Vertical Transport (IVT) of moisture composites for wet and dry years for each cluster indicated that Bay of Bengal is the major source of moisture for extreme rainfall for all of India, except for WG. Arabian Sea and Bay of Bengal both provide moisture for the seasonal rainfall for western and eastern halves of India, respectively. Trend analysis revealed a decline in seasonal rainfall over CE, NE and WG clusters and increase in extreme rainfall over CW region. These are consistent with increasing IVT trends over Bay of Bengal and decreasing over Arabian Sea and Indian Ocean. Teleconnections to tropical Pacific Sea surface temperatures (SSTs) were reminiscent of El Nino Southern Oscillation (ENSO) patterns, with cooler SSTs in central and eastern Pacific favoring stronger monsoon rainfall and to a lesser extent the extremes. Further, warmer Indian Ocean in recent decades is likely a mediator in the moisture transport by reducing seasonal rainfall and enhancing the extremes. These interesting insights brighten the prospects for skillful forecast of monsoon rainfall and extremes. [ABSTRACT FROM AUTHOR]

The California-Nevada 1997 New Year's flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. The joint occurrence of saturated soils, rainfall, and snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, the warming sensitivity of their flood drivers across scales remains understudied. We leverage the regionally refined mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) to recreate the 1997 New Year's flood with horizontal grid spacings of 3.5 km across California, with forecast lead times of up to 4 days, and across six warming levels ranging from pre-industrial conditions to + 3.5 ∘ C. We describe the sensitivity of the flood drivers to warming including AR duration and intensity, precipitation phase, intensity and efficiency, snowpack mass and energy changes, and runoff efficiency. Our findings indicate current levels of climate change negligibly influence the flood drivers. At warming levels ≥ 1.7 ∘ C, AR hazard potential increases, snowpack nonlinearly decreases, antecedent soil moisture decreases (except where the snowline retreats), and runoff decreases (except in the southern Sierra Nevada where antecedent snowpack persists). Storm total precipitation increases, but at rates below warming-induced increases in saturation-specific humidity. Warming intensifies short-duration, high-intensity rainfall, particularly where snowfall-to-rainfall transitions occur. This study highlights the nonlinear tradeoffs in 21st-century RoS flood hazards with warming and provides water management and infrastructure investment adaptation considerations. [ABSTRACT FROM AUTHOR]

Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of "anchor" observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes. Significance Statement: This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data. [ABSTRACT FROM AUTHOR]

Arctic sea ice has undergone a significant decline in the Barents–Kara Sea (BKS) since the late 1990s. Previous studies have shown that the decrease in sea ice caused by increased poleward moisture transport is modulated by tropical sea temperature changes (mainly referring to La Niña events). The occurrence of multi-year La Niña (MYLA) events has increased significantly in recent decades, and their impact on Arctic sea ice needs to be further explored. In this study, we investigate the relationship between sea-ice variation and different atmospheric diagnostics during MYLA and other La Niña (OTLA) years. The decline in BKS sea ice during MYLA winters is significantly stronger than that during OTLA years. This is because MYLA events tend to be accompanied by a warm Arctic–cold continent pattern with a barotropic high pressure blocked over the Urals region. Consequently, more frequent northward atmospheric rivers intrude into the BKS, intensifying longwave radiation downward to the underlying surface and melting the BKS sea ice. However, in the early winter of OTLA years, a negative North Atlantic Oscillation presents in the high latitudes of the Northern Hemisphere, which obstructs the atmospheric rivers to the south of Iceland. We infer that such a different response of BKS sea-ice decline to different La Niña events is related to stratospheric processes. Considering the rapid climate changes in the past, more frequent MYLA events may account for the substantial Arctic sea-ice loss in recent decades. [ABSTRACT FROM AUTHOR]

This study proposes a model‐based methodology to estimate design precipitation for long durations during the winter and spring seasons (October to June) through its application to the drainage areas of two dams in the Columbia River Basin, United States. For basins with large reservoir storage or snowpack, design precipitation and floods need to be estimated based on long‐duration processes rather than focusing only on flood peaks or single storm durations. This study used the advanced research version of weather research and forecasting (WRF) model to maximize the target precipitation over the drainage areas by means of the Atmospheric Boundary Condition Shifting and Relative Humidity Perturbation with relaxed moisture flux thresholds. The greatest cumulative basin‐average precipitation depths during Oct–Jun were estimated to be 1220.5 and 1595.4 mm for the drainage areas of Bonneville and Libby Dams, respectively. The 95% confidence interval (CI) of the exceedance probabilities of the estimated design precipitation depths were found to range from 10−3 to 10−5 at Bonneville Dam's drainage area. Those orders were found to be comparable with the documented exceedance probabilities of PMP/PMF in the US. The estimated design precipitation and corresponding atmospheric/land‐surface fields together will drive a physical model to estimate the design flood. [ABSTRACT FROM AUTHOR]

Weather features, such as extratropical cyclones, atmospheric rivers (ARs), and fronts, contribute to substantial amounts of precipitation globally and are associated with different precipitation characteristics. However, future changes in these characteristics, as well as their representation in climate models, remain uncertain. We attribute 6‐hourly accumulated precipitation to cyclones, moisture transport axes (AR‐like features), fronts, and cold air outbreaks, and the combinations thereof in 10 ensemble members of the CESM2‐LE between 1960 and 2100 under the SSP3‐7.0 scenario. We find that, despite some biases in both precipitation and weather features, CESM2‐LE adeptly represents the precipitation characteristics associated with the different combinations of weather features. The combinations of weather features that contribute most to precipitation in the present climate also contribute the most to future changes, both due to changes in intensity as well as frequency. While the increase in precipitation intensity dominates the overall response for total precipitation in the storm track regions, the precipitation intensity for the individual weather features does not necessarily change significantly. Instead, approximately half of the increase in precipitation intensity in the storm track regions can be attributed to a higher occurrence of the more intensely precipitating combinations of weather features, such as the co‐occurrence of extratropical cyclones, fronts, and moisture transport axes. Plain Language Summary: Most precipitation is associated with weather features such as storms, atmospheric rivers, and fronts. Different combinations of these weather features are associated with different precipitation characteristics, but how these characteristics are represented in climate models as well as their possible future changes is not known. We attribute 6‐hourly accumulated precipitation to weather features, such as storms, fronts, and atmospheric rivers, from 1960 to 2100 under a high greenhouse emission scenario in a climate model. Despite some biases, the climate model represents the precipitation characteristics associated with these weather features well. We find that the weather features with the largest contribution to precipitation in the current climate also contribute the most to future changes in precipitation. The changes are caused by changes in both frequency of occurrence and precipitation intensity. Key Points: We attribute precipitation to cyclones, fronts, moisture transport axes, and cold air outbreaks in 10 ensemble members CESM2‐LE in 1960–2100CESM2‐LE adeptly represents the precipitation characteristics associated with the different weather features and their combinationsCo‐occurring weather features, associated with intense precipitation, contribute to a larger fraction of the precipitation in the future [ABSTRACT FROM AUTHOR]

We present a suite of new atmospheric river (AR) metrics that are designed for quick analysis of AR characteristics and statistics in gridded climate datasets such as model output and reanalysis. This package is expected to be particularly useful for climate model evaluation. The metrics include mean bias and spatial pattern correlation, which are efficient for diagnosing systematic AR biases in climate models. For example, the package identifies that in CMIP5 and CMIP6 models, AR tracks in the south Atlantic are positioned farther poleward compared to the ERA5 reanalysis, while in the south Pacific, tracks are generally biased towards the equator. For the landfalling AR peak season, we find that most climate models simulate a completely opposite seasonal cycle over western Africa. This tool is also useful for identifying and characterizing structural differences among different AR detectors (ARDTs). For example, ARs detected with the Mundhenk algorithm exhibit systematically larger size, width and length compared to the TempestExtremes (TE) method. The AR metrics developed from this work can be routinely applied for model benchmarking and during the development cycle to trace performance evolution across model versions or generations and set objective targets for the improvement of models. They can also be used by operational centers to perform near real-time climate and extreme events impact assessment as part of their forecast cycle. [ABSTRACT FROM AUTHOR]

The quantitative characterization and prediction of localized severe weather events that emerge as coherences generated by the highly non-linear interacting multivariate dynamics of global weather systems poses a significant challenge whose solution is increasingly important in the face of climate change where weather extremes are on the rise. As weather measurement systems (multiband satellite, radar, etc) continue to dramatically improve, increasingly complex time-dependent multivariate 3D datasets offer the potential to inform such problems but pose an increasingly daunting computational challenge. Here we describe the application to global weather systems of a novel computational method called the Entropy Field Decomposition (EFD) capable of efficiently characterizing coherent spatiotemporal structures in non-linear multivariate interacting physical systems. Using the EFD derived system configurations, we demonstrate the application of a second novel computational method called Space-Time Information Trajectories (STITs) that reveal how spatiotemporal coherences are dynamically connected. The method is demonstrated on the specific phenomenon known as atmospheric rivers (ARs) which are a prime example of a highly coherent, in both space and time, severe weather phenomenon whose generation and persistence are influenced by weather dynamics on a wide range of spatial and temporal scales. The EFD reveals how the interacting wind vector field and humidity scalar field couple to produce ARs, while the resulting STITS reveal the linkage between ARs and large-scale planetary circulations. The focus on ARs is also motivated by their devastating social and economic effects that have made them the subject of increasing scientific investigation to which the EFD may offer new insights. The application of EFD and STITs to the broader range of severe weather events is discussed. [ABSTRACT FROM AUTHOR]

Atmospheric Rivers (ARs) are long and narrow bands of strong horizontal water vapour transport concentrated in the lower troposphere. ARs play an important role in producing some high-impact weather events such as extreme precipitation and flooding, damaging winds, and temperature anomalies. To facilitate various studies on the short- and long-term variability of ARs and their impacts, I compiled a multi-decade global dataset containing 12 relevant meteorological variables for AR analysis. These variables were derived from the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis version 5 (ERA5). They are available at 6-hour intervals from 1940 to present. Also included in the dataset is an interactive web browser-based graphical tool for visualising the AR evolution on regional (North America) and global scales. This ERA5-based Dataset for Atmospheric River Analysis (EDARA) may serve as a valuable resource for many AR-related research and applications. [ABSTRACT FROM AUTHOR]

Recently, climate extremes have been grabbing attention as important drivers of environmental change. Here, we assemble an observational inventory of energy and mass fluxes to quantify the ice loss from glaciers on the Russian High Arctic archipelago of Novaya Zemlya. Satellite altimetry reveals that 70 ± 19% of the 149 ± 29 Gt mass loss between 2011 and 2022 occurred in just four high-melt years. We find that 71 ± 3% of the melt, including the top melt cases, are driven by extreme energy imports from atmospheric rivers. The majority of ice loss occurs on leeward slopes due to foehn winds. 45 of the 54 high-melt days (>1 Gt d−1) in 1990 to 2022 show a combination of atmospheric rivers and foehn winds. Therefore, the frequency and intensity of atmospheric rivers demand accurate representation for reliable future glacier melt projections for the Russian High Arctic. Moist, warm air from mid-latitudes lead to a speed-up of glacial ice loss in Novaya Zemlya, High Russian Arctic. Foehn winds enable especially high melt rates. These conditions became more frequent over the past decade. [ABSTRACT FROM AUTHOR]

Atmospheric Rivers (ARs) are narrow filaments of high moisture flux responsible for most of the horizontal transport of water vapor from the tropics to mid-latitudes. Improving forecasts of ARs through numerical weather prediction (NWP) is important for increasing the resilience of the western US to flooding and droughts. These NWP forecasts rely on the improved understanding of AR physics and dynamics from satellite, radar, aircraft, and in situ observations, and now airborne radio occultation (ARO) can contribute to those goals. The ARO technique is based on precise measurements of Global Navigation Satellite Systems (GNSS) signal delays collected from a receiver onboard an aircraft from setting or rising GNSS satellites. ARO inherits the advantages of high vertical resolution and all-weather capability of spaceborne RO observations and has the additional advantage of continuous and dense sampling of the targeted storm area. This work presents a comprehensive ARO dataset recovered from four years of AR Reconnaissance (AR Recon) missions over the eastern Pacific. The final dataset is comprised of ∼ 1700 ARO profiles from 39 flights (∼ 260 flight hours) from multiple GNSS constellations. Profiles extend from aircraft cruising altitude (13–14 km) down into the lower troposphere, with more than 50 % of the profiles extending below 4 km, below which the receiver loses or cannot initiate lock. The horizontal drift of the tangent points that comprise a given ARO profile greatly extends the area sampled from just underneath the aircraft to both sides of the flight track (up to ∼ 400 km). The estimated refractivity accuracy with respect to dropsondes is ∼ 1.2 %, in the upper troposphere where the sample points are closely collocated. For the lower troposphere, the agreement is within ∼ 7 % which is the level of consistency expected given the nature of atmospheric variations over the 300–700 km separation between the lowest point and the dropsonde. [ABSTRACT FROM AUTHOR]

The White Hills Gravel is the coarsest fluvial gravel within Victoria, southeastern Australia, and outcrops discontinuously across a substantial part (>35 000 km2) of this region. It overlies a major regional unconformity representing >300 Ma, and is dominated by conglomerate (61% of beds), with less abundant sandy lithofacies (36% of beds) and rare mudstones (3% of beds). The sedimentary characteristics of the basal conglomerates (e.g. thick bedding, common boulders, very poor sorting and in places chaotic appearance) indicate deposition by significant paleofloods, including debris floods, within braided streams that had similar flow directions to the modern drainage. Paleohydraulic calculations estimate that the paleofloods had discharges exceeding >7200 m3 s−1, over 25 times the largest recorded floods in the same valleys. These paleofloods were probably caused by exceptionally severe storm systems, consisting of many individual thunderstorms. Deposition of the White Hills Gravel has previously been attributed to the mid-Cretaceous uplift that affected southeastern Australia, but the maximum possible age of the formation (early Paleocene) is too young for this to be a viable explanation. Instead, the White Hills Gravel may have been deposited during a period of increased precipitation at the Paleocene–Eocene Thermal Maximum (ca 56 Ma), when an intensification of the hydrological system generated significant sediment responses within fluvial systems around the world. Modelling indicates that this climatic event had a substantial impact in Victoria, where increases in mean annual precipitation (50–60%) and the most extreme rainfall rates achieved by the strongest storms (60–70%) were some of the largest globally, and frequent and intense Atmospheric Rivers were an important part of the hydrological system. These factors, together with a period of disturbed vegetation cover, resulted in major paleofloods that could have deposited the coarse-grained conglomerates of the White Hills Gravel. KEY POINTS: The White Hills Gravel in central Victoria is dominated by conglomerate (∼61% of beds), with lesser sandstone (∼36% of beds) and rare mudstone (∼3% of beds). In valleys across the central Victorian Highlands, White Hills Gravel outcrops are characteristically elevated above the Calivil Formation, and in the Campaspe and Loddon valleys, the long profiles of both formations cross over downstream. There is no evidence of a tectonic origin for the formation; instead, the White Hills Gravel was most likely deposited in response to a sharp rise in precipitation. The very-coarse-grained base of the formation was deposited by major thunderstorm-generated paleofloods with discharges up to and exceeding 7200 m3 s−1 in small catchments. The White Hills Gravel may denote a significant fluvial response to climate modification across the Paleocene–Eocene Thermal Maximum. [ABSTRACT FROM AUTHOR]

Understanding the hydrometeorological impacts of atmospheric rivers (ARs) on mountain snowpack is crucial for water resources management in the snow-fed river basins such as the Euphrates-Tigris (ET). In this study, we investigate the contribution of wintertime (December-January–February) ARs to precipitation and snowpack in the headwater regions of the ET Basin for the period of 1979–2019 using a state-of-the-art AR catalog and ERA5 reanalysis data. The results show that AR days in the headwaters region could be warmer by up to 3 °C and wetter by over 5 mm day−1 compared to non-AR days. The contribution of ARs to the total winter precipitation varies from year to year, with a maximum contribution of over 80% in 2010 and an average contribution of 60% over the 40-year period. While snow accumulation on AR days shows spatial variability, the average snow contribution is 27% of the seasonal average, ranging from 12 to 57% for different years. The south-facing parts of the mountain range experience significant snowmelt, with contributions ranging from 15 to 80% for different years. The high total precipitation (60%) and low snowpack (27%) contribution can be attributed to the semi-arid characteristics of the region and the occurrence of rain-on-snow events, where rain falling on existing snow rapidly melts the snowpack. The findings have implications for water resource management and call for continued research to improve our knowledge of ARs and their interactions with the complex terrain of the ET Basin. [ABSTRACT FROM AUTHOR]

This study investigates the origins and the atmospheric circulation patterns that led to the occurrence of Wet-Cold compound events (WCCEs) recorded by the Hellenic National Meteorological Service (HNMS) between 1980 and 2004. The study employed two methods to provide insights into the issue. The first method involved identifying clusters from backward trajectories (BTs) at three heights above the arrival point (500, 1500 and 5500 m). The second method aimed at detecting weather patterns (WPs) that result from the clustering of atmospheric pressure at sea level (SLP) and the geopotential height (GH500) at 500 hPa using ERA5 reanalysis data. To detect clusters for both methods, k-means clustering was applied. The analysis of backward trajectories produced seven clusters of BTs at each height level. Most of these clusters originated from the northwest or north, with medium, short, and long-distance clusters observed at the heights of 500 m, 1500 m and 5500 m, respectively. Despite analysing the temporal data, no clear connection was established between the clusters and months. From the cluster of GH500 and SLP variables for 94 different dates, we derived three main weather patterns. All weather patterns showed high GH500 values in Western Europe and lower GH500 values in Eastern Europe. Although SLP values differed significantly among the clusters, they helped to identify distinct weather patterns. Finally, we found that on 7 out of a total of 94 different dates with WCCEs, large amounts of moisture are transported through the atmosphere to Greece over long distances by atmospheric rivers (ARs). [ABSTRACT FROM AUTHOR]

Large‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models. Plain Language Summary: A machine learning model is employed to bias correct the large‐scale dynamical and thermodynamical fields simulated by a low‐resolution global climate model. The impact of the machine learning model on the large‐scale storm environment associated with tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) was evaluated. It is found that the ML bias correction can effectively reduce the mean climate biases in large‐scale wind, temperature, and humidity fields associated with the three types of high‐impact weather systems. For storms such as ETCs and ARs that can be partly resolved by the low‐resolution climate models, the machine learning bias correction shows skills in improving the long‐term statistics of these weather events. For storms such as TCs that can not be well resolved in the low‐resolution climate models, the machine learning approach produces more realistic statistics of the tropical cyclone tracks by providing more realistic large‐scale steering winds for downscaling approaches. By reducing model biases without affecting the climate change signals in large‐scale storm environments derived from the low‐resolution climate model simulations, machine learning bias correction has the potential to provide more reliable projections for assessing future changes in extreme weather events. Key Points: A machine learning approach with a long short‐term memory network is used to bias correct climate simulations from the E3SM atmosphere modelThe approach effectively reduces biases in the simulated large‐scale model states while preserving their responses to climate changeImproving large‐scale storm environment with bias correction facilitates modeling of high‐impact weather systems and their future changes [ABSTRACT FROM AUTHOR]

Record high temperatures were documented in the McMurdo Dry Valleys, Antarctica, on 18 March 2022, exceeding average temperatures for that day by nearly 30°C. Satellite imagery and stream gage measurements indicate that surface wetting coincided with this warming more than 2 months after peak summer thaw and likely exceeded thresholds for rehydration and activation of resident organisms that typically survive the cold and dry conditions of the polar fall in a freeze‐dried state. This weather event is notable in both the timing and magnitude of the warming and wetting when temperatures exceeded 0°C at a time when biological communities and streams have typically entered a persistent frozen state. Such events may be a harbinger of future climate conditions characterized by warmer temperatures and greater thaw in this region of Antarctica, which could influence the distribution, activity, and abundance of sentinel taxa. Here we describe the ecosystem responses to this weather anomaly reporting on meteorological and hydrological measurements across the region and on later biological observations from Canada Stream, one of the most diverse and productive ecosystems within the McMurdo Dry Valleys. Plain Language Summary: An atmospheric river event in March 2022 led to record warm temperatures over much of Antarctica. Here we report the ecological and hydrological responses to these extraordinary temperatures. The McMurdo Dry Valleys are unique among other regions of Antarctica that experienced this extreme weather event because contemporary observations of ecosystem responses to unseasonable weather can be anchored to a 30‐year record of meteorology, stream flow, and soil community data supported by the National Science Foundation Long Term Ecological Research program. As such weather anomalies are expected to be more common in future Antarctic climate regimes our insights are broadly relevant to the scientific community seeking to predict future ecosystem and organismal responses to ongoing climate change. Key Points: An atmospheric river caused extreme weather in the Antarctic Dry Valleys in March 2022 with temperatures 25°C above average conditionsRecord fall temperatures drove mobilization of liquid water and reactivation of biota at a time when organisms are entering winter dormancyBiotic responses to unseasonable warm and wet conditions may influence diversity and life‐history characteristics of biotic communities [ABSTRACT FROM AUTHOR]

This study emulates dropsondes to elucidate the extent to which sporadic airborne sondes adequately represent divergence of moisture transport in Arctic atmospheric rivers (ARs). The convergence of vertically integrated moisture transport (IVT) plays a crucial role as it favours precipitation that significantly affects Arctic sea ice properties. Long-range research aircraft can transect ARs and drop sondes to determine their IVT divergence. In order to assess the representativeness of future sonde-based IVT divergence in Arctic ARs, we disentangle the sonde-based deviations from an ideal instantaneous IVT divergence, which result from undersampling by a limited number of sondes and from the flight duration. Our synthetic study uses C3S Arctic Regional Reanalysis (CARRA) reanalyses to set up an idealised scenario for airborne AR observations. For nine Arctic spring ARs, we mimic flights transecting each AR in CARRA and emulate sonde-based IVT representation by picking single vertical profiles. The emulation quantifies IVT divergence observability by two approaches. First, sonde-based IVT and its divergence are compared to the continuous IVT interpolated onto the flight cross-section. The comparison specifies uncertainties of discrete sonde-based IVT variability and divergence. Second, we determine how temporal AR evolution affects IVT divergence values by contrasting time-propagating sonde-based values with the divergence based on instantaneous snapshots. For our Arctic AR cross-sections, we find that coherent wind and moisture variabilities contribute less than 10 % to the total transport. Both quantities are uncorrelated to a great extent. Moisture turns out to be the more variable quantity. We show that sounding spacing greater than 100 km results in errors greater than 10 % of the total IVT along AR cross-sections. For IVT divergence, the Arctic ARs exhibit similar differences in moisture advection and mass convergence across the embedded front as mid-latitude ARs, but we identify moisture advection as being dominant. Overall, we confirm the observability of IVT divergence with an uncertainty of around 25 %–50 % using a sequence of at least seven sondes per cross-section. Rather than sonde undersampling, it is the temporal AR evolution over the flight duration that leads to high deviations in divergence components. In order to realise the estimation of IVT divergence from dropsondes, flight planning should consider not only the sonde positioning, but also the minimisation of the flight duration. Our benchmarks quantify sonde-based uncertainties as essential preparatory work for the upcoming airborne closure of the moisture budget in Arctic ARs. [ABSTRACT FROM AUTHOR]

Extreme (i.e., centennial‐scale) floods are by definition rare and can therefore be difficult to study. As a result, case studies of response to extreme floods can provide unique insights. On 15 November 2021, the Nicola River in British Columbia, Canada, experienced a 200‐year flood that increased the average width of the Nicola River by more than 50% (35 m), leaving the only highway in the region impassable for nearly 12 months. This research assesses the spatial variability of erosion along a 71‐km‐long segment of the Nicola River during a period with a large flood (2015–2018) and a second period containing the extreme flood in 2021 (2018–2021). We use a random forest statistical analysis to explore the most important valley and channel characteristics affecting relative widening during both periods. Unit stream power, gradient and valley confinement were the primary determinants of erosion during the extreme flood, whereas vegetation cover, channel pattern and surficial material were less important. Erosion during the large flood event was not related to these variables, with channel pattern providing a better indication of widening potential. As the Nicola River is flanked by erodible glacial deposits, this work provides important insight into river response in the paraglacial environments that are common throughout much of Canada and the northern United States, as well as Europe and Asia, but less intensely studied. In these settings, the geomorphic response to large floods may not be representative of the potential erosion (and infrastructure impacts) that can occur during extreme events, which destabilize glacial terraces in confined reaches. Historical observations should therefore be used with caution in paraglacial settings as extreme events may not be captured in available data, creating a perception of stability in confined reaches that may have the potential to erode dramatically during extreme events. [ABSTRACT FROM AUTHOR]

Drought intensity is commonly characterized using meteorologically‐based metrics that do not provide insight into water deficits within deeper hydrologic systems. In contrast, global positioning system (GPS) displacements are sensitive to both local and regional hydrologic‐storage fluctuations. While a few studies have leveraged this sensitivity to produce geodetic drought indices, hydrologic drought characterization using GPS is not commonly accounted for in drought assessment and management. To motivate this application, we produce a new geodetic drought index (GDI) and quantify its ability to characterize hydrologic drought conditions in key surface and sub‐surface hydrologic reservoirs/pools across California. In northern California, the GDI exhibits a strong regional association with surface‐reservoir storage at the 1‐month time scale (correlation coefficient: 0.83) and groundwater levels at the 3‐month time scale (correlation coefficient: 0.87), along with moderate associations with stream discharge at the daily (instantaneous) time scale (correlation coefficient: 0.50). Groundwater in southern California is best characterized with a 12‐month GDI (correlation coefficient: 0.77), and surface‐reservoir storage is optimized with the 3‐month GDI (correlation coefficient: 0.72). Two sigma uncertainties are ±0.03. Differences between northern and southern California reveal that the GDI is sensitive to unique aquifer and drainage basin characteristics. In addition to capturing long‐term hydrologic trends, rapid changes in the GDI initiate during clusters of large atmospheric river events that closely mirror fluctuations in traditional hydrologic and meteorological observations. We show that GPS‐based hydrologic drought indices provide a significant opportunity to improve drought assessment, in California and beyond, by improving our understanding of the hydrologic cycle. Plain Language Summary: Although quantifying the total volume of water loss is of critical importance during periods of drought, drought intensity is often characterized using meteorologic observations, such as precipitation, rather than using more holistic hydrologic observations, such as reservoir levels and groundwater. While precipitation is a good measure of the amount of water entering a region, precipitation models do not determine the amount of water retained in a watershed or the amount lost due to runoff and evapotranspiration, which are important factors for drought management. We address this need by producing a hydrologically based drought index that captures changes in both surface and subsurface hydrologic reservoirs/pools using surface‐loading geodesy, which quantifies changes in water volume based on how the shape of the Earth changes under the weight of the water. In this study, we use three‐dimensional global positioning system data to develop a geodetic drought index (GDI). Comparison with independent hydrologic observations indicates strong regional and temporal correlations with reservoir storage, groundwater fluctuations, and stream discharge observations, suggesting the GDI can effectively characterize variations in total hydrologic storage. The GDI provides an opportunity to improve hydrologic models for drought management and to advance our understanding of the water cycle. Key Points: Current drought assessment methods rely primarily on meteorologic drought indices that do not characterize total water storageThe geodetic drought index quantifies hydrologic drought and is especially sensitive to groundwater and surface‐reservoir storageDrought metrics based on geodetic data improve characterization of total water storage, providing unique insight for drought management [ABSTRACT FROM AUTHOR]

The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretable machine learning to identify and describe drivers of AR flood damage. Hazard features, particularly measures of AR intensity such as total precipitation, increase the probability of damage with increasing values up to a threshold point, after which the probability of damage saturates. Although hazard is generally the most important risk dimension across all models, exposure and vulnerability contribute up to a third of the explanatory power. Exposure and variability features generally increase the probability of damage with increasing values, apart from a few instances which can be explained by physical intuition, but tend to affect the probability of damage less for the largest AR events. Comparisons between random forest models at different spatial and temporal scales showed general agreement. We then examine limitations inherent in publicly available exposure, vulnerability, and loss data, focusing on the difference in temporal resolution between variables from different risk dimensions and discrepancies between NFIP claims and total flood losses, and describe how those limitations may affect the model results. Overall, the application of interpretable machine learning to understand the contributions of exposure and vulnerability to AR-driven flood risk has identified potential community risk drivers and strategies for resilience, but the results must be considered in the context of the data that produced them. [ABSTRACT FROM AUTHOR]

The state and fate of snow on sea ice are crucial in the mass and energy balance of sea ice. The function of atmospheric rivers (ARs) on snow depth over sea ice has not been measured thus far, limiting the understanding of the mechanism of snow depth changes. Here, the effect of ARs on snow depth changes was explored. We found that increased AR frequency is responsible for winter‐autumn snow accumulation and spring‐summer snow melting. The 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and cloud radiative effect (CRE) during ARs explain the changes in snow depth triggered by AR occurrence. This work helps us understand how ARs affect snow depth changes through related physical processes, promotes an understanding of climate systems and provides a theoretical basis for snow treatment in sea ice models. Plain Language Summary: Snowpack, one of the most essential parts of the cryosphere, affects the energy exchange between sea ice and the atmosphere, thus contributing to sea ice change and the global climate system. Atmospheric rivers (ARs) are long and narrow transient corridors of water vapor. Because ARs transmit more than 90% of water vapor to high latitudes, increasing scientific and societal interest has focused on the impact of ARs on sea ice. However, little attention has been given to the relationship between ARs and snow depth. Therefore, reliable ARs and snow depths from 2002 to 2022 were obtained. We found that increased ARs drive autumn‐winter snow depth increases and spring‐summer snow depth decreases based on ARs and snow depth records. ARs cause changes in snow depth by altering physical processes related to ARs (e.g., 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and the cloud radiative effect (CRE)). Key Points: A novel snow depth record over Arctic sea ice was generated using the triple collocation methodMore frequent atmospheric rivers (ARs) lead to autumn‐winter snow depth increases and spring‐summer snow depth reductionsThe 2 m air temperature, rainfall, snowfall, and cloud radiative effect (CRE) are important in the AR‐induced changes in snow depth [ABSTRACT FROM AUTHOR]

Given the important role of Atmospheric River precipitation (ARP) in the global hydrological cycle, accurate representation of ARP is significant. However, general circulation models (GCMs) demonstrate bias in simulating ARP. The target of this study is to quantify the performance of ARP intensity/frequency for CMIP6 simulations, and further to improve ARP estimation of CMIP6 using Cycle‐Consistent Generative Adversarial Networks (CycleGAN) with highlighting the more accurate ARP features under the global warming background. The findings of this study are as follows: (a) although ARP intensity/frequency in reserved‐optimal CMIP6 overall reproduces the observation, it is still underestimated at the stronger Atmospheric river (AR) scales, particularly for the AR highly active mid‐latitude regions. (b) The CycleGAN‐based bias correction approach markedly diminishes the bias of the CMIP6 simulations within most of the AR scales among both global and the four AR highly active regions. Moreover, the performance of the ARP in AR highly active regions is significant improvement, which is mainly due to the reduction of the bias at the strongest scale. (c) Relative to reference period (1986–2005), ARP intensity/frequency at the strongest scale increase notably under 3°C warming level, with an average value of 373.3% in intensity and 415.9% in frequency for global and the four key regions before correction, and the value is 451.9% and 492.5% after bias correction. The results illustrate that CycleGAN can effectively improve the ARP simulations of GCMs, and an early warning implies that future strong extreme ARP should potentially surpass the current expected. Plain Language Summary: Despite the important role of Atmospheric River precipitation (ARP) in global hydrological cycle, accurately simulating global ARP remains a great challenge. Here, the content of our study consists of three main parts. Firstly, we quantify the performance of CMIP6 general circulation models (GCMs) on ARP intensity/frequency simulations and find that CMIP6 GCMs underestimates ARP at the Atmospheric river (AR) highly active regions, especially in the strongest AR intensity scale. Secondly, we utilize a deep learning method named CycleGAN to improve global ARP intensity/frequency simulations of CMIP6 GCMs. The results indicate that CycleGAN effectively corrects the ARP intensity/frequency bias within most of the AR intensity scales among both global and the AR highly active regions. Moreover, the performance of ARP in the AR highly active regions is significant improvement, which is mainly due to the reduction of the bias in the strongest scale. Finally, the trained CycleGAN model is used to evaluate ARP under global warming of 1.5, 2, and 3°. The bias correction results show that ARP in the strongest AR intensity scale surpasses the current expected, especially under 3° warming level. Key Points: CMIP6 models underestimates Atmospheric River precipitation (ARP) at the Atmospheric river (AR) highly active regions, especially in the strongest AR scaleThe CycleGAN‐based deep learning method reduces model bias notably for ARP intensity and frequency, respectivelyARP in the strongest AR scale potentially surpasses the current GCM‐based projections expected in different degrees of warming after bias correction [ABSTRACT FROM AUTHOR]

Extreme precipitation events (EPEs) present potentially an enormous societal risk and often lead to major human and economic impacts. In the mid-latitudes, such EPEs are often triggered by intense extratropical cyclones and their associated frontal systems. Over the last decade, several studies have shown the important and specific role played by Atmospheric Rivers (ARs) in the occurrence of EPEs in western Europe, particularly in the Iberia Peninsula. In this study we analyze the all-time 24 h record-breaking precipitation (120.3 mm) recorded in the historical Dom Luiz Observatory (since 1863) in Lisbon, Portugal, between 12 and 13 December 2022. A synoptic evaluation of surface and upper-level fields from 5 to 14 December is performed using ERA-5 reanalysis. The week before the EPE, there was a combined effect of a large-scale SLP gradient resembling the NAO negative phase, a southerly position of the jet stream and an above normal positive SST anomalies over the North Atlantic leading to the development of several low-pressure systems at relatively low latitudes, all travelling along the same mean path towards western Europe. The atmospheric river associated with this event was first detected on late 10 December associated with a deep extratropical cyclone. The combination of high Integrated Vapor Transport (and moisture inflow by the warm conveyor belt), with a dynamical component characterized by a suitable uplift motion, allowed the system to evolve and maintain its AR characteristics for 72 h. The extratropical cyclone and associated AR moved northeast towards Iberia, making landfall in Portugal, on 12 December, as an extreme AR event, leading to the 24 h precipitation record breaking event. [ABSTRACT FROM AUTHOR]

Contrasting the extensive research on summer atmospheric rivers (ARs) in the Antarctic Peninsula (AP), winter AR impacts are less understood. This study examines a unique warming event from 1 to 3 July 2023, using in situ winter observations and ERA5 reanalysis. On 2 July, Frei station experienced an extreme warm event with a temperature of 2.7°C and a significant rise in the freezing level, coinciding with winter rainfall. A pressure dipole pattern over the AP, with contrasting circulations over Bellingshausen and Weddell Seas, facilitated an AR, carrying warm, humid air initially from South America/Atlantic and then the southeast Pacific. This shift resulted in anomalous water stable isotope composition in precipitation. Trends suggest a strengthening winter pressure dipole, associated with increased AR frequency and higher temperatures in northern AP. These findings highlight the importance of winter observations in exploring AR impacts, bridging knowledge gaps about winter AR behaviors. Plain Language Summary: The Antarctic Peninsula is increasingly witnessing climate extremes during summer, while the understanding of such extremes in winter remains limited. Our study explores a significant warming event in the northern Peninsula in early July 2023, utilizing recent winter in situ observations and atmospheric analysis. On 2 July, an exceptional temperature of 2.7°C was recorded, significantly altering the atmospheric freezing level and causing rainfall instead of snow. Our analysis revealed a unique atmospheric pattern around the Peninsula, characterized by varying air movements over the Bellingshausen and Weddell Seas. This pattern facilitated an "atmospheric river," a flow of warm and moist air from lower latitudes, including continental South America/Atlantic and the southeast Pacific, as reflected in the precipitation's water stable isotope composition. This event is indicative of a potential trend toward more frequent and severe occurrences, emphasizing the urgent need for comprehensive winter research in the Antarctic Peninsula to understand the broader implications of extreme weather events and climate change. Key Points: Early July 2023 had extreme winter warmth with rainfall in the northern Antarctic Peninsula, favored by an atmospheric riverIn recent decades, strengthened winter pressure dipole has directed northerly warm and moist air, resulting in warm events in the regionWe highlight the need for expanded winter research in the Antarctic Peninsula to better understand climate change and extreme weather events [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are known to produce both beneficial and extreme rainfall, leading to natural hazards in Chile. Motivated to understand moisture transport during AR events, this study performs a moisture budget analysis along 50 zonally elongated ARs reaching the western coast of South America. We identify the convergence of moist air masses of tropical/subtropical origin along the AR as the primary source of vertically integrated water vapor (IWV). Over the open ocean, moisture convergence is nearly balanced by precipitation. The advection of moisture along the AR, although smaller compared to mass convergence, significantly increases toward the landfalling region. The near conservation of IWV over the open ocean, observed by tracking a Lagrangian atmospheric column along the ARs, is the explanation behind the seemingly tropical origin of ARs in time‐lapse visualizations of IWV. Plain Language Summary: Imagine atmospheric rivers (ARs) as massive, flowing rivers in the sky, but instead of water, they carry vapor from the ocean. When these "sky rivers" travel and hit the Andes Mountains in South America, they can cause a lot of rain and snow to fall. This precipitation is often good because it helps fill reservoirs and water crops. However, sometimes there's too much rain, leading to floods and landslides, which can be dangerous. Over the ocean, the amount of water vapor these atmospheric rivers pick up is almost exactly balanced by the rain that falls from them. As these atmospheric rivers get closer to South America, the movement of moisture along the river, though generally less significant than the gathering of moist air, becomes more pronounced. This means that as the atmospheric river approaches the land, it starts carrying more moisture toward its destination. We were able to see this process in action by following a moving "slice" of the atmosphere (a Lagrangian atmospheric column) as it travels along the path of the atmospheric river. This helped us understand how atmospheric rivers maintain their water content as they move. It also shows why atmospheric rivers seem to originate from tropical areas when we look at them in time‐lapse images of water vapor. Key Points: We calculate the moisture transport budget of 50 events of zonal atmospheric rivers over the Pacific that reach South AmericaHorizontal convergence of tropical and subtropical air masses act to maintain atmospheric rivers over the ocean while advection dominates near the coastFollowing a Lagrangian column, precipitable water is roughly conserved along atmospheric rivers, except near landfalling [ABSTRACT FROM AUTHOR]

The increase in the number and quality of numerical moisture tracking tools has greatly improved our understanding of the hydrological cycle in recent years. However, the lack of observations has prevented a direct validation of these tools, and it is common to find large discrepancies among the results produced by them, especially between Eulerian and Lagrangian methodologies. Here, we evaluate two diagnostic tools for moisture tracking, WaterSip and UTrack, using simulations from the Lagrangian model FLEXPART. We assess their performance against the Weather Research and Forecasting (WRF) model with Eulerian Water Vapor Tracers (WRF-WVTs). Assuming WRF-WVTs results as a proxy for reality, we explore the discrepancies between the Eulerian and Lagrangian approaches for five precipitation events associated with atmospheric rivers and propose some physics-based adjustments to the Lagrangian tools. Our findings reveal that UTrack, constrained by evaporation and precipitable water data, has a slightly better agreement with WRF-WVTs than WaterSip, constrained by specific humidity data. As in previous studies, we find a negative bias in the contribution of remote sources, such as tropical ones, and an overestimation of local contributions. Quantitatively, the root-mean-square-error (RMSE) for contributions from selected source regions is 5.55 for WaterSip and 4.64 for UTrack, highlighting UTrack's narrowly superior performance. Implementing our simple and logical corrections leads to a significant improvement in both methodologies, effectively reducing the RMSE by over 50 % and bridging the gap between Eulerian and Lagrangian outcomes. Our results suggest that the major discrepancies between the different methodologies were not rooted in their inherently different nature, but in the obviation of basic physical considerations that may be easily straightened out. [ABSTRACT FROM AUTHOR]

The Gulf Stream (GS) ocean front exhibits intense ocean–atmosphere interaction in winter, which has a significant impact on the genesis and development of extratropical cyclones in the North Atlantic. The atmospheric rivers (ARs), closely related with the cyclones, transport substantial moisture from the North Atlantic towards the Western European coast. While the influence of the GS front on extratropical cyclones has been extensively studied, its effect on ARs remains unclear. In this study, two sets of ensemble experiments are conducted using a high-resolution global Community Atmosphere Model forced with or without the GS sea surface temperature front. Our findings reveal that the inclusion of the GS front leads to approximately 25% enhancement of water vapor transport and precipitation associated with ARs in the GS region, attributed to changes in both AR frequency and intensity. Furthermore, this leads to a more pronounced downstream response in Western Europe, characterized by up to 60% (40%) precipitation increases (reductions) around Spain (Norway) for the most extreme events (exceeding 90 mm/day). The influence of the GS front on ARs is mediated by both thermodynamic and dynamic factors. The thermodynamic aspect involves an overall increase of water vapor in both the GS region and Western Europe, promoting AR genesis. The dynamic aspect encompasses changes in storm tracks and Rossby wave train, contributing to downstream AR shift. Importantly, we find the co-occurrence of ARs and the GS front is crucial for inducing deep ascending motion and heating above the GS front, which perturbs the deep troposphere and triggers upper-level Rossby wave response. These findings provide a further understanding of the complex interaction between the oceanic front in the western boundary current regions and extratropical weather systems and the associated dynamics behind them. [ABSTRACT FROM AUTHOR]