Search

Abstract 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.

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.

This study unravels the characteristics, mechanisms, and predictability of four consecutive record‐breaking heatwaves hitting North China in June and July 2023. The first three heatwaves primarily influenced the northern part of North China and were accompanied by consistent anticyclonic anomalies in the upper troposphere. The anomalous anticyclone was caused by the British–Baikal corridor teleconnection along the polar front jet, particularly during the second heatwave. In contrast, the fourth heatwave was induced by a distinct low‐pressure system, attributed to the Silk Road pattern along the subtropical jet. The presence of this low‐pressure system and its interaction with atmospheric rivers and local topography led to the foehn wind, further contributing to the rise in surface temperatures. Sub‐seasonal to seasonal models can effectively predict the occurrence of all heatwaves 2–5 days in advance despite underestimating the intensity. However, models exhibit limitations in providing reliable predictions when the lead time exceeds 2 weeks. Plain Language Summary: In the summer of 2023, North China experienced four consecutive extreme high‐temperature events, which are called heatwaves. This study investigates the main factors that cause the four events and shows how well the operational numerical models can predict the heatwaves. The first three heatwaves shared similar circulation, with high‐pressure systems controlling North China. This local circulation anomaly was related to an upstream quasi‐stationary wave train along the polar front jet. The fourth heatwave was associated with a low‐pressure system over North China and an upstream quasi‐stationary wave train along the subtropical jet. Moreover, predictions from operational models demonstrate their capability to forecast the occurrence of high temperatures 2–5 days ahead, with an underestimation in the intensity. However, models are not reliable when it comes to predicting heatwaves 2 weeks in advance. Key Points: North China witnessed record‐breaking heatwaves in June and July 2023, consisting of four sequential synoptic‐scale eventsThe first three and the last heatwaves are controlled by distinct local circulations induced by atmospheric wave trains across EurasiaS2S models capture the heatwave occurrences 2–5 days in advance, but predictive skill notably diminishes beyond 2 weeks [ABSTRACT FROM AUTHOR]

Elevated moisture transport over the Antarctic ice sheet can increase snowfall and ice mass. Previous studies used ground‐based observations, reanalysis products, and atmospheric models to evaluate the relationship between extreme moisture transport and snowfall properties. Here, we build on previous studies by combining reanalysis and CloudSat radar snowfall retrievals to examine impacts of extreme moisture intrusions on changes in snowfall frequency and intensity over glacier basins on the Antarctic ice sheet. We examine the impacts of enhanced moisture transport events on snowfall frequency and intensity over two different regions on the Antarctic ice sheet: the Amery Ice Shelf of East Antarctica and the Thwaites and Pine Island glacier basins of West Antarctica. We determine when the median integrated water vapor transport from reanalysis exceeds the 95th percentile within the glacier basins of interest to define enhanced moisture transport events. We then use CloudSat radar snowfall retrievals to evaluate differences between snowfall frequency and intensity during enhanced water vapor transport events compared to the seasonal means for 2007–2010. We find that enhanced moisture transport events over the Amery Ice Shelf and Thwaites and Pine Island glacier basins coincide with higher snowfall frequency and intensity. These enhanced moisture transport events have the potential to alter surface mass balance within glacier basins, with implications for future rates of sea level rise. Plain Language Summary: Large‐scale features of the atmosphere can transport large amounts of water vapor over Antarctica. These instances of enhanced moisture transport can precede enhanced snowfall events. Prior studies have examined this relationship using ground‐based observations and modeling results. Our study uses spaceborne radar observations to show that enhanced moisture transport corresponds to elevated snowfall frequency and rates within the Amundsen Sea and the Amery Ice Shelf regions. This work shows that the increased snowfall penetrates far inland, from the coast and to the interior of the ice sheets. Key Points: Enhanced moisture transport events correspond to CloudSat observations of increased snowfall frequencies and rates over Antarctic regionsObservations indicate that increased snowfall during extreme moisture transport events persists deep into the interior of ice sheetsEnhanced moisture transport events have the potential for elevated snowfall accumulation over Antarctic glacier basins [ABSTRACT FROM AUTHOR]

During wildfires and fossil fuel combustion, biomass is converted to black carbon (BC) via incomplete combustion. BC enters the ocean by rivers and atmospheric deposition contributing to the marine dissolved organic carbon (DOC) pool. The fate of BC is considered to reside in the marine DOC pool, where the oldest BC 14C ages have been measured (>20,000 14C y), implying long-term storage. DOC is the largest exchangeable pool of organic carbon in the oceans, yet most DOC (>80%) remains molecularly uncharacterized. Here, we report C measurements on size-fractionated dissolved BC (DBC) obtained using benzene polycarboxylic acids as molecular tracers to constrain the sources and cycling of DBC and its contributions to refractory DOC (RDOC) in a site in the North Pacific Ocean. Our results reveal that the cycling of DBC is more dynamic and heterogeneous than previously believed though it does not comprise a single, uniformly "old" 14C age. Instead, both semilabile and refractory DBC components are distributed among size fractions of DOC. We report that DBC cycles within DOC as a component of RDOC, exhibiting turnover in the ocean on millennia timescales. DBC within the low-molecular-weight DOC pool is large, environmentally persistent and constitutes the size fraction that is responsible for long-term DBC storage. We speculate that sea surface processes, including bacterial remineralization (via the coupling of photooxidation of surface DBC and bacterial co-metabolism), sorption onto sinking particles and surface photochemical oxidation, modify DBC composition and turnover, ultimately controlling the fate of DBC and RDOC in the ocean. [ABSTRACT FROM AUTHOR]

Accurate forecasting of atmospheric rivers (ARs) holds significance in preventing losses from extreme precipitation. However, traditional numerical weather prediction (NWP) models are computationally expensive and can be limited in accuracy due to inaccurate physical parameter settings. To overcome these limitations, we propose a deep learning (DL) model, called GAN‐UNet, to forecast the AR occurrence, position, and intensity in East Asia. GAN‐UNet can capture the complex nonlinear relationship between the inputs at the past moment, including the vertically integrated water vapor transport (IVT), zonal wind at 850 hPa (U850), and meridional wind at 850 hPa (V850), and the forecast output (IVT, U850, or V850), whose results are comparable to NWP models. In addition, the average model (AM) by integrating the results generated by GAN‐UNet and European Centre for Medium‐Range Weather Forecasts (ECMWF) outperforms all the NWP models selected in this study, demonstrating its potential to improve the performance of NWP through the DL method. Specifically, the 5‐day average F1 scores of the AM are 0.777 and 0.845, whose values are significantly better than those obtained by ECMWF (0.712 and 0.794) in the two key regions of East Asia; The AM 5‐day average intersection over unions are 0.706 and 0.688 while the values of ECMWF are 0.675 and 0.64; in terms of intensity forecast, GAN‐UNet and AM exhibited lower differences in most of the intensity bins, except for the final bin with IVT more than 825 kg m−1 s−1. With this thorough analysis, GAN‐UNet is shown as an effective model to forecast ARs. Plain Language Summary: This work presents a deep learning (DL) model (called GAN‐UNet) for the spatio‐temporal atmospheric rivers (ARs) forecast. Our goal is to perform long lead‐time AR event forecasts accurately and efficiently. The data from 1959 to 2022 over 40°E−180°E, 20°S−60°N are used for training, validation, and testing. The results show that the proposed GAN‐UNet can forecast the AR occurrence, position, and intensity accurately and its performance is comparable with the state‐of‐the‐art numerical weather prediction (NWP) model. In addition, the ensemble of GAN‐UNet and the NWP model outperform all the NWP models selected in this study. However, The DL model still has certain limitation. Specifically, while GAN‐UNet and the ensemble model demonstrate superior forecast capabilities for most intensity bins in AR events compared to NWP models, they are severely underestimated for the grids of the final bin. The results show that the DL model can effectively forecast AR events in East Asia, and further improve the forecast accuracy of NWP by combining it with NWP model, which holds significance in extreme precipitation forecast. Key Points: A deep learning model (GAN‐UNet) is developed for efficient and accurate atmospheric river forecastGAN‐UNet achieves comparable performance to ECMWF and NCEP on atmospheric river forecastThe outcomes obtained from the average of GAN‐UNet and NWP exhibit superior performance compared to all the individual NWP models selected in this study [ABSTRACT FROM AUTHOR]

The atmospheric river (AR) is a long, narrow, and transient corridor of strong horizontal water vapor transport. Various AR detection methods have been proposed, which have introduced significant uncertainty to the identified AR characteristics. This study has designed a data fusion algorithm to merge 12 data sets of different global and regional AR identification algorithms published by the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) covering the period from 1980 to 2016. It aims to conduct frequency statistics to further research the global distribution, interannual variation, the trends in poleward shifting, and the impacted factors of AR occurrence. The quantitative results indicate an overall increasing trend in interannual variation, with a more pronounced growth trend observed in the oceanic region between 40 and 60ºS. Additionally, the study identifies a poleward shift in the peak latitude of AR occurrence frequency, with speeds of 0.589° and 0.769° per decade in the Northern and Southern Hemispheres, respectively. This shift may be associated with the tropical poleward expansion. Upon examining the relationship between AR frequency and sea surface temperature (SST) as well as zonal wind, the study finds that distinct dominant factors influence AR in different regions. AR events near the 30°N/S ocean are influenced more significantly by zonal wind than by SST. These findings shed light on the global characteristics of AR occurrences and provide insights into the factors governing their variability across different areas. Plain Language Summary: Researchers employ an array of tools to detect a weather phenomenon event known as the atmospheric river (AR), which is a long, narrow corridor in the atmosphere that transports water vapor originating from the tropics. Each tool employs a distinct methodology for identifying these atmospheric rivers, which can lead to varying results. This diversity makes it challenging for us to select a single methodology to study AR on a global scale and over long periods. To address this problem, we have developed an innovative algorithm. This algorithm merges the results from different tools to create a more consistent and comprehensive data set of atmospheric rivers. Utilizing this novel data set, we investigated how atmospheric rivers have evolved over time and across different locations. Our research indicates a poleward shift of AR positions throughout the research period. Moreover, we also examined the sea surface temperature and wind patterns, and their connections to the frequency of atmospheric rivers. These insights could provide insightful implications for how climate changes might influence these important weather phenomena in the future. Key Points: Proposed a data fusion algorithm merging 12 Atmospheric River Detection Tools data sets based on their identification results conflictsThe poleward shifts of the AR frequency peak and tropical expansion have a moderate correlation during the research periodAR events near the 30° N/S ocean are influenced more significantly by zonal wind than by sea surface temperature [ABSTRACT FROM AUTHOR]

Cool-season (November–March) precipitation contributes critically to California's water resources and flood risk. In the Sierra Nevada, approximately half of cool-season precipitation is derived from a small proportion of storms classified as atmospheric rivers (ARs). The frequency and intensity of ARs are highly variable from year to year and unreliable climate teleconnections limit forecasting. However, previous research provides intriguing evidence of cycles with biennial (2.2 years) and decadal (10–20 years) periodicities in Sierra Nevada cool-season precipitation, suggesting it is not purely stochastic. To identify the source of this cyclicity, we decompose daily precipitation records (1949–2022) into contributions from ARs versus non-ARs, as well as into variations in frequency and intensity. We find that the biennial and decadal spectral peaks in Sierra Nevada precipitation totals are entirely due to precipitation delivered by ARs, and primarily due to variations in the frequency of days with AR precipitation. While total non-AR precipitation correlates with sea surface temperature (SST) and atmospheric pressure patterns associated with the El Niño–Southern Oscillation, AR precipitation shows no consistent remote teleconnections at any periodicity. Supporting this finding, atmospheric simulations forced by observed SSTs do not reproduce the biennial or decadal precipitation variations identified in observations. These results, combined with the lack of long-term stable cycles in previously published tree-ring reconstructions, suggest that the observed biennial and decadal quasi-cyclicity in Sierra Nevada precipitation is unreliable as a forecasting tool. Significance Statement: In California's Sierra Nevada, where most of the state's above-ground water resources originate, cool-season precipitation totals exhibited year-to-year and decadal cyclicity over the past century. Long-range forecasts are notoriously unskillful in this region, so nonrandom cycles would be intriguing to water managers challenged to simultaneously minimize flood and drought risk. Over 1949–2022, precipitation cycles were driven by variations in the number of atmospheric river (AR) storms per year even though ARs account for just half of total precipitation. These findings bring us a step closer to understanding the causes of precipitation cyclicity, but we find no evidence that the cycles were underpinned by larger-scale ocean–atmosphere circulations so we caution against relying on continued cycles into the future. [ABSTRACT FROM AUTHOR]

Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored "sub‐grid" scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases. Plain Language Summary: We present a general framework to design machine learned correction operators to improve the predicted statistics of low‐resolution climate simulations. We illustrate the approach, which acts on existing data in a post‐processing manner, on a simplified prototype climate model as well as a realistic climate model, namely the Energy Exascale Earth System Model (E3SM) with 110 km resolution. For the latter, we show that the developed approach is able to correct the low‐resolution E3SM output to closely reflect the climate statistics of historical observations as quantified by the ERA5 data set. We also demonstrate that our model significantly improves the prediction of atmospheric rivers, an example of extreme weather events resolvable by the low resolution model. Key Points: Development of non‐intrusive correction operators for coarse scale climate simulationsDesign of a training procedure that improves dynamics and allows for characterization of extremes with return period longer than the training dataApplication to Energy Exascale Earth System Model and demonstration of improvement on global and regional statistics [ABSTRACT FROM AUTHOR]

During a 6-day intensive observing period in January 2021, Atmospheric River Reconnaissance (AR Recon) aircraft sampled a series of atmospheric rivers (ARs) over the northeastern Pacific that caused heavy precipitation over coastal California and the Sierra Nevada. Using these observations, data denial experiments were conducted with a regional modeling and data assimilation system to explore the impacts of research flight frequency and spatial resolution of dropsondes on model analyses and forecasts. Results indicate that dropsondes significantly improve the representation of ARs in the model analyses and positively impact the forecast skill of ARs and quantitative precipitation forecasts (QPF), particularly for lead times > 1 day. Both reduced mission frequency and reduced dropsonde horizontal resolution degrade forecast skill. On the other hand, experiments that assimilated only G-IV data and experiments that assimilated both G-IV and C-130 data show better forecast skill than experiments that only assimilated C-130 data, suggesting that the additional information provided by G-IV data is necessary for improving forecast skill. Although this is a case study, the 6-day period studied encompassed multiple AR events that are representative of typical AR behavior. Therefore, the results indicate that future operational AR Recon missions incorporate daily mission or back-to-back flights, maintain current dropsonde spacing, support high-resolution data transfer capacity on the C-130s, and utilize G-IV aircraft in addition to C-130s. [ABSTRACT FROM AUTHOR]

Atmospheric Rivers (ARs), corridors of intensive water vapor transport, have received much attention recently due to their potentially large economic and societal impacts. However, a comprehensive understanding of the interannual variability of ARs over East Asia is still lacking. This study examines the frequency of AR occurrence over East Asia based on the EAR5 reanalysis. It is shown that the frequency of AR occurrence over East Asia displays obvious seasonality, with its peak in early boreal summer. ARs contribute to up to 50% of total precipitation and largely modulate the interannual variability of precipitation in East Asia during early boreal summer. The interannual variability of ARs over East Asia during early boreal summer is largely controlled by the Indo-western Pacific Ocean Capacitor (IPOC) effect, which is mainly triggered by El Niño-Southern Oscillation and sometimes by Indian Ocean dipole. During early boreal summer, a higher frequency of AR occurrence over East Asia is associated with anomalous anticyclonic circulation (AAC) over the Indo-western Pacific region. The AAC, the atmospheric signal of the IPOC mode, indicates an enhanced East Asian summer monsoon and a northwestward shift of the western North Pacific subtropical high, favoring the AR occurrence over East Asia. This study illustrates that a strong positive Indian Ocean dipole event could impact AR activity over East Asia in the following summer by triggering the IPOC effect. The IPOC-related signals 2–4 months ahead could be useful to predict the AR occurrence over East Asia during early boreal summer. [ABSTRACT FROM AUTHOR]

The global increase in atmospheric water vapor due to climate change tends to heighten the dangers associated with both humid heat and heavy precipitation. Process-linked connections between these two extremes, particularly those which cause them to occur close together in space or time, are of special concern for impacts. Here we investigate how atmospheric rivers relate to the risk of summertime humid heat in the United States. We find that the hazards of atmospheric rivers and humid heat often occur in close proximity, most notably across the northern third of the United States. In this region, high levels of water vapor – resulting from the spatially organized horizontal moisture plumes that characterize atmospheric rivers – act to amplify humid heat, generally during the transition from dry high-pressure ridge conditions to wet low-pressure trough conditions. In contrast, the US Southeast, Southwest, and Northwest tend to experience atmospheric rivers and humid heat separately, representing an important negative correlation of joint risk. [ABSTRACT FROM AUTHOR]

This study investigates the recurring floods within the Nekor Basin of NE Morocco, a phenomenon dating back to 1944. A multitude of factors has contributed to these recurrent floods, which profoundly affect the northern region encompassing the Nekor Plain. The research primarily investigates the influence of specific weather conditions on the surge of water levels in "Wadi Nekor" and the ensuing consequences when the valley veers from its primary course. The research findings strongly indicate that a significant portion of these flood events can be attributed to "Cut-Off Low" (COL) and atmospheric rivers (ARs), frequently aligning with mean sea level pressure (MSLP) systems located over, or in proximity to, the Moroccan region. This study employs a systematic classification and arrangement methodology for both upper and lower atmospheric levels in analyzing pertinent cases. [ABSTRACT FROM AUTHOR]

As a result of their important role in weather and the global hydrological cycle, understanding atmospheric rivers' (ARs) connection to synoptic‐scale climate patterns and atmospheric dynamics has become increasingly important. In addition to case studies of two extreme AR events, we produce a December climatology of the three‐dimensional structure of water vapor and O3 (ozone) distributions associated with ARs in the northeastern Pacific from 2004 to 2014 using MERRA‐2 reanalysis products. Results show that positive O3 anomalies reside in dry intrusions of stratospheric air due to stratosphere‐to‐troposphere transport (STT) behind the intense water vapor transport of the AR. In composites, we find increased excesses of O3 concentration, as well as in the total O3 flux within the dry intrusions, with increased AR strength. We find that STT O3 flux associated with ARs over the NE Pacific accounts for up to 13% of total Northern Hemisphere STT O3 flux in December, and extrapolation indicates that AR‐associated dry intrusions may account for as much as 32% of total NH STT O3 flux. This study quantifies STT of O3 in connection with ARs for the first time and improves estimates of tropospheric ozone concentration due to STT in the identification of this correlation. In light of predictions that ARs will become more intense and/or frequent with climate change, quantifying AR‐related STT O3 flux is especially valuable for future radiative forcing calculations. Plain Language Summary: Long filaments of rapidly moving water vapor in the atmosphere, known as atmospheric rivers (ARs), play a vital role in the Earth's water cycle. Because of this, research continues to expand into ARs' relationship with large‐scale climate patterns. In this paper, we use data from the Modern Era Retrospective analysis for Research Applications to examine several extreme ARs that made landfall on the U.S. West Coast and their relationship to the transport of ozone from the stratosphere to the troposphere. We then combine 11 years of December AR and ozone data in order to study the average trend of ozone transport in connection with ARs. We quantify the AR‐related ozone transport for the first time, and we find ARs with more intense water vapor transport result in the transport of higher concentrations of ozone. Quantifying ozone transport into the troposphere in connection with ARs is important as ARs may become more intense and/or more frequent with climate change, and ozone in the troposphere has consequences for the greenhouse effect. Key Points: Case studies and December climatology using MERRA‐2 reveal positive tropospheric ozone anomalies within dry intrusions associated with ARsAverage excess ozone concentrations are 10–13 ppbv at 400 hPa, and are even greater for increasing intensity of ARsSTT of ozone associated with ARs in the NE Pacific may account for (13 ± 2)% of the total December Northern Hemisphere STT ozone flux [ABSTRACT FROM AUTHOR]

Extreme precipitation events associated with atmospheric rivers (ARs) trigger floods, landslides, and avalanches that threaten lives and livelihoods in Southeast Alaska. Six rural and indigenous communities (Hoonah, Klukwan, Skagway, Yakutat, Craig, and Kasaan) identified specific needs regarding these hazards and joined the Southeast Alaska Coastlines and People (CoPe) Kutí Hub to address the shared challenge of understanding and predicting these events. This study presents a climatology (1980–2019) of synoptic, mesoscale, and local meteorological characteristics of ARs and heavy precipitation across this region. High‐amplitude upper‐level patterns across the northeastern Pacific Ocean favor ARs reaching Southeast Alaska, where moisture is orographically lifted, resulting in heavy precipitation. In the six communities, ARs occur 8–15 days per month, yet only 6 AR days per year account for up to 68%–91% of precipitation extremes. Furthermore, 80%–96% of days with extreme precipitation have >75th percentile integrated water vapor transport (IVT), demonstrating the strong relationship between IVT and extreme precipitation. This study also highlights the relationship between IVT direction and complex coastal topography in determining precipitation extremes. For example, in Klukwan and Skagway, 80%–90% of extreme AR days have south‐southwesterly or south‐southeasterly IVT. Coastal communities like Yakutat experience higher IVT and precipitation overall, and although southeasterly IVT is more common, extreme precipitation events are most common with southwesterly IVT. Collaboration with the National Weather Service in Juneau, Alaska will lead to improved situational awareness, forecasts, and Impact Decision Support Services to communities, saving lives and property in a region vulnerable to the impacts of climate change. Plain Language Summary: Extreme precipitation events associated with atmospheric rivers (ARs) trigger floods, landslides, and avalanches that threaten lives and livelihoods in Southeast Alaska. ARs, long and narrow regions of intense water vapor transport, reach Southeast Alaska 8–15 days per month, yet only six ARs per year account for up to 91% of precipitation extremes. This study shows that ARs that result in extreme precipitation in six rural and indigenous communities (Hoonah, Klukwan, Skagway, Yakutat, Craig, and Kasaan) are more likely to have stronger moisture transport, and that the direction of the moisture transport plays a role in precipitation outcomes in each community. Coastal communities like Yakutat experience higher moisture transport and precipitation overall, and although moisture transport from the southeast is more common, extreme precipitation events are more common when moisture transport comes from the southwest. Communities located further inland, such as Klukwan and Skagway, have lower moisture transport, but similar precipitation outcomes, and 80%–90% of extreme AR days have south‐southwesterly or south‐southeasterly moisture transport. The results illustrate the opportunity to incorporate additional characteristics of Southeast Alaskan ARs to improve situational awareness, forecasts, and messaging from the National Weather Service Office in Juneau, Alaska for the vulnerable communities that they serve. Key Points: Atmospheric rivers occur on ∼120 days per year in Southeast Alaska, but ∼6 days produce 68%–91% of precipitation days >95th percentileIn six rural and indigenous Southeast AK communities, 80%–96% of days with extreme precipitation have >75th percentile moisture transportExtreme precipitation in Southeast Alaska is more likely during atmospheric rivers with south‐southwesterly moisture transport [ABSTRACT FROM AUTHOR]

We present a comprehensive analysis of Arctic surface energy budget (SEB) components during atmospheric river (AR) events identified by integrated water vapor transport exceeding the monthly 85th percentile climatological threshold in 3-hourly ERA5 reanalysis data from January 1980 to December 2015. Analysis of average anomalies in SEB components, net SEB, and the overall AR contribution to the total seasonal SEB reveals clear seasonality and distinct land – sea – sea ice contrast patterns. Over the sea ice-covered central Arctic Ocean, ARs significantly impact net SEB, inducing substantial surface warming in fall, winter, and spring, primarily driven by large anomalies in surface downward longwave radiation. We find that ARs make a substantial relative contribution to the mean SEB in spring (32 %), exceeding their corresponding occurrence frequency (11 %). However, in other seasons, ARs contribute relatively less to the mean SEB than their frequency, indicating a diminished role compared to their occurrence frequency. Over sub-polar oceans, ARs have the most substantial positive impact on net SEB in cold seasons, mainly attributed to significant positive turbulent heat flux anomalies, with a maximum contribution to the mean SEB in spring averaging 65 %. In summer, ARs induce negative impacts on net SEB, primarily due to reduced shortwave radiation from increased cloud cover during AR events. Over continents, ARs generate smaller absolute impacts on net SEB but contribute significantly to the mean SEB in cold seasons, far surpassing their corresponding frequency, highlighting their crucial role in determining the net SEB over continents during cold seasons. Greenland, especially western Greenland, exhibits significant downward longwave radiation anomalies associated with ARs, which drive large net SEB anomalies and contribute >54 % to mean SEB, and induce amplified surface warming year-round. This holds significance for melt events, particularly during summer. This study quantifies the role of ARs on surface energy budget, contributing to our understanding of the Arctic warming and sea ice decline in ongoing Arctic amplification. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are the primary mechanism for transporting water vapor from low latitudes to polar regions, playing a significant role as drivers of extreme weather, such as heavy precipitation and heat waves in both the Arctic and Antarctica. With the rapidly growing interest in polar ARs during the past decade, it is imperative to establish an objective framework to quantify the strength and impact of these ARs for both scientific research and practical application. The AR scale introduced by Ralph et al. (2019) ranks ARs based on the duration of AR conditions and the intensity. However, the thresholds of integrated water vapor transport (IVT) used to rank ARs are selected based on the IVT climatology at middle latitudes. These thresholds are insufficient for polar regions due to the substantially lower temperature and moisture content. In this study, we analyze the IVT climatology in polar regions, focusing on the coasts of Antarctica and Greenland. Then we introduce an extended version of the AR scale tuned to polar regions by adding lower IVT thresholds of 100, 150, and 200 kg m-1 s-1 to the standard AR scale, which starts at 250 kg m-1 s-1. The polar AR scale is utilized to examine AR frequency, seasonality, trends, and associated precipitation and surface melt over the Antarctic and Greenland coasts. The polar AR scale better characterizes the strength and impacts of ARs in the Antarctic and Arctic regions, and has the potential to enhance communications across observation, research, and forecasts for polar regions. [ABSTRACT FROM AUTHOR]

Weather features, such as extratropical cyclones (ETCs), atmospheric rivers (ARs), and fronts, contribute to substantial amounts of precipitation globally. However, previous estimates of how much these individual features contribute to precipitation are very sensitive to subjectively chosen metrics. Furthermore, there is no information on how these weather features contribute to precipitation poleward of 60° latitude. To alleviate these shortcomings, we introduce a more robust attribution method applicable at all latitudes. Based on ERA5, we present the first global climatology of the contributions from cyclones, fronts, moisture transport axes (MTAs; AR-like features), and cold air outbreaks, as well as their combinations, to summer and winter precipitation as well as extreme precipitation. Most of the precipitation in the midlatitudes relates to the combination of ETC, fronts, and MTAs (28%), while in polar regions most precipitation occurs within the ETC-only category (27%). Extreme precipitation events in all extratropical regions are predominantly associated with the combination of ETCs, fronts, and MTAs (46%). In the midlatitudes, the combination of ETCs, fronts, and MTAs occurs almost 4 times as often during extreme events compared to regular events. [ABSTRACT FROM AUTHOR]

This study analyzed the sensitivity of atmospheric rivers (ARs) to aerosol treatment in regional climate simulations. Three experiments covering the Iberian Peninsula for the period from 1991 to 2010 were examined: (1) an experiment including prescribed aerosols (BASE); (2) an experiment including direct and semi-direct aerosol effects (ARI); and (3) an experiment including direct, semi-direct, and indirect aerosol effects (ARCI). A new regional-scale AR identification algorithm, AIRA, was developed and used to identify around 250 ARs in each experiment. The results showed that spring and autumn ARs were the most frequent, intense, and long-lasting and that ARs could explain up to 30 % of the total accumulated precipitation. The inclusion of aerosols was found to redistribute precipitation, with increases in the areas of AR occurrence. The analysis of common AR events showed that the differences between simulations were minimal in the most intense cases and that a negative correlation existed between mean direction and mean latitude differences. This implies that more zonal ARs in ARI or ARCI with respect to BASE could also be linked to northward deviations. The joint analysis and classification of dust and sea salt aerosol distributions allowed for the common events to be clustered into eight main aerosol configurations in ARI and ARCI. The sensitivity of ARs to different aerosol treatments was observed to be relevant, inducing spatial deviations and integrated water vapor transport (IVT) magnitude reinforcements/attenuations with respect to the BASE simulation depending on the aerosol configuration. Thus, the correct inclusion of aerosol effects is important for the simulation of AR behavior at both global and regional scales, which is essential for meteorological predictions and climate change projections. [ABSTRACT FROM AUTHOR]

Understanding the present and future features of atmospheric rivers (ARs) is critical for effective disaster prevention and mitigation efforts. This study comprehensively assesses the performance of ARs in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models on both seasonal and interannual timescales within the historical period and investigates the future projection of ARs under different emission scenarios on a global scale. The multi‐model mean results obtained using the PanLu detection algorithm consistently exhibit agreement with the observational AR climatology and capture interannual fluctuations as well as the relationships with large‐scale drivers. The future projections reveal increased AR frequency, intensity, duration, and spatial extent and decreased landfall intervals with regional variations and seasonal fluctuations. Besides, the AR frequency increase will accelerate around the middle of the century, attributed to a non‐linear rise in surface temperature. Furthermore, mid‐latitude ARs are gradually shifting toward higher latitudes in both hemispheres under SSP585, with Greenland experiencing a substantial increase in AR frequency and AR‐induced precipitation. The hydrological implications arising from more frequent ARs are manifested more prominently in AR‐induced heavy precipitation (HP), with regions historically characterized by lower AR occurrence also receiving a higher percentage of precipitation from ARs. At last, an incremental decomposition highlights the dominant role of thermal effects and relatively limited contributions from dynamical effects in AR changes. Besides, the interplay between regionally divergent temperature amplification results in different dynamically driven AR responses across the globe. Plain Language Summary: Understanding atmospheric rivers (ARs) is crucial for disaster prevention and mitigation. This study evaluates the performance of CMIP6 simulation on historical ARs on seasonal and interannual timescales, assesses their future projection under different emission scenarios, and examines their hydrological implications. The seasonal and interannual AR patterns in models are consistent with the observations. Future projections reveal that ARs will become more frequent, intense, and longer‐lasting while the time between landfall AR events will be shorter, with the magnitude varying by region and season. Interestingly, AR frequency is expected to increase even faster after the middle of the century due to a non‐linear rise in Earth surface temperature. The study also shows that mid‐latitude ARs are shifting toward higher latitudes, bringing more precipitation to the polar regions. The changes in AR characteristics can lead to increased risks of heavy precipitation, especially in regions where ARs are relatively not frequent in the past time. The study also shows that the changes in AR characteristics are primarily contributed by the increase in atmospheric moisture. The contribution from the changes in wind patterns is limited and varies across different regions as a result of regionally divergent temperature amplification due to global warming. Key Points: CMIP6 simulates atmospheric rivers (ARs) well in seasonal and interannual timescales globallyARs will play a more significant role in the hydrological cycle as global warming alters various AR characteristicsThermal effects dominate AR changes globally, while dynamical effects associated with temperature rise vary regionally [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) polarimetric radio occultation (PRO) observations sense the presence of hydrometeor particles along the ray path by measuring the difference of excess phases in horizontally and vertically polarised carrier waves. As a first step towards using these observations in data assimilation and model diagnostics, a forward operator for the GNSS-PRO observable ΦDP (polarimetric differential phase shift) has been implemented by extending the existing two-dimensional forward operator for radio occultation bending-angle observations. Evaluation of heavy-precipitation cases showed that the implemented forward operator can simulate the observed ΦDP in synoptic-scale atmospheric river (AR) cases very accurately. For tropical cyclone cases it is more challenging to produce reasonable ΦDP simulations, due to the high sensitivity of ΦDP with respect to displacement of the position of the tropical cyclones. It was also found that snow is the dominant contributor to the simulated ΦDP and that the ability to compute the ray paths in two dimensions is essential to accurately simulate ΦDP. [ABSTRACT FROM AUTHOR]

This study explores the seasonal variations in snow grain size on the East Antarctic Plateau, where dry metamorphism occurs, by using microwave radiometer observations from 2000 to 2022. Local meteorological conditions and large-scale atmospheric phenomena have been considered in order to explain some peculiar changes in the snow grains. We find that the highest ice divide is the region with the largest grain size in the summer, mainly because the wind speed is low. Moreover, some extreme grain size values with respect to the average (over +3σ) were identified. In these cases, the ERA5 reanalysis revealed a high-pressure blocking close to the onsets of the summer increase in the grain size. It channels moisture intrusions from the mid-latitudes, through atmospheric rivers that cause major snowfall events over the plateau. If conditions of weak wind and low temperature occur during the following weeks, dry snow metamorphism is facilitated, leading to grain growth. This determines anomalous high maximums of the snow grain size at the end of summer. These phenomena confirm the importance of moisture intrusion events in East Antarctica and their impact on the physical properties of the ice sheet surface, with a co-occurrence of atmospheric rivers and seasonal changes in the grain size with a significance of over 95 %. [ABSTRACT FROM AUTHOR]

Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30°–40°C temperature anomalies across the ice sheet. In Part I, we assessed the meteorological drivers that generated an intense atmospheric river (AR) that caused these record-shattering temperature anomalies. Here, we continue our large collaborative study by analyzing the widespread and diverse impacts driven by the AR landfall. These impacts included widespread rain and surface melt that was recorded along coastal areas, but this was outweighed by widespread high snowfall accumulations resulting in a largely positive surface mass balance contribution to the East Antarctic region. An analysis of the surface energy budget indicated that widespread downward longwave radiation anomalies caused by large cloud-liquid water contents along with some scattered solar radiation produced intense surface warming. Isotope measurements of the moisture were highly elevated, likely imprinting a strong signal for past climate reconstructions. The AR event attenuated cosmic ray measurements at Concordia, something previously never observed. Last, an extratropical cyclone west of the AR landfall likely triggered the final collapse of the critically unstable Conger Ice Shelf while further reducing an already record low sea ice extent. Significance Statement: Using our diverse collective expertise, we explored the impacts from the March 2022 heat wave and atmospheric river across East Antarctica. One key takeaway is that the Antarctic cryosphere is highly sensitive to meteorological extremes originating from the midlatitudes and subtropics. Despite the large positive temperature anomalies driven from strong downward longwave radiation, this event led to huge amounts of snowfall across the Antarctic interior desert. The isotopes in this snow of warm airmass origin will likely be detectable in future ice cores and potentially distort past climate reconstructions. Even measurements of space activity were affected. Also, the swells generated from this storm helped to trigger the final collapse of an already critically unstable Conger Ice Shelf while further degrading sea ice coverage. [ABSTRACT FROM AUTHOR]

Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30°–40°C temperature anomalies across the ice sheet. This record-shattering event saw numerous monthly temperature records being broken including a new all-time temperature record of −9.4°C on 18 March at Concordia Station despite March typically being a transition month to the Antarctic coreless winter. The driver for these temperature extremes was an intense atmospheric river advecting subtropical/midlatitude heat and moisture deep into the Antarctic interior. The scope of the temperature records spurred a large, diverse collaborative effort to study the heat wave's meteorological drivers, impacts, and historical climate context. Here we focus on describing those temperature records along with the intricate meteorological drivers that led to the most intense atmospheric river observed over East Antarctica. These efforts describe the Rossby wave activity forced from intense tropical convection over the Indian Ocean. This led to an atmospheric river and warm conveyor belt intensification near the coastline, which reinforced atmospheric blocking deep into East Antarctica. The resulting moisture flux and upper-level warm-air advection eroded the typical surface temperature inversions over the ice sheet. At the peak of the heat wave, an area of 3.3 million km2 in East Antarctica exceeded previous March monthly temperature records. Despite a temperature anomaly return time of about 100 years, a closer recurrence of such an event is possible under future climate projections. In Part II we describe the various impacts this extreme event had on the East Antarctic cryosphere. Significance Statement: In March 2022, a heat wave and atmospheric river caused some of the highest temperature anomalies ever observed globally and captured the attention of the Antarctic science community. Using our diverse collective expertise, we explored the causes of the event and have placed it within a historical climate context. One key takeaway is that Antarctic climate extremes are highly sensitive to perturbations in the midlatitudes and subtropics. This heat wave redefined our expectations of the Antarctic climate. Despite the rare chance of occurrence based on past climate, a future temperature extreme event of similar magnitude is possible, especially given anthropogenic climate change. [ABSTRACT FROM AUTHOR]

Aerosols play a key role in polar climate, and are affected by long‐range transport from the mid‐latitudes, both in the Arctic and Antarctic. This work investigates poleward extreme transport events of aerosols, referred to as polar aerosol atmospheric rivers (p‐AAR), leveraging the concept of atmospheric rivers (AR) which signal extreme transport of moisture. Using reanalysis data, we build a detection catalog of p‐AARs for black carbon, dust, sea salt and organic carbon aerosols, for the period 1980–2022. First, we describe the detection algorithm, discuss its sensitivity, and evaluate its validity. Then, we present several extreme transport case studies, in the Arctic and in the Antarctic, illustrating the complementarity between ARs and p‐AARs. Despite similarities in transport pathways during co‐occurring AR/p‐AAR events, vertical profiles differ depending on the species, and large‐scale transport patterns show that moisture and aerosols do not necessarily originate from the same areas. The complementarity between AR and p‐AAR is also evidenced by their long‐term characteristics in terms of spatial distribution, seasonality and trends. p‐AAR detection, as a complement to AR, can have several important applications for better understanding polar climate and its connections to the mid‐latitudes. Plain Language Summary: The extreme transport of aerosol‐containing air masses, from the mid‐latitudes to the polar regions, can be characterized and quantified by leveraging polar Aerosol Atmospheric Rivers (p‐AARs). This is similar to the Atmospheric Rivers (ARs) which carry large amounts of water to the poles and affect the overall stability of polar ecosystems. In this work, we establish a detection algorithm for p‐AARs and evaluate it for different well‐known aerosol intrusions or AR events. The areas most affected by p‐AARs are described, their trends are investigated and we discuss the potential applications of p‐AAR detection for a better understanding of polar climate. Key Points: A catalog of polar aerosol atmospheric rivers (p‐AAR) is provided for 1980–2022 by adapting an atmospheric river (AR) detection schemeImportant p‐AAR events, representing rapid poleward transport of aerosol‐enriched air masses, are presentedCombining AR and p‐AAR can improve our understanding of the links between mid‐ and polar‐latitudes, in the past, present and future climate [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) play important roles in various extreme weather events across the US. While AR features in western US have been extensively studied, there remains limited understanding of their variability in the eastern US (EUS). Using both observations and a state-of-the-art climate model, we find a significant increase (~10% dec-1) in winter AR frequency in the EUS during the past four decades. This trend is closely linked to recent changes in the Pacific/North America (PNA) teleconnection pattern, accompanied by a poleward shift of the mid-latitude jet stream. We further reveal a strong correlation (R = 0.8; P < 0.001) between interannual variations in AR occurrence and the PNA index. This linkage has been verified in various model simulations. A statistical model, built on this linkage, has proven effective in predicting the AR frequency using the PNA index at both monthly and seasonal scales. These promising results have important implications for addressing concerns related to AR-associated extreme precipitation and flooding in this region. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) in East Asia have garnered increasing attention for their crucial roles in understanding extreme precipitation events and regional hydrological cycles. Here, we investigate the summer AR frequency over the East Asia‐western North Pacific region (EA‐WNP) during 1959–2022. Interestingly, the results show that the AR frequency over the two regions experienced opposite variations, with the boundary between them at around 135°–140°E. On interdecadal timescales, such variations are mainly synchronized with a zonal dipole pattern characterized by cyclonic (anticyclonic) anomalies centered to the northwest of Taiwan and anticyclonic (cyclonic) anomalies centered to the southeast of Japan. This interdecadal dipole pattern is induced by interdecadal changes in the western North Pacific subtropical high under different Pacific Decadal Oscillation (PDO) phases. Particularly, the internal variability accounts for about 77–90% of the observed trends in high response areas. The displacements of ARs are also consistent with the opposite trends of precipitation over EA‐WNP. ARs exert a dominant influence on the precipitation trend in the WNP region but a weaker contribution to the precipitation trend in the EA region, which reflects the different impacts of ARs on EA and WNP precipitation. These results indicate that the PDO has the potential for predicting EA‐WNP ARs on interdecadal timescales, and the opposite AR trends are crucial in understanding the complexity of precipitation over the EA‐WNP region. Plain Language Summary: Atmospheric rivers (ARs) are long, narrow corridors of intense water vapor transport, and considered a crucial source of extreme precipitation events and floods over coastal regions. Previous research has revealed southward‐shifting trends of ARs over East Asia (EA) in recent decades. However, the possibility of opposing AR trends within different areas of the entire EA and western North Pacific region (EA‐WNP) has not previously been considered. In this study, we examine the summer AR frequency over EA‐WNP based on reanalysis data during the past six decades. The results show that EA‐WNP ARs have experienced opposite interdecadal variations and formed opposite trends between EA and the WNP during specific periods. Further analysis reveals that these interdecadal shifts of ARs can be explained well by a zonal dipole pattern modulated by the interdecadal change in the western North Pacific subtropical high under a strong influence of the Pacific Decadal Oscillation. Moreover, such AR variations have caused opposite precipitation trends in EA‐WNP and contributed differently to the formation of the distinct precipitation regimes in EA and the WNP. These findings provide insights into the decadal prediction of ARs and their diverse roles in shaping regional precipitation variations over the EA‐WNP region. Key Points: Summer atmospheric rivers (ARs) over East Asia and the western North Pacific (EA‐WNP) have experienced opposite trends in recent decadesOpposite AR trends are modulated by a zonal dipole pattern, which correlates closely with the Pacific Decadal OscillationSuch AR variations lead to opposite precipitation trends and contribute differently to the precipitation regimes between EA and the WNP [ABSTRACT FROM AUTHOR]

Landfalling atmospheric rivers (LARs) are important drivers of mid-latitude climate; however, our understanding of the water vapour sources, storm trajectories, and receiving waters of ARs is limited. This study aims to characterize LARs in southwest British Columbia by their isotopic composition and storm track trajectories and to better understand how AR-derived precipitation is manifested in watershed waters. ARs were depleted (−11.71‰ δ18O, −85.80‰ δ2H, n = 19) compared to non-ARs (−9.47‰ δ18O, −69.58‰ δ2H, n = 32) (p = 0.03); however, the difference is minimal. LAR storm tracks did not show any obvious correlation to their isotopic composition, despite the large variability in their source regions across the Pacific Ocean. The lack of correlation is attributed to mixing air parcels, thereby incorporating moisture with different isotopic compositions into the main transport mechanism. D-excess values for ARs and non-ARs were statistically similar, although seasonal differences were observed. ARs with higher d-excess were sourced from the central Pacific, whereas ARs with lower d-excess had storm tracks through the northern Pacific. Watershed water d-excess values (mean = 8.58 ± 2.97‰) were more similar to winter precipitation (mean = 10.1 ± 5.1‰), compared to summer (mean = 2.8 ± 4.3‰), likely due to their source of winter precipitation at high elevation. A greater range in AR d-excess winter values relative to summer values (3.6–16.6‰, −0.3–6.0‰, respectively) is attributed to storm track variability. [ABSTRACT FROM AUTHOR]

Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25 ∘ × 0.25 ∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid sea ice decrease through turbulent heat fluxes and winds. We carry out further statistical analysis of the meteorological conditions and sea ice variations for 1981–2020 over the entire Arctic Ocean. We find that on weather timescales the atmospheric moisture content anticorrelates significantly with the sea ice concentration tendency almost everywhere in the Arctic Ocean, while the dynamic sea ice motion driven by northward winds further reduces the sea ice concentration. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate performance sensitivity to the Cumulus Parameterization Scheme (CPS) used for the Weather Research and Forecasting (WRF) model to predict the atmospheric river‐related precipitation (ARP) event with 206 and 57 mm, highest and area‐averaged precipitation (AAP) per 24‐h, respectively, that occurred over the central mountainous basins of Iran on 31 March 2019. For this purpose, experiments were designed using the 12 (almost all) CPSs available in WRF v4. To verify the predicted precipitation (from the inner 4‐km domain), both point‐scale and grid‐scale comparisons were performed against gauge‐ and satellite‐based observational data at three accumulation time‐scales (12‐, 18‐, and 24‐h) and in three distinct sub‐regions. All scores obtained from the different statistical metrics used, are in complete agreement with a strongly dependent performance of WRF on the CPS used. In addition, the use of Kain‐Fritsch, KF‐CuP, and Grell‐3 CPSs could provide a realistic picture of impending heavy precipitation for WRF. Contrary, the New SAS, Tiedtke, and Zhang‐McFarlane CPSs did not perform satisfactorily in predicting the ARP event. As a result, CPSs with the "momentum transport" option in their modification mechanism are unlikely to adequately simulate the conversion of incoming low‐level moisture from atmospheric river to precipitation. However, precipitation predictions are more accurate at the 24‐h accumulation time‐scale than at the 12‐ and 18‐h. Also, a dry bias in the predictions is expected as the terrain elevation and accumulation time‐scale decrease and the distance from the core of the precipitation field increases. [ABSTRACT FROM AUTHOR]

This study describes a generalized computational mathematical framework, Scalable Feature Extraction and Tracking (SCAFET), that extracts and tracks features from large climate data sets. SCAFET utilizes novel shape-based metrics that can identify and compare features from different mean states, data sets, and between distinct regions. Features of interest such as atmospheric rivers, tropical and extratropical cyclones, and jet streams are extracted by segmenting the data based on a scale-independent bounded variable called the shape index (SI). The SI gives a quantitative measurement of the local geometric shape of the field with respect to its surroundings. Compared to other widely used frameworks in feature detection, SCAFET does not use a posteriori assumptions about the climate model or mean state to extract features of interest and levelize the comparison between different models and scenarios. To demonstrate the capabilities of the method, we illustrate the detection of atmospheric rivers, tropical and extratropical cyclones, sea surface temperature fronts, and jet streams. Cyclones and atmospheric rivers are extracted to show how the algorithm identifies and tracks both the nodes and areas from climate data sets. The extraction of sea surface temperature fronts exemplifies how SCAFET effectively handles curvilinear grids. Last, jet streams are extracted to demonstrate how the algorithm can also detect three-dimensional features. As a generalized framework, SCAFET can be implemented to extract and track many weather and climate features across scales, grids, and dimensions. [ABSTRACT FROM AUTHOR]

In global meteorological literature, atmospheric rivers are defined as long and narrow pathways of intense water vapor transport towards the Polar Regions in the middle latitudes, typically associated with low-level jet streams along the leading edge of extratropical cyclones. In this study, to identify the origins of the incoming atmospheric rivers to the study area, precipitation systems that occurred at more than half of the region's stations were selected. Then, using vertically integrated water vapor flux data from the east and north of the study area with a spatial resolution of 0.5 x 0.5 degrees, the magnitude of the water vapor flux was calculated. To calculate the magnitude of the flux, data including specific humidity and meridional and zonal winds at pressure levels from 1000 to 300 hPa were used. Showed that these rivers have entered northwest and west of Iran from four moisture sources. The sources are the warm southern seas (the Sudan - Red Sea low-pressure pattern), the convergence zone region, the combined source of the Sudan low-pressure system and the Mediterranean circulation, and the Mediterranean Sea. Among these sources, the warm seas of Arabia and Oman and the Red Sea had the largest share in the incoming rivers to the region. These atmospheric rivers have been the strongest in terms of both temporal continuity and moisture flux. They first enter southwest Iran and then into the study area. The atmospheric rivers with the convergence zone source rank second in terms of their contribution to the region's precipitation. After passing through the eastern Mediterranean coastal countries and Iraq, these rivers enter the study area. Overall, the incoming atmospheric rivers to this region have the highest probability of occurrence in the time interval from 00 to 06 Greenwich Mean Time. [ABSTRACT FROM AUTHOR]

Abstract The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual‐frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis‐derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high‐latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user‐friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation.

Abstract Atmospheric Rivers (ARs) are synoptic‐scale conduits for poleward transport of heat and water, often associated with extreme rainfall. Using NASA surface heat flux observations and climate model simulations, we assess whether ARs are “rivers”, transporting heat and moisture over longer distances, or mostly local convergence. The observations indicate that ARs reduce extratropical surface energy fluxes, even during early development. This damping of surface fluxes during ARs is also simulated in the Goddard Institute for Space Studies ModelE, 2.1 (GISS‐E2.1) nudged to (MERRA2) reanalysis winds. Furthermore, water provenance tracers in GISS‐E2.1 identify the moisture source for ∼7,500 ARs globally during 2018–2022 as farther upstream and equatorward compared to climatology. These results quantitatively show that ARs source relatively less moisture from the surface beneath them and more from a greater distance than during non‐AR times.

Abstract Using high temporal resolution satellite observations and reanalysis data, we classify daily weather into distinct regimes and quantify their associated cloud radiative effect (CRE) to better understand the roles of various weather systems in affecting Earth's top‐of‐atmosphere radiation budget. These regimes include non‐precipitation, drizzle, wet non‐storm, and storm days, which encompass atmospheric rivers (AR), tropical storms (TS), and mesoscale convection systems (MCS). We find that precipitation (wet) days account for roughly 80% (60%) of global longwave (LW) and shortwave (SW) CREs due to their large frequency and high intensity in CRE. Despite being rare globally (13%), AR, TS, and MCS days together account for 32% of global LW CRE and 27% of SW CRE due to their higher intensity in LW and SW CRE. These results enhance our understanding of how various weather systems, particularly severe storms, influence Earth's radiative balance, and will help to better constrain climate models.

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

Atmospheric rivers (ARs) that reach the complex terrain of High Mountain Asia (HMA) cause significant hydrological impacts for millions of people. While ARs are often associated with precipitation extremes and can cause floods and debris flows affecting populated communities, little is known about ARs that reach as far inland as HMA. This paper characterizes AR types and investigates dynamical mechanisms associated with the development of ARs that typically affect HMA. Combined empirical orthogonal function (cEOF) analysis using integrated water vapor transport (IVT) is applied to days where an AR reaches HMA. K-means cluster analysis applied to the first two principal components uncovered three subtypes of AR events with distinct synoptic characteristics during winter and spring months. The first subtype increases precipitation and IVT in Western HMA and is associated with a zonally oriented wave train propagating within the westerly jet waveguide. The second subtype is associated with enhanced southwesterly IVT, anomalous upper-level cyclonic circulation centered on 45 ∘ E, and precipitation in Northwestern HMA. The third subtype shows anomalous precipitation in Eastern HMA and southwesterly IVT across the Bay of Bengal. Interannual variations in the frequency of HMA ARs and relationships with various teleconnection patterns show that western HMA AR subtypes are sensitive to well-known remote large-scale climate factors, such as the El Niño Southern Oscillation, Arctic Oscillation, and the Siberian High. These results provide synoptic characterization of the three types of ARs that reach HMA and reveal the previously unexplored significance of their contribution to winter and spring precipitation.Supplementary informationThe online version contains supplementary material available at 10.1007/s00382-021-06008-z.

The range of synoptic patterns that North Pacific landfalling atmospheric rivers form under are objectively identified using genesis day 500 hPa geopotential height anomalies in a self-organizing map (SOM). The SOM arranges the synoptic patterns to differentiate between two groups of climate modes—the first group with ENSO (El Niño Southern Oscillation), PDO (Pacific Decadal Oscillation), PNA (Pacific North American) and NP (North Pacific index) and the second group with AO (Arctic Oscillation), EPO (East Pacific Oscillation), and WPO (West Pacific Oscillation). These two groups have their positive and negative modes organized in opposite corners of the SOM. The ARs produced in each of the synoptic patterns have distinct lifecycle characteristics (such as genesis and landfall location, duration, velocity, meridional/zonal movement) and precipitation impacts (magnitude and spatial distribution). The conditions that favour AR trajectories closer to the tropics tend to produce higher amounts of precipitation. The large-scale circulation associated with AR genesis shows a close relationship between the genesis location and the location and intensity of the upper-level jet in the west/central pacific as well as anomalous, low-level southwesterly winds in the east pacific.

In July 2023, Baoding in Hebei Province experienced unprecedented torrential rainfall, breaking historical records and causing severe flooding. However, our understanding of the multi-scale circulation systems and physical mechanisms driving this extreme precipitation event remains incomplete. This study utilizes multi-source observational data and the Weather Research and Forecasting (WRF) numerical model to conduct a weather diagnosis and numerical simulation of this extreme rainfall event, focusing on the impact of atmospheric rivers (ARS) and urban rooftop roughness on the precipitation process against the background of climate warming. The study found that this extremely heavy rainstorm occurred in the circulation background formed by the factors of subtropical high ectopics, typhoon residual vortex retention, double typhoon water-vapor transmission, and stable high-level divergence. The ARS provided abundant moisture, with its vapor pathway significantly altered following the landfall of Typhoon Doksuri. The interaction between the ARS and the Taihang Mountains was crucial in triggering and intensifying the rainstorm in the foothills. Urbanization significantly affected the distribution of precipitation, with moderate urban roughness enhancing rainfall in and around the city, whereas excessive roughness suppressed it. These results contribute to a deeper understanding of the mechanisms behind extreme precipitation under climate change and provide a scientific basis for improving the forecasting and mitigation of such events.

Study region: Limburg, the Netherlands; west Germany; Rur / Roer River basin. Study focus: Atmospheric rivers (ARs) are long (≥ 2000 kilometers) tropospheric bands of water vapor responsible for 90% of poleward atmospheric water transport linked to extreme rainfall events (EREs). The meteorological impacts of ARs have been well-studied in high-relief regions with the implication being that rainfall generated by such events is exacerbated by the orographic influence of mountainous topography. While prior research has examined linkages between ARs and precipitation with ad-hoc flood events, few studies have systematically examined the influence of ARs to streamflow variability in regions low-to-moderate relief. We assess the effects of ARs on high precipitation and high streamflow events in northwestern Europe, and specifically for the Rur (Roer) River basin, a region characterized by low to moderate relief that has received little hydrologic research attention related to atmospheric rivers. New hydrological insights for the region: The study is novel in that it reveals that daily precipitation and streamflow are significantly increased, by 2.84 mm and 1.91 m3/s, respectively, during AR conditions. Moreover, 47.7% of EREs and 23.2% of high streamflow events were found to be associated with ARs. An AR occurrence also increases the likelihood of a rain day occurring by 36.6%. Lastly, AR presence was significantly correlated (p

Precipitation events exhibit a spatiotemporal continuity since they move across both space and time, however, the joint spatiotemporal characteristics of precipitation events are often neglected, and the role of Atmospheric rivers (ARs) and tropical cyclones (TCs) in the events has not been systematically explored. To solve these problems, this study seeks to detect and characterize the three-dimensional (latitude + longitude + time) structures of spatiotemporally contiguous precipitation events, distinguish the heavy events associated with ARs or TCs, and systematically explore the different roles of these events. We do so by combining Forward-in-Time algorithm, an object-based physical event detecting algorithm, with TMPA precipitation estimates to detect spatiotemporally contiguous precipitation events over China, and an AR detection technique considering a combination of fixed lower limit and Integrated Water Vapor Transport percentile threshold is adopted to identify ARs. The heavy events are defined to analyze the differences of the events associated with TCs and ARs as a function of precipitation total, duration, and intensity. Results show that approximately half or more of precipitation events are shorter than 6-hour, and the precipitation total of each event is strongly correlated with maximum intensity and average intensity across China. The heavy events normally occur in southern, center, northern, and southwestern regions, where have approximately 10 to 40 heavy events at each grid. The fraction of heavy events associated with TCs generally increase for events with larger precipitation totals and for events with higher maximum intensity, until all of events are associated with TCs. The percentages of events associated with ARs as well normally increase as precipitation totals and maximum intensity increase up to around 150 mm and 30 mm/h, respectively. Our findings show that the events associated with TCs or ARs exhibit distinct different characteristics, and suggest that the different spatiotemporal dynamics should be sufficient considered in contiguous events analyses in different regions.

Precipitation efficiency refers to the amount of water that is lost from the atmosphere through precipitation compared to the available water vapor in the atmosphere. This metric plays a critical role in understanding precipitation patterns. However, calculating precipitation efficiency for extratropical cyclones can be challenging because cyclones are dynamic and move through the atmosphere as they evolve. To overcome this challenge, our study uses ERA5 reanalysis data to estimate precipitation efficiencies for 400 Southern Ocean cyclones, with a frame of reference that moves with the individual cyclones. Our findings indicate that at maximum intensity, average precipitation efficiencies reach a maximum of 60%/6 hr near the warm front where ascent rates are the largest. Typically, within 24–36 hr after cyclogenesis, all of the initial water vapor available within 500 km of a cyclone center is lost due to precipitation. However, a cyclone's precipitating phase is prolonged due to local evaporation and moisture flux convergence (MFC), which replenish the moisture lost via precipitation. Close to the cyclone center, MFC provides additional moisture from the environment into which cyclones are traveling. On average, this extends a cyclone's precipitation phase to over 60 hr after cyclogenesis. Thus, while moisture from the genesis location is quickly removed from the cyclone via precipitation, cyclones are replenished by moisture along their track, which doubles the timescale for a cyclone's precipitating phase. Plain Language Summary: Precipitation efficiency is a measure of how much water in the atmosphere falls as rain compared to how much is available to fall. In this study, we estimate how efficient precipitation is for 400 cyclones in the Southern Ocean. We find that when the cyclones are strongest, about 60% of the available water vapor turns into rain every 6 hr near the warm front of the cyclone where air is rising rapidly. Normally, within a day or two of a cyclone forming, all of the available water vapor in the cyclone gets turned into rain. However, the rain can persist for longer because cyclones pick up more moisture as they move, which gets turned into more rain. This can make the rain from a cyclone last on average for 60 hr, even after the initial moisture is all gone. Key Points: The initial moisture content of extratropical cyclones is removed via precipitation within 30 hr after cyclogenesisLocal evaporation and moisture flux convergence doubles the precipitating phase of extratropical cyclonesThe feeder airstream provides a continuous supply of moisture to cyclones in the developing stage of their evolution [ABSTRACT FROM AUTHOR]

Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical weather data to improve the underlying model. Here, we introduce GraphCast, a machine learning–based method trained directly from reanalysis data. It predicts hundreds of weather variables for the next 10 days at 0.25° resolution globally in under 1 minute. GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets, and its forecasts support better severe event prediction, including tropical cyclone tracking, atmospheric rivers, and extreme temperatures. GraphCast is a key advance in accurate and efficient weather forecasting and helps realize the promise of machine learning for modeling complex dynamical systems. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are synoptic-scale features that transport moisture poleward and have been shown to cause short duration, high-volume melt events on the Greenland ice sheet (GrIS). This project investigates the effectiveness of variable-resolution (VR) grids in modeling ARs and their subsequent precipitation around the GrIS using a study period of 1 January 1979 to 31 December 1998. VR simulations from the Community Earth System Model (CESM2.2) bridge the gap between limitations of global climate models and regional climate models while maximizing computational efficiency. VR grids improve the representation of ARs, in part by resolving small-scale processes. ARs from CESM2.2 simulations using three grid types (VR, latitude-longitude, and quasi-uniform) with varying resolutions are compared to output of ERA5 and MERRA2 observation-based reanalysis products. The VR grids produce ARs with smaller areal extents and lower integrated precipitation over the GrIS compared to latitude-longitude and quasi-uniform grids. We hypothesize that the smaller areal extents in VR grids are produced by the refined topography resolved in these grids. In contrast, smoothing from coarser resolution latitude-longitude and quasi-uniform grids allow ARs to penetrate further inland on the GrIS. The reduced areal extent in VR grids also likely contributes to the lower area-integrated cumulative precipitation, whereas the area-average cumulative precipitation is similar for VR, latitude-longitude, and quasi-uniform grids. The VR grids most closely match the AR overlap extent and precipitation in ERA5 and MERRA2, suggesting the most realistic behavior among the three configurations. [ABSTRACT FROM AUTHOR]

This study investigated ice cloud formation associated with marine bioaerosols over the Southern Ocean (SO) using a combination of cloud particle sensor (CPS) sonde observations, satellite products, reanalysis data, and backward trajectory analysis. The CPS sonde detected ice clouds at temperatures higher than −10°C in the mid‐troposphere near an atmospheric river at high‐latitudes over the SO. Backward trajectory analyses indicated that a mid‐latitude air mass with a high concentration of atmospheric dimethylsulfide (DMS) in the atmospheric boundary layer (<1 km) arrived at the ice cloud formation layer over the high‐latitudes. The DMS in the boundary layer began to increase under high wave conditions, coincident with the highest chlorophyll‐a concentrations in the ocean. These results suggest that bioaerosols emitted from the ocean over the mid‐latitudes acted as ice‐nucleating particles for ice cloud formation over high‐latitudes. Plain Language Summary: Polar region clouds play a key role in Earth's climate. Knowledge of the cloud phase (i.e., liquid water, ice, or mixed) is important for determining the surface heat budget because the reflection of solar radiation at the cloud top depends on cloud phase. Although the development of numerical climate models allows the investigation of clouds globally, there still is a cloud phase (water or ice) bias in the models. Therefore, an observational study is required to investigate cloud formation environments. During a cruise in the SO by a research ship, ice clouds were observed at relatively high temperatures in the mid‐troposphere at high‐latitudes. Analysis of the trajectory of the air mass at the ice formation height indicated that the air mass had traveled from the surface in the mid‐latitudes to the mid‐troposphere over high‐latitudes. Biogenic material emitted from the ocean under strong wind and high wave conditions was transferred to the air mass near the surface in the mid‐latitudes and eventually reached the mid‐troposphere at the observation point. These results suggest that marine biogenic material transported from the mid‐latitudes influenced ice cloud formation under relatively high temperatures. Key Points: A cloud particle sensor sonde measured ice clouds at air temperatures higher than −10 °C in the mid‐troposphere near an atmospheric riverMid‐latitude air mass with high dimethylsulfide (DMS) in the boundary layer reached the ice cloud formation layer over high‐latitudesSimulated high surface atmospheric DMS was associated with observed high chlorophyll‐a concentration under simulated high wave conditions [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support. Plain Language Summary: California winter weather can alternate between very wet conditions from atmospheric rivers making landfall along the Pacific coast to hot, dry, and windy conditions brought by Santa Ana winds blowing in from the Southwest interior. Atmospheric rivers are important for water resources while also causing flooding, whereas Santa Ana winds are often associated with wildfire, especially following prolonged dry periods. Preparing for these types of weather events is important for managing resources and protecting life and property, yet reliable forecasts beyond about 7–10 days remain a challenge. We have developed a new prediction system that combines information about approaching atmospheric weather patterns from weather forecast models along with historical information relating those patterns to impacts over California to predict the likelihood of impactful weather at 1–4 weeks lead time. By extending the window of opportunity to take management action, this new approach should aid in resource and emergency planning in water, land, and fire sectors as well as protecting residents through improved warning systems. Key Points: A hybrid dynamical‐statistical model is described for 1–4‐week forecasts of impactful California winter weather using circulation regimesThis hybrid framework reduces the number of forecasts produced, but the ones issued can be interpreted with higher confidenceThis new methodology provides skillful subseasonal forecasts with potential to improve early warnings for impactful weather events [ABSTRACT FROM AUTHOR]