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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. Plain Language Summary: Using detailed satellite observations and reanalysis data, we categorize daily weather patterns into different types and measure the cloud radiative effects (CRE) associated with each type. The weather patterns we study include non‐precipitation days, drizzle, wet non‐storm days, and storm days, which include events like atmospheric rivers, tropical storms, and mesoscale convective systems. We found that precipitation days, which include both drizzle and wet days, contribute to about 80% of global longwave (LW) and shortwave (SW) CRE due to their high frequency and intensity. Even though storm days are rare globally (only 13%), they collectively contribute to around 32% of global LW CRE and 27% of SW CRE because of their stronger impact on both LW and SW CRE. These findings are important for understanding how different weather systems influence the Earth's radiation balance and will help improve the accuracy of climate models. Key Points: Cloud radiative effect (CRE) associated with various daily weather regimes including atmospheric rivers (ARs), tropical storms (TSs), and mesoscale convection systems (MCSs) are derived using satellite observations and reanalysis dataPrecipitation (wet) days account for roughly 80% (60%) of global longwave (LW) and shortwave (SW) CRE due to their large frequency and high intensity in CREDespite their rare occurrence, AR, TS, and MCS days together account for 32% of LW CRE and 27% of SW CRE due to their higher intensity CRE [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Atmospheric rivers are thin, river‐like, streams of moisture connecting the mid‐ and high‐latitudes to the tropics, often associated with extratropical storms. These events are regions that have large amounts of moisture and can generate heavy precipitation. We show that moisture and energy fueling the ARs do not come from the ocean directly beneath the ARs. First, we use a dataset derived from satellite retrievals of surface winds to demonstrate that the energy transferred into ARs from the ocean are weaker than usual during AR events. Then we use a global climate model to show that the origins of the moisture in the ARs arrives via atmospheric transport from regions outside of the ARs, farther away and nearer to the equator. Key Points: When atmospheric rivers form, ocean fluxes are reduced, pulling in moisture from farther away than during non‐atmospheric river eventsAfter formation, atmospheric rivers decrease surface heat fluxes from the ocean into the atmosphereAtmospheric rivers source moisture from ∼10° equatorward of non‐atmospheric rivers [ABSTRACT FROM AUTHOR]

Black carbon (BC) has emerged as an integral part of the global carbon (C) cycle, constituting 12% ± 5% of the organic C pool in rivers and soils, with the potential to generate negative climate feedback. However, its ability to sequester C depends on the recalcitrant nature of BC in the environment, which is under debate. Using CTO‐375 method and by measuring concentrations and isotopic compositions of particulate BC (δ13CPBC), we explore the transformation of particulate black carbon (PBC) along the atmosphere‐river‐ocean continuum. Significantly high δ13CPBC in the ocean compared to rivers and atmospheric particulate matter indicates (a) degradation of PBC, potentially through photodegradation and leaching, and/or (b) availability of an enriched source other than fluvial or aeolian inputs. This evidence for degradation of PBC in aquatic systems warrants rethinking on its C sequestration potential and role in aquatic C biogeochemistry and further raises concerns regarding the use of sedimentary BC as a paleoenvironmental proxy. Plain Language Summary: Particulate black carbon (PBC) forms ∼12% ± 5% of the particulate organic carbon pool in aquatic systems. Black carbon has been considered highly recalcitrant and therefore a potent sink for carbon. Paleo‐environmental and biogeochemical studies have been conducted assuming BC as highly stable and its isotopic composition to remain unaltered after formation. This study explores the variability in stable isotopic composition of PBC in the riverine, atmospheric, and oceanic systems and provides evidence contrary to the general belief of the refractory nature of BC, which warrants revisiting and rethinking of the BC dynamics in aquatic systems. Key Points: Particulate black carbon (PBC) concentration decreases along the river ocean continuumStable carbon isotopic composition of the oceanic PBC pool is higher compared to the riverine and atmospheric poolsIsotopic evidence suggests degradation of PBC with the preferential loss of lighter carbon [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: This paper summarizes the creation of a new, gridded data set containing precipitation, atmospheric water vapor content, and atmospheric river (AR) information from May 2014 to December 2022. The precipitation and water vapor data are both remotely‐sensed data products using the Global Precipitation Measurement Core Observatory Microwave Imager (GMI). The AR algorithm detection is more sensitive to moisture transport than global algorithms and is thus better suited to colder and drier environments. We temporally match and interpolate all data to a common grid and provide examples of the merged data to illustrate the seasonality and impacts of AR presence on precipitation. Lastly, we demonstrate the utility of this data set by investigating the assumptions used to estimate precipitation from GMI measurements. The data set confirms that ARs are associated with warmer temperatures, higher water vapor content, and more intense precipitation rates. Key Points: A new data set is presented that collocates satellite‐derived precipitation rates and water vapor with reanalysis‐derived Atmospheric rivers (ARs)Higher precipitation rates and water vapor content are coincident with AR events in the high‐latitude regionsAn example application illustrates warmer, more moist conditions during precipitating AR events that are present in the GPROF database [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) serve a narrow, filamentary channels responsible for intense moisture and heat transport, which are vital for the Arctic sea ice loss and surface air warming, particularly in winter. In this study, ERA5 reanalysis data from 1979 to 2020 are used to investigate the evolution of ARs in the Arctic and the corresponding atmospheric circulation patterns in winter. Our findings indicate that atmospheric circulation is a key factor that facilitates the transport and dispersion of ARs. Cyclonic circulation anomalies provide the dynamical support for ARs in the early development stages, while anticyclonic anomalies are essential for maintaining the thermal and moisture conditions of ARs, particularly after their intrusions into the Arctic region. The cooperation between cyclonic and anticyclonic anomalies plays a critical role in the infiltration and dispersion of ARs, governed by distinct circulation modes for regional intrusions. A positive Arctic Oscillation‐like pattern manifests as a cyclonic system over the Arctic, increasing the likelihood of AR intrusion into Baffin Bay. Conversely, a negative pattern induces a more intense anticyclonic system over the Arctic, thereby raising the frequency of ARs in the Bering Strait. The Pacific‐North American pattern not only directly affects ARs entering the Arctic via the Pacific but also has downstream impacts on ARs intruding the Arctic through Baffin Bay. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations. Plain Language Summary: Atmospheric rivers (ARs) are a weather pattern that brings high amounts of atmospheric water and winds in a relatively narrow region. ARs are typically considered a "west coast" phenomenon, largely because the majority of the scientific research on ARs has focused on ARs in western coastal regions: particularly the western United States (US). ARs occur in continental interiors, but there has been some debate about whether these ARs represent the same type of weather as those in western coastal regions. This paper uses two objective methods for identifying ARs and finds times when ARs are present in three locations in the eastern half of the US: Norman, OK, Bloomington, IN, and Washington, DC. Examination of weather conditions during these AR times shows remarkable similarity to conditions associated with west coast ARs. This gives strong evidence that ARs do occur in the eastern half of the US. This result is important because it suggests that ARs may be important for water resources and extreme weather in the eastern half of the US, just as they are in the western US. This result also suggests that ARs may be important for water resources and extremes in other continental interiors. Key Points: Atmospheric rivers (ARs) east of the Rockies are associated with baroclinic wavesWestern coastal ARs and eastern/midwest ARs are dynamically similarSynoptic‐scale uplift, combined with convective instability, provide efficient mechanisms for generating precipitation [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty. [ABSTRACT FROM AUTHOR]

This study introduces the ROTated Atmospheric river coordinaTE (ROTATE) system — a novel storm‐centric coordinate system designed specifically for analyzing atmospheric rivers (ARs). It effectively preserves key AR signals in the time mean that may be lost or obscured in simple averaging due to diverse AR orientations and shapes. By applying the ROTATE system, we compared climatological characteristics for northern hemisphere ARs. Composites of four key meteorological variables, integrated vapor transport, integrated water vapor, precipitation, and windspeed, indicate distinct and clearer patterns of ARs compared to the conventional non‐rotated AR centroid‐based compositing approach. Moreover, the ROTATE system improves precipitation rates, particularly around the AR center and its head and tail regions, providing more distinct delineations of the precipitation signals between landfalling and oceanic ARs. Overall, the ROTATE system has the potential to serve as a valuable tool for better comparing and understanding the characteristics, processes, and impacts of ARs across different regions. Plain Language Summary: This study introduces a new method called ROTATE, designed to study long, narrow filamentary regions of intense water vapor transport in the lower atmosphere or so‐called atmospheric rivers (ARs). ROTATE helps us retain essential details in weather systems when averaging over time that otherwise might get lost using the usual averaging method. We used ROTATE to look at crucial characteristics of ARs such as how wet the air is, how fast the wind blows, how much water vapor is being transported, and how much rain falls. We found more apparent AR patterns with ROTATE compared to the traditional way of averaging ARs. The new method also helps us see finer details in rain distributions over land versus over the oceans. The results indicate ROTATE is a valuable tool to help us better compare and understand mean characteristics of ARs in different places. Key Points: Introduces a new coordinate system for analyzing atmospheric rivers (ARs)Provides clearer AR composite patterns and their multi‐variate relationships when compared to the conventional non‐rotated AR centroidEnhances the distinction of precipitation signals between landfall and oceanic ARs, especially around the AR center and its head/tail [ABSTRACT FROM AUTHOR]

The extratropical west coast of South America has one of the largest frequencies of landfalling atmospheric rivers (ARs), with dozens of events per season that account for ~50% of the annual precipitation and can produce extreme rainfall events in south-central Chile. Most ARs form an acute angle with the Andes, but, in some cases, the moist stream impinges nearly perpendicular to the mountains, referred to as zonal atmospheric rivers (ZARs). Enhanced surface-based and upper-air measurements in Concepcion (36.8° S), as well as numerical simulations, were used to characterize a ZAR and a meridionally oriented AR in July 2022. They represent extremes of the broad distribution of winter storms in this region and exhibit key features that were found in a composite analysis based on larger samples of ZARs and tilted ARs. The latter is associated with an upper-level trough, broad-scale ascent, extratropical cyclone, and cold front reaching southern Chile. Instead, ZARs are associated with tropospheric-deep, strong zonal flow and a stationary front across the South Pacific, with ascent restricted upstream of the Andes. Consequently, ZARs have minimum precipitation offshore but a marked orographic precipitation enhancement and exhibit relatively warm temperatures, thus resulting in an augmented risk of hydrometeorological extreme events. [ABSTRACT FROM AUTHOR]

Based on the historical precipitation (1950–2018), daily data from summer (May–October) and winter (November–April) are reviewed to identify several extreme observation characteristics affecting four municipalities in Northwestern Mexico. The municipalities were chosen based on population, growth rate, health services, and economic importance. An extreme daily precipitation (EDP) event was defined by first filtering records larger than 30 mm from the stations located 50 km around each municipality. Then, each resulting series was fitted to the Weibull continuous distribution to obtain the 99th series percentile (P99). Observations larger than or equal to P99 from the original series were EDP events. The results show 46.6% of summer EDP events associated with tropical cyclones (TCs), where September is the most prolific month followed by the North American monsoon (27.3%) and Mesoscale Convective Systems (26.1%). During the winter, in this period, 75% EDP events are associated with extra-tropical cyclones or frontal systems and 25% with atmospheric rivers. The analyses also show that most EDP events occur under El Niño Southern Oscillation (ENSO) neutral and warm phases. Historical series display an increase in relative frequencies of EDP events in the last decades with an evident rise since 2010, which implies an increase in population and infrastructure vulnerabilities in the region. [ABSTRACT FROM AUTHOR]

A vertical superposition of the polar and subtropical jet streams constitutes a unique synoptic-scale environment with the potential to induce high-impact weather, including anomalously strong surface cyclones that are accompanied by heavy precipitation and strong winds. Jet superpositions are not always a sufficient condition for the occurrence of high-impact weather, however, so understanding the dynamical and thermodynamic environments that favor the development of high-impact weather in association with jet superpositions is essential for improving sensible weather forecasts during these events. In this study, we pair a climatology of jet superpositions with climatologies of atmospheric rivers and surface cyclones to determine the frequency with which these features accompany jet superpositions. We subsequently construct two subsets of jet superpositions for further analysis. "High-impact" jet superposition cases are defined as those that feature an atmospheric river and a highly anomalous surface cyclone relative to climatology, which can potentially support extreme near-surface winds and precipitation. In contrast, "null" cases are defined as jet superposition cases that are not associated with a surface cyclone and are therefore less likely to support widespread high-impact weather. Composite analyses are then performed to identify discriminating environmental factors between high-impact and null cases, and how these factors influence jet superposition dynamics. We find that stronger environmental baroclinicity and a sufficient moisture supply within the near-jet environment are common characteristics of high-impact cases. These characteristics subsequently support the development of a more vigorous ageostrophic transverse circulation beneath the superposed jet's exit region during high-impact cases and more intense surface cyclogenesis. Significance Statement: A jet superposition event occurs when the normally separate polar and subtropical jets combine to form a single jet. This study aims to understand what factors differentiate jet superposition events that coincide with strong winds and heavy precipitation, or "high-impact weather," from those that are less likely to coincide with such weather conditions. We identified several important environmental characteristics that tend to precede jet superposition events with a large potential to induce high-impact weather, including increased moisture and a strengthened pole-to-equator temperature gradient. These results provide indicators forecasters may consider when predicting the impacts of a jet superposition event. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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 HMA's hydrological cycle and the associated hazards to vulnerable communities living in the region. [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]

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]

An atmospheric river (AR) is a highly accumulated line of moisture, which often preconditions heavy rainfall events. Considering the short-lived nature of ARs, we aim to understand their characteristics over the western North Pacific (WNP) in boreal summer, focusing on the difference between the post–El Niño (pEN) and non-post–El Niño (npEN) cases in weekly segments rather than the seasonal mean. We found that during AR-active weeks in pEN summers, a meridional dipole pattern of anomalous geopotential height with a more westward-extended WNP subtropical high dominates, favoring AR activity over southern China to the central Pacific. In npEN summers, in contrast, a zonal dipole pattern with an anomalous low over southeast China and an anomalous high over subtropical WNP is pronounced during the AR-active weeks, which is accompanied with warm sea surface temperature anomalies and greater downward shortwave radiation to the southeast of Japan. Such anomalous circulation leads to enhanced AR occurrence over the northern South China Sea (SCS) to Japan. The above differences in the AR features between the two cases appear as the distinct regionality in the AR-related rainfall over eastern China and Japan. Temporal decompositions reveal that both seasonal mean and submonthly variations play dominant roles in the anomalous circulation during AR-active weeks in pEN summers, while the submonthly variations are more pronounced in npEN summers. Further analysis also suggests that tropical cyclones could impact AR occurrence in both cases with different patterns of atmospheric responses, inducing remarkable AR enhancement over the northern SCS in npEN summers. [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]

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

The results of sensing of East Antarctica and the adjoining areas of the Southern Ocean by MTVZA-GYа microwave satellite radiometers at frequency ν = 10–190 GHz and AMSR2 at ν = 6–89 GHz in conditions of warm and humid air (an atmospheric river (AR)) invasion from the area of Tasmania area in March 2022 are presented. The surface air warming caused by AR was recorded by the Automatic Weather Station at the coast and at the Vostok, Concordia, and Dome CII stations in East Antarctica. The variability of atmospheric characteristics above Antarctica was studied using readings of radiosondes launched from the Casey station at the coast and Concordia station at a height of 3230 m and time series of brightness temperatures averaged over a circular area 200 km in diameter with the center at a distance of ~200 km from the Concordia station. The influence of air and surface temperature and atmospheric water-vapor content variations on brightness temperature Tb(ν) variations was estimated from the results of modeling of microwave radiation transfer in the atmosphere–firn system using radiosonde profiles from the Concordia station. It was shown that the increase in Tb(ν) at frequencies of 89–92 GHz of a large part of East Antarctica was caused mainly by an increase in the firn temperature. The increase at frequencies of ∼176–190 GHz in the area of the water vapor absorption line was caused by an increase of both the firn temperature and air temperature and humidity. Based on measurements of brightness temperature Tb(ν) over the open ocean at frequencies in the atmospheric-transparency windows of ∼6–48 and 88–92 GHz, wind speed W, cloud liquid-water content Q, and atmospheric water-vapor content V were determined and the temporal variability of parameters in the AR area was studied. [ABSTRACT FROM AUTHOR]

Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used. Significance Statement: Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models. [ABSTRACT FROM AUTHOR]

In October 2023, Scotland experienced heavy rainfall and flooding due to an atmospheric river and a quasi-stationary front. This event was unusual and different from previous flood events in Scotland. The flooding caused widespread damage, including landslides, road closures, and loss of crops. The synoptic meteorological situation involved a continuous advection of a humid air mass and a temperature and humidity contrast across the front. It is important to note that extreme precipitation events like this may become more severe in a warmer climate. [Extracted from the article]

Atmospheric rivers (ARs) have the potential to generate large-impact hydrometeorological events over mountainous topography. In this study, we investigate ARs' impacts on the hydrology of Indus basin (IB) and Ganga basin (GB), two highly populated basins of the Himalayas. We used the recently developed 37-yr-long ERA5-based AR database over the Himalayas to explore the influence of ARs on total and extreme precipitation, snowfall, and floods over these basins. We find that ARs contribute ∼25% to the annual rainfall in the IB and ∼15% in the GB. Over the mountainous regions, ARs contribute more than 50% to winter precipitation in Karakoram (KA), Hindu–Kush (HK), central (CH), and western Himalayas (WH), and respectively explain over 75%, 57%, 42%, and 30% of their interannual variability. The seasonal rainfall extremes over the mountain foothills are most often (50%–100%) associated with ARs in winter and spring, whereas the summer and autumn extremes over the plains and mountains foothills appear moderately associated with ARs (10%–40%). The two most catastrophic flood events (2014 Kashmir flood and 2013 Uttarakhand flood) in these basins are found to be linked with category 5 ARs. Upon further examination of floods over a long period, we noted that 56% and 73% of the floods in IB and GB, respectively, are related to ARs. Thus, our results establish that the variance of ARs is a major source of hydroclimate variability in the two Himalayan basins. [ABSTRACT FROM AUTHOR]

Research and shared interest in atmospheric rivers (ARs) have increased significantly in recent years, alongside technological improvements that allow better comprehension of these storms. The Nechako River Basin (NRB) in British Columbia, Canada, is significantly affected by ARs originating in the Pacific Ocean. This work analyses the frequency, intensity, duration and trends of ARs in two regions (South and North) near the NRB. Analyses are based on data provided by an updated AR catalogue. The AR catalogue is matched on a daily scale to an adaptation of the AR scale to compile so‐called AR‐Days (ARDs). In the South region, ARDs exhibit stronger associations with hydroclimatic variables total precipitation, rain, snow and snow depth water equivalent (SWE). The Mann–Kendall (MK) trend test was applied to 364 time series created by combining the two closest AR‐monitored regions to the NRB with the annual and seasonal scales of climate data and the adapted AR scale (ARD0‐ARD5). Results show higher AR frequency of mainly beneficial ARDs during fall and a significant reduction of ARD1‐ARD3 in both analysed regions. Rain and total precipitation related to ARD2‐ARD3 also present significant decreasing trends for most sub‐basins of the NRB. The MK test shows a shift in water contribution from total precipitation and rainfall linked to more potentially dangerous ARDs to short‐duration, beneficial ARDs (ARD0). Rain from non‐AR‐related meteorological systems presents an increasing trend for the Upper Nechako sub‐basin, where the Nechako Reservoir is located. Trends are mainly for AR‐related total precipitation and rainfall, and in the northern part of the NRB, results point to the increase of AR‐related SWE. [ABSTRACT FROM AUTHOR]

In the arid and semiarid southwestern United States, both cool- and warm-season storms result in flash flooding, although the former storms have been much less studied. Here, we investigate a catalog of 52 flash-flood-producing storms over the 1996-2021 period for the arid Las Vegas Wash watershed using rain gauge observations, reanalysis fields, radar reflectivities, cloud-to-ground lightning flashes, and streamflow records. Our analyses focus on the hydroclimatology, convective intensity, and evolution of these storms. At the synoptic scale, cool-season storms are associated with open wave and cutoff low weather patterns, whereas warm-season storms are linked to classic and troughing North American monsoon (NAM) patterns. At the storm scale, cool-season events are southwesterly and southeasterly under open wave and cutoff low conditions, respectively, with long duration and low to moderate rainfall intensity. Warm-season storms, however, are characterized by short-duration, high-intensity rainfall, with either no apparent direction or southwesterly under classic and troughing NAM patterns, respectively. Atmospheric rivers and deep convection are the principal agents for the extreme rainfall and upper-tail flash floods in cool and warm seasons, respectively. Additionally, intense rainfall over the developed low valley is imperative for urban flash flooding. The evolution properties of seasonal storms and the resulting streamflows show that peak flows of comparable magnitude are "intensity driven" in the warm season but "volume driven" in the cool season. Furthermore, the distinctive impacts of complex terrain and climate change on rainfall properties are discussed with respect to storm seasonality. [ABSTRACT FROM AUTHOR]

The Sierra Nevada and Southern Cascades—California's snowy mountains—are primary freshwater sources and natural reservoirs for the states of California and Nevada. These mountains receive precipitation overwhelmingly from wintertime storms including atmospheric rivers (ARs), much of it falling as snow at the higher elevations. Using a seven-decade record of daily observed temperature and precipitation as well as a snow reanalysis and downscaled climate projections, we documented historical and future changes in snow accumulation and snowlines. In four key subregions of California's snowy mountains, we quantified the progressing contribution of ARs and non-AR storms to the evolving and projected snow accumulation and snowlines (elevation of the snow-to-rain transition), exploring their climatology, variability and trends. Historically, snow makes up roughly a third of the precipitation affecting California's mountains. While ARs make up only a quarter of all precipitating days and, due to their relative warmth, produce snowlines higher than do other storms, they contribute over 40% of the total seasonal snow. Under projected unabated warming, snow accumulation would decline to less than half of historical by the late twenty-first century, with the greatest snow loss at mid elevations (from 1500 to 3300 m by the mountain sub-regions) during fall and spring. Central and Southern Sierra Nevada peaks above 3400 m might see occasionally extreme snow accumulations in January–February resulting entirely from wetter ARs. AR-related snowlines are projected to increase by more than 700 m, compared to about 500 m for other storms. We discuss likely impacts of the changing climate for water resources as well as for winter recreation. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) are efficient mechanisms for transporting atmospheric moisture from low latitudes to the Antarctic continent. AR events induce intense snowfall episodes, which increase crustal deformation. Here, we used an AR detection algorithm, via a spatial matrix operation to quantify the contribution of AR-induced snowfall in the Antarctic continent, 2010–2019. Our results reveal that the AR snowfall contribution to Antarctica primarily ranged from 9.28 to 29.73 per cent from 2010 to 2019, and there was an evident increasing trend from 2015 to 2019 (20.66 per cent in 2015 to 29.30 per cent in 2019). AR-induced snowfall is one of the factors influencing the surface deformation of the Antarctic continent, based on the hourly AR snowfall deformation calculations for the Antarctic continent, both the average and maximum crustal deformation or displacement tends to be greatest near the coastline, while the displacement is less affected by AR further inland. [ABSTRACT FROM AUTHOR]

Extreme warm events in the South Orkney Islands (SOIs) are investigated using synoptic observations from Signy and Orcadas stations for 1947-1994 and 1956-2019 respectively. Defining the extremes as temperatures exceeding the 95th percentile of the temperature distribution, we reveal the characteristics and associated drivers of the warm events, especially the top 10 events in both summer and winter. At both stations, extreme warm events often involve a combined effect of atmospheric rivers (ARs) and localised föhn warming, with distinct characteristics due to the station locations relative to Coronation Island, the largest and highest island of the SOIs. For example, warm events at Signy are warmer (by an average of around 3°C) than the corresponding concurrent temperatures at Orcadas. The number of warm events per year has significantly increased over the record periods at both stations, which could potentially impact ecosystems by increasing melting of snow and ice. Extreme warm events at Signy are dominated by föhn warming in combination with ARs originating from the Southern Atlantic Ocean, where warm, moisture-rich air is rapidly advected towards the islands by enhanced northerly winds. By contrast, the Orcadas warm extremes involve both warm-air advection and föhn warming associated with enhanced northwesterlies/westerlies with ARs originating in the Pacific Ocean that travel across the Drake Passage. Simulation of one of the top 10 warm events for Signy station using a 1-km grid spacing configuration of the atmosphere-only UK Met Office Unified Model is used to disentangle the role of local versus large-scale forcing. We find that the majority of the warming can be attributed to föhn effects for the case study. These results demonstrate the complexity of Antarctic temperature extremes. [ABSTRACT FROM AUTHOR]

The June‐July Yangtze flooding in 1998 and 2020 drew incredible attention owing to the extreme precipitation events and devastating societal/economic damages. However, the quantitative estimation of the moisture transport mechanism is intensely discussed but still unresolved. Here we investigated two events from a unique perspective of Eulerian atmospheric water tracers that tries to explain the two events from model physics. The results showed that the moisture supplies from the Northwest Pacific decreased despite of different inducements, whereas the southwest summer monsoon (SWSM)‐related moisture supplies exhibited conspicuous enhancements in both events, suggesting that the SWSM‐related moisture supplies controlled the occurrence of Yangtze flooding. The physical processes of the two events were further compared. The 2020 flooding was more severe than the 1998 event, which was related to the stronger advective convergences and in‐stratus condensations of the SWSM‐related moisture. Plain Language Summary: In 1998 and 2020, the Yangtze River valley experienced extreme rainfalls during June–July. The associated atmospheric river was detected to be significantly enhanced by the moisture from the southwest summer monsoon‐related source regions (such as the Bay of Bengal, Arabian Sea, Tropical Indian Ocean, Indo‐China Peninsula, and India) in both events. On the contrary, the moisture supplies from the Northwest Pacific declined. These results suggested that the southwest summer monsoon‐related moisture supplies played vital roles in the formations of extreme rainfalls over the Yangtze River valley. Additionally, our study investigated the differences in the physical mechanisms of the two extreme rainfall events. The stronger advective transport and cloud processes forced more southwest summer monsoon‐related moisture to be converted into precipitation around the Yangtze River, ultimately leading to more severe flooding in 2020 than in 1998. Key Points: The southwest summer monsoon (SWSM)‐related moisture supplies regulate the emergence of the Yangtze flooding during June–JulyAlong with the flooding appearances, the moisture supplies from the Northwest Pacific to the Yangtze River valley decreaseStronger advective transport and in‐stratus condensations of the SWSM‐related moisture avail the larger flooding in 2020 than in 1998 [ABSTRACT FROM AUTHOR]

The term jet stream generally refers to a narrow region of intense winds near the top of the midlatitude or subtropical troposphere. It is in the midlatitude jet stream where instabilities and waves may develop into synoptic‐scale systems, which in turn makes accurately resolving the structure of the jet stream and associated features critical for atmospheric development, predictability, and impacts, such as extreme precipitation and winds. Using dropwindsonde observations collected during the Atmospheric River Reconnaissance (AR Recon) campaign from 2020 to 2022, this study assesses the North Pacific jet stream structure in the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). Results show that the IFS has a slow‐wind bias on the lead times assessed, with the strongest winds (≥50 m·s−1) having a bias of up to −1.88 m·s−1 on forecast day 4. Also, the IFS cannot resolve the sharp potential vorticity (PV) gradient across the jet stream and tropopause, and this PV gradient weakens with forecast lead time. Cases with larger wind biases are characterized by higher PV biases and PV biases tend to be larger for cases with a higher horizontal PV gradient. These results suggest that further model‐based experiments are needed to identify and address these biases, which could ultimately yield increased forecast accuracy. [ABSTRACT FROM AUTHOR]

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

Proxy reconstructions and model simulations of precipitation during Earth's glacial periods suggest that the locations and mechanisms of atmospheric moisture transport have changed considerably during Earth's past. We investigate the hydroclimate of the Last Glacial Maximum (LGM) using simulations with the Community Earth System Model, with a focus on the extratropics and the influence of atmospheric rivers (ARs), a key driver of modern‐day moisture transport globally. Mean and extreme precipitation increase significantly over southwestern Patagonia, Iberia, and southwestern North America—mid‐latitude regions affected by ARs in the modern climate—despite overall decreases elsewhere. In each, the associated moisture transport changes are different, with increased transport and AR activity mainly occurring in the North Atlantic. The overall LGM response is dominated by the response to ice sheets, with other forcings causing additional cooling and drying over the extratropics and a strong decrease of moisture transport over the subpolar North Atlantic. Plain Language Summary: During the last ice age, when glaciers reached their maximum extent, atmospheric CO2 was lower and Earth's orbit was different. All of these influences caused dramatic changes in precipitation patterns around the planet. Using climate model simulations, we find that the majority of the changes to atmospheric moisture transport and precipitation patterns result from the presence of the ice sheets alone, with lower CO2 and insolation differences enhancing the ice sheet‐induced patterns. These changes mainly comprise decreases in moisture transport and lower precipitation, with three notable exceptions in Patagonia, Iberia, and southwestern North America. Of these, precipitation increases over Iberia result from increased transport by plumes of moisture, known as atmospheric rivers , across the North Atlantic. Elsewhere in the world, AR activity decreases. Key Points: Average and extreme precipitation declined globally during the Last Glacial Maximum with three notable mid‐latitude exceptions over landMoisture transport changes mainly caused by ice sheets, with lower CO2 and altered insolation causing enhanced drying regionallyAtmospheric river activity decreased globally except over the North Atlantic, where it enhanced precipitation over Iberia [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 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 on heavy precipitation cases showed that the implemented forward operator can simulate very accurately the observed ΦDP in synoptic-scale atmospheric river (AR) cases. For tropical cyclone cases it is more challenging to produce reasonable ΦDP simulations, due to the highly sensitive 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]