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Atmospheric rivers (ARs) generate most of the economic losses associated with flooding in the western United States and are projected to increase in intensity with climate change. This is of concern as flood damages have been shown to increase exponentially with AR intensity. To assess how AR-related flood damages are likely to respond to climate change, we constructed county-level damage models for the western 11 conterminous states using 40 years of flood insurance data linked to characteristics of ARs at landfall. Damage functions were applied to 14 CMIP5 global climate models under the RCP4.5 "intermediate emissions" and RCP8.5 "high emissions" scenarios, under the assumption that spatial patterns of exposure, vulnerability, and flood protection remain constant at present day levels. The models predict that annual expected AR-related flood damages in the western United States could increase from $1 billion in the historical period to $2.3 billion in the 2090s under the RCP4.5 scenario or to $3.2 billion under the RCP8.5 scenario. County-level projections were developed to identify counties at greatest risk, allowing policymakers to target efforts to increase resilience to climate change.

Atmospheric rivers (ARs) are now widely known for their association with high-impact weather events and long-term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR-related metrics based on 20+ different AR identification and tracking methods applied to Modern-Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria-based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all-method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR-related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR-related research to consider.

Daily precipitation in California has been projected to become less frequent even as precipitation extremes intensify, leading to uncertainty in the overall response to climate warming. Precipitation extremes are historically associated with Atmospheric Rivers (ARs). Sixteen global climate models are evaluated for realism in modeled historical AR behavior and contribution of the resulting daily precipitation to annual total precipitation over Western North America. The five most realistic models display consistent changes in future AR behavior, constraining the spread of the full ensemble. They, moreover, project increasing year-to-year variability of total annual precipitation, particularly over California, where change in total annual precipitation is not projected with confidence. Focusing on three representative river basins along the West Coast, we show that, while the decrease in precipitation frequency is mostly due to non-AR events, the increase in heavy and extreme precipitation is almost entirely due to ARs. This research demonstrates that examining meteorological causes of precipitation regime change can lead to better and more nuanced understanding of climate projections. It highlights the critical role of future changes in ARs to Western water resources, especially over California.

In 2016, a team of earth scientists directly engaged a team of computer scientists to identify cyberinfrastructure (CI) approaches that would speed up an earth science workflow. This paper describes the evolution of that workflow as the two teams bridged CI and an image segmentation algorithm to do large scale earth science research. The Pacific Research Platform (PRP) and The Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI) resources were used to significantly decreased the earth science workflow's wall-clock time from 19.5 days to 53 minutes. The improvement in wall-clock time comes from the use of network appliances, improved image segmentation, deployment of a containerized workflow, and the increase in CI experience and training for the earth scientists. This paper presents a description of the evolving innovations used to improve the workflow, bottlenecks identified within each workflow version, and improvements made within each version of the workflow, over a three-year time period.

Water years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation and snow records along with mid‐tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical seasonal ENSO‐precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO‐independent weather patterns was a major cause of anomalous precipitation in WY2017, record‐setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983, 1998, and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal precipitation forecasts. Plain Language Summary: In 2017 and 2023, California experienced unexpectedly wet conditions despite predictions of dry winters due to La Niña. In 2016, seasonal predictions in California favored wet conditions due to the very strong El Niño, but the season was normal‐to‐dry statewide. Understanding relationships between El Niño/La Niña and recurring atmospheric weather patterns driving individual storms is needed to improve seasonal forecasts. We studied historical relationships between weather patterns that bring rain and snow to the region and the El Niño Southern Oscillation (ENSO). We find ENSO influences important characteristics of weather patterns once they make landfall in California, making El Niño storms generally wetter in coastal southern California and Desert Southwest. However, ENSO does not strongly affect how often these patterns occur in a season, which makes seasonal precipitation forecasts challenging. The frequency of certain weather patterns not tied to ENSO played important roles in the unusual rainfall of 2017, the heavy snowfall of 2023, and the drier than expected winter of 2016. Understanding these weather patterns provides operationally and scientifically relevant context for future seasonal forecasts by highlighting that while ENSO only minimally influences the frequency of certain impactful storm types, it does change the precipitation characteristics of these storms. Key Points: Weather regime type and frequency are key drivers of winter seasons with anomalous precipitation and/or snow accumulation in CaliforniaENSO does not modulate the seasonal frequency of weather regimes impacting the coast, presenting a challenge for seasonal forecastingENSO modulates synoptic circulation characteristics of key weather regimes which produces the canonical ENSO‐precipitation relationship [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Human communities in the western United States rely on seasonal mountain snowpack and precipitation for agriculture, industry, and the civilian water supply. Over the past two decades, increasingly severe drought conditions have plagued western states. In this study, we investigate gains in water storage related to recent atmospheric rivers, which are long and narrow corridors of atmospheric moisture associated with extreme precipitation, using an interdisciplinary approach called hydrogeodesy. Hydrogeodesy uses satellite‐ and ground‐based sensors to track subtle changes in the shape of the Earth and its gravity field caused by the redistribution of water through the Earth system. By treating the Earth as a natural scale, we can infer how much water is gained or lost from a particular area over time. Advancing our understanding of water‐resource availability, the Earth's water cycle, and the Earth's changing climate is critical for improving water‐resource management, sustaining natural ecosystems, and mitigating natural hazards. Key Points: We use surface deformation to assess changes in terrestrial water storage (TWS) associated with families of strong atmospheric rivers (ARs) in 2022–2023Large jumps in mountain water storage from ARs spurred recoveries from major California droughts in 2012–2015 and 2019–2022Early‐season water‐storage gains, driven by ARs, nearly equal precipitation inputs, indicating extensive reservoir recharge [ABSTRACT FROM AUTHOR]

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month proof of concept trial run designed to illustrate the utility and feasibility of the ARTMIP project.

https://doi.org/10.15447/sfews.2018v16iss4art1California is regularly affected by floods and droughts, primarily as a result of too many or too few atmospheric rivers (ARs). This study analyzes a 2-decade-long hourly precipitation data set from 176 California weather stations and a 3-hourly AR chronology to report variations in rainfall events across California and their association with ARs. On average, 10–40 and 60–120 hours of rainfall in southern and northern California, respectively, are responsible for more than half of annual rainfall accumulations. Approximately 10% to 30% of annual precipitation at locations across the state is from only one large storm. On average, northern California receives 25 to 45 rainfall events annually (40% to 50% of which are AR-related). These events typically last longer and have higher event-precipitation totals than those in southern California. Northern California also receives more AR landfalls with longer durations and stronger Integrated Vapor Transport (IVT). On average, ARs contribute 79%, 76%, and 68% of extreme-rainfall accumulations (i.e., top 5% events annually) in the north coast, northern Sierra, and Transverse Ranges of southern California, respectively.The San Francisco Bay Area terrain gap in the California Coast Range allows more AR water vapor to reach inland over the Delta and Sacramento Valley, and thus influences precipitation in the Delta’s catchment. This is particularly important for extreme precipitation in the northern Sierra Nevada, including river basins above Oroville Dam and Shasta Dam. This study highlights differences between rainfall and AR characteristics in coastal versus inland northern California — differences that largely determine the regional geography of flood risks and water reliability. These analyses support water resource, flood, levee, wetland, and ecosystem management within the catchment of the San Francisco Estuary system by describing regional characteristics of ARs and their influence on rainfall on an hourly time-scale.

The Russian River Hydrometeorological Observing Network (RHONET) is a unique suite of high-resolution in situ and remote sensing observations deployed over 20 years to address both scientific and operational gaps [...]