This presentation introduces the ROTated Atmospheric river coordinaTE (ROTATE) system – a storm-centric coordinate system designed specifically for analyzing long, narrow filamentary regions of intense water vapor transport in the lower atmosphere or so-called 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. We used ROTATE to look at crucial characteristics of atmospheric rivers 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 conventional non-rotated AR centroid-based compositing approach. The new method also helps us see finer details in rain distributions over land versus over the oceans. It is further apparent that the ROTATE system more distinctly delineates the finer details in precipitation distributions for 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. Details about the analysis and challenges associated with the current results will be discussed in this presentation.

The value of improving longer-lead precipitation forecasting in the water-stressed, semi-arid western United States cannot be overstated, especially considering the severity and frequency of droughts that have plagued the region for much of the 21st century. Seasonal prediction skill of current operational forecast systems, however, remain insufficient for decision-making purposes across a variety of applications. To address this capability gap, we develop a seasonal forecasting system that leverages the long-term memory of leading global and basin-scale modes of sea surface temperature variability. This approach focuses on characterizing and capitalizing on the spatiotemporal evolution of predictor modes over multiple antecedent seasons, instead of the customary use of predictive information from just the current season. Another distinctive methodological feature is the incorporation of sources of predictability spanning multiple timescales, from interannual to decadal-multidecadal. An evaluation of the forecast system's performance from cross-validation analyses demonstrates skill over core winter precipitation regions-California, Pacific Northwest, and the Upper Colorado River basin. The developed model exhibits superior skill compared to dynamical and statistical benchmarks in predicting winter precipitation. Experimental seasonal precipitation forecasts from the model have the potential to provide critical situational awareness guidance to stakeholders in the water resources, agriculture, and disaster preparedness communities.

Atmospheric motion vectors (AMVs) represent horizontal wind derived by tracking cloud or water vapor features on successive satellite images. The launch of the Geostationary Operational Environmental Satellite-R Series (GOES-R), including GOES-16 (GOES-East) and GOES-17 (GOES-West), has significantly enhanced AMV data volume and geographic coverage over the contiguous United States (U.S.) and adjacent oceans. AMVs from GOES-16/17 products can augment wind data in data-sparse oceanic areas such as those frequented by atmospheric rivers (ARs). However, AMVs exhibit biases and uncertainties, especially due to height assignment issues, and there are fewer conventional data (e.g., radiosondes) to assess GOES-17 AMVs over oceans. The AR Reconnaissance (AR Recon) samples ARs to improve forecast skill over the U.S. West and provides a unique opportunity to compare GOES-17 AMVs. This study quantifies biases and uncertainties in GOES-17 AMVs in the northeast Pacific using dropsondes from AR Recon, and assesses Global Forecast System (GFS) model wind analyses and background fields during AR events. Results for four representative AR cases show that GOES-R AMVs improved wind data distribution compared to cases prior to GOES-R becoming operational, particularly in the upper and lower troposphere. A comparison with dropsondes reveals a small vector wind speed bias of -0.7 m s-1. The uncertainty for AMVs is estimated at 5-6 m s-1. Comparison of collocated GFS model background wind fields shows small biases. Data assimilation reduces root-mean-squared differences, but the small biases in operational AMVs need further attention as they are a predominant wind data source in the GFS over oceanic regions.

This study employs CYGNSS data to explore the life cycle of atmospheric rivers (ARs) on a near global scale, focusing on ocean surface turbulent heat flux variations. Using spaceborne measurements matched to different stages of full AR life cycles for the first time, we show declining latent and sensible heat fluxes throughout the AR life cycle, with the latent heat flux dominating. The observed reduction in heat fluxes, despite increasing wind speed, suggests that temperature and humidity vertical gradients are the primary drivers of these changes during the AR life cycle. The decreasing temperature and humidity gradients indicate rapid declines in air temperature and humidity levels relative to the ocean surface. Our findings suggest the potential role of air‐sea interactions in driving moisture transport from the tropics to higher latitudes within ARs, and offer a satellite‐based benchmark for improving model fidelity in representing air‐sea processes during ARs.

California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks' time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022-January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1-6 month lead time) and subseasonal (2-6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Nina), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2-3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.

We describe a novel scanning microwave limb sounder (SMLS) instrument that performs rapid and broad azimuth conical scans of Earth's limb while simultaneously scanning the limb in the vertical. This azimuthal scanning capability gives dramatic improvement in temporal and spatial coverage over that of previous limb sounding instruments. In a 1500-kilometer altitude, 52 degrees-inclination Earth orbit, SMLS provides 6-8 vertical profile measurements separated by 1.9 hours every 24 hours everywhere between +/- 65 degrees latitude, and 2-4 such measurements everywhere between +/-(65-82 degrees). Horizontal resolution is similar to 50x50 km. Vertical resolution is similar to 2 km for water vapor and cloud ice and similar to 1-3 km for chemical species. In an equatorial orbit, emphasizing the tropics and subtropics, SMLS produces profile measurements every 1.9 hours everywhere between +/- 35 degrees latitude. SMLS measurements address scientific issues of relevance to the upper troposphere, stratosphere, mesosphere, and lower thermosphere regions of the atmosphere (heights from similar to 10 km to similar to 100 km).

Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle. The continually developing Tracking Atmospheric Rivers Globally as Elongated Targets (tARget) algorithm identifies AR objects at individual time steps based on thresholding integrated water vapor transport (IVT) and other requirements, and tracks each AR object in time and space. Building on previous versions of tARget, this paper discusses further refinements to the algorithm to better handle ARs in tropical and polar areas, as well as “zonal” ARs which the previous versions of the algorithm were not designed to capture. This further regionally refined algorithm is applied to the ERA5 reanalysis over 1940–2023 at 6 h intervals and a 0.25° × 0.25° horizontal resolution. The AR detection results are evaluated in terms of key AR characteristics. We anticipate this regionally refined global AR database will aid further understanding of ARs such as AR process studies, evaluation of AR simulations and predictions, and assessment of climate change impacts on ARs.

There is growing urgency for improved public and commercial services to support a resilient, secure, and thriving United States (US) in the face of mounting decision-support needs for environmental stewardship and hazard response, as well as for climate change adaptation and mitigation. Sustained space-based Earth observations are critical infrastructure to support the delivery of science and decision-support information with local, national, and global utility. This is reflected in part through the United States' sustained support of a suite of weather and land-imaging satellites. However, outside of these two areas, the US lacks an overarching, systematic plan or framework to identify, prioritize, fund, and implement sustained space-based Earth observations to meet the Nation's full range of needs for science, government policy, and societal support. To aid and accelerate the discussion on our nation's needs, challenges and opportunities associated with sustained critical space-based Earth observations, the Keck Institute for Space Studies (KISS) sponsored a multi-week think-tank study to offer ways forward. Based on this study, the KISS study team suggests the establishment of a robust coordination framework to help address US needs for sustained Earth observations. This coordination framework could account for: (a) approaches to identify and prioritize satellite observations needed to meet US needs for science and services, (b) the rapidly evolving landscape of space-based Earth viewing architecture options and technology improvements with increasing opportunities and lower cost access to space, and (c) the technical and programmatic underpinnings required for proper and comprehensive data stewardship to support a wide range of research and public services. The Keck Institute of Space Studies has carried out a think tank study to codify best practices, articulate successes, and identify challenges and opportunities in the prioritization, acquisition, curation, and stewardship of sustained space-based Earth observations. The goal of the study is to accelerate discussion and plans for a greater and more impactful US contribution to the global satellite observing system that will support decision-making regarding climate change, environmental hazards, and national security. Based on this study, the KISS study team suggests the establishment of a nimble and responsive coordination framework to help guide and shepherd US concerns regarding sustained Earth observations. This coordination framework should account for: (a) approaches to identify and prioritize satellite observations needed to meet US needs for science and services, (b) the rapidly evolving landscape of space-based Earth viewing architecture options and technology improvements with increasing opportunities and lower cost access to space and (c) the technical and programmatic underpinnings required for proper and comprehensive data stewardship with a broad science and services user base in mind. There is growing urgency for improved public and commercial services to support a resilient, secure, and thriving US Space-based Earth observations represent an essential component of the infrastructure needed to support the delivery of needed information The US would benefit from an overarching plan for sustained Earth observations to support our science, policy, and resilience goals

AbstractThis 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.

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

Atmospheric rivers (ARs) are narrow, elongated, transient corridors of enhanced water vapor transport that play important roles in the global water cycle. Increasing scientific and societal interests in ARs prompted the introduction of an AR scale (ranks 1–5, from weak to strong) initially focused on western North America. Aided by a global AR detection algorithm, the current study explores the insights the AR scale can help provide from a global perspective. It is found that AR event count is inversely related to AR rank and peaks in midlatitude oceans. Out of all precipitation occurrences, ARs account for an increasing fraction as the precipitation intensity considered increases. Over the oceans, this fraction is composed of comparable contributions from the five AR ranks, but the contribution of AR 5 is much smaller over land. Higher‐ranked ARs tend to initiate at lower latitudes, terminate at higher latitudes, but follow a less sloped track due to longer zonal displacement. Sensitivity analysis indicates that if ARs are defined using a sole IVT threshold of 250 kg m −1 s −1 globally but with the tropics excluded, the spatiotemporal patterns of key AR event characteristics are largely similar to those described above, except AR events are more frequent by a factor of ∼1.5–2 depending on the AR rank. The results demonstrate the potential of the AR scale in helping evaluate and communicate ARs' influences globally, where the uniform scaling facilitates intercomparisons across different regions and the perception of impacts can be adjusted according to local climatology.

The Daily Tropical Cyclone Probability (DTCP), defined as the probability of tropical cyclone occurrence within 500 km of a location in 1 day, is proposed and used in evaluating subseasonal to seasonal (S2S) predictions from the subseasonal to seasonal Prediction Project Database, from May 1 to October 31, 1999 to 2010. The ensemble forecasts are collected from eleven operational centers, the Bureau of Meteorology (BoM), China Meteorological Administration (CMA), Environment and Climate Change Canada (ECCC), European Centre for Medium-Range Weather Forecasts (ECMWF), Hydrometeorological Centre of Russia (HMCR), the Institute of Atmospheric Sciences and Climate of the National Research Council of Italy (ISAC), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), Météo-France/Centre National de Recherche Meteorologiques (METFR), the United States National Centers for Environmental Prediction (NCEP), and the United Kingdom Met Office (UKMO). In both observation and these eleven forecast models, the daily tropical cyclone probability is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), depicted by the two indices, boreal summer intraseasonal oscillation 1 and boreal summer intraseasonal oscillation 2. During boreal summer intraseasonal oscillation 1 phases 1, 5, 6, 7, and 8, the daily tropical cyclone probability in the western North Pacific region is ∼3.5 times higher. Similarly, during phases 1, 2, 3, 4, and 8 of boreal summer intraseasonal oscillation 2, the daily tropical cyclone probability is ∼2.5 times higher. Among the eleven models, the European Centre for Medium-Range Weather Forecasts model best reproduces the climatological daily tropical cyclone probability and its modulation by the boreal summer intraseasonal oscillation in the western North Pacific region, followed by the United States National Centers for Environmental Prediction, the Korea Meteorological Administration, the Japan Meteorological Agency models. Using the daily tropical cyclone probability metric, the highest debiased Brier Skill Score of the eleven models is from European Centre for Medium-Range Weather Forecasts, which has a slightly less skillful prediction than the reference climatological forecast with lead time 11–30 days. The skill of the eleven models is higher during the non-active phases of tropical cyclone activity than their skill during the active phases.

This study investigates the impacts of aerosol atmospheric rivers (AARs) on extreme Particulate Matter 2.5 (PM2.5) levels (PM2.5 > 15mgm-3, as per the WHO) and on aerosol optical depth (AOD) extremes (AOD > 98th percentile) over the US and the globe, respectively, between 1997-2020. Results show that over various regions over the US, extreme PM2.5values are associated with AARs up to 70% of the time. Dust (sulfate) AARs are responsible for extreme PM2.5 levels over the southwestern (northeastern and the east coastal) US. Organic and black carbon AARs are associated with extreme PM2.5 levels over the Midwest region of the US. Globally, AARs are associated with 40-80% of the extreme AOD levels over the US, Sahel, Europe, Middle East, US, South America, East Asia, India, and South Africa. Such associations often lead to the highest or the second highest PM2.5 and AOD levels recorded over those stations between 1997-2020.

Given the increasing use of climate projections and multi-model ensemble weighting for a diverse array of applications, this project assesses the sensitivities of climate model weighting strategies and their resulting ensemble means to multiple components, such as the weighting schemes, climate variables, or spatial domains of interest. The purpose of this study is to assess the sensitivities associated with multi-model weighting strategies. The analysis makes use of global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their statistically downscaled counterparts created with the localized constructed analogs (LOCA) method. This work focuses on historical and projected future mean precipitation and daily high temperatures of the south-central United States. Results suggest that the model weights and the corresponding weighted model means can be sensitive to the weighting strategy that is applied. For instance, when estimating model weights based on Louisiana precipitation, the weighted projections show a wetter and cooler south-central domain in the future compared to other weighting strategies. Alternatively, for example, when estimating model weights based on New Mexico temperature, the weighted projections show a drier and warmer south-central domain in the future. However, when considering the entire south-central domain in estimating the model weights, the weighted future projections show a compromise in the precipitation and temperature estimates. As for uncertainty, our matrix of results provided a more certain picture of future climate compared to the spread in the original model ensemble. If future impact assessments utilize weighting strategies, then our findings suggest that how the specific weighting strategy is used with climate projections may depend on the needs of an impact assessment or adaptation plan.

The ability of a suite of models contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate observed large-scale atmospheric circulation patterns over the Pacific Northwest of North America is evaluated. Twelve representative patterns of 500-hPa geopotential height (Z500) anomalies are identified using the self-organizing map method applied to reanalysis. CMIP6 Z500 anomalies from simulations of the historical period are mapped to each reanalysis-derived pattern, and the resulting differences between patterns, as well as differences in frequency of pattern occurrence, are quantified. In general, models are able to simulate the range of Z500 patterns with reasonable fidelity, although model skill varies across the 25-member ensemble. Surface temperature and precipitation anomalies associated with each Z500 pattern are found to also be reasonably simulated by the models, with some biases noted. This boosts confidence that the models are simulating temperature and precipitation patterns for the correct physical reasons. The models exhibit a range of skill at simulating pattern occurrence frequency and pattern persistence, with more agreement in winter than summer. Results indicate that the CMIP6 models are appropriate for assessing future projections of key atmospheric circulation patterns and their impacts on temperature and precipitation over the region.

Increases in climate hazards and their impacts mark one of the major challenges of climate change. Situations in which hazards occur close enough to one another to result in amplified impacts, because systems are insufficiently resilient or because hazards themselves are made more severe, are of special concern. We consider projected changes in such compounding hazards using the Max Planck Institute Grand Ensemble under a moderate (RCP4.5) emissions scenario, which produces warming of about 2.25 °C between pre-industrial (1851–1880) and 2100. We find that extreme heat events occurring on three or more consecutive days increase in frequency by 100%–300%, and consecutive extreme precipitation events increase in most regions, nearly doubling for some. The chance of concurrent heat and drought leading to simultaneous maize failures in three or more breadbasket regions approximately doubles, while interannual wet-dry oscillations become at least 20% more likely across much of the subtropics. Our results highlight the importance of taking compounding climate extremes into account when looking at possible tipping points of socio-environmental systems.

Leveraging the concept of atmospheric rivers (ARs), a detection technique based on a widely utilized global algorithm to detect ARs (Guan and Waliser, 2019, 2015; Guan et al., 2018) was recently developed to detect aerosol atmospheric rivers (AARs) using the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis (Chakraborty et al., 2021a). The current study further characterizes and quantifies various details of AARs that were not provided in that study, such as the AARs' seasonality, event characteristics, vertical profiles of aerosol mass mixing ratio and wind speed, and the fraction of total annual aerosol transport conducted by AARs. Analysis is also performed to quantify the sensitivity of AAR detection to the criteria and thresholds used by the algorithm. AARs occur more frequently over, and typically extend from, regions with higher aerosol emission. For a number of planetary-scale pathways that exhibit large climatological aerosol transport, AARs contribute up to a maximum of 80 % to the total annual transport, depending on the species of aerosols. Dust (DU) AARs are more frequent in boreal spring, sea salt AARs are often more frequent during the boreal winter (summer) in the Northern (Southern) Hemisphere, carbonaceous (CA) AARs are more frequent during dry seasons, and often originate from the global rainforests and industrial areas, and sulfate AARs are present in the Northern Hemisphere during all seasons. For most aerosol types, the mass mixing ratio within AARs is highest near the surface. However, DU and CA AARs over or near the African continent exhibit peaks in their aerosol mixing ratio profiles around 700 hPa. AAR event characteristics are mostly independent of species with the mean length, width, and length / width ratio around 4000 km, 600 km, and 7–8, respectively.

Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.

S.1 Maps from the CMIP5 ensembles -precipitationAmong the 288 ensemble means created from this experimental setup, there are numerous times when results are duplicated.For example, applying a given weighting combination created using the full domain to Louisiana would have the same value as the same weighting combination created using the full domain applied to the full domain and examining only the Louisiana area.As such, the results in this and the following sections will focus only on those ensemble means created from the various combinations of weighting schemes applied to the full domain for each ensemble.In this way, one can then examine the effects for the Louisiana and New Mexico domains and other regions of the full domain.