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.

We estimate the spatial distribution of daily melt-season snow water equivalent (SWE) over the Sierra Nevada for March to August, 2000-2012, by two methods: reconstruction by combining remotely sensed snow cover images with a spatially distributed snowmelt model and a blended method in which the reconstruction is combined with in situ snow sensor observations. We validate the methods with 17 snow surveys at six locations with spatial sampling and with the operational snow sensor network. We also compare the methods with NOAA's operational Snow Data Assimilation System (SNODAS). Mean biases of the methods compared to the snow surveys are -0.193 m (reconstruction), 0.001 m (blended), and -0.181 m (SNODAS). Corresponding root-mean-square errors are 0.252, 0.205, and 0.254 m. Comparison between blended and snow sensor SWE suggests that the current sensor network inadequately represents SWE in the Sierra Nevada because of the low spatial density of sensors in the lower/higher elevations. Mean correlation with streamflow in 19 Sierra Nevada watersheds is better with reconstructed SWE (r = 0.91) versus blended SWE (r = 0.81), snow sensor SWE (r = 0.85), and SNODAS SWE (r = 0.86). On the other hand, the correlation with blended SWE is generally better than with reconstructed, snow sensor, and SNODAS SWE late in the snowmelt season when snow sensors report zero SWE but snow remains in the higher elevations. Sensitivity tests indicate downwelling longwave radiation, snow albedo, forest density, and turbulent fluxes are potentially important sources of errors/uncertainties in reconstructed SWE, and domain-mean blended SWE is relatively insensitive to the number of snow sensors blended. ©2013. American Geophysical Union. All Rights Reserved.

We investigate transport pathways of water vapor associated with landfalling atmospheric river (AR) events that result in precipitation along the West Coast of the U.S. for winters of 1997-2010. The water vapor transport pathways are determined by computing back trajectories with a trajectory model using the Modern Era Retrospective analysis for Research and Applications reanalysis data set. The majority of AR events (86%) over the West Coast of the U.S. are grouped into three trajectory types, and two of them are closely associated with the AR events. We designate the first type as Ascending near landfall and of Tropical Origin (AT), the second type as Ascending near landfall and of Extratropical Origin (AE), and the third type as Descending or parallel near landfall and of Extratropical Origin (DE), which is accompanied but not directly associated with the AR events. The magnitude and spatial distribution of precipitation of a given AR event are found to be strongly determined by the type of trajectories. In general, AR events composed of both AT and AE trajectories have more frequent precipitation over a broad region of the western U.S. and AR events composed of both AT and DE trajectories have intense precipitation over the southwestern U.S. due to AT trajectories. AR events of AT-only trajectories have intense precipitation, especially over the northwestern U.S., but are less frequent compared to those of AT + AE trajectories. In addition, different patterns of trajectory types among AR events are closely linked to upper level potential vorticity (PV) anomalies; 66% of AR events are associated with anticyclonic Rossby wave breaking events.

AbstractA 30-yr climatology of lightning days and associated synoptic meteorological patterns are characterized across the interior western United States (WUS). Locally centered composite analyses show preferred synoptic meteorological patterns with positive 500-hPa geopotential height anomalies located to the northeast and negative sea level pressure anomalies to the northwest and collocated with local lightning days. Variations in preferred patterns for local lightning days are seen across the interior WUS. Areas not commonly affected by the North American monsoon system including the western Great Basin and northern Rocky Mountains show higher-amplitude anomalies of geopotential height, moisture, and midtropospheric instability patterns suggesting the importance of episodic midlatitude dynamics to lightning days in such locations. By contrast, locations closer to the core of the North American monsoon show weaker anomalies, likely reflecting the prevalence of favorable mesoscale dynamics key to lightning production during warm-season months in locations in the interior Southwest. Meteorological patterns for select locations are explored in more detail and two case studies of notably active lightning events are presented. Results from this observational analysis provide a foundation for evaluating meteorological conditions on lightning days in climate model simulations for the interior WUS.

Observation-based climate model evaluation and future projections help policymakers in developing action plans for efficient management of water resources and mitigation of the impacts of hazardous extremes. Apart from this socioeconomic importance, the scientific value cannot be overstated, especially in light of the upcoming Fifth U.S. National Climate Assessment (NCA) report. In this study, we evaluate the realism of hydroclimate variability in the historical simulations of a suite of coupled general circulation models (CGCMs) participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Our results demonstrate systematic biases in the simulated seasonal precipitation-most prominently, wet bias over the mountainous western United States in winter, and dry bias over the U.S. central plains in summer. A distinctive feature of this work is our focus on the examination of the atmospheric water budget, in particular the relative importance of remote and local contributions-convergence of moisture fluxes and local land surface processes (evapotranspiration) respectively-in helping produce precipitation. This diagnosis reveals that the leading contribution of the remote influence in winter is overestimated by the CMIP6 multimodel mean (MMM), whereas the local influence, which is more influential in summer, is underestimated. Our results aid in understanding the drivers of seasonal precipitation over the United States, where precipitation will likely increase by the end of the century but with significant model disagreement for the summer and fall. In support of ongoing NCA efforts, our study aims to contribute a comprehensive, regional-level analysis of the moisture budget and emphasizes the importance of realistically simulating its major components in CGCMs.

Virtual Workshop on Subseasonal to Seasonal Climate Forecasting for Water Management in the Western U.S. What: Scientists and stakeholders came together to discuss forecast priorities for western U.S. water resource management and to review existing and emerging methodologies that can improve prediction of precipitation, circulation regimes, and atmospheric rivers at lead times of weeks to months. When: 15–17 March 2022 Where: Online, hosted by IRI, Columbia Climate School

An analysis of diabatic heating and moistening processes from 12 to 36 h lead time forecasts from 12 Global Circulation Models are presented as part of the "Vertical structure and physical processes of the Madden-Julian Oscillation (MJO)" project. A lead time of 12-36 h is chosen to constrain the large-scale dynamics and thermodynamics to be close to observations while avoiding being too close to the initial spin-up of the models as they adjust to being driven from the Years of Tropical Convection (YOTC) analysis. A comparison of the vertical velocity and rainfall with the observations and YOTC analysis suggests that the phases of convection associated with the MJO are constrained in most models at this lead time although the rainfall in the suppressed phase is typically overestimated. Although the large-scale dynamics is reasonably constrained, moistening and heating profiles have large intermodel spread. In particular, there are large spreads in convective heating and moistening at midlevels during the transition to active convection. Radiative heating and cloud parameters have the largest relative spread across models at upper levels during the active phase. A detailed analysis of time step behavior shows that some models show strong intermittency in rainfall and differences in the precipitation and dynamics relationship between models. The wealth of model outputs archived during this project is a very valuable resource for model developers beyond the study of the MJO. In addition, the findings of this study can inform the design of process model experiments, and inform the priorities for field experiments and future observing systems.

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.