Precipitation regime change in Western North America: The role of Atmospheric Rivers.

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

From California’s Extreme Drought to Major Flooding: Evaluating and Synthesizing Experimental Seasonal and Subseasonal Forecasts of Landfalling Atmospheric Rivers and Extreme Precipitation during Winter 2022/23

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 Niña), 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

Observations and Simulations of the Western United States' Hydroclimate

<p>While very important from an economical and societal point of view, estimating precipitation in the western United States remains an unsolved and challenging problem. This is due to difficulties in observing and modeling precipitation in complex terrain. This research examines this issue by (i) providing a systematic evaluation of precipitation observations to quantify data uncertainty and (ii) investigating the ability of the Ocean-Land-Atmosphere Model (OLAM) to simulate the winter hydroclimate in this region. This state-of-the-art, non-hydrostatic model has the capability of simulating simultaneously all scales of motions at various resolutions.</p><p>This research intercompares nine precipitation datasets commonly used in hydrometeorological research in two ways. First, using principal component analysis, a precipitation climatology is conducted for the western U.S. from which five unique precipitation climates are identified. From this analysis, data uncertainty is shown to be primarily due to differences in (i) precipitation over the Rocky Mountains, (ii) the eastward wet-to-dry precipitation gradient during the cold season, (iii) the North American Monsoon signal, and (iv) precipitation in the desert southwest during spring and summer. The second intercomparison uses these five precipitation regions to provide location-specific assessments of uncertainty, which is shown to be dependent on season and location.</p><p>Long-range weather forecasts on the order of a season are important for water-scarce regions such as the western U.S. The modeling component of this research looks at the ability of the OLAM to simulate the hydroclimate in the western U.S. during the winter of 1999. Six global simulations are run, each with a different spatial resolution over the western U.S. (360 km down to 11 km). For this study, OLAM is configured as for a long-range seasonal hindcast but with observed sea surface temperatures. OLAM precipitation compares well against observations, and is generally within the range of data uncertainty. Observed and simulated synoptic meteorological conditions are examined during the wettest and driest events. OLAM is shown to reproduce the appropriate anomaly fields, which is encouraging since it demonstrates the capability of a global climate model, driven only by SSTs and initial conditions, to represent meteorological features associated with daily precipitation variability.</p>Dissertatio

An Analysis of Precipitation Variability, Persistence, and Observational Data Uncertainty in the Western United States

Abstract This paper presents an intercomparison of precipitation observations for the western United States. Using nine datasets, the authors provide a comparative climatology and season- and location-specific evaluations of precipitation uncertainty for the western United States and for five subregions that have distinct precipitation climates. All data are shown to represent the general climate features but with high bias among datasets. Interannual variability is similar among datasets with respect to the timing of precipitation excesses and deficits, but important differences occur in the spatial distribution of specific anomalous events. Dataset distribution differences, as represented by their cumulative density functions (CDFs), are statistically significant for 80% of data combinations stratified by subregion and season. The CDFs of anomaly fields are more similar but uncertainty remains, as data differences are significant for 40% of dataset comparisons. Observational uncertainty is low for persistence studies because the data are found to be similar with respect to (i) grid cell estimates of a characteristic persistence time scale and (ii) distributions of anomaly length scales. Spatially, the greatest uncertainty in magnitude differences occurs along the Rocky Mountains in winter, spring, and fall, and along the California coastline in summer. In linear (phase) association, the greatest differences occur in northern Mexico during all seasons; along the Rocky Mountains in winter, spring, and fall; and in California, Nevada, and the intermountain region in summer. Overall, data similarity is lowest in summer as a result of a reduction in phase association and an increase in amplitude differences

A Precipitation Climatology and Dataset Intercomparison for the Western United States

Abstract This paper presents the results of a regionalization study of the precipitation climate of the western United States using principal component analysis. Past eigen-based regionalization studies have relied on rain gauge networks, which is restrictive because rain gauge coverage is sparse, especially over complex terrain that exists in the western United States. Here, the use of alternate data products is examined by conducting a comparative regionalization using nine precipitation datasets used in hydrometeorological research. Five unique precipitation climates are identified within the western United States, which have centers and boundaries that are physically reasonable and that highlight the relationship between the precipitation climatology and local topography. Using the congruence coefficient as the measure of similarity between principal component solutions, the method is found to be generally stable across datasets. The exception is the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis 2, which frequently demonstrates only borderline agreement with the other datasets. The loading pattern differences among datasets are shown to be primarily a result of data differences in the representation of (i) precipitation over the Rocky Mountains, (ii) the eastward wet-to-dry precipitation gradient that occurs during the cold season, (iii) the magnitude and spatial extent of the North American monsoon signal, and (iv) precipitation in the desert southwest during spring and summer. Sensitivity tests were conducted to determine whether the spatial resolution and temporal domain of the input data would dramatically affect the solution, and these results show the methodology to be stable to differences in spatial/temporal data features. The results suggest that alternate data products can be used in regionalization studies, which has applications for rain gauge installation and planning, climate research, and numerical modeling experiments

Interannual variability in associations between seasonal climate, weather, and extremes: wintertime temperature over the Southwestern United States

Temperature variability in the Southwest US is investigated using skew-normal probability distribution functions (SN PDFs) fitted to observed wintertime daily maximum temperature records. These PDFs vary significantly between years, with important geographical differences in the relationship between the central tendency and tails, revealing differing linkages between weather and climate. The warmest and coldest extremes do not necessarily follow the distribution center. In some regions one tail of the distribution shows more variability than does the other. For example, in California the cold tail is more variable while the warm tail remains relatively stable, so warm years are associated with fewer cold extremes but not necessarily more warm extremes. The opposite relationship is seen in the Great Plains. Changes in temperature PDFs are conditioned by different phases of El Niño-La Niña (ENSO) and the Pacific decadal oscillation (PDO). In the Southern Great Plains, La Niña and/or negative PDO are associated with generally warmer conditions. However, in terms of extremes, while the warm tails become thicker and longer, the cool tails are not impacted—extremely warm days become more frequent but extremely cool days are not less frequent. In contrast, in coastal California, La Niña or negative PDO bring generally cooler conditions with more/stronger cold extremes but the warm extreme probability is not significantly affected. These results could have implications for global warming. If a rigid shift of the whole range occurs, then warm years are not necessarily a good analogue for a warmer climate. If global warming instead brings regional changes more aligned with a preferred state of dominant climate variability modes, then we may see asymmetric changes in the tails of local temperature PDFs