Simulating and Evaluating Atmospheric River‐Induced Precipitation Extremes Along the U.S. Pacific Coast: Case Studies From 1980–2017

Abstract

Atmospheric rivers (ARs) are responsible for a majority of extreme precipitation and flood events along the U.S. West Coast. To better understand the present‐day characteristics of AR‐related precipitation extremes, a selection of nine most intense historical AR events during 1980–2017 is simulated using a dynamical downscaling modeling framework based on the Weather Research and Forecasting Model. We find that the chosen framework and Weather Research and Forecasting Model configuration reproduces both large‐scale atmospheric features—including parent synoptic‐scale cyclones—as well as the filamentary corridors of integrated vapor transport associated with the ARs themselves. The accuracy of simulated extreme precipitation maxima, relative to in situ and interpolated gridded observations, improves notably with increasing model resolution, with improvements as large as 40–60% for fine scale (3 km) relative to coarse‐scale (27 km) simulations. A separate set of simulations using smoothed topography suggests that much of these gains stem from the improved representation of complex terrain. Additionally, using the 12 December 1995 storm in Northern California as an example, we demonstrate that only the highest‐resolution simulations resolve important fine‐scale features—such as localized orographically forced vertical motion and powerful near hurricane‐force boundary layer winds. Given the demonstrated ability of a targeted dynamical downscaling framework to capture both local extreme precipitation and key fine‐scale characteristics of the most intense ARs in the historical record, we argue that such a configuration may be highly conducive to understanding AR‐related extremes and associated changes in a warming climate.

Understanding the Spatial Organization of Simultaneous Heavy Precipitation Events Over the Conterminous United States

“…The filament is directed along the same path as the anomalous upper‐level winds. This pattern is consistent with the archetypical atmospheric river (AR) circulation that delivers the strongest precipitation events to the region (see more details in Gershunov et al, 2017; Guan et al, 2018; Huang et al, 2020; Jeon et al, 2015; Ralph et al, 2019; Waliser & Guan, 2017). As a reminder the composite include SHPEs that occur in the states of Washington and California and therefore the averaging used to generate the composite smooths the sharper features found in individual events.…”

Future Warming and Intensification of Precipitation Extremes: A “Double Whammy” Leading to Increasing Flood Risk in California

“…Here we review the properties of the 40 most extreme AR events simulated in LENS‐WRF in the historical and future time. Although here we focus on the most extreme ones, the frequency of future ARs and/or precipitation extremes in the LENS are also projected to increase significantly, as shown in Hagos et al (2016), Swain et al (2018), and Huang, Swain, and Hall (2020). Overall, the average total precipitation per event over the SN watersheds is around ~350 and ~430 mm/event during the historical and future cases, respectively.…”

“…We use the Noah‐MP land surface model (LSM) (Niu et al, 2011) (see the supporting information for further discussion), which is also the default LSM option coupled to Weather Research and Forecasting (WRF) model, to simulate changes to snowpack and runoff in response to very extreme AR events impacting the SN region. As introduced previously, the AR events we studied here were previously selected for WRF downscaling at 3 km from the 40 CESM‐LENS ensemble members (hereafter, we refer to this data set as LENS‐WRF) (Huang, Swain, & Hall, 2020). The 40 most extreme AR events impacting the SN were chosen in both the historical (1996–2005) and future (2071–2080) periods.…”

Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California

“…Our LENS-WRF framework allows us to provide this information at the spatial and temporal granularity typically associated with operational weather forecasts (23). While formal validation of hypothetical future extreme weather events is not possible, we emphasize that our approach uses a modeling framework that has previously been demonstrated to successfully reproduce observed historical extremes of a comparable magnitude (23). Thus, for present-day natural hazard mitigation and future climate adaptation purposes, this approach may offer a more detailed picture of plausible worst-case hydroclimate scenarios than traditional observational or modeling exercises alone can provide.…”