Using Backwards Trajectories to Estimate Atmospheric Rivers’ Contributions to Colorado’s Wettest Days

Heavy precipitation in Colorado (CO) is key to water resources, and the presence or absence of a few strong storms can make or break the yearly snowpack that delivers water to four major river basins. However, predicting precipitation in CO is challenging because it has high spatial and temporal variability. Atmospheric rivers (ARs) are one type of storm that results in a large fraction of extreme precipitation in the western U.S. and lends itself to improved forecasts over the region. Extensive knowledge of AR frequency, intensity, impacts, and key meteorological processes has been developed for U.S. West Coast landfalling ARs; however, relatively limited research has examined AR characteristics further inland, particularly for Colorado (CO), where high and complex topography, as well as the distance from the coast, complicate attempts to track ARs, AR-derived moisture, and AR-related impacts. Previous research efforts attributing precipitation to ARs based on their spatial footprint have yielded less than 30% of cool-season precipitation in CO as related to ARs. However, a large volume of anecdotal evidence suggests that ARs play a larger role in CO precipitation. To quantify this, we used trajectory-based methods to quantify the contribution of landfalling ARs to top-decile precipitation in subbasins throughout CO. Moisture sourced from landfalling ARs penetrates inland along relatively low-elevation corridors through the Interior West, and exhibits substantial geographic and interannual variability. Using the backward trajectory approach, we found that landfalling ARs contribute 21–78% of western CO’s top-decile cool season precipitation. Most of the AR-related precipitation across western CO during the cool-season is sourced from landfalling ARs near Southern California, the Baja Peninsula, and the Pacific Northwest. These results indicate a larger role for ARs in CO weather and hydroclimate than previous research suggests and highlight the importance of AR representation in forecast models to improve predictability of precipitation in CO.