Sensitivity of seasonal Snowfall Attribution to Atmospheric Rivers and Their Reanalysis-Based Detetcion

We characterize the sensitivity of atmospheric river (AR)-derived seasonal snowfall estimates to their atmospheric reanalysis-based detection over Sierra Nevada, USA. We use an independent snow data set and the ARs identified with a single detection method applied to multiple atmospheric reanalyses of varying horizontal resolutions, to evaluate orographic relationships and contributions of individual ARs to the seasonal cumulative snowfall (CS). Spatial resolution differences have relatively minor effects on the number of ARs diagnosed, with higher-resolution data sets identifying four more AR days per year, on average, during the 1985-2015 winters. However, this can lead to similar to 10% difference in AR attribution to the mean domain-wide seasonal CS and differences up to 47% snowfall attribution at the seasonal scale. We show that identifying snow-bearing ARs provides more information about the seasonal CS than simply knowing how many ARs occurred. Overall, we find that higher-resolution atmospheric reanalyses imply greater attribution of seasonal CS to ARs. Plain Language Summary While it is known that elongated moisture-rich atmospheric features, known as atmospheric rivers (ARs), play an important role in the water resources of the mountainous western United States, less is known about how the atmospheric data sets used to diagnose the presence of ARs influence the amount of AR-attributed snowfall estimated each winter. Nonetheless, this missing information can be important for managing water resources and improving seasonal snowfall forecasts for areas depending on AR-derived snowfall. We show that using a single AR detection algorithm, applied to multiple atmospheric reanalyses to identify ARs, higher-resolution atmospheric reanalyses diagnose up to four more ARs per winter implying 10% greater AR attribution to the mean seasonal snowfall across Sierra Nevada, USA. Understanding how different atmospheric reanalyses play a role in our interpretation of AR impacts for hydrologic studies and water resources management, as we investigate here, is important especially as more ARs are projected to occur in a warmer future atmosphere.