Atmospheric rivers (ARs) are narrow bands of enhanced integrated water vapor transport, modulated by large-scale and synoptic-scale variability. Here, we investigate how ARs and snowpack are shaped by large-scale variability such as arctic oscillation (AO) by examining the synoptic conditions and characteristics of ARs and snowpack in the different phases of AO. Using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data, Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) reanalysis data, and in-situ observation data over the eastern Pacific and western United States. we found that more precipitation is observed in lower latitudes (35° N–45° N) during negative AO months and farther north (north of 45° N) in latitude during positive AO months. These are associated with wavelike synoptic patterns in negative AO months and more straight-line type synoptic patterns in positive AO months. The different phases of AO also modulate the AR characteristics: 2.6% less intense (5.3% more intense) integrated water vapor transport and total precipitation, and 16.0% shorter (21.1% longer) duration of ARs than the climatological mean (1980–2019) for positive AO (negative AO) phase. AR frequency is also higher (~50.4%) than the climatological mean for negative AO phase, but there is no statistically significant difference between either negative AO or positive AO phase, especially in southern California. In addition, the snow water equivalent (SWE) tends to be reduced in the positive AO phase and under high-temperature conditions, especially in recent years (2010s). The similar relationships are found in the early 1990s and 2000s, but their statistical significances are low. Considering that lower atmospheric temperature keeps increasing over the eastern Pacific and the western U.S., and SWE tends to be reduced in the positive AO phase in recent years, SWE may decrease over northern California if the warming condition persists. These findings highlight how the characteristics of local extreme weather can be shaped by large-scale climate variability.
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