Abstract

Atmospheric rivers (ARs), referring to long and narrow filamentary bands of intense water vapor transport in the atmosphere, can cause extreme precipitation, floods, and drought events. Their variability has been linked to various climate modes, such as El Nino/Southern Oscillation, Pacific Decad... Show moreAtmospheric rivers (ARs), referring to long and narrow filamentary bands of intense water vapor transport in the atmosphere, can cause extreme precipitation, floods, and drought events. Their variability has been linked to various climate modes, such as El Nino/Southern Oscillation, Pacific Decadal Oscillation, and Pacific-North America pattern. Understanding and improving simulation of this linkage can provide the potential to predict ARs at subseasonal-to-decadal timescales. Up to now, the extent to which climate model simulations of ARs are dependent on model resolutions has not been fully assessed. Here, we compare and evaluate ARs in a pair of high-resolution (HR) and a low-resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results show that within this CESM framework, climatological AR strength and associated precipitation are severely underestimated by LR compared to the observations, and their relationships with major modes of climate variability are also poorly reproduced. These deficiencies in LR are alleviated to a significant extent in HR. Using a linear regression analysis, we show that HR is able to capture with high fidelity the observed relationships between ARs and major climate modes in the Northern Hemisphere at seasonal-to-decadal time scales. These results suggest that the use of HR CESM may potentially lead to an improved forecast skill of ARs at season-to-decadal time scales. Show less