A Distinct Atmospheric Mode for California Precipitation. Issue 12 (22nd June 2021) - Chen, Di; Norris, Jesse; Goldenson, Naomi; Thackeray, Chad; Hall, Alex
Abstract: The hydrologic cycle in California is strongly influenced by wet‐season (November–April) precipitation. Here, we demonstrate the existence of an influential mode of North Pacific atmospheric pressure variability that regulates wet‐season precipitation variability over both northern and southern California. This mode, named as the "California precipitation mode" (CPM), is statistically distinct from other well‐known modes of pressure variability such as the Pacific‐North American pattern. In addition to controlling wet‐season mean precipitation, positive days of the CPM coincide with up to 90% of the extreme (>99th percentile) precipitation days and 76% of detected atmospheric rivers (ARs) days, while the negative days correspond with 60% of the dry days. CMIP6 models capture the CPM remarkably well, including its statistical separation from the other well‐known modes of pressure variability. The models also reproduce the CPM's strong association with California wet‐season precipitation, giving confidence in the models' dynamics relating to regional hydrologic extremes. However, the models also exhibit biases in regional hydrologic extremes. The CPM is a useful way to understand the origins of those biases and select the more credible models for further analysis: Models with unrealistically strong gradients in the CPM pressure pattern generally oversimulate larger wet extremes and produce excessively long dry intervals in the historical period. Thus the hydrologic biases can be traced to the particular aspects of North Pacific atmospheric dynamics. Key Points: We identified a distinct mode of atmospheric variability that regulates California's mean and extreme wet‐season precipitation In general, CMIP6 models reproduce the mode and its connections with California precipitation remarkably well Model biases in regional hydrologic extremes can be understood in terms of biases in the magnitude of this mode