11:00 〜 13:00
[AAS03-P02] A Hierarchical Dissection of the Multi-Scale Forcing on the Springtime Mesoscale Convective Systems in the United States
キーワード：mesoscale convective system, multi-scale processes, large-scale circulation, atmospheric instability, frontal system, extreme precipitation
During boreal warm season (March-August), in an environment lack of distinct airmass boundaries, an Mesoscale Convective System (MCS) often forms over the central US where cumulonimbus clouds aggregate, inducing a mesoscale “layered” overturning circulation typically characterized by lines of intense convective cells followed by a broader zone of stratiform precipitation. These storms account for about 30-70% of total warm season rainfall and an even greater portion of extreme rainfall in these regions, becoming the single most important factor that modulates variabilities in regional water and energy cycle and determines the occurrence frequency and magnitude of hydrological extremes. Here, based upon a new MCS dataset created through feature (brightness temperature) tracking, a hierarchical dissection of the multi-scale forcing of the springtime MCS over US is conducted to reveal distinct hemispheric-scale circulation anomalies prior to MCS geneses as well as a variety of local dynamic and thermodynamic forcing (frontal lifting, low-level jet, moisture convergence etc). Five types of large-scale forcing are identified, all of which lead to pronounced troughs over western US and are dynamically either locally growing modes or remotely forced through downstream energy propagation from the North Pacific. Local forcing analysis suggests that frontal lifting induced potential instability and moisture convergence driven by low level jets collectively determine the precise location of MCS geneses, given a similar large-scale forcing. Variability and trend analysis indicate that the recently observed increase of US MCS frequency is largely associated with a positive trend of the Pacific Decadal Oscillation (PDO). The implications of the findings for MCS prediction and attribution of MCS biases in global climate models are discussed.