Multiple Equilibria and Self-aggregation of Deep Tropical Convection

热带深对流在没有明显外部影响的情况下的聚集趋势是大气科学中一个尚未解决的重要问题。自我聚合可能是气旋形成的前兆，因此对某些飓风的早期阶段可能很重要。马登-朱利安振荡，这是一个突出的，但难以捉摸的问题，在预测方面，也可能是一个表现的对流自我聚集。本研究的目的是利用云解析模式来研究热带对流中的自聚集现象。研究人员将利用一种新的策略，该策略利用了大域中的自聚集和有限域模拟中的多重平衡之间的类比。了解控制自聚集的基本机制将构成一项重大成就，并可以大大提高大尺度模式的能力，以代表对流和大尺度热带气流之间的相互作用过程。因此，这一努力具有重大而广泛的影响。除了本科生和研究生培训，还有一个工作计划，通过该计划，PI将帮助教育K-12教师气候相关问题。

The tendency for deep tropical convection to aggregate in the absence of any obvious external influences represents an important unsolved problem in the atmospheric sciences. Self-aggregation may serve as a precursor to cyclogenesis, and therefore may be important to the early stages of some hurricanes. The Madden-Julian Oscillation, which represents a prominent but elusive problem in terms of prediction, may also be a manifestation of convective self-aggregation. The goal of the proposed research is to utilize a cloud resolving model to investigate the phenomenon of self-aggregation in tropical convection. The investigators will utilize a novel strategy which exploits the analogy between self-aggregation in large domains and multiple equilibria in limited domain simulations. Understanding the underlying mechanisms which control self-aggregation would constitute a major achievement and could greatly improve the ability of large scale models to represent interaction processes between convection and large-scale tropical flows. Thus this effort has significant broader impacts. In addition to undergraduate and graduate student training, there is a work plan through which the PI will help educate K-12 teachers on climate-related issues.