Studies in the Climate Dynamics of Moist Processes

决定是否下雨和下雨多少的潮湿过程是气候动力学的中心议题，更好地了解它们是如何运作的可能具有重大的实用价值。这种过程通常在云中发现的小空间尺度和快时间尺度上运行，但它们是在大尺度缓慢变化的大气条件下发生的。快速、小尺度降水过程对环境条件的依赖性提供了希望，至少在统计或平均意义上，它们的行为可以被理解为潮湿过程发生地区的温度、湿度和其他因素的缓慢变化的函数。 该项目致力于从理论上理解这种潮湿过程的统计数据，主要是在温暖的热带环境中的对流降水的情况下。需要考虑的具体问题包括降水开始及其与总柱水汽（CWV）的关系、降水概率的非正态行为、给定量级降水事件发生的频率（如在特定位置的总累积降水量所定义），以及降水特性对参数的依赖性，如干燥空气夹带到云中的速率。这项工作的动机来自PI的以前的工作表明，热带对流降水的行为可以简单地封装通过重新缩放技术，例如通过确定一个温度依赖的CWV值，超过该值，降雨率随着CWV的增加而急剧增加。 当降水量被认为是CWV的函数时，很明显，降雨率随CWV的增加在很宽的温度范围内遵循相同的曲线，只有温度依赖的偏移，因此可以通过将降雨率视为CWV与其临界值之比的函数来考虑温度依赖性。 PI认为，这种重新标度可以很好地帮助发展对湿过程的行为及其对环境条件的依赖性的基本认识，建议中的工作被组织成四个主题：1）湿过程的快速过程诊断，特别是降水的开始和结束过程; 2）对流准平衡动力邻域内大尺度气流与降水过程相互作用的理论和层次模式;（3）有助于评估潮湿过程对气候变率和变化的敏感性;（4）发展对潮湿过程的观测限制，以减少模式模拟中的结构不确定性。 这项工作包括对观测和气候模式输出（来自共同体地球系统模式CESM）的诊断分析、CESM中的参数敏感性实验以及简单随机模式的开发和使用。 在一个随机模型中，给定量级的降雨事件发生的频率被计算为大气柱中水分收敛的函数，因此极端事件概率的变化可以被确定为大尺度气候变率和变化的函数。这项工作具有更广泛的影响，因为理解降水统计数据的重要性，包括极端降水事件和干旱的可能性，由于环境条件的变化而变化，例如与厄尔尼诺事件和长期气候变化有关的环境条件。 此外，这项工作还试图了解潮湿过程对环境条件的依赖性，以便开发更好的天气预测和气候预测模型。

The moist processes which determine whether and how much it rains are central topics in climate dynamics, and a better understanding of how they operate could have substantial practical value. Such processes generally operate on the small spatial scales and fast time scales found within clouds, yet they occur in response to large-scale slowly varying atmospheric conditions. The dependence of the fast, small-scale precipitation process on ambient conditions offers hope that their behavior can be understood, at least in a statistical or average sense, as a function of slower variations of temperature, moisture, and other factors occurring over the regions in which the moist processes occur. This project strives to develop a theoretical understanding of the statistics of such moist processes, primarily in the case of convective precipitation in warm tropical environments. Specific issues to be considered include precipitation onset and its relationship to total column water vapor (CWV), non-normal behavior of precipitation probability, the frequency of occurrence of precipitation events of a given magnitude (as defined by total accumulated precipitation at a specific location), and the dependence of precipitation properties on parameters like the rate of entrainment of dry air into clouds.Motivation for the work comes from the PI's previous work showing that the behavior of tropical convective precipitation can be simply encapsulated through rescaling techniques, for example by identifying a temperature-dependent value of CWV beyond which rain rate increases dramatically with increasing CWV. When precipitation is considered as a function of CWV, it becomes apparent that the increase in rain rate with CWV follows the same curve over a wide range of temperatures, only with a temperature-dependent offset, so that the temperature dependence can be factored out by considering rain rate as a function of the ratio of CWV to its critical value. The PI argues that this sort of rescaling can be quite helpful for developing fundamental insights into the behavior of moist processes and their dependence on ambient conditions.The work in the proposal is organized into four themes: 1) fast-process diagnostics for moist processes, especially for precipitation onset and termination processes; 2) theory and hierarchical modeling for precipitation processes interacting with the large-scale flow in the dynamic neighborhood of convective quasi-equilibrium; 3) contributions to assessing moist process sensitivity to climate variability and change; and 4) development of observational constraints on moist processes to reduce structural uncertainty in model simulations. The work includes a combination of diagnostic analysis of observations and climate model output (from the Community Earth System Model, CESM), parameter sensitivity experiments in CESM, and the development and use of simple stochastic models. In one stochastic model the frequency of occurrence of rainfall events of a given magnitude is computed as a function of moisture convergence into the atmospheric column, so that changes in the probability of extreme events can be determined as a function of large-scale climate variability and change.The work has broader impacts due to the importance of understanding how the statistics of precipitation, including the likelihood of extreme precipitation events and droughts, varies due to changes in ambient conditions such as those that are associated with El Nino events and secular climate change. In addition, the work seeks to develop understanding of the dependence of moist processes on ambient conditions that can be used to develop better models for weather predictions and climate projections.