Studies in the Climate Dynamics of Moist Processes

决定是否降雨和降雨多少的潮湿过程是气候动力学的中心话题，更好地了解它们的运行方式可能具有重大的实用价值。这种过程通常在云中的小空间尺度和快速时间尺度上运行，但它们发生在对大范围缓慢变化的大气条件的反应中。快速、小规模降水过程对环境条件的依赖提供了希望，即它们的行为至少在统计或平均意义上可以被理解为温度、湿度和发生潮湿过程的地区发生的其他因素变化较慢的函数。该项目致力于从理论上理解这种潮湿过程的统计数据，主要是在温暖热带环境中的对流降水的情况下。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、降水概率的非正常行为、给定级别(由特定地点的总累积降水量定义)的降水事件发生的频率，以及降水性质对干空气夹带到云中的速率等参数的依赖。这项工作的动机来自于PI之前的工作，该工作表明，热带对流降水的行为可以通过重新定标技术来简单地封装，例如通过确定CWV的温度相关值，超过该值的降雨率随着CWV的增加而急剧增加。当降雨量被认为是CWV的函数时，很明显，在很宽的温度范围内，降雨率随CWV的增加遵循相同的曲线，只是与温度有关的偏移量，因此可以通过将降雨率作为CWV与其临界值的函数来考虑温度相关性。PI认为，这种重新定标有助于发展对湿过程行为及其对环境条件的依赖的基本见解。建议中的工作分为四个主题：1)湿过程的快速诊断，特别是降水的开始和终止过程；2)降水过程与对流准平衡动态邻域中的大尺度气流相互作用的理论和分层模拟；3)对湿过程对气候变异性和变化的敏感性评估；以及4)对湿过程的观测约束的发展，以减少模式模拟中的结构不确定性。这项工作包括结合观测和气候模型输出的诊断分析(来自共同体地球系统模型，CESM)，CESM的参数敏感性实验，以及开发和使用简单的随机模型。在一个随机模型中，给定等级的降雨事件的发生频率是作为水汽汇聚到大气柱中的函数来计算的，因此极端事件的概率的变化可以作为大范围气候变异性和变化的函数来确定。这项工作具有更广泛的影响，因为了解降水统计数据(包括极端降水事件和干旱的可能性)如何因环境条件的变化而变化的重要性，例如与厄尔尼诺事件和长期气候变化有关的环境条件的变化。此外，这项工作寻求加深对潮湿过程对环境条件的依赖的理解，以用于开发更好的天气预报和气候预测模型。

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.