Studies in the Climate Dynamics of Moist Processes

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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.