Exploring the Linkages among Mid-Latitude Dynamics, Clouds, and Climate Sensitivity

这项研究的目标是更好地了解中纬度云与观测中的动力学之间的联系。将研究为什么这种联系在一些全球气候模型中得到了很好的体现，而在另一些模型中却没有得到很好的体现。还将审查这些过程对模式气候敏感性和未来气候预测的影响。在该项目的第一部分，将根据南北半球中纬度海洋上的气旋和反气旋中心合成观测到的和模式云的辐射特性，以检查与模式偏差和未来气候预测中的不确定性有关的天气系统部门。在该项目的第二部分，将使用两个版本的共同体大气模式进行理想化的模式实验，这两个版本对南大洋云的辐射特性有非常不同的表示。在这两个模型版本中，将在启用和禁用云的辐射特性的情况下进行四倍二氧化碳实验，以探索不同的云辐射效应在控制南半球中纬度急流对温室气体强迫增加的反应中所起的作用。通过更好地了解观测中的云-辐射-动力学联系以及它们在模型中是如何被曲解的，拟议的项目不仅可以提高关于云在气候系统中的作用的理论知识，而且还可以对云参数提供额外的约束，这将有助于减少未来模型预测中的不确定性。

The goal of this research is to better understand the connection between mid-latitude clouds and dynamics in observations. The reasons for why the such linkages are well-represented in some global climate models but not in others will be studied. The implications of these processes on model climate sensitivity and future climate projections will also be examined. In the first part of the project, observed and model cloud radiative properties will be composited with respect to the centers of cyclones and anticyclones over the mid-latitude oceans of the Northern and Southern Hemispheres, to examine in the sectors of weather systems linked to model biases and uncertainties in future climate projections. In the second part of the project, idealized model experiments will be performed using two versions of the Community Atmosphere Model with very different representations of Southern Ocean cloud radiative properties. In both model versions, quadrupled carbon dioxide experiments will be run with the radiative properties of the clouds enabled and disabled, in order to explore the role of varying cloud-radiative effects in governing the response of the Southern Hemisphere mid-latitude jet to increased greenhouse gas forcing. By better understanding cloud-radiative-dynamical connections in observations and how they are misrepresented in models, the proposed project not only can improve theoretical knowledge of the role of clouds in the climate system, but also can provide additional constraints on cloud parameterizations that will help to reduce uncertainty in future model projections.