Relationships Between Cloud and Thermodynamic Variables

气候变化理论中一组极其重要的不确定性是与云/辐射/热力学/动力相互作用有关的不确定性。在这些不确定性中，最麻烦的是那些与大规模热力学过程的性质有关的不确定性，这些热力学过程导致在任何给定的地点和时间观测到的云的复杂结构。目前，气候模型通常将云层分数和液态水含量与当地相对湿度或对流降雨率联系起来。所使用的公式是基于理论结果和一些短期详细的现场试验。需要对全球长期观测进行分析，以验证这些参数，并测试其对区域、季节或气候时期的适用性，这些区域、季节或气候时期没有在实地试验中直接抽样。这些数据将包括国际卫星云气候学项目得出的七种云“类型”的卫星覆盖率估计值、欧洲中期天气预报中心业务分析得出的热力学和动力学变量以及地面测量的降水标准观测值。除了使用以前导出的云类型外，PI还将导出并使用标准气压水平下的云分数的新规范。全球大部分地区这些参数在季节和年际时间尺度上的变化将被用来得出新的相互关系，并评估气候云参数和长期预报模式中使用的当前相互关系。

An extremely important set of uncertainties in climate change theories are those associated with cloud/radiative/thermodynamic/ dynamic interactions. Among the most troublesome of these uncertainties are those associated with the nature of the large- scale thermodynamics processes leading to the observed complex structure of clouds at any given location and time. Currently, climate models generally relate cloud fractions and liquid water contents to local relative humidity or convection precipitation rates. The formulas used are based on the results of theory and a few short-term detailed field experiments. Analyses of global, long-term observations are needed to verify these parameterizations and to test their applicability to regions, seasons or climatic eras, which were not directly sampled in the field experiments This project will carry out such analyses with the best available regularly observed data. These data will consist of satellite derived estimates of fractional cover of seven cloud 'types' derived from the International Satellite Cloud Climatology Project, thermodynamics and dynamics variables from the operational analyses of the European Centre for Medium-Range Weather Forecasting and standard observations of precipitation form surface measurements. In addition to using the previously derived cloud types, the PI will derive and utilize a new specification of cloud fraction at standard pressure level. Variations of these parameters for much of the globe over seasonal and interannual time scales will be used to derive new and assess current interrelationships used in the cloud parameterizations of climate and long-range forecast models.