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，这些过程导致在任何给定地点和时间观察到云的复杂结构。目前，气候模式一般将云组分和液态水含量与当地相对湿度或对流降水率联系起来。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，以验证这些参数化，并检验它们对在实地试验中没有直接取样的地区、季节或气候年代的适用性。该项目将利用现有的最佳定期观测数据进行此类分析。这些数据将包括来自国际卫星云气候学项目的7种云“类型”的部分覆盖的卫星估算、来自欧洲中期天气预报中心业务分析的热力学和动力学变量以及来自地面测量的降水标准观测。除了使用先前导出的云类型外，PI还将导出并使用标准压力水平下的云分数的新规格。这些参数在全球大部分地区在季节和年际时间尺度上的变化将用于推导和评估气候和长期预报模式的云参数化中使用的新的和当前的相互关系。

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.