Parametric behaviors of CLUBB in simulations of low clouds in the Community Atmosphere Model (CAM)
Parametric behaviors of CLUBB in simulations of low clouds in the Community Atmosphere Model (CAM)
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DOI：
10.1002/2014ms000405
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发表时间：
2015-09-01
影响因子：
6.8
通讯作者：
Zhou, Tianjun
中科院分区：
文献类型：
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作者：
Guo, Zhun;Wang, Minghuai;Zhou, Tianjun
在本研究中，我们研究了模拟低云对双法线统一云层 (CLUBB) 的 14 个选定可调参数、高阶闭合 (HOC) 方案以及社区大气模型版本 5 (CAM5) 中的 Zhang-McFarlane (ZM) 深对流方案的四个参数的敏感性。采用准蒙特卡罗（QMC）采样方法有效探索高维参数空间，并应用广义线性模型研究模拟云场对可调参数的响应。我们的结果表明，模拟的低云特性（云分数和液态水路径）的方差可以通过所选的可调参数以两种不同的方式来解释：宏观物理本身及其与微观物理的相互作用。首先，与动力和热力学湍流结构和双高斯闭合相关的参数被发现是模拟低云最有影响的参数。参数贡献的空间分布显示出明显的云域依赖性。其次，由于云宏观物理和云微物理之间的耦合，总水方差方程中耗散项的系数有影响。该参数影响云内云水的方差，从而进一步影响微物理过程速率，例如自动转换，并最终影响低云部分。这项研究提高了对与参数不确定性相关的 HOC 行为的理解，并为宏观物理和微观物理的相互作用提供了宝贵的见解。
In this study, we investigate the sensitivity of simulated low clouds to 14 selected tunable parameters of Cloud Layers Unified By Binormals (CLUBB), a higher-order closure (HOC) scheme, and four parameters of the Zhang-McFarlane (ZM) deep convection scheme in the Community Atmosphere Model version 5 (CAM5). A Quasi-Monte Carlo (QMC) sampling approach is adopted to effectively explore the high-dimensional parameter space and a generalized linear model is applied to study the responses of simulated cloud fields to tunable parameters. Our results show that the variance in simulated low-cloud properties (cloud fraction and liquid water path) can be explained by the selected tunable parameters in two different ways: macrophysics itself and its interaction with microphysics. First, the parameters related to dynamic and thermodynamic turbulent structure and double Gaussian closure are found to be the most influential parameters for simulating low clouds. The spatial distributions of the parameter contributions show clear cloud-regime dependence. Second, because of the coupling between cloud macrophysics and cloud microphysics, the coefficient of the dissipation term in the total water variance equation is influential. This parameter affects the variance of in-cloud cloud water, which further influences microphysical process rates, such as autoconversion, and eventually low-cloud fraction. This study improves understanding of HOC behavior associated with parameter uncertainties and provides valuable insights for the interaction of macrophysics and microphysics.