An emulator approach to stratocumulus susceptibility
An emulator approach to stratocumulus susceptibility
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DOI：
10.5194/acp-19-10191-2019
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发表时间：
2019-08-13
影响因子：
6.3
通讯作者：
Feingold, Graham
中科院分区：
文献类型：
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作者：
Glassmeier, Franziska;Hoffmann, Fabian;Feingold, Graham
气溶胶-云相互作用的气候相关性取决于云的辐射效应对云滴数N和液态水路径LWP的敏感性。我们推导了云分数Cf、云反照率A(C)和相对云辐射效应rCRE=Cf的依赖关系。来自159个夜间层积云大涡模拟的N和LWP的A(C)。这些模拟对温度、湿度、边界层高度和气溶胶浓度的初始条件有所不同，但对地表通量和下沉的边界条件是相同的。我们的方法是基于高斯过程仿真，这是一种与机器学习相关的统计技术。我们成功地构建了模拟器，对于给定的N和LWP值，可以准确地预测Cf、A(C)和rCRE的模拟值。模拟器的偏导数INRCRE/偏导数INN和偏导数INRCRE/偏导数InLWP覆盖了非毛毛细雨、全阴天和有破碎云层的毛毛雨区域。重现了仅限于非毛毛雨区域的理论结果。磁化率偏导数lnrCRE/偏导数lnN反映了云辐射效应对云量的强烈敏感性，而磁化率偏导数lnrCRE/偏导数lnLWP描述了云量对云反照率的影响，与云量无关。我们基于仿真的方法提供了一个强大的工具，用于在一个简单的框架中汇总复杂的数据，该框架可以捕获各种状态下云场属性的敏感性。
The climatic relevance of aerosol-cloud interactions depends on the sensitivity of the radiative effect of clouds to cloud droplet number N, and liquid water path LWP. We derive the dependence of cloud fraction CF, cloud albedo A(C), and the relative cloud radiative effect rCRE = CF . A(C) on N and LWP from 159 large-eddy simulations of nocturnal stratocumulus. These simulations vary in their initial conditions for temperature, moisture, boundary-layer height, and aerosol concentration but share boundary conditions for surface fluxes and subsidence. Our approach is based on Gaussian-process emulation, a statistical technique related to machine learning. We succeed in building emulators that accurately predict simulated values of CF, A(C), and rCRE for given values of N and LWP. Emulator-derived susceptibilities partial derivative lnrCRE/partial derivative lnN and partial derivative lnrCRE/partial derivative lnLWP cover the nondrizzling, fully overcast regime as well as the drizzling regime with broken cloud cover. Theoretical results, which are limited to the nondrizzling regime, are reproduced. The susceptibility partial derivative lnrCRE/partial derivative lnN captures the strong sensitivity of the cloud radiative effect to cloud fraction, while the susceptibility partial derivative lnrCRE/partial derivative lnLWP describes the influence of cloud amount on cloud albedo irrespective of cloud fraction. Our emulation-based approach provides a powerful tool for summarizing complex data in a simple framework that captures the sensitivities of cloud-field properties over a wide range of states.