Accurate representation of cloud phase partitioning is critical for understanding the cloud feedback to climate change, but the supercooled liquid fraction is often underestimated in global climate models, in part due to the assumption of homogeneous distributions of hydrometeors in mixed-phase clouds. In this study, we take into account the heterogeneous liquid-ice mixing in modeling the ice depositional growth using airborne in situ measurements. The impact of heterogeneous liquid-ice mixing on the Wegener-Bergeron-Findeisen process is parameterized as the fraction of ice that is mixed with liquid water, which is a function of liquid-ice mixing homogeneity and liquid fraction. The liquid-ice mixing homogeneity, quantified using the information entropy theory, is parameterized using the total condensed water content and temperature. With this observationally constrained parameterization incorporated in the Community Atmospheric Model version 6, the modeled cloud phase partitioning and cloud radiative forcing are improved.

中文翻译：

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在 CAM6 中通过 Wegener-Bergeron-Findeisen 过程对冰生长进行建模中参数化非均相液态冰混合


准确表示云相分区对于理解云对气候变化的反馈至关重要，但在全球气候模型中，过冷液体分数经常被低估，部分原因是假设水汽凝结体在混合相云中分布均匀。在这项研究中，我们在使用空气原位测量对冰沉积生长进行建模时考虑了非均质液态冰混合。非均相液态冰混合对 Wegener-Bergeron-Findeisen 过程的影响参数化为冰与液态水混合的分数，这是液态冰混合均匀性和液体分数的函数。使用信息熵理论量化的液态-冰混合均匀性使用总冷凝水含量和温度进行参数化。通过将这种观测约束参数化纳入社区大气模型版本 6 中，建模的云相位分区和云辐射强迫得到了改进。