Evaluation of the CAM6 Climate Model Using Cloud Observations at McMurdo Station, Antarctica
Evaluation of the CAM6 Climate Model Using Cloud Observations at McMurdo Station, Antarctica
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DOI：
10.1029/2021jd034653
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发表时间：
2021-08
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影响因子：
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通讯作者：
J. Yip;M. Diao;Tyler R. Barone;I. Silber;A. Gettelman
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文献类型：
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作者：
J. Yip;M. Diao;Tyler R. Barone;I. Silber;A. Gettelman
利用南极麦默多站的观测资料和共同体大气模式第6版（CAM 6）的模拟资料，对云的特征及其热力条件进行了对比分析。Ka波段天顶雷达（KAZR）和高光谱分辨率激光雷达（HSRL）反演被用作云分数和云相识别的基础。以12小时为增量释放的无线电探空仪提供了评估模拟热力学条件的大气廓线。我们的研究结果表明，CAM6模拟一致高估（低估）云分数以上（以下）3公里，在一年的四个季节。通过总云中样本进行归一化，在云分数高于0.6时，模型低估了冰和混合相的出现频率，高估了液相频率，而在云分数低于0.6时，模型高估了冰相频率，低估了含液相频率。云分数偏差与相对湿度（RH）的并发偏差密切相关，即2 km以上（以下）的高（低）RH偏差。当RH的绝对偏差减小时，正确模拟冰和含液体相的频率增加。云分数偏差也表现出与RH偏差的正相关。水汽混合比偏差是RH偏差的主要贡献者，因此可能是控制云偏差的关键因素。对麦默多站CAM6模拟中云特征表示的明显不足的诊断为改进其中的管理模式物理带来了新的见解。
A comparative analysis between observational data from McMurdo Station, Antarctica and the Community Atmosphere Model version 6 (CAM6) simulation is performed focusing on cloud characteristics and their thermodynamic conditions. Ka‐band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) retrievals are used as the basis of cloud fraction and cloud phase identifications. Radiosondes released at 12‐h increments provide atmospheric profiles for evaluating the simulated thermodynamic conditions. Our findings show that the CAM6 simulation consistently overestimates (underestimates) cloud fraction above (below) 3 km in four seasons of a year. Normalized by total in‐cloud samples, ice and mixed phase occurrence frequencies are underestimated and liquid phase frequency is overestimated by the model at cloud fractions above 0.6, while at cloud fractions below 0.6 ice phase frequency is overestimated and liquid‐containing phase frequency is underestimated by the model. The cloud fraction biases are closely associated with concurrent biases in relative humidity (RH), that is, high (low) RH biases above (below) 2 km. Frequencies of correctly simulating ice and liquid‐containing phase increase when the absolute biases of RH decrease. Cloud fraction biases also show a positive correlation with RH biases. Water vapor mixing ratio biases are the primary contributor to RH biases, and hence, likely a key factor controlling the cloud biases. This diagnosis of the evident shortfalls of representations of cloud characteristics in CAM6 simulation at McMurdo Station brings new insight in improving the governing model physics therein.