Evaluation of the CAM6 Climate Model Using Cloud Observations at McMurdo Station, Antarctica

• Published in: Journal of Geophysical Research: Atmospheres
• Main Authors: Yip, Jackson, Diao, Minghui, Barone, Tyler, Silber, Israel, Gettelman, Andrew
• Format: Text
• Language: unknown
• Published: SJSU ScholarWorks 2021
• Subjects: Antarctica; AWARE campaign; CAM model; cloud fraction; cloud phase; polar clouds; Meteorology and Climate Science; McMurdo Station; Antarc*
• Online Access: https://scholarworks.sjsu.edu/faculty_rsca/2560; https://doi.org/10.1029/2021JD034653; https://scholarworks.sjsu.edu/context/faculty_rsca/article/3559/viewcontent/EvaluationoftheCAM6ClimateModel.pdf

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.