Evaluation of Climate Simulations Using Observations of Clouds at McMurdo Station, Antarctica

A comparative analysis between McMurdo Station, Antarctica observational data and global climate model simulation is performed with respect to cloud microphysical and macrophysical properties. Using a suite of remote sensing instrumentation and radiosonde soundings, a comprehensive dataset of thermodynamic and cloud properties was collected encompassing an entire year of observed meteorological conditions. For the analysis of observations, the method defined in Silber et al., 2018 was used as a basis to derive cloud microphysical (e.g. ice water content, ice water path, phase, liquid water path) and macrophysical (e.g. hydrometeor fraction) properties using the Ka-Band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) located at the research station. Following this step, variables were quality controlled using Microwave Radiometer (MWR) and Cielometer (cloud base height) returns collected at the station. The observational data were then regridded to CAM6 pressure levels allowing for comparison on equal spatial scales to model simulation. The presented CAM6 simulation was nudged towards reanalysis temperature and wind fields (Modern-Era Retrospective analysis for Research and Application version 2 (MERRA-2)). Model biases were then analyzed for macrophysical (cloud fraction), microphysical (phase, ice water path, liquid water path), and thermodynamic (water vapor mixing ratio, temperature, and relative humidity (RH)) variables. Evidence suggests that when the presented nudged CAM6 simulation underestimates or overestimates cloud fraction, model thermodynamic profile also exhibits large deviations from observed RH. Using the method defined in Diao et al. (2014), RH was decomposed into contributing differences in temperature or water vapor mixing ratio, where it was seen that biases in water vapor mixing ratio are the primary contributing variable to RH model bias. With the overarching goal of diagnosing the shortfalls of cloud macrophysical and microphysical model parameterization, this comparative analysis brings new insight in improving the representations of clouds at McMurdo Station in CAM6.