Evaluation of Cloud and Precipitation Simulations in CAM6 and AM4 Using Observations Over the Southern Ocean. Issue 2 (5th February 2021) - Zhou, Xiaoli; Atlas, Rachel; McCoy, Isabel L.; Bretherton, Christopher S.; Bardeen, Charles; Gettelman, Andrew; Lin, Pu; Ming, Yi
Abstract: This study uses cloud and radiative properties collected from in situ and remote sensing instruments during two coordinated campaigns over the Southern Ocean between Tasmania and Antarctica in January–February 2018 to evaluate the simulations of clouds and precipitation in nudged‐meteorology simulations with the CAM6 and AM4 global climate models sampled at the times and locations of the observations. Fifteen SOCRATES research flights sampled cloud water content, cloud droplet number concentration, and particle size distributions in mixed‐phase boundary layer clouds at temperatures down to −25°C. The 6‐week CAPRICORN2 research cruise encountered all cloud regimes across the region. Data from vertically pointing 94 GHz radars deployed was compared with radar simulator output from both models. Satellite data were compared with simulated top‐of‐atmosphere (TOA) radiative fluxes. Both models simulate observed cloud properties fairly well within the variability of observations. Cloud base and top in both models are generally biased low. CAM6 overestimates cloud occurrence and optical thickness while cloud droplet number concentrations are biased low, leading to excessive TOA reflected shortwave radiation. In general, low clouds in CAM6 precipitate at the same frequency but are more homogeneous compared to observations. Deep clouds are better simulated but produce snow too frequently. AM4 underestimates cloud occurrence but overestimates cloud optical thickness even more than CAM6, causing excessive outgoing longwave radiation fluxes but comparable reflected shortwave radiation. AM4 cloud droplet number concentrations match observations better than CAM6. Precipitating low and deep clouds in AM4 have too little snow. Further investigation of these microphysical biases is needed for both models. Key Points: CAM6 and AM4 simulate observed cloud properties and compositions fairly well within the variability of observations CAM6 clouds are "too frequent, too bright"; AM4 clouds are "too few, too bright" Cloud droplet number concentration in CAM6 is typically too low; AM4 clouds include too much small ice and too little snow