Performance of McRAS-AC in the GEOS-5 AGCM: aerosol-cloud-microphysics, precipitation, cloud radiative effects, and circulation - Y. C. Sud; D. Lee; L. Oreopoulos; D. Barahona; A. Nenes; M. J. Suarez (Geoscientific Model Development, 2013)
A revised version of the Microphysics of clouds with Relaxed Arakawa-Schubert and Aerosol-Cloud interaction scheme (McRAS-AC) including, among others, a new ice nucleation parameterization, is implemented in the GEOS-5 AGCM. Various fields from a 10-yr-long integration of the AGCM with McRAS-AC are compared with their counterparts from an integration of the baseline GEOS-5 AGCM, as well as satellite observations. Generally McRAS-AC simulations have smaller biases in cloud fields and cloud radiative effects over most of the regions of the Earth than the baseline GEOS-5 AGCM. Two systematic biases are identified in the McRAS-AC runs: one is underestimation of cloud particle numbers around 40° S–60° S, and one is overestimate of cloud water path during the Northern Hemisphere summer over the Gulf Stream and North Pacific. Sensitivity tests show that these biases potentially originate from biases in the aerosol input. The first bias is largely eliminated in a test run using 50% smaller radius of sea-salt aerosol particles, while the second bias is substantially reduced when interactive aerosol chemistry is turned on. The main weakness of McRAS-AC is the dearth of low-level marine stratus clouds, a probable outcome of lack of explicit dry-convection in the cloud scheme. Nevertheless, McRAS-AC largely simulates realistic clouds and their optical properties that can be improved further with better aerosol input. An assessment using the COSP simulator in a 1-yr integration provides additional perspectives for understanding cloud optical property differences between the baseline and McRAS-AC simulations and biases against satellite data. Overall, McRAS-AC physically couples aerosols, the microphysics and macrophysics of clouds, and their radiative effects and thereby has better potential to be a valuable tool for climate modeling research.