Examining Cirrus Cloud Microphysical Properties and Aerosol Indirect Effects Using In-Situ Observations of NSF and NASA Campaigns and the Community Atmosphere Model Version 6 (CAM6)

Abstract:

With a widespread global coverage, cirrus clouds remain an important cloud type for climate studies. Key components of cirrus cloud microphysical properties, such as ice crystal number concentration (Ni) and mean number-weighted diameter (Di), control the sign of cirrus clouds’ radiative effect. However, correctly quantifying and simulating cirrus radiative effects remains an elusive task. Mitigating these uncertainties lies in identifying the individual effects of thermodynamics conditions (i.e., temperature and relative humidity with respect to ice (RHi), dynamic conditions (i.e., vertical velocity (w)), nucleation mechanisms, and aerosol number concentrations (Na) and size distributions on cirrus cloud microphysical properties. Using a comprehensive dataset of seven NSF flight campaigns we investigate the effects of temperature, RHi, w, and Na₁₀₀ and Na₅₀₀ (Na > 100 nm and > 500 nm, respectively) on ice particles larger than 62.5 µm (Patnaude et al., 2020). Regional variabilities of cirrus cloud microphysical properties are quantified for tropics, midlatitudes and polar regions in the Northern and Southern Hemispheres. Continuing on that work, we expand our in situ dataset to include five NASA flight campaigns and investigate aerosol indirect effects on cirrus clouds using a wider ice particle size range (> 3 µm). In addition, using carbon monoxide (CO) and black carbon (BC) concentrations we investigate the impacts of anthropogenic emissions on cirrus clouds based on the spatial correlations between cirrus microphysical properties and these chemical tracers. These results are compared with the NCAR CAM6 model and the model is found to underestimate aerosol indirect effects on ice microphysical properties, particularly in the Northern Hemisphere midlatitudes. These model biases underscore the continued effort in evaluating and improving climate model parameterizations on cirrus clouds.