Observations of Microphysical Properties of Single-Layer Stratocumulus During the Mixed-Phase Arctic Cloud Experiment

McFarquhar, G., University of Oklahoma

Cloud Distributions/Characterizations

Cloud-Aerosol-Precipitation Interactions

Fridlind, A.M., A.S. Ackerman, G.M. McFarquhar, G. Zhang, M.R. Poellot, P.J. DeMott, A.J. Prenni and A.J. Heymsfield, 2007: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment (M-PACE): Part II, Model results. J. Geophys. Res., In press.

Luo, Y., K.-M. Xu., H. Morrison, and G.M. McFarquhar, 2007: Arctic mixed-phase clouds simulated by a cloud-resolving model with ARM observations and sensitivity to microphysics parameterizations. J. Atmos. Sci., In press.

McFarquhar, G.M., G. Zhang, M.R. Poellot, G.L. Kok, R. McCoy, T. Tooman, A. Fridlind, and A.J. Heymsfield, 2007: Ice properties of single layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment (M-PACE): Part I. Observations. J. Geophys. Res., In press.

Morrison, H., J.O. Pinto, J.A. Curry and G.M. McFarquhar, 2007: Sensitivity of modeled arctic mixed-phase stratocumulus to cloud condensation and ice nuclei over regionally-varying surface conditions. J. Geophys. Res., Under review.

Profiles of arctic stratocumulus show: 1) liquid-topped clouds with precipitating ice near base, 2) rei and ice/water number concentration N independent of normalized altitude zn, 3) rew increasing with zn

Profiles of arctic stratocumulus show: 1) liquid-topped clouds with precipitating ice near base, 2) rei and ice/water number concentration N independent of normalized altitude zn, 3) rew increasing with zn

Additional Key Contacts: Ann Fridlind, Hugh Morrison, and Yali Luo

Large atmospheric, oceanic and terrestrial changes are occurring in the Arctic yet complex interactions between sea ice, snow cover, clouds, ocean, and atmosphere are not well understood. The surface energy budget and profiles of radiative heating are very sensitive to the assumed cloud properties. Thus, assumptions made about clouds in large-scale models have a big impact on climate simulations and future projections of climate change. The representation of Arctic clouds in models offers unique challenges because Arctic clouds have a net warming effect on the surface over the course of a year (unlike other clouds), because mixed-phase clouds are hard to model yet ubiquitous during transition seasons in the Arctic and because few datasets describing the microphysical properties of Arctic clouds exist.

To overcome some of these difficulties, an unprecedented data set describing the vertical structure of mixed-phase arctic stratocumulus was acquired during the ARM Climate Research Facility's recent Mixed-Phase Arctic Cloud Experiment (M-PACE) in the fall of 2004. During M-PACE, the University of North Dakota's Citation aircraft executed spiral ascents and descents over Barrow and Oliktok Point, Alaska, and flew ramped ascents and descents in between the sites so that 101 profiles of Arctic stratus clouds were obtained. Data from a full complement of in-situ microphysical probes allowed us to derive the size, shape, and phase distributions over the complete range of possible hydrometeor sizes (1 μm to 10,000 μm). With clouds defined as locations where mass contents were greater than 0.001 g m-3, 71% of the observations were in mixed-phase conditions (defined to be conditions where both ice and liquid occurred within a 5 km horizontal distance), 23% in ice-phase and 6% in liquid-phase. Observations in mixed-phase conditions were dominated by contributions from the liquid, with the liquid mass fraction fl averaging 0.89±0.18 and 75% of cases having fl > 0.9. Even though the liquid dominated the cloud radiative properties, it is still important to understand the role of ice in these clouds because it affects their longevity. Thus, studies were conducted using observations in conjunction with numerical models to identify the physical processes through which these phases co-exist for long time periods and that are hence responsible for the persistence of the Arctic mixed-phase clouds.

To get a database suitable for evaluating model simulations, the distributions of the size, shape, and phase of cloud particles were used to derive vertical profiles of bulk cloud properties (liquid water content or LWC, ice water content or IWC, fraction of liquid or fl equals LWC/(IWC+LWC), effective radius of cloud droplets or rew, effective radius of ice crystals or rei, number concentration of water droplets or Nw, number concentration of ice crystals or Ni) were derived (McFarquhar et al. 2007). Despite the wide variability in the observed clouds, most had a common structure of a liquid dominated top with a greater fraction of ice near base. These trends were quantified as a function of normalized cloud altitude zn, defined to linearly increase from 0 at cloud base to 1 at cloud top. For clouds occurring in a vertically continuous single layer, fl increased with zn averaging 0.96±0.13 near zn=1 and 0.70±0.30 near zn=0. The effective radius of water droplets rew also increased with zn from 6.9±1.8 um near zn=0 to 11.4±2.4 um near zn=1, whereas the effective radius of ice crystals rei (25.2±3.9 um) was nearly independent of zn. The averaged cloud droplet number concentration and concentrations of ice crystals with maximum dimensions greater than 53 um were 43.6±30.5x103 L-1 and 2.8±6.9 L-1, respectively, and nearly independent of zn. Comparing the modeled fields against the observations allowed us to test the physical processes that might be responsible for the formation of ice. The tests have shown that the formation of ice nuclei from drop evaporation residuals or drop freezing during evaporation might explain the ice formation in these clouds (Fridlind et al. 2007). Further tests have shown that the sensitivity of Arctic mixed-phase clouds to changes in aerosols depends in part on the underlying surface conditions (Morrison et al. 2007) and that more complicated ice microphysical parameterization schemes that predict two moments of the size distribution should be used to describe the processes occurring and to provide optimum agreement with observed fields (Luo et al. 2007). The data are now being used to evaluate retrievals of cloud properties and radiative heating profiles derived from the ARM Climate Research Facility ground-based sensors.