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What does a CRM need to model cloud-aerosol interaction in mixed-phase clouds? Mikhail Ovtchinnikov 1 , Jiwen Fan 1 , Jennifer Comstock 1 , Sally McFarlane 1 , Alexander Khain 2 , Vaughn Phillips 3
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What does a CRM need to model cloud-aerosol interaction in mixed-phase clouds? Mikhail Ovtchinnikov1, Jiwen Fan1, Jennifer Comstock1, Sally McFarlane1, Alexander Khain2, Vaughn Phillips3 1 Pacific Northwest National Laboratory, Richland , WA, USA; 2 The Hebrew University of Jerusalem, Jerusalem, Israel; 3 University of Hawaii, Honolulu, USA Total liquid water Total ice water Total liquid water Total ice water Motivation Mixed-phase clouds are wide-spread and long-lived in the Arctic. Their response to changing aerosol forcing is not well understood. Second indirect effect is expected to be stronger than in warm cloud. Cloud-resolving models (CRMs) need to simulate mixed-phase cloud properties correctly to provide a reliable information on cloud-aerosol interaction. Cloud properties 12-hour 2D simulations, dx=100m, dz=20m, dt=2s Summary The mixed-phase clouds are not uniform; spatial structure is important. Spectral bin microphysics scheme provides a tool to test uncertain nucleation mechanisms but … … global models can only handle a two-moment scheme, at best. Models with prognostic IN are rare but needed (Fridlind et al. 2007) 1MOM • Nearly equal split between cloud liquid and cloud ice; • Liquid and ice amounts in cloud are diagnosed and therefore highly correlated in time and in space. • What is needed ? • IN concentrations measured by CFDC (condensation freezing and deposition modes) are often comparable to observed ice particle (IP) concentrations. • IN are depleted quickly by precipitation. • Therefore, • a prognostic IN concentration(s) is needed, and • another nucleation mechanism and/or • IN source is needed to maintain the IP concentration and the mixed-phase structure of clouds for prolonged period of time. Aerosol • Approach • Use the System for Atmospheric Modeling (SAM) with three microphysics options: • standard bulk (1MOM): predicted non-precipitating and precipitating condensed water mixing ratios; saturation adjustment-based condensation; temperature dependent liquid-ice partitioning (Khairoutdinov and Randall, 2003) • limited 2-moment bulk (2MOM): predicted number and mass mixing ratios for cloud liquid and ice and mass mixing ratios for rain, snow, and graupel; linearized supersaturation prediction scheme (Phillips et al, 2007) • spectral bin microphysics (SBM): predicted size distributions for CCN, cloud droplets, 3 types of ice crystals, snow, graupel, and hail (Khain et al. 2004); added CCN regeneration scheme, prognostic ice nucleus number mixing ratio is available uncertain Cloud Condensation Nuclei Total liquid water 2MOM Ice Nuclei IN recycling and pre- activation evaporation IN ? Total ice water Cloud droplets Ice Particles evaporative freezing ? Total liquid water • Negligible cloud ice amount; • Most ice is in precipitation; • Liquid and ice amounts in cloud are anti-correlated. Total ice water SBM LWP and IWP Mixed-Phase Arctic Cloud Experiment (M-PACE) October 9-10, 2004 case (McFarquhar et al. 2007). Both liquid and ice water paths are highly sensitive to the treatment of microphysics and in case of 2MOM and SBM to the concentrations of CCN and IN. REFERENCES Fridlind et al (2007), J. Geophys. Res., doi:10.1029/2007JD008646. Khain, A. P., A. Pokrovsky, M. Pinsky, A. Seifert, and V. Phillips (2004), J. Atmos. Sci., 61, 2963–2982. Khairoutdinov and Randall (2003), J. Atmos. Sci., 60, 607–625. McFarquhar et al (2007), J. Geophys. Res., doi:10.1029/2007JD008633. Phillips V. T. J., L. Donner, and S. T. Garner (2007), J. Atmos. Sci., 64, 738–761. • Comparable amount of ice in the form of primary ice crystals and snow; • Complex spatial structure with no unique liquid-ice relation. Acknowledgments This research was supported by the DOE Atmospheric Radiation Measurements Program (ARM) and by the PNNL Directed Research and Development (LDRD) program as part of the Aerosol Climate Initiative. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC02-05CH11231. PNNL is operated by Battelle for the U.S. Department of Energy. LWP and IWP are important but do not tell the whole story. MODEL AIRCRAFT OBS