An Investigation of Ice Production Mechanisms in Small Cumuliform Clouds Using a 3D Model with Explicit Microphysics. Part I: Model Description

Ovtchinnikov, Mikhail; Kogan, Yefim L.

doi:10.1175/1520-0469(2000)057<2989:aioipm>2.0.co;2

Cited by 58 publications

(52 citation statements)

References 74 publications

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“…This formula reveals much less temperature dependence of active IN than BIGG. Therefore the ice production from this scheme is much less effective [Ovtchinnikov and Kogan, 2000;Khain et al, 2000].…”

Section: Homogeneous Nucleation and Immersion-freezing Parameterizationsmentioning

confidence: 99%

Tropical anvil characteristics and water vapor of the tropical tropopause layer: Impact of heterogeneous and homogeneous freezing parameterizations

Fan¹,

Comstock²,

Ovchinnikov³

et al. 2010

J. Geophys. Res.

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[1] Two isolated deep convective clouds (DCCs) that developed in clean-humid and polluted-dry air masses, observed during the Tropical Pacific Warm Pool International Cloud Experiment (TWP-ICE) and U.K. Aerosol and Chemical Transport in Tropical Convection (ACTIVE) campaigns, respectively, are simulated using a three-dimensional cloud-resolving model with size-resolved aerosol and cloud microphysics. We examine the impacts of different homogeneous and immersion freezing parameterizations on the anvil characteristics and the water vapor content (WVC) in the tropical tropopause layer (TTL) for the two DCCs that developed in contrasting environments. The modeled cloud properties such as liquid/ice water path and precipitation generally agree with the available radar and satellite retrievals and in situ aircraft measurements. We find that anvil microphysical properties such as ice number concentration and ice effective radius (r ei ) are sensitive to the homogeneous freezing parameterizations (HomFPs) under both the clean-humid and polluted-dry conditions, while upper level convection and WVC in the TTL air are only sensitive to HomFPs under the polluted-dry condition. Specifically, the cloud anvil with the Koop et al. (2000) relative humidity dependent scheme has up to 50% and 70% lower ice number than those with other schemes (temperature dependent) for the clean-humid and polluted-dry cases, respectively. Consequently, the r ei is increased by 20-30 mm in both cases. As a result, extinction coefficient of cloud anvils is reduced by over 30%. Anvil size and evolution are also much affected by HomFPs. Higher immersion-freezing rate (Bigg, 1953) leads to a stronger convective cloud due to larger latent heat release resulted from much higher freezing rates, with larger ice water path but less precipitation in both humid and dry conditions. Consequently, the domain-averaged homogeneous freezing rates are enhanced by over 15%. Also, the higher immersion-freezing rate results in over 1.5 times higher ice number concentrations, much reduced r ei in the cloud anvil, and larger anvil size. The moistening effect of deep convection on the TTL clear air is very significant, with increases of a few times relative to the WVC before convection under both humid and dry conditions. Different HomFPs does not make much difference in WVC and upper level convection in the clean-humid case, but in the polluted-dry case, the HomFPs with lower nucleation rates predict about 25% lower WVC relative to the HomFPs with higher nucleation rates. Under both humid and dry conditions, the Bigg (1953) immersion freezing predicts about 25% higher WVC relative to the Vali (1975) parameterization, due to stronger transport and the larger anvil area in the domain.

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Section: Homogeneous Nucleation and Immersion-freezing Parameterizationsmentioning

confidence: 99%

Tropical anvil characteristics and water vapor of the tropical tropopause layer: Impact of heterogeneous and homogeneous freezing parameterizations

Fan¹,

Comstock²,

Ovchinnikov³

et al. 2010

J. Geophys. Res.

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“…There are many BRM cloud models that are "warm" rain models (e.g., Cotton, 1972a;Ogura and Takahashi, 1973;Clark, 1973;Tzvion et al, 1989;Bott et al, 1990;Kogan,4748 I. Sednev et al: Simulating mixed-phase Arctic stratus clouds Ovtchinnikov and Kogan, 2000a). In these models only one type of solid hydrometeors that is ice crystals is considered or the bins with the smallest ice sizes are assigned to ice crystals while the rest of bins are interpreted as graupel.…”

Section: Introductionmentioning

confidence: 99%

Simulating mixed-phase Arctic stratus clouds: sensitivity to ice initiation mechanisms

2009

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Abstract. The importance of Arctic mixed-phase clouds on radiation and the Arctic climate is well known. However, the development of mixed-phase cloud parameterization for use in large scale models is limited by lack of both related observations and numerical studies using multidimensional models with advanced microphysics that provide the basis for understanding the relative importance of different microphysical processes that take place in mixed-phase clouds. To improve the representation of mixed-phase cloud processes in the GISS GCM we use the GISS single-column model coupled to a bin resolved microphysics (BRM) scheme that was specially designed to simulate mixed-phase clouds and aerosol-cloud interactions. Using this model with the microphysical measurements obtained from the DOE ARM Mixed-Phase Arctic Cloud Experiment (MPACE) campaign in October 2004 at the North Slope of Alaska, we investigate the effect of ice initiation processes and BergeronFindeisen process (BFP) on glaciation time and longevity of single-layer stratiform mixed-phase clouds. We focus on observations taken during 9-10 October, which indicated the presence of a single-layer mixed-phase clouds. We performed several sets of 12-h simulations to examine model sensitivity to different ice initiation mechanisms and evaluate model output (hydrometeors' concentrations, contents, effective radii, precipitation fluxes, and radar reflectivity) against measurements from the MPACE Intensive Observing Period. Overall, the model qualitatively simulates ice crystal concentration and hydrometeors content, but it fails to predict quantitatively the effective radii of ice particles and their vertical profiles. In particular, the ice effective radii are overestimated by at least 50%. However, using the same definition as used for observations, the effective radii simulated and that observed were more comparable. We find that for the singleCorrespondence to: I. Sednev (isednev@lbl.gov) layer stratiform mixed-phase clouds simulated, process of ice phase initiation due to freezing of supercooled water in both saturated and subsaturated (w.r.t. water) environments is as important as primary ice crystal origination from water vapor. We also find that the BFP is a process mainly responsible for the rates of glaciation of simulated clouds. These glaciation rates cannot be adequately represented by a water-ice saturation adjustment scheme that only depends on temperature and liquid and solid hydrometeors' contents as is widely used in bulk microphysics schemes and are better represented by processes that also account for supersaturation changes as the hydrometeors grow.

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“…Bin microphysical models explicitly calculate particle-number concentration in different size categories (e.g., Hall 1980;Khain et al 1996Khain et al , 1999Khain et al , 2000Ovtchinnikov and Kogan 2000;Khain et al 2001Khain et al , 2004Lynn et al 2005). This leads to large numbers of predicted variables and high computational expense, especially for 3D models.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Resolving Modeling of Deep Convection during KWAJEX. Part I: Comparison to TRMM Satellite and Ground-Based Radar Observations

Zipser

Krueger

et al. 2008

Monthly Weather Review

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A global TRMM database of tropical cloud system precipitation features (PFs), which provides useful observational constraints on cloud system properties, is used to evaluate the bulk microphysics schemes in a cloud-resolving model (CRM). The simulation of the Mesoscale Convective System (MCS) of 11-12 August 1999 during the Kwajalein Experiment (KWAJEX) is executed using the 3D University of Utah CRM, which employs a one-moment bulk, three-ice category microphysical parameterization. The simulated precipitation features are compared with climatological "norms" for Kwajalein locations from the TRMM PF database to evaluate the precipitation microphysics of the cloud model simulation. The modelsimulated reflectivities are also compared with vertical profiles of radar reflectivity obtained from a groundbased precipitation radar.Comparison of simulation results with the TRMM observation statistics indicates that the model tends to underestimate microwave brightness temperatures at ice-scattering frequencies and overestimate radar reflectivities, especially for those associated with larger ice particles. The differences between the statistics of KWAJEX simulation and available ground-based precipitation radar observations are relatively small at the levels below 5 km. Above 6 km, the differences increase with height and reach a maximum near 9 km. The simulated radar reflectivities are statistically 5-13 dBZ higher than those from radar observations at levels between 7 and 10.5 km, where graupel is the dominant simulated ice species. The largest graupel mixing ratios, as high as 8 g kg Ϫ1 , are the most likely reason for the unrealistically high simulated radar reflectivity. Comparison of model-simulated graupel mixing ratio with available microphysics data from the Citation aircraft indicates that the model overestimates graupel content at the level the Citation flew (about 6.4 km).

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An Investigation of Ice Production Mechanisms in Small Cumuliform Clouds Using a 3D Model with Explicit Microphysics. Part I: Model Description

Cited by 58 publications

References 74 publications

Tropical anvil characteristics and water vapor of the tropical tropopause layer: Impact of heterogeneous and homogeneous freezing parameterizations

Tropical anvil characteristics and water vapor of the tropical tropopause layer: Impact of heterogeneous and homogeneous freezing parameterizations

Simulating mixed-phase Arctic stratus clouds: sensitivity to ice initiation mechanisms

Cloud-Resolving Modeling of Deep Convection during KWAJEX. Part I: Comparison to TRMM Satellite and Ground-Based Radar Observations

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