Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests

Morrison, Hugh; Milbrandt, Jason A.

doi:10.1175/jas-d-14-0065.1

Cited by 403 publications

(355 citation statements)

References 126 publications

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“…6c). This may be a result of the use of distinct and different definitions of ice-phase hydrometeor categories in the two schemes, which have been shown to cause deficiencies in simulations of observed squall lines (Morrison and Milbrandt, 2015).…”

Section: Wrf Congo Basinmentioning

confidence: 99%

“…Differences in ice-phase microphysics in bulk schemes have been shown to affect cloud biases, especially at upper levels (Cintineo et al, 2014), and to affect ice-cloud-radiation feedbacks with impacts on tropospheric stability, triggering of deep convection and surface precipitation (Hong et al, 2009). Such limitations have led to the development in more recent years of new representations of ice microphysics in bulk schemes, such as approaches which separately prognose ice mass mixing ratios grown by riming and vapour deposition (Morrison and Grabowski, 2008), approaches where particle habit evolution is predicted by prognosing the mixing ratios of ice crystal axes (Harrington et al, 2013) and approaches where ice-phase particles are represented by several physical properties that evolve freely in time and space (Morrison and Milbrandt, 2015). Although these developments are relatively new, they have already been shown to improve simulations of observed squall lines and orographic precipitation when compared to traditional two-moment bulk schemes (Morrison et al, 2015a).…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects

White

Gryspeerdt²,

Stier

et al. 2017

Atmos. Chem. Phys.

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Abstract. This study investigates the hydrometeor development and response to cloud droplet number concentration (CDNC) perturbations in convection-permitting model configurations. We present results from a real-data simulation of deep convection in the Congo basin, an idealised supercell case, and a warm-rain large-eddy simulation (LES). In each case we compare two frequently used double-moment bulk microphysics schemes and investigate the response to CDNC perturbations. We find that the variability among the two schemes, including the response to aerosol, differs widely between these cases. In all cases, differences in the simulated cloud morphology and precipitation are found to be significantly greater between the microphysics schemes than due to CDNC perturbations within each scheme. Further, we show that the response of the hydrometeors to CDNC perturbations differs strongly not only between microphysics schemes, but the inter-scheme variability also differs between cases of convection. Sensitivity tests show that the representation of autoconversion is the dominant factor that drives differences in rain production between the microphysics schemes in the idealised precipitating shallow cumulus case and in a subregion of the Congo basin simulations dominated by liquidphase processes. In this region, rain mass is also shown to be relatively insensitive to the radiative effects of an overlying layer of ice-phase cloud. The conversion of cloud ice to snow is the process responsible for differences in cold cloud bias between the schemes in the Congo. In the idealised supercell case, thermodynamic impacts on the storm system using different microphysics parameterisations can equal those due to aerosol effects. These results highlight the large uncertainty in cloud and precipitation responses to aerosol in convectionpermitting simulations and have important implications not only for process studies of aerosol-convection interaction, but also for global modelling studies of aerosol indirect effects. These results indicate the continuing need for tighter observational constraints of cloud processes and response to aerosol in a range of meteorological regimes.

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Section: Wrf Congo Basinmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects

White

Gryspeerdt²,

Stier

et al. 2017

Atmos. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

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“…As a result, particles with high rime fractions have a smaller projected area and thus a higher fall speed than their weakly rimed counterparts of the same mass. Morrison and Milbrandt (2015) showed in a regional model that this adjustment of particle…”

Section: The Rime Fractionmentioning

confidence: 99%

“…We will use the microphysical properties of ice described in Morrison and Milbrandt (2015) (hereafter MM15) and embed them in the ECHAM6-HAM2 microphysics scheme. A short summary of their scheme is given in Sect.…”

mentioning

confidence: 99%

Prognostic parameterization of cloud ice with a single category in the aerosol-climate model ECHAM(v6.3.0)-HAM(v2.3)

Dietlicher¹,

Neubauer²,

Lohmann³

2017

Preprint

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Abstract.A new scheme for stratiform cloud microphysics has been implemented in the ECHAM6-HAM2 general circulation model. It features a widely used description of cloud water with two categories for cloud droplets and rain drops. The unique aspect of the scheme is the break with the traditional approach to describe cloud ice analogously. Here we parameterize cloud ice with a single, prognostic category as it has been done in regional models and most recently also in the global model CAM5.A single category does not rely on heuristic conversion rates from one category to another. At the same time it is conceptually 5 easier and closer to first principles.This work shows that a single category is a viable approach to describe cloud ice in climate models. Prognostic representation of sedimentation is achieved by a nested approach for sub-stepping the microphysics scheme. This yields good results in terms of numerical stability and accuracy as compared to simulations with high temporal resolution.The improvement of the representation of cloud ice in ECHAM6-HAM2 is twofold. Not only are we getting rid of heuristic 10 conversion rates but we also find that the prognostic treatment of sedimenting ice allows to unbiasedly represent the ice formation pathway from nucleation over growth by deposition and collisions to sedimentation.

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“…One of them is to partition the ice crystals into cloud ice and snow categories, while using D cs to convert cloud ice to snow. Thus a more physical treatment of ice crystal evolution such as using bin microphysics (e.g., Bardeen et al, 2013;Khain et al, 2015) or a single category to represent all ice-phase hydrometeors (Morrison and Milbrandt, 2015;Eidhammer et al, 2017) is needed.…”

Section: Discussionmentioning

confidence: 99%

Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

Liu

Diao

et al. 2017

Atmos. Chem. Phys.

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Abstract. In this study we evaluate cloud properties simulated by the Community Atmosphere Model version 5 (CAM5) using in situ measurements from the HIAPER Poleto-Pole Observations (HIPPO) campaign for the period of 2009 to 2011. The modeled wind and temperature are nudged towards reanalysis. Model results collocated with HIPPO flight tracks are directly compared with the observations, and model sensitivities to the representations of ice nucleation and growth are also examined. Generally, CAM5 is able to capture specific cloud systems in terms of vertical configuration and horizontal extension. In total, the model reproduces 79.8 % of observed cloud occurrences inside model grid boxes and even higher (94.3 %) for ice clouds (T ≤ −40 • C). The missing cloud occurrences in the model are primarily ascribed to the fact that the model cannot account for the high spatial variability of observed relative humidity (RH). Furthermore, model RH biases are mostly attributed to the discrepancies in water vapor, rather than temperature. At the micro-scale of ice clouds, the model captures the observed increase of ice crystal mean sizes with temperature, albeit with smaller sizes than the observations. The model underestimates the observed ice number concentration (N i ) and ice water content (IWC) for ice crystals larger than 75 µm in diameter. Modeled IWC and N i are more sensitive to the threshold diameter for autoconversion of cloud ice to snow (D cs ), while simulated ice crystal mean size is more sensitive to ice nucleation parameterizations than to D cs . Our results highlight the need for further improvements to the sub-grid RH variability and ice nucleation and growth in the model.

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Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests

Cited by 403 publications

References 126 publications

Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects

Uncertainty from the choice of microphysics scheme in convection-permitting models significantly exceeds aerosol effects

Prognostic parameterization of cloud ice with a single category in the aerosol-climate model ECHAM(v6.3.0)-HAM(v2.3)

Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

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