A modelling study of aerosol impacts on cloud microphysics and radiative properties

Cheng, Chao‐Tzuen; Wang, Wei‐Chyung; Chen, Jen‐Ping

doi:10.1002/qj.25

Cited by 64 publications

(45 citation statements)

References 42 publications

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“…For example, Lynn et al (2005) found that precipitation processes and rainfall were suppressed by enhancing the aerosol concentration for a Florida squall line. Cheng et al (2007) also found that increasing aerosols inhibited precipitation for a Oklahoma warm cloud system.…”

Section: F Aerosol-precipitation Interactive Processesmentioning

confidence: 86%

Cloud Resolving Modeling

Tao

2007

Journal of the Meteorological Society of Japan

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One of the most promising methods to test the representation of cloud processes used in climate models is to use observations together with cloud resolving models (CRMs). CRMs use more sophisticated and realistic representations of cloud microphysical processes, and they can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems (with sizes ranging from about 2-200 km). CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results.This paper provides a brief discussion and review of the main characteristics of CRMs as well as some of their major applications. These include the use of CRMs to improve our understanding of: (1) convective organization, (2) cloud temperature and water vapor budgets, and convective momentum transport, (3) diurnal variation of precipitation processes, (4) radiative-convective quasi-equilibrium states, (5) cloud-chemistry interaction, (6) aerosol-precipitation interaction, and (7) improving moist processes in large-scale models. In addition, current and future developments and applications of CRMs will be presented.

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Section: F Aerosol-precipitation Interactive Processesmentioning

confidence: 86%

Cloud Resolving Modeling

Tao

2007

Journal of the Meteorological Society of Japan

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“…Additional case studies addressing microphysical and PBL processes, including more comprehensive microphysical sensitivity testing (e.g., turning off certain conversion processes from one cloud species to another), will be considered in future research. Recently, a spectral bin microphysics scheme (Khain et al 2004) and a two-moment scheme (Cheng et al 2007) were implemented into WRF; additional sensitivity tests with these sophistical microphysical schemes will be needed. Sensitivity tests on model resolution (horizontal grid spacing from 1 up to 5 km * , and 31 to 81 vertical layers), terrain height (lifting and blocking, see discussion in section 4) and sea surface temperature (surface latent heat fluxes, see discussion in section 5.2) will be conducted and discussed in Part II.…”

Section: Discussionmentioning

confidence: 99%

High-Resolution Numerical Simulation of the Extreme Rainfall Associated with Typhoon Morakot. Part I: Comparing the Impact of Microphysics and PBL Parameterizations with Observations

Wei¹,

Shi²,

Lin³

et al. 2011

Terr. Atmos. Ocean. Sci.

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Typhoon Morakot hit Taiwan the night of 7 August 2009 as a Category 1 storm and caused up to 3000 mm of rain, leading to the worst flooding there in 50 years as well as devastating mudslides. The Weather Research and Forecasting model (WRF) is used at high resolution to simulate this extreme weather event. The model results indicate that WRF is able to capture the amount and location of the observed surface rainfall and that the typhoon-induced circulation, orographic lifting and a moisture-abundant southwest flow are the main mechanisms that together produced the tremendous rainfall in this case. Furthermore, the model results suggest that the agreement with the observed rainfall is due to the simulated storm track and intensity being in relatively good agreement with the observed. Additional simulations were made to examine the sensitivity of this case to model physics (microphysics and planetary boundary layer or PBL). Both warm rain only as well as improved microphysics yield similar significant rain amounts at the same locations as the control case. The improved microphysics lead to a better storm intensity early on but later exceed the observed intensities by about 10 hPa. The stronger storm arises from less evaporative cooling from cloud and rain and consequently weaker simulated downdrafts. Warm rain results closely match the control (i.e., the track, intensity, and maximum rainfall locations/amounts), implying ice processes (i.e., additional heat release due to ice processes) have only a secondary effect on surface rainfall. Results are less sensitive to using different PBL schemes than different microphysics.

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“…This drives a delay in rain formation and can affect the amount of surface rain (Rosenfeld, 1999(Rosenfeld, , 2000Cheng et al, 2007;Khain, 2009;Levin and Cotton, 2009;Koren et al, 2012;Hazra et al, 2013a, b;Dagan et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

et al. 2017

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Abstract. Large eddy simulations (LESs) with bin microphysics are used here to study cloud fields' sensitivity to changes in aerosol loading and the time evolution of this response. Similarly to the known response of a single cloud, we show that the mean field properties change in a nonmonotonic trend, with an optimum aerosol concentration for which the field reaches its maximal water mass or rain yield. This trend is a result of competition between processes that encourage cloud development versus those that suppress it. However, another layer of complexity is added when considering clouds' impact on the field's thermodynamic properties and how this is dependent on aerosol loading. Under polluted conditions, rain is suppressed and the non-precipitating clouds act to increase atmospheric instability. This results in warming of the lower part of the cloudy layer (in which there is net condensation) and cooling of the upper part (net evaporation). Evaporation at the upper part of the cloudy layer in the polluted simulations raises humidity at these levels and thus amplifies the development of the next generation of clouds (preconditioning effect). On the other hand, under clean conditions, the precipitating clouds drive net warming of the cloudy layer and net cooling of the sub-cloud layer due to rain evaporation. These two effects act to stabilize the atmospheric boundary layer with time (consumption of the instability). The evolution of the field's thermodynamic properties affects the cloud properties in return, as shown by the migration of the optimal aerosol concentration toward higher values.

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A modelling study of aerosol impacts on cloud microphysics and radiative properties

Cited by 64 publications

References 42 publications

Cloud Resolving Modeling

Cloud Resolving Modeling

High-Resolution Numerical Simulation of the Extreme Rainfall Associated with Typhoon Morakot. Part I: Comparing the Impact of Microphysics and PBL Parameterizations with Observations

Time-dependent, non-monotonic response of warm convective cloud fields to changes in aerosol loading

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