Aerosol-Cloud-Precipitation Interactions in WRF Model: Sensitivity to Autoconversion Parameterization

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  • Corresponding author: XIE Xiaoning, xnxie@ieecas.cn
  • Funds:

    Supported by the National Basic Research and Development (973) Program of China (2011CB403406), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05110101), and National Natural Science Foundation of China (41105071 and 41290255).

  • doi: 10.1007/s13351-014-4065-8

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  • Cloud-to-rain autoconversion process is an important player in aerosol loading, cloud morphology, and precipitation variations because it can modulate cloud microphysical characteristics depending on the participation of aerosols, and affects the spatio-temporal distribution and total amount of precipitation. By applying the Kessler, the Khairoutdinov-Kogan (KK), and the Dispersion autoconversion parameterization schemes in a set of sensitivity experiments, the indirect effects of aerosols on clouds and precipitation are investigated for a deep convective cloud system in Beijing under various aerosol concentration backgrounds from 50 to 10000 cm-3. Numerical experiments show that aerosol-induced precipitation change is strongly dependent on autoconversion parameterization schemes. For the Kessler scheme, the average cumulative precipitation is enhanced slightly with increasing aerosols, whereas surface precipitation is reduced signifi-cantly with increasing aerosols for the KK scheme. Moreover, precipitation varies non-monotonically for the Dispersion scheme, increasing with aerosols at lower concentrations and decreasing at higher concentrations. These different trends of aerosol-induced precipitation change are mainly ascribed to differences in rain wa-ter content under these three autoconversion parameterization schemes. Therefore, this study suggests that accurate parameterization of cloud microphysical processes, particularly the cloud-to-rain autoconversion process, is needed for improving the scientific understanding of aerosol-cloud-precipitation interactions.
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Aerosol-Cloud-Precipitation Interactions in WRF Model: Sensitivity to Autoconversion Parameterization

    Corresponding author: XIE Xiaoning, xnxie@ieecas.cn
  • 1. State Key Laboratory of Loess and Quaternary Geology,Institute of Earth Environment,Chinese Academy of Sciences,Xi'an 710075;
  • 2. Department of Environmental Science and Technology,School of Human Settlements and Civil Engineering,Xi'an Jiaotong University,Xi'an 710049
Funds: Supported by the National Basic Research and Development (973) Program of China (2011CB403406), Strategic Priority Research Program of the Chinese Academy of Sciences (XDA05110101), and National Natural Science Foundation of China (41105071 and 41290255).

Abstract: Cloud-to-rain autoconversion process is an important player in aerosol loading, cloud morphology, and precipitation variations because it can modulate cloud microphysical characteristics depending on the participation of aerosols, and affects the spatio-temporal distribution and total amount of precipitation. By applying the Kessler, the Khairoutdinov-Kogan (KK), and the Dispersion autoconversion parameterization schemes in a set of sensitivity experiments, the indirect effects of aerosols on clouds and precipitation are investigated for a deep convective cloud system in Beijing under various aerosol concentration backgrounds from 50 to 10000 cm-3. Numerical experiments show that aerosol-induced precipitation change is strongly dependent on autoconversion parameterization schemes. For the Kessler scheme, the average cumulative precipitation is enhanced slightly with increasing aerosols, whereas surface precipitation is reduced signifi-cantly with increasing aerosols for the KK scheme. Moreover, precipitation varies non-monotonically for the Dispersion scheme, increasing with aerosols at lower concentrations and decreasing at higher concentrations. These different trends of aerosol-induced precipitation change are mainly ascribed to differences in rain wa-ter content under these three autoconversion parameterization schemes. Therefore, this study suggests that accurate parameterization of cloud microphysical processes, particularly the cloud-to-rain autoconversion process, is needed for improving the scientific understanding of aerosol-cloud-precipitation interactions.

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