Sensitivity of Clouds and Precipitation to the Auto-conversion and Accretion Schemes in the MIROC-SPRINTARS Model

Takuro Michibata, Kyushu University, Fukuoka, Japan and Toshihiko Takemura, Kyushu University, Research Institute for Applied Mechanics, Fukuoka, Japan
Abstract:
This study examines schemes of autoconversion (mass transfer from the cloud water to the rain water by the coalescence of cloud droplets) based on sensitivity experiments by an aerosol climate model, MIROC-SPRINTARS. The autoconversion scheme proposed by Berry (1968), which is a default in MIROC-SPRINTARS, predicts larger autoconversion rate than other autoconversion schemes (e.g., Beheng 1994; Khairoutdinov and Kogan 2000) that have non-linear dependence on the cloud droplet number concentration. Such a larger autoconversion rate of Berry parameterization results in faster production of precipitating cloud.

In addition, we estimated the conversion rate (sum of autoconversion and accretion rates) from the active CloudSat and passive MODIS retrieved data as well. The inter-comparison of the frequency distribution diagrams as a function of the liquid water path and conversion rate, associated with conversion time, between the CloudSat/MODIS observations and single-column model simulation showed the characteristics of larger conversion rate and faster conversion time in the parameterization of Berry, than that of the CloudSat/MODIS observations. These characteristics and results support the findings of a previous study (e.g., Suzuki et al., 2013) by the method of "contoured frequency by optical depth diagram (CFODD)".

Practically all the climate models generally have non-negligible large uncertainty in cloud–precipitation parameterizations due to the difficulty in representing the complex aerosol–cloud interactions. The cloud (and water vapor) plays an important role in the global radiation budget and climate system, therefore to improve the reproducibility related to the cloud and precipitation leads to the improvement in performance of climate models as a whole.

This study was partly supported by the Environment Research and Technology Development Fund (S-12-3) of the Ministry of the Environment, Japan.

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