Reply on RC2

Jinfang Yin

doi:10.5194/gmd-2021-230-ac3

Abstract

Cloud and precipitation processes remain among the largest sources of uncertainties in weather and climate modeling, and considerable attention has been paid to improve the representation of the cloud and precipitation processes in numerical models in the last several decades. In this study, we develop a weighted ensemble (named as EN) scheme by employing several widely used autoconversion (ATC) schemes to represent the ATC from cloud water to rainwater. One unique feature of the EN approach is that ATC rate is a weighted mean value based on the calculations from several ATC schemes within a microphysics scheme with a negligible increase of computation cost. The EN scheme is compared with the several commonly used ATC schemes by performing a real case simulations. In terms of accumulated rainfall and extreme hourly rainfall rate, the EN scheme provides better simulations than that by using the single Berry-Reinhardt scheme which was originally used in the Thompson scheme. It is worth emphasizing, in the present study, we only pay our attention to the ATC process from cloud water into rainwater with the purpose to improve the modeling of the extreme rainfall events over southern China. Actually, any (source/sink) term in a cloud microphysics scheme can be dealt with the same approach. The ensemble method proposed herein appears to have important implications for developing cloud microphysics schemes in numerical models, especially for the models with variable grid resolution, which would be expected to improve of the representation of cloud microphysical processes in the weather and climate models.

Highlights

Panel on Climate Change (IPCC) (Houghton et al, 2001)
Owing to the complex microphysical processes in clouds and their interactions with dynamical and thermodynamic processes, considerable attention has been devoted to developing cloud microphysics schemes in the numerical weather and climate models in the last several decades, which is summarized in several review articales (e.g., Grabowski et al, 2019; Khain et al, 2015; Morrison et al, 2020)
The objective of this paper is to address how to reduce the negative effects of inherent uncertainties in the ATC parameterization within a cloud microphysics scheme to make the weather and climate models behave realistically

Summary

Introduction

Panel on Climate Change (IPCC) (Houghton et al, 2001). Owing to the complex microphysical processes in clouds and their interactions with dynamical and thermodynamic processes, considerable attention has been devoted to developing cloud microphysics schemes in the numerical weather and climate models in the last several decades, which is summarized in several review articales (e.g., Grabowski et al, 2019; Khain et al, 2015; Morrison et al., 2020). Because of fundamental gaps in the knowledge of cloud microphysics, there are still a large number of empirical values derived and assumptions in microphysics schemes based on limited observations, even from numerical simulations (Tapiador et al, 2019). Simulations are quite sensitive to microphysical parameter settings (Falk et al, 2019; Freeman et al, 2019; Gilmore et al, 2004), and obvious differences occur frequently from different simulations due to the poor representation of the empirical values and assumptions (Lei et al, 2020; White et al, 2017). Collision–coalescence between cloud droplets forming riandrops is named as the autoconversion (ATC) , which is a significant microphysical process in warm clouds. Raindrop is initiated by ATC process in warm clouds, which plays a significant role in the onset of a rainfall event. ATC process has important influence on cloud microphysical properties by bridging aerosols, cloud droplets, and raindrops (White et al., 2017). Local circulation may be modified to a certain extent due to falling down of the initialized raindrops because of terminal velocity of raindrop (Doswell, 2001)

Objectives

Results

Conclusion