[Paper Review] Role of modified cloud microphysics parameterization in coupled climate model for studying ISM rainfall: small-scale cloud model and climate model work better together

Moumita Bhowmik, Anupam Hazra+5 more|arXiv (Cornell University)|Mar 2, 2023

Climate variability and modelsEnvironmental Science3 citations

TL;DR

This study proposes a physically based modification to cloud microphysics parameterization in the CFSv2 coupled climate model, using diffusional growth rates and relative dispersion from a Lagrangian particle-by-particle small-scale cloud model to improve autoconversion and droplet growth. The modified scheme significantly reduces biases in the probability distribution function (PDF) of Indian Summer Monsoon (ISM) rainfall, enhancing simulation of specific humidity, liquid water content, and outgoing longwave radiation (OLR), leading to more realistic deep convection and improved monsoon rainfall representation.

ABSTRACT

An unresolved problem of present generation coupled climate models is the realistic distribution of rainfall over Indian monsoon region, which is also related to the persistent dry bias over Indian land mass. Therefore, quantitative prediction of the intensity of rainfall events has remained a challenge for the state-of-the-art global coupled models. Guided by the observation, it is hypothesized that insufficient growth of cloud droplets and processes responsible for the cloud to rain water conversion are key components to distinguish between shallow to convective clouds. The new diffusional growth rates and relative dispersion based autoconversion from the Eulerian-Lagrangian particleby-particle based small-scale model provide a pathway to revisit the parameterizations in climate models for monsoon clouds. The realistic information of cloud drop size distribution is incorporated in the microphysical parameterization scheme of climate model. Two sensitivity simulations are conducted using coupled forecast system (CFSv2) model. When our physically based small-scale derived modified parameterization is used, a coupled climate model simulates the probability distribution (PDF) of rainfall and accompanying specific humidity, liquid water content, and outgoing long-wave radiation (OLR) with increasing accuracy. The improved simulation of rainfall PDF appears to have been aided by much improved simulation of OLR and resulted better simulation of the ISM rainfall.

Motivation & Objective

• To address the persistent dry bias and inaccurate PDF of ISM rainfall in global coupled climate models.
• To investigate how improved microphysical processes—particularly autoconversion and diffusional growth—can reduce biases in precipitation and cloud properties.
• To evaluate whether physically based parameterizations derived from small-scale cloud models enhance large-scale climate model performance.
• To reduce the overestimation of light rain and underestimation of moderate/heavy rainfall events in CFSv2.
• To explore the feedback between improved microphysics, cloud condensate, and radiation (OLR) in simulating ISM dynamics.

Proposed method

• A Lagrangian particle-by-particle numerical model is used to compute diffusional growth rates and relative dispersion of cloud droplets, informing microphysical parameterization.
• The study derives a modified autoconversion parameterization based on Sundqvist-type and Liu-Daum-type formulations, with dispersion-dependent rates.
• These physically derived coefficients are integrated into the CFSv2 coupled climate model’s microphysics scheme, replacing standard parameterizations.
• Two sensitivity simulations are conducted: a control (CTL) and a modified (MOD) version, with MOD incorporating the new microphysical coefficients.
• The model evaluates changes in rainfall PDF, specific humidity, liquid water content (LWC), and outgoing longwave radiation (OLR).
• Results are validated against GPCP observations and in-situ disdrometer data from HACPL, India, focusing on drop size distribution (RDSD) and diurnal cycle fidelity.

Experimental results

Research questions

• RQ1 Can a small-scale cloud model’s physically based microphysical parameterization improve the simulation of ISM rainfall PDF in a global coupled climate model?
• RQ2 How do modified diffusional growth rates and dispersion-based autoconversion affect the representation of light and heavy rainfall events?
• RQ3 To what extent does improved microphysics reduce biases in OLR, specific humidity, and cloud condensate in the CFSv2 model?
• RQ4 What is the role of cloud microphysics in modulating convection-radiation feedbacks and the diurnal cycle of ISM rainfall?
• RQ5 How does the integration of Lagrangian particle-based microphysics improve the realism of cloud type (shallow vs. deep) in monsoon systems?

Key findings

• The modified parameterization significantly improves the probability distribution function (PDF) of ISM rainfall, reducing the overestimation of light rain and underestimation of moderate and heavy rainfall events.
• The MOD simulation shows better agreement with observations in specific humidity and liquid water content (LWC), indicating improved thermodynamic and microphysical representation.
• Outgoing longwave radiation (OLR) is simulated more realistically in the MOD experiment, indicating improved representation of cloud radiative effects and convective activity.
• The MOD model produces more deep convective clouds and fewer shallow clouds compared to the control, aligning better with observed monsoon dynamics.
• The use of dispersion-based Liu-Daum autoconversion and improved diffusional growth coefficients leads to a more accurate cloud droplet size distribution (DSD), reducing model biases.
• The study demonstrates that integrating small-scale cloud model physics into large-scale climate models reduces generic PDF biases across multiple variables, including rainfall, humidity, and LWC.