Autoconversion in Coupled Climate Models and Monsoon Subseasonal Oscillations

Methodology

The study also contains a preliminary analysis of another set of sensitivity experiments with high resolution (∼ 38 km) CFSv2-T382 model (SE382 hereafter), which compares two different types of autoconversion schemes, i.e., and Liu-Daum scheme simulating the active-break conditions. The extended empirical orthogonal function (EEOF) analysis has been applied to the ten-year unfiltered daily rainfall data for June-July-August-September (JJAS).

Study Design

A similar quantitative analysis of variance for this mode yields that both the SE underestimates the mean-variance over EIMR and AI (Table 3 ).

The results from the subseasonal variance analysis show that proper modification of the autoconversion process leads to a better representation of subseasonal variability.

Results & Findings

This study explores the role of autoconversion parameterization in microphysical schemes for the simulation of MISO with the coupled climate model, e.g., the Climate Forecast System version 2 (CFSv2), by conducting sensitivity experiments in two resolutions (~100 km and ~38 km). Results reveal that the modified autoconversion parameterization better simulates the active-break spells of the ISM rainfall.

• This study explores the role of autoconversion parameterization in microphysical schemes for the simulation of MISO with the coupled climate model, e.g., the Climate Forecast System.
• Results reveal that the modified autoconversion parameterization better simulates the active-break spells of the ISM rainfall.
• The improvements are qualitatively and quantitatively more significant in the higher-resolution simulations, particularly regarding rainfall spatial patterns over the Indian subcontinent during active spells.
• This study concludes that proper autoconversion parameterization in the coupled climate model can lead to enhanced representation of active/break spells and sub-seasonal variability of ISM.
• In this paper, we have shown the importance of the proper combination of convective and microphysical autoconversion coefficients for better simulation of subseasonal oscillation of ISM.

Abstract

Abstract The Indian summer monsoon (ISM) and associated monsoon intraseasonal oscillations (MISOs) influence the billions of people living in the Indian subcontinent. This study explores the role of autoconversion parameterization in microphysical schemes for the simulation of MISO with the coupled climate model, e.g., the Climate Forecast System version 2 (CFSv2), by conducting sensitivity experiments in two resolutions (~100 km and ~38 km). Results reveal that the modified autoconversion parameterization better simulates the active-break spells of the ISM rainfall. The main improvements include the contrasting features of rainfall over land.

Plain Language Summary:

Plain Language Summary: Prediction of the Indian summer monsoon (ISM) and associated subseasonal variabilities is very important to policymakers and common people. For better simulation, cloud microphysical processes associated with rainfall must be parameterized properly. Autoconversion is a crucial cloud microphysical process that controls rain formation. In this paper, we have shown the importance of the proper combination of convective and microphysical autoconversion coefficients for better simulation of subseasonal oscillation of ISM in a coupled climate model, e.g., CFSv2.

MPHY (c). Bias in simulation for sensitivity experiments in (d) and (e). Difference in model simulation in (f). Pattern correlation of sensitivity experiments with observation over the box (shown in b) are in parenthesis.

MPHY (c). Bias in simulation for sensitivity experiments in (d) and (e). Difference in model simulation in (f). Pattern correlation of sensitivity experiments with observation over the box (shown in b) are in parenthesis. However, both the SE126 underestimate the spatial extent of positive rainfall anomaly over the BoB. The negative rainfall anomaly is observed over the equatorial Indian Ocean (EIO) during an active spell (Fig. 1a ). The spatial extent and intensity of rainfall anomaly over EIO are better captured in CFSv2.MPHY than CFSv2.CTL (Fig. 1b , c .

Convective Rainfall

Convective Rainfall (S. B. Saha et al., 2014) found that during active spells, precipitating clouds are more of a stratiform type (~20%) than a convective type (~5%) over the monsoon trough region. Therefore, proper representation of the convective to total rain (RCT) ratio in the global climate models is essential and requires improvement (Hazra, Chaudhari, Saha, Pokhrel, et al., 2017) . In general, most of the climate models tend to simulate 95% of the rain as convective (Dai, 2006) , whereas satellite observations show that 40-50% of rainfall originates from.

Figures Explained

The paper’s visual material highlights the workflow and the main system components.

• Figure 1 :: Figure 1: Rainfall anomaly (mm/day) for active composite from observation (a), CFSv2.CTL (b) and CFSv2.MPHY (c). Bias in simulation for sensitivity experiments in (d) and (e). Difference in model simulation in (f). Pattern correlation of sensitivity experiments with observation over the box (shown in b) are in parenthesis.
• Figure 2 .: Figure 2. Same as Figure 1 but for break composite.
• Figure 3: CFSv2.MPHY is realistically closer to the observed value of OLR during the active spells. The mean OLR value for break composite is also better simulated in CFSv2.MPHY. The reduced CCA limits the conversion of cloud condensate to convective precipitation, increasing the cloudiness in the mid to upper troposphere through detrainment of moisture (J. Y. Han et al., 2016) . On the other hand, the increased CMA increases the conversion of cloud liquid water to microphysical rain by reducing the characteristic time for the collision-coalescence process. Therefore, proper choice/combination of autoconversion coefficients can play a pivotal role in modulating the convection during the monsoon oscillation.
• Figure 3 .: Figure 3. The bias of convective rainfall for CFSv2.CTL during (a) active spell and (b) break spell. Similar bias for CFSv2.MPHY during (c) active spell and (d) break spell. The difference in convective rainfall between two sensitivity experiments, i.e., CFSv2.MPHY minus CFsv2.CTL for (e) active and (f) break spells.
• Figure 4 .: Figure 4. Mean value of high cloud fraction (%) for active (a-c) and break (d-f) composite from reanalysis (a, d), CFSv2.CTL (b, e), and CFSv2.MPHY (c, f).

Limitations and Cautions

A useful limitation and caution is that this article summarizes the available paper text and extracted evidence; readers should consult the source paper before treating any interpretation as definitive.

The paper’s conclusions may depend on its source selection, definitions, assumptions, and the scope of its analysis, so follow-up reading is important.

Conclusion

Therefore, designed sensitivity experiments based on a different combination of convective and microphysical “autoconversion” in the CFSv2 to improve the biases in the simulation of tropical oscillations (i.e., MISO and MJO) in the standard CFSv2 model. Therefore, this study investigates the impact of modified autoconversion rates on the simulation of active-break spells of ISM.

Therefore, the scientific question arises: Can a high-resolution model with more generalized autoconversion parameterization be more useful in simulating the active break spell?

Therefore, the combination of autoconversion coefficients for two sensitivity experiments (SE) with the CFSv2-T126 model (SE126 hereafter) in this current study is as follows: a) CFSv2.CTL: CCA= 0.002 m -1 ; CMA = 1.0 x 10 -4 s -1 b) CFSv2.MPHY: CCA= 0.001 m -1 ; CMA = 1.5 x 10 -4 s -1 .