Impact of Stochastic Entrainment in the NCAR CAM Deep Convection Parameterization on the Simulation of South Asian Summer Monsoon

Pathak, Raju; Sahany, Sandeep; Mishra, Saroj K.

doi:10.1007/s00382-021-05870-1

Cited by 7 publications

(4 citation statements)

References 109 publications

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“…In addition, by substituting the deterministic entrainment rate parameterization with a more realistic stochastic approach proposed by Plant and Craig (2008) and G. Wang et al (2017) successfully enhanced the precipitation intensity in CAM5. Using a similar method but with the stochastic entrainment approach proposed by Sušelj et al (2013) and Pathak et al (2021) alleviated the Indian summer monsoon bias in CAM5.…”

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confidence: 99%

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

Chu

Wang

2023

J Adv Model Earth Syst

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Stabilizing the atmosphere and ventilating the sub-cloud layer through vertical transport of moisture, moist convection is an important sub-grid scale process that still needs to be parameterized in general circulation models (GCMs) in the coming decades (Plant & Yano, 2016). The purpose of convection parameterization is to provide feedback on sub-grid scale convection to the large-scale fields as tendency terms. It is important to keep in mind that for achieving this goal, we do not have to introduce full details of convection into a parameterization. Rather, it must constitute a caricature of the reality of convection, as emphasized by Yano (2014b). Therefore, early convection schemes only considered the feedback without describing the convective clouds (e.g., the moist adjustment scheme of Manabe et al. (1965) or the moisture convergence scheme of Kuo (1965)). However, some shortcomings, including fixed tropical temperature profiles (Arakawa, 2004) and unreasonable convective moistening profiles (Emanuel, 1994), are related to such an oversimplified approach, indicating that an idealized caricature cannot encompass the collective effect of a series of complex processes during the convection. A practical way to mitigate this problem is to describe more basic features of convective clouds in detail in convection schemes. As a result, convection schemes started to adopt a bulk or spectrum mass-flux (plume) formulation (e.g.,

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confidence: 99%

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

Chu

Wang

2023

J Adv Model Earth Syst

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“…On the other hand, incorporating mesoscale stratiform heating structures by altering vertical heating structures in climate models could also significantly improve MJO simulations (e.g., Seo and Wang 2010;Cao and Zhang 2017). In addition to traditional convective parameterization, superparameterization (e.g., Khairoutdinov et al 2005;Hannah et al 2015) and stochastic parameterization (e.g., Deng et al 2015;Goswami et al 2017;Pathak et al 2021) have been implemented to improve MJO simulations and predictions.…”

Section: Introductionmentioning

confidence: 99%

Simulation of MJO with improved deep convection scheme in different resolutions of BCC-CSM2 models

Zheng,

Wu,

Xin

et al. 2024

Clim Dyn

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This study investigates the impacts of modifying the deep convection scheme on the ability to simulate the Madden–Julian Oscillation (MJO) in the Beijing Climate Center Climate System Model version 2 with a medium resolution (BCC-CSM2-T159) and a high resolution (BCC-CSM2-T382). On the basis of the original deep convection scheme, a modified scheme is suggested, which involves the transport processes of deep convective cloud water. The liquid cloud water that is detrained is transferred horizontally to its neighboring grids, and a portion of the cloud water that is horizontally transported is allowed to be transported downward into the lower troposphere. Both BCC-CSM2-T159 and BCC-CSM2-T382 with the modified deep convection scheme perform better than that used the original deep convection scheme in reproducing the major features of the MJO, such as its spectrum, period, intensity, eastward propagation and life cycle. Further analysis shows that those pronounced improvements in the MJO features in both BCC-CSM2-T159 and BCC-CSM2-T382 with the modified scheme are caused by transport processes of deep convective cloud water. The modified deep convection scheme enhances moisture and energy exchange from the lower troposphere to the upper troposphere around convective cloud, and promotes the convergence of moisture in the lower troposphere to the east of the MJO convection center, and then induces eastward propagation of the MJO. The comparisons between the coupled experiments and their corresponding experiments following Atmospheric Model Intercomparison Project (AMIP) simulations indicated that atmosphere–ocean interactions are also important to improve MJO simulations in the models.

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“…Attempts have been made by the researchers to improve the performance of the NCAR‐CAM5 model by increasing horizontal resolution, and manual and semi‐automatic tuning of parameters (Anand et al ., 2018; 2019; Mishra et al ., 2018a), and modification in the convective parametrization scheme (Pathak et al ., 2021) for climate simulations over India as well as the south Asian region. However, biases still persist in the model; specifically, for precipitation during the summer monsoon, the model shows wet (dry) biases over peninsular (Indo‐Gangetic Plain) India (Pathak et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Impact of the similarity functions of surface layer parametrization in a climate model over the Indian region

Namdev

Sharan

Mishra

2022

Quart J Royal Meteoro Soc

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Impact of the similarity functions of surface layer parametrization in National Centre for Atmospheric Research Community Atmosphere Model version 5 (NCAR-CAM5) is examined over the Indian region. The surface layer parametrization is based on the Monin-Obukhov similarity theory (MOST),which uses momentum and heat similarity functions to parametrize surface turbulent fluxes. In the present study, some of the well-established similarity functions under stable as well as unstable stratification have been incorporated in the surface layer scheme of NCAR-CAM5. Eight simulations have been conducted using different similarity functions with and without a limit on stability parameter along with the simulation using the default surface layer scheme. The impact of the incorporated functions on surface fluxes, near-surface variables, and precipitation has been assessed in this climate model over different climatic zones for the boreal winter and summer seasons. The model represents the timing of diurnal variation of 2 m temperature and turbulent fluxes accurately with higher correlation in all simulations, but the performance of the schemes is found to be sensitive to the climatic zones and seasons. During the winter season, most of the schemes overestimate the night-time surface air temperature over different climatic zones, but the simulation having a model performance index (MPI) of about 0.91 alleviates the bias. Under unstable stratification, the default scheme performs better than incorporated schemes in the winter season, whereas in boreal summer under unstable (stable) stratification, the incorporated functional form having an MPI of about 0.94 (0.97) does better than default and other schemes. As the performance of the functions is spatially and temporally dependent, based on the region and season of interest, the schemes may judiciously be chosen for the climate simulations.

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Impact of Stochastic Entrainment in the NCAR CAM Deep Convection Parameterization on the Simulation of South Asian Summer Monsoon

Cited by 7 publications

References 109 publications

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

Simulation of MJO with improved deep convection scheme in different resolutions of BCC-CSM2 models

Impact of the similarity functions of surface layer parametrization in a climate model over the Indian region

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