Performance of CMIP5 models in the simulation of Indian summer monsoon

Jain, Shipra; Salunke, Popat; Mishra, Saroj K.; Sahany, Sandeep

doi:10.1007/s00704-018-2674-3

Cited by 59 publications

(32 citation statements)

References 59 publications

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“…Previous studies (Ashfaq et al, 2017;Jain et al, 2019;Mishra, Sahany, et al, 2018;Sabeerali et al, 2015;Saha et al, 2014) based on the CMIP5-GCMs reported that the majority of GCMs fail to capture the summer monsoon variability over South Asia. Consistent with the CMIP5-GCMs, we find that the majority of the CMIP6-GCMs are not able to capture the South Asian summer monsoon variability (Table S3 and Figures 3 and S4).…”

Section: Discussionmentioning

confidence: 99%

“…However, high bias and poor seasonality in the summer monsoon precipitation during the observed climate raise questions on the performance of the CMIP6‐GCMs. Based on the CMIP5‐GCMs, previous studies (Ashfaq et al, 2017; Jain et al, 2019; Mishra, Sahany, et al, 2018; Sabeerali et al, 2015) assume that the projections based on the skillful GCMs are more reliable and can be used for the decision making related to climate change adaptation in South Asia. Our results based on BEST‐GCMs can be considered more reliable for drought projections in South Asia in comparison to the multimodel ensemble mean that can be highly influenced by POOR‐GCMs.…”

Section: Discussionmentioning

confidence: 99%

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On the Projected Decline in Droughts Over South Asia in CMIP6 Multimodel Ensemble

Aadhar

Mishra

2020

JGR Atmospheres

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Recent studies based on the ensemble mean of Coupled Model Intercomparison Project 6 (CMIP6) General Circulation Models (GCMs, CMIP6-GCMs hereafter) reported a decline in drought frequency over South Asia in the projected future climate. Here using the simulations from 16 CMIP6-GCMs, we examine the potential causes of declining droughts in South Asia. We show that the projections based on the multimodel ensemble mean CMIP6-GCMs are not reliable over South Asia. The multimodel ensemble mean is influenced mainly by the low-skill GCMs, which show high bias in simulating the monsoon (June-September) season precipitation during the observed period (1951-2014). The low-skill GCMs show a higher (20-30%) increase in the convective precipitation with a rise in the global mean temperature under the warming (1.5°C, 2.0°C, and 2.5°C worlds) climate. The GCMs with less bias (BEST-GCMs) in the monsoon season precipitation and better seasonal cycle representation show lower sensitivity of convective precipitation to rise in global mean temperature. BEST-GCMs exhibit significantly different projections in comparison to the multimodel ensemble mean from all 16 GCMs (ALL-GCMs). In contrast to ALL-GCMs, BEST-GCMs project an increase in the frequency of droughts in South Asia under the future climate. Therefore, the projected risk of droughts over South Asia under the 1.5°C, 2.0°C, and 2.5°C warming levels is higher than previously reported based on the ensemble mean of CMIP6-GCMs. A projected increase in the drought frequency in South Asia will have considerable implications for agricultural production and water availability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

On the Projected Decline in Droughts Over South Asia in CMIP6 Multimodel Ensemble

Aadhar

Mishra

2020

JGR Atmospheres

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“…Further, the frequency distribution of precipitation rate (Figure 5b) shows that the frequency of light precipitation rate (1-10 mm/day) and moderate precipitation rate (10-20 mm/day) in DefCAM5 is overestimated, while the frequency of very heavy (extreme) precipitation rate (greater than 40 mm/day) is underestimated (also seen in CMIP5 models by Jain et al 2019 andSalunke et al 2019). StochCAM5 improves the frequency distribution of precipitation rate, as well as the contributions of light to extreme precipitation rates to total precipitation (Figure 5c).…”

Section: Precipitation Patternmentioning

confidence: 92%

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

2021

Self Cite

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Model simulations are highly sensitive to the formulation of the atmospheric mixing process or entrainment in the deep convective parameterizations used in their atmospheric component. In this paper, we have implemented stochastic entrainment in the deep convection scheme of NCAR CAM5 and analyzed the improvements in model simulation, focusing on the South Asian Summer Monsoon (SASM), as compared to the deterministic entrainment formulation in the default version of the model. Simulations using stochastic entrainment (StochCAM5) outperformed default model simulations (DefCAM5), as inferred from multiple metrics associated with the SASM. StochCAM5 significantly alleviated some of the longstanding SASM biases seen in DefCAM5, such as precipitation pattern and magnitude over the Arabian Sea and western Equatorial Indian ocean, early monsoon withdrawal, and the overestimation (underestimation) in the frequency of light (large-to-extreme) precipitation. Related SASM dynamical and thermodynamical features, such as Somali Jet, low-level westerly winds, and meridional tropospheric temperature gradient (MTTG), are improved in StochCAM5. Further, the simulation of monsoon intra-seasonal oscillation (MISO), Madden Julian Oscillation (MJO), and equatorial Kelvin waves are improved in StochCAM5. Many essential climate variables, such as shortwave and longwave cloud forcing, cloud cover, relative and specific humidity, and precipitable water, show significant improvement in StochCAM5.

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“…We consider five homogeneous zones of India as shown and described in Bhatla et al (2020) (Figure S1) for spatial analysis. We consider the models from the same modeling group, as Jain et al (2019) have shown that even the models of the same origin simulate quite different trend patterns using examples of four MIROC models (MIROC4h, MIROC5, MIROC-ESM, and MIROC-ESM-CHEM) and three Hadley Center models (Had-CM3, HadGEM2-CC, and HadGEM2-ES). For each MME model, we use consistent initial condition realization r1i1p1.…”

Section: Datamentioning

confidence: 99%

“…Several studies have analyzed the performance of different GCM outputs for simulating observed precipitation and temperature in the literature (Jain et al., 2019; Jena et al., 2016; Khan et al., 2018; Mishra et al., 2014; Sarthi et al., 2016). Recently, Raju and Kumar (2020) have reviewed approaches for selecting and ensembling GCMs for global studies and different parts of the world like China, Indian Subcontinent, Middle East (Iran, Iraq, Syria), Asia, Europe, and America.…”

Section: Datamentioning

confidence: 99%

Depth‐Duration‐Frequency of Extreme Precipitation Events Under Internal Climate Variability: Indian Summer Monsoon

2021

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Performance of CMIP5 models in the simulation of Indian summer monsoon

Cited by 59 publications

References 59 publications

On the Projected Decline in Droughts Over South Asia in CMIP6 Multimodel Ensemble

On the Projected Decline in Droughts Over South Asia in CMIP6 Multimodel Ensemble

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

Depth‐Duration‐Frequency of Extreme Precipitation Events Under Internal Climate Variability: Indian Summer Monsoon

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