Relationship of tropospheric temperature anomaly with Indian southwest monsoon rainfall

Singh, G. P.; Chattopadhyay, Joydev

doi:10.1002/(sici)1097-0088(19980615)18:7<759::aid-joc288>3.0.co;2-p

Cited by 11 publications

(3 citation statements)

References 8 publications

(2 reference statements)

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“…The stronger temperature gradient leads to more enhanced rainfall. The influx of large amounts of water vapour coupled with the release of latent heat due to the increase in temperature gradient leads to an increase in the summer monsoon rainfall (Singh & Chattopadhyay, 1998). The large influx of moisture helps in the formation of precipitating clouds and increases the overall precipitation process.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of the characteristics of Indian summer monsoon simulated by CMIP6 models

Saha,

Singh

2024

Intl Journal of Climatology

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The present research is aimed at evaluating the climate coupled models with respect to the observational datasets for the investigation of the characteristics of the Indian summer monsoon (ISM). The monthly averaged historical simulations from 12 coupled climate models that participated in Coupled Model Intercomparison Project Phase 6 (CMIP6) are compared with the ground based observational data, satellite and reanalysis datasets for the study period of 1980–2014. This study uses these high‐resolution models to investigate monsoon features, onset and withdrawal dates, primarily focusing on individual model performances rather than highly documented multi‐model mean performance. Performance evaluation of the models suggests that sea surface temperature (SST) simulations by the models are in better agreement for the Arabian Sea than the Bay of Bengal with respect to the ERA5 reanalysis data sets. Further, inter‐model differences amongst the CMIP6 models in estimating the spatial distribution of various monsoon system variables during pre‐monsoon and monsoon seasons are noted which may be attributed to the different model components and varying physics configuration, nonetheless, models like NESM3 and INM‐CN5 are able to reproduce ISM pattern reasonably well. The annual precipitation cycle demonstrates a good agreement between most climate models and IMD data. Evolution of tropospheric temperature gradient (ΔTT) estimated from the CMIP6 models mimics the temporal pattern of the annual rainfall and therefore, is used to estimate the onset and withdrawal dates from CMIP6 models; however, high variability is noted amongst the CMIP6 models in retrieving the onset and withdrawal dates when compared with the IMD observations. Most of the models show a shorter rainy season except NorESM2‐MM. Overall our results suggest that the CMIP6 models can be used for the seasonal mean evaluation of monsoon system parameters and process‐based studies to improve our present understanding of the ISM system.

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Section: Resultsmentioning

confidence: 99%

Evaluation of the characteristics of Indian summer monsoon simulated by CMIP6 models

Saha,

Singh

2024

Intl Journal of Climatology

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“…The tropospheric temperature gradients (TTG) between 850 and 300 hPa over the Indian Subcontinent during the monsoon season play an important role in determining the strength of the monsoon circulation (Parthasarathy et al, 1990;Singh and Chattopadhyay, 1998). The north-south temperature gradient manifests the position of the easterly shear, the dynamical feedback to the MLLJ, and the formation of the tropical easterly jet (Krishnamurti et al, 1976;Chen and van Loon, 1987;Attada et al, 2018).…”

Section: Tropospheric Temperature Gradient (Ttg) and Moist Static Ene...mentioning

confidence: 99%

Enhanced Simulation of the Indian Summer Monsoon Rainfall Using Regional Climate Modeling and Continuous Data Assimilation

et al. 2022

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This study assesses a Continuous Data Assimilation (CDA) dynamical-downscaling algorithm for enhancing the simulation of the Indian summer monsoon (ISM) system. CDA is a mathematically rigorous technique that has been recently introduced to constrain the large-scale features of high-resolution atmospheric models with coarse spatial scale data. It is similar to spectral nudging but does not require any spectral decomposition for scales separation. This is expected to be particularly relevant for ISM, which involves various interactions between large-scale circulations and regional physical processes. Along with a control simulation, several downscaling simulations were conducted with the Weather Research and Forecasting (WRF) model configured over the Indian monsoon region at 10 km horizontal resolution using CDA, spectral (retaining different wavenumbers) and grid nudging for three contrasting ISM rainfall seasons: normal (2016), excess (2013), and drought (2009). The simulations are nested within the global NCEP Final Analysis data available at 1 × 1° horizontal resolution. The model outputs are evaluated against the India Meteorological Department (IMD) gridded precipitation and the fifth generation ECMWF atmospheric reanalysis (ERA-5). Compared to grid and spectral nudging, the simulations using CDA produce enhanced ISM features over the Indian subcontinent including the low-level jet, tropical easterly jet, easterly wind shear, and rainfall distributions for all investigated ISM seasons. The major ISM processes, in particular the monsoon inversion over the Arabian Sea, tropospheric temperature gradients and moist static energy over central India, and zonal wind shear over the monsoon region, are all better simulated with CDA. Spectral nudging outputs are found to be sensitive to the choice of the wavenumber, requiring careful tuning to provide robust simulations of the ISM system. In contrast, control and grid nudging generally fail to well-reproduce some of the main ISM features.

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“…On interannual timescales, the Indian monsoon rainfall has a strong and positive correlation with the premonsoon spring tropospheric temperature anomaly (Verma, 1980;Parthasarathy et al, 1990;Singh & Chattopadhyay, 1998). Parameters representing these conditions, global as well as regional, provide a handle for seasonal prediction.…”

Section: Influence Of Climate Variables On Indian Monsoonmentioning

confidence: 99%

Regional Rainfall Forecasting using Large Scale Climate Teleconnections and Artificial Intelligence Techniques

Kumar¹,

Reddy²,

Maity³

2007

Journal of Intelligent Systems

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This paper presents an Artificial Intelligence approach for regional rainfall forecasting for Orissa state, India on monthly and seasonal time scales. The possible relation between regional rainfall over Orissa and the large scale climate indices like El-Niflo Southern Oscillation (ENSO), EQUitorial INdian Ocean Oscillation (EQUINOO) and a local climate index of Ocean-Land Temperature Contrast (OLTC) are studied first and then used to forecast monsoon rainfall. To handle the highly non-linear and complex behavior of the climatic variables for forecasting the rainfall, this study employs Artificial Neural Networks (ANNs) methodology. To optimize the ANN architecture, Genetic Optimizer (GO) is used. After identifying the lagged relation between climate indices and monthly rainfall, the rainfall values are forecast for the summer monsoon months of June, July, August, and September (JJAS)individually, as well as for total monsoon rainfall. The models are trained individually for monthly and for seasonal rainfall forecasting. Then the trained models are tested to evaluate the performance of the model. The results show reasonably good accuracy for monthly and seasonal rainfall forecasting. This study emphasizes the value of using large-scale climate teleconnections for regional rainfall forecasting and the significance of Artificial Intelligence approaches like GO and ANNs in predicting the uncertain rainfall.

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Relationship of tropospheric temperature anomaly with Indian southwest monsoon rainfall

Cited by 11 publications

References 8 publications

Evaluation of the characteristics of Indian summer monsoon simulated by CMIP6 models

Evaluation of the characteristics of Indian summer monsoon simulated by CMIP6 models

Enhanced Simulation of the Indian Summer Monsoon Rainfall Using Regional Climate Modeling and Continuous Data Assimilation

Regional Rainfall Forecasting using Large Scale Climate Teleconnections and Artificial Intelligence Techniques

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