Relationship between tropospheric temperature and Indian summer monsoon rainfall as simulated by RegCM3

Pattnayak, Kanhu Charan; Panda, Subrat Kumar; Saraswat, Vaishali; Dash, S. K.

doi:10.1007/s00382-015-2758-z

Cited by 23 publications

(12 citation statements)

References 25 publications

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“…IMD data are also validated against another observational data set namely APHRODITE (Yatagai et al ., ) and are found to be nearly identical (Rajeevan and Bhate, ). The IMD data set is used in numerous works to study various observed dynamical aspects of ISM precipitation (Roja Raman et al ., ; Panda et al ., ; Panda and Kumar, ; Prakash et al ., ; Rai et al ., ) as well as a reference in evaluation of climate models (Das et al ., ; Kumar et al ., ; Pattnayak et al ., ; ; Das et al ., ; Maharana and Dimri, ; ; Dash et al ., ). Furthermore, it is available at a resolution of 0.5° which matches with that of CORDEX‐SA RCMs that are considered for bias correction here.…”

Section: Methodsmentioning

confidence: 99%

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

Choudhary

Dimri

2018

Intl Journal of Climatology

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In the present study, four different bias correction methods are assessed in improving the precipitation output from a set of two regional climate simulations for 1971–2005 period. The simulations come from Coordinated Regional Climate Downscaling Experiment over South Asia (CORDEX‐SA). The bias correction methods applied here are linear scaling (Scaling), empirical quantile mapping (EQM), power transformation (PTR) and local intensity scaling (LOCI). All the bias correction techniques show skill in improving the model results, but there are clear differences in the degrees of success depending upon the characteristic of precipitation studies—like seasonal mean, seasonal extremes or inter‐annual variability. Most of the methods show equivalently high ability in correcting the seasonal mean precipitation values; however, there also exist residual biases which vary spatially over India. The largest reduction in bias was observed for the frequency of dry days and precipitation on very wet days, which are significantly altered after adjustment by EQM and PTR. It is found that distribution‐based EQM method is more skilful followed by PTR and LOCI in most of the cases including correction of frequency‐based indices (e.g., dry days number and inter‐annual variability).

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Section: Methodsmentioning

confidence: 99%

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

Choudhary

Dimri

2018

Intl Journal of Climatology

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“…The experiments conducted by Giorgi and Marinucci (1996) with different topography indicated that resolution has a greater impact on the simulation through the model physics and dynamics rather than through the topography itself, especially for summer convection. The RegCM version 3 (RegCM3) has also been successfully integrated to simulate the salient features of Indian summer monsoon circulation and rainfall (Dash et al 2013;Pattnayak et al 2013aPattnayak et al , 2016b. Dash et al (2006) has shown that the RegCM3 simulates intraseasonal variability of Indian summer monsoon rainfall over India reasonably well during the period 1993-1996.…”

Section: Introductionmentioning

confidence: 99%

“…ISMR exhibits significant temporal and spatial variability (Mooley and Parthasarathy 1984;Thapliyal and Kulshrestha 1991;Kripalani and Kulkarni 2001;Sahai et al 2003;Naidu et al 2015). Several scientists have investigated the inter-annual and intra-seasonal variations of monsoon rainfall and the associated phenomena over the last several years using observed data (Webster et al 1998;Gadgil et al 2003) and conducting modeling studies (Ratna et al 2011;Satyanarayana and Kar 2016;Pattnayak et al 2016a). The key features of Indian summer monsoon include the sudden onset and gradual withdrawal, which signify the beginning and end of the rainy season respectively.…”

Section: Introductionmentioning

confidence: 99%

Assessment of two versions of regional climate model in simulating the Indian Summer Monsoon over South Asia CORDEX domain

et al. 2017

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zones in reproducing the mean monsoon rainfall and interannual variation of rainfall. Further, the monsoon onset, low-level Somali Jet and the upper level tropical easterly jet are better represented in the RegCM4.3 than RegCM4.2. Thus, RegCM4.3 has performed better in simulating the mean summer monsoon circulation over the South Asia. Hence, RegCM4.3 may be used to study the future climate change over the South Asia.Keywords Indian summer monsoon · RegCM4.2 · RegCM4.3 · CORDEX · Moisture flux · Rainfall homogenous zones · Surface net downward shortwave flux

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“…In 24 years out of the 30 years, the bias is less than 2 days. In the summer monsoon rainfall contrasting years such as 1982 and 1983, 1987 and 1988, 2002 and 2003 (Pattnayak et al, ; Pattnayak et al, ), the onset dates simulated by RegCM4 are close to observed onset dates. The aim of presenting the MOK and all India summer monsoon rainfall on the same plot (Figure a) is to show the relation between these two.…”

Section: Resultsmentioning

confidence: 63%

How good is regional climate model version 4 in simulating the monsoon onset over Kerala?

Pattnayak

Panda

Saraswat

et al. 2019

Intl Journal of Climatology

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This study assesses the performance of regional climate model version 4 (RegCM4) in simulating the monsoon onset over Kerala (MOK). It also examines any possible relationship between the onset dates with the summer monsoon rainfall over India as whole as well as each grid points of the India land points and also the moisture inflow into Indian subcontinent. A 30-year long simulation starting from 1979 till 2008 was carried out with the lateral boundary forcings provided by European Centre for Medium Range Weather Forecasts Reanalysis (ERA-interim) at 25 km horizontal resolution. The simulated climatological MOK date is found to be 28th May, while as per the India Meteorological Department, climatological normal onset date is 1st June. The model has performed well in simulating the inter-annual variation of MOK during the study period. The correlation coefficient between model simulated and observed MOK is 0.83 significant at 95% confidence level. In both model and observations, the MOK is weakly correlated with All India Summer Monsoon Rainfall. Again, the model skill was examined through equitable threat score (ETS). The ETS score is high for normal (0.48) and delayed (0.42) onset years, while the score is very low in early onset years. The spatial patterns of rainfall over central India are very similar in early and normal onset years. The model has performed well in reproducing the moisture inflow in to the Indian subcontinent from all the directions in most of the years, but there is no one-to-one relation between different categories of MOK years with total rainfall and net moisture inflow. Based on this study, it is found that RegCM4 reproduces different aspects of MOK reasonably well. K E Y W O R D Sequitable threat score, ERA-interim, monsoon onset over Kerala, moisture Inflow, RegCM4

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Relationship between tropospheric temperature and Indian summer monsoon rainfall as simulated by RegCM3

Cited by 23 publications

References 25 publications

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

On bias correction of summer monsoon precipitation over India from CORDEX‐SA simulations

Assessment of two versions of regional climate model in simulating the Indian Summer Monsoon over South Asia CORDEX domain

How good is regional climate model version 4 in simulating the monsoon onset over Kerala?

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