South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4

Kripalani, R. H.; Oh, Jai-Ho; Kulkarni, Ashwini; Sabade, S. S.; Chaudhari, Hemantkumar S.

doi:10.1007/s00704-006-0282-0

Cited by 338 publications

(214 citation statements)

References 63 publications

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“…(iv) Coefficient of variation (CV) is defined as the ratio of standard deviation to the mean of the sample. Scatter plots of the seasonal mean rainfall versus the CV are plotted to analyse the variability of the models in terms of CV variation (Kripalani et al ., 2007). (v) The spatial plots of the rainfall and the mean sea level pressure of the models are obtained to identify the spatial behaviour of the models in comparison with the corresponding observed dataset for the region of interest.…”

Section: Methodsologymentioning

confidence: 99%

“…Climate modelling groups around the world have performed a comprehensive, systematic, well-designed and well-coordinated set of 20th and 21st century climate change experiments for the IPCC AR4 (IPCC, 2007;Kripalani et al ., 2007). The IPCC AR4 coupled climate model datasets of the 20th century experiments, forced by changing greenhouse gas concentrations, as well as those for the control simulations, are available as ensembles through the Program for Climate Model Diagnosis and Intercomparison (PCMDI) website (http://www-pcmdi.llnl.gov).…”

Section: Introductionmentioning

confidence: 99%

“…It has been found that for a particular region, the models chosen for simulating the current climate conditions perform differently (Annamalai et al ., 2007;Kripalani et al ., 2007;Carolina and Gabriel, 2009;Errasti et al ., 2011). Furthermore, different methodologies lead to differing selections of models, even for one particular region (Annamalai et al ., 2007;Kripalani et al ., 2007). The general consensus is that although a model may be good in one region, it may not to be good in simulating the climate conditions of other regions.…”

Section: Introductionmentioning

confidence: 99%

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Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

Almazroui

Abid

Athar

et al. 2012

Intl Journal of Climatology

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ABSTRACT:The interannual rainfall variability derived from the 22 Global Climate Model (GCM) simulations of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) for the duration 1979-2000 is analysed and compared with the gridded observed dataset over the Arabian Peninsula. The annual cycle of the rainfall derived from these models is validated for the entire Arabian Peninsula, and separately for its two sub-regions, named northern and southern Arabian Peninsula. The spatial patterns of the rainfall and the mean sea level pressure are analysed to judge the ability of the models to simulate the mean climatology of the Peninsula. This analysis reveals that out of the 22 IPCC AR4 GCM multi-model datasets, only one group (composed of 5 models) is relatively better than all the others in simulating the interannual variability of the wet season rainfall for the northern sub-region, and another group (also composed of 5 models) is likewise for the dry season rainfall climatology of the southern sub-region, compared with the gridded dataset. The above two groups of models tend to fall within one-sigma standard deviation of the mean seasonal rainfall derived from the gridded dataset. Moreover, only one model [CCCMA-CGCM3 (T47) from Canada] is found to be relatively better in simulating the rainfall climatology for both the wet and the dry seasons (i.e. for the northern and the southern sub-regions) simultaneously, compared with the observed data.

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Section: Methodsologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

Almazroui

Abid

Athar

et al. 2012

Intl Journal of Climatology

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“…Among the seven models used in their study, ing and mpi are the two models common to this study. In their study, Kripalani et al (2007b) used monthly precipitation values to calculate the annual cycle over the south Asian region and found bar and ips models unable to simulate the annual cycle accurately. The results in this study show a higher skill for the ips model in the Annual category for the Indian region.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Evaluation of the daily precipitation simulated by the GCMs exclusively for the Indian region at finer scales has not been conducted (Table 1). Past studies have evaluated the precipitation simulated by GCMs at larger timescales (monthly, seasonal, and annual) over India region and larger spatial scales (e.g., south Asia) (Annamalai et al 2007;Bollasina and Nigam 2009;Kripalani et al 2007b;Preethi et al 2010;Rajeevan and Nanjundiah 2009). A good review of the methods available to evaluate the performance of GCMs may be found in (Errasti et al 2010;Johnson and Sharma 2009).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

Anandhi

Nanjundiah

2014

Theor Appl Climatol

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The ability of Coupled General Circulation Models (CGCMs) participating in the Intergovernmental Panel for Climate Change's fourth assessment report (IPCC AR4) for the 20th century climate (20C3M scenario) to simulate the daily precipitation over the Indian region is explored. The skill is evaluated on a 2.5°×2.5°grid square compared with the Indian Meteorological Department's (IMD) gridded dataset, and every GCM is ranked for each of these grids based on its skill score. Skill scores (SSs) are estimated from the probability density functions (PDFs) obtained from observed IMD datasets and GCM simulations. The methodology takes into account (high) extreme precipitation events simulated by GCMs. The results are analyzed and presented for three categories and six zones. The three categories are the monsoon season (JJASO -June to October), non-monsoon season (JFMAMND -January to May, November, December) and for the entire year ("Annual"). The six precipitation zones are peninsular, west central, northwest, northeast, central northeast India, and the hilly region. Sensitivity analysis was performed for three spatial scales, 2.5°grid square, zones, and all of India, in the three categories. The models were ranked based on the SS. The category JFMAMND had a higher SS than the JJASO category. The northwest zone had higher SSs, whereas the peninsular and hilly regions had lower SS. No single GCM can be identified as the best for all categories and zones. Some models consistently outperformed the model ensemble, and one model had particularly poor performance. Results show that most models underestimated the daily precipitation rates in the 0-1 mm/day range and overestimated it in the 1-15 mm/day range.

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Paradox in South Asian summer monsoon circulation change: Lower tropospheric strengthening and upper tropospheric weakening

2014

Geophysical Research Letters

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In the literature, there exist contradictory conclusions on the South Asian summer monsoon (SASM) precipitation and circulation changes: whether the circulation change contributes positively by strengthening or negatively by weakening to the rainfall enhancement, on a background of moisture content increase. Based on Representative Concentration Pathway 4.5 simulations by 18 Coupled Model Intercomparison Project phase 5 models, this study explains these puzzles by illustrating that the SASM circulation changes oppositely between the lower and upper troposphere, with tipping point at 450 hPa. However, this indicates a new paradox, created by competing mechanisms. By analyzing the intermodel variability, we determine that the mean advection of stratification change mechanism weakens the upper tropospheric circulation, while the enhanced surface land-sea thermal contrast strengthens the lower level and surface winds. Our moisture budget analysis shows that the SASM precipitation enhancement (8% K À1 ) attributes to moisture increase (5% K À1 ) and lower tropospheric circulation strengthening (3% K À1 ).

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South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4

Cited by 338 publications

References 63 publications

Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

Interannual variability of rainfall over the Arabian Peninsula using the IPCC AR4 Global Climate Models

Performance evaluation of AR4 Climate Models in simulating daily precipitation over the Indian region using skill scores

Paradox in South Asian summer monsoon circulation change: Lower tropospheric strengthening and upper tropospheric weakening

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