Assessment of Indian summer monsoon simulation by Community Atmosphere Model (CAM3)

Das, Sukanta; Deb, Sanjib; Kishtawal, C. M.; Pal, P. K.

doi:10.1007/s00704-011-0565-y

Cited by 7 publications

(10 citation statements)

References 24 publications

(30 reference statements)

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“…During the analysis of model climatology, a systematic rain-bias has been observed and removed by statistical bias correction technique that is being plugged along with the model configuration (figure 1). The rain-bias correction methodology, the validation of model generated rainfall climatology along with a glimpse of seasonal rainfall forecast (without any validation) for ISM 2011 for a single ensemble CAM experiment during the June-phase were discussed in the earlier work (Das et al 2012). However, there was no scope for the detailed analysis of all seasonal forecast experiments of ISM 2011 which had been done in three experimental phases during April, May and June, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Seasonal prediction of Indian summer monsoon: Sensitivity to persistent SST

et al. 2013

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In the present study, the assessment of the Community Atmosphere Model (CAM) developed at National Centre for Atmospheric Research (NCAR) for seasonal forecasting of Indian Summer Monsoon (ISM) with different persistent SST is reported. Towards achieving the objective, 30-year model climatology has been generated using observed SST. Upon successful simulation of climatological features of ISM, the model is tested for the simulation of ISM 2011 in forecast mode. Experiments have been conducted in three different time-phases, viz., April, May and June; using different sets of initial conditions (ICs) and the persistent SSTs of the previous months of the time-phases. The spatial as well as temporal distribution of model simulated rainfall suggest a below normal monsoon condition throughout the season in all the experiments. However, the rainfall anomaly shows some positive signature over northeast part of India in the month of June and August whereas the central Indian landmass had positive anomaly during August and September. The monthly accumulated All-India rainfall (AIR) over land for June to September 2011 are predicted to be 101% (17.6 cm), 86% (24.3 cm), 83% (21.0 cm) and 95% (15.5 cm) of normal AIR, respectively. This makes the seasonal accumulated AIR 78.4 cm which is 11% below the normal rainfall of 87.6 cm. The model prediction for the months of June and July is comparable with the observation; however, the simulation would not be able to capture the high rainfall during August and September. The intention behind this work is to assess the shortcomings in the CAM model prediction, which can later be improved for future monsoon forecast experiments.

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Section: Introductionmentioning

confidence: 99%

“…The present work is a continuation of Das et al (2012), with slight difference in objective. The actual performance of different phases of forecast experiments sensitive towards the different persistent SSTs when compared with observations is the main focus of the work.…”

Section: Introductionmentioning

confidence: 99%

Seasonal prediction of Indian summer monsoon: Sensitivity to persistent SST

et al. 2013

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“…IMD data are also validated against another observational dataset namely APHRODITE (Yatagai et al, ) and are found to be nearly identical (Rajeevan and Bhate, ). The IMD dataset is used in numerous works to study various observed dynamical aspects of ISM precipitation 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, , ). Furthermore, it is available at a resolution of 0.5° which is on the same grid as CORDEX‐SA RCMs, therefore they can be directly compared without interpolation.…”

Section: Methodsmentioning

confidence: 99%

Added value of CORDEX‐SA experiments in simulating summer monsoon precipitation over India

Choudhary

Dimri

Paeth

2018

Intl Journal of Climatology

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Regional climate models (RCMs) serve as an important tool to perform high resolution climate simulations for a limited area. However, there remains a crucial issue regarding the efficacy of RCMs that whether they are able to produce added value (AV) in representing the regional climatic features compared to their coarse scale driving Global climate models (GCMs). The present study aims at assessing the AV of a set of RCM simulations performed under the Coordinated Regional Climate Downscaling Experiments-South Asia (CORDEX-SA) compared with their respective driving GCMs in representing the Indian summer monsoon (ISM) precipitation and its dynamical features. For this purpose, the performance of RCMs and GCMs is compared for the present day climate against observations and reanalysis datasets in terms of various ISM features, that is, the spatial distribution of precipitation, the evolution of vertical shear of zonal wind and the onset phase of ISM in terms of delayed or early arrival of monsoon. The RCMs show a clear improvement upon their corresponding driving GCMs in simulating spatial features of precipitation distribution that are characteristically associated with ISM. Regionally, the RCMs also show skill in capturing the precipitation amount as a reduction in bias is found compared with their driving GCMs. There is a significant improvement in the simulation of onset dates of ISM by only some of the experiments particularly, RegCM4 driven with IPSL-CM5A-LR. The study concludes that the present set of CORDEX-SA RCM simulations do indeed add value to their driving GCMs for a number of features associated with the ISM precipitation but this value varies with region, driving GCM and the particular feature of ISM under focus.

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“…Initially, the model was tested for simulating ISM rainfall, and the model climatology was generated using a long-term simulation of 30 years. During the validation of model climatology a spatial and temporal bias in the rainfall simulation was noticed, and subsequently, a bias correction methodology was developed and implemented in the model for the Indian monsoon region (DAS et al 2012). The experimental forecast system predicts the daily, monthly and seasonal rainfall for June-September over the Indian landmass region through ensemble CAM-CLM simulation in T85 (*1.41°9 1.41°) and T170 (*0.7°9 0.7°) resolution.…”

Section: Seasonal Forecast Of Ism Rainfallmentioning

confidence: 99%

“…Presently, the atmosphere-land component of the CCSM model is used (DAS et al 2011) for experimental seasonal forecast at SAC. Every year upon completion of the summer monsoon season, the experimental seasonal and 30-day forecasts have been assessed with actual observations, and shortcomings in the forecast system have been identified for improvement in the subsequent year's forecasts (DAS et al 2012(DAS et al , 2013. On completion of 5 years since 2009, it is required to quantify the overall validation of the forecast system, and to assess the scope for further improvements of these experimental seasonal forecasts over time, if any.…”

Section: Introductionmentioning

confidence: 99%

Validation of Seasonal Forecast of Indian Summer Monsoon Rainfall

Das

Deb

Kishtawal

et al. 2015

Pure Appl. Geophys.

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The experimental seasonal forecast of Indian summer monsoon (ISM) rainfall during June through September using Community Atmosphere Model (CAM) version 3 has been carried out at the Space Applications Centre Ahmedabad since 2009. The forecasts, based on a number of ensemble members (ten minimum) of CAM, are generated in several phases and updated on regular basis. On completion of 5 years of experimental seasonal forecasts in operational mode, it is required that the overall validation or correctness of the forecast system is quantified and that the scope is assessed for further improvements of the forecast over time, if any. The ensemble model climatology generated by a set of 20 identical CAM simulations is considered as the model control simulation. The performance of the forecast has been evaluated by assuming the control simulation as the model reference. The forecast improvement factor shows positive improvements, with higher values for the recent forecasted years as compared to the control experiment over the Indian landmass. The Taylor diagram representation of the Pearson correlation coefficient (PCC), standard deviation and centered root mean square difference has been used to demonstrate the best PCC, in the order of 0.74-0.79, recorded for the seasonal forecast made during 2013. Further, the bias score of different phases of experiment revealed the fact that the ISM rainfall forecast is affected by overestimation in predicting the low rain-rate (less than 7 mm/day), but by underestimation in the medium and high rain-rate (higher than 11 mm/day). Overall, the analysis shows significant improvement of the ISM forecast over the last 5 years, viz. 2009-2013, due to several important modifications that have been implemented in the forecast system. The validation exercise has also pointed out a number of shortcomings in the forecast system; these will be addressed in the upcoming years of experiments to improve the quality of the ISM prediction.

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Assessment of Indian summer monsoon simulation by Community Atmosphere Model (CAM3)

Cited by 7 publications

References 24 publications

Seasonal prediction of Indian summer monsoon: Sensitivity to persistent SST

Seasonal prediction of Indian summer monsoon: Sensitivity to persistent SST

Added value of CORDEX‐SA experiments in simulating summer monsoon precipitation over India

Validation of Seasonal Forecast of Indian Summer Monsoon Rainfall

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