Simulation of monsoon intraseasonal variability in NCEP CFSv2 and its role on systematic bias

Deshpande, Medha; Mukhopadhyay, P.; Saha, Subodh Kumar; Rao, Suryachandra A.; Murthugudde, Raghu; Goswami, B. N.

doi:10.1007/s00382-014-2089-5

Cited by 84 publications

(90 citation statements)

References 68 publications

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“…The systematic biases of CFSv2 in simulating the Indian summer monsoon features have been reported by many researchers [ Yuan et al ., ; Pokhrel et al ., ; Jiang et al ., ; Saha et al ., ; Goswami et al ., ; George et al ., ]. The annual cycle of rainfall over the Indian landmass simulated at two different horizontal resolution runs of the CFSv2 is given in Figure along with observations.…”

Section: Resultsmentioning

confidence: 81%

“…Both low‐ and high‐resolution (T126 and T382) runs of the CFSv2 reasonably simulate the locations of the rainfall maximum (figure not shown). However, both these runs show strong dry bias over the Indian landmass and a wet bias over the tropical ocean basins (Figures b and c) consistent with previous studies [ Saha et al ., ; Goswami et al ., ; George et al ., ]. Recently, Sabeerali et al .…”

Section: Resultsmentioning

confidence: 99%

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Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution

et al. 2016

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This study compares the simulation and prediction skill of the Indian summer monsoon at two different horizontal resolutions viz., T126 (~100 km) and T382 (~38 km) using 28 years of hindcast runs of the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) model. It is found that the simulation of the mean state of the South Asian summer monsoon, its variance, and prediction skill of the all India summer monsoon rainfall (AISMR) are better represented in the high-resolution configuration (T382) of the CFSv2 compared to the low-resolution (T126) configuration. In the high-resolution run, the systematic bias in the teleconnection between the AISMR and Indian Ocean Dipole (IOD) has considerably reduced and the teleconnections between the AISMR and El Niño-Southern Oscillation (ENSO) remained same. We hypothesize that the better simulation of mean climate and IOD-AISMR teleconnection in highresolution configuration (T382) of CFSv2 are responsible for the improved prediction skill of AISMR in T382 configuration. Although the T382 configuration of CFSv2 has shown a significant improvement in the simulation and prediction of Indian summer monsoon as compared to the T126 configuration, several parallel efforts are still essential to understand the processes controlling some of the systematic biases of CFSv2 and those efforts are underway as part of the Monsoon Mission project.

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Section: Resultsmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution

et al. 2016

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“…Sabeerali et al . [] have demonstrated that the Climate Forecast System version 2 (CFSv2) [ Saha et al ., ] simulates good ISO characteristics when compared to the best CMIP5 models and it has demonstrated skill at simulating the ISMR and its different modes of variability [ Chattopadhyay et al ., ; George et al ., ; Ramu et al ., ; Abhik et al ., ; Joseph et al ., ; Abhilash et al ., ; Borah et al ., ; Srivastava et al ., ] even though it simulates a dry bias over the Indian landmass [ Goswami et al ., ; Saha et al ., ; George et al ., ]. Goswami et al .…”

Section: Introductionmentioning

confidence: 99%

“…Goswami et al . [] have demonstrated that the northward propagation of the low frequency ISO mode (30–60 day) in the CFSv2 is reasonable. Sharmila et al .…”

Section: Introductionmentioning

confidence: 99%

Structure, characteristics, and simulation of monsoon low‐pressure systems in CFSv2 coupled model

Srivastava

Rao

et al. 2017

JGR Oceans

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Indian Summer Monsoon (ISM) synoptic scale systems (low‐pressure systems, LPS) are known to produce increased rainfall over central India (CI). Fidelity of the Climate Forecast System version 2 (CFSv2) at simulating the LPS and their characteristics is evaluated in this study using a feature tracking algorithm. The model is able to reproduce the clustering of LPS by monsoon intraseasonal oscillations and the associated precipitation over eastern‐central India. It is found that mean biases in circulation and moisture stem from cold sea surface temperature (SST) bias in the model which results in weak LPS linked rainfall events over central India. Two sensitivity experiments were carried out to study the effect of coupled dynamics of tropical basins on LPS. Suppression of active dynamics of the tropical Indian Ocean in CFSv2 causes a reduction in cold SST bias and enhanced cyclogenesis in the northern Bay of Bengal. The reduced low‐level anticyclonic bias and enhanced moisture availability result in a better simulation of LPS structure, and associated precipitation over CI. Suppression of active ocean dynamics in tropical Pacific Ocean causes a perennial El‐Niño type bias which restricts LPS propagation over the Indian landmass, possibly due to time‐mean subsidence induced by remote El‐Niño forcing. Sensitivity experiments indicate the need for improvements in the representation of tropical Indian Ocean coupled dynamics as well as convective parameterization schemes in the model for subsequent improvements in the simulation of ISM at various time scales.

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“…It is generally found that, even though the global climate models (GCMs) like CFSv2 simulate or forecast the global (large-scale) climate adequately (Sahai et al 2013a;Sabeerali et al 2013;Sharmila et al 2013;Abhilash et al 2013Abhilash et al , 2014aGoswami et al 2014), forecast skill reduces, once the information is sought on smaller spatial scales and at longer lead-times (Borah et al 2014). There can be many reasons for this discrepancy depending on the application; however, several common causes have been identified.…”

Section: Introductionmentioning

confidence: 99%

A bias-correction and downscaling technique for operational extended range forecasts based on self organizing map

et al. 2016

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Simulation of monsoon intraseasonal variability in NCEP CFSv2 and its role on systematic bias

Cited by 84 publications

References 68 publications

Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution

Indian summer monsoon rainfall simulation and prediction skill in the CFSv2 coupled model: Impact of atmospheric horizontal resolution

Structure, characteristics, and simulation of monsoon low‐pressure systems in CFSv2 coupled model

A bias-correction and downscaling technique for operational extended range forecasts based on self organizing map

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