Variability and teleconnectivity of northeast monsoon rainfall over India

Nayagam, Lorna R.; Janardanan, Rajesh; Mohan, H. S. Ram

doi:10.1016/j.gloplacha.2009.10.005

Cited by 17 publications

(14 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…precipitation and temperature for different applications, general circulation models (GCMs) are known to have a very important role over different parts of the world (TOMEI et al 2009;INES et al 2011;MOHANTY et al 2013;TIWARI et al 2014;NAGES-WARARAO et al 2015). Many studies (WANG et al 2005;KRISHNAMURTI et al 2006;Kripalani et alet al 2007;NAIR et al 2014) were available on evaluation performance of GCMs for predicting Indian summer monsoon rainfall. However, limited number of studies exist in documenting the skill of these models in predicting winter precipitation over India (KRIPALANI and KUMAR 2004;HATWAR et al 2005;ZUBAIR and ROPELEWSKI 2006;NAYAGAM et al 2009;TIWARI et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Many studies (WANG et al 2005;KRISHNAMURTI et al 2006;Kripalani et alet al 2007;NAIR et al 2014) were available on evaluation performance of GCMs for predicting Indian summer monsoon rainfall. However, limited number of studies exist in documenting the skill of these models in predicting winter precipitation over India (KRIPALANI and KUMAR 2004;HATWAR et al 2005;ZUBAIR and ROPELEWSKI 2006;NAYAGAM et al 2009;TIWARI et al 2014). HATWAR et al (2005 did two case studies for intense WD, which affected the northwest India using the IMD operational limited area analysis, forecast system, and concluded that 24-hour model forecasts are in fine agreement with the observations in respect of WD movement and intensification.…”

Section: Introductionmentioning

confidence: 99%

“…HATWAR et al (2005 did two case studies for intense WD, which affected the northwest India using the IMD operational limited area analysis, forecast system, and concluded that 24-hour model forecasts are in fine agreement with the observations in respect of WD movement and intensification. However, KRIPALANI and KUMAR (2004); ZUBAIR and ROPELEWSKI (2006);NAYAGAM et al (2009) show that GCMs have poor predictability skill for generating seasonal winter precipitation over India. ZORITA andVON STORCH (1999) andPRINCE et al (2007) pointed out several causes of dynamical models' failure on small-scale seasonal predictions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Evaluation of Performances of Two Versions of NCEP Climate Forecast System in Predicting Winter Precipitation over India

Nageswararao

Mohanty

Nair

et al. 2015

Pure Appl. Geophys.

View full text Add to dashboard Cite

The precipitation during winter (December through February) over India is highly variable in terms of time and space. Maximum precipitation occurs over the Himalaya region, which is important for water resources and agriculture sectors over the region and also for the economy of the country. Therefore, in the present global warming era, the realistic prediction of winter precipitation over India is important for planning and implementing agriculture and water management strategies. The National Centers for Environmental Prediction (NCEP) issued the operational prediction of climatic variables in monthly to seasonal scale since 2004 using their first version of fully coupled global climate model known as Climate Forecast System (CFSv1). In 2011, a new version of CFS (CFSv2) was introduced with the incorporation of significant changes in older version of CFS (CFSv1). The new version of CFS is required to compare in detail with the older version in the context of simulating the winter precipitation over India. Therefore, the current study presents a detailed analysis on the performance of CFSv2 as compared to CFSv1 for the winter precipitation over India. The hindcast runs of both CFS versions from 1982 to 2008 with November initial conditions are used and the model's precipitation is evaluated with that of India Meteorological Department (IMD). The models simulated wind and geopotential height against the National Center for Atmospheric Research (NCEP-NCAR) reanalysis-2 (NNRP2) and remote response patterns of SST against Extended Reconstructed Sea Surface Temperatures version 3b (ERSSTv3b) are examined for the same period. The analyses of winter precipitation revealed that both the models are able to replicate the patterns of observed climatology; interannual variability and coefficient of variation. However, the magnitude is lesser than IMD observation that can be attributed to the model's inability to simulate the observed remote response of sea surface temperatures to all India winter precipitation. Of the two, CFSv1 is appreciable in capturing year-to-year variations in observed winter precipitation while CFSv2 failed in simulating the same. CFSv1 has accounted for less mean bias and RMSE errors along with good correlations and index of agreements than CFSv2 for predicting winter precipitation over India. In addition, the CFSv1 is also having a high probability of detection in predicting different categories (normal, excess and deficit) of observed winter precipitation over India.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Evaluation of Performances of Two Versions of NCEP Climate Forecast System in Predicting Winter Precipitation over India

Nageswararao

Mohanty

Nair

et al. 2015

Pure Appl. Geophys.

View full text Add to dashboard Cite

show abstract

“…Eventhough interannual variation and prediction of the southwest monsoon has been widely studied, as documented by numerous publications, the NE monsoon over south peninsular India has received much less attention, with a limited number of studies (e.g., Doraiswamy 1946;Rao and Jagannathan 1953;Rao 1963;Ramaswamy 1972;Srinivasan and Ramamurthy 1973;Dhar and Rakecha 1983;Krishnan 1984;Raj and Jamadar 1990;Sridharan and Muthusamy 1990;Singh and Sontakke 1999;Kripalani et al 2004;Zubair and Ropelewski 2006;Kumar et al 2007;Nayagam et al 2009). Some of these studies have discussed about global teleconnection patterns related to NE monsoon.…”

Section: Introductionmentioning

confidence: 99%

“…Balachandran et al (2006) defined a zonal temperature anomaly gradient index (ZTAGI) between eastern equatorial Pacific and western equatorial Pacific and found a stable significant inverse relationship with NE monsoon rainfall. In a recent study, the spatial and temporal variability of rainfall over peninsular India during the NE monsoon season has been discussed (Nayagam et al 2009). In this study, the dominant modes of the NE monsoon rainfall were identified using Empirical Orthogonal Function (EOF) analysis and the power over the identified scales was extracted using wavelet analysis.…”

Section: Introductionmentioning

confidence: 99%

Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

Acharya¹,

Kar

Kulkarni³

et al. 2011

J Earth Syst Sci

View full text Add to dashboard Cite

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.

show abstract

The influence of Indian Ocean sea surface temperature on the variability of monsoon rainfall over India

Nayagam

Rajesh

Mohan

2012

Intl Journal of Climatology

Self Cite

View full text Add to dashboard Cite

This study focuses on the spatial and temporal variability of southwest monsoon (SWM) and northeast monsoon (NEM) rainfall over India, after the 1976-1977 regime shift. Through wavelet analysis the dominant mode of variability in the SWM and NEM rainfall is found to be in the 2-8-year scale (ENSO band). Therefore, scale-averaged wavelet power (SAP) of SWM (SAPSWM) and NEM (SAPNEM) rainfall obtained from a high-resolution dataset for the period 1977-2001 was subjected to empirical orthogonal function analysis, in order to understand the homogenous regions of rainfall variability. As the study utilizes SAP, the decadal variability is observed in the spatial and temporal patterns of rainfall. The maximum variability of SAPSWM rainfall is found to be in the 2-4-year period, whereas, for the SAPNEM rainfall, the biennial mode explains the variability over the core region of NEM rainfall. Significant power is also observed in the 4-year period. The evolution of the principal components (PCs) is consistent with the above/below normal rainfall received over the region. Similar analysis has been carried out also for the SAP of monthly sea surface temperature (SAPSST) over Indian Ocean for the months from January to September. Compared with any other month, the decadal changes of the PCs of SAPSWM and SAPNEM rainfall are highly correlated with the decadal variations of PC1 of SAPSST for the month of March. Therefore, the relation between the rainfall and SST was explained using SAPSST for the month of March. Significant biennial power is also observed in March SST over southeast Indian Ocean (85-110°E and 5-40°S) after the climate shift of 1976. In order to identify any change in the relation between NEM rainfall and SAPSST (March) before and after 1976, a correlation between NEM rainfall and PC1 of SAPSST (March) was found for the period 1951-1975 and 1977-2001. Results show that correlation has increased after 1976. Therefore, this PC can be made use of in the prediction of NEM rainfall.

show abstract

Variability and teleconnectivity of northeast monsoon rainfall over India

Cited by 17 publications

References 39 publications

Comparative Evaluation of Performances of Two Versions of NCEP Climate Forecast System in Predicting Winter Precipitation over India

Comparative Evaluation of Performances of Two Versions of NCEP Climate Forecast System in Predicting Winter Precipitation over India

Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

The influence of Indian Ocean sea surface temperature on the variability of monsoon rainfall over India

Contact Info

Product

Resources

About