Indian winter monsoon: Present and past

Dimri, A. P.; Yasunari, Tetsuzo; Kotlia, Bahadur Singh; Mohanty, U. C.; Sikka, D. R.

doi:10.1016/j.earscirev.2016.10.008

Cited by 103 publications

(50 citation statements)

References 110 publications

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“…The Indian summer monsoon rainfall (ISMR) during June-September solely contributes to around 80% of the total rainfall on which the Indian economy is highly dependent (Ghosh et al, 2009;Kumar et al, 2010;Jain and Kumar, 2012). Some contribution to the annual rainfall also comes from the Indian winter monsoon rainfall (IWMR) during November-January (Palazzi et al, 2013, Dimri et al, 2016. Therefore, rainfall distributions over India are highly seasonal.…”

Section: Introductionmentioning

confidence: 99%

Changes in rainfall seasonality pattern over India

Rai

Dimri

2019

Meteorological Applications

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Rainfall seasonality over India is studied using an individual seasonality index. The spatial distribution of the individual seasonality index and the mean individual seasonality index over two periods, that is, early (1901–1970) and recent (1971–2015), is analysed. Trend analysis using linear regression during these two periods shows a declining trend in seasonality index (i.e. decreasing values of the index), which impends shorter dry periods resulting in more consistent rainfall throughout the year in some parts of India during the recent period (1971–2015).

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Section: Introductionmentioning

confidence: 99%

Changes in rainfall seasonality pattern over India

Rai

Dimri

2019

Meteorological Applications

View full text Add to dashboard Cite

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“…Similarly, warm tropical Pacific and Indian Oceans are related to drier or wetter winter anomalies in southeastern India or northern India, respectively. It is indicated that ENSO events are related to the modulation of the westerly disturbances that bring copious rainfall to northern India in the winter season (Dimri et al ., ). Therefore, the teleconnections exhibited by the winter anomalies over northern India in this paper is consistent with such an interpretation.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, despite the strong variability of the Indian Winter Monsoon (IWM) rainfall and its relatively strong teleconnection with ENSO (Ramaswamy, 1972;Zubair and Ropelewski, 2006;Kumar et al, 2007;Rajeevan et al, 2012;Dimri et al, 2016;Misra and Bhardwaj, 2019;Sengupta and Nigam, 2019) the models continue to display poor fidelity of the IWM (Rajeevan et al, 2012;Sengupta and Nigam, 2019).…”

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confidence: 99%

The impact of varying seasonal lengths of the rainy seasons of India on its teleconnections with tropical sea surface temperatures

Misra

Bhardwaj

2020

Atmospheric Science Letters

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We present in this paper the interannual variability of seasonal temperature and rainfall in the Indian meteorological subdivisions (IMS) for boreal winter and summer seasons that take in to account the varying length of the seasons. Our study reveals that accounting for the variations in the length of the seasons produces stronger teleconnections between the seasonal anomalies of surface temperature and rainfall over India with corresponding sea surface temperature anomalies of the tropical Oceans (especially over the northern Indian and the equatorial Pacific Oceans) compared to the same teleconnections from fixed length seasons over the IMS. It should be noted that the IMS show significant spatial heterogeneity in these teleconnections.

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“…The Himalayan region has the largest area under the glacier (70% of the non-polar glacier) and permafrost outside polar caps (Bothale et al 2015;Mir et al 2015). The rainfall over this region is highly influenced by the unique direction of the Indian monsoon from June to September and western disturbances from December to March (Dimri 2012;Das et al 2014;Meher et al 2014Meher et al , 2017aDimri et al 2016). Orography, altitude, topography and lower tropospheric circulation patterns are the other factors which regulated the rainfall over this region (Rao 1997;Dimri 2012;Das et al 2014;Meher et al 2014Meher et al , 2017a.…”

Section: Introductionmentioning

confidence: 99%

Gridded data as a source of missing data replacement in station records

Meher

Das

2019

J Earth Syst Sci

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The quality of available station data over western Himalayan region (WHR) of India is poor due to missing values, and hence quantifying climate change information at the station level is more challenging. The present study investigated the extent to which the available two different resolutions gridded rainfall data from the India Meteorological Department (IMD) namely, IMD-0.25 • × 0.25 • (IMD.25) and IMD-1 • × 1 • (IMD1) and global observational gridded data from the Climate Research Unit (CRU-0.5 • × 0.5 •) of UK can be used as a substitute to replace the missing values in IMD station data. Long-term time series produced at each station location showed that IMD.25 data was much closer to observation both in magnitude and patterns compared to CRU and IMD1. The seasonal and annual scale performance of these gridded data has been evaluated through different agreement and error indices. The agreement indices showed higher values in case of IMD.25 data while IMD1 and CRU indicated poor results. Similarly, the estimated errors are minimum in IMD.25 and maximum in CRU data. Finally, an overall agreement index (OAI) was developed by the combined influence of all the agreement indices. The results of OAI for each station revealed that in more than 77% of cases, the performance of IMD.25 gridded data to reproduce station level rainfall is superior as compared to IMD1 (<63%) and CRU (<46%) data. Therefore, it is concluded that the IMD.25 gridded data may be reliably used as a substitute data source in place of missing rain-gauge data over the WHR.

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Indian winter monsoon: Present and past

Cited by 103 publications

References 110 publications

Changes in rainfall seasonality pattern over India

Changes in rainfall seasonality pattern over India

The impact of varying seasonal lengths of the rainy seasons of India on its teleconnections with tropical sea surface temperatures

Gridded data as a source of missing data replacement in station records

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