Vol.:(0123456789) 1 3 Meteorology and Atmospheric Physics https://doi.org/10.1007/s00703-018-0589-2 ORIGINAL PAPER Predicting summer monsoon of Bhutan based on SST and teleconnection indices Singay Dorji 1,6  · Srikantha Herath 2,3,4  · Binaya Kumar Mishra 5  · Ugyen Chophel 6 Received: 15 March 2017 / Accepted: 17 February 2018 © Springer-Verlag GmbH Austria, part of Springer Nature 2018 Abstract The paper uses a statistical method of predicting summer monsoon over Bhutan using the ocean-atmospheric circulation variables of sea surface temperature (SST), mean sea-level pressure (MSLP), and selected teleconnection indices. The predictors are selected based on the correlation. They are the SST and MSLP of the Bay of Bengal and the Arabian Sea and the MSLP of Bangladesh and northeast India. The Northern Hemisphere teleconnections of East Atlantic Pattern (EA), West Pacifc Pattern (WP), Pacifc/North American Pattern, and East Atlantic/West Russia Pattern (EA/WR). The rainfall station data are grouped into two regions with principal components analysis and Ward’s hierarchical clustering algorithm. A support vector machine for regression model is proposed to predict the monsoon. The model shows improved skills over traditional linear regression. The model was able to predict the summer monsoon for the test data from 2011 to 2015 with a total monthly root mean squared error of 112 mm for region A and 33 mm for region B. Model could also forecast the 2016 monsoon of the South Asia Monsoon Outlook of World Meteorological Organization (WMO) for Bhutan. The reliance on agriculture and hydropower economy makes the prediction of summer monsoon highly valuable information for farmers and various other sectors. The proposed method can predict summer monsoon for operational forecasting. 1 Introduction Monsoon is defned as a seasonally reversing wind system accompanied by seasonal changes in atmospheric circula- tion and precipitation. The reversal in the wind direction is caused due to diferential heating of the continents and the adjacent oceans (Trenberth et al. 2000). The monsoon of South Asia afects countries like Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka. Predicting monsoon is difcult but undeniably important due to enormous eco- nomic and societal impacts. Climate change is projected to intensify the global water cycle, with the likely increase of both mean and extreme precipitation (IPCC 2013). Monsoon winds are likely to weaken, but the monsoon precipitation is likely to intensify (IPCC 2013). Studies have shown that there is substantial variability in the Asian summer mon- soon (Annamalai and Slingo 2001) and there are changes in the extreme wet and dry spells of the South Asian mon- soon from 1951 to 2011 (Singh et al. 2014). El Niño and the Southern Oscillation (ENSO) (Walker 1923) will remain the dominant mode of inter-annual climate variability (Stock- dale et al. 2010; IPCC 2013) and thus a major source of predictability. However, some studies have shown that the relationship is weakening. The inverse relationship of ENSO with the Indian monsoon has broken in recent decades (Kumar et al. 1999). They argue that it is possibly due to two reasons, the south-eastward shift in the Walker circula- tion anomalies and the enhanced land–ocean thermal gradi- ent due to the increased surface temperatures over Eurasia, which helped sustain the normal monsoon even in strong ENSO events. In addition to the ENSO, studies have shown that the Equatorial Indian Ocean Oscillation (EQUINOO) Responsible Editor: A.-P. Dimri. * Singay Dorji singaydor@yahoo.co.in 1 Institute for the Advanced Study of Sustainability (UNU-IAS), United Nations University, 5 Chome-53-70 Jingumae, Shibuya, Tokyo 150-8925, Japan 2 Ministry of Megapolis and Western Development, Battaramulla, Sri Lanka 3 United Nations University (UNU-IAS), IR3S-University of Tokyo, Tokyo, Japan 4 University of Peradeniya, Peradeniya, Sri Lanka 5 United Nations University, (UNU-IAS), Tokyo, Japan 6 National Center for Hydrology and Meteorology, Thimphu, Bhutan