Received: 23 August 2017 Revised: 4 July 2018 Accepted: 21 August 2018 Published on: 30 September 2018 DOI: 10.1002/joc.5853 RESEARCH ARTICLE A statistical seasonal forecast model of North Indian Ocean tropical cyclones using the quasi-biennial oscillation Md Wahiduzzaman1 | Eric C. J. Oliver1,2,3 | Philip J. Klotzbach4 | Simon J. Wotherspoon1,5 | Neil J. Holbrook1,6 1Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, Previous studies have shown that the skill of seasonal forecasts of tropical cyclone Australia (TC) activity over the North Indian Ocean (NIO) tends to be poor. This paper 2Department of Oceanography, Dalhousie investigates the forecast potential of TC formation, trajectories and points of land- University, Halifax, Nova Scotia, Canada fall in the NIO region using an index of the stratospheric quasi-biennial oscillation 3 Australian Research Council Centre of Excellence (QBO) as the predictor variable in a new statistical seasonal forecast model. Gene- for Climate System Science, Hobart, Tasmania, Australia sis was modelled by kernel density estimation, tracks were fitted using a general- 4Department of Atmospheric Science, Colorado ized additive model (GAM) approach with an Euler integration step, and landfall State University, Fort Collins, Colorado, USA location was estimated using a country mask. The model was trained on 30 years 5Australian Antarctic Division, Kingston, of TC observations (1980–2009) from the Joint Typhoon Warning Center and the Tasmania, Australia QBO index at lags from 0 to 6 months. Over this time period, and within each sea- 6Australian Research Council Centre of Excellence son and QBO phase, the kernel density estimator modelled the distribution of gene- for Climate Extremes, Hobart, Tasmania, Australia sis points, and the cyclone trajectories were then fit by the GAM along the Correspondence Md Wahiduzzaman, Institute for Marine and observed cyclone tracks as smooth functions of location. Trajectories were simu- Antarctic Studies, University of Tasmania, lated from randomly selected genesis points in the kernel density estimates. Ensem- Tasmania 7001, Australia. bles of cyclone paths were traced, taking account of random innovations every 6-hr Email: [email protected] along the GAM-fitted velocity fields, to determine the points of landfall. Lead–lag Funding information analysis was used to assess the best predictor timescales for TC forecast potential. Australian Research Council (ARC), Grant/Award Number: CE170100023; G. Unger Vetlesen We found that the best model utilized the QBO index with a 3-month lead. Two Foundation; Tasmania Graduate Research hindcast validation methods were applied. First, leave-one-out cross-validation was Scholarship, Grant/Award Number: 172141 performed where the country of landfall was decided by the majority vote of the simulated tracks. Second, the distances between the landfall locations in the obser- vations and simulations were calculated. Application of seasonal forecast analysis further indicated that including information on the state of the QBO has the poten- tial to improve the skill of TC seasonal forecasts in the NIO region. KEYWORDS landfall, North Indian Ocean, quasi-biennial oscillation, statistical modelling, tropical cyclone genesis, tropical cyclone trajectories 1 | INTRODUCTION Patwardhan, 2013; Fadnavis et al., 2014; Girishkumar et al., 2014; Mohapatra et al., 2014; Rajasekhar et al., 2014; Tropical cyclones (TCs) are one of the most significant Girishkumar et al., 2015; Nath et al., 2015). Globally, the weather hazards in the tropics and represent a major hazard impacts of TCs can be devastating, with numerous previous to North Indian Ocean (NIO) rim countries (Alam et al., events causing enormous loss of life and property damage, 2003; Ali et al., 2007; Alam and Collins, 2010; Girishkumar representing significant financial risk to insurance and rein- et al., 2012; Mohapatra et al., 2012; Paliwal and surance companies (Rumpf et al., 2007). TCs pose multiple 934 © 2018 Royal Meteorological Society wileyonlinelibrary.com/journal/joc Int J Climatol. 2019;39:934–952. WAHIDUZZAMAN ET AL. 935 meteorological hazards (e.g., storm surge, heavy rain, strong • Easterly anomalies are generally stronger (30–35 m/s) winds) that can last several days (Ebert et al., 2010). One than westerly anomalies (15–20 m/s). remarkable example was TC Nargis in 2008, which killed • Highest amplitude of both E and W phases typically over 135,000 people in Myanmar (Webster, 2008; Lin et al., occurs near 20 hPa. 2009) and ranked as the second deadliest disaster in the • Average periodicity is slightly greater than 2 years. decade from 2000–2009 according to the Centre for • Oscillation period and amplitude vary considerably from Research on Epidemiology of Disasters (Rodriguez et al., cycle to cycle. 2009). Due to the significant loss of life as well as economic damage caused by TCs making landfall in NIO rim countries The circulation of the QBO extends below the tropo- (Webster, 2008; Islam and Peterson, 2009; Ng and Chan, pause and modulates the amount of water vapour in the air 2012; Mohapatra et al., 2014; Pattanaik and Mohapatra, (Zhou et al., 2004; Liang et al., 2011). The QBO has been 2016) it is important to assess and anticipate risks as much shown to modulate tropical deep convection (Collimore as possible. Due to the threats that TCs pose to life and prop- et al., 2003; Ho et al., 2009; Liess and Geller, 2012), the erty, accurate TC landfall probability forecasts can be poten- occurrence of Atlantic hurricanes (Gray, 1984a) and typhoon tially beneficial to local populations regarding risks tracks in the western North Pacific (Ho et al., 2009). Gray (Tolwinski-Ward, 2015). (1984a) demonstrated impacts of the QBO on Atlantic TC Skilful forecasts of TC tracks have particular importance activity and showed that TC activity in the Atlantic was (Fraedrich and Leslie, 1989; Carter and Elsner, 1997; greater during the QBO westerly phase, or during the transi- Carson, 1998; Elsner et al., 2006; Camargo et al., 2007; Chu tion to the westerly phase, than during the easterly phase. In and Zhao, 2007; Basu and Bhagyalakshmi, 2010; Chu et al., addition, more major Atlantic hurricanes occurred during 2010; Chand and Walsh, 2011; Balachandran and Geetha, westerly QBO years (Gray, 1984b). The QBO effect on 2012; Camp et al., 2015). In the present study, we have Atlantic hurricane activity has been discussed by several developed a statistical model to better understand and predict authors since Gray’s initial publications (Shapiro, 1989; NIO region TC activity in the upcoming TC season. The per- Hess and Elsner, 1994; Landsea et al., 1998; Elsner and cent contribution of NIO region TCs to the global total dif- Kara, 1999; Arpe and Leroy, 2009). Camargo and Sobel fers between studies and time periods investigated, ranging (2010) have also explored the QBO connection with TC from 5 to 7% (Mohapatra et al., 2014; Schreck et al., 2014; activity in the North Atlantic. They found a statistically sig- Sahoo and Bhaskaran, 2016) and averaging ~5 TCs per year nificant relationship from the 1950s to the 1980s but did not (Mohapatra et al., 2014). Despite the relatively small per- see a significant correlation after the 1980s. Camargo and centage of the global total, the socio-economic impacts of Sobel (2010) concluded that the QBO–TC frequency rela- TCs in this region are greater than those in other TC basins tionship was generally weak in most global TC basins. Fad- (Singh, 2010). This is due to the high population density navis et al. (2014) proposed that the phase of the QBO may along coastlines in low-lying areas, as well as other physical influence NIO TC trajectories by modulation of tropopause and socio-economic factors, particularly in Bangladesh pressure and associated steering winds. (Hossain and Paul, 2017). Furthermore, NIO region TCs are Recently, a potentially useful climatic relationship has typically characterized by shorter life cycles, and their devel- been demonstrated between the stratospheric QBO and TC opment relatively close to the coast compared to other basins trajectories in the Bay of Bengal (BoB) region of the NIO allows little time for preparation. So, any improvements in (Fadnavis et al., 2014). The impact of the QBO on TC tra- TC forecasting for this region can provide significant bene- jectories was demonstrated to be stronger during the QBO fits (Singh et al., 2012). easterly phase than the QBO westerly phase. Also, the num- The quasi-biennial oscillation (QBO) is the primary ber of TCs was higher during the easterly phase compared to mode of wind variability in the tropical stratosphere. It repre- the westerly phase, at least during the more active pre- sents a quasi-periodic (with a mean phase of 28–29 months) monsoon and post-monsoon seasons. This follows earlier oscillation of the equatorial easterly (E) and westerly research work that also proposed relationships between the (W) zonal winds in the stratosphere (Baldwin et al., 2001). stratospheric QBO and hurricane activity in the North Atlan- The major features of the QBO are discussed in several tic region (Gray et al., 1992; 1994), although the use of the papers including Naujokat (1986) and Baldwin et al. (2001). QBO as a predictor for North Atlantic TC activity has been We highlight several of these major features below: discontinued in recent years (Klotzbach, 2007; Camargo and Sobel, 2010). Other analyses have also tested the effect of • Downwards propagation of easterly and westerly winds the QBO on TC activity in the western North Pacific (Chan, in which the propagation of the westerlies is usually fas- 1995; Baik and Paek, 1998; Lander and Guard, 1998; Ho ter and more regular than that of the easterlies. et al., 2009), the eastern North Pacific (Whitney and Hob- • Transition to easterlies is often delayed between 30 and good, 1997), the NIO (Balachandran and Guhathakurta, 50 hPa. 1999), the South Indian Ocean (Jury, 1993; Jury et al., 936 WAHIDUZZAMAN ET AL.