The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology 3.1 Prediction skill in the Tropical Indian Ocean Current Status of the Indian Ocean Dipole (IOD) Prediction in Seasonal Forecast Systems 1 Introduction  The Indian Ocean Dipole (IOD) has been recognized as a strong climate driver that not only significantly impacts the rainfall variability in the countries surrounding the Indian Ocean but also may influence the global climate beyond the El Niño-Southern Oscillation (ENSO).  Some air-sea coupled models show skilful prediction up to 4-6 months in advance for those extreme positive IOD events (e.g., Luo et al. 2007, 2010, Wajsowicz 2007 and Zhao and Hendon, 2009). However, the IOD prediction skill for those real-time seasonal forecast systems around the world has not been well assessed as what the ENSO has been done Motivation  Assessing the current status of the real-time IOD prediction skill in seasonal forecast systems around the world.  Note: 3-month-Mean (3mM) along the lead months has been applied for the purpose of figure. 3mM: lt(i)_after_filter = (lt(i)+lt(i+1)+lt(i+2))/3_before_filter 4 Summary  The forecast skill of IOD is much less predictable than El Niño for all forecast systems, for instance, the skilful prediction (TCC  0.6) of IOD for the peak phase of the observed IOD (boreal autumn season) of all seasonal forecast systems is up to 3-4 months lead time;  More than half of the observed positive IOD and negative IOD events in the SON season can be correctly predicted by all seasonal forecast systems at least 4-5 months in advance in despite of the false alarm rate of all seasonal forecast systems are also obviously greater than 0.5 after 1-month lead time;  Some common deficiencies suffered by most of the seasonal forecast systems, such as, relatively larger ensemble standard deviation, obviously cold bias of SST mean state in both tow poles of IOD, underpredicted the relationship between the IOD and ENSO, etc. 1: Centre for Australian Weather and Climate Research (CAWCR) 2: Frontier Research Center for Global Change, JAMSTEC, Yokohama, Japan 3: European Centre for Medium-Range Weather Forecasting, Reading, United Kingdom L. Shi 1, H. Hendon 1, O. Alves 1, J-J Luo 2, M. Balmaseda 3 Acknowledgement: NCEP-CFS Team, D. Anderson, G. Liu and G. Wang (a) Temporal correlation coefficients (TCC) between monthly SST anomalies in the WIO region (averaged over 50-70ºE, 10ºS-10ºN) of the seasonal forecast systems and the corresponding observations for all initial months during the period ; (b) As in (a), except for the EIO region (averaged over ºE, 10ºS-0ºN); (c) As in (a), except for the IOD index (defined as WIO-EIO); (d)-(f) As in (a)-(c), except for the normalized ensemble standard deviation (normalized by corresponding observed standard deviation). X axis is the lead time 0-6 months. a) b) c) d) e) f) 2 Seasonal Forecast Systems and Hindcast Datasets 3.2 Mean State Bias in the Tropical Indian Ocean Temporal correlation coefficients of IOD indices of the seasonal forecast systems for all verification months (January-December) during the period at 0-, 2-, and 4-month lead time, respectively. X axis is the verification month from January to December. The filter has been applied. Temporal correlation coefficients of IOD indices of the seasonal prediction systems and the statistic model (dot-dashed purple curve; following the Dommenget and Jansen, 2009) in the September-October- November (SON) season during the period Contingency table of forecasting trichotomous event Plume of the monthly IOD indices of the seasonal forecast systems at the initial date 1st May (a), 1st July (b) and 1st September (c) for the period X axis is the time series from 1st January 1981 to 31st December The unit of Y axis is degree ºC a) b) c) The hit rate of the positive IOD events (a), the hit rate of the negative IOD events (b) and the false alarm rate (c) for the seasonal forecast systems in the SON season during the period