A new method for extracting the ENSO-independent Indian Ocean Dipole: application to Australian region tropical cyclone counts

Abstract

We introduce a simple but effective means of removing ENSO-related variations from the Indian Ocean Dipole (IOD) in order to better evaluate the ENSO-independent IOD contribution to Australian climate-specifically here interannual variations in Australian region tropical cyclogensis (TCG) counts. The ENSO time contribution is removed from the Indian Ocean Dipole Mode index (DMI) by first calculating the lagged regression of the DMI on the sea surface temperature anomaly (SSTA) index NINO3.4 to maximum lags of 8 months, and then removing this ENSO portion. The new ENSO-independent time series, DMI NOENSO , correlates strongly with the original DMI at r = 0.87 (significant at [99% level). Despite the strength of the correlation between these series, the IOD events classified based on DMI NOENSO provide important differences from previously identified IOD events, which are more closely aligned with ENSO phases. IOD event composite maps of SSTAs regressed on DMI NOENSO reveal a much greater ENSO-independence than the original DMI-related SSTA pattern. This approach is used to explore relationships between Australian region TCG and IOD from 1968 to 2007. While we show that both the DMI and DMI NOENSO have significant hindcast skill (on the 95% level) when used as predictors in a multiple linear regression model for Australian region annual TCG counts, the IOD does not add any significant hindcast skill over an ENSO-only predictor model, based on NINO4.Correlations between the time series of annual TCG count observations and ENSO ? IOD model cross-validated hindcasts achieve r = 0.68 (significant at the 99% level).

Review of tropical cyclones in the Australian region: Climatology, variability, predictability, and trends

“…Similarly, Wijnands et al () showed that the IOD Mode Index (DMI) was the most commonly occurring predictor of the best‐performing models in the region. In contrast, Werner, Maharaj, and Holbrook () found that the IOD added no additional predictive value over using traditional ENSO indices (i.e., Niño 4 SST). Finally, Ramsay, Richman, and Leslie () identified a southwest‐to‐northeast dipole pattern of SST anomalies in the Indian Ocean and showed that a simple index based on this dipole resulted in improved prediction of seasonal TC counts for the region compared with models that incorporated only ENSO‐based predictors.…”

Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM

“…The October 2006 global SST anomaly pattern (Figure c) indicates the expected negative SST gradient from west to east across the Indian Ocean associated with a +IOD event. By several metrics, the 2006 +IOD event was one of the strongest in the 1965–2012 period [ Werner et al ., ; Wilson et al ., ]. Coexisting SST anomalies resembling El Niño occur in the equatorial Pacific Ocean [ Harrison and Larkin , ; Okumura and Deser , ].…”

Climatology in support of climate risk management

“…Of a range of factors, the role of regional and remote sea surface temperatures (Dailey et al, 2009;Jin et al, 2013;Rumpf et al, 2010;Wang et al, 2011) and large-scale modes of atmospheric or oceanic variability such as El Niño-Southern Oscillation, the Madden-Julian Oscillation and the Indian Ocean Dipole (Klotzbach, 2011b(Klotzbach, , 2012Larson et al 2012;Lin et al, 2012;Werner et al, 2012) have received attention. Of course, a fundamental requirement for robust hurricane risk assessment is the availability of quality assured homogenous tropical cyclone frequency, intensity and track data.…”