Tropical cyclones (TCs) are strong atmospheric vortex with warm-cored low pressure structure, forming over tropical or subtropical oceans. The Australian region (0–30°S, 100°-165°E) is climatologically active for TCs, with a typical season average of 12.5 TCs (Liu et al. 2012; Chand et al. 2019). TCs can also bring about plentiful precipitation, flooding, and storm surge, which poses a recurring and growing threat to coastal communities throughout the Australian TC region (Holmes 2021). Proper guidance for seasonal and climate variability of Australian TC activity is conductive to mechanism analysis and effective forecast of TC activity, which relies on a solid understanding of how atmospheric and oceanic factors influence TC activity.
The influence of various environmental and climate factors on the variability of TC activity in the Australian region has been widely investigated since the late 1970s. Several important large-scale environmental factors have been examined and verified to be responsible for impacting the TC activity in the Australian region. These include sea surface temperature (SST) and a deep thermocline, weak deep-tropospheric vertical wind shear, conditional instability through a deep-tropospheric layer and relatively large values of low-level cyclonic vorticity (Ramsay et al. 2008). Kuleshov et al. (2009) repeated these studies and proposed high values of relative humidity in the mid-troposphere as another major modulator for the genesis and continued support of TCs. In addition to the effect of environmental variables, Mcbride and Keenan (2010) suggested a strong seasonal relationship between the intertropical convergence zone (ITCZ) and tropical TCG in the Australian basin from the 1950s to the 1970s. Intraseasonally, the Madden–Julian oscillation (MJO) plays a significant role in the Australian region TCG (Hall et al. 2001). More (fewer) TCs tend to form in the active (inactive) phase of the MJO. Note that nearly 50% of the TCs in their study formed within 300 km of land, quite different from those in the Atlantic and western North Pacific, more than 80% of which are formed far offshore (Werner and Holbrook 2011; Parker et al. 2018).
On the interannual time scale, many existing studies have investigated the relationship between the El Niño–Southern Oscillation (ENSO) phenomenon and TC activity in the Australian region. Werner and Holbrook (2011) investigated the influence of SST on variations in Australian TC activity and discovered that ENSO acts as a dominant modulator. Kuleshov et al. (2008) examined the connection of the ENSO to the TC activity in the Southern Hemisphere and showed the differences in TCG in El Niño and La Niña years. Evans and Allan (2012) explored the link between ENSO extremes, the Australian monsoon trough, with TC activity. Significant differences in the structure of the monsoon trough and associated TC activity were related to ENSO phase. ENSO is considered to be a significant contributing factor influencing the mean TC genesis location in the Australian region.
In addition to the effect of ENSO on TCG, Nicholls (2010) found a relation between interannual variations in TC numbers in the Australian region and Darwin sea level pressure (SLP) averaged over June to August. After the introduction of satellite, which aided in the detection of TCs, the relationship between Darwin SLP and TC number was found to be even stronger. Grant and Walsh (2001) examined the relation between the interdecadal variability of TC formation in the northeastern Australian region and the Interdecadal Pacific Oscillation. Broadbridge and Hanstrum (1998) examined TC activity in the Australian region and its relationship with the Southern Oscillation index (SOI). Increases in TC frequency and in landfalling impacts were found for strongly positive SOI values. Various studies have confirmed the relationship between TCG in the Australian region and ENSO indices, SST in the Niño regions or the SOI (Butler and Callaghan 2007; Hastings 2010; Kuleshov et al. 2014). The geographical distribution of TCG shifts westward (eastward) and southward (northward) during positive (negative) phase of SOI.
Additionally, some previous studies have also developed seasonal predictions of TC activity in the Australian region. McDonnell and Holbrook (2004) proposed a statistical model for the seasonal forecast of TC activity based on the SOI and the saturated equivalent potential temperature gradient. Flay and Nott (2010) also constructed a statistical model for the prediction of TC landfalls in Queensland using the SOI. In a later study, Goebbert (2009) developed a prediction scheme for the northwest Australian TC frequency based on a set of NECP-NCAR reanalysis fields (e.g., geopotential height, air temperature, and components of wind) highly correlated with TC frequency.
In the above studies on the interannual variability of TCG over the Australian region, there are two outstanding problems that have received wide attention. The first one is that climate factors related to the TC activity are limited to local environmental drivers in the Australian region. Few previous studies have examined impact factors of TCG in the entire Southern Hemisphere or the whole globe. Thus, the restricted selection of environmental factors (i.e., predictors) inevitably causes inaccuracy for seasonal prediction of TC activity. For example, predictors such as SOI and SST in the South Indian and South Pacific Oceans are most commonly used to build statistical or dynamical models (2003). The second scientific challenge has been how to analyze the relationship between various climate factors and TCG. Traditionally, the most commonly used research method in climate science is the time-delayed correlation analysis. That may be not appropriate, as there has been strong argument in philosophy against using correlation analysis for relation identification, because, for example, correlation lacks the needed asymmetry or directedness between dynamical events (Liang 2015). Therefore, how to extract the causal relations in climate-cyclone interactions is an important problem in atmospheric science.
For the first problem, recent studies have suggested that the Atlantic Ocean variability, such as SST and atmospheric variability, can influence the ENSO variability and its predictability through the atmospheric circulation response (Jansen et al. 2010; Frauen and Dommenget 2012; Ding et al. 2012). Ham et al. (2013) investigated a subtropical teleconnection between the north tropical Atlantic (NTA) SST with the Pacific atmospheric anomalies by modulating the tropical Pacific atmospheric circulation and SST. Thus, it is possible that the Atlantic Ocean variability may modulate the Pacific or, specifically, Australian region climate variability.
For another problem, the research of causality analysis in the modern sense was started by Granger (1969), whose Granger causality test has become classical. Other approaches include the transfer entropy (TE) analysis by Schreiber (2000) and its derivatives. But recently Smirnov (2015) argued that TE analysis will “give spurious causal inference in a wide range of situations”. Recently, based on Granger causality test and TE, information flow (IF) put forward by Liang (2008; 2015) is an emerging casual analysis method. He realized that causality is in fact a real physical notion and can be put on a rigorous footing. In his formalism, causality of both liner systems and highly nonlinear system could be measured by IF remarkably. Then Liang (2015) normalized the obtained IF in order to assess the relative importance of an identified causality.
In this study, we extend the investigation to the whole globe to determine the importance of different climate factors in influencing TC formation in the Australian region, using the recently developed rigorous and quantitative causality analysis tool (i.e., IF). Interestingly, besides reconfirming the existing relations, it is also found that the Atlantic anomalies could impact the Australian region TCG remotely. And the teleconnection from the Atlantic could be new predictors that can significantly improve the seasonal forecast of TCG in the Australian region. In the current study, we first introduce the new tool and theory, and then show the analysis results. Possible physical mechanisms are suggested for the teleconnection between TCG in the Australian region and the Atlantic variability. From the analysis, three causal factors are particularly examined: the Atlantic meridional mode (AMM), Atlantic multidecadal oscillation (AMO), and north tropical Atlantic (NTA) sea surface temperature (SST). Finally, these factors are taken as predictors to build a statistical model for the prediction of the Australian region TCG.