The use of steering flow variations is also key for predictions of year-to-year tropical cyclone movement over regions of the Northwest Pacific (Chan 1994). In this forecasting methodology, Chan utilizes the zonal winds at both 850 and 500 mb before the season commences in a cross-validated linear OLS regression model to forecast the tropical cyclone numbers moving through designated regions. This allows for predictions of the annual number of westward moving tropical cyclones through the Philippines by early April (Table 3) and the annual number of northward moving tropical cyclones through the region just south of Japan by early March. Both methods appear to provide skill in predicting the number of tropical cyclones substantially above that of climatology. However, the physical link - presumably persistence of steering flow conditions before the season begins in March to the end of the season in late autumn - has not yet been convincingly shown.
C. Australia/South Pacific basin
Research into seasonal tropical cyclone forecasting in the Australian region (5-32S, 105-165E) began with Nicholls' (1979) report on the usage of an index of ENSO to predict the upcoming October-May cyclone season. Nicholls utilized the Darwin surface pressure (one-half of the SOI) to hindcast about 40% of the variance of activity, skill coming primarily through the early and mid-season tropical cyclones. In Nicholls (1984), this work was extended to show that local SSTs and SSTs in the equatorial eastern Pacific (a direct measure of ENSO) could also be utilized in a forecast mode (Fig. 11). Holland et al. (1988) showed that most of the local SST predictive signal was due directly to ENSO effects.
Recently, Nicholls (1992) noticed a temporal change in the behavior of the tropical cyclone numbers versus the ENSO predictors. The predicted values have been consistently too high compared to what has been observed since the season of 1986/87. The values in the correlation for independent data dropped to only about 20% of the variability. He suggests that most of this bias is artificial due to change in tropical cyclone monitoring policies. Instead, utilizing the year-to-year differences in cyclone numbers and the SOI gives a predictive association explaining about 65% of the variability. Thus with just one predictor (the SOI, or more recently, the one year change in SOI), a regression scheme is able to capture almost two-thirds of the interannual variability in Australian basin tropical cyclone activity by early December and slightly less by early September. Nicholls' real-time forecasts are posted in November of each year and can be found at the following World Wide Web site:http://www.bom.gov.au/bmrc/.
While Nicholls has utilized El Niño's effect of reducing tropical cyclone activity in the Australian basin, Basher and Zheng (1995) use the tendency for El Niño increases of South Pacific tropical cyclones for forecasting purposes in that region. Again making use of an OLS multiple regression, Basher and Zheng use SOI and local SSTs during September to forecast the number of cyclone occurrences in 20 longitude by 12 latitude boxes extending across the South Pacific. Using just the September SOI, they find that they can explain 52% of the variance for the entire region extending from 170E to 130W. The local SSTs do not contribute significantly above this value. It is just in the region from 150E to 170E where the local SSTs are able to forecast 35% of the variance, and the SOI provides no additional information. This is due to ENSO's influence reversing between inhibiting to enhancing of tropical cyclogenesis (for El Niño events) around the longitude of 160E.
Discussion and Conclusions
In section II, discussion centered on the need for favorable synoptic-scale variations of the environment to allow genesis and development of tropical cyclones to proceed. Since the El Niño-Southern Oscillation (ENSO) produces such large, widescale changes in the tropical circulation, perhaps it is not surprising that tropical cyclones are strongly altered by ENSO. However, these changes are not uniform throughout the global tropics. In some regions, an El Niño event would bring increases in tropical cyclone formation (e.g. the South Pacific and the North Pacific between 140W to 160E) while others see decreases (e.g. the North Atlantic, the Northwest Pacific west of 160E, the Australian region). Las Niñas typically bring opposite conditions. These alterations in tropical cyclone activity are due to a variety of ENSO effects: by modulating the intensity of the local monsoon trough, by repositioning the location of the a monsoon trough and by altering the tropospheric vertical shear. Thus based on the global nature of ENSO alone, assumptions of independence between different tropical cyclone basins would be incorrect.
In addition to ENSO, three basins (the Atlantic, Southwest Indian and Northwest Pacific) show systematic alterations of tropical cyclone frequency by the stratospheric Quasi-Biennial Oscillation (QBO). This intuitively is unexpected given that tropical cyclones are primarily a tropospheric phenomenon, but may be due to alterations in the static stability and dynamics near the tropopause. Certainly more research is needed to provide a thorough explanation of these relationships. However, given the robustness of these alterations in tropical cyclone activity that match the QBO phases, it appears unlikely that the association is purely a chance correlation.
Interannual tropical cyclone variations have also been linked to more localized, basin-specific features such as sea surface temperatures (SST), monsoon strength and rainfall, sea level pressures and tropospheric vertical changes. These regional factors can be as large as the forcing due to ENSO, though most are not. Together with ENSO and the QBO, these factors produce changes in the frequency, intensity, formation region and track of tropical cyclones in all basins. However, understanding how tropical cyclone variability relates to the surrounding environmental conditions is hampered by having only a few decades worth of reliable tropical cyclone records. The emerging field of "paleotempestology" - the study of pre-historic tropical cyclones (e.g. Liu and Fearn 1993, Keen and Slingerland 1993) - may be able in coming years to assist in analyzing how and why tropical cyclones change from year to year.
Despite the current lack of accurate long-term tropical cyclone records, some of the tropical cyclone-environmental associations have led to methodologies in seasonal forecasting by the onset of the tropical cyclone season. Given that the typical lifecycle for a particular phase of ENSO of a year or greater, one can utilize this for the basis of seasonal predictions with lead times of up to several months. These are now being done (or can be performed) for the Atlantic basin tropical cyclones, those near Australia and in the South Pacific basin. These forecasts assume no knowledge of the future state of ENSO, except that the current ENSO phase will persist for at least the next few months in the future.
Over the last fifteen years, seasonal forecasting in various basins has evolved to the point that up to 50% (or more) of the tropical cyclone variability can be predicted at the start of the cyclone season. In addition to the frequency and intensity of the all basin storms, statistical methodologies have also introduced to forecast track frequency and the likelihood of hurricane landfall in certain coastal zones. Currently, such statistical models are the only feasible methodology for seasonal tropical cyclone forecasting, because the lack of skill in global circulation models (GCMs). Because of the complexity of difficulties faced in utilizing these numerical models, it may take a decade or two of concerted research effort before such GCM forecasting is feasible, if ever. However, for the current time, creative use of statistical regression schemes can provide and will continue to provide skilled and useful predictions of tropical cyclone activity around the world.
A sensible question would be how can seasonal forecasts of tropical cyclones be used when they are for large geographic regions such as the entire North Atlantic Ocean, Caribbean Sea and Gulf of Mexico. There are a number of reasons for issuing such predictions. Practically, most people in the general public cannot - and should not - utilize the forecast directly. As an example, it would be foolish if John Q. Public decided to ignore hurricane preparedness and mitigation plans because the year was one predicted to be below average. 1992 serves as an excellent warning against such actions: the Atlantic hurricane season was very successfully forecasted by Prof. Gray (see Table 2) as a quiet year with only four hurricanes, yet one of those was Hurricane Andrew - the most destructive U.S. hurricane on record (Mayfield et al. 1994). Strong wording should be added to any seasonal hurricane forecast to discourage an individual from using the forecast in such a way.
Corporations and governments, however, because of their size and scope of operations, are starting to make reasonable use of such forecasts each year. One large, private manufacturing company with interests all along the U.S. coastline serves as an example of the many utilizations that have been made with these predictions: decisions on the amount of "hurricane" liability insurance that covers preparations, damages and repair costs; determinations of annual budgets and preventative maintenance schedules; an aid in the production and inventory storing planning; plans for data processing disaster recovery; and schedules for workers' shifts in the upcoming year. Such usage can be better enhanced by increases in skill, by providing longer-range accurate predictions, and by regionalizing the forecast for smaller locales (such as being pursued by Lehmiller et al. ).
Another justification for such forecasting efforts is that it leads to further advances in our understanding of linkages in the climate system. It was the failure of the 1989 seasonal Atlantic hurricane forecast (Table 2) that led to the discovery that the West African monsoon is intimately tied to the occurrence of Atlantic hurricanes on an interannual basis. Successes in seasonal tropical cyclone activity may also provide insight into forecasts of other tropical phenomena such as droughts/flooding associated with anomalous changes in the strength and location of the ITCZ and the monsoons (e.g. Hastenrath 1995).
Finally, the seasonal tropical cyclone forecasts at times generate public and media interest, whether or not individuals can actually make use of the predictions. A beneficial side effect of such interest is that it heightens the awareness of the public to the danger of hurricanes and hopefully prompts more people to take precautions and make preparations.