Statistical Deterministic and Ensemble Seasonal Prediction of Tropical Cyclones in the Northwest Australian Region

Statistical seasonal prediction of tropical cyclones (TCs) has been ongoing for quite some time in many different ocean basins across the world. While a few basins (e.g., North Atlantic and western North Pacific) have been extensively studied and forecasted for many years, Southern Hemispheric TCs have been less frequently studied and generally grouped as a whole or into two primary basins: southern Indian Ocean and Australian. This paper investigates the predictability of TCs in the northwest Australian (NWAUS) basin of the southeast Indian Ocean (105°–135°E) and describes two statistical approaches to the seasonal prediction of TC frequency, TC days, and accumulated cyclone energy (ACE). The first approach is a traditional deterministic seasonal prediction using predictors identified from NCEP–NCAR reanalysis fields using multiple linear regression. The second is a 100-member statistical ensemble approach with the same predictors as the deterministic model but with a resampling of the dataset with replacement and smearing input values to generate slightly different coefficients in the multiple linear regression prediction equations. Both the deterministic and ensemble schemes provide valuable forecasts that are better than climatological forecasts. The ensemble approach outperforms the deterministic model as well as adding quantitative uncertainty that reflects the predictability of a given TC season

Impact of large-scale dynamic versus thermodynamic climate conditions on contrasting tropical cyclone genesis frequency

Significant advances have been made in understanding the key climate factors responsible for tropical cyclone (TC) activity, yet any theory that estimates likelihood of observed TC formation rates from mean climate states remains elusive. The present study investigates how the extremes of observed TC genesis (TCG) frequency during peak TC seasons are interrelated with distinct changes in the large-scale climate conditions over different ocean basins using the global International Best Track Archive for Climate Stewardship (IBTrACS) dataset and ERA-Interim for the period 1979–2014. Peak TC seasons with significantly high and low TCG frequency are identified for five major ocean basins, and their substantial spatial changes in TCG are noted with regionally distinct differences. To explore the possible climate link behind such changes, a suite of potentially relevant dynamic and thermodynamic climate conditions is analyzed. Results indicate that the observed changes in extreme TCG frequency are closely linked with distinct dominance of specific dynamic and thermodynamic climate conditions over different regions. While the combined influences of dynamic and thermodynamic climate conditions are found to be necessary for modulating TC formation rate over the North Atlantic, eastern Pacific, and southern Indian Oceans, significant changes in large-scale dynamic conditions appear to solely control the TCG frequency over the western Pacific and South Pacific basins. Estimation of the fractional changes in genesis-weighted climate conditions also indicates the coherent but distinct competing effects of different climate conditions on TCG frequency. The present study further points out the need for revising the existing genesis indices for estimating TCG frequency over individual basins

Tropical cyclones and the ecohydrology of Australia's recent continental-scale drought

The Big Dry, a recent drought over southeast Australia, began around 1997 and continued until 2011. We show that between 2002-2010, instead of a localized drought, there was a continent-wide reduction in water storage, vegetation and rainfall, spanning the northwest to the southeast of Australia. Trends in water storage and vegetation were assessed using Gravity Recovery and Climate Experiment (GRACE) and Normalized Difference Vegetation Index (NDVI) data. Water storage and NDVI are shown to be significantly correlated across the continent and the greatest losses of water storage occurred over northwest Australia. The frequency of tropical cyclones over northwest Australia peaked just prior to the launch of the GRACE mission in 2002. Indeed, since 1981, decade-scale fluctuations in tropical cyclone numbers coincide with similar variation in rainfall and vegetation over northwest Australia. Rainfall and vegetation in southeast Australia trended oppositely to the northwest prior to 2001. Despite differences between the northwest and southeast droughts, there is reason to believe that continental droughts may occur when the respective climate drivers align

Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis

ENSO (El Nino-Southern Oscillation) has a large influence on tropical cyclone activity. The authors examine how different environmental factors contribute to this influence, using a genesis potential index developed by Emanuel and Nolan. Four factors contribute to the genesis potential index: low-level vorticity (850hPa), relative humidity at 600hPa, the magnitude of vertical wind shear from 850 to 200hPa and potential intensity (PI). Using monthly NCEP Reanalysis data in the period of 1950-2005, we calculate the genesis potential index on a latitude strip from 60°S to 60°N. Composite anomalies of the genesis potential index are produced for El Nino and La Nina years separately. These composites qualitatively replicate the observed interannual variations of the observed frequency and location of genesis in several different basins. This justifies producing composites of modified indices in which only one of the contributing factors varies, with the others set to climatology, to determine which among the factors are most important in causing interannual variations in genesis frequency. Specific factors that have more influence than others in different regions can be identified. For example, in El Nino years, relative humidity and vertical shear are important for the reduction in genesis seen in the Atlantic basin, and relative humidity and vorticity are important for the eastward shift in the mean genesis location in the western North Pacific

01 Southern Hemisphere Tropical Cyclone Climatology

Southern Hemisphere Tropical Cyclone Climatology: Each year, around 80 tropical cyclones (TCs) form around the world, with about one-third of them in the Southern Hemisphere (SH) (Gray, 1979). Tropical cyclones within the South Indian Ocean (SIO) and the South Pacific Ocean (SPO) are frequent and intense, and they dramatically affect maritime navigation and the lives of communities in coastal areas. Australia and the island nations are affected each year by TCs. In extreme cases they can have devastating consequences on life, property and the economic well-being of the communities directly affected and the country as a whole, as in the case of one of Australia’s most notorious TCs, Tracy, which devastated Darwin, the capital of the Northern Territory, on 25 December 1974 (Australian Government, 1977).https://digitalcommons.usu.edu/modern_climatology/1000/thumbnail.jp

Sensitivity of northwest Australian tropical cyclone activity to ITCZ migration since 500 CE

Tropical cyclones (TCs) regularly form in association with the intertropical convergence zone (ITCZ), and thus, its positioning has implications for global TC activity. While the poleward extent of the ITCZ has varied markedly over past centuries, the sensitivity with which TCs responded remains poorly understood from the proxy record, particularly in the Southern Hemisphere. Here, we present a high-resolution, composite stalagmite record of ITCZ migrations over tropical Australia for the past 1500 years. When integrated with a TC reconstruction from the Australian subtropics, this time series, along with downscaled climate model simulations, provides an unprecedented examination of the dependence of subtropical TC activity on meridional shifts in the ITCZ. TCs tracked the ITCZ at multidecadal to centennial scales, with a more southward position enhancing TC-derived rainfall in the subtropics. TCs may play an increasingly important role in Western Australia’s moisture budgets as subtropical aridity increases due to anthropogenic warming

Seasonal Tropical Cyclone Forecasting

This paper summarizes the forecast methods, outputs and skill offered by twelve agencies for seasonal tropical cyclone (TC) activity around the world. These agencies use a variety of techniques ranging from statistical models to dynamical models to predict basinwide activity and regional activity. In addition, several dynamical and hybrid statistical/dynamical models now predict TC track density as well as landfall likelihood. Realtime Atlantic seasonal hurricane forecasts have shown low skill in April, modest skill in June and good skill in August at predicting basinwide TC activity when evaluated over 2003-2018. Real-time western North Pacific seasonal TC forecasts have shown good skill by July for basinwide intense typhoon numbers and the ACE index when evaluated for 2003-2018. Both hindcasts and real-time forecasts have shown skill for other TC basins. A summary of recent research into forecasting TC activity beyond seasonal (e.g., multi-year) timescales is included. Recommendations for future areas of research are also discussed

Adaptive machine learning approaches to seasonal prediction of tropical cyclones

Tropical cyclones (TCs) are devastating phenomena that cause loss of life and catastrophic damage, owing to destructive winds, flooding rains and coastal inundation from storm surges. Accurate seasonal predictions of TC frequency and intensity are required, with a lead-time appropriate for preemptive action. Current schemes rely on linear statistics to generate forecasts of the TC activity for an upcoming season. Such techniques employ a suite of intercorrelated predictors; however, the relationships between predictors and TCs violate assumptions of standard prediction techniques. We extend tradition linear approaches, implementing support vector regression (SVR) models. Multiple linear regression (MLR) is used to create a baseline to assess SVR performance. Nine predictors for each calendar month (108 total) were inputs to MLR. MLR equations were unstable, owing to collinearity, requiring variable selection. Stepwise multiple regression was used to select a subset of three attributes adaptive to specific climatological variability. The R2 for the MLR testing data was 0.182. The SVR model used the same predictors with a radial basis function kernel to extend the traditional linear approach. Results of that model had an R2 of 0.255 (∼ 40% improvement over linear model). Refinement of the SVR to include the Quasi-Biennial Oscillation (QBO) improved the SVR predictions dramatically with an R2 of 0.564 (∼ 121% improvement over SVR without QBO). © 2012 Published by Elsevier B.V