A Comparison of Southern Hemisphere Cyclone Track Climatology and Interannual Variability in Coarse-Gridded Reanalysis Datasets

Southern Hemisphere (SH) extratropical cyclones have received less study than their Northern Hemisphere (NH) counterparts. Generating SH cyclone tracks from global reanalysis datasets is problematic due to data reliability, especially prior to 1979. It is therefore prudent to compare the climatology and variability of SH cyclone tracks from different reanalysis datasets. We generate cyclone track frequency and intensity climatologies from three reanalysis datasets: The National Center for Environmental Prediction’s Reanalysis I and Reanalysis II datasets and the European Centre for Medium Range Weather Forecasts ERA-40 dataset. Our results show that ERA-40 produces more intense cyclones in the SH active cyclone region compared to NCEP reanalyses. More intense storms are also found in the SH active cyclone region in NCEP reanalyses data post-1979 reflecting the positive trend in the AAO in the past few decades. When evaluating interannual variability, our results show Rossby wave trains including the Pacific South American (PSA) and the East Indian Ocean pattern in response to anomalous heating linked to El Ni？o and the Indian Ocean Dipole (IOD), respectively. Response to the AAO shows a robust annular structure for cyclone track frequency, but not intensity suggesting a weak relationship between cyclone frequency and cyclone intensity. 1. Introduction Extratropical cyclones are an important manifestation of the Southern Hemisphere (SH) general circulation and are associated with serious socioeconomic impacts. For example, the economic costs associated with coastal cyclones in South Africa are quite large as demonstrated by infrastructure costs associated with a 2007 cyclone that resulted in R100 million along the Durban coast [1]. While cyclone tracks for the SH have been less studied than the NH, several studies have been conducted. These include those that detect cyclones in the upper levels (e.g., 300？hPa, 500？hPa) using band-passed or high-pass filtered data (e.g., Trenberth [2], J. S. Frederiksen and C. S. Frederiksen [3], Kidson and Sinclair [4], C. S. Frederiksen and J. S. Frederiksen [5], Berbery and Vera [6], Rao et al. [7], Inatsu and Hoskins [8], Nakamura and Shimpo [9], Solman and Menéndez [10], Ashok et al. [11], and Carmo and de Souza [12]). Other studies use a Lagrangian method to determine cyclones from the low levels (e.g., sea level pressure (SLP)) such as Lim and Simmonds [13], Pezza et al. [14], Pezza et al. [15], Mendes et al. [16], and Yuan et al. [17]. Hoskins and Hodges [18] utilized both Eularian and Lagrangian methods in their