Comparison of tropical cyclogenesis indices on seasonal to interannual timescales

Abstract

International audienceThis paper evaluates the performances of four cyclogenesis indices against observed tropical cyclone genesis on a global scale over the period 1979-2001. These indices are: the Genesis Potential Index; the Yearly Genesis Parameter; the Modified Yearly Convective Genesis Potential Index; and the Tippett et al. Index (J Clim, 2011), hereafter referred to as TCS. Choosing ERA40, NCEP2, NCEP or JRA25 reanalysis to calculate these indices can yield regional differences but overall does not change the main conclusions arising from this study. By contrast, differences between indices are large and vary depending on the regions and on the timescales considered. All indices except the TCS show an equatorward bias in mean cyclogenesis, especially in the northern hemisphere where this bias can reach 5°. Mean simulated genesis numbers for all indices exhibit large regional discrepancies, which can commonly reach up to ±50%. For the seasonal timescales on which the indices are historically fitted, performances also vary widely in terms of amplitude although in general they all reproduce the cyclogenesis seasonality adequately. At the seasonal scale, the TCS seems to be the best fitted index overall. The most striking feature at interannual scales is the inability of all indices to reproduce the observed cyclogenesis amplitude. The indices also lack the ability to reproduce the general interannual phase variability, but they do, however, acceptably reproduce the phase variability linked to El Niño/Southern Oscillation (ENSO)--a major driver of tropical cyclones interannual variations. In terms of cyclogenesis mechanisms that can be inferred from the analysis of the index terms, there are wide variations from one index to another at seasonal and interannual timescales and caution is advised when using these terms from one index only. They do, however, show a very good coherence at ENSO scale thus inspiring confidence in the mechanism interpretations that can be obtained by the use of any index. Finally, part of the gap between the observed and simulated cyclogenesis amplitudes may be attributable to stochastic processes, which cannot be inferred from environmental indices that only represent a potential for cyclogenesis

Tropical cyclone genesis potential across palaeoclimates

“…The grid resolution of most GCMs is not sufficiently refined to simulate the mesoscale processes required to adequately capture tropical cyclones. Many studies have used genesis potential indices as a less computationally intensive and more practical approach to describe how favourable climate conditions are for tropical cyclogenesis (Bruyère et al, 2012;Camargo et al, 2007;Emanuel and Nolan, 2004;Korty et al, 2012a, b;Menkes et al, 2012;Tippett et al, 2011). Gray (1975) pioneered work on a GPI by demonstrating the use of selected atmospheric properties to characterise climatic conditions that are favourable for cyclone genesis.…”

“…It is therefore anticipated that the climate change signal will be easier to detect in the palaeoclimate simulations. In transient simulations, large-scale forcings may not fully account for the observed variability (Menkes et al, 2012) as stochastic effects may potentially account for up to half of the observed variability (Jourdain et al, 2010).…”

Modeling high-impact weather and climate: lessons from a tropical cyclone perspective

“…These approaches were typically designed to capture the hemispheric seasonal cycle and typically do not do well on regional and interannual scales. For example, Menkes et al (2011) applied four genesis potential indices to different reanalysis datasets and found poor reproduction of interannual amplitude and phase variability on regional scales. However, recent work has begun to show skill on regional, seasonal scales.…”

“…Bruyère et al (2012) showed that for interannual variability and longer-term changes, the relative humidity and vorticity terms contribute nothing to the skill, though this could be due to the specific formulation of the index rather than having a physical interpretation (a general limitation highlighted by Menkes et al 2011). Bruyère et al (2012) also found care needs to be taken when selecting an index averaging area: for the North Atlantic, a basin-wide average was not optimal in explaining total basin cyclone frequency, whereas an average taken over the eastern tropical Atlantic (5-20 o N, 60-15 o W), was able to explain 72 % of the annual variance of total basin cyclone frequency.…”

Seasonal Environmental Characteristics for the Tropical Cyclone Genesis in the Indian Ocean during the CINDY2011/DYNAMO Field Experiment

“…The JRA-25 dataset has been widely used in investigating the environmental characteristics for the generation of TCs [8,[23][24][25][26]. There is also a study [27] which compared the representations of the environmental conditions for TCs in various reanalysis datasets and found that there are no pronounced differences among the datasets. Therefore, the JRA-25 dataset is considered to be useful in the present analyses.…”