Use of a Genesis Potential Index to Diagnose ENSO Effects on Tropical Cyclone Genesis

Abstract

ENSO (El Niño–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 (850 hPa), relative humidity at 600 hPa, the magnitude of vertical wind shear from 850 to 200 hPa, and potential intensity (PI). Using monthly NCEP Reanalysis data in the period of 1950–2005, the genesis potential index is calculated on a latitude strip from 60°S to 60°N. Composite anomalies of the genesis potential index are produced for El Niño and La Niña 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 Niño 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.

Dynamical downscaling of tropical cyclones from CCSM4 simulations of the Last Glacial Maximum

“…Emanuel [2005] and Webster et al [2005] related increases in intense storms to concurrent increases in sea surface temperatures, but questions regarding the accuracy and completeness of the historical TC record persist [e.g., Landsea et al, 2006;Klotzbach and Landsea, 2015], and reliable global data are limited only to the recent decades of the satellite era. Efforts to understand how storms may respond to projections of climate changes over the next century have largely focused on analysis of model simulations; these include analysis of large-scale environmental factors known to favor genesis and intensification [e.g., Camargo et al, 2007aCamargo et al, , 2007bCamargo et al, , 2014, analysis of explicitly resolved storms simulated directly in global climate models [e.g., Camargo, 2013;Camargo and Wing, 2016], and several downscaling techniques to simulate storms in higher-resolution models better suited to resolving storm structure and intensity [e.g., Emanuel, 2013;Knutson et al, 2015].…”

A dynamical‐statistical forecast model for the annual frequency of western Pacific tropical cyclones based on the NCEP Climate Forecast System version 2

“…The variations of TC activity over the western North Pacific (WNP) are primarily due to the El Niño-Southern Oscillation (ENSO) effects by modulating the intensity and location of local monsoon trough and changing the vertical wind shear [e.g., Camargo and Sobel, 2005;Camargo et al, 2007a;Chan, 2000;Chen et al, 1998;Wu et al, 2012]. During an El Niño event, TCs tend to form over the southeastern WNP and can develop very intensively [Chan, 2000;Wang and Chan, 2002;Camargo and Sobel, 2005;Chan, 2007;Camargo et al, 2007aCamargo et al, , 2007b. Moreover, the central Pacific El Niño (also known as El Niño Modoki) events [Ashok et al, 2007] are shown to shift the TC formation westward to the western Pacific .…”

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

“…Such an enhanced overturning circulation provides a stronger shear condition, which negatively impacts GPI. The shear factor was shown to be one of the major contributors that negatively influence the GPI anomaly during a La Niña phase [36]. The present analysis for the CINDY2011/DYNAMO period suggests that the shear factor is influenced more by the negative impact from the east-west circulation induced by the La Niña phase than by the positive impact through the active MJO events.…”

“…The contributions from each factor in Equation (1) to the total GPI anomaly are examined with the procedure of Camargo et al [6,36] in order to diagnose the degrees of each environmental property in Equation (1) in characterizing the GPI anomaly of 2011. The procedure to compute those The contributions from each factor in Equation (1) to the total GPI anomaly are examined with the procedure of Camargo et al [6,36] in order to diagnose the degrees of each environmental property in Equation (1) in characterizing the GPI anomaly of 2011. The procedure to compute those contributions is the same as that used in TT14 [8].…”