Systematic Characterization of Cyclogenesis in High Resolution Climate Model Simulations

In this study we develop a systematic methodology to analyze cyclogenesis in high resolution climate model simulations. The motivation for this study is to understand how cyclones develop in simulations with the objective of improving the theoretical foundations of cyclogenesis.

We use the toolkit for extreme climate analysis (TECA) [Prabhat et al., ICCS 2012] to detect and track cyclones (TCs) in recent high resolution simulations (25km) of current day and climate change scenarios [Wehner et al, J Climate 2015]. We systematically adjust the tracking criteria to identify developing and non-developing TCs.

The detection and tracking criteria are based on (i) the local relative vorticity maximum being above a certain value, (ii) the colocation of vorticity maximum, surface pressure minimum and warm core temperature maximum, (iii) surface pressure gradient around the storm center to be above a certain value, and (iv) temperature gradient around the warm core center to be above a certain value. To identify non-developing TCs, we systematically characterize the sensitivity of cyclone detection to these criteria. This is done by the following two-step process: (1) We systematically lower the thresholds values set for the above-mentioned criteria used to detect and track TCs. This results in an increase in the number of TC-like systems and trajectory lengths compared to the baseline case. (2) The new TC-like systems that did not exist in the baseline case are labelled as non-developing TCs as they do not grow into TCs, while those that are extensions of the original TCs from the baseline case are labelled as developing TCs.

We contrast the behavior of developing and non-developing TCs by constructing multivariate joint PDFs of various environmental conditions along their trajectories, including SST, entropy excess (a measure of buoyancy), vertical wind shear and outgoing longwave radiation. We also compute the standard genesis potential index, max potential intensity and ventilation index along these trajectories.

Existing indices do not appear to capture the probability of cyclogenesis accurately. We suggest ways in which multivariate joint PDF analysis could provide the foundation to develop new methods of characterizing and predicting cyclogenesis.