NH51F-1957
The atmospheric influence on climatological tropical cyclone frequency and intensity

Friday, 18 December 2015
Poster Hall (Moscone South)
Sarah Strazzo, United States Military Academy, Geography and Environmental Engineering, West Point, NY, United States, Thomas Jagger, Florida State University, Denver, CO, United States and James Elsner, Florida State University, Tallahassee, FL, United States
Abstract:
The relationship between tropical cyclones (TCs) and climate variability/change continues to be an area of significant scientific inquiry. Much of this research focuses on the influence of rising sea surface temperatures (SSTs) on various metrics of TC activity. For example, previous research employing a spatial tessellation approach suggests that a 1 °C increase in SST is associated with a 7.9 m s-1 increase in the upper limit of TC intensity. Although this prior research indicates that SST describes nearly 70% of the variability in the upper limit of TC intensity, the role of various atmospheric variables has not been examined using the spatial tessellation approach. This study utilizes a similar spatial methodology to quantify the influence of both the atmosphere (e.g., 400—700 hPa relative humidity, 200—850 hPa vertical shear, 100 hPa temperature) and SST on climatological TC frequency and intensity for the North Atlantic basin.

Results from observed and reanalyzed data are compared to climate model output from the Florida State University/Center for Ocean-Atmospheric Prediction Studies global spectral model. It is shown that of the variables considered, SST and 400—700 hPa relative humidity are the only significant predictors of observed TC intensity. Conversely, relative humidity is the only significant predictor of climate model-simulated TC intensity. Regarding frequency, results indicate statistically significant differences in SST, shear, and relative humidity during years with versus years without TCs for portions of the North Atlantic basin. These preliminary results will help inform the development of probabilistic models that use output from global climate model simulations as predictors of regional TC frequency and intensity.