The observed historical record of North Atlantic tropical cyclones (TCs) is relatively short and is subject to potential biases owing to a lack of observation platforms such as aircraft reconnaissance and satellite imagery in earlier decades. Therefore, studies of long-term variability in TC activity are hindered by the limitations and uncertainty within the historical data. An alternative approach is to study long-term Atlantic TC variability within the framework of a coupled ocean-atmosphere climate model simulation. We have taken such an approach using a simulation of the National Center for Atmospheric Research Climate System Model 1.4 forced with estimated natural and anthropogenic forcing over the past millennium. Atmospheric variables from the long-term model simulation are used to drive a recently developed downscaling relationship that simulates TC genesis and tracking over the course of the 1150 year model simulation. This downscaling process generates a long-term synthetic TC track data set, free of observational biases, though subject to limitations in the model climatology. The synthetic TC data are used to perform an analysis of long-term variability in Atlantic TCs, specifically focusing on TC landfalls, within the context of the coupled model simulation. Ultimately, analysis of various TC time series reveals that counts of landfalling TCs and even landfalling hurricanes track relatively well with the total basin-wide TC activity on multidecadal and longer timescales. Key Points Long-term Atlantic TC activity is analyzed within a downscaled model simulation Simulated TC counts track hurricane landfall counts well on multidecadal scales ©2013. American Geophysical Union. All Rights Reserved.
CITATION STYLE
Kozar, M. E., Mann, M. E., Emanuel, K. A., & Evans, J. L. (2013). Long-term variations of North Atlantic tropical cyclone activity downscaled from a coupled model simulation of the last millennium. Journal of Geophysical Research Atmospheres, 118(24), 13,383-13,392. https://doi.org/10.1002/2013JD020380
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