Objective tropical cyclone extratropical transition detection in high‐resolution reanalysis and climate model data
Abstract This paper describes an objective technique for detecting the extratropical transition (ET) of tropical cyclones (TCs) in high‐resolution gridded climate data. The algorithm is based on previous observational studies using phase spaces to define the symmetry and vertical thermal structure o...
Published in: | Journal of Advances in Modeling Earth Systems |
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Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
American Geophysical Union (AGU)
2017
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Subjects: | |
Online Access: | https://doi.org/10.1002/2016MS000775 https://doaj.org/article/27cd876e50ad41f1bbeb6f6588803ad1 |
Summary: | Abstract This paper describes an objective technique for detecting the extratropical transition (ET) of tropical cyclones (TCs) in high‐resolution gridded climate data. The algorithm is based on previous observational studies using phase spaces to define the symmetry and vertical thermal structure of cyclones. Storm tracking is automated, allowing for direct analysis of climate data. Tracker performance in the North Atlantic is assessed using 23 years of data from the variable‐resolution Community Atmosphere Model (CAM) at two different resolutions ( ΔX∼55 km and 28 km), the Climate Forecast System Reanalysis (CFSR, ΔX∼38 km), and the ERA‐Interim Reanalysis (ERA‐I, ΔX∼80 km). The mean spatiotemporal climatologies and seasonal cycles of objectively detected ET in the observationally constrained CFSR and ERA‐I are well matched to previous observational studies, demonstrating the capability of the scheme to adequately find events. High‐resolution CAM reproduces TC and ET statistics that are in general agreement with reanalyses. One notable model bias, however, is significantly longer time between ET onset and ET completion in CAM, particularly for TCs that lose symmetry prior to developing a cold‐core structure and becoming extratropical cyclones, demonstrating the capability of this method to expose model biases in simulated cyclones beyond the tropical phase. |
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