Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis
We analyze, using Poisson regressions, the main climate influences on North Atlantic tropical cyclone activity. The analysis is performed using not only various time series of basin-wide storm counts but also various series of regional clusters, taking into account shortcomings of the hurricane data...
| Published in: | Journal of Geophysical Research: Atmospheres |
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| Main Authors: | , , |
| Other Authors: | |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/2117/104581 https://doi.org/10.1002/2016JD026103 |
| Summary: | We analyze, using Poisson regressions, the main climate influences on North Atlantic tropical cyclone activity. The analysis is performed using not only various time series of basin-wide storm counts but also various series of regional clusters, taking into account shortcomings of the hurricane database through estimates of missing storms. The analysis confirms that tropical cyclones forming in different regions of the Atlantic are susceptible to different climate influences. We also investigate the presence of trends in these various time series, both at the basin-wide and cluster levels, and show that, even after accounting for possible missing storms, there remains an upward trend in the eastern part of the basin and a downward trend in the western part. Using model selection algorithms, we show that the best model of Atlantic tropical cyclone activity for the recent past is constructed using Atlantic sea surface temperature and upper tropospheric temperature, while for the 1878–2015 period, the chosen covariates are Atlantic sea surface temperature and El Niño–Southern Oscillation. We also note that the presence of these artificial trends can impact the selection of the best covariates. If the underlying series shows an upward trend, then the mean Atlantic sea surface temperature captures both interannual variability and the upward trend, artificial or not. The relative sea surface temperature is chosen instead for stationary counts. Finally, we show that the predictive capability of the statistical models investigated is low for U.S. landfalling hurricanes but can be considerably improved when forecasting combinations of clusters whose hurricanes are most likely to make landfall. The authors would like to thank all the people and organizations who made their data available: the National Hurricane Center, the Earth System Research Laboratory (NOAA), the Climate Prediction Center (NOAA), the Hadley Centre, the National Climatic Data Center, the Joint Institute for the Study of the Atmosphere and Ocean at the University of Washington, the Climatic Research Unit of East Anglia, and the Solar Influences Data Analysis Center (SIDC) of the Royal Observatory of Belgium. Data sources are detailed in sections 2.1 and 2.2. We are also grateful to Phil Klotzbach for his helpful input and Jean-Philippe Baudouin for helping develop the R code used to produce the heatmaps. S.J.C. acknowledges support from NSF grant AGS 1143959 and NOAA grants NA15OAR43100095 and NA16OAR43100079. M.B. acknowledges support from the Natural Sciences and Engineering Research Council of Canada. L.P.C. acknowledges financial support from the Ministerio de Economía y Competitividad (MINECO; project CGL2014-55764-R). Peer Reviewed Postprint (published version) |
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