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...
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ftcolumbiauniv:oai:academiccommons.columbia.edu:10.7916/d8-adr5-6c89 2023-05-15T17:34:55+02:00 Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis Boudreault, Mathieu Caron, Louis-Philippe Camargo, Suzana J. 2017 https://doi.org/10.7916/d8-adr5-6c89 English eng https://doi.org/10.7916/d8-adr5-6c89 Atmospherics Cyclones Climatic changes Cluster analysis Hurricanes articles 2017 ftcolumbiauniv https://doi.org/10.7916/d8-adr5-6c89 2019-04-04T08:18:19Z 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. Article in Journal/Newspaper North Atlantic Columbia University: Academic Commons |
institution |
Open Polar |
collection |
Columbia University: Academic Commons |
op_collection_id |
ftcolumbiauniv |
language |
English |
topic |
Atmospherics Cyclones Climatic changes Cluster analysis Hurricanes |
spellingShingle |
Atmospherics Cyclones Climatic changes Cluster analysis Hurricanes Boudreault, Mathieu Caron, Louis-Philippe Camargo, Suzana J. Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis |
topic_facet |
Atmospherics Cyclones Climatic changes Cluster analysis Hurricanes |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Boudreault, Mathieu Caron, Louis-Philippe Camargo, Suzana J. |
author_facet |
Boudreault, Mathieu Caron, Louis-Philippe Camargo, Suzana J. |
author_sort |
Boudreault, Mathieu |
title |
Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis |
title_short |
Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis |
title_full |
Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis |
title_fullStr |
Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis |
title_full_unstemmed |
Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis |
title_sort |
reanalysis of climate influences on atlantic tropical cyclone activity using cluster analysis |
publishDate |
2017 |
url |
https://doi.org/10.7916/d8-adr5-6c89 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
https://doi.org/10.7916/d8-adr5-6c89 |
op_doi |
https://doi.org/10.7916/d8-adr5-6c89 |
_version_ |
1766133895757037568 |