Estimation of the maximum annual number of North Atlantic tropical cyclones using climate models

• Published in: Science Advances
• Main Authors: Lavender, Sally L., Walsh, Kevin J.E., Caron, Louis-Philippe, King, Malcolm, Monkiewicz, Sam, Guishard, Mark, Zhang, Qiong, Hunt, Barrie
• Other Authors: Barcelona Supercomputing Center
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Association for the Advancement of Science 2018
• Subjects: Àrees temàtiques de la UPC::Energies; Seasonal climate forecasting; Hurricanes > Atlantic Ocean; North Atlantic tropical cyclones; Climate models; Canvis climàtics; Huracans; Cierva; North Atlantic
• Online Access: http://hdl.handle.net/2117/120601; https://doi.org/10.1126/sciadv.aat6509

Using millennia-long climate model simulations, favorable environments for tropical cyclone formation are examined to determine whether the record number of tropical cyclones in the 2005 Atlantic season is close to the maximum possible number for the present climate of that basin. By estimating both the mean number of tropical cyclones and their possible year-to-year random variability, we find that the likelihood that the maximum number of storms in the Atlantic could be greater than the number of events observed during the 2005 season is less than 3.5%. Using a less restrictive comparison between simulated and observed climate with the internal variability accounted for, this probability increases to 9%; however, the estimated maximum possible number of tropical cyclones does not greatly exceed the 2005 total. Hence, the 2005 season can be used as a risk management benchmark for the maximum possible number of tropical cyclones in the Atlantic. This work was funded by the Bermuda Institute of Ocean Sciences’ Risk Prediction Initiative (RPI). L.-P.C.’s contract is cofinanced by the Ministerio de Economı́a y Competitividad (MINECO) under Juan de la Cierva Incorporacion postdoctoral fellowship number IJCI-2015-23367. This research was partially supported through funding from the Earth System and Climate Change Hub of the Australia’s National Environmental Science Programme. L.-P.C. acknowledges financial support from MINECO (project CGL2015-70353-R). Author contributions: K.J.E.W. and L.-P.C. designed the research. S.L.L., K.J.E.W., M.K., and S.M. performed the analysis with input from L.-P.C., B.H., and M.G. The CSIRO Mk2 and EC-Earth data were made available by B.H. and Q.Z., respectively. S.L.L. wrote the article with input from all living authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: Datasets used in this report can be made available upon request from the lead and second authors. In addition, these data are archived by RPI. The MRI data were available from the database for Policy Decision Making for Future Climate Change (d4PDF), which was produced under the SOUSEI program. EC-Earth simulation was performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at Linköping University and ECMWF’s computing and archive facilities. Peer Reviewed Postprint (published version)