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There are several different estimates of the observed cyclone damage potential of tropical cyclones based on observations of size, intensity and track. For the analysis of climate model data, previous work identified an index, the cyclone damage potential climate index (CDPClimate), based on relative sea surface temperature (SST) and tropical cyclone steering flow to estimate the damage potential in climate models. Using millennia-long climate models, CDPClimate is estimated for the North Atlantic basin and compared against values from reanalyses and the observed damage potential. The peak in SSTs in the cyclone main development region with respect to the tropical mean SSTs is smaller in these models than reanalyses, resulting in smaller variations in CDPClimate. Although the year 1995 had the highest observed cyclone damage potential, the year 2010 is a maximum for CDPClimate in the reanalysis data. The models exceed this 2010 value in less than 1% of model years. Using a model with 100 ensemble members, the variability in CDPClimate is examined further. The interannual variability of the ensemble mean results has a very high correlation (R = 0.95) with reanalyses. The high decadal variability is evident and interannual variability is found to have increased during the 30 years after 1981 relative to those prior. The 2010 ensemble mean value is exceeded in other years by individual ensemble members 1.1% of the time. The results from this study suggest that although it is possible to exceed the observed CDP, this is rare in the current climate. However, this study does not consider changes as we move to future climates., This study was funded by Bermuda Institute of Ocean Sciences’ Risk Prediction Initiative (RPI). Calculations were performed on the National Computational Infrastructure supercomputer, funded by the Australian Government. SLL is currently funded by Meat and Livestock Australia (MLA), the Queensland Government, and University of Southern Queensland through the Northern Australian Climate Program (NACP)., Peer Reviewed, Postprint (author's final draft)

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.

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)