FIGURE 1: GLOBAL ACE AT DIFFERENT DATA QUALITY: 1945-2013

FIGURE 2: HADCRUT SEA SURFACE TEMPERATURE DATA 1945-2013

FIGURE 3: GLOBAL ACE VS SST AT DECADAL TIME SCALE

FIGURE 4: CORRELATION & DETRENDED CORRELATION

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1. SUMMARY: The proposed relationship between sea surface temperature (SST) and tropical cyclone activity is tested with data for global mean Accumulated Cyclone Energy (ACE) in all six basins and global mean SST in the study period 1945-2013.  Three different time scales from annual to decadal are studied. Although some strong correlations are seen in the source time series, no correlation is found in the detrended data. A test with only Northern Hemisphere tropical cyclone basins and Northern Hemisphere SST also failed to find the needed correlation. We conclude that no evidence is found in these data to relate the ACE measure of tropical cyclone activity to mean SST. 
2. BACKGROUND: Sea surface temperature (SST) is the link that connects climate change research with tropical cyclone research. Rising SST is observed (Hadley Centre, 2017) and thought to be an effect of Anthropogenic global warming or AGW (Hansen, 2005) . At the same time, the theory of tropical cyclones holds that cyclone formation, and particularly cyclone intensification are related to SST (Vecchi, 2007) (Knutson, 2010). Testable implications of the theory for empirical research are derived from climate model simulations (Knutson, 2010) and also from sedimentary evidence of land-falling hurricanes over a 1500-year period (Mann, 2009). These studies suggest some guidelines and testable implications for empirical tests of the theory that AGW affects tropical cyclone activity (Knutson, 2010). These guidelines as described by Knutson etal are described in a related post at this site [LINK] . A challenge for empirical tests in this line of research in light of Knutson’s work is extremely high variance in tropical cyclone data at an annual time scale or for any single cyclone basin. The variance problem suggests that trend and correlation analysis of tropical cyclone data should be carried out on a global basis for all six tropical cyclone basins and time scales of longer than annual should be used.
3. DATA: The “best track” cyclone data were used as received from the NCDC without corrections, adjustments, additions, or deletions with the exception that the years 1848-1944 were not used because they did not contain data for all six basins. It is generally assumed that these data may contain a measurement bias over time and across basins because of differences in data collection methods and procedures (Kossin, 2013). Although aircraft reconnaissance of tropical cyclones in selected basins began as early as the 1940s, these data did not reach a level of coverage and sophistication until the C-130 was deployed in the 1960s. Satellite data gathering for tropical cyclones began in the 1970s.  Figure 1 is a display of the ACE data for the two different data quality periods. This study uses the longer time span 1945-2013. Global mean and Northern Hemisphere mean SST data for the corresponding period are displayed in Figure 2. 
4. THEORY: The effect of rising atmospheric carbon dioxide and sea surface temperature (SST) in the climate change era on the formation and intensification of tropical cyclones is not well understood (Walsh, 2014). The conventional theory is that rising SST under the right atmospheric conditions will increase both the formation and intensification of tropical cyclones (Gray W. , 1967) (McBride, 1995) (Emanuel K. , The dependence of hurricane intensity on climate, 1987) (Gray W. , 1979). However, historical tropical cyclone data in a warming world as well as future tropical cyclone conditions generated by general circulation climate models imply that the relationship between the warming trend in the climate change era and tropical cyclone formation and intensification may be more complicated (Hodges, 2007) (Kozar, 2013) (Lin, 2015) (Walsh, 2014). For example, localized SST relatively higher than surrounding waters may produce a greater extent of the rainfall area (Lin, 2015). It is also possible that a complex relationship exists between SST and the frequency and intensity of tropical cyclones with rising temperatures implying fewer but more intense storms (Hodges, 2007). On the other hand, a simulation on a millennial time scale by Kozar, Mann, Emanuel, and others suggests that warming will increase the decadal frequency of North Atlantic hurricanes and proportionately, the decadal frequency of hurricanes that make landfall (Kozar, 2013). In this study we use detrended correlation analysis to test whether a relationship between SST and ACE can be found that would imply that ACE is responsive to SST at the time scale of interest. The test is carried out at time scales of annual, 5 years, and 10 years.
5. DATA ANALYSIS AND RESULTS: The results of the detrended correlation analysis of global mean ACE against SST for two regional references (global and northern hemisphere) and three time scales (1, 5, & 10 years) are displayed in Figure 3 and Figure 4. Since the SST series differs among calendar months, the relationship between SST and ACE is studied separately for each calendar month particularly since tropical cyclone activity is strongly seasonal. In Figure 3, the relationship between SST and ACE is depicted graphically for each calendar month in a GIF animation that cycles through the twelve calendar months.
6. Figure 4 is a comparison of the correlations and detrended correlations between ACE and SST (in the ordinate) for the twelve calendar months (labeled 1 to 12  along the coordinate) and the two regional extents studied namely global (GL) and northern hemisphere (NH). Figure 4 consists of three panels for the three time scales with two frames in each panel (global extent in the left frame and northern hemisphere extent on the right frame). These graphics show that source data correlations (in blue) are high and statistically significant for all twelve calendar months with the correlation being highest in the decadal time scale and lowest in the annual time scale. These correlations are stronger for the global extent than they are for the hemispheric extent. Not much difference among calendar months is seen in the global extent but the hemispheric extent appears to show stronger correlations in summer.
7. Source data correlations derive from two independent sources. They are (1) share long term trends and (2) the responsiveness of ACE to SST. It is only the latter that is of interest in this study. The two sources are separated in the detrended correlations shown in red that contain only the responsiveness of ACE to SST at each of the three time scales. Here we find that none of the source data correlation survives into the detrended series for any calendar month and that therefore the correlations seen in the source data are driven only by shared trends and that therefore they do not contain any information about the responsiveness of ACE to SST in any of the twelve calendar months.
8. CONCLUSION:  The data do not show that the total tropical cyclone energy for all six basins worldwide or for the four basins in the northern hemisphere is responsive to the corresponding regional sea surface temperature at any of the three time scales studied from annual to decadal. In a related post it is shown that the apparent rising trend in tropical cyclone activity is mostly the creation of changing data quality with no statistically significant change in tropical cyclone activity measured as total global ACE [LINK] . In this post the the further study of the ACE data we find no evidence that total global ACE is responsive to changes in SST.

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CITATIONS

1. American Meteorological Society. (2014). State of the climate in 2013. Bulletin of the American Meterological Society, V. 95, No. 7, July 2014.
2. Balaguru, K. (2014). Increase in the intensity of post monsoon Bay of Bengal tropical cyclones. Geophysical Research Letters, 3594-3601.
3. Bister, M. (1998). Dissipative heating and hurricane intensity. Meteorology and Atmospheric Physics, 52: 233-240.
4. Chan, J. (2005). Interannual and interdecadal variations of tropical cyclone activity over the western North Pacific. Meteorology and Atmospheric Physics, 89: 143-152.
5. Chan, J. (2006). Comments on “changes in tropical cyclone number, duration, and intensityi a warming environment”. Science, 311: 1731b.
6. Dodla, V. (2007). GIS based analysis of climate change impacts on tropical cyclones over Bay of Bengal. Jackson, MS, 39217, USA: Trent Lott Geospatial and Visualization Research Center, Jackson State University.
7. Draper, N. a. (1981). Applied regression analysis. NY: Wiley.
8. Elsner, J. (2008). The increasing intensity of the strongest tropical cyclones. Nature, 455, 92-95.
9. Emanuel, K. (1987). The dependence of hurricane intensity on climate. Nature, 326: 483-485.
10. Emanuel, K. (1988). The maximum intensity of hurricanes. Journal of Atmospheric Sciences, 45: 11431155.
11. Emanuel, K. (2005). Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436: 686-688.
12. Eric, K. (2012). Interannual variations of tropical cyclone activity over the North Indian Ocean. International Journal of Climatology, Volume 32, Issue 6, pages 819–830.
13. Frank, W. (2007). The interannual variability of tropical cyclones. Monthly Weather Review, 135: 3587-3598.
14. Girishkumar, M. (2012). The influences of ENSO on tropical cyclone activity in the Bay of Bengal during October-December. Journal of Geophysical Research, V.117, C02033, doi:10.1029/2011JC007417.
15. Gray, W. (1967). Global view of the origins of tropical disturbances and storms. Fort Collins, CO: Technical Paper #114, Dept of Atmospheric Sciences, Colorado State University.
16. Gray, W. (1979). Hurricanes: their formation, structure, and likely role in the tropical circulation. In D. Shaw, Meteorology over tropical oceans. Bracknell: Royal Meteorological Society.
17. Held, I. (2011). The response of tropical cyclone statistics to an increase in CO2 … Journal of Climate, 24: 5353-5364.
18. Hodges, K. (2007). How may tropical cyclones change in a warmer climate. Tellus A: Dynamic Meteorology and Oceanography, 59(4): pp. 539-561.
19. Holland, G. (1997). The maximum potential intensity of tropical cyclones. Journal of Atmospheric Sciences, 54: 2519-2541.
20. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Holm, S. (1979). “A simple sequentially rejective multipScandinavian Journal of Statistics, 6 (2): 65–70.
21. Hurricane Science. (2010). 1970 The great Bhola cyclone. Retrieved 2015, from hurricanescience.org: http://www.hurricanescience.org/history/storms/1970s/greatbhola/
22. IPCC. (2007). Climate change 2007. Retrieved 2015, from ipcc.ch: https://www.ipcc.ch/pdf/assessment-report/ar4/wg2/ar4_wg2_full_report.pdf
23. Islam, T. (2008). Climatology of landfalling tropical cyclones in Bangladesh 1877–2003. Natural Hazards, 48(1), 115–135.
24. JAXA. (2015). Typhoon Search. Retrieved 2015, from The Japan Aerospace Exploration Agency : http://sharaku.eorc.jaxa.jp/cgi-bin/typ_db/typ_track.cgi?lang=e&area=IO
25. JMA. (2005). Tropical cyclone basins. Retrieved 2015, from Japan Meteorological Agency: http://ds.data.jma.go.jp/gmd/jra/atlas/eng/indexe_time3.htm
26. Johnson, V. (2013). Revised standards for statistical evidence. Proceedings of the National Academy of Sciences, http://www.pnas.org/content/110/48/19313.full.
27. Kikuchi, K. (2010). Formation of tropical cyclones in the northern Indian Ocean. Journal of the Meteorological Society of Japan, Vol. 88, No. 3, pp. 475–496.
28. Knutson, T. (2010). Tropical cyclones and climate change. Nature Geoscience, 3.3 (2010): 157-163.
29. Knutson-McBride-Landsea-Emanuel-Chan. (2010). Tropical cyclones and climate change. Nature Geoscience, 3.3 (2010): 157-163.
30. Klotzbach, P. (2006). Trends in global tropical cyclone activity over the past twenty years 1986-2005. Geophysical research letters, 33: L10805.
31. Knapp, K. (2010). The International Best Track Archive for Climate Stewardship (IBTrACS) . Bulletin of the American Meteorological Society, 91, 363–376.
32. Kossin, J. (2013). Trend analysis with a new global record of cyclone intensity. Journal of Climate, 26: 9960-8876.
33. Kozar, M. (2013). Long term variations of North American tropical cyclone activity … Journal of Geophysical Research, 118: 13383-13392.
34. Kumar, R. (2013). A brief history of Indian cyclones. Retrieved 2015, from dnaindia.com: http://www.dnaindia.com/india/report-a-brief-history-of-indian-cyclones-1902774
35. Landsea, C. (2007). Counting Atlantic tropical cyclones back to 1900. EOS Transactions of the American Geophysical Union, 88:18.197-208.
36. Li, T. (2003). Satellite data analysis and numerical simulation of tropical cyclones. Geophysical Research Letters, V. 30 #21 2122.
37. Li, T. (2010). Global warming shifts Pacific tropical cyclone location. Geophysical Research Letters, 37: 1-5.
38. Lin, H. (2015). Recent decrease in typhoon destructive potential and global warming implications. Nature Communications, DOI: 10.1038/ncomms8182.
39. Lin, Y. (2015). Tropical cyclone rainfall area controlled by relative sea surface temperature. Nature Communications, DOI: 10.1038/ncocoms7591.
40. Mann, M. (2007). Atlantic tropical cyclones revisited. EOS Transactions American Geophysical Union, 88:36:349-350.
41. Mann, M. (2007). Evidence of a modest undercount bias in early historical Atlantic tropical cyclone counts. Geophysical Research Letters, 34: L22707.
    McBride, J. (1995). Tropical cyclone formation. In W. Frank, A global view of tropical cyclones (pp. 63-100). Geneva: World Meteorological Organization.
42. Munshi, J. (2015). Global cyclone paper data archive. Retrieved 2015, from Dropbox: [LINK]
43. Murakami, H. (2014). Contributing Factors to the Recent High Level of Accumulated Cyclone Energy (ACE) and Power Dissipation Index (PDI) in the North Atlanti. Journal of Climate, v.27, n. 8.
44. Neetu, S. (2012). Influence of upper ocean stratification on tropical cyclone induced surface cooling in the Bay of Bengal. Journal of Geophysical Research, V117 C12020.
45. NHC. (2015). National Hurricane Center. Retrieved 2015, from NOAA: http://www.nhc.noaa.gov/
46. NOAA. (2015). La Nina. Retrieved 2015, from NOAA: http://www.publicaffairs.noaa.gov/lanina.html
47. NOAA. (2015). NOAA. Retrieved 2015, from NOAA: http://www.noaa.gov/
48. NOAA/NCDC. (2015). IBTRACS. Retrieved 2015, from NCDC: https://www.ncdc.noaa.gov/ibtracs/index.php?name=ibtracs-data
49. Royer, J. (1998). A GCM study of the impact of greenhouse gas increase on the frequency of tropical cyclones. Climate Change, 38: 307-343.
50. Scoccimarro, E. (2014). Intense precipitation events associated with landfalling tropical cyclones in response to a warmer climate and increased CO2. Journal of Climate, 27: 4642-4654.
51. Sengupta, D. (2008). Cyclone-induced mixing does not cool SST in the post-monsoon north Bay of Bengal. Atmospheric Science Letters, 9(1), 1–6.
52. Sharkov, E. (2012). Global tropical cyclogenesis. Berlin: Springer-Verlag.
    Singh, O. P. (2001). Has the frequency of intense tropical cyclones increased in the north Indian Ocean? Current Science, 80(4), 575–580.
53. Sriver, R. (2006). Low frequency variability in globally integrated tropical cyclone power dissipation. Geophysical Research Letters, 33: L11705.
54. Stormfax. (2015). El-Nino. Retrieved 2015, from el-nin.com: http://el-nino.com/
55. Walsh, K. (2014). Hurricanes and climate. Bulletin of the American Meteorological Society, DOI: 10.1175/BAMS-D-13-00242.1.
56. Webster, P. (2005). Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309: 1844-1846.
57. Zhao, H. (2011). Interannual Changes of Tropical Cyclone Intensity in the Western North Pacific . Journal of the Meteorological Society of Japan, Vol. 89, No. 3, pp. 243–253, 2011 .
58. Zhao, M. (2009). Simulations of global hurricane climatology, interannual variability, and reponse to global warming. Journal of Climate, 22: 6653-6678.
59. Zhao, M. (2012). GCM simulations of hurricane frequency response to sea surface temperature anomalies. Journal of Climate, 25: 2995-3009.