Thongchai Thailand

Trends in Tropical Cyclone Activity

Posted on: November 28, 2018







































  1. SUMMARY: In a general linear model for global mean annual Accumulated Cyclone Energy (ACE) in six basins and seven decades from 1945 to 2014 we find some evidence of a rising trend in tropical cyclone activity in the early part of the study period prior to the decade D03 [1965-1974]. No global trends are found after this decade. The same pattern is found in three of the six cyclone basins studied, namely, EP [Eastern Pacific], SI [South Indian], and SP [South Pacific]; with each basin showing a rising trend relative to the decades prior to D03 and none since D03. No trends could be detected in the other three cyclone basins in the study, namely, NA [North Atlantic], NI [North Indian], and WP [Western Pacific]. The global model found significant differences in mean overall ACE index among the six basins. The Western Pacific Basin was the most active and the North Indian Basin was the least active. Not much separates the other four basins except that the South Indian Basin was more active than the South Pacific Basin.
  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).
  3. These guidelines are as follows: 1. Globally averaged intensity of tropical cyclones will rise as AGW increases SST. Models predict globally averaged intensity increase of 2% to 11% by 2100. 2. Models predict falling globally averaged frequency of tropical cyclones with frequency decreasing 6%-34% by 2100. 3. The globally averaged frequency of “most intense tropical cyclones” should increase as a result of AGW. The intensity of tropical cyclones is measured as the ACE (Accumulated Cyclone Energy). 4. Models predict increase in precipitation within a 100 km radius of the storm center. A precipitation rise of 20% is projected for the year 2100.
  4. Complications of empirical tests in this line of research are (Knutson, 2010): 1. Extremely high variance in tropical cyclone data at an annual time scale suggests longer, perhaps a decadal time scale which in turn greatly reduces statistical power. 2. Limited data availability and poor data quality present barriers to research. 3. Limited theoretical understanding of natural variability makes it difficult to ascertain whether the variability observed in the data is in excess of natural variability. 4. Model projections for individual cyclone basins show large differences and conflicting results. Thus, no testable implication can be derived for studies of individual basins. It is necessary that empirical studies have a global geographical span. 5. Advances in data collection activity, methods, and technology create trends in the data that must be separated from climate change effects (Landsea, 2007) (Landsea, 2010). A high level of interest in tropical cyclones derives from an unusually active hurricane season in 2004 when more than 14 tropical cyclones formed in the North Atlantic basin . Four of these storms intensified to Category 4 or greater and made landfall in the USA causing considerable damage. The even more dramatic 2005 season followed in its heels with more than thirty depressions. Four of them intensified to Category 5 and three made landfall. The most intense was Hurricane Wilma but the most spectacular was Hurricane Katrina which made landfall in Florida and again in Louisiana. Its devastation was facilitated by a breach in the levee system that was unrelated to AGW but its dramatic consequences made it an icon of the possible extreme weather impacts of AGW.
  5. 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.  The undercount bias in the oldest data explains why a rising trend in cyclone activity is found only against the early part of the study period. The findings presented here are entirely empirical and their utility depends on the validity of the ACE index as a measure of tropical cyclone activity. All data and computational details are available in the online data archive for this paper [LINK] . The full text of the source paper for this post may be downloaded from [SSRN.COM] or [ACADEMIA.EDU] .
  6. 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). Perhaps it has to do with the amount and extent of rainfall associated with tropical cyclones with higher SST producing more rain (Scoccimarro, 2014) and localized SST relatively higher than surrounding waters producing a greater extent of the rainfall area (Lin, 2015). It is also possible that a complex relationship exists between SST and the frequency2 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). An extensive study by the US CLIVAR hurricane working group3 (HWG) with multiple general circulation climate models found that warming may cause the frequency of tropical cyclones to decline in the long term and that rising CO2 may have its own independent effect on hurricane activity (Walsh, 2014) (Held, 2011) (Royer, 1998). The authors of the Walsh study included the disclaimer that the effect of climate change on tropical cyclones is “uncertain” and the sobering implication that we don’t really know the relationship between climate change and tropical cyclones. At the root of the tropical cyclone conundrum is the extreme inter-annual variation in the number and maximum intensity of tropical cyclones and the seemingly independent and unrelated behavior of the six major tropical cyclone basins (Hodges, 2007) (Frank, 2007) (Mann, 2007) (Zhao M. , Simulations of global hurricane climatology, interannual variability, and reponse to global warming, 2009) (Zhao H. , 2011) (Eric, 2012) (Chan, Interannual and interdecadal variations of tropical cyclone activity over the western North Pacific, 2005). Although apparent patterns may be visualized in decadal and multi-decadal means, their differences can be interpreted only within the low statistical power imposed by the high variance at the annual level, and their utility is constrained by the limited historical reach of the data along with a measurement bias imposed on the time series by changing measurement technology (Kozar, 2013) (Mann, Evidence of a modest undercount bias in early historical Atlantic tropical cyclone counts, 2007) (Landsea, 2007).
  7. DATA ANALYSIS: There are six tropical and sub-tropical oceanic regions where tropical cyclones form from an isolated patch of relatively higher sea surface temperature. They are, alphabetically, The East Pacific, North Atlantic, North Indian, South Indian, South Pacific, and West Pacific. North Atlantic tropical cyclones are called Hurricanes and those in the West Pacific are called Typhoons. In the other basins they are called cyclones. Figure 1 shows their relative locations of the six tropical cyclone basins as well as the General Linear Model used used to combine them at a decadal time scale in this study of long term trends in global tropical cyclone activity in six basins and seven decades.
  8. RESULTS: The results of the general linear model analysis of global mean ACE for all six tropical cyclone zones at a decadal time scale [as suggested by (Knutson 2010)], are displayed in Figure 2. The left panel is a tabulation of the regression coefficients and their statistical significance. The right panel is a plot across time of the derived global decadal mean ACE for each of the seven decades in the study period 1945-2015. The 21 possible differences in global mean ACE among the seven decades are tested for statistical significance in Figure 3. In these tests, only 2 of the 21 hypothesis tests show statistically significant differences. It shows that decade#5 (1985-1994) and decade#6 (1995-2004) had higher mean global ACE than decade#1 (1955-1964). No other statistically significant difference is found.
  9. The general linear model depicted in Figures 1&2 is also used to compare tropical cyclone activity among the six cyclone basins net of the variation among the seven decades. The mean annual ACE index in each basin for the entire study period 1945-2014 is shown in Figure 3 where the six basins are compared graphically. Hypothesis tests for all pairwise comparisons of the six basins are listed in Figure 3. They show that the Western Pacific (WP) is the most active basin and that North Indian (NI) is the least active. No difference among the other four basins is found except that the South Indian basin (SI) is more active than the South Pacific (SP). Interestingly, the North Atlantic (NA) basin that gets a great deal of attention from researchers due its proximity and relevance to the USA, is not a particularly active basin in the global context. It is more active than only one  basin – the least active North Indian (NI) basin. Tropical cyclone research is therefore biased by a lopsided attention to the North Atlantic basin such that many of the conclusions drawn may not be relevant in a global context, the only context for tests of the effect of global warming on tropical cyclone activity (Knutson 2010). 
  10. The trends for each basin are studied in Figure 4 to Figure 9 alphabetically from EP to WP. Some trends are found in the Eastern Pacific (EP), South Indian (SI), and the South Pacific (SP) basins relative to the earliest decades. No trends are found in the other three basins. In particular, no trend is found in the most active basin WP or in the most popular research basin NA. Cyclonic activity in the EP basin in the twenty-year period 1975-1994 was greater than in the decade 1945-1954 and greater in the decade 1985-1994 than in the decade 1955-1964. No overall trend is found. In particular, there is no evidence that tropical cyclone activity has increased in subsequent decades since the decade D03 [1965-1974]. That in the SI basin is found to be higher in 1965-2004 than in the decade 1945-1954 and higher in the decade1995-2004 than in the decade 1955-1964. However, no sustained trend is found in the sample period 1945-2014 and in particular we find no evidence of an increase in cyclonic activity since the decade D03 [1965-1974]. In the SP basin, cyclonic activity shows a difference between the period 1975-2004 and the decade 1945-1954. However, no sustained trend in cyclonic activity is found. In particular, there is no evidence that cyclonic activity has increased since the decade D02 [1955-1964].
  11. In CONCLUSION, in this work, the ACE index is used to compare decadal mean tropical cyclone activity worldwide in all six basins among seven decades from 1945 to 2014. Some increase in tropical cyclone activity is found relative to the earliest decades. No trend is found after the decade 1965-1974. A comparison of the six cyclone basins in the study shows that the Western Pacific Basin is the most active basin and the North Indian Basin the least. These findings are best understood in terms of the known undercount bias in the data in the earliest decades; and not in terms of the theory of anthropogenic global warming and climate change.
  12.  The full text of this work may be downloaded from [ACADEMIA.EDU] or [SSRN.COM] .






  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
  22. IPCC. (2007). Climate change 2007. Retrieved 2015, from
  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 :
  25. JMA. (2005). Tropical cyclone basins. Retrieved 2015, from Japan Meteorological Agency:
  26. Johnson, V. (2013). Revised standards for statistical evidence. Proceedings of the National Academy of Sciences,
  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
  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:
  46. NOAA. (2015). La Nina. Retrieved 2015, from NOAA:
  47. NOAA. (2015). NOAA. Retrieved 2015, from NOAA:
  48. NOAA/NCDC. (2015). IBTRACS. Retrieved 2015, from NCDC:
  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
  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.







Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: