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Posted on: December 11, 2020

Landmark United in Science report informs Climate Action Summit — IPCC



Hurricane Katrina: flooding in New Orleans 2

  1. BACKGROUND INFORMATION: Sea surface temperature (SST) is the link that connects climate change research with tropical cyclone research. Rising SST is observed 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 for the testable implications of of the impact of AGW on tropical cyclone activity (Knutson, 2010).
  2. A high level of interest in tropical cyclones derives from an unusually active hurricane season in 2004 when more than 15 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.
  3. The research paper (Emanuel, 2005) came in the heels of these events and after the breathless media had already decided that Katrina was a creation of climate change and a taste of the climate change horror yet to come. (Emanuel 2005) is possibly best understood in this context. The assumed attribution by the media of the epic devastation to AGW set the stage for climate science to claim the destructiveness of hurricanes as extreme weather effects of AGW. The Emanuel 2005 paper was one of several published in the heels of these hurricane seasons. It presents a new measure of tropical cyclone intensity which the author calls “Power Dissipation Index” and to which he assigns the acronym PDI. The paper finds a statistically significant rising trend in the aggregate annual PDI of North Atlantic Hurricanes in the study period 1949-2004 in tandem with rising sea surface temperature (SST) for the appropriate zone where hurricanes form. The graphical depiction of this result is reproduced above in the chart labeled “findings of Emanuel 2005”.
  4. The usual measure of tropical cyclone activity is the ACE or Accumulated Cyclone Energy. It is computed as the sum of squares of the maximum sustained wind speed in each 6-hour window during the life of the cyclone. It represents the total amount of kinetic energy generated by a tropical cyclone and this energy has been related to the energy in the ocean surface as measured by surface temperatures and temperature differentials such that the cyclone can be described as a heat engine (Rotunno, 1987) (Emanuel, 1987) (Goni, 2003) (Latif, 2007) (Klotzbach, 2006) (Emanuel, 2003), but the ACE measure did not show the trend that the author had thought that he would find. It is at this point the research leaves the realm of objective and unbiased scientific inquiry.
  5. Here, the respected scientist decided to cube the velocities instead of squaring them as a way of increasing the differences among annual values. This innovation produced the desired result and a trend became evident over the last 30 years of the 55-year study period as seen in the chart above labeled “the finding of Emanual 2005.
  6. Since the sum of cubes could not be called ACE, the author gave it a new name and called it the Power Dissipation Index or PDI, a new terminology invented on the spot and post hoc. The object variable in the hypothesis was thus changed from ACE to PDI. The PDI hypothesis was then modified to exclude the first 22 years of the study period where no trend and very little correspondence between PDI and SST are seen in the chart above. Thus, the tailor made post hoc hypothesis to be tested was whether there is a rising trend in the PDI in the most recent 30 years (1975-2004) of the study period.
  7. This hypothesis was then tested with the same data over the same time span that was used to construct it. The procedure of testing a hypothesis with the data used to construct the hypothesis constitutes circular reasoning because the methodology subsumes and ensures the desired result.
  8. At this point, a rising trend is seen in the PDI time series 1975-2004 at an annual time scale but the trend is not statistically significant because of extreme year to year variability in cyclone formation and intensification (Knutson, 2010). To smooth out the variance, the author took a 5-year moving average of the PDI data; and when that also failed to show a statistically significant trend, he took 5-year moving averages of the 5-year moving averages (in effect a 10-year moving average) and was finally able to find statistical significance for rising PDI over the last 30 years of the study period (Watkins, 2007). The findings presented by the paper are based on this rising trend and the visual correspondence between PDI and SST seen in the chart above.
  9. However, in his hypothesis test computations the author failed to correct for degrees of freedom lost in the computation of moving averages. When moving averages are computed some data values are used more than once. It can be shown that the average multiplicity of use is given by the relationship M = (λ/N)*(N-λ+1) where M is the average multiplicity, N is the sample size, and λ is the width of the moving window (Munshi, 2016) (VonStorch, 1995). In the case of a window with λ=10 years moving through a time series of N=30 years, the average multiplicity is M=7. The effective sample size is computed as EFFN=N/M or EFFN=30/7 = 4.285 and the degrees of freedom for the t-test for trend is DF=EFFN-2 or DF=2.285. The statistical significance reported by the author at N=30 and DF=28 is not found when the sample size is corrected for multiplicity. A false sense of statistical power was created by the methodology used when decadal moving averages were taken (Watkins, 2007) (Munshi, 2016). Full text download links for the paper on moving averages  [SSRN.COM]  [ACADEMIA.EDU]  .
  10. The North Atlantic basin is just one of six major cyclone basins around the world. The other five are The West Pacific, the East Pacific, the South Pacific, the North Indian, and the South Indian. The most active basin is the West Pacific. The theory of anthropogenic global warming as expressed in terms of climate models indicates that only long term changes in global averages of all six cyclone basins may be interpreted in terms of the impacts of climate change (Knutson, 2010). The study of a single basin is unlikely to contain useful information relevant to AGW. Data for all six basins over a 70-year study period 1945-2014 does not show trends in total aggregate annual ACE that can be interpreted as an impact of warming as shown in three related posts on this site  [LINK] [LINK] [LINK] . 
  11. It is likely that (Emanuel, 2005) was a product of climate activism that had reached a high level of intensity in the years leading up to 2005 by way of the push for the ratification of the Kyoto Protocol for CO2 emission reduction as well as the European heat wave of 2003 that was claimed and widely accepted to be caused by AGW. It was a time when the extreme weather effect of AGW was given credence by the IPCC and generally taken for granted. Given the theoretical basis that connected SST to tropical cyclones, the truth of AGW driven hurricane intensity was thus taken to be a given and then apparently proven by the 2004/2005 hurricane seasons. It remained for climate science only to tend to the details of presenting the data in the appropriate format.
  12. Thus the ultimate form of circular reasoning is found in (Emanuel, 2005) in which a high level of confidence ex-ante in the truth of the proposition that AGW causes extreme tropical cyclone activity left the presentation of empirical evidence of that relationship as mere detail. The role of confirmation bias in research of this nature is discussed in a related post [CONFIRMATION BIAS] .FOOTNOTE: Guidelines for attribution of tropical cyclone properties to climate change: 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.
  13. Complications of empirical tests in this line of research: 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).


Making History of a Hurricane | National Geographic Society


Albright, W. (1973). From the Patriarchs to Moses II. Moses out of Egypt. The Biblical Archaeologist, 36.2 (1973): 48-76.


Allen, R. (2009). The British industrial revolution in global perspective. Vol. 1. Cambridge. Cambridge: Cambridge University Press, 2009.

Alley, R. (2005). Ice-sheet and sea-level changes. science, 310.5747 (2005): 456-460.

Altmetrics. (2017). Altmetrics. Retrieved 2017, from Altmetrics:

Bless, C. (2006). Fundamentals of social research methods: An African perspective. Juta and Company Ltd, 2006.

Britt, C. (2001). Testing theory and the analysis of time series data. Journal of quantitative criminology, 17.4 (2001): 343-357.

Carbon Brief. (2017). Carbon Brief. Retrieved 2017, from Carbon Brief:

Church, J. (2006). A 20th century acceleration in global sea‐level rise. Geophysical research letters, 33.1 (2006).

Church, J. (2011). Sea-level rise from the late 19th to the early 21st century. Surveys in geophysics, 32.4-5 (2011): 585-602.

Cooper, D. (2006). Business research methods. Vol. 9. New York: McGraw-Hill Irwin, 2006.

Cross, F. (1973). Albright’s View of Biblical Archaeology and Its Methodology. The Biblical Archaeologist, 36.1 (1973): 2-5.

CU Sea Level Research Group. (2018). CU Sea Level Research Group. Retrieved from CU Sea Level Research Group:

Curry, J. (2006). Mixing politics and science in testing the hypothesis that greenhouse warming is causing a global increase in hurricane intensity. Bulletin of the American Meteorological Society, 87.8 (2006): 1025-1.

Curry, J. (2011). Reasoning about climate uncertainty. Climatic Change, 108.4 (2011): 723.

Dangendorf, S. (2016). Human influence on sea-level rise. Nature Climate Change, 6 (2016): 661-662.

Dever, W. (2003). Who were the early Israelites, and where did they come from? Wm. B. Eerdmans Publishing, 2003.

Douglas, B. (1992). Global sea level acceleration. Journal of Geophysical Research: Oceans, 97.C8 (1992): 12699-12706.

Douglas/Kearney/Leatherman. (2000). Sea level rise: History and consequences. Vol. 75. Cambridge, MA: Academic Press, 2000.

Easterbrook, P. (1991). Publication bias in clinical research. The Lancet, 337.8746 (1991): 867-872.

Emanuel, K. (1987). The dependence of hurricane intensity on climate. Nature, 326.6112 (1987): 483-485.

Emanuel, K. (2003). Tropical cyclones. Annual Review of Earth and Planetary Sciences , 31 (2003).

Emanuel, K. (2005). Increasing destructiveness of tropical cyclones over the past 30 years. Nature , 436.7051 (2005): 686.

Enright, J. (1989). The parallactic view, statistical testing, and circular reasoning. Journal of biological rhythms, 4.2 (1989): 183-192.

Finkelstein, I. (1996). The archaeology of the United Monarchy: an alternative view. Levant, 28.1 (1996): 177-187.

Finkelstein, I. (1998). Bible archaeology or archaeology of Palestine in the Iron Age? Levant, 30.1 (1998): 167-174.

Finkelstein, I. (1998). Bible archaeology or archaeology of Palestine in the Iron Age? A rejoinder. Levant, 30.1 (1998): 167-174.

Finkelstein, I. (2002). The Bible Unearthed: Archaeology’s New Vision of Ancient Isreal and the Origin of Sacred Texts. NY: Simon and Schuster, 2002.

Finkelstein, I. (2010). A Great United Monarchy? Archaeological and Biblical Perspectives, 405 (2010): 3.

Finkelstein, I. (2011). The “Large Stone Structure” in Jerusalem: Reality versus Yearning. Zeitschrift des Deutschen Palästina-Verein, s (1953-) H. 1 (2011): 1-10.

Freedman, D. (1991). Statistical models and shoe leather. Sociological methodology, (1991): 291-313.

Goni, G. (2003). Ocean thermal structure monitoring could aid in the intensity forecast of tropical cyclones. Transactions American Geophysical Union, 84.51 (2003): 573-578.

Gornitz&Hansen. (1982). Global sea level trend in the past century. Science , 215.4540 (1982): 1611-1614.

Grinsted/Moore/Jevrejeva. (2010). Reconstructing sea level from paleo and projected temperatures 200 to 2100 AD. Climate Dynamics, 34.4 (2010): 461-472.

Hadley Centre. (2017). Met Office Hadley Centre. Retrieved 2017, from SST:

Hansen, J. (2005). A slippery slope: How much global warming constitutes “dangerous anthropogenic interference”? Climatic Change, 68.3 (2005): 269-279.

Hansen, J. (2005). Earth’s energy imbalance: Confirmation and implications. Science, 308.5727 (2005): 1431-1435.

Hansen, J. (2007). Scientific reticence and sea level rise. Environmental research letters, 2.2 (2007): 024002.

Hansen, J. (2016). Ice melt, sea level rise and superstorms: evidence from paleoclimate data, climate modeling, and modern observations that 2 C global warming could be dangerous. Atmospheric Chemistry and Physics , 16.6 (2016): 3761-3812.

Harper, D. (2008). Paleontological data analysis. John Wiley & Sons.

Hodges, J. (1992). Is it you or your model talking?: A framework for model validation. Santa Monica, CA: Rand.

Horton, B. (2014). Expert assessment of sea-level rise by AD 2100 and AD 2300. Quaternary Science Reviews, 84 (2014): 1-6.

Huybrechts, P. (2002). Sea-level changes at the LGM from ice-dynamic reconstructions of the Greenland and Antarctic ice sheets during the glacial cycles. Quaternary Science Reviews, 21.1-3 (2002): 203-231.

IPCC. (2007). Climate change 2007: The physical science basis. IPCC.

IPCCAR5. (2013). Chapter 6: Carbon and other biogeochemical cycles. Geneva: IPCC.

Jakeman, A. (2006). Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software, 21.5 (2006): 602-614.

Jansen, W. (2010). Directions in security metrics research. Diane Publishing.

Jevrejeva, S. (2008). Recent global sea level acceleration started over 200 years ago? Geophysical Research Letters , 35.8 (2008).

Jevrejeva, S. (2009). Anthropogenic forcing dominates sea level rise since 1850. Geophysical Research Letters, 36.20 (2009).

Jevrejeva, S. (2014). Trends and acceleration in global and regional sea levels since 1807. Global and Planetary Change, 113 (2014): 11-22.

Juhl, C. (2007). Fine‐Tuning and Old Evidence. Noûs, 41.3 (2007): 550-558.

Kaptchuk, T. (2003). Effect of interpretive bias on research evidence. Bmj , 326.7404 (2003): 1453-1455.

Kemp, A. (2009). Timing and magnitude of recent accelerated sea-level rise (North Carolina, United States. Geology , 37.11 (2009): 1035-1038.

Kemp, A. (2011). Climate related sea-level variations over the past two millennia. Proceedings of the National Academy of Sciences, 108.27 (2011): 11017-11022.

Klotzbach, P. (2006). Trends in global tropical cyclone activity over the past twenty years (1986–2005). Geophysical Research Letters , 33.10 (2006).

Knutson, T. (2010). Tropical cyclones and climate change. Nature Geoscience, 3.3 (2010): 157-163.

Knutson-McBride-Landsea-Emanuel-Chan. (2010). Tropical cyclones and climate change. Nature Geoscience, 3.3 (2010): 157-163.

Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method. American Psychologist, 56.1 (2001): 16.

Lacis, A. (2010). Atmospheric CO2: Principal control knob governing Earth’s temperature. Science, 330.6002 (2010): 356-359.

Landsea, C. (1999). ATLANTIC BASIN HURRICANES: INDICES OF CLIMATIC CHANGES. Climatic Change, 42(1):89–129.

Landsea, C. (2007). Counting Atlantic tropical cyclones back to 1900. Eos, Transactions American Geophysical Union, 88.18 (2007): 197-202.

Landsea, C. (2010). Impact of duration thresholds on Atlantic tropical cyclone counts. Journal of Climate, 23.10 (2010): 2508-2519.

Latif, M. (2007). Tropical sea surface temperature, vertical wind shear, and hurricane development. Geophysical Research Letters , 34.1 (2007).

Mann, M. (2009). Atlantic hurricanes and climate over the past 1,500 years. Nature, 460.7257 (2009): 880.

McDonald, R. (2013.). Test theory: A unified treatment. Psychology Press.

Mehta, T. (2004). Towards sound epistemological foundations of statistical methods for high-dimensional biology. Nature genetics, 36.9 (2004): 943.

Mingers, J. (2006). A critique of statistical modelling in management science from a critical realist perspective: its role within multimethodology . Journal of the Operational Research Society, 57.2 (2006): 202-219.

Moberg, A. (2005). Highly variable Northern Hemisphere temperatures reconstructed from low-and high-resolution proxy data.” . Nature, 433.7026 (2005): 613.

Montgomery, D. (2010). Applied statistics and probability for engineers. NY: John Wiley & Sons.

Munshi, J. (2015). A General Linear Model for Trends in Tropical Cyclone Activity. SSRN, r

Munshi, J. (2016). Illusory Statistical Power in Time Series Analysis. SSRN

Munshi, J. (2016). Spurious Correlations in Time Series Data. SSRN

Munshi, J. (2017). Limitations of the TCRE. SSRN

Munshi, J. (2017). A Test of the Anthropogenic Sea Level Rise Hypothesis. SSRN

Munshi, J. (2017). Correlation of Regional Warming with Global Emissions. SSRN Negative Results eJournal

Munshi, J. (2017). Responsiveness of Atmospheric CO2 to Fossil Fuel Emissions. Retrieved 2017, from SSRN:

Munshi, J. (2017). The Effect of Fossil Fuel Emissions on Sea Level Rise. Retrieved 2017, from SSRN:

Nerem, R. (2018). Climate-change–driven accelerated sea-level rise detected in the altimeter era. Proceedings of the National Academy of Sciences, (2018): 201717312.

Nicholls, N. (1999). Cognitive illusions, heuristics, and climate prediction. Bulletin of the American Meteorological Society , 80.7 (1999): 1385-1397.

Nickerson, R. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of general psychology, 2.2 (1998): 175.

NOAA. (2013). Tropical Cyclones. Retrieved 2017, from NOAA:

PSMSL. (2018). PSMSL. Retrieved from PSMSL:

Ragin, C. (2014). The comparative method: Moving beyond qualitative and quantitative strategies. Berkeley: Univ of California Press.

Rahmstorf, S. (2007). A semi-empirical approach to projecting future sea-level rise. Science, 315.5810 (2007): 368-370.

Rahmstorf, S. (2010). A new view on sea level rise. Nature reports climate change , 4.4 (2010): 44-45.

Rignot, E. (2011). Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise. Geophysical Research Letters, 38.5 (2011).

Rockström, J. (2009). A safe operating space for humanity. Nature, 461.7263 (2009): 472.

Rotunno, R. (1987). An air–sea interaction theory for tropical cyclones. Part II: Evolutionary study using a nonhydrostatic axisymmetric numerical model. Journal of the Atmospheric Sciences , 44.3 (1987): 542-561.

Rykiel, E. (1996). Testing ecological models: the meaning of validation. Ecological modelling, 90.3 (1996): 229-244.

Sallenger, J. (2012). Hotspot of accelerated sea-level rise on the Atlantic coast of North America. Nature Climate Change, 2.12 (2012): 884.

Slangen, A. (2016). Anthropogenic forcing dominates global mean sea-level rise since 1970. Nature Climate Change, 6.7 (2016): 701-705.

Stern, J. (1997). Publication bias: evidence of delayed publication in a cohort study of clinical research projects. BMJ, 315.7109 (1997): 640-645.

Trenberth, K. (2005). Uncertainty in hurricanes and global warming. Science , 308.5729 (2005): 1753-1754.

UCSUSA. (2017). Impacts of global warming. Retrieved 2017, from UCSUSA:

UHSLC. (2018). UHSLC. Retrieved from UHSLC:

Vecchi, G. (2007). Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature, 450.7172 (2007): 1066.

Vermeer/Rahmstorf. (2009). Global sea level linked to global temperature. Proceedings of the National Academy of Sciences, 106.51 (2009): 21527-21532.

VonStorch, H. (1995). Analysis of cliimate variability. Sprnger Verlag.

Vul, E. (2010). Begging the question: The non-independence error in fMRI data analysis. In M. Press, Foundational issues for human brain mapping (pp. 71-91). Boston: MIT Press.

Watkins, T. (2007). The use of moving averages can create the appearance of confirmation of theories. Retrieved 2017, from Thayer Watkins:

Webster, P. (2005). Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309.5742 (2005): 1844-1846.

Woodworth, P. (2009). Evidence for the accelerations of sea level on multi‐decade and century timescales. International Journal of Climatology, 29.6 (2009): 777-789.

  1. FOOTNOTE: Guidelines for attribution of tropical cyclone properties to climate change: 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.

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