Thongchai Thailand

TCRU: A Parody of the TCRE

Posted on: December 3, 2018

 

 

 

 

FIGURE 1: CUMULATIVE UFO SIGHTINGS AND CUMULATIVE GLOBAL WARMING01

 

FIGURE 2: TCRU: TRANSIENT CLIMATE RESPONSE TO CUMULATIVE UFO SIGHTINGSGLOBAL-CHART

 

FIGURE 3: TCRU VALUES BY CALENDAR MONTHGLOBAL-TABLE

 

 

FIGURE 4: TCRU ESTIMATES FOR REGIONAL TEMPERATURE RECONSTRUCTIONS0203040506

 

 

 

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  1. The TCRE (Transient Climate Response to Cumulative Emissions) serves a crucial role in climate science. First, it provides a direct causal link between emissions and warming in support of the two key elements of climate change theory theory that (i) the observed warming since the LIA is driven by fossil fuel emissions, and (ii) that the rate of warming can be moderated by climate action in the form of emission reduction. Even more important, the TCRE provides climate science with a metric for estimating the so called “carbon budget” used by climate action policy makers to determine the maximum total emissions possible to meet total warming targets such as the IPCC 1.5ºC and 2.0ºC targets. For more information about the TCRE and its applications in climate science, please see [2018: Matthews, Damon, “Focus on cumulative emissions, global carbon budgets and the implications for climate mitigation targets.” Environmental Research Letters 13.1 (2018)].
  2. The Environmental Research Letters focus issue on ‘Cumulative Emissions, Global Carbon Budgets and the Implications for Climate Mitigation Targets‘ was launched in 2015 to highlight the emerging science of the climate response to cumulative emissions, and how this can inform efforts to decrease emissions fast enough to avoid dangerous climate impacts. There is also a related post on the TCRE at this site [LINK] where it is argued and demonstrated that the observed proportionality between temperature and cumulative emissions is spurious and that therefore, the TCRE metric and carbon budgets derived from it are specious because the correlation derives from a fortuitous sign pattern in the data where annual emissions are always positive and, in an era of global warming, the amount of warming each year is mostly positive.
  3. This work is a parody of the TCRE that further demonstrates the speciousness of the TCRE metric showing that any variable that matches the sign convention offered by cumulative emissions creates just as good a proportionality as emissions. The variable chosen for this parody demonstration is UFO sightings. Like emissions, UFO sightings each year are either zero or positive but never negative. UFO activity data are available from numerous sources for different regions and periods of time (Bader, 2017) (Donderi, 2013) (Hopkins, 1987) (Picknett, 2001) (Sheaffer, 1998) (Spencer, 1993) (UFO-Info, 2017). A convenient summary is also provided by Wikipedia (Wikipedia, 2018). The data are cross checked against the Wikipedia compilation for completeness.
  4. The sightings data are available as individual sightings and complied into total number of UFO sightings worldwide for each year 1910-2015. It is noted that individual sightings are usually for a number of different spaceships that vary from sighting to sighting and in different reports of the same sighting. For the purpose of this study, UFO activity is defined in terms of sightings without consideration for the number of ships per sighting. The annual sightings data are sparse in the first half of the study period with most years containing no sightings. The data are compiled into a cumulative values series along the lines of the CCR/TCRE procedure in climate science (Allen, 2009) (Matthews, 2009) (Matthews/Solomon, 2012) (Munshi, 2018). The proportionality π between cumulative sightings and surface temperature is computed both as a linear regression coefficient and also as a correlation coefficient and tested for statistical significance. The null hypothesis H0: π=0 is tested against the alternate HA: π>0 in a one-tailed test. Here π represents proportionality estimated as a combination of the strength of the linear regression coefficient and the correlation coefficient.
  5. Global surface temperature reconstructions for the period 1910-2015 are provided by the Hadley Centre of the Met Office of the Government of the UK (Morice, 2012). The data are available as monthly mean temperatures for each calendar month in four distinct region and surface combinations. They are Land in the Northern Hemisphere, Sea in the Northern Hemisphere, Land in the Southern Hemisphere, and Sea in the Southern Hemisphere. Data for each calendar month in each of four distinct surface and region specifications are studied for a total of forty eight different statistical tests of the hypothesis that surface temperature in the study period 1910-2015 is driven by UFO activity. The beginning of the study period of 1910-2015 is constrained by the availability of UFO data and the end is constrained by the data availability at the time the study was carried out.
  6. Figure 1 is a graphical display of the UFO sightings and temperature data used in this work. The results of the analysis of these data using the TCRE methodology is displayed in Figure 2 and tabulated in Figure 3. The left frame of Figure 2 is a graphical display of the correlation between annual mean global temperature and cumulative UFO sightings. The right frame is a presentation of the results for monthly mean temperatures. The numbers 1 to 12 along the coordinate represent the twelve calendar months from January to December. There are two ordinate parameters. The TCRU coefficients for the calendar months, computed as the regression coefficient of monthly mean global temperature against cumulative UFO sightings is shown in blue. The corresponding correlation that supports the validity of the regression coefficient is shown in red. The numerical values for both the TCRU and corresponding correlation are tabulated in Figure 3. Details of the month by month analysis are shown in Figure 4.
  7. the empirical test with available UFO sighting data and surface temperature reconstructions 1910-2015 presented in Figure 1, Figure 2, and Figure 3 shows a strong statistically significant proportionality between temperature and cumulative UFO sightings. We conclude that the data are consistent with the proposition that the observed warming since 1910 can be explained as an effect of UFO sightings perhaps by way of their unnatural perturbation of earth’s gravitational and magnetic fields as suggested by various authors.
  8. It has been proposed that UFO spacecraft contain no mechanism for flying known to man. The consensus among scientists is that the method of flight employed by these craft involve interactions with the earth’s own gravitational and geomagnetic system. Analysis of artifacts retrieved from crashed UFOs as well as the study of the intensification of the Aurora Borealis in the presence of UFOs reveal details of UFO propulsion dynamics that imply a massive and intense interference in the earth’s gravitational and magnetic fields (Potter, 2016) (Mike, 2011) (Ensley, 2013) (LaViolette, 2008) (Sarg, 2009). These electromagnetic and gravitational effects alter the way the earth interacts with its sun (Potter, 2009). Based on these effects of UFOs on the atmosphere and the results of our analysis presented above, we propose that the observed warming since 1910 is related to atmospheric perturbations of UFO activity.

 

 

INTENSIFICATION OF NORTHERN LIGHTS BY UFOsnorthernLights

 

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CITATIONS

  1. Allen, M. (2009). Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature, 458.7242 (2009): 1163.
  2. Bader, C. (2017). Paranormal America: Ghost encounters, UFO sightings, bigfoot hunts, and other curiosities in religion and culture. NY: NYU Press, 2017.
  3. Box, G. (1994). Time series analysis: forecasting and control. . Englewood Cliffs, NJ: : Prentice Hall.
  4. Donderi, D. (2013). UFOs, ETs, and Alien Abductions: A Scientist Looks at the Evidence. Hampton Roads Publishing (2013).
  5. Draper&Smith. (1998). Applied Regression Analysis. . Wiley.
    Ensley, H. (2013). Alien technology. CreateSpace Independent Publishing Platform.
  6. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6:2:65-70.
  7. Hopkins, B. (1987). Intruders: The Incredible Visitations at Copley Woods. New York: Random House, 1987.
  8. IPCC. (2007). IPCC AR4 The Physical Science Basis. Retrieved 2017, from IPCC: https://www.ipcc.ch/report/ar4/wg1/
  9. IPCC. (2013). AR5 The Physical Science Basis. Retrieved 2017, from IPCC: http://www.ipcc.ch/report/ar5/wg1/
  10. Johnson, V. (2013). Revised standards for statistical evidence. Proceedings of the National Academy of Sciences, 110.48 (2013): 19313-19317.
  11. Kriebel, D. (2001). The precautionary principle in environmental science.” . Environmental health perspectives , 109.9 (2001): 871.
  12. LaViolette, P. (2008). Secrets of Antigravity Propulsion: Tesla, UFOs, and Classified Aerospace Technology. Bear & Company.
  13. Matthews, H. (2009). The proportionality of global warming to cumulative carbon emissions. Nature, 459.7248 (2009): 829.
  14. Matthews/Solomon. (2012). Cumulative carbon as a policy framework for achieving climate stabilization. Phil. Trans. R. Soc. A, 370.1974 (2012): 4365-4379.
  15. Mike, J. (2011). The anatomy of a flying saucer. CreateSpace Independent Publishing Platform.
  16. Morice, e. (2012). Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 dataset. J.Geophys. Res.,, 117.
  17. Munshi, J. (2017). Limitations of the TCRE: Transient Climate Response to Cumulative Emissions. SSRN, https://ssrn.com/abstract=3000932 or http://dx.doi.org/10.2139/ssrn.3000932.
  18. Munshi, J. (2017). OLS Trend Analysis of CET Daily Mean Temperatures 1772-2016. SSRN.
  19. Munshi, J. (2018). From Equilibrium Climate Sensitivity to Carbon Climate Response. SSRN, https://ssrn.com/abstract=3142525.
  20. Picknett, L. (2001). The Mammoth Book of UFOs. Consteble & Robinson Ltd, 2001.
  21. Potter, P. (2009). Physics of UFO gravity manipulation. Retrieved from Physics of UFO gravity manipulation: http://www.zamandayolculuk.com/html-2/ufogravity.htm
  22. Potter, P. (2016). Anti-Gravity Propulsion Dynamics: UFOs and Gravitational Manipulation. Adventures Unlimited Press.
  23. Prodobnik. (2008). Detrended cross correlation analysis. Physical Review Letters, 100: 084102.
  24. Sarg, S. (2009). Field Propulsion by Control of Gravity: Theory and Experiments. CreateSpace Independent Publishing Platform.
  25. Sheaffer, R. (1998). UFO sightings: The evidence. Prometheus Books, 1998.
  26. Spencer, J. (1993). The UFO Encyclopedia. . New York: Avon Books, New York.
  27. UFO-Info. (2017). UFO Info. Retrieved from UFO Info: http://www.ufoinfo.com
  28. Von-Storch, H. (1999). Misuses of statistical analysis in climate research. Analysis of Climate Variability. Springer, Berlin, Heidelberg, , 1999. 11-26.
  29. Wikipedia. (2018). UFO Sightings. Retrieved from Wikipedia: https://en.wikipedia.org/wiki/List_of_reported_UFO_sightings

 

 

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