Thongchai Thailand

Archive for April 2019

 

FIGURE 1: HURRICANE DATAxxx

 

FIGURE 2: APRIL TEMPERATURES IN HONG KONG 1955-2016illusory-april-1illusory-april-2

 

 

[LIST OF POSTS ON THIS SITE]

 

 

  1. Moving average and autoregressive models in time series analysis such as MA, AR, ARMA, ARIMA, and data smoothing methods make use of a moving window with a fixed length of time (Box, 1994) (Chatfield, 1989) (Draper&Smith, 1998) (Mudelsee, 2014) (Granger, 2008). The window moves forward, at increments of one unit of time and the object parameters are computed from the data in the window at each increment. In most cases the moving window procedure creates a preprocessed series which is subjected to further statistical analysis. A popular procedure of this kind is the moving average. In this procedure, the simple or weighted average of the data within the window is computed at each increment of time in the journey of the window from the beginning to the end of the time series. These averages form the filtered series and this series serves as the time series for further statistical analysis perhaps for trends, correlations, regression coefficients or other parameters. This post is an examination of a common error in this  procedure.
  2. The well-known study of trends in North Atlantic Hurricane intensity in the context of climate change by high profile climate scientist Kerry Emanuel of MIT is an example of the use of moving averages in the study of trends (Emanuel, 2005) as shown in a related post [LINK] . Such procedures are used when the researcher feels that the random scatter in the source time series is an impediment to discovering its underlying structure and behavior. The motivation for preprocessing the data prior to trend analysis is to reduce the residual variance of the data around the trend line. As an example of such analysis of time series data, consider the hurricane data in Figure 1 where the random variation of the data at an annual time scale may be an impediment to understanding the underlying pattern of hurricane counts. In this case five-year moving averages are used to discover trend patterns at a five year time scale.
  3. Some objections have been raised by Professor Watkins and others to the use of preprocessed series for trend analysis of this kind because the filtered time series does not contain much of the uncertainty in the source data time series and no explanation can be given for what appears to be a magical gain in statistical power (Blumel, 2015) (Briggs, 2008) (Watkins, 2006) . In this post we examine this issue from a perspective of degrees of freedom lost when the same data item in the source time series is used multiple times in the preprocessing algorithm. A procedure is proposed for adjusting degrees of freedom to account for multiplicity in the use of the data. The visual indication in Figure 1 is that the filtered series indicated by the red line contains less uncertainty and more information than the source data indicated by the black line; but where did this new information come from? The apparent reduction in uncertainty and the implied gain in information and statistical power is illusory. Our source of information is unchanged and no new information was gathered. It is proposed that the illusion of increased statistical power is created by multiplicity in data usage. When moving windows are used, the first and last data points are used only once but the other data values in the time series are used more than once. Therefore, an adjustment of the effective sample size and degrees of freedom in the filtered time series is necessary to account for multiplicity.
  4. A moving window of length λ advancing by an increment of one time unit through a time series of length N will generate a total of N-λ+1 windows. Since each window contains λ numbers, a total of λ*(N-λ+1) numbers are used by the moving window. Yet, there are only N numbers in the time series. Therefore, the average multiplicity is M = (λ/N)*(N-λ+1). Each number in the series is used M times on average. The effective value of N (EFFN) is then computed as ξ = N/M. For some procedures a second pass of a moving window is used. If the length of the second window is ϒ then sample size for the second pass is N-λ and the additional number of times that the data are used may be written as of ϒ*(N-λ-ϒ+1). The grand total for both passes is Σ = λ*(N-λ+1) + ϒ*(N-λ-ϒ+1). The equation for multiplicity may be written as M = Σ/N and the effective value of N as ξ = N/M. The degrees of freedom for any given statistic can then be computed as the DF = ξ – K where K is the number of constraints contained in the statistic. Although the number of values generated by the moving window is N-λ for the first pass and N-λ-ϒ for the second pass, the computation of multiplicity requires the full length N of the source data series from which the moving window series was derived.
  5. For example in a time series of 70 years if we generate a moving average series with λ=5 as in Figure 1, N=70 and N-λ=65. The average multiplicity is M = (5/70)*(70-5+1) = 4.714286. The effective value of the sample size is computed as ξ = 70/4.714286 ≈ 14.84848. Note that the computed value of the effective sample size may be approximated by ξ = N/λ = 70/5 = 14. If a second pass is made with ϒ=5, the multiplicity increases. The number of values used by both moving windows is Σ = 5*(70-5+1) + 5*(70-5-5+1) = 635. Multiplicity is therefore M = 635/70 = 9.07. The effective sample size is ξ = 70/9.07= 7.72. Note that the computed value of the effective sample size may be approximated by ξ = N/(λ+ϒ) = 70/10 = 7.
  6. Figure 2 is a presentation of how the effective value of N and the reduction in degrees of freedom can change the conclusions of statistical analysis of preprocessed time series data. Here we find that April temperatures in Hong Kong 1955-2016 show a warming trend and that the rate of warming appears to be higher in the preprocessed series than in the source data. At the same time the preprocessed series show less random scatter and therefore increasingly greater statistical power (R-squared = 0.041 in the source data, 0.233 in the 5-year moving averages (MA5), and 0.370 in the five year moving averages of the five year moving averages (MA5,5). In the hypothesis test for H0: β=0 without correcting for multiplicity we find that the probability of observing these sample results (or more extreme) in the H0 distribution is P-VALUE = 0.1145970 in the source data series and P-VALUE= 0.0000713, and 0.0000002 in the preprocessed series. At α=0.001 we fail to reject H0 in the source data but we are able to reject H0 in both the filtered series. This result appears to show greater statistical power in the filtered series than in the source data series.
  7. To determine whether this gain in statistical power is illusory or real we correct for multiplicity in the preprocessed series and compute ADJUSTED DEGREES OF FREEDOM = 12.848 for MA(5) and 5.717 for MA(5,5). The corresponding ADJUSTED P-VALUEs are 0.0010927 for MA(5) and 0.0019396 for MA(5,5). At α=0.001 we fail to reject H0. This result implies that the apparent greater statistical power observed in the filtered series without adjustment for multiplicity is illusory and an artifact of multiplicity.
  8. A well known example of climate science research that failed to take these considerations into account is the Emanuel 2005 paper where high profile MIT climate scientist Kerry Emanuel concluded erroneously that his data proved that climate change was causing North Atlantic Hurricanes to become more destructive. This faux finding has encouraged decades of activism against fossil fuels fueled by fear of destructive hurricanes. The Emanuel 2005 paper is discussed in depth in a related post [LINK] .
  9. CONCLUSION: All moving window processes in time series analysis involve repeated use of the same data value. If the same data value is used multiple times, it creates a false sense of information because this piece of data brings with it new information only in the first use. It is therefore proposed that the information content of a filtered series and therefore its degrees of freedom must be adjusted for multiplicity. A procedure is presented for estimating the average multiplicity in the use of the source data series in generating the filtered series. The average multiplicity is used to estimate an effective sample size and the effective degrees of freedom. Hypothesis tests must be checked to ensure that rejection of H0 survives when the degrees of freedom are adjusted for multiplicity.

 

 

 

[LIST OF POSTS ON THIS SITE]

 

 

  1. Blumel, K. (2015). Does climate change affect period available field time and required capacities for grain harvesting in Brandenburg Germany/reviews/72069. Retrieved 2016, from Researchgate: https://www.researchgate.net/publication/270793652_Does_clima
  2. Bowley, A. (1928). The standard deviation of the correlation coefficient. Journal of the American Statistical Association, 31-34.
  3. Box, G. (1994). Time series analysis: forecasting and control. Englewood Cliffs, NJ: Prentice Hall.
  4. Briggs, W. (2008). Do not smooth time series. Retrieved 2016, from wmbriggs.com: http://wmbriggs.com/post/195/
  5. Chatfield, C. (1989). The Analysis of Time Series: An Introduction. NY: Chapman and Hall/CRC.
  6. Emanuel, K. (2005). Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436.7051 (2005): 686-688.
  7. Granger, C. (2008). ACRONYMS IN TIME SERIES ANALYSIS (ATSA). Journal of Time Series Analysis, 3(2):103 – 107 · June 2008.
  8. Hong Kong Observatory. (2016). Climatology. Retrieved 2016, from Hong Kong Observatory: http://www.hko.gov.hk/cis/climat_e.htm
  9. Johnson, V. (2013). Revised standards for statistical evidence. Retrieved 2015, from Proceedings of the National Academy of Sciences: http://www.pnas.org/content/110/48/19313.full
  10. Mudelsee, M. (2014). Climate Time Series Analysis: Classical Statistical and Bootstrap Methods. Springer.
  11. Munshi, J. (2015). Decadal Fossil Fuel Emissions and Decadal Warming. Retrieved 2016, from ssrn.com/author=2220942: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2662870
  12. Munshi, J. (2016). Illusory-power data archive. Retrieved 2016, from Google Drive: https://drive.google.com/open?id=0ByzA6UNa41ZfbXpuOHozTm5sems
  13. Siegfried, T. (2010). Odds Are, It’s Wrong. Retrieved 2016, from Science News: https://www.sciencenews.org/article/odds-are-its-wrong
  14. Watkins, T. (2006). How the Use of Moving Averages Can Create the Appearance of Confirmation of Theories. Retrieved 2016, from Thayer Watkins SJSU: http://www.sjsu.edu/faculty/watkins/movingaveraging.htm

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

falling13c

 

FIGURE 1: 13C MEASURING STATIONS USED IN THIS STUDYfig01

 

FIGURE 2: CORRELATION ANALYSIS: BARROW ALASKA12

 

FIGURE 3: CORRELATION ANALYSIS: SOUTH POLE12

 

FIGURE 4: CORRELATION ANALYSIS: CAPE KUMUKAHI12

 

FIGURE 5: CORRELATION ANALYSIS: LA JOLLA12

 

FIGURE 6: CORRELATION ANALYSIS: CHRISTMAS ISLAND12

 

FIGURE 7: CORRELATION ANALYSIS: ALERT, CANADA12

 

 

 

 

 

[LIST OF POSTS ON THIS SITE]

 

 

  1. Fossil fuel reservoirs deep under the ground contain a large inventory of carbon that has been sequestered for millions of years from the delicately balanced surface-atmosphere carbon cycle that sustains a stable climate system and life on earth as we know it. The theory of anthropogenic global warming and climate change (AGW) addresses this issue in terms of the response of the surface-atmosphere system to the perturbation caused by fossil fuel emissions that inject extraneous carbon into it.
  2. In view of a concurrent and corresponding rise in fossil fuel emissions and atmospheric CO2 since the Industrial Revolution, it is taken as axiomatic that these changes are causally related and empirical evidence is provided in terms of correlations between cumulative values and also in terms of observed changes in isotopic ratios of carbon in atmospheric CO2 (Callendar, 1938) (IPCC, 2007) (IPCC, 2014) (Kheshgi, 2005) (Levin, 2000) (Matthews, 2009) (Raupach, 2007) (Revelle, 1956) (Hansen, Impact of Increasing Atmospheric Carbon Dioxide, 1981) (Stuiver M. , 1987) (Stuiver M. , 1981) (Suess, 1953).
  3. In a related post, we examine this relationship in terms of detrended fluctuation analysis [LINK]  and also in terms of the 14C/12C ratio in atmospheric CO2 [LINK] . In this post we extend this line of inquiry by examining changes in the 13C/12C ratio in atmospheric CO2 to determine whether these changes can serve as empirical evidence of a relationship between fossil fuel emissions and changes in atmospheric CO2 as claimed by these climate scientists (Stuiver M. , 1987) (Stuiver, 1984) (TANS, 1980) (Robertson, 1887) (RealClimate, 2004) (Severinghaus, 2014).
  4. Data for the 13C/12C ratio in atmospheric CO2 are available form a large number of stations around the world maintained by the Scripps Institute of Oceanography (SIO) and these data are made available online (Keeling, 2005) (Graven, 2016) (ScrippsCO2, 2016). Six SIO measuring stations are selected for this study based on the somewhat arbitrary condition that at least twenty years of data should be available up to and including the year 2014. Typically there are about a thousand observations made during this period. If there are fewer than five hundred observations the data are considered sparse and that station is eliminated from consideration. The stations that meet our conditions are listed in Figure 1.
  5. The relevance of this measure to the idea that the observed increase in atmospheric CO2 derives from fossil fuel emissions is that plant photosynthesis has an isotope bias and prefers 12C. For this reason, plants, and therefore fossil fuels, contain a lower ratio of 13C/12C than the atmosphere. In this context, the combustion of large quantities of fossil fuels is like a sudden injection of low 13C/12C CO2 into the atmosphere that should cause a measurable reduction of the 13C/12C ratio in atmospheric CO2. In fact, what we find in the data is that indeed there has been a gradual reduction of the 13C/12C ratio in atmospheric CO2 during a period of rising fossil fuel emissions as shown in Figure 2. The data are from the Christmas Island dataset. AGW theory attributes increases in atmospheric CO2 to fossil fuel emissions and this attribution implies a negative correlation between 13C in atmospheric CO2 and fossil fuel emissions. In fact, if we look at the numbers we find that the 13C/12C ratio is strongly negatively correlated with cumulative emissions. The Pearson correlation coefficient of R = -0.987. It is this correlation that has led climate science to claim that the 13C/12C data support the attribution that the observed rise in atmospheric CO2 to fossil fuel emissions.
  6. However, this correlation is unreliable because it is a correlation between cumulative values. It has been shown in a related post that such correlations are spurious and that no conclusions can be drawn from correlations between cumulative values  [LINK] . To support causation, it must be shown that a correlation exists at the theoretical time scale at which the causation is supposed to work. In the case of fossil fuel emissions, it is generally held that the time scale for its effect on the atmosphere is one year (IPCC, 2014) (IPCC, 2007) (Falkowski, 2000) (Rodhe, 1990) (Keeling C. , 1995). For a time scale of one year, it is necessary to show that annual changes in atmospheric 13C can be related to annual emissions. To test that hypothesis we compute the correlation between annual fossil fuel emissions and the change in atmospheric 13C from the previous year to the current year. Data for fossil fuel emissions from 1977-2010 are available from the CDIAC4 and the ORNL5 (Marland-Andres, 2016). It is found that most measurement stations show a seasonal cycle in the 13C/12C ratio for atmospheric CO2. The data are therefore deseasonalized as a first step. Further analysis refers only to the deseasonalized series. All hypothesis tests for statistical significance are carried out at a maximum false positive error rate of α=0.001 as recommended in “Revised standards for statistical evidence” in a PNAS publication to address the unacceptably high rate of irreproducible results in research publications (Johnson, 2013). Where necessary an appropriate adjustment is made for multiple comparisons (Holm, 1979). The data and their correlations are presented in Figure 2 to Figure 7. No statistically significant correlation is found to support the hypothesis that observed year to year increases in atmospheric CO2 are attributable to fossil fuel emissions. In Figures 2-7, the term “C13” refers to the 13C/12C ratio in atmospheric CO2.
  7. SUMMARY: In all six stations studied we find that the 13C/12C ratio in atmospheric CO2 has declined in the study period from about -7.5 to about -8.5 At the same time fossil fuel emissions have risen from 5 GTY (gigatons per year) to more than 9.7 GTY. These charts present the correlation of changes at an annual time scale. Here, we find no correlation between annual changes in the 13C/12C ratio and annual fossil fuel emissions. The highest value of R-squared is found  for Cape Kumukahi as R-squared = 0.0104. For N=25 observations, the corresponding t-statistic is t=1.94 and the corresponding p-value is p=0.0318. At our maximum error rate of α=0.001, we fail to reject H0: R=0 and find no evidence that changes in the 13C/12C ratio are related to fossil fuel emissions.

CONCLUSION: We conclude that the 13C data do not provide empirical evidence that observed changes in atmospheric CO2 concentration can be attributed to fossil fuel emissions. We further note that the high correlation between cumulative changes in the 13C/12C ratio in atmospheric CO2 and cumulative emissions is unreliable and unacceptable as empirical evidence because of the spuriousness of correlations between cumulative values discussed in a related post [LINK] .

 

 

 

 

[LIST OF POSTS ON THIS SITE]

 

 

REFERENCES

  1. Box, G. (1994). Time series analysis: forecasting and control. Englewood Cliffs, NJ: Prentice Hall.
  2. Callendar, G. (1938). The Artificial Production of Carbon Dioxide and Its Influence on Climate. Quarterly Journal of the Royal Meteorological Society, 64: 223-40.
  3. Canadell, J. (2007). Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proceedings of the national academy of sciences, 18866-18870.
  4. CO2.Earth. (2016). Global CO2 emissions. Retrieved 2016, from co2.earth: https://www.co2.earth/global-co2-emissions?itemid=1
  5. Draper&Smith. (1998). Applied Regression Analysis. Wiley.
  6. Earth System Science Data. (2016). Global carbon budget 2015. Retrieved 2016, from Earth System Science Data: http://www.earth-system-science-data.net/about/news_and_press/2015-12-07_global-carbon-budget.html
  7. Falkowski, P. (2000). The global carbon cycle: a test of our knowledge of earth as a system. Science, 290.5490 (2000): 291-296.
  8. Graven, H. (2016). Scripps CO2 Program. La Jolla, CA: Scripps Institution of Oceanography, University of California.
  9. Hansen, J. (1981). Impact of Increasing Atmospheric Carbon Dioxide. Science, 213: 957-66.
  10. Hansen, J. (2016). Ice melt, sea level rise and superstorms:. Atmos. Chem. Phys., 16, 3761–3812, 2016.
  11. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6:2:65-70.
  12. IPCC. (2007). AR4 WG1 Chapter 7: Couplings between changes in the climate system and biogeochemistry. Geneva: IPCC.
  13. IPCC. (2014). Climate Change 2013 The Physical Science Basis. Geneva: IPCC/UNEP.
  14. Jacob, D. (1999). Introduction to atmospheric chemistry. Princeton University Press.
  15. Johnson, V. (2013). Revised standards for statistical evidence. Retrieved 2015, from Proceedings of the National Academy of Sciences: http://www.pnas.org/content/110/48/19313.full
  16. Keeling, C. (1995). Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980. Nature, 375.6533 (1995): 666-670.
  17. Keeling, C. (2005). Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and oceans from 1978 to 2000: observations and carbon cycle implications. In J. Ehleringer, History of atmospheric CO2 and its effects on plants, animals, and ecosystems (pp. 83-113). NY: Springer Verlag.
  18. Kheshgi, H. (2005). Emissions and atmospheric CO2 stabilization: Long-term limits and paths. In Mitigation and Adaptation Strategies for Global Change (pp. 10.2 213-220).
  19. Lacis, A. (2010). Principal Control Knob Governing Earth’s Temperature. Science, 330.
  20. Levin, I. (2000). Radiocarbon – a unique tracer of global carbon cycle dynamics. Radiocarbon, v42, #1, pp69-80.
  21. Marland-Andres. (2016). Regional and National Fossil-Fuel CO2 Emissions. Oak Ridge, TN: Oak Ridge National Laboratory.
  22. Matthews, H. (2009). The proportionality of global warming to cumulative carbon emissions. Nature, 459.7248 (2009): 829-832.
  23. Munshi, J. (2015). Responsiveness of Atmospheric CO2 to Anthropogenic Emissions. Retrieved 2016, from ssrn.com: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2642639
  24. Munshi, J. (2016). 13C Paper Archive. Retrieved 2016, from Google Drive: https://drive.google.com/open?id=0ByzA6UNa41ZfQXB4eE5vYUVPQVk
  25. Munshi, J. (2016). Dilution of Atmospheric Radiocarbon CO2 by Fossil Fuel Emissions. Retrieved 2016, from ssrn.com: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2770539
  26. Munshi, J. (2016). The Spuriousness of Correlations between Cumulative Values. Retrieved 2016, from ssrn.com: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2725743
  27. NASA-GISS. (2016). GLOBAL MEAN CO2. Retrieved 2016, from DATA.GISS.NASA.GOV: http://data.giss.nasa.gov/modelforce/ghgases/Fig1A.ext.txt
  28. NOAA/ESRL. (2010). The Data: What 14C Tells Us. Retrieved 2016, from Stable and Radiocarbon Isotopes of Carbon Dioxide: http://www.esrl.noaa.gov/gmd/outreach/isotopes/c14tellsus.html
  29. Raupach, M. (2007). Global and regional drivers of accelerating CO2 emissions. Proceedings of the National Academy of Sciences, 104.24 (2007): 10288-10293.
  30. RealClimate. (2004). How do we know that recent CO2 increases are due to human activities? Retrieved 2016, from realclimate.org: http://www.realclimate.org/index.php/archives/2004/12/how-do-we-know-that-recent-cosub2sub-increases-are-due-to-human-activities-updated/
  31. Revelle, R. (1956). Carbon dioxide exchange between atmosphere and ocean and the question of an increase in atmospheric CO2 during the past decades. UC La Jolla, CA: Scripps Institution of Oceanography.
  32. Robertson, I. (1887). Signal strength and climate relationships in 13C/12C ratios of tree ring cellulose from oak in southwest Finland. Geophysical Research Letters, 24.12 (1997): 1487-1490.
  33. Rodhe, H. (1990). A comparison of the contribution of various gases to the greenhouse effect. Science, 248.4960 (1990): 1217.
  34. ScrippsCO2. (2016). Atmospheric CO2 data. Retrieved 2016, from scrippsco2: http://scrippsco2.ucsd.edu/data/atmospheric_co2
  35. Severinghaus, J. (2014). University of California Television (UCTV). Retrieved 2016, from Youtube: https://www.youtube.com/watch?v=uSbpydoPxu0
  36. Shumway, R. (2011). Time series analysis. Springer. . Springer.
  37. Solomon, S. (2009). Irreversible climate change due to carbon dioxide emissions. Proceedings of the national academy of sciences, pnas-0812721106.
  38. Stuiver. (1984). 13C/12C ratios in tree rings and the transfer of biospheric carbon to the atmosphere. Journal of Geophysical Research: Atmospheres, 89.D7 (1984): 11731-11748. (PICTURED ABOVE)
  39. Stuiver, M. (1981). Atmospheric 14C changes resulting from fossil fuel CO2 release and cosmic ray flux variability. Earth and Planetary Science Letters, 53: 349-362.
  40. Stuiver, M. (1987). Tree cellulose 13C/12C isotope ratios and climatic change. Nature , 328.6125 (1987): 58-60.
  41. Suess, H. (1953). Natural Radiocarbon and the rate of exchange of carbon dioxide between the atmosphere and the sea. Washington, DC: National Academy of Sciences.
  42. Tans, P. (1980). Past atmospheric CO2 levels and the 13C/12C ratios in tree rings. Tellus, 32.3 (1980): 268-283.
  43. UNEP. (2016). Paris Agreement. Retrieved 2016, from COP121: http://web.unep.org/climatechange/cop21

 

 

 

bandicam 2019-04-26 09-48-29-127

 

 

 

[LIST OF POSTS ON THIS SITE]

 

 

  1. A Newsweek article dated 4/25/2019 features a video by David Wallace Wells saying that “by the end of the century, if we do not take action on climate change, the damage from climate change will surpass twice as much as all the wealth that exists in the world today”. In a related article Newsweek had a somewhat scarier assessment that the current warming trend will take us to such an extreme climate condition that we have to go back 50 million years to find climate conditions as extreme. This statement is false. For example, as described in a related post [LINK] , if we go back just 120,000 years to the previous interglacial called the Eemian, we find much more extreme climate than in the current interglacial, the Holocene. Most of the climate change fear mongering arguments can be understood in the context of what one would expect to see in nature’s own interglacials. Yet known natural interglacial phenomena are being sold as evidence of unnatural human caused climate change.
  2. The article goes on to promote the ugly and reprehensible use of school children in the activism against fossil fuels and claims that the fossil fuel industry’s influence with government is to blame for the recent calls by government officials to prevent elementary school teachers from preaching climate catastrophe to their very young children. The most visible child climate change activist is Swedish teenager Greta Thunberg. She recently publicly admitted to having been indoctrinated into fearing climate change by elementary school teachers. In her words, “I first heard about this when I was 7, 8, or 9 years old. In school the teacher explained what climate change was and how it was caused and they showed us pictures of starving polar bears”. Elementary school teachers have a fixed and well defined curriculum of things like language, grammar, arithmetic, and so on. This curriculum does not include climate science and these teachers are not qualified to teach climate science. The syllabus of courses they teach is well defined and it is their job to follow that curriculum. They are not allowed to bring their own personal causes, religions, or other beliefs to preach to their very young students or to indoctrinate them in causes they happen to believe in. This sad and ugly chapter in climate activism that involves child abuse and child exploitation should, in itself, disqualify the climate change movement.
  3. The text goes on to state that ” This assault on our children (that is restricting teachers to the curriculum) is reprehensible for so many reasons. To start with an obvious one, it is keeping kids in the dark about an urgent global problem that will affect the rest of their lives. Yet, the reading writing and arithmetic syllabus of elementary school education does not include these topics nor does it give the teacher the freedom to preach environmentalism as he or she sees it. In fact what is reprehensible about the indoctrination of school children and their deployment in climate activism is that it is or it should be illegal to use children in this way to further one’s activism needs because it is surely a case of child exploitation.
  4. The Michael Mann statement continues with the reference to fear mongering climate change impacts stating that “We’re already seeing the harm of floods, fires, heat and drought, but it’s the next generation that will bear the brunt of climate change. The least we can do is give our kids the tools to rise to the immense challenge they will face as the climate change generation. Actually, the very least we could do is not lie to them about it”. Yet, it is an indefensible position that elementary school students as young as seven years old can be taught climate science that even climate scientists don’t full understand. Most of the extreme weather fears that they peddle is without empirical evidence. Also, the fundamental relationship on which all of AGW theory rests is climate sensitivity and the extreme state of uncertainty in its empirical values is an unsettled question in climate science. Yet another weakness in climate science is that it is a product of errors in statistics as explained in a related post [LINK] . If climate scientists themselves can’t get the science right, how can we expect elementary school teachers to teach it?
  5. The claim that climate change has caused extreme weather and made tropical cyclones more destructive is without empirical evidence. The famous paper by MIT climate scientist Kerry Emanuel that climate change has increased the destructiveness of North Atlantic hurricanes contains gross methodological and statistical errors that one would expect to see only at the undergraduate level. The Emanuel paper is discussed in a related post [LINK] .  The failure of the case by climate scientists that climate change is making tropical cyclones more extreme is discussed in two related posts [LINK]  [LINK] . Evidence of tropical cyclones “in pre industrial times” (that is prior to AGW) that are worse than what we see today in post industrial times, is presented in a related post [LINK] . The attribution of extreme weather events to climate change after the fact with what is called “Event Attribution Science” is not science but a combination of circular reasoning and confirmation bias described in related posts [LINK] [LINK] .
  6. Rather than be the accusers of the fossil fuel industry as evil activists who are trying to keep elementary school teachers from teaching climate science to elementary school students, climate scientists should look in a mirror. There, they will see the evil of child abuse and child exploitation to further their activism against fossil fuels. The scary reality of climate change is that someday these charges and related lawsuits will be brought against the perpetrators of these crimes against children to shore up a failed case against fossil fuels that their own science is unable to defend.

 

 

[LIST OF POSTS ON THIS SITE]

 

 

 

 

 

 

 

 

 

 

 

 

bandicam 2019-04-25 15-33-37-018

bandicam 2019-04-25 15-34-14-151

 

 

[LIST OF POSTS ON THIS SITE]

 

 

FIGURE 1: LOW LATITUDE COUNTRIES IN THE SAMPLELOW-LATITUDE

 

FIGURE 2: HIGH LATITUDE COUNTRIES IN THE SAMPLEHIGH-LATITUDE

 

FIGURE 3: LOW LATITUDE PER CAPITA GDP 1960-1980: COOLINGlow-gdp-cooling

 

FIGURE 4: HIGH LATITUDE PER CAPITA GDP 1960-1980: COOLINGhigh-gdp-cooling

 

FIGURE 5: INCOME INEQUALITY 1960-1980: COOLINGinequality-cooling

 

FIGURE 6: LOW LATITUDE PER CAPITA GDP 1981-2017: WARMINGlow-gdp-warming

 

FIGURE 7: HIGH LATITUDE PER CAPITA GDP 1981-2017: WARMINGhigh-gdp-warming

 

FIGURE 8: INCOME INEQUALITY 1981-2017: WARMINGinequality-warming

 

FIGURE 9: LOW LATITUDE %GDP GROWTH FULL SPAN 1960-2017LOWLATGROWTH

 

FIGURE 10: HIGH LATITUDE %GDP GROWTH FULL SPAN 1960-2017HIGHLATGROWTH

 

FIGURE 11: GROWTH INEQUALITY FULL SPAN 1960-2017PCTGROWTHINEQUALITY

 

 

  1. Climate scientists published a paper in 2019 with the finding that climate change causes income inequality between hot low latitude countries, eg India, and cool high latitude countries, eg Sweden.  (CITATION: Global warming has increased global economic inequality, Noah S. Diffenbaugh, Marshall Burke, Proceedings of the National Academy of Sciences Apr 2019, 201816020). The income inequality is shown as an increasing spread between per capital GDP between the cool rich country and the hot poor country. The abstract appears below in the next paragraph. The full text of the paper may be downloaded from an online archive [LINK]
  2. The ABSTRACT of the article states as follows: Understanding the causes of economic inequality is critical for achieving equitable economic development. To investigate whether global warming has affected the recent evolution of inequality, we combine counterfactual historical temperature trajectories from a suite of global climate models with extensively replicated empirical evidence of the relationship between historical temperature fluctuations and economic growth. Together, these allow us to generate probabilistic country-level estimates of the influence of anthropogenic climate forcing on historical economic output. We find very high likelihood that anthropogenic climate forcing has increased economic inequality between countries. For example, per capita gross domestic product (GDP) has been reduced 17–31% at the poorest four deciles of the population-weighted country-level per capita GDP distribution, yielding a ratio between the top and bottom deciles that is 25% larger than in a world without global warming. As a result, although between-country inequality has decreased over the past half century, there is ∼90% likelihood that global warming has slowed that decrease. The primary driver is the parabolic relationship between temperature and economic growth, with warming increasing growth in cool countries and decreasing growth in warm countries. Although there is uncertainty in whether historical warming has benefited some temperate, rich countries, for most poor countries there is >90% likelihood that per capita GDP is lower today than if global warming had not occurred. Thus, our results show that, in addition to not sharing equally in the direct benefits of fossil fuel use, many poor countries have been significantly harmed by the warming arising from wealthy countries’ energy consumption.
  3. The authors also provide a summary about the significance of their finding as follows: SIGNIFICANCE: We find that global warming has very likely exacerbated global economic inequality, including ∼25% increase in population-weighted between-country inequality over the past half century. This increase results from the impact of warming on annual economic growth, which over the course of decades has accumulated robust and substantial declines in economic output in hotter, poorer countries—and increases in many cooler, wealthier countries—relative to a world without anthropogenic warming. Thus, the global warming caused by fossil fuel use has likely exacerbated the economic inequality associated with historical disparities in energy consumption. Our results suggest that low-carbon energy sources have the potential to provide a substantial secondary development benefit, in addition to the primary benefits of increased energy access.
  4. The essence of the argument is that global warming is inherently unfair in terms of per capital GDP because the per capita GDP of the rich temperature countries that are mostly to blame for fossil fuel emissions face a lesser impact of climate change than the per capita GDP of poor equatorial countries where it is hot. This unequal and unfair impact of climate change therefore results in a rising wealth gap between the rich industrialized temperate countries like Sweden and the poor struggling countries like India located in the hotter equatorial zone of the planet.
  5. This post is a test of the this income inequality hypothesis. The data are per capita GDP values for all countries 1960 to 2017 provided by the World Bank. Two samples of countries are taken from the World Bank dataset. A large sample of hot equatorial countries is taken with absolute value of latitude from Φ=0 to Φ=25. A smaller sample of rich industrialized temperate countries is take at the higher latitudes of Φ=46 (France) to Φ=64 (Finland). The selected countries are listed above in Figure 1 and Figure 2.
  6. The sample period of the GDP data, 1960 to 2017, includes a period of cooling 1960-1980 and a period of warming thereafter 1981-2017. In Figure 3, Figure 4, and Figure 5, we use the per capital GDP data for the hot low latitude countries and the cool high latitude countries in the cooling period 1960-1980 to compute the trend in income inequality. Figure 5 shows a rising trend in the difference between cool rich countries and and hot poor countries.
  7. This comparison is repeated for the warming period 1981-2017 in Figure 6, Figure 7, and Figure 8. We find that the warming period and the cooling period are indistinguishable in terms of growth in income inequality. This result is not consistent with the attribution of the growing difference between the per capita GDP of rich and poor countries to global warming.
  8. This is because of the nature of economic growth in which the richer the country is the more it can invest and therefore the faster it can increase its wealth. Therefore the difference between per capita GDP is not the appropriate metric for this comparison. Instead, the comparison must be made with percent growth in per capita GDP. This comparison is made in Figure 9, Figure 10, and Figure 11. The income inequality displayed in Figure 11 does not show rising income inequality.
  9. CONCLUSION: We conclude that the evidence of rising inequality in the Diffenbaugh 2019 paper cited above is an artifact of the nature of economic growth and not a valid computation of income inequality. We find that when this error is corrected and percent economic growth is compared in Figure 11, no evidence of rising income inequality is found. Therefore, the Diffenbaugh 2019 paper cited above has not shown that climate change has caused rising income inequality between rich cool countries and poor hot countries. The per capita GDP of cool rich countries grows faster than the per capita GDP of hot poor countries not because they are cooler but because they are richer.
  10. For example, if you invest $100,000 in 10-year treasuries at 2.5% and Al Gore invests 1,000,000 in the same instrument. Ten years from now you will have $102,500 and Al will have $1025,000. After the next decade you will have $105,062 and Al Gore will have $1050625 and so on with your income inequality increasing as shown in the chart below. This is why small countries and large countries and poor countries and rich countries can’t be compared on the basis of dollar per capita GDP but must be evaluated on the basis of percent growth.  ALGORE

 

 

 

[LIST OF POSTS ON THIS SITE]

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[LIST OF POSTS ON THIS SITE]

 

 

THE TEXT OF THE HARRISON FORD CLIMATE CHANGE SPEECH

sumatra

  1. Point#1: The destruction of nature accounts for more global emissions than all the cars and trucks in the world. We can put solar panels on every house, we can turn every car into an electric vehicle, but as long as Sumatra burns, we will have failed. So long as the Amazon’s great forests are slashed and burned, so long as the protected lands of tribal people, indigenous people, are allowed to be encroached upon, so long as wetlands and peat bogs are destroyed, our climate goals will remain out of reach and we will be shut out of time.
  2. Point#2: If we don’t stop the destruction of our natural world nothing else will matter. Why? Because protecting and restoring forests, mangroves, wetlands, these huge dense carbon sinks, represent at least 30% of what needs to be done to avoid catastrophic warming. It is at this time the only feasible solution for absorbing carbon on a global scale. Simply put, if we can’t protect nature we can’t protect ourselves.
  3. Point#3: Stop protecting people who don’t believe in science – or worse than that, pretend they don’t believe in science for their own self interest. They know who they are. We know who they are. We are all, rich or poor, powerful or powerless, at risk. We will all suffer the effects of climate change and ecosystem destruction and we are facing what is quickly becoming the greatest moral crisis of our time – that those least responsible will bear the greatest cost.
  4. About Harrison Ford: The author, once a famous Hollywood actor is now Vice Chairman of Conservation International, a large and well funded ecological activism group. Their view of the world and their ecological activism goals and methods are described on the CI website [LINK]. This text indicates that CI is in the business of protecting nature (specifically identified as forests, wetlands, primitive human communities, and animals perceived as endangered by activities of technologically advanced human communities such as the Western industrialized civilization.
  5. Statement from the CI website describing their organization: ​​​​​​​​​​​​​​​For more than 30 years, Conservation International (CI) has been protecting nature for the benefit of all​. Humanity is totally dependent on nature, and by saving nature, we’re saving ourselves. To that end, Conservation International is working to build a healthier, more prosperous and more productive planet. We do this through s​​cience, policy and partnerships with countries, communities and companies. We employ nearly 1,000 people and work with more than 2,000 partners in 30 countries. Over the years, we have helped support 1,200 protected areas and interventions across 77 countries, protecting more than 601 million hectares of land, marine and coastal areas.​​
  6. CI Activities: Human drugs are polluting the water and animals are swimming in it. Pharmaceuticals are flowing from homes and factories into freshwater rivers, streams and lakes, harming aquatic species. The story: Medication is entering freshwater ecosystems worldwide through our toilets and sinks — and its trip through the human digestive tract isn’t dampening its effectiveness, According to recent research, a platypus living in a pharmaceutical-contaminated stream in Melbourne is likely to ingest more than half the recommended adult dose of antidepressants every day. The big picture: While symptoms from exposure depend on the species and dosage, scientists have already observed a measurable effect on wildlife. Atlantic salmon smolts that are exposed to anti-anxiety medications such as Xanax and Valium migrate twice as fast as unmedicated smolts, which causes them to arrive at sea before they’re fully developed and harms their chances of survival. Scientists estimate that if humans continue releasing pharmaceuticals into waterways at the current rate, the concentration of these drugs in freshwater ecosystems will likely increase by two-thirds in the next 30 years.
  7. CI Activities: Invasions of indigenous lands: Emboldened by President Jair Bolsonaro, armed invaders are encroaching on Brazil’s tribal lands in the Amazon. Invasions of indigenous lands have increased 150 percent since Bolsonaro was elected president of Brazil in October. During his presidential campaign, Bolsonaro condemned federal protections for indigenous peoples, whose lands make up about 13% of Brazil’s territory. In response to Bolsonaro’s antagonistic statements against indigenous rights and in support of development during his campaign, attacks on indigenous reservations rose and deforestation rates climbed almost 50 percent.Brazil’s Amazon rain forest is home to 850,000 indigenous peoples. The president’s incendiary remarks have been viewed by many as approval or even incentive to invade indigenous spaces and “stake their claims.” Not only does this put people at risk, it threatens generations of traditional knowledge that are key to fighting climate change.
  8. CI Activities: Ocean heat waves threatening marine life: Ocean heat waves, defined as at least five consecutive days of warmer-than-usual ocean temperatures, are more severe and longer-lasting because of greenhouse gas emissions, a new study finds. Oceans have absorbed more than 90 percent of the heat from greenhouse gas emissions since the 1950s. This excess heat translates to an uptick in heat waves. These marine heat waves can kill off fish, coral reefs and vital coastal ecosystems such as seagrass meadows and kelp forests that store “blue” carbon. For the approximately 3 billion people dependent on oceans for their protein, these heat waves pose a serious threat to their food security.
  9. CI Activities: Komodo dragons: Komodo Island in Indonesia may temporarily close its borders to tourists to enable dragon populations to recover. Komodo dragon numbers have been dwindling due in large part to smugglers. A temporary tourism ban would help protect the dragons from smugglers and let authorities replenish the dragons’ food supply by planting native vegetation. Komodo dragons are an endangered species. There are only about 5,700 in the wild and they’re being trafficked for food and traditional medicinal use. The dragons are an essential part of the food chain on the island, and could be significant to science, too: Antimicrobial peptides in their blood give them the ability to recover from the venomous bites of other Komodo dragons, which scientists believe could provide the foundation of a new antibiotic for human medicine.
  10. CI Activities: Save the Mangroves: In one of the most biodiverse regions of Colombia, the national government has proposed building a port within the protected area of the Tribuga-Corrientes cape, on Colombia’s northern Pacific coast. This port would destroy mangroves and the ecosystem services (that is, the tangible benefits that nature provides) that local communities rely on. A new study puts an exact price tag on the cost of destroying those mangroves: If the port is built, it would cost US$ 230 million per year in lost ecosystem services such as providing habitat for fish, protecting the coast from storms and storing carbon. Plans for the port have been discussed for close to a decade, and local organizations have been trying to stop it for just as long with little success. To prove the detrimental impact that the port and by default, the destruction of the mangroves would have on the economy, the researchers analyzed the value of the mangroves through three distinct lenses: monetary (the economic value to fisheries, other natural resources), sociocultural (the value to surrounding communities); and ecological (storing carbon, biodiversity). By putting a price tag on mangrove ecosystem services, researchers are able to show the mangroves’ economic importance not only to surrounding communities, which rely on fishing, agricultural and tourism that the mangrove forest provides, but to the country at large. The data from this study was presented to the president of Colombia, senators and the Ministry of Environment with the goal of stopping the construction of the port. “Mangroves are vital for human well-being and provide valuable ecosystem services to the country as a whole. The port will harm the country economically more than it would help it. Hopefully this is enough to stop the progression of the port in Congress and save the mangrove forest and all of the benefits that it provides.
  11. Summary of CI activities: It is clear from the above that CI is an environmental activism organization with the generic purpose of saving nature from human impacts and from its own complexities. The role of climate change in these activities is mostly in terms of protecting nature from climate change impacts that have been claimed by climate science. In addition there is some concern that nature’s ability to store carbon should not be disturbed lest natural emissions of carbon from natural storage sinks exacerbate climate change. We can now understand the Harrison Ford lecture in this context as follows:
  12. Context for Point #1: The context is that the CI priority of protecting forests ties in with climate change because protecting and preserving forests can prevent release of carbon stored in forests. His reference to Sumatra is relates to the large forest fire there in 2015 that involved the combustion of peat in the forest floor seen as climate change causing carbon being released to the atmosphere. These fires have recurred several times since then and are thought to be natural. His concern for the preservation of the Amazon also relates to the release of CO2 if the forest is “slashed and burned”. His reference encroachment on indigenous lands is mysterious in this context. Here is a link to more information about the Sumatra peat forest fires  [LINK]
  13. Context for Point #2: In Point number 2, the control of carbon release to the atmosphere by preserving forests is generalized to protecting “the natural world” from destruction by human activity. It should be mentioned that the Sumatra fires are thought to be natural and so the CI goal of protecting nature applies both to protecting nature from humans and protecting nature from itself.
  14. Context for Point #3: Here he appears to have identified national leaders such as President Trump (USA) and President Bolsonaro (Brazil) as enemies of nature because they “don’t believe in science“. It is understood that CI activity extends to opposing and neutralizing world leaders who do not believe in science and who therefore pose a threat in terms of the CI priority of protecting nature from destruction. It is common among environmentalists to think of science as a belief system such that whatever scientists say become biblical truth that must not be questioned although science itself works in exactly the opposite way.
  15. CONCLUSION: The theory of Anthropogenic Global Warming (AGW) relates to the use of fossil fuels by the industrial economy of humans in which large quantities of carbon are dug up from under the ground where they had been sequestered from nature’s carbon cycle and climate system. The concern is that the carbon that is released into the atmosphere when these fossil fuels are burnt does not belong in the current account of the carbon cycle. It is feared that such external carbon is a perturbation of nature’s delicately balanced current account of the carbon cycle and that such perturbation can cause an unnatural accumulation of CO2 in the atmosphere such as to cause unnatural man-made and therefore dangerous global warming and climate change. This aspect of AGW theory is not addressed in the Ford lecture and surely plays no role in CI priorities. Instead, AGW theory is seen only in terms of CI goals and priorities. The sources of carbon mentioned are all natural and the exchange of carbon between these sources and the atmosphere and oceans is also natural and not foreign to nature and therefore not a perturbation of the carbon cycle. They are instead the carbon cycle itself. The only unnatural role for humans in terms of deforestation is the encouragement given to Indonesia by climate science to clear forests for palm oil plantations and the production of climate friendly biofuels that can be derived from palm oil. The climate change presentation given by Harrison Ford is derived completely from the CI agenda and its priorities with little or no understanding of or relevance to the theory of AGW. This climate change presentation serves as a high profile example of an unusual aspect of the climate change movement in that it has attracted and become kind of a “promised land” for environmental, ecological, and new age activism groups of all colors. They all talk about climate change but in the details what they are talking about are really their own agenda placed into a climate change context.

 

 

 

[LIST OF POSTS ON THIS SITE]

 

 

 

 

 

 

 

 

 

 

 

 

 

timelinegif

 

 

  1. According to researchers at University College London, “One surprising effect of European colonization of the Americas was a cooling of the Earth’s climate that explains the Little Ice Age (LIA). The LIA is described in a related post [LINK] . These researchers estimate that the indigenous population of the Americas at the end of the 15th century was 60 million. Over the next century, this population declined by some 90 percent, largely due to epidemics introduced by Europeans. As a result, around 215,000 square miles of cultivated land, roughly the area of France, was left fallow and reverted to forest. This sucked up enough carbon dioxide—a greenhouse gas that traps heat in the atmosphere—to lead to cooling. This process took place amid an extended cold stretch known as the Little Ice Age, which lasted from around 1250 to 1850. Other factors that contributed to cooling during this period included numerous widespread volcanic eruptions and natural fluctuations in solar radiation. But, Koch says, the effects of colonization played a key role in driving temperatures down in the early seventeenth century, adding, “This is thought to be the coolest part of the Little Ice Age.”
  2. This assessment likely derives from a decline in atmospheric CO2 concentration of ≈10 ppm from ≈282 ppm in ≈1580 to ≈271.6 ppm in ≈1612  as seen in the chart below. This drop of 10.4ppm in atmospheric CO2 implies a temperature decline of ≈0.12C over a period of 32 years or 0.00375C/year by way of climate sensitivity to atmospheric CO2 at the Manabe climate sensitivity value of λ=2. lawdomeco2
  3. The paleo temperature record for the Northern Hemisphere in Mann etal 2008 is shown in the chart below. Here we find a correspondingly undetectable change in temperature from 1580 to 1612.      mann2008chart
  4. The data presented above is consistent with the assessment that there was a minor CO2 decline of ≈10 ppm during the time of the European settlement of North America. However, the attribution of the change to European colonization is speculative and its proposed contribution to the the cooling from the Medieval Warm Period (MWP) to the Little Ice Age (LIA) is inconsistent with the minute amount of cooling implied by the climate sensitivity and the absence of cooling in the paleo record shown above.
  5. CONCLUSION: We find no evidence to support the attribution of the 10 ppm drop in atmospheric CO2 from 1580 to 1612 to the European colonization of North America or for its alleged contribution as a cause of the Little Ice Age. Without the necessary supporting data, the proposed relationship between European colonization of North America, the drop in atmospheric CO2, and its contribution to the Little Ice Age proposed in the research paper appear speculative.
  6. Related posts: [MWP]  [LIA] .

 

 

 

 

 

 

 

 

 

 

 

  1. CITATION: Wilkerson, J., Dobosy, R., Sayres, D. S., Healy, C., Dumas, E., Baker, B., and Anderson, J. G.: Permafrost nitrous oxide emissions observed on a landscape scale using the airborne eddy-covariance method, Atmos. Chem. Phys., 19, 4257-4268, https://doi.org/10.5194/acp-19-4257-2019, 2019. ABSTRACT: The microbial by-product nitrous oxide (N2O), a potent greenhouse gas and ozone depleting substance, has conventionally been assumed to have minimal emissions in permafrost regions. This assumption has been questioned by recent in situ studies which have demonstrated that some geologic features in permafrost may, in fact, have elevated emissions comparable to those of tropical soils. However, these recent studies, along with every known in situ study focused on permafrost N2O fluxes, have used chambers to examine small areas (less than 50 square meters). In late August 2013, we used the airborne eddy-covariance technique to make in situ N2O flux measurements over the North Slope of Alaska from a low-flying aircraft spanning a much larger area: around 310 square km. We observed large variability of N2O fluxes with many areas exhibiting negligible emissions. Still, the daily mean averaged over our flight campaign was 3.8 (2.2–4.7) mg N2O m−2 d−1 with the 90 % confidence interval shown in parentheses. If these measurements are representative of the whole month, then the permafrost areas we observed emitted a total of around 0.04–0.09 g m−2 for August, which is comparable to what is typically assumed to be the upper limit of yearly emissions for these regions. FULL TEXT: [LINK] .
  2. INTERPRETATION OF THESE FINDINGS IN TERMS OF CLIMATE CHANGE APOCALYPSE:  “Emissions from thawing Arctic permafrost may be 12 times higher than thought, scientists say. ‘This needs to be taken more seriously than it is right now,’ says author of new study. [LINK]  .  “Emissions from thawing Arctic permafrost may be 12 times higher than previously thought, scientists have discovered.  #ClimateBreakdown #EcologicalEmergency” [LINK] . 
  3. TESTABLE IMPLICATION: The extreme heat trapping effect of N2O in conjunction with the large outflow of emissions from thawing Arctic permafrost in the North Slope of Alaska on August 2, 2013 is interpreted in the sources cited above as a dangerous positive feedback of greenhouse effect global warming. The testable implication is that this extreme event should have left a mark in the temperature record that should show a warming event. 
  4. A TEST FOR A TEMPERATURE EFFECT: Shown below are UAH satellite (deseasonalized) temperature anomalies for land in the North Polar region for each of the twelve calendar months in the sample period 2008-2018. The year 2013 falls in the middle of the study period 2008-2018.
  5. Figure 1 shows full span trends for each of the 12 calendar months. These trends are depicted graphically in the GIF image of Figure 3 which cycles through the twelve calendar months. The month of August, when the N2O emission was detected, does not appear to be different from the other months in either Figure 1 or Figure 3.
  6. Figure 2 is a GIF image that displays the trend across the twelve calendar months for each year in the study period 2008-2018. Nothing unusual is found in the year 2013 when the N2O emission was detected.
  7. The testable implication of the N2O event of August of 2013 is that if the GHG effect of the released N2O had an effect on temperature there ought to be something unusual about the month of August in Figure 3 or something unusual about the year 2013 in Figure 2. No such evidence is found in the data.
  8. Figure 1: Full span trends for each calendar month  fullspanTrends
  9. Figure 2: Temperature trends across calendar months January-December for each year in the sample period. The vertical red line marks the year 2013.    years-gif
  10. Figure 3: Temperature trends across the sample period 2008-2018 for each calendar month. The vertical red line marks the month of August.      months-gif
  11. CONCLUSION: It is noteworthy that the authors were able to detect a large release of N2O from thawing permafrost in the North Slope of Alaska but their further interpretation of the data in terms catastrophic runaway positive feedback warming due to the extreme GHG effect of N2O is not evident in the data.