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[RELATED POST ON THE CARBON CYCLE]

RELATED POST:  AN EXCLUSIVE RELIANCE ON FOSSIL FUEL EMISSIONS OVERLOOKS NATURAL CARBON FLOWS. [LINK]  

FIGURE 1: FOSSIL FUEL EMISSIONS AND ATMOSPHERIC COMPOSITION

FIGURE 2: EMISSIONS AND CHANGE IN ATMOS-CO2 AT FIVE TIME SCALES

FIGURE 3: CORRELATION BETWEEN  ΔCO2 AND EMISSIONS

FIGURE 4: SUMMARY OF AIRBORNE FRACTION

FIGURE 5: SUMMARY OF DETRENDED CORRELATION ANALYSIS

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[RELATED POST ON THE CARBON CYCLE]

1. Figure 1 shows that atmospheric CO2 concentration as measured at Mauna Loa has been rising steadily since 1958 while at the same time post industrial humans have been injecting increasing amounts of carbon dioxide from fossil fuels into the atmosphere. It is in this context that the usual assumption is made that observed changes in atmospheric CO2 concentration (ΔCO2) are driven by fossil fuel emissions. This assumed relationship appears to be visually validated in the left panels of the five charts in Figure 3 where changes in atmospheric carbon dioxide (ΔCO2) appear to be strongly correlated with the rate of emissions.
2. The correlation was tested in a related work [LINK] where it was shown with detrended correlation analysis that there is insufficient evidence to claim that atmospheric CO2 concentration is responsive to fossil fuel emissions at an annual time scale and that therefore the attribution of rising atmospheric CO2 to emissions is without empirical support. Detrended correlation analysis extracts the portion of the observed source data correlation that derives from responsiveness at the chosen time scale by removing the portion that derives from shared trends. The motivation for this procedure is described in a related post [LINK] . Briefly, the trend is removed from the data so that only the regression residuals remain and a correlation between these residuals is used to measure the responsiveness of ΔCO2 to emissions.
3. This work is a further investigation into the relationship between changes in atmospheric CO2 concentration and fossil fuel emissions. The failure of the prior study to find a responsiveness of atmospheric CO2 to fossil fuel emissions at an annual time scale leaves open the possibility that a responsiveness may exist at longer time scales. Five time scales from one year to five years in increments of one year are studied. The data for the five time scales are displayed in Figure 2 which contains five charts one for each time scale. Each chart consists of three frames. The left frame shows emissions at the time scale of the chart in gigatons of carbon equivalent (GTC). The middle frame displays the corresponding increase in atmospheric CO2 converted from parts per million in volume (ppmv) to GTC equivalent. The last frame contains the ratio of ΔCO2 to emissions. This ratio, called the “Airborne Fraction (A/F)” is considered to be a constant with a value of approximately 50%. It describes the portion of emissions that end up in the atmosphere. The spread of the Airborne Fraction appears to include the value of A/F = 0.5 and the spread appears to narrow as the time scale is increased. Curiously, a slight downward trend is seen in the A/F at all time scales. The Airborne Fraction concept appears to assume a causal relationship between emissions and change in atmospheric CO2 concentration. The results are summarized in Figure 4. The volatility of the Airborne Fraction decreases sharply from Range=0.8 to Range =0.29 as the time scale is increased from T/S=1 to T/S=5 and at the longer time scales the median A/F converges nicely to the original IPCC figure of A/F=0.5. Later claims to reduced figures of A/F=0.42 seems arbitrary and perhaps a case of circular reasoning as explained in a related post [LINK]
4. The correlation analysis is presented in Figure 3. There are five charts one for each time scale. Each chart consists of two frames, a left frame that displays correlation in the source data and a right frame that shows the correlation between the detrended series. Both of these correlations rise as the time scale is increased from one to five years. At all five time scales we find a significant loss in correlation when the data are detrended. The correlation that survives into the detrended series serves as evidence of responsiveness at each of the five time scales. The survival fraction also rises as the time scale is increased from annual to five years. The results are summarized in Figure 5. Here we see that the source correlation rises from CORR=0.742 to CORR=0.921 as we increase the time scale from T/S=1 to T/S=5. The corresponding detrended correlation also rises from DETCOR=0.145 to DETCOR=0.314 with the survival fraction rising sharply from 19.5% to 34.1%.
5. The higher and higher detrended correlations and survival fractions at longer time scales raise the intriguing possibility that the failure to find a responsiveness of atmospheric composition to the rate of fossil fuel emissions was an inappropriate choice of an annual time scale. Perhaps a longer time scale will resolve the issue. To test that hypothesis we present one tailed hypothesis tests for each of the five detrended correlations at the five selected time scales. Here the alternate hypothesis is that the detrended correlation is positive or HA: DETCOR>0. The corresponding null hypothesis is that is not positive or H0: DETCOR<=0. The maximum false positive error rate is set to α=0.001, much lower than the usual values of α=0.01 to α=05, in accordance with Revised Standards for Statistical Significance (Johnson, 2013) published by the NAS to address an unacceptable rate of irreproducible results in published research (Siegfried, 2010). Since five comparisons are made for the five different time scales, the probability of finding at least one significant correlation in random data is increased by a factor of five to 0.005 (Holm, 1979). The results of the hypothesis tests are presented in Figure 5. Here EFFN=effective value of the sample size corrected for time scale which decreases from EFFN=60 to EFFN=12 as the time scale is increased from T/S=1 to T/S=5 to account for residual unique information in the time series. The procedure and rationale for this computation are described in a related work [LINK] . Along with the effective sample size, the degrees of freedom also falls since in this case degrees of freedom is computed as DF=EFFN-2. Thus, although the T-statistic rises somewhat as the time scale is increased from T/S=1 to T/S=5, none of the five PVALUEs is low enough to reject H0 even at alpha=0.05 where with the Holm adjustment for multiple comparison, a p-value of pval=0.01 would be required. We therefore fail to reject H0: DETCOR<=0  and conclude that the data do not provide evidence that atmospheric CO2 concentration is responsive to fossil fuel emissions at any of the time scales studied. Thus the interpretation of the Airborne Fraction in terms of the contribution of fossil fuel emissions to ΔCO2 requires the use of circular reasoning with an assumed responsiveness that is not found in the data. This issue is described in greater detail in a related post.  [LINK] .
6. A rationale for the inability to relate changes in atmospheric CO2 to fossil fuel emissions is described by Geologist James Edward Kamis in terms of natural geological emissions due to plate tectonics [LINK] and by Viv Forbes in terms of the natural Henry’s Law equilibrium with much larger store of CO2 in the ocean [LINK] . The essential argument is that, in the context of much larger natural flows of carbon dioxide and other carbon based compounds, it requires circular reasoning to describe changes in atmospheric CO2 only in terms of fossil fuel emissions. It is shown in a related post, that in the context of large uncertainties in carbon cycle flows, it is not possible to detect the presence of fossil fuel emissions without the help of circular reasoning  [LINK] . 
7. Carbon cycle flows cannot be directly measured and they must therefore be inferred. These inferred carbon cycle flows contain large uncertainties. The essence of this argument is that the in climate science mass balance of the carbon cycle with and without fossil fuel emissions, the declared uncertainties in carbon cycle flows are ignored. In the related post cited above [LINK] it is shown that when the declared uncertainties are taken into account, the much smaller fossil fuel emissions cannot be detected net of uncertainties in the much larger carbon cycle flows because the carbon cycle balances with and without fossil fuel emissions within its uncertainty bounds.
8. Circular reasoning in this case can be described in terms of the “Assume a spherical cow” fallacy [LINK] which refers to the use of simplifying assumptions needed to solve a problem that change the context of the problem so that the solution no longer answers the original research question. In the case of climate science the corresponding spherical cow assumption is “assume that there are no uncertainties in carbon cycle flows and no geological flows of carbon” [LINK] . 
9. The results of detrended correlation analysis at five time scales shows that the failure to find a responsiveness of atmospheric composition to fossil fuel emissions in a related work  [LINK] cannot be ascribed to the annual time scale used in the study as the result is validated at longer time scales to the point of diminishing returns.
10. We conclude that atmospheric composition specifically in relation to the CO2 concentration is not responsive to the rate of fossil fuel emissions. This finding is a serious weakness in the theory of anthropogenic global warming by way of rising atmospheric CO2 attributed to the use of fossil fuels in the industrial economy; and of the “Climate Action proposition of the UN that reducing fossil fuel emissions will moderate the rate of warming by slowing the rise of atmospheric CO2. The finding also establishes that the climate action project of creating Climate Neutral Economies, that is Economies that have no impact on atmospheric CO2, is unnecessary because the global economy is already Climate Neutral. 

[LIST OF POSTS ON THIS SITE]

REFERENCE

1. Achen, C. (1982). Interpreting and using regression. Vol. 29. Sage.
2. Ackerman, S. (2006). Meteorology: Understanding the Atmosphere. Jones and Barlett Titles in Physical Science.
3. Allen, M. (2009). Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature , 458.7242 (2009): 1163-1166.
4. Anselin, L. (1990). Some robust approaches to testing and estimation in spatial econometrics. Regional Science and Urban Economics , 20.2 (1990): 141-163.
5. Armstrong, R. (2014). Ophthalmic and Physiological Optics. When to use the Bonferroni correction , 34.5 (2014): 502-508.
6. Aubinet, M. (2012). Eddy covariance: a practical guide to measurement and data analysis. Springer Science & Business Media, 2012.
7. Bachelet, D. (2001). Climate change effects on vegetation distribution and carbon budget in the United States. Ecosystems , 4.3 (2001): 164-185.
8. Baltagi, B. (1985). Pooling cross-sections with unequal time-series lengths. Economics Letters , 18.2-3 (1985): 133-136.
9. Bengtsson, L. (2004). Can climate trends be calculated from reanalysis data? Journal of Geophysical Research: Atmospheres , 109.D11 (2004).
10. Bopp, L. (2002). Climate‐induced oceanic oxygen fluxes: Implications for the contemporary carbon budget.” Global Biogeochemical Cycles. Global Biogeochemical Cycles , 16.2 (2002).
11. Bousquet, P. (2000). Regional changes in carbon dioxide fluxes of land and oceans since 1980. Science , 290.5495 (2000): 1342-1346.
12. Bowley, A. (1928). The standard deviation of the correlation coefficient. Journal of the American Statistical Association , 23.161 (1928): 31-34.
13. Box, G. (1994). Time series analysis: forecasting and control. Englewood Cliffs, NJ: Prentice Hall.
14. Callendar, G. (1938). The artificial production of carbon dioxide and its influence on temperature. Quarterly Journal of the Royal Meteorological Society , 64.275 (1938): 223-240.
15. 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 , 104.47 (2007): 18866-18870.
16. CarbonBrief. (2016). What global emissions in 2016 mean for climate change goals. Retrieved 2017, from Carbon Brief: https://www.carbonbrief.org/what-global-co2-emissions-2016-mean-climate-change
17. CDIAC. (2017). CDIAC. Retrieved 2017, from ORNL.GOV: http://cdiac.ornl.gov/trends/emis/meth_reg.html
18. CDIAC. (2014). Global Fossil-Fuel CO2 Emissions. Retrieved 2017, from CDIAC / ORNL: http://cdiac.ornl.gov/trends/emis/tre_glob_2013.html
19. Chatfield, C. (1989). The Analysis of Time Series: An Introduction. NY: Chapman and Hall/CRC.
20. Chen, W. (2000). An integrated terrestrial ecosystem carbon-budget model based on changes in disturbance, climate, and atmospheric chemistry. Ecological Modelling , 135.1 (2000): 55-79.
21. Davis, S. (2010). Future CO2 emissions and climate change from existing energy infrastructure. Science , 329.5997 (2010): 1330-1333.
22. Davison, M. (2016). Man made global warming. Retrieved 2017, from Ministry for the Environment. Government of New Zealand: http://www.mfe.govt.nz/sites/default/files/media/NZETS_reviewstage2%20-%20Davison,%20Michael%2004035(b).pdf
23. Dufrêne, E. (2005). Modelling carbon and water cycles in a beech forest: Part I: Model description and uncertainty analysis on modelled NEE. Ecological Modelling , 185.2 (2005): 407-436.
24. Easterling, D. (2009). Is the climate warming or cooling? Geophysical Research Letters , 36.8 (2009).
25. Edwards, P. (1999). Global climate science, uncertainty and politics: Data‐laden models, model‐filtered data. Science as Culture , 8.4 (1999): 437-472.
26. ESRL. (2017). NOAA/ESRL. Retrieved 2017, from NOAA/ESRL: https://www.esrl.noaa.gov/gmd/dv/data/index.php
27. Flannery, T. (2006). The weather makers: How man is changing the climate and what it means for life on earth. Grove Press, 2006.
28. Friedlingstein, P. (2006). Climate–carbon cycle feedback analysis: results from the C4MIP model intercomparison. Journal of Climate , 19.14 (2006): 3337-3353.
29. Garamszegi, L. (2006). Comparing effect sizes across variables: generalization without the need for Bonferroni correction. Behavioral Ecology , 17.4 (2006): 682-687.
30. Giering, S. (2014). Reconciliation of the carbon budget in the ocean/’s twilight zone. Nature , 507.7493 (2014): 480-483.
31. Gillett, N. (2013). Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. Journal of Climate , 26.18 (2013): 6844-6858.
32. Hansen, J. (1981). Climate impact of increasing atmospheric carbon dioxide. Science , 213.4511 (1981): 957-966.
33. 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.
34. Haverd, V. (2013). The Australian terrestrial carbon budget. Biogeosciences , 10.2 (2013).
35. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics , 6:2:65-70.
36. Hu, K. (2001). Physical Review , E 64.1 (2001): 011114.
37. Hulme, M. (2010, November 16). The Year Climate Science Was Redefined. The Guardian .
38. IPCC. (2014). Climate Change 2014: Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC.
39. IPCC. (2014). Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC, 2014. Geneva: IPCC.
40. IPCC. (2000). Special report on emissions scenarios (SRES), a special report of Working Group III. Cambridge: Cambridge University Press.
41. Ito, A. (2005). Climate-related uncertainties in projections of the twenty-first century terrestrial carbon budget: off-line model experiments using IPCC greenhouse-gas scenarios and AOGCM climate projections. Climate Dynamics , 24.5 (2005): 435-448.
42. JIR. (2017). Journal of Irreproducible Results. Retrieved 2017, from Journal of Irreproducible Results: http://www.jir.com/
43. 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
44. Kantelhardt, J. (2001). Detecting long-range correlations with detrended fluctuation analysis. Physica A: Statistical Mechanics and its Applications , 295.3 (2001): 441-454.
45. Lacis, A. (2010). Atmospheric CO2: Principal control knob governing Earth’s temperature. Science , 330.6002 (2010): 356-359.
46. Le Quéré, C. (2009). Trends in the sources and sinks of carbon dioxide. Nature Geoscience , 2.12 (2009): 831-836.
47. Lundberg, L. (1996). A Nordic Seas–Arctic Ocean carbon budget from volume flows and inorganic carbon data. Global Biogeochemical Cycles , 10.3 (1996): 493-510.
48. Massman, W. (2002). Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agricultural and Forest Meteorology , 113.1 (2002): 121-144.
49. McGuire, A. (2001). Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land use effects with four process‐based ecosystem models. Global Biogeochemical Cycles , 15.1 (2001): 183-206.
50. Meinshausen, M. (2009). Greenhouse-gas emission targets for limiting global warming to 2 C. Nature , 458.7242 (2009): 1158-1162.
51. Moran, M. (2003). Arguments for rejecting the sequential Bonferroni in ecological studies. Oikos , 100.2 (2003): 403-405.
52. Munshi, J. (2017). 2017 Atmospheric CO2 paper Data Archive. Retrieved 2017, from Google Drive: https://drive.google.com/open?id=0ByzA6UNa41ZfX1dhR1VxbmdaRGs
53. Munshi, J. (2015). An Empirical Study of Fossil Fuel Emissions and Ocean Acidification. SSRN , http://dx.doi.org/10.2139/ssrn.2669930.
54. Munshi, J. (2015). Decadal Fossil Fuel Emissions and Decadal Warming: A Note. SSRN , http://dx.doi.org/10.2139/ssrn.2662870.
55. Munshi, J. (2016). Effective Sample Size of the Cumulative Values of a Time Series. SSRN , http://dx.doi.org/10.2139/ssrn.2853163.
56. Munshi, J. (2016). Generational Fossil Fuel Emissions and Generational Warming: A Note. SSRN , http://dx.doi.org/10.2139/ssrn.2845972.
57. Munshi, J. (2017). Long Term Temperature Trends in Daily Station Data: Australia. Retrieved 2017, from SSRN.COM: https://ssrn.com/abstract=2968352
58. Munshi, J. (2015). Responsiveness of Atmospheric CO2 to Anthropogenic Emissions: A Note . Retrieved 2017, from SSRN: https://ssrn.com/abstract=2642639 or http://dx.doi.org/10.2139/ssrn.2642639
59. Munshi, J. (2016). Responsiveness of Atmospheric CO2 to Fossil Fuel Emissions: Part 2. SSRN , http://dx.doi.org/10.2139/ssrn.2862438.
60. Munshi, J. (2016). Some Methodological Issues in Climate Science. SSRN , http://dx.doi.org/10.2139/ssrn.2873672.
61. Munshi, J. (2016). Spurious Correlations in Time Series Data. SSRN , http://dx.doi.org/10.2139/ssrn.2827927.
62. Munshi, J. (2017). The Correlation between Emissions and Warming in the CET. SSRN , http://dx.doi.org/10.2139/ssrn.2956179.
63. Munshi, J. (2016). The Spuriousness of Correlations between Cumulative Values. SSRN , http://dx.doi.org/10.2139/ssrn.2725743.
64. Munshi, J. (2015a). Uncertain Flow Accounting and the IPCC Carbon Budget. SSRN , http://dx.doi.org/10.2139/ssrn.2654191.
65. Netherlands Environmental Assessment Agency. (2016). Trends in global CO2 emissions. Retrieved 2017, from European Commission Joint Research Centre: http://edgar.jrc.ec.europa.eu/news_docs/jrc-2016-trends-in-global-co2-emissions-2016-report-103425.pdf
66. Parmesan, C. (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature , 421.6918 (2003): 37-42.
67. Peltoniemi, M. (2006). Factors affecting the uncertainty of sinks and stocks of carbon in Finnish forests soils and vegetation. Forest Ecology and Management , 232.1 (2006): 75-85.
68. Plattner, G. (2002). Revision of the global carbon budget due to changing air‐sea oxygen fluxes. Global Biogeochemical Cycles , 16.4 (2002).
69. Podobnik, B. (2008). Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Physical review letters , 100.8 (2008): 084102.
70. Revelle, R. (1957). Carbon dioxide exchange between atmosphere and ocean and the question of an increase of atmospheric CO2 during the past decades. Tellus , 9.1 (1957): 18-27.
71. Rodhe, H. (2000). Avoiding circular logic in climate modeling. Climatic Change , 44.4 (2000): 419-422.
72. Rogelj, J. (2016). Paris Agreement climate proposals need a boost to keep warming well below 2 C. Nature , 534.7609 (2016): 631-639.
73. Rosón, G. (2003). Carbon distribution, fluxes, and budgets in the subtropical North Atlantic Ocean (24.5 N). Journal of Geophysical Research: Oceans , 108.C5 (2003).
74. Sarmiento, J. (1998). Simulated response of the ocean carbon cycle to anthropogenic climate warming. Nature , 393.6682 (1998): 245-249.
75. Scripps CO2 Program. (2017). Scripps CO2 Program. Retrieved 2017, from UCSD: http://scrippsco2.ucsd.edu/data/atmospheric_co2/
76. Shvidenko, A. (1996). Carbon budget of the Russian boreal forests: a systems analysis approach to uncertainty. In Forest ecosystems, forest management and the global carbon cycle (pp. 145-152). Berlin: Springer, Berlin, Heidelberg.
77. Siegfried, T. (2010). Odds Are, It’s Wrong. Retrieved 2016, from Science News: https://www.sciencenews.org/article/odds-are-its-wrong
78. Smith, J. (2001). Identifying influences on model uncertainty: an application using a forest carbon budget model. Environmental Management , 27.2 (2001): 253-267.
79. Solomon, S. (2009). Irreversible climate change due to carbon dioxide emissions. Proceedings of the national academy of sciences , 106.6 (2009): 1704-1709.
80. Stern, N. (2007). The economics of climate change: the Stern review. cambridge, England: cambridge University press.
81. Stocker, T. (2013). Climate change 2013: the physical science basis. Intergovernmental panel on climate change, working group I contribution to the IPCC fifth assessment report (AR5). New York (2013): IPCC.
82. The Conversation. (2016). Fossil fuel emissions have stalled. Retrieved 2017, from The Conversation: https://theconversation.com/fossil-fuel-emissions-have-stalled-global-carbon-budget-2016-68568

GEOLOGICAL CARBON BIBLIOGRAPHY

1. 2015: James Edward Kamis, Deep Ocean Rock Layer Mega-Fluid Flow Systems  [LINK]  Fluid flow of chemically charged seawater through and within very deep ocean rock layers is virtually unknown until recently. It is here proposed that the flow rate, flow amount, and flow duration of these systems is many orders of magnitude greater than previously thought. As a result the affect these systems have on our climate has been dramatically underestimated. It is proposed that Deep Ocean Rock Layer fluid Flow Systems are quite possibly an extremely important factor in influencing earth’s atmospheric climate, earth’s ocean climate, and earth’s ocean biologic communities. The mechanism for these relationships are strong El Nino’s / La Nina’s, altering major ocean currents, locally altering polar ice cap melting, infusing the ocean with needed minerals, affecting ocean fish migration patterns, acting to maintain huge chemosynthetic communities, acting to spread new species, and acting to eliminate weak species. It is possible that these systems will be proved to be unique/ different from land based hydrodynamic systems in many ways, and if proven correct this would be an extremely important new concept. Scientists have assumed that land based fluid flow / hydrologic systems would be a good analogy. It is here contended that this is an incorrect assumption. These deep ocean systems do not act like land based systems. The major difference of deep ocean fluid flow systems is that they likely flow significantly greater amounts of heat and chemically charged fluid than previously realized. Deep ocean hydrothermal vents and cold seeps are here hypothesized be a just a small part of these here-to-for unrecognized and much larger deep ocean fluid flow systems. This is a very different way of perceiving fluid flow through deep ocean basin rock and sediment layers. To date most scientists have thought of deep ocean rock and sediment layers as basically bottom seals that largely did not and do not interact with the overlying ocean. It is here contended that these systems will be some day be proven to be immense, many of them covering huge regions and extending to great depths of many thousands of feet into ocean rock and sediment layers. In essence they will be found to be part of a continuum between the ocean crust, which they are part of, and upper mantle. Some of the perceived important differences between deep ocean fluid flow systems and land hydrologic systems are as follows
2. 2016: James Edward Kamis, How Geological Forces Rock the Earth’s Climate [LINK]  Geological forces influence the planet’s climate in many specific and measurable ways. They melt the base of polar glaciers, abruptly change the course of deep ocean currents, influence the distribution of plankton blooms, infuse our atmosphere with volcanic sulfur rich ash, modify huge sub-ocean biologic communities, and generate all El Niño / La Niñas’ cycles. Given all of this very convincing information, many of today’s supposedly expert scientists still vehemently insist that our climate is completely / exclusively driven by atmospheric forces. This work challenges that orthodoxy. Three new game-changing pieces of geological information have been revealed: the discovery of an extensive field of active seafloor volcanoes and faults in the far western Pacific, iron enrichment of a huge ocean region off the coast of Antarctica, and the timing of western Pacific Ocean earthquakes vs. El Niños. A significant portion of the Earth’s climate is driven by massive fluid flow of super-heated and chemically charged seawater up and out from major fault zones and associated volcanic features. New geological information is changing the way we view long term climate variability. The data covers significant areas of the ocean measured in hundreds of miles laterally and thousands of feet vertically, and lastly the data is clearly related to geological forces and rather than the exclusive domain of the atmosphere.
3. 2017: James Edward Kamis, Global Warming and Plate Climatology Theory [LINK] The Plate Climatology Theory was originally posted on Climate Change Dispatch October 7, 2014. Since that time other information in the form of several relatively new publications has been incorporated into the theory, and as a result key aspects of the theory have been strengthened. Not proven, but strengthened. This new information does prove one thing, that this theory should be given strong consideration by all scientists studying Global Climate. I am in no way attempting to prove the other guys wrong. Rather Plate Climatology is intended to be additive to the excellent work done to date. It may open the way to resolving the “Natural Variation” question currently being debated by Climate Scientists. What could be more natural than geological events influencing Climate? It is expected that this work will act as a catalyst for future research and provide a platform to join what are now several independently researched branches of science; Geology, Climatology, Meteorology, and Biology. The science of Climate is extremely complex and necessitates a multi-disciplinary approach.
4. 2018: James Edward Kamis, The influence of oceanic and continental fault boundaries on climate [LINK] Another giant piece of the climate science puzzle just fell into place, specifically that geological heat flow is now proven to be the primary force responsible for anomalous bottom melting and break-up of many West Antarctica glaciers, and not atmospheric warming. This new insight is the result of a just released National Aeronautics and Space Administration (NASA) Antarctica geological research study (see here). Results of this study have forever changed how consensus climate scientists and those advocating the theory of Climate Change / Global Warming, view Antarctica’s anomalous climate and climate related events. In a broader theoretical sense, results of the NASA study challenge the veracity of the most important building block principle of the Climate Change Theory, specifically that emissions of CO2 and carbon by humans is responsible for the vast majority of earth’s anomalous climate phenomena. This article will provide evidence that geological forces associated with major oceanic and continental fault boundaries influence and in some cases completely control a significant portion of earth’s anomalous climate and many of its anomalous climate related events.
5. 1983: Garrels, ROBERT M. “The carbonate-silicate geochemical cycle and its effect on atmospheric carbon dioxide over the past 100 million years.” Am J Sci 283 (1983): 641-683. [FULL TEXT PDF DOWNLOAD]
6. 1992: Raymo, Maureen E., and William F. Ruddiman. “Tectonic forcing of late Cenozoic climate.” nature 359.6391 (1992): 117. Global cooling in the Cenozoic, which led to the growth of large continental ice sheets in both hemispheres, may have been caused by the uplift of the Tibetan plateau and the positive feedbacks initiated by this event. In particular, tectonically driven increases in chemical weathering may have resulted in a decrease of atmospheric C0₂concentration over the past 40 Myr.
7. 1995: Keeling, Charles D., et al. “Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980.” Nature375.6533 (1995): 666. OBSERVATIONS of atmospheric CO₂ concentrations at Mauna Loa, Hawaii, and at the South Pole over the past four decades show an approximate proportionality between the rising atmospheric concentrations and industrial CO₂ emissions¹. This proportionality, which is most apparent during the first 20 years of the records, was disturbed in the 1980s by a disproportionately high rate of rise of atmospheric CO₂, followed after 1988 by a pronounced slowing down of the growth rate. To probe the causes of these changes, we examine here the changes expected from the variations in the rates of industrial CO₂emissions over this time², and also from influences of climate such as El Niño events. We use the¹³C/¹²C ratio of atmospheric CO₂ to distinguish the effects of interannual variations in biospheric and oceanic sources and sinks of carbon. We propose that the recent disproportionate rise and fall in CO₂ growth rate were caused mainly by interannual variations in global air temperature (which altered both the terrestrial biospheric and the oceanic carbon sinks), and possibly also by precipitation. We suggest that the anomalous climate-induced rise in CO₂ was partially masked by a slowing down in the growth rate of fossil-fuel combustion, and that the latter then exaggerated the subsequent climate-induced fall.
8. 1995: Kerrick, Derrill M., et al. “Convective hydrothermal C02 emission from high heat flow regions.” Chemical Geology121.1-4 (1995): 285-293.In addition to volatiles released from volcanoes, the flux of CO2 to the atmosphere from other sources (e.g., metamorphism and subsurface magmatism) represents an important aspect of the global carbon cycle. We have obtained a direct estimate of the present-day atmospheric CO2 flux from convective hydrothermal systems within subaerial, seismically-active, high heat flow regions. Geothermal systems of the Salton Trough (California, U.S.A.) and the Taupo Volcanic Zone (New Zealand) provide benchmarks for quantifying convective hydrothermal CO2 fluxes from such regions. CO2 fluxes from the Salton Trough ( ∼ 109 mol yr−1) and the Taupo Volcanic Zone (∼ 8·109 mol yr−1) were computed using data on convective heat flow and the temperatures and CO2 concentrations of reservoir fluids. The similarity in specific CO2 flux ( ∼ 106 mol km−2 yr−1) from these two disparate geologic/tectonic settings implies that this flux may be used as a baseline to compute convective hydrothermal CO2 emission from other areas of high heat flow. If this specific flux is integrated over high heat flow areas of the circum-Pacific and Tethyan belts, the total global CO2 flux could equal or exceed 1012 mol yr−1 Adding this flux to a present-day volcanic CO2 flux of ∼ 4·1012 mol yr−1 the total present-day Earth degassing flux could balance the amount of CO2 consumed by chemical weathering ( ∼ 7·1012 mol yr−1).
9. 1996: Sano, Yuji, and Stanley N. Williams. “Fluxes of mantle and subducted carbon along convergent plate boundaries.” Geophysical Research Letters 23.20 (1996): 2749-2752. The potential impact of increases in atmospheric CO₂ is a topic of considerable controversy. Even though volcanic emission of CO₂ may be very small as compared to anthropogenic emissions, evaluation of natural degassing of CO₂ is important for any model of the geochemical C cycle and evolution of the Earth’s atmosphere. We report here the mantle C flux in subduction zones based on He and C isotopes and CO₂/³ He ratios of high‐temperature volcanic gases and medium‐ and low‐temperature fumaroles in circum‐Pacific volcanic regions. The calculated volcanic C flux of 3.1 × 10¹² mol/a from subduction zones is larger than the flux of 1.5 × 10¹² mol/a from mid‐ocean ridges, while contributions from the mantle in subduction zone is only 0.30 × 10¹² mol/a, equivalent to about 20% of the C flux in mid‐ocean ridges. Since the estimated mantle C flux in hot spot regions is insignificant, 0.029 × 10¹² mol/a, we propose that the global mantle C flux is 1.8 × 10¹² mol/a in total. The flux, if accumulated over 4.5 billion year of geological time, amounts to 8.3 × 10²¹ mol which agrees well with 9 × 10²¹ mol of the present inventory of C at the Earth’s surface. This may support a continuous degassing model of C or the idea that subducted C is recycled into the lower mantle.
10. 1998: Marty, Bernard, and Igor N. Tolstikhin. “CO 2 fluxes from mid-ocean ridges, arcs and plumes.” Chemical Geology 145.3 (1998): 233-248. Estimates of CO₂ emissions at spreading centres, convergent margins, and plumes have been reviewed and upgraded using observed CO₂/³He ratios in magmatic volatiles, ³He content estimates in the magmatic sources, and magma emplacement rates in the different tectonic settings. The effect of volatile fractionation during magma degassing, investigated using new rare gas and CO₂ abundances determined simultaneously for a suite of Mid-Ocean Ridge (MOR) basalt glasses, is not the major factor controlling the spread of data, which mainly result from volatile heterogeneity in the mantle source. The computed C flux at ridges (2.2±0.9)×10¹² mol/a, is essentially similar to previous estimates based on a more restricted data base. Variation of the C flux in the past can be simply scaled to that of spreading rate since the computed C depends mainly on the volatile content of the mantle source, which can be considered constant during the last 10⁸ a. The flux of CO₂ from arcs may be approximated using the CO₂/³He ratios of volcanic gases at arcs and the magma emplacement rate, assuming that the ³He content of the mantle end-member is that of the MORB source. The resulting flux is ∼2.5×10¹² mol/a, with approx. 80% of carbon being derived from the subducting plate. The flux of CO₂from plumes, based on time-averaged magma production rates and on estimated contributions of geochemical sources to plume magmatism, is ≤3×10¹² mol/a. Significant enhancements of the CO₂ flux from plumes might have occurred in the past during giant magma emplacements, depending on the duration of these events, although the time-integrated effect does not appear important. The global magmatic flux of CO₂ into the atmosphere and the hydrosphere is found to be 6×10¹² mol/a, with a range of (4–10)×10¹² mol/a. Improvement on the precision of this estimate is linked to a better understanding of the volatile inventory at arcs on one hand, and on the dynamics of plumes and their mantle source contribution on the other hand.
11. 2001: Kerrick, Derrill M. “Present and past nonanthropogenic CO2 degassing from the solid Earth.” Reviews of Geophysics 39.4 (2001): 565-585. Global carbon cycle models suggest that CO₂ degassing from the solid Earth has been a primary control of paleoatmospheric CO₂ contents and through the greenhouse effect, of global paleotemperatures. Because such models utilize simplified and indirect assumptions about CO₂ degassing, improved quantification is warranted. Present‐day CO₂ degassing provides a baseline for modeling the global carbon cycle and provides insight into the geologic regimes of paleodegassing. Mid‐ocean ridges (MORs) discharge 1–3 × 10¹² mol/yr of CO₂ and consume ∼3.5 × 10¹² mol/yr of CO₂ by carbonate formation in MOR hydrothermal systems. Excluding MORs as a net source of CO₂ to the atmosphere, the total CO₂ discharge from subaerial volcanism is estimated at ∼2.0–2.5 × 10¹² mol/yr. Because this flux is lower than estimates of the global consumption of atmospheric CO₂ by subaerial silicate weathering, other CO₂ sources are required to balance the global carbon cycle. Nonvolcanic CO₂ degassing (i.e., emission not from the craters or flanks of volcanos), which is prevalent in high heat flow regimes that are primarily located at plate boundaries, could contribute the additional CO₂ that is apparently necessary to balance the global carbon cycle. Oxidation of methane emitted from serpentinization of ultramafics and from thermocatalysis of organic matter provides an additional, albeit unquantified, source of CO₂ to the atmosphere. Magmatic CO₂degassing was probably a major contributor to global warming during the Cretaceous. Metamorphic CO₂ degassing from regimes of shallow, pluton‐related low‐pressure regional metamorphism may have significantly contributed to global warming during the Cretaceous and Paleocene/Eocene. CO₂ degassing associated with continental rifting of Pangaea may have contributed to the global warming that was initiated in the Jurassic. During the Cretaceous, global warming initiated by CO₂ degassing of flood basalts, and consequent rapid release of large quantities of CH₄ by decomposition of gas hydrates (clathrates), could have caused widespread extinctions of organisms.
12. 2008: Zachos, James C., Gerald R. Dickens, and Richard E. Zeebe. “An early Cenozoic perspective on greenhouse warming and carbon-cycle dynamics.” Nature 451.7176 (2008): 279. By the year 2400, it is predicted that humans will have released about 5,000 gigatonnes of carbon (Gt C) to the atmosphere since the start of the industrial revolution if fossil-fuel emissions continue unabated and carbon-sequestration efforts remain at current levels¹. This anthropogenic carbon input, predominantly carbon dioxide (CO₂), would eventually return to the geosphere through the deposition of calcium carbonate and organic matter². Over the coming millennium, however, most would accumulate in the atmosphere and ocean. Even if only 60% accumulated in the atmosphere, the partial pressure of CO₂ (pCO2pCO2) would rise to 1,800 parts per million by volume (p.p.m.v.) (Fig. 1). A greater portion entering the ocean would decrease the atmospheric burden but with a consequence: significantly lower pH and carbonate ion concentrations of ocean surface layers¹
13. 2010: Dasgupta, Rajdeep, and Marc M. Hirschmann. “The deep carbon cycle and melting in Earth’s interior.” Earth and Planetary Science Letters 298.1-2 (2010): 1-13. Carbon geochemistry of mantle-derived samples suggests that the fluxes and reservoir sizes associated with deep cycle are in the order of 10^12–13 g C/yr and 10^22–23 g C, respectively. This deep cycle is responsible for the billion year-scale evolution of the terrestrial carbon reservoirs. The petrology of deep storage modulates the long-term evolution and distribution of terrestrial carbon. Unlike water, which in most of the Earth’s mantle is held in nominally anhydrous silicates, carbon is stored in accessory phases. The accessory phase of interest, with increasing depth, typically changes from fluids/melts → calcite/dolomite → magnesite → diamond/Fe-rich alloy/Fe-metal carbide, assuming that the mass balance and oxidation state are buffered solely by silicates. If, however, carbon is sufficiently abundant, it may reside as carbonate even in the deep mantle. If Earth’s deep mantle is Fe-metal saturated, carbon storage in metal alloy and as metal carbide cannot be avoided for depleted and enriched domains, respectively. Carbon ingassing to the interior is aided by modern subduction of the carbonated oceanic lithosphere, whereas outgassing from the mantle is controlled by decompression melting of carbonated mantle. Carbonated melting at > 300 km depth or redox melting of diamond-bearing or metal-bearing mantle at somewhat shallower depth generates carbonatitic and carbonated silicate melts and are the chief agents for liberating carbon from the solid Earth to the exosphere. Petrology allows net ingassing of carbon into the mantle in the modern Earth, but in the hotter subduction zones that prevailed during the Hadean, Archean, and Paleoproterozoic, carbonate likely was released at shallow depths and may have returned to the exosphere. Inefficient ingassing, along with efficient outgassing, may have kept the ancient mantle carbon-poor. The influence of carbon on deep Earth dynamics is through inducing melting and mobilization of structurally bound mineral water. Extraction of carbonated melt on one hand can dehydrate the mantle and enhance viscosity; the presence of trace carbonated melt on other may generate seismic low-velocity zones and amplify attenuation.