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Reference: After Nobel Gore should go for the next big prize, Bangkok Post, Oct 16, 2007

The goofiness of the Nobel Committee is grossly underrated. Robert Merton and Myron Scholes must have thought they had been certified as Lords of Finance when they were awarded the Nobel Prize in Economics until their hedge fund called Long Term Capital Management did a nose dive and almost brought down the American financial system. More recently the Nobel Prize was awarded to scientists who had warned us that that human activity was causing ozone depletion and making the ozone hole bigger. They were allowed to keep their prizes even after it became apparent that the observed changes in the ozone layer were part of a natural cycle having to do with shifting winds in the upper atmosphere and NOT due to human activity.

In its latest goof, the Nobel Committee has awarded a prize for service to humanity to people who see humanity as the enemy of the earth and whose stated goal could be achieved simply by eliminating humanity from the face of the earth. Besides, the movie about global warming An Inconvenient Truth has been widely discredited as being biased and containing not only exaggerations but outright lies and scientific fraud.

The award of the Nobel Prize to these snake oil salesmen does not prove that the Anthropogenic Global Warming hypothesis is correct. That proof can only be provided by empirical evidence. The fact that Gore was awarded a prize by a committee of five Norwegians appointed by the Norwegian legislature does not vindicate Gore. If anything it discredits the Nobel committee. If the Nobel Prize is to be made into a global prize then it should be removed from the confines of Norwegian goofiness and opened up to a more global evaluation.

algorenobel

salby

 

 

 

 

ASSUME A SPHERICAL COWSphericalCow2

 

 

[LIST OF POSTS ON THIS SITE]

 

 

FIGURE 1: CO2 AIRBORNE FRACTIONRF-FIG1

 

  1. Paleo climate data tell us that prior to the Industrial era the mean annual CO2 concentration of the atmosphere stayed in range 180-290 ppm (IPCCAR5, 2013), a difference of 234 gigatons of carbon equivalent (GTC). The range is equivalent to total global fossil fuel emissions in the 33-year period 1985-2017 but since the Paleo changes occurred prior to the industrial age, these changes are ascribed to volcanic eruptions which inject both aerosols and CO2 into the atmosphere. Changes in solar activity are also considered as they can change the equilibrium partial pressure of CO2 over the oceans in accordance with the Henry’s Law relationship of the temperature dependence of the solubility of carbon dioxide in water (IPCCAR5, 2013).
  2. However, in the postindustrial era, these changes are shown to be much more rapid and are therefore explained in terms of anthropogenic fossil fuel emissions with the mathematics of the attribution computed in the context of the carbon cycle that describes the natural flows of carbon dioxide to and from the atmosphere. The IPCC describes the carbon cycle in terms of carbon dioxide flows among multiple sources and sinks. The atmosphere plays a role in nine of these flows. These mean flows, averaged over the decade 2000-2009 (Figure 7) and their standard deviations (SD) as reported by the IPCC are listed below in units of GTC/y (IPCCAR5, 2013). Non availability of data is indicated by N/A.
  3. Natural: Ocean surface to atmosphere:Mean=78.4,SD=N/A.
  4. Natural: Atmosphere to ocean:surface:Mean=80.0,SD=N/A
  5. Human: Fossil fuel emissions:surface to atmosphere:Mean=7.8,SD=0.6
  6. Human: Land use change:surface to atmosphere:Mean=1.1,SD=0.8
  7. Natural: Photosynthesis:atmosphere to surface:Mean=123.0,SD=8.0
  8. Natural: Respiration/fire:surface to atmosphere:Mean=118.7,SD=N/A
  9. Natural: Freshwater to atmosphere:Mean=1.0,SD=N/A
  10. Natural: Volcanic emissions surface to atmosphere:Mean=0.1,SS =N/A
  11. Natural: Rock weathering:surface to atmosphere:Mean=0.3,SD=N/A
  12. A simple flow accounting of the mean values without consideration of uncertainty shows a net CO2 flow from surface to atmosphere of 4.4 GTC/y. The details of this computation are as follows. In the emissions and atmospheric composition data we find that during the decade 2000-2009 total fossil fuel emissions were 78.1 GTC and that over the same period atmospheric CO2 rose from 369.2 to 387.9 ppm for an increase of 18.7 ppm equivalent to 39.6 GTC in atmospheric CO2 or 4.4 GTC/y. The ratio of the observed increase in atmospheric carbon to emitted carbon is thus =39.6/78.2=0.51. This computation is the source of the claim that the so called “Airborne Fractionis about 50%; that is to say that about half of the emitted carbon accumulates in the atmosphere on average and the other half is absorbed by the oceans, by photosynthesis, and by terrestrial soil absorption. The Airborne Fraction of AF=50% later had to be made flexible in light of a range of observed values (Figure 1).
  13. The left frame of Figure 1 above shows that a large range of values of the decadal mean Airborne Fraction of 0<DMAF<4 .5 for decades ending in 1860 to 2017. This sample period includes ice core CO2 data from the Law Dome for years prior to 1958. However, when the sample period is restricted to the more precise Mauna Loa data from 1958, a much smaller range of values are seen in the right frame of Figure 1 with 0.45<DMAF<0.65. These data appear to support the usual assumption in climate science that fossil fuel emissions have contributed about half of the decadal mean increase in atmospheric CO2 concentration since 1958; but as demonstrated in a related post [LINK] , without a correlation between emissions and changes in atmospheric CO2 concentration, airborne fractions can be computed but they have no interpretation in terms of cause and effect in the phenomenon being studied [LINK] .
  14. When uncertainties are not considered, the flow accounting appears to show an exact match of the predicted and computed carbon balance. It is noted, however, that this exact accounting balance is achieved, not with flow measurements, but with estimates of unmeasurable flows constrained by the circular reasoning that assigns flows according to an assumed flow balance.
  15. However, a very different picture emerges when uncertainties are included in the balance. Published uncertainties for three of the nine flows are available in the IPCC reports. Uncertainty for the other six flows are not known. However, we know that they are large because no known method exists for the direct measurement these flows. They can only be grossly inferred based in assumptions that exclude or minimize geological flows.
  16. We therefore set up a Monte Carlo simulation to estimate the highest value of the unknown standard deviations at which we can detect the presence of human emissions in the carbon cycle. For the purpose of this test we propose that an uncertain flow account is in balance as long as the Null Hypothesis that the sum of the flows is zero cannot be rejected. The alpha error rate for the test is set to a high value of alpha=0.10 to ensure that any reasonable ability to discriminate between the flow account WITH Anthropogenic Emissions from a the flow account WITHOUT Anthropogenic Emissions is taken into evidence that the relatively small fossil fuel emissions can be detected in the presence of much larger and uncertain natural flows. The spreadsheet used in this determination is available for download from an online data archive Data Archive Link .
  17. In the simulation we assign different levels of uncertainty to the flows for which no uncertainty data are available and test the null hypothesis that the flows balance with anthropogenic emissions (AE) included and again with AE excluded. If the flows balance when AE are included and they don’t balance when AE are excluded then we conclude that the presence of the AE can be detected at that level of uncertainty. However, if the flows balance with and without AE then we conclude that the stochastic flow account is not sensitive to AE at that level of uncertainty because it is unable to detect their presence. If the presence of AE cannot be detected no role for their effect on climate can be deduced from the data at that level of uncertainty in natural flows.
  18. The balance is computed from the atmospheric perspective as Balance=Input-Output where Input is flow to the atmosphere and Output is flow from the atmosphere. The p-values for hypothesis tests for uncertainties in the natural flows from 1% of mean to 6.5% of mean are presented below both as a tabulation and as a line chart.
  1. In the tabulation the PCT column shows the assumed percent standard deviation in the natural flows for which no uncertainty information is available. In the”base case”, the blanket statement by the IPCC that the uncertainty is 20% is interpreted to mean that the width of the 95% confidence interval is 20% of the mean and the corresponding standard deviation computed as (20/2)/1.96 is almost identical to that in the 5% (5PC) row. The data in each row shows the p-values of two hypothesis tests labeled as WITH and WITHOUT. The WITH column shows p-values when the AE are included in the balance computation. The WITHOUT column shows the p-values when the AE are left out of the balance computation.
  2. We use a critical p-value of alpha=0.1 for the test of the null hypothesis that Balance=0. Balance=0 means that the stochastic flow account is in balance. If the p-value is less than apha we reject the null hypothesis and conclude that the stochastic flow account is not in balance. If we fail to reject the null then we conclude the stochastic flow account is in balance.
  3. The p-values for WITH and WITHOUT in each row taken together tell us whether the stochastic flow system is sensitive to AE, that is whether the relatively small AE flow can be detected in the context of uncertainty in much larger natural flows. If we fail to reject the null hypothesis that Balance=0 in both WITH and WITHOUT columns, the stochastic flow account balances with and without the AE flows. In these cases the stochastic flow account is not sensitive to AE, that is it is unable to detect the presence of the AE flows. This is true for the five rows in which the uncertainty in natural flows is 3% of mean or higher.
  4. For the two lower uncertainty levels of 2% and 1% we find that the null hypothesis Balance=0 is not rejected when AE are included (the stochastic flow account is in balance) but rejected when AE are not included (the stochastic flow account is not in balance). Under these uncertainty conditions, the stochastic flow account is sensitive to the presence of AE, that is the flow account can detect the presence of the relatively small AE flows. The chart shows that the crossover uncertainty lies somewhere between 2% and 3% and in fact it is found by trial and error that the crossover occurs at 2.3%.
  5. These results imply that the IPCC carbon cycle stochastic flow balance is not sensitive to the presence of the relatively low flows from human activity involving fossil fuel emissions and land use change. The large natural flows of the carbon cycle cannot be directly measured and they can only be indirectly inferred. These inferred values contain uncertainties much larger than 2.3% of the mean. It is not possible to carry out a balance of the carbon cycle under these conditions.
  6. In the case of the conclusion by climate scientists that the observed increase in atmospheric CO2 concentration is caused by fossil fuel emissions, natural flows in the carbon cycle that are an order of magnitude larger than fossil fuel emissions and that cannot be directly measured are inferred with the implicit assumption that the increase in atmospheric CO2 comes from fossil fuel emissions. The flow balance can then be carried out and it does of course show that the increase in atmospheric CO2 derives from fossil fuel emissions The balance presented by the IPCC with inferred flows thus forces an exact balance by way of circular reasoning. Therefore, the IPCC carbon cycle balance does not contain useful information that may be used to ascertain the impact of fossil fuel emissions on the carbon cycle or on the climate system.
  7. 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] . The essential argument is that, in the context of significant geological flows of carbon dioxide and other carbon based compounds, it is a form of circular reasoning to describe changes in atmospheric CO2 only in terms of human activity. It is shown in a related post, that in the context of large uncertainties in natural flows, changes in atmospheric CO2 is not responsive to the rate of emissions [LINK] .
  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.

 

SphericalCow2

 

The following posts on this site are relevant to this discussion:

  1. Fossil Fuel Emissions and Atmospheric Composition
  2. Will Emission Reduction Change the Rate of Warming?
  3. Ocean Acidification by Fossil Fuel Emissions
  4. Spurious Correlations in Climate Science

 

 

 

 

THE GLOBAL CARBON PROJECT

bandicam 2018-06-26 09-38-07-779

  1. The Global Carbon Project, with a goal to “fully understand the carbon cycle” has instead evolved into the world’s foremost and most trusted accountants of fossil fuel emissions. The Project keeps records of fossil fuel emissions the world over on a country by country and year by year basis and these data are made publicly available and also analyzed for trends as well as implications for future climate change scenarios. According to Wikipedia, “The Global Carbon Project (GCP) was established in 2001. The organisation seeks to quantify global carbon emissions and their sources.”
  2. Their pretension to the study of the carbon cycle is not as useful as their emissions data because it is presented completely in the context of circular reasoning. Observed changes of CO2 concentration in natural systems are assumed to derive wholly from fossil fuel and land use change. The carbon cycle accounting is carried out on that basis. Uncertainties are noted but ignored in the accounting calculations. The procedure is described below for the decade 2008-2017.
  3. The average annual carbon cycle for the decade 2008-2017 is presented here as an example of the reliance of the Global Carbon Project on circular reasoning. The flows used in the flow accounting are provided by the Global Carbon Project as GTY of carbon dioxide. They have been converted to GTY of carbon (GTCY) for ease of comparison with the IPCC figures above.
  4. The use of net flows (net flow to land sink and net flow to ocean sink) insert assumptions and circular reasoning into the carbon cycle flow account. These are differences between very large flows in the order of 100 GTY with large uncertainties in their measurement. These differences therefore contain even larger uncertainties.
  5. In cases where a net flow is a direct measurement, it’s interpretation subsumes that which the flow account is carried out to determine. For example, if the net flow to ocean sink is measured as a change in the average inorganic carbon concentration of the oceans, then the flow account assumes that the change was caused by surface phenomena such as fossil fuel emissions ignoring emissions from plate tectonics, submarine volcanoes, hydrothermal vents, and hydrocarbon seeps.
  6. Carbon cycle flow accounts of this nature are not pure data that can be used to test theory but rather the theory itself expressed in terms of flow account values.

Fossil fuel emissions: mean=9.3545, stdev=0.5454 [IPCC 2000-2009: 7.8]

Land use change emissions: mean=1.485, stdev=0.818 [IPCC 2000-2009: 1.1]

Net flow to land sink: mean=3.054, stdev=0.818 [IPCC 2000-2009: 4.3]

Net flow to ocean sink: mean=2.37, stdev=0.545 [IPCC 2000-2009: 1.6]

Growth in atmospheric CO2: mean=4.718. stdev=0.0545 [IPCC 2000-2009: 4.4]

 

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FIGURE 1: CLIMATE SCIENTISTS

 

 

FIGURE 2: FEAR OF MELTING ICE AND SEA LEVEL RISE 

 

 

FIGURE 3: SPURIOUS CORRELATIONS

 

 

 

 

[LIST OF POSTS ON THIS SITE]

 

 

  1. DETRENDED CORRELATION ANALYSIS OF TIME SERIES DATA: Correlation between  x and y in time series data derive from responsiveness of y to x at the time scale of interest and also from shared long term trends. These two effects can be separated by detrending both time series as explained by Alex Tolley in the video frame of Figure 3. When the trend effect is removed only the responsiveness of y to x remains. This is why detrended correlation is a better measure of responsiveness than source data correlation as explained very well by Alex Tolley in the video. The full video may be viewed on Youtube [LINK] . That spurious correlations can be found in time series data when detrended analysis is not used is demonstrated with examples at the Tyler Vigen Spurious Correlation website [LINK] . Spurious correlations are common in climate science where many critical relationships that support the fundamentals of anthropogenic global warming (AGW) are found to  be based on spurious correlations.
  2. EXAMPLE 1:  For example, climate science assumes that changes in atmospheric CO2 concentration since pre-industrial times are due to fossil fuel emissions of the industrial economy. This attribution is supported by a strong correlation between the rate of emissions and the rate of increase in atmospheric CO2 concentration in the time series of the source data. However, when the two time series are detrended, the correlation is not found. This result of detrended correlation analysis implies that the correlation seen in the source data derives from shared trends and not from responsiveness at an annual time scale. Details of this test are presented in a related post  [LINK] .
  3. EXAMPLE 2: A similar relationship is found in the ocean acidification hypothesis which claims that changes in the inorganic carbon concentration of oceans are driven by fossil fuel emissions. There, too the source data do show a strong correlation but that correlation vanishes when the two time series are detrended. As before, this pattern implies that the correlation in the source data derives from shared trends and not from responsiveness at an annual time scale. [LINK] .
  4. EXAMPLE 3: It is claimed that the observed rise in atmospheric methane concentration is due to human caused methane emissions in activities such as cattle ranching and dairy farming as well as rice cultivation and oil and gas production. Here too, a strong correlation is found in the time series of the source data but this correlation does not survive into the detrended series. This result implies that the correlation between human caused methane emissions and the rise in atmospheric methane derives from shared trends and not from responsiveness at an annual time scale. Such responsiveness is a necessary, though not sufficient, condition for causation.  Details of this work may be found in a related post at this site [LINK] .
  5. EXAMPLE 4: A cornerstone of climate science is the effectiveness of proposed climate action in the form of reducing fossil fuel emissions. That the rate of warming can be attenuated by reducing fossil fuel emissions requires that the rate of warming must be responsive to the rate of emissions at the appropriate time scale for this causation to occur (thought to be a decade or perhaps longer (Ricke&Caldeira 2014). And in fact, we find a strong correlation between the rate of warming and the rate of emissions in the time series of the source data at five different time scales (10, 15, 20, 25, & 30 years). Both of these source time series show an upward trend such that the shared trend can create spurious correlations as in the Alex Tolley lecture. When the two time series are detrended, the correlation disappears. The absence of detrended correlation implies that the observed correlation was a faux relationship driven by shared trends and not by responsiveness at the time scales tested in the analysis as demonstrated in a related post [LINK] . Thus no evidence is found in the data that reducing emissions will slow down the rate of warming.
  6. EXAMPLE 5: It is also claimed in climate science that reducing emissions will slow down the rate of sea level rise. This relationship requires a responsiveness of the rate of sea level rise to the rate of emissions at the appropriate time scale for this causation. And in fact, we find a strong correlation between the rate of sea level rise and the rate of emissions in the time series of the source data at five different time scales ranging from 30 to 50 years. Both of these source time series show an upward trend such that the shared trend can create a faux correlation. When the two time series are detrended, the correlation disappears. The absence of detrended correlation implies that the observed correlation was a spurious relationship driven by shared trends and not by responsiveness at the time scales tested in the analysis. This work may be found in a related post [LINK] .
  7. EXAMPLE 6: Climate science supports the greenhouse gas heat trapping theory of atmospheric CO2 and the relevance of their climate models with a strong correlation between model projections of surface temperature and actual observations (see for example Santer 2019). However, this correlation is also between two time series with rising trends. In a related post it is shown that there is indeed a strong correlation between the source data but this correlation is not found in the detrended series [LINK]
  8. EXAMPLE 7: Arctic sea ice extent has played an important role in climate change fear based activism because of periods of diminishing summer minimum sea ice extent in September and the forecasts of “ice free Arctic” that these trends have engendered. The underlying fear of human caused climate change causing Arctic sea ice melt was thus created. The evidence for the causal connection for this causation is a correlation between the rate of warming and the rate of summer sea ice decline; but detrended correlation analysis shows that this correlation is spurious as no year to year responsiveness of September Arctic sea ice extent to the rate of warming is found in the detrended series [LINK] .
  9. EXAMPLE 8: With the assumption that the observed rise in atmospheric CO2 concentration is driven by fossil fuel emissions (discussed in example 1) the effect of higher atmospheric CO2 concentration on climate is then established in terms of climate sensitivity, that is the responsiveness of surface temperature to the logarithm of atmospheric CO2 concentration. The validity of the climate sensitivity function can be shown with strong and statistically significant correlations between the climate model temperature series and observations. However, as shown in a related post [LINK] , this correlation does not survive into the detrended series and is therefore a spurious correlation, similar to the Tyler Vigen examples, that derives from shared trends and not from responsiveness at an annual or other fixed and finite time scale.
  10. EXAMPLE 9: The theory of the greenhouse gas effect of atmospheric CO2 predicts that as the CO2 concentration rises, it will cause tropospheric temperatures to rise and at the same time will cause lower stratospheric temperatures to fall. Thus we expect that that the lower stratospheric temperature will be responsive to mid-tropospheric temperature at an annual time scale and climate scientists claim that this is exactly what we find in the observational data. The evidence presented is a strong correlation between tropospheric temperature and lower stratospheric temperature. However, detrended correlation shows that this correlation derives from shared trends and not from a responsiveness of lower stratospheric temperature to mid tropospheric temperature at an annual time scale. The details of this analysis is described in a related post  [LINK] .
  11. EXAMPLE 10:  An additional argument for the attribution of increases in atmospheric CO2 to fossil fuel emissions is presented by climate science in terms of the observed dilution of the 14C isotope fraction of carbon in atmospheric CO2. It is claimed that this dilution proves that fossil fuel emissions accumulate in the atmosphere because fossil fuel carbon is known to contain low or no 14C having been dead and underground for millions of years. A test of this hypothesis shows that the correlation presented by climate science as empirical evidence in support of this theory is spurious. Details in a related post [LINK] .
  12. MOVING AVERAGES AND OTHER PRE-PROCESSED TIME SERIES DATA. When moving averages or moving sums of a time series are used to construct a derived time series, care must be taken to correct for the effective sample size (EFFN) in hypothesis tests because multiplicity (the use of the same data point more than once) reduces the effective sample size. When the reduction in degrees of freedom is not taken into account faux statistical significance can lead to spurious findings . This issue is discussed in some detail in a related post [LINK] and an example of this statistical error in climate science is presented in another related post [LINK] .
  13. A TIME SERIES OF THE CUMULATIVE VALUES OF ANOTHER TIME SERIES: An extreme case of such multiplicity is the construction of a time series of the cumulative values of another time series. In these cases it can be shown that the effective sample size is always EFFN=2 so that the degrees of freedom in hypothesis tests is DF=0. This relationship is described in an online paper [LINK] with the relevant text reproduced in paragraph#8 below. It should also be noted that the time series of the cumulative values of another time series does not contain a time scale. Thus, without either time scale or degrees of freedom, it is not possible to test for the statistical significance of any statistic for a time series of the cumulative values of another time series. The spuriousness of such correlations is demonstrated with Monte Carlo simulation in paragraph#9 below.
  14. EFFECTIVE SAMPLE SIZE OF THE CUMULATIVE VALUES OF A TIME SERIES. If the summation starts at K=2, series cumulative values of a time series X of length N is computed as Σ(X1 to X2), Σ(X1 to X3), Σ(X1 to X4), Σ(X1 to X5) … Σ(X1 to XN-3), Σ(X1 to XN-2), Σ(X1 to XN-1), Σ(X1 to XN). In these N-K+1 cumulative values, XN is used once, XN-1 is used twice, XN-2 is used three times, XN-3 is used four times, X4 is used N-3 times, X3 is used N-2 times, X2 is used N-1 times , X1 is used N-1 times. In general, each of the first K data items will be used N-K+1 times. Thus, the sum of the multiples for the first K data items may be expressed as K*(N-K+1). The multiplicities of the remaining N-K data items form a sequence of integers from one to N-K and their sum is (N-K)*(N-K+1)/2. The average multiplicity of the N data items in the computation of cumulative values may be expressed as AVERAGE-MULTIPLE = [(K*(N-K+1) + (N-K)*(N-K+1)/2]/N. Since multiplicity of use reduces the effective value of the sample size we can express the effective sample size as: EffectiveN = N/(AVERAGE-MULTIPLE) = N2/(K*(N-K+1) + (N-K)*(N-K+1)/2). To be able to determine the statistical significance of the correlation coefficient it is necessary that the degrees of freedom (DF) computed as effectiveN -2 should be a positive integer. This condition is not possible for a sequence of cumulative values that begins with Σ(X1 to X2). Effective-N can be increased to values higher than two only by beginning the cumulative series at a later point K>2 in the time series so that the first summation is Σ(X1 to XK) where K>2. In that case, the total multiplicity is reduced and this reduction increases the value of effectiveN somewhat but not enough to reach values much greater than two.
  15. MONTE CARLO SIMULATION OF SPURIOUS CORRELATION BETWEEN CUMULATIVE VALUES OF TIME SERIES DATA
  16. EXAMPLE 1: An example of the use of cumulative values in climate science is the so called TCRE or Transient Climate Response to Cumulative Emissions. It is the correlation between cumulative emissions and cumulative warming (note that temperature = cumulative warming). This relationship shows a nearly perfect proportionality that is thought to provide convincing evidence of a causal relationship between emissions and temperature and provides a convenient metric for the computation of the so called remaining “carbon budget”, that is the amount of additional emissions possible for a given constraint on the amount of warming. The spuriousness of the TCRE proportionality is described in a related post on this site [LINK] and its spuriousness is further supported with a parody of the procedure that shows that UFO visitations are the real cause of global warming [LINK] . A related post shows that when a finite time scale is inserted into the TCRE, the correlation disappears [LINK] .
  17. EXAMPLE 2: A paper by Peter Clark of Oregon State University extended the TCRE methodology to sea level rise to provide empirical evidence that fossil fuel emissions cause sea level rise and that climate action in the form of reducing fossil fuel emissions should moderate the rate of sea level rise. (Clark, Peter U., et al. “Sea-level commitment as a gauge for climate policy” Nature Climate Change 8.8 2018: 653). In a related post on this site it is shown that this correlation is spurious [LINK] . In another, we show that when finite time scales are inserted so that both time scale and degrees of freedom are available for carrying out hypothesis tests, the correlation seen in the cumulative series is not found [LINK] .
  18. EXAMPLE 3: It is claimed that a correlation between cumulative values provides evidence that the decay in atmospheric 13C/12C isotope ratio is related to fossil fuel emissions and proves that the observed increase in atmospheric CO2 is driven by fossil fuel emissions. This claim and spurious correlation are addressed in a related post [LINK] .
  19. EXAMPLE 4: Climate science claims that dilution of the 13C isotope of carbon in atmospheric CO2 provides evidence that the observed increase in atmospheric CO2 concentration is caused by fossil fuel emissions. A strong correlation is presented as evidence but the correlation is between cumulative values and therefore spurious. When that error is corrected, no correlation is found [LINK] .
  20. THE INTERPRETATION OF VARIANCE IN CLIMATE SCIENCE STATISTICS. A related issue in statistical analysis methods of climate scientists is the way variance is interpreted. In statistics and also in information theory, high variance implies low information content. In other words, the higher the variance the less we know. In this context high variance is undesirable because it degrades the information we can derive from the data. However, high variance also yields large confidence intervals making it possible for high variance to be interpreted not as absence of information but as information about a danger of how extreme it could be. This interpretation of variance is common in climate science. In conjunction with the precautionary principle, it leads to a perverse interpretation of uncertainty such that uncertainty about the mean becomes transformed into certainty of extreme values. For example if the mean value of empirical climate sensitivity is found to have no statistical significance because of a large variance over a range of λ=2 to λ=6, the conclusion drawn by climate science from these data is not that we don’t really know what the value of λ is or even whether this concept can be verified with empirical evidence, but an obsession with the high value of λ=6 along with the alarming fear of the highest possible value in a range that actually implies that we don’t know. This interpretation of variance is aided by the use of the precautionary principle which holds that if a possible value of something that is harmful is high it is better to take precaution against that possibility than to interpret the data in a strictly rational way. In other words, the less you know the more extreme it COULD be and this use of the word “could” is common in climate science in the use of ignorance in the form of high variance to create fear.
  21. THE USE OF CIRCULAR REASONING IN CLIMATE SCIENCE STATISTICS:  In carrying out the flow accounting of the carbon cycle as a way of determining the effect of carbon in fossil fuel emissions on the carbon cycle, climate science is faced with the impossibility of measuring the much larger flows of carbon to and from the atmosphere in the carbon cycle. This difficulty is overcome by using the time series of atmospheric CO2 concentration from the Mauna Loa observatory that shows atmospheric CO2 concentration rising over time. By attributing the changes in atmospheric CO2 to fossil fuel emissions, a flow account of the unmeasurable carbon cycle can be inferred. The inferred flow account is then used to determine that the observed rise in atmospheric CO2 concentration is explained in terms of fossil fuel emissions.  This issue is presented in detain in two related posts [LINK] [LINK] .

 

 

 

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Figure 1: Screen grab from the Youtube video “Forklift causes whole warehouse to collapse”. An example of dependence, nonlinear dynamics and chaos.  Link to video: https://www.youtube.com/watch?v=XqmDRRZjaeU

 

 

 

Figure 2: Demonstration of chaotic behavior of Hurst persistence in time series data

 

 

  1. The video in Figure 2 plots the same time series twice – in red and in blue. In the red line, the events in the time series evolve as a deterministic system; independently with no dependence or persistence. Such independence is an important assumption in OLS (ordinary least squares) regression. In the blue line the events in the time series evolve as a chaotic system with dependence and persistence. A small dependence has been inserted as a 1% tendency for persistence. Without persistence rising and falling tendencies are always equal with 50% probability at each event. The result of that is seen in the red line where the ups and downs are not visible because they are small.
  2. The 1% persistence in the blue line means that if the prior change was positive, the probabilities change from 50%/50% to 51%/49% favoring a positive change for the next event. If it is positive again in the next event, the probabilities are changed to 52%/48% but if it is negative they change back to 50%/50%. The probabilities keep changing to favor the direction of change in the prior event. This behavior is called persistence and it is very common in nature, particularly in surface temperature.
  3. When you play the video you will see the blue line take various shapes and trends both rising and falling mostly staying very close to the Gaussian red line. BUT, it is capable of sudden departures from the Gaussian to form what appears to be patterns such as rising and falling trends. These shapes do not imply cause and effect phenomena. They are the random behavior of a chaotic system.
  4. All of these shapes are representations of the same underlying phenomenon. The differences among these curves have no interpretation because they represent randomness. The human instinct to look for causes for unusual patterns derives from Darwinian survival but it leads us astray when we study chaotic systems.
  5. The shapes and trends the blue line forms may be found to be statistically significant if the system is assumed to be deterministic and the violations of OLS assumptions are ignored; but that statistical significance is meaningless. Yet this kind of analysis is common. The conclusions they imply in terms of the phenomena of nature have no interpretation because they are the product of violations of assumptions.
  6. Surface temperature and other climate variables are known to be chaotic and therefore, not all observed patterns in climate data contain information about cause and effect phenomena.That nature is chaotic is well understood. These three books present that argument with data, examples, and case studies: EA Jackson, Exploring Nature’s Dynamics; C Letellier, Chaos in Nature; Cushing et al, Chaos in Ecology.
  7. References to chaos in nature are also found in the climate change literature. These two excellent papers by Timothy Palmer are a good introduction to the subject of chaos in climate data: “A nonlinear dynamical perspective on climate prediction” Journal of Climate 12.2 (1999): 575-591, and “Predicting uncertainty in forecasts of weather and climate” Reports on Progress in Physics 63.2 (2000): 71.
  8. A common method of detecting fractal/chaotic behavior in long time series of field data is to compute the so called Hurst exponent “H” of the time series as a way of detecting persistence in the data.  Persistence implies that the the data in the time series do not evolve independently but contain a dependence on prior values such that prior changes tend to persist into the next time slice. This method was first described by Harold Edwin Hurst in 1951 in the only paper he ever published which is still cited hundreds of times a year every year ( Hurst, H.E. (1951). “Long-term storage capacity of reservoirs”. Transactions of American Society of Civil Engineers).
  9. The Hurst exponent method of detecting non-linear dynamics in time series data is used in climate change research. This trend in climate science has been led by the very charismatic and controversial hydrologist Demetris Koutsoyiannis, Professor of Hydrology, National Technical University of Athens. Demetris finds that many hydrology time series behaviors that climate science ascribes to emissions can be explained in terms of non-linear dynamics.
  10. Here are some examples from the literature of  Hurst persistence analysis of climate data: Cohn, Timothy etal “Nature’s style: Naturally trendy.” Geophysical Research Letters 32.23 (2005) (by “naturally trendy” Tim means that things that look like trends are actually randomness); Weber, Rudolf etal. “Spectra and correlations of climate data from days to decades.” Journal of Geophysical Research: Atmospheres 106.D17 (2001): 20131-20144; Koutsoyiannis, Demetris. “Climate change, the Hurst phenomenon, and hydrological statistics.” Hydrological Sciences Journal 48.1 (2003): 3-24; Markonis, Yannis, “Climatic variability over time scales spanning nine orders of magnitude: Connecting Milankovitch cycles with Hurst–Kolmogorov dynamics.” Surveys in Geophysics 34.2 (2013): 181-207; Pelletier, Jon etal. “Long-range persistence in climatological and hydrological time series: analysis, modeling and application to drought hazard assessment.” Journal of Hydrology 203.1-4 (1997): 198-208; Rybski, Diego, et al. “Long‐term persistence in climate and the detection problem.” Geophysical Research Letters 33.6 (2006).
  11. Much of the empirical work in climate science is presented in terms of time series of field data (field data means data made by nature over which the researcher has no control – as opposed to data collected in controlled experiments). Information about climate contained in these data is usually gathered by researchers in terms of OLS (ordinary least squares) regression analysis. Surprisingly, even at the highest levels of climate research little or no attention is paid to the assumptions of OLS analysis which include for example the assumption that the data in the time series evolve as I.I.D, (independent identically distributed). The stationarity assumption further enforces the requirement that the distribution must not change as the time series evolves.
  12. More information on regression analysis may be found in a companion post on SPURIOUS CORRELATIONS. A good reference for regression analysis of time series data is “Time Series Analysis: Forecasting and Control, Wiley 2015, by George Box & Gwilym Jenkins. Another is Time Series Analysis and Its Applications with Examples, Springer 2017, by Robert Shumway. These authors have shown that when time series analysis goes awry you can bet it has to do with violations of OLS assumptions.
  13. In my work with temperature, sea level rise, precipitation, solar activity, and ozone depletion, I tested the time series for OLS violations by computing the Hurst exponent H. The theoretical neutral value with no serial dependence is H=0.5 but it has been shown that the neutral value for comparison in empirical research needs to be adjusted for the specific sub-sampling strategy used in the estimation of H. In all my work, this estimation is carried out twice – once with the data and again with a Monte Carlo simulation of the data that generates a corresponding IID/stationary series.
  14. The two values of H are then compared. If the difference between the two values of H is not statistically significant, we conclude that there is no evidence of Hurst behavior in the data and OLS regression results may therefore be interpreted in terms of the phenomena under study. However if the value of H in the data is greater than the value of H in the IID simulation, then we can conclude that the data contain Fractal/Chaotic behavior by virtue of Hurst persistence and that therefore OLS results may not be interpreted strictly in terms of the phenomena under study. Nonlinear dynamics must be considered. A list of these studies appears below with links to the freely downloadable full text.
  1. Ozone Depletion Latitudinally Weighted Mean Global Ozone 1979-2015
  2. Warming Seasonality and Dependence in Daily Mean USCRN Temperature 
  3. Precipitation The Hurst Exponent of Precipitation
  4. Warming A Robust Test for OLS Trends in Daily Temperature Data
  5. Solar Activity The Hurst Exponent of Sunspot Counts
  6. Warming The OLS Warming Trend at Nuuk, Greenland
  7. Warming The Hurst Exponent of Surface Temperature
  8. Warming OLS Trend Analysis of CET Daily Mean Temperatures 1772-2016
  9. Precipitation The Hurst Exponent of Precipitation: England and Wales 1766-2016

 

 

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greenhouseTasmania

  1. Before it was expropriated by the global warming/climate change movement, the term “Greenhouse Effect” referred to the effect of elevated carbon dioxide in greenhouses on crop chemistry. We know from greenhouse studies going back to the late 19th century that crop chemistry reflects the balance between soil chemistry, air chemistry, and light intensity. The important features of air chemistry are the availability of carbon dioxide for photosynthesis and of oxygen for plant respiration. The important features of soil chemistry are the availability of water, nitrates, phosphates, and minerals.
  2. Climate science is apparently concerned about the effect of elevated atmospheric CO2 on agriculture. Initially it was assessed that the effects of climate change would devastate agriculture but later the concern shifted to the effect of elevated atmospheric CO2 on the nutritional quality of crops. These concerns appear to be disconnected from the extensive literature on elevated CO2 agriculture in greenhouses that have been with us for more than a century.
  3. Greenhouse operations include irrigation, air circulation to maintain air quality, heating for temperature control, the introduction of carbon dioxide to maintain elevated carbon dioxide levels of 1000 to 2000 parts per million for photosynthesis enrichment, and the availability of sufficient light for photosynthesis to occur. Photosynthesis enrichment improves crop yield. Corresponding changes to soil chemistry are required to preserve the nutritional quality of the crops.
  4. It has been found in numerous greenhouse studies since the 19th century that if elevated carbon dioxide is not matched by corresponding changes to soil chemistry, crop chemistry may shift in the direction of higher starch content and lower nutritional quality. These effects are crop specific and vary greatly among crop types.
  5. Proper greenhouse management is responsive to these dynamics and involves the management of light and soil chemistry that is appropriate for any given level of carbon dioxide so that crop nutritional quality is maintained. These relationships are described in some detail in the Stitt&Krapp1999 paper listed below and highlighted in bold.
  6. The various works of Bruce Kimball of the US Water Conservation Laboratory (with full text available free from the USDA) are unique in this line of research as they are not greenhouse studies but a survey of a large number of such studies carried out to estimate the impact of climate change on crop yield.
  7. His work followed in the heels of the landmark “Climate Sensitivity” presentation made by Jule Charney in 1979 in which he presented the finding from climate model studies that a doubling of atmospheric carbon dioxide will cause mean global temperature to rise by 1.5C to 4.5C. The Charney Climate Sensitivity still serves as the fundamental relationship in climate science for the “greenhouse warming effect” thought to be caused by atmospheric carbon dioxide.
  8. Kimball followed the Charney format and presented his finding that  a doubling of atmospheric carbon dioxide will increase crop yields worldwide by about 30% with some differences among crops and for different conditions and latitudes. The relevant citations appear below.
  9. Kimball, Bruce A. “Carbon Dioxide and Agricultural Yield: An Assemblage and Analysis of 430 Prior Observations 1.” Agronomy journal 75.5 (1983): 779-788.
  10. Kimball, B. A., and S. B. Idso. “Increasing atmospheric CO2: effects on crop yield, water use and climate.” Agricultural water management 7.1-3 (1983): 55-72.
  11. Kimball, B. A., et al. “Effects of increasing atmospheric CO2 on vegetation.” CO2 and Biosphere. Springer, Dordrecht, 1993. 65-76.
  12. Mauney and Kimball. “Growth and yield of cotton in response to a free-air carbon dioxide enrichment (FACE) environment.” Agricultural and Forest Meteorology 70.1-4 (1994): 49-67.
  13. Kimball, Bruce A., et al. “Productivity and water use of wheat under free‐air CO2 enrichment.” Global Change Biology 1.6 (1995): 429-442.
  14. Kimball, B. A., K. Kobayashi, and M. Bindi. “Responses of agricultural crops to free-air CO2 enrichment.” Advances in agronomy. Vol. 77. Academic Press, 2002. 293-368.
  15. Idso and Kimball. “Effects of atmospheric CO2 enrichment on plant growth: the role of air temperature.” Agriculture, ecosystems & environment 20.1 (1987): 1-10.
  16. The findings of a selection of GREENHOUSE STUDIES from Besford 1990 to Galtier 1995 presenting measurements of nutritional loss due to an imbalance in CO2 and soil nutrients are listed below. The greenhouse management implications of these findings are described best in the Stitt and Krapp 1999 paper.
  17. RT Besford, et al 1990, Journal of Experimental Botany 41.8: 925-931: Compared with tomato plants grown in normal ambient CO2, the 1000 ppm CO2 grown leaves, when almost fully expanded, contained only half as much RuBPco protein. Note: corresponding soil enrichment was not used.
  18. Peter Curtis et al, 1998Oecologia 113.3: 299-313: Total biomass and net CO2 assimilation increased significantly at about twice ambient CO2, regardless of growth conditions. Low soil nutrient availability reduced the CO2 stimulation of total biomass by half, from +31% under optimal conditions to +16%, while low light increased the difference to +52%.
  19. Kramer, Paul J. 1981BioScience 31.1: 29-33: The long-term response to high CO2 varies widely among species. Furthermore, the rate of photosynthesis is limited by various internal and environmental factors in addition to the COconc.
  20. Curtis, P. S. 1996Plant, Cell & Environment 19.2: 127-137: Growth at elevated [CO2] resulted in moderate reductions in gs in unstressed plants, but there was no significant effect of CO2 on gs in stressed plants. Leaf dark respiration (mass or area basis) was reduced strongly by growth at high [CO2] > while leaf N was reduced only when expressed on a mass basis.
  21. Shahidul Islam et al, 1996Scientia Horticulturae 137-149: CO2 enriched tomatoes had lower amounts of citric, malic and oxalic acids, and higher amounts of ascorbic acid, fructose, glucose and sucrose synthase activity than the control. Elevated CO2 enhanced fruit growth and colouring during development.
  22. Stitt & Krapp 1999Plant, Cell & Environment 22.6-583-621: Increased rates of growth in elevated [CO2] will require higher rates of inorganic nitrogen uptake and assimilation. An increased supply of sugars can increase the rates of nitrate and ammonium uptake and assimilation, the synthesis of organic acid acceptors, and the synthesis of amino acids. Interpretation of experiments in elevated [CO2] requires that the nitrogen status of the plants is monitored.
  23. Galtier, Nathalie, et al. 1995Journal of Experimental Botany 1335-1344:  At elevated CO2, the rate of sucrose synthesis was increased relative to that of starch and sucrose/starch ratios were higher throughout the photoperiod in the leaves of all plants expressing high SPS activity. At high C02 the stimulation of photosynthesis was more pronounced. We conclude that SPS activity is a major point of control of photosynthesis particularly under saturating light and C02.

 

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The eco scare that human activity is killing off the fish in the oceans predates climate change. In the BC days (before-climate), a combination of over-fishing, seafaring, and discharges of plastics and pollution into the oceans by humans were cited (“Sea’s riches running out”, 1977). In AC times (after climate) there is of course only one cause for all things and that is human caused global warming by way of fossil fuel emissions (Oceans running out of fish, Bangkok Post, 1994), (Ocean’s fish could disappear, Bangkok Post, May 19, 2010), (A New Warning Says We Could Run Out of Fish by 2048, HuffPost, Dec 14, 2017), (All seafood will run out in 2050, say scientists, The Telegraph, 22 May 2018), (Oceans are running out of fish much faster than previously thought, ZME Science, 20 January 2016).

 

In the AC after-climate era, causes of the fish apocalypse is described in terms of rising ocean temperature and ocean acidification by fossil fuel emissions. As well, the language of fish apocalypse is changed from gradual reduction in numbers to “depletion at alarming rates” and that marine life on earth is “at a breaking point”. There is also a timeline given for when the oceans will become devoid of fish. That will happen in the year 2050. Unless of course we get serious about the Paris Accord, stop using dirty polluting fossil fuels, and save the planet. And the fish.

bandicam 2018-08-14 18-42-38-365

THE DEARTH OF SCIENTIFIC KNOWLEDGE ONLY ADDS TO THE ALARM

  1. Global warming scientists cited the shrinking of the Chorabari Glacier in the eastern Himalayan Mountains as evidence that carbon dioxide emissions from fossil fuels is causing global warming and that global warming in turn is causing Himalayan glaciers to melt. Although the data are insufficient and conflicting, they project that in a hundred years, the glacial loss will affect water supply to a vast region whose rivers get their water from these glaciers. With respect to the absence of sufficient data to support this projection, they propose the odd logic that “the dearth of scientific knowledge only adds to the alarm”.
  2. There are a thousand glaciers in the Himalayan Mountains. Some of them are retreating. Some of them are expanding. Some are doing neither. We don’t have sufficient data to know what most of them are doing except that there has been a gradual net retreat of the glaciers since the year 1850 which marks the glacial maximum of the Little Ice Age.
  3. The Himalayans are folded mountains and the folding is currently in process. It is a geologically active area. There is a lot of geothermal activity in these mountains particularly in Uttaranchal where Chorabari Glacier is located. Steamy hot springs are a major tourist attraction in Uttaranchal.
  4. Neither the geothermal nor volcanic activity is included in the assessment of glacial melt as an effect of fossil fuel emissions. The assessment is that the end is near for Himalayan glaciers due to fossil fuel emissions. The end may very well be near but the prediction of its coming would be more credible if their computer model included volcanic and geothermal activity both on land and in the bottom of the ocean.
  5. A computer model based on the assumption that all surface anomalies of the planet are due to human activity is not the appropriate tool for the determination of the role of human activity in surface anomalies.

 

FOSSIL FUEL EMISSIONS MELTING ICE IN GREENLAND

  1. Although there has been some thinning of coastal ice in Greenland, the total ice mass there is actually increasing because of a rapid increase in ice thickness at higher elevations. If we could cause all of Greenland’s ice to melt into the sea, it would raise the sea level by 7 meters, as the scaremongers say, but that scenario does not appear likely given the data.
  2. One should also take note that during the last decade, Greenland has not become warmer. It has become colder. It is therefore not possible to ascribe changes in its ice mass to global warming or to fossil fuel emissions. As a footnote, Greenland’s coast was in fact green with vegetation in the tenth century when it was discovered by Nordic sailors. It was warmer then than it is now.
  3. Since then it has been through the Little Ice Age from which it is currently recovering. Studies of ice mass balance in Greenland by climate scientists ignore geothermal activity and begin with the assumption that all observed ice loss are due to fossil fuel emissions and that they can be attenuated by taking climate action in the form of cutting emissions.

 

FOSSIL FUEL EMISSIONS CAUSE DROUGHT IN AFRICA

  1. Africa is a drought prone continent and has suffered numerous tragic droughts over the last 500 years. These droughts are natural occurrences. They are not caused by carbon dioxide emissions from fossil fuels. There is no trend in the severity of these droughts and the current one is not the most severe. African scholars have written to refute efforts to associate the current drought with the global warming agenda. One of these scholarly articles was recently published in the Bangkok Post.
  2. The dropping of water levels in Lake Victoria and other lakes there is a known effect of a cascade of dams on the Nile and cannot in any way be related to the use of fossil fuels.
  3. The New York Times columnist who makes these alarming charges is the same individual who once fell for the oldest trick in book in Cambodian brothels and paid a large sum of money to “purchase freedom” for a young prostitute and then wrote a column about his heroic deed. The young lady had by then returned to the brothel. This is the level of gullibility we are dealing with in this column as well. One should use a big dose of critical thinking when consuming this kind of information.

 

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