Intro by David Linden
What is becoming more and more evident, as the research featured below describes, is that global temperature rises precede rises in CO2. In other words, rises in CO2 do not cause rising temperatures but rising temperatures, through mechanisms that honest scientists do not pretend to understand yet, cause or contribute to rises in CO2 levels.
This fact blows the current climate psyop out of the water.
This doesn’t mean we should not take care of the environment.
It means we need to pick the correct point of address from which improvement can stem and making CO2 the villain of the piece ain’t it.
Another New Paper Shows Temp Changes Before CO2
by Demetris Koutsoyiannis, Christian Onof, Zbigniew Kundzewicz, Antonis Christofides
SOURCE Principia Scientific
This is taken from a very long paper, so we have reproduced the most important parts of it. It will likely be ignored by the mainstream media and politicians. The full paper can be seen via the see more here link
The scientific and wider interest in the relationship between atmospheric temperature (T) and concentration of carbon dioxide ([CO2]) has been enormous
According to the commonly assumed causality link, increased [CO2] causes a rise in T.
However, recent developments cast doubts on this assumption by showing that this relationship is of the hen-or-egg type, or even unidirectional but opposite in direction to the commonly assumed one.
These developments include an advanced theoretical framework for testing causality based on the stochastic evaluation of a potentially causal link between two processes via the notion of the impulse response function.
Using, on the one hand, this framework and further expanding it and, on the other hand, the longest available modern time series of globally averaged T and [CO2], we shed light on the potential causality between these two processes.
All evidence resulting from the analyses suggests a unidirectional, potentially causal link with T as the cause and [CO2] as the effect. That link is not represented in climate models, whose outputs are also examined using the same framework, resulting in a link opposite the one found when the real measurements are used.
The mainstream assumption of the causality direction [CO2] → T makes a compelling narrative, as everything is blamed on a single cause, the human CO2 emissions. Indeed, this has been the popular narrative for decades.
However, popularity does not necessarily mean correctness, and here we have provided strong arguments against this assumption.
Since we have identified atmospheric temperature as the cause and atmospheric CO2 concentration as the effect, one may be tempted to ask the question: What is the cause of the modern increase in temperature?
Apparently, this question is much more difficult to reply to, as we can no longer attribute everything to any single agent.
We do not claim to have the answer to this question, whose study is far beyond the article’s scope.
Neither do we believe that mainstream climatic theory, which is focused upon human CO2 emissions as the main cause and regards everything else as feedback of the single main cause, can explain what happened on Earth for 4.5 billion years of changing climate.
Nonetheless, as a side product, in the Appendices to the paper, we provide several indications of the following:
The dependence of the carbon cycle on temperature is quite strong and indeed major increases of [CO2] can emerge as a result of temperature rise. In other words, we show that the natural [CO2] changes due to temperature rise are far larger (by a factor > 3) than human emissions (Appendix A.1).
There are processes, such as the Earth’s albedo (which is changing in time as any other characteristic of the climate system), the El Niño–Southern Oscillation (ENSO) and the ocean heat content in the upper layer (represented by the vertically averaged temperature in the layer 0–100 m), which are potential causes of the temperature increase, unlike what is observed with [CO2], their changes precede those of temperature (Appendix A.2, Appendix A.3 and Appendix A.4).
On a large timescale, the analysis of paleoclimatic data supports the primacy of the causal direction T → [CO2], even though some controversy remains about this issue (Appendix A.5).
In terms of the carbon cycle (point 1 above), several physical, chemical, biochemical and human processes are involved in it. The human CO2 emissions due to the burning of ‘fossil fuels’ have largely increased since the beginning of the industrial age.
However, the global temperature increase began succeeding the Little Ice Period, at a time when human CO2 emissions were very low.
To cast light on the problem, we examine the issue of CO2 emissions vs. atmospheric temperature further in the Supplementary Information, where we provide evidence that they are not correlated with each other.
The outgassing from the sea is also highlighted sometimes in the literature among the climate-related mechanisms. On the other hand, the role of the biosphere and biochemical reactions is often downplayed, along with the existence of complex interactions and feedback.
This role can be summarized in the following points, examined in detail and quantified in Appendix A.1.
Terrestrial and maritime respiration and decay are responsible for the vast majority of CO2 emissions [32], Figure 5.12.
Overall, natural processes of the biosphere contribute 96 percent to the global carbon cycle, the rest, four percent, being human emissions (which were even lower in the past [33]).
The biosphere is more productive at higher temperatures, as the rates of biochemical reactions increase with temperature, which leads to increasing natural CO2 emission [2].
Additionally, a higher atmospheric CO2 concentration makes the biosphere more productive via the so-called carbon fertilization effect, thus resulting in greening of the Earth [34,35], i.e., amplification of the carbon cycle, to which humans also contribute through crops and land-use management [36].
In addition to the biosphere, there are other factors that drive the Earth’s climate in periodic and non-periodic way.
Orbital parameters of Earth’s revolution change quasi-cyclically in a multi-millennial scale (variations in eccentricity, axial tilt, and precession of Earth’s orbit), as interpreted by Milanković [37,38,39,40,41], and changes in the orbit geometry influence the amount of insolation.
The non-periodic drivers of the Earth’s climate variability include volcanic eruptions and collisions with large extraterrestrial objects, e.g., asteroids. An important climate driver is water in its three phases [33].
Another apparent factor is solar activity (including solar cycles) and the solar radiation (im)balance on Earth (e.g., albedo changes; see [33] and Appendix A.2). Notably, a recent study [42], by assessing 20 years of direct observations of energy imbalance from Earth-orbiting satellites, showed that the global changes observed appear largely from reductions in the amount of sunlight scattered by Earth’s atmosphere.
ENSO and ocean heating, both of which affect temperature, are examined in Appendix A.3 and Appendix A.4, respectively. The results of Appendix A.2, Appendix A.3 and Appendix A.4 are summarized in the schematic of Figure 13.
Changes in all three examined processes, albedo, ENSO and the upper ocean heat, precede in time the changes in temperature and even more so those in [CO2]. Generally, the time lags shown in Figure 13 complete a consistent picture of potential causality links among climate processes and always confirm the 𝑇→[CO2] direction.
Figure 13. Schematic of the examined possible causal links in the climatic system, with noted types of potential causality, HOE or unidirectional, and its direction. Other processes, not examined here, could be internal of the climatic system or external.
The examined processes in the Appendices are internal to the climatic system. Other processes affecting T, not examined here, could also be external (e.g., solar and astronomical [43,44] and geological [45,46,47,48,49]).
Generally, in complex systems, an identified causal link, even though it gives some explanation of a phenomenon, raises additional questions, e.g., what caused the change in the identified cause, etc. In turn, causal links in complex systems may form endless sequences.
For this reason, it is naïve to expect complete answers to problems related to complex systems or to assume that a complex system is in permanent equilibrium and that an external agent is needed to “kick” it out of the equilibrium and produce change.
Yet the investigation of a single causal link between two processes, as is the focus of this paper, provides useful information, with possible significant scientific, technical, practical, epistemological and philosophical implications.
These are not covered in this paper. Readers interested in epistemological and philosophical aspects of causality are referred to Koutsoyiannis et al. [6], while those interested in the perennial changes in complex systems are referred to Koutsoyiannis [50,51].
As already clarified, the scope of our work is not to provide detailed modeling of the processes studied but to check causality conditions. We highlight the fact that the relationship we established explains only about 1/3 of the actual variance of Δln[CO2].
This is not negligible for investigating causality, but also leaves a margin for many other climatic factors to act.
Nonetheless, our results can certainly be improved if we change our scope to more detailed modeling. To illustrate this, we provide the following toy model. Based on our result that the T-[CO2] system is potentially causal with direction Δ𝑇→Δln[CO2], we estimate Δln[CO2] as
Δln[CO2]=∑𝑗=020𝑔𝑗Δ𝑇𝜏−𝑗+𝜇𝑣 (8)
and we proceed a step further, assuming that the mean 𝜇𝑣 also depends on past temperature, averaged at timescale m, i.e.,
𝜇𝑣=𝛼(𝑇𝑚−𝑇0)(9)
where 𝑇𝑚 is the average temperature of the previous m years, and 𝛼 and 𝑇0 are constants (parameters). Such a simple linear relationship is supported by the above-listed points related to the productivity of the biosphere. Equation (9) will result in negative values 𝜇𝑣 if 𝑇𝑚<𝑇0 and positive otherwise.
By re-evaluating the IRF coordinates 𝑔𝑗 simultaneously with the parameters of Equation (9), we find the modified version of the IRF plotted in Figure 14. The optimized additional parameters are 𝑚=4 (years), 𝛼=0.0034, 𝑇0=285.84 K.
Similarly to [6], we used a common spreadsheet software solver to perform the optimization, adding the two parameters α and 𝑇0 to the unknown coordinates 𝑔𝑗 of the IRF and performing the (nonlinear) optimization for all unknowns (𝑚 was found by trial-and-error).
A graphical comparison of the actual Δln[CO2] and [CO2] with those simulated by the model of Equations (8) and (9) is given in Figure 15. The explained variance for Δln[CO2] was drastically increased from 34 to 55.5 percent and that for [CO2] is an impressive 99.9 percent.
Figure 14. Modified IRF for temperature–CO2 concentration based on the NCEP/NCAR Reanalysis temperature at 2 m and Mauna Loa time series, respectively, similar to Figure 2 but with IRF coordinates simultaneously optimized with the parameters of Equation (9).
Figure 15. Comparison of the actual Δln[CO2] (upper) and [CO2]
(lower) with those simulated by the model of Equations (8) and (9).
For the convenience of the readers who are interested in repeating the calculations, we also give a parametric expression of IRF and summarize the toy model as:
Δln[CO2]=∑𝑗=020𝑔𝑗Δ𝑇𝜏−𝑗+𝜇𝑣,𝑔𝑗=0.00076 𝑗0.67𝑒−0.2𝑗/K,𝜇𝑣=0.0034 (𝑇4/K−285.84)
(10)
(where K is the unit of kelvin).
We emphasize, however, that we do not exploit the impressive result of explained variance of 99.9 percent to assert that we have built a decent model, even though this toy model is both accurate (in the lower panel of Figure 15, the simulated curve is indistinguishable from the actual) and parsimonious (the model expression in (10) contains only 5 fitted parameters).
We prefer to highlight the fact that the hugely complex climate system entails high uncertainty, and its study needs reliable data that provide the basis for the modeling and testing of hypotheses.
Conclusions
With reference to points 1–7 of the Introduction setting the paper’s scope, the results of our analyses can be summarized as follows.
All evidence resulting from the analyses of the longest available modern time series of atmospheric concentration of [CO2] at Mauna Loa, Hawaii, along with that of globally averaged T, suggests a unidirectional, potentially causal link with T as the cause and [CO2] as the effect.
This direction of causality holds for the entire period covered by the observations (more than 60 years).
Seasonality, as reflected in different phases of [CO2] time series at different latitudes, does not play any role in potential causality, as confirmed by replacing the Mauna Loa [CO2] time series with that in South Pole.
The unidirectional 𝑇→ln[CO2] potential causal link applies to all timescales resolved by the available data, from monthly to about two decades.
The proposed methodology is simple, flexible and effective in disambiguating cases where the type of causality, HOE or unidirectional, is not quite clear.
Furthermore, the methodology defines a type of data analysis that, regardless of the detection of causality per se, assesses modeling performance by comparing observational data with model results. In particular, the analysis of climate model outputs reveals a misrepresentation of the causal link by these models, which suggest a causality direction opposite to the one found when the real measurements are used.
Extensions of the scope of the methodology, i.e., from detecting possible causality to building a more detailed model of stochastic type, are possible, as illustrated by a toy model for the T-[CO2] system, with explained variance of [CO2] reaching an impressive 99.9 percent.
While some of the findings of this study seem counterintuitive or contrary to mainstream opinions, they are logically and computationally supported by arguments and calculations given in the Appendices.
Overall, the stochastic notion of a causal system, based on the concept of the impulse response function, proved to be very effective in studying demanding causality problems.
A crucial characteristic of our methodology is its direct use of the data per se, in contrast with other methodologies that are based on uncertain estimates of autocorrelation functions or on the more uncertain tool of the power spectrum, i.e., the Fourier transform of the autocorrelation function.
The methodology has the potential for further advances, as we first reported here (e.g., the asymmetric time lag window, the definition of a type of data analysis to be used in assessing modeling performance, and the extensions of its scope from detecting possible causality to building a more detailed model).
See more here mdpi.com
Another New Paper Slays CO2 Greenhouse Gas ‘Thought Experiment’September 27, 2017
The above was featured on UK Reloaded
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