Ed Berry, PhD, Theoretical Physics, CCM
Koutsoyiannis et al. (2023) prove that increase in global temperature causes the increase in atmospheric CO2, and not vice-versa. That is a significant proof.
Koutsoyiannis et al. also proves Theory (1) is false.
IPCC Theory (1) says the natural CO2 level remained constant at 280 ppm and human CO2 controls the CO2 level above 280 ppm.
Koutsoyiannis proves global temperature controls the CO2 level. But global temperature does not control human CO2 emissions. Global temperature controls only natural CO2 emissions.
Therefore, human CO2 emissions do not control the CO2 level, and Theory (1) is false.
So, their paper supports the conclusions — of Berry (2018, 2019, 2020, 2021, 2023a, 2023b), Harde (2017, 2019, 2023), Harde and Salby (2021a, 2021b, 2022), and Salby (2013, 2016, 2018) — that natural CO2 causes the increase in atmospheric CO2 and human emissions are insignificant to climate, e.g., Theory (1) is also false.
Koutsoyiannis et al. (2023) write the following (quotes):
Nonetheless, as a side product, in the Appendices to the paper, we provide several indications of the following (Page 18):
- 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 (Appendices A.2–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).
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.
This role can be summarized in the following points, examined in detail and quantified in Appendix A.1. (Page 19)
- Terrestrial and maritime respiration and decay are responsible for the vast majority of CO2 emissions , Figure 5.12.
- Overall, natural processes of the biosphere contribute 96% to the global carbon cycle, the rest, 4%, being human emissions (which were even lower in the past ).
- The biosphere is more productive at higher temperatures, as the rates of biochemical re- actions increase with temperature, which leads to increasing natural CO2 emission .
- 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 .
Conclusions (Page 22):
- 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%.
- 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.