From notrickszone New Study: The Rising-CO2-Causes-Warming Perception Not Supported By Real-World Observation.  Excerpts in italics with my bolds.

One of the most basic concepts in physics is that causes precede effects and effects follow causes. Determining the directionality sequence is thus essential in any causality analysis.

The assumed CO₂→T causality direction cannot be scientifically supported

The assumption in climate models is that CO₂ causes changes in temperature, or T. More specifically, it is assumed modern global warming has been caused by increases in anthropogenic CO₂ emissions.

However, scientists (Koutsoyiannis et al., 2023) have now expanded upon last year’s 2-part study on stochastics-formulated causality published in The Royal Society (Koutsoyiannis et al., 2022 (1) and Koutsoyiannis et al., 2022 (2)) where they notably contend:

“Clearly the results […] suggest a (mono-directional) potentially causal system with T as the cause and [CO₂] as the effect. Hence the common perception that increasing [CO₂] causes increased T can be excluded as it violates the necessary condition for this causality direction.”

The analysis is in complete agreement with several posts here, especially:

Temps Cause CO2 Changes, Not the Reverse. 2023 Update

The paper is On Hens, Eggs, Temperatures and CO2: Causal Links in Earth’s Atmosphere by Demetris Koutsoyiannis et al.  Excerpts in italics with my bolds.

Abstract

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.

Discussion and Further Results

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.

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. 

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.

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.

1. 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).
2. 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.
3. The unidirectional 𝑇→ln[CO2] potential causal link applies to all timescales resolved by the available data, from monthly to about two decades.
4. The proposed methodology is simple, flexible and effective in disambiguating cases where the type of causality, HOE or unidirectional, is not quite clear.
5. 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.
6. 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%.
7. 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.