No experimental evidence for the significant anthropogenic climate change (1907.00165v1)

Abstract: In this paper we will prove that GCM-models used in IPCC report AR5 fail to calculate the influences of the low cloud cover changes on the global temperature. That is why those models give a very small natural temperature change leaving a very large change for the contribution of the green house gases in the observed temperature. This is the reason why IPCC has to use a very large sensitivity to compensate a too small natural component. Further they have to leave out the strong negative feedback due to the clouds in order to magnify the sensitivity. In addition, this paper proves that the changes in the low cloud cover fraction practically control the global temperature.

• The paper challenges IPCC models by demonstrating that a 1% increase in low cloud cover lowers global temperature by approximately 0.11°C.
• It finds that current climate models overstate CO2 impacts, calculating a CO2 doubling sensitivity of only 0.24°C compared to typical 2–5°C estimates.
• The study underscores the need to integrate comprehensive low cloud feedbacks into future climate models for accurate temperature projections.

Analysis of Cloud Cover Influence on Global Temperature

The paper "No experimental evidence for the significant anthropogenic climate change," authored by J. Kauppinen and P. Malmi, presents a critical analysis of General Circulation Models (GCMs) used in the IPCC's Fifth Assessment Report (AR5). This paper challenges the reported climate sensitivities and suggests a reevaluation of how low cloud cover changes are factored into climate models.

Overview of the Paper

Kauppinen and Malmi argue that GCMs employed by the IPCC fail to accurately compute the effects of low cloud cover on global temperature. This miscalculation results in an understatement of the natural components of climate change and consequently overstates the impact of greenhouse gases, like CO$_2$. Their analysis contends that significant changes in low cloud cover, which are not adequately represented in current climate models, have a substantial influence on global temperature variations.

The authors support their claims by presenting observational data illustrating that a 1% increase in low cloud cover is associated with a decrease in global temperature by approximately 0.11°C. This figure is corroborated by past theoretical work by the authors. Additionally, they argue that the observed global temperature changes, primarily occurring over the past few decades, can be explained largely by this cloud cover effect, leaving minimal room for significant contributions from anthropogenic CO$_2$ emissions.

Numerical Results and Claims

The paper puts forth the notion that the IPCC's climate sensitivity estimates are inflated, primarily due to neglecting the negative feedback effects of clouds. The derived climate sensitivity of CO$_2$ doubling in this paper is 0.24°C, starkly lower than the commonly reported figures ranging between 2–5°C. Furthermore, when viewed through the lens of cloud cover impact, they assert that the human-induced temperature increase is negligible, at about 0.01°C over the past century.

Implications and Speculations

From a theoretical perspective, the paper highlights the gaps in the current climate models and the potential for significant realignment in understanding the drivers of climate change if low cloud cover effects are more accurately accounted for. The authors propose that low clouds have a controlling influence on global temperature, suggesting that future climate change projections should consider this parameter with greater emphasis.

Practically, this analysis urges climate researchers to reinvestigate the role of cloud cover and feedback mechanisms in climate models. If the assertions hold, this would necessitate revisions in global climate policies and strategies that are heavily reliant on current climate model outputs.

Future Developments

As the paper dissects the inadequacies in prevailing climate models, it opens pathways for subsequent research aimed at refining both observational techniques and modeling methodologies to integrate comprehensive cloud cover data. Future developments could involve the adaptation of models to cases with variable cloud cover conditions and validating these models against a broader range of empirical data.

In conclusion, while the paper's assertions challenge central tenets in current climate science narratives, they underscore the need for continuous scrutiny and adaptation within the domain of climate modeling. As future research either validates or refutes these findings, the implications could steer the paradigm through which anthropogenic climate change is understood and addressed.