(CMIP) global climate models (GCMs) assess that nearly 100% of global surface warming observed between 1850-1900 and 2011-2020 is attributable to anthropogenic drivers like greenhouse gas emissions. These models also generate future climate projections based on shared socioeconomic pathways (SSPs), aiding in risk assessment and the development of costly "Net-Zero" climate mitigation strategies. Yet, as this study discusses, the CMIP GCMs face significant scientific challenges in attributing and modeling climate change, particularly in capturing natural climate variability over multiple timescales throughout the Holocene. Other key concerns include the reliability of global surface temperature records, the accuracy of solar irradiance models, and the robustness of climate sensitivity estimates. Global warming estimates may be overstated due to uncorrected non-climatic biases, and the GCMs may significantly underestimate solar and astronomical influences on climate variations. The equilibrium climate sensitivity (ECS) to radiative forcing could be lower than commonly assumed; empirical findings suggest ECS values lower than 3 deg C and possibly even closer to 1.1 +/- 0.4 deg C. Empirical models incorporating natural variability suggest that the 21 st -century global warming may remain moderate, even under SSP scenarios that do not necessitate Net-Zero emission policies. These findings raise important questions regarding the necessity and urgency of implementing aggressive climate mitigation strategies. While GCMs remain essential tools for climate research and policymaking, their scientific limitations underscore the need for more refined modeling approaches to ensure accurate future climate assessments. Addressing uncertainties related to climate change detection, natural variability, solar influences, and climate sensitivity to radiative forcing will enhance predictions and better inform sustainable climate strategies. Throughout human history, climate change has profoundly influenced both the natural environment and societal structures, leaving a lasting imprint through its wide-ranging positive and negative effects. The negative impacts encompass a range of potential threats, including diminished food production, increased transmission of diseases, and the disruption of ecosystems and infrastructure caused by extreme weather events such as heatwaves, floods, hurricanes, sea level rise, and droughts (IPCC, 2023). Importantly, the effects of climate change are unevenly distributed across the globe, with socio-economic disparities accentuating differences in exposure and vulnerability. Marginalized communities, particularly those facing economic constraints, are disproportionately affected by climate-related hazards. Such disparities underscore the enduring concern humanity has had regarding climate change. Nevertheless, the science underpinning climate change, its causative factors, and the reliability of future climate projections remain subjects of significant scientific uncertainty (Curry, 2023). This contrasts with prevalent public assertions claiming that climate change is "settled science". on Climate Change (IPCC) has , which address the detection and attribution of climate change (IPCC, 1990(IPCC, , 1995(IPCC, , 2001(IPCC, , 2007(IPCC, , 2013(IPCC, , 2021)). These reports are regarded by many scientists, policymakers, and members of the public as providing robust evidence of a significant anthropogenic contribution to unprecedented global warming since the pre-industrial period of 1850-1900. Fossil fuel combustion -resulting in the release of greenhouse gases, particularly carbon dioxide (CO 2 ) -is identified as the primary driver of increased radiative forcing leading to rising global surface temperatures. Global climate models (GCMs) process a number of radiative forcing inputs and simulate global temperature increases, attributed in gran part to the activation of allegedly-positive climatic feedback mechanisms. The outputs of these GCMs are subsequently employed to assess future climate scenarios based on hypothetical shared socioeconomic pathways (SSPs) and associated hazards. This information forms the basis for the IPCC recommendations aimed at guiding policymakers in crafting comprehensive and costly climate mitigation strategies to avert projected adverse climate change outcomes (IPCC, 2023). For instance, the European Union has adopted the "Green Deal", a policy initiative targeting at least a 55% reduction in net greenhouse gas emissions by 2030 (compared to 1990 levels) and achieving carbon neutral-ity, or "Net-Zero", by 2050 (European Union, 2024). This policy framework seeks to balance greenhouse gas emissions with their removal via natural absorption processes and emerging carbon sequestration technologies. The overarching goal of the Net-Zero policies is to keep the "increase in the global average temperature to well below 2 deg C above pre-industrial levels" with efforts "to limit the temperature increase to 1.5 deg C above pre-industrial levels" (Paris Agreement, 2016). Strategies include transitioning from fossil fuels to renewable energy sources (geothermal, hydroelectric, solar, and wind power) and replacing internal combustion vehicles with electric alternatives. However, the implementation of the Green Deal policies entails significant economic investments, potentially jeopardizing industrial development and economic growth. Therefore, a thorough evaluation of the feasibility of these policies and the underlying scientific justifications is imperative. Serious economic challenges render Net-Zero targets difficult, if not impossible to achieve. As of 2023, the European Union's 27 member states collectively contributed only 6.08% of global greenhouse gas emission, which continues to grow globally by approximately 2% annually, despite reductions in Europe (European Commission, 2024). The continued global reliance on fossil fuels -evident in the approximately 1,000 coalfired power plants across Asia currently under construction or planned, particularly in China and India (2024) -suggests that achieving Net-Zero emissions may remain impractical throughout the 21 st century (Climate Action Tracker, 2024;Hausfather and Peters, 2020;IEA, 2024). Also the depletion of critical metals essential for low-carbon technologies and electric vehicles (e.g., Co, Ni, Cu, Se, Ag, Cd, In, Te, and Pt) by 2060 could hinder further technological advancements (Groves et al., 2023). Furthermore, economic meta-analyses indicate that the costs associated with implementing Net-Zero policies to meet the targets of the Paris Agreement could outweigh the anticipated benefits even in the most favorable scenario (Tol, 2023). The above findings suggest the need of a meticulous evaluation of energy and climate policies by carefully weighing their potential advantages and disadvantages (OhAiseadha et al., 2020). To accurately assess the risks associated with projected climate changes and the effectiveness of mitigation policies, it is essential to first evaluate the reliability of current climate science. The IPCC Assessment Reports directly address this point by explaining in great details how the (CMIP6) rigorously tests global climate models (GCMs) against historical climate records.
These models undergo thorough a number of statistical validation in an attempt to ensure that their confidence intervals align well with the observed temperature trends. Additionally, climate models are cross-checked with paleoclimate data from ice cores and tree rings, as well as insights from oceanography, atmospheric physics, and geophysics. By integrating evidence from multiple disciplines, the IPCC attempts to maintain consistency between climate projections and real-world observations. However, unresolved major scientific questions regarding the science of climate change persist. In fact, contrary to some popular perception, climate science remains unsettled and highly debated. Recent research highlights persistent uncertainties in the detection, attribution, and prediction of climate change, and advocate for a more cautious approach to this multifaceted complex issue. Scientific challenges in climate detection, attribution, and modeling stem from three primary issues: 1. the inherent uncertainty of what measurements really indicate complicates the detection of climate change and its causative factors; 2. the anthropogenic contribution is superimposed to natural climate variability, necessitating comprehensive understanding and accurate modeling of the latter; 3. key physical processes, such as cloud formation and solar contributions to climate dynamics, remain poorly characterized. Anthropogenic climate change becomes meaningful only if it is significant relative to expected natural variability. Accurate attributions require precise modeling of external forcing mechanisms and climate responses. However, ongoing uncertainties in these areas continue to impede the accurate attribution of observed climatic changes to either natural or human influences (Curry, 2023). Section 2 of this paper outlines the evolution of the "" (AGWT) as presented in the IPCC Assessment Reports to 2023 (IPCC, 1990(IPCC, , 1995(IPCC, , 2001(IPCC, , 2007(IPCC, , 2013(IPCC, , 2021)). Sections 3 and 4 critically examine uncertainties surrounding the reliability of global temperature records, the extent of solar influences on climate change, and climate sensitivity to radiative forcing. Evidence suggests that estimates of global surface warming since the pre-industrial period may have been exaggerated (e.g. : Scafetta and Ouyang, 2019;Scafetta, 2021a;Katata et al., 2023;Soon et al., 2023) and that solar and astronomical contributions to climate dynamics may have been underestimated (e.g. : Connolly et al., 2023;Scafetta, 2023a). Additionally, contemporary GCMs remain insufficiently validated, and climate sensitivity parameters may be lower than previously anticipated (e.g. : Lewis, 2023;Scafetta, 2022Scafetta, , 2023b)). Empirical models incorporating natural variability are discussed in Section 5 and suggest that global warming during the 21 st century under realistic SSPs may be moderate (e.g. : Connolly et al., 2020;Scafetta, 2024) and not as severe as projected by the IPCC (2021,2023). Consequently, it is investigated whether the risks associated with future climate change have been overestimated, underscoring the need to address several physical uncertainties prior to develop and implement effective climate policies. This section outlines the key arguments supporting the Intergovernmental Panel on Climate Change's theory of anthropogenic global warming, and traces the evolution of the AGWT from the 1990s to the most recent assessment report (AR6, IPCC, 2021, 2023). The IPCC's Sixth Assessment Report (AR6, 2023) claims definitive evidence linking human activities to the observed global warming. According to the report: "Global surface temperature was 1.09 [0.95 to 1.20] deg C higher in 2011-2020 than 1850-1900, with larger increases over land (1.59 [1.34 to 1.83] deg C) than over the ocean (0.88 [0.68 to 1.01] deg C)"; further, "It is unequivocal that human influence has warmed the atmosphere, ocean and land" owing to "observed increases in well-mixed greenhouse gas (GHG) concentrations since around 1750 are unequivocally caused by human activities". The report concludes: "The likely range of total human-caused global surface temperature increase from 1850-1900 to 2010-2019 is 0.8 deg C to 1.3 deg C, with a best estimate of 1.07 deg C". These statements support the conclusion that nearly 100% of the global surface warming observed from the pre-industrial period to 2011-2020 is attributable to anthropogenic drivers, which forms the basis of the (AGWT). However, the IPCC's attribution of natural versus anthropogenic contributions to climate change has evolved significantly over time. The IPCC's First Assessment Report (FAR, 1990) acknowledged that in the 20 th century human activities had begun influencing global climate, but precise quantification of their contribution remained elusive due to uncertainties surrounding natural variability. FAR highlighted the paleoclimatic temperature record of the last millennium by Lamb (1965). This was a proxy temperature reconstruction for England that could more broadly represent the European and North Atlantic climates. This reconstruction identified a pronounced Medieval Warm Period (MWP) (900-1300 AD), which was estimated definitely warmer than the first half of the 20 th century, followed by the Little Ice Age (LIA) (1300-1850 AD). On the basis of such evidence, FAR noted that the warming observed since 1900 was "broadly consistent with predictions of climate models, but it is also of the same magnitude as natural climate variability", and suggested that "the observed increase could be largely due to this natural variability; alternatively this variability and other human factors could have offset a still larger human-induced greenhouse warming" (FAR Executive Summary, p. xii). (SAR, 1995) largely corroborated FAR's findings, estimating that anthropogenic activities could have accounted for approximately 50% of the observed global warming of the 20 th century with natural variability contributing the remainder. SAR and FAR also noted a moderate global cooling from the 1940s to the 1970s. The IPCC's Third Assessment Report (TAR, 2001) posited that human activities likely accounted for around 70% of the observed warming since 1900, emphasizing the role of greenhouse gas emissions (e.g., carbon dioxide, methane, nitrous oxide) from industrial processes, agriculture, and deforestation. TAR introduced a novel reconstruction of the Northern Hemisphere temperatures over the past millennium (Mann et al., 1999), which depicted the controversial "hockey-stick" pattern showing a very modest ~0.2 deg C cooling from MWP to LIA followed by ~1 deg C warming from 1900 to 2000.
This graph suggested that the 20 th -century warming was unprecedented and that the overall temperature pattern of the last millennium correlated well with historical greenhouse gas concentration records; such a correlation increased confidence in climate models and in their climate attribution assessments (cf. Crowley, 2000). However, the hockey-stick graph contradicted earlier paleoclimate research (e.g. Lamb, 1965) and sparked controversy (e.g. Soon and Baliunas, 2003;Soon et al., 2003;von Storch et al., 2004) due also to methodological concerns (McIntyre and McKitrick, 2003), rapidly becoming even a politically charged debate (e.g. : Deming, 2006;Gore, 2006). (AR4, 2007) proposed again the hockey-stick graph (Mann et al., 1999) and concluded that "most of the observed increase in global average temperatures since the mid-20 th century is 'very likely' (i.e., >90% probability) due to the observed increase in anthropogenic greenhouse gas concentrations". Meanwhile, several novel paleoclimatic studies (e.g. : Moberg et al., 2005;Mann et al., 2008) revealed a more pronounced MWP, sometimes comparable to the Current Warm Period (CWP) of the last of the last decades of the 20 th century (e.g. : Ljungqvist, 2010;Christiansen and Ljungqvist, 2012). These records were acknowledged in the IPCC Fifth Assessment Report (AR5, 2013, its Finally, AR6 (IPCC, 2021, 2023) attributed 0.8-1.3 deg C of the observed 0.95-1.20 deg C warming (from 1850-1900 to 2011-2020) to anthropogenic emissions, and relied on the PAGES-2K Consortium (2019) proxy model, which again attenuated the Medieval Warm Period although more moderately than in the original hockey-stick temperature record proposed by Mann et al. (1999). However, representing the Common Era temperature history through a single paleoclimatic reconstruction has been recently criticized as reductive (Esper et al., 2024), indicating that the debate remains ongoing. Accurate assessments of past climates are necessary to validate the AGWT, as discussed below. Despite significant uncertainties in paleoclimate temperature reconstructions, the IPCC's understanding of climate change attribution from the pre-industrial period to the present largely relies on computational experiments conducted with sophisticated numerical climate models. The principal characteristics and findings of these models are extensively summarized in the IPCC's Fourth, Fifth, and Sixth Assessment Reports (IPCC, 2007(IPCC, , 2013(IPCC, , 2021)). Numerical climate models, commonly referred to as "General Circulation Models" or "Global Climate Models" (GCMs), are advanced computational tools designed to simulate the dynamics of the climate system. These models capture interactions between the ocean, atmosphere, and land, predicting variations in climate parameters based on imposed radiative forcing functions. Radiative forcing functions include alterations driven by greenhouse gas concentrations (e.g., carbon dioxide [CO 2 ], methane [CH 4 ]), atmospheric aerosols (e.g., industrial sulphate aerosols, black carbon), land-use changes (e.g., deforestation, urbanization), solar radiation fluctuations, and volcanic activity, which introduces aerosols and gases into the atmosphere: see Figures 1A and 1C. Each of these factors can perturb the energy balance of the climate system, resulting in either warming or cooling effects. GCMs simulate the system's integrated response to the combination of these forcing functions. The climate system's response to variations in radiative forcing is mediated by mechanisms known as climate feedbacks. These feedbacks may amplify or diminish the initial changes induced by the forcing; they are categorized as positive (leading to enhanced warming) or negative (leading to mitigated warming). Key  Cloud Feedback -Particularly challenging to quantify, as cloud formation, type, and distribution are sensitive to warming; certain clouds cool the surface by reflecting solar radiation, while others trap emitted heat, making their net contribution highly uncertain;  Lapse Rate Feedback -A negative feedback involving modifications to atmospheric temperature vertical gradients;  Carbon Cycle Feedback -Activated by warming-induced CO 2 release from soils and oceans (per Henry's law), further increasing atmospheric CO 2 concentrations;  Vegetation Feedback -Temperature and precipitation changes alter vegetation cover, which influences carbon storage and surface albedo. While all available GCMs indicate that the positive feedbacks surpass the negative ones thus amplifying the effects of radiative forcing, large uncertainties associated with crucial feedback mechanisms -particularly those related to water vapor and cloud formation -remain substantial. A doubling of atmospheric CO 2 from pre-industrial levels (280 ppm) to 560 ppm is estimated to increase radiative forcing by approximately F 2xCO 2 = 3.7 W/m 2 at the Earth's surface. Under idealized assumptionsneglecting feedbacks and treating Earth as a black body governed by the Stefan-Boltzmann law -this forcing would theoretically result in 1 deg C warming upon restoration of energy balance: T 2xCO 2 = F 2xCO 2 /4T 3  1 deg C (Rahmstorf, 2008). However, the 6 (CMIP6) GCMs adopted in AR6 (IPCC, 2021) estimate equilibrium climate sensitivity (ECS) -the projected warming at equilibrium due to a CO 2 doubling -ranging from 1.8 deg C to 5.6 deg C, reflecting substantial theoretical uncertainty in the net climate feedback amplification (cf. Hausfather et al., 2022;IPCC, 2021;Scafetta, 2024). CMIP6 -Initiated in 2013 and ongoing, incorporating 55 models that formed the basis for AR6 (IPCC, 2021;Eyring et al., 2016). Through CMIP, the scientific community continues to refine the GCMs, yet uncertainties in feedback processes and ECS highlight the need for ongoing improvements in climate modeling physical reliability and accuracy. Figures 1A and 1C show that the combined contributions of the solar and volcanic effective radiative forcings are substantially smaller relative to the cumulative impact of anthropogenic forcings. Solar forcing exhibits a modest 11-year cycle superimposed on an almost negligible upward trend, while volcanic forcing is characterized by sharp negative spikes resulting from major eruptions. These volcanic spikes induce shortterm cooling effects lasting only a few years (Zanchettin et al., 2022). The GCM simulations depicted in Figures 1B and 1D strongly support the (AGWT). When GCMs are driven solely by solar and volcanic forcing functions -the only two natural components assumed to influence Earth's climate -the numerical simulations fail to produce any substantial warming or cooling trends throughout the 20 th century. This indicates that, absent anthropogenic contributions, the models predict a relatively stable climate from the pre-industrial era (1850-1900) to present. However, by incorporating anthropogenic radiative forcing functions, the GCMs successfully replicate the observed global surface warming trend.
This comparison implies that, according to these GCMs, nearly 100% of the warming recorded since the 1850-1900 period is attributable to anthropogenic factors, as explicitly concluded in AR6 (IPCC, 2021). The CMIP6 GCMs are also employed to simulate future climate scenarios based on hypothetical radiative forcing functions derived from Shared Socioeconomic Pathways (SSPs). The ones mainly adopted in the IPCC AR6 are:  SSP1-2.6 -low greenhouse gas emissions, with robust adaptation and mitigation measures leading to Net-Zero CO 2 emissions between 2050-2075;  SSP2-4.5 -intermediate emissions, where CO 2 levels remain near current levels until 2050 and subsequently decline without achieving Net-Zero by 2100;  SSP3-7.0 -high emissions, with CO 2 concentrations doubling by 2100 under minimal policy interven- tion;  SSP5-8.5 -very high emissions, with CO 2 levels tripling by 2075 under a worst-case scenario devoid of mitigation measures. (5-6 deg C). The right panels of Figure 3 illustrate the projected impacts and risks of climate change associated with various global Reasons for Concern (RFCs). These projections suggest that only SSP1-2.6, which employs aggressive mitigation and Net-Zero policies, can limit the 21 st -century warming to below 2 deg C relative to preindustrial levels, which is the threshold identified as critical under the Paris Agreement (2016). Over the span of approximately three decades, from the publication of the First Assessment Report (FAR, IPCC, 1990) to the Sixth Assessment Report (AR6, IPCC, 2021), the Intergovernmental Panel on Climate Change (IPCC) has significantly advanced its understanding of the role of anthropogenic emissions in driving global warming. In the 1990s the IPCC posited that both natural mechanisms and human activities could have contributed roughly equally (50% each) to the observed warming of the 20 th century. However, since the years 2000s the prevailing scientific opinion has shifted, and the IPCC (AR6, 2021) now asserts that human activities are almost exclusively responsible (100%) for the global warming and climate change observed from 1850-1900 to 2011-2020. The most recent assessment reports IPCC (2021IPCC ( , 2023) ) underscore this conclusion with striking clarity. As shown in Figure 2, the average contribution of natural factors -solar and volcanic forcing and internal natural variability -to global warming during the aforementioned period is estimated to be approximately 0 deg C. Consequently, from the CMIP GCM perspective, concerns about future climate warming due to additional anthropogenic greenhouse gas (GHG) emissions are well-founded. However, this conclusion depends on the reliability of global surface temperature records and the robustness of the physical science underpinning global climate models (GCMs). Under the worst-case scenario, the SSP5-8.5, which assumes very high and accelerating GHG emissions without mitigation, the GCM projections indicate alarming potential warming of 4 deg C to 8 deg C by 2080-2100. Despite these concerning projections, the IPCC (AR6, 2021, its This evolution of understanding underscores the complexity of the climate system and the importance of continuously refining projections through improved climate modeling and robust observational datasets. Scientific critiques of the (AGWT) emerge from diverse perspectives, reflecting significant concerns about persistent physical uncertainties and the potential validity of alternative explanations for climate change. First of all, it is crucial to differentiate scientifically substantiated critiques from various misguided claims often circulating online, which tend to oversimplify the physics of climate change. One such incorrect argument asserts that additional CO 2 emissions cannot affect the climate due to a supposed saturation of the infrared (IR) absorption bands of such gas. This claim is demonstrably inaccurate, as the CO 2 IR absorption bands have not reached their maximum capacity yet. This fact is also acknowledged by van Wijngaarden and Happer (2023, their figure 7) -two noted skeptics of climate change alarmism -who calculated that doubling CO 2 concentrations from 400 to 800 ppm would reduce the upward IR heat flux by approximately 3 W/m 2 at the mesopause altitude (86 km) under standard atmospheric conditions without clouds, assuming a surface temperature of 288.7 K. This result is slightly lower and does not differ significantly from what already reported in the scientific literature and in the IPCC reports, and can be easily verified using the MODTRAN Infrared Light in the Atmosphere model (available online at accessed on April 10, 2025), which also estimates a downward IR heat flux at the surface of about 3.6 W/m 2 under identical physical conditions. In fact, the CO 2 infrared (IR) absorption bands are quasi-saturated, leading to a logarithmic relationship between CO 2 concentration and radiative forcing. For instance, a doubling of atmospheric CO 2 concentration from 400 to 800 ppm results in a typically-estimated increase in radiative forcing of approximately 3.7 W/m 2 , yielding to a theoretical radiative forcing function of the type: F CO 2  3.7 log 2 (P CO 2 /400) W/m 2 with a 10% uncertainty (Myhre, 1998;Etminan et al., 2016); here P CO 2 is the CO 2 atmospheric concentration. This evidence directly contradicts the saturation hypothesis that incorrectly infers that any increase in CO 2 levels would result in a radiative forcing increase of 0.0 W/m 2 . As already explained in Section 2.2, the CMIP6 GCMs predict that a radiative forcing of 3.7 W/m 2 could induce, at equilibrium, a warming from about 1.8 to 5.7 deg C, depending on the modeled net feedback strength. It is important to realize that the principal scientific challenge lies not in debating the CO 2 saturation argument but rather in accurately determining the climate sensitivity to radiative forcings that depends on the sign and magnitude of the climate feedback, the actual forcings of the climate system, and the quantitative detection of climate change. Addressing these complex scientific issues requires a detailed understanding of the climate system, of its astronomical environment and of its feedback mechanisms, domains that remain highly uncertain and form a critical focus of ongoing research, as elaborated below. van Wijngaarden and Happer (2023) expressed skepticism only toward climate change alarmism by arguing that the net climate feedback should predominantly be negative.
This is a legitimate physical expectation that the authors (2001) and Lindzen and Choi (2011) obtained by comparing high-frequency deseasonalized fluctuations in sea surface temperatures and the concurrent fluctuations in top-of-atmosphere (TOA) outgoing radiation using ERBE and CERES satellite data from 1985to 2008. However, van Wijngaarden and Happer (2023) specifically invoked also Le Chatelier's Principle -which states that when a system at equilibrium experiences a disturbance, it will adjust to counteract the disturbance and establish a new equilibrium -to suggest that the climate system should inherently resists radiative perturbations. Citing Le Chatelier's Principle to conclude that the net climate feedback should be strictly negative appears to oversimplify the complexity of the climate system, which incorporates both negative feedbacks (such as increased cloud cover reflecting sunlight) and positive feedbacks (like ice-albedo effects, where melting ice reduces reflectivity and accelerates warming). It is the interplay between these positive and negative feedbacks that ultimately determines the thermodynamic conditions of the new equilibrium of the climate system. The new equilibrium temperature will depend on the prevailing feedback mechanisms; whether the net feedback is negative or positive just determines whether the resulting temperature deviates downward or upward from the level expected solely due to increased atmospheric CO 2 , which is 1 deg C for CO 2 doubling (Rahmstorf, 2008). Thus, in this regard, van Wijngaarden andHapper (2023) appear to express only an opinion against the possibility of a catastrophic runaway greenhouse effect scenario where Le Chatelier's Principle is not satisfied because the new equilibrium is never reached. Such a catastrophic scenario did not occur even during the Cambrian Period (~540-485 million years ago) when the Earth's temperature was much higher than today and atmospheric CO 2 levels were estimated to be 4,000-5,000 ppm (Royer et al., 2001), that is ten times larger than current levels. In this regard, van Wijngaarden and Happer (2023) do not appear to have actually developed a solid critique of the AGWT advocated by the IPCC, which is perfectly compatible with Le Chatelier's Principle. Indeed, none of the CMIP GCMs used in IPCC reports predicts a catastrophic runaway greenhouse effect scenario. Only critiques grounded in empirical observations could offer a solid foundation for challenging the AGWT. In the following, Section 3 provides a summary of the primary scientific critiques of the AGWT outlined in The concept of "consensus" holds political significance but is less meaningful within scientific discourse, where the core principle is the verifiability of results through empirical evidence. Yet, in the realm of climate change, an appeal to consensus is often employed to bolster the validity of the (AGWT), its associated alarmist claims and Net-Zero policies. For instance, assumptions that climate change science is settled have led editors of Earth science and health journals to advocate for urgent global action on climate change (McNutt, 2015), assessing that "our planet is Maibach et al., 2016, pp. 5 and 8). Only 29% of respondents agreed that global warming was predominantly or entirely (81% to 100%) attributable to human activity -a stance even more moderate than the definitive 100% attribution claimed by the IPCC (AR6, 2021, 2023), as shown in Figures 1 and 2. in crisis" (Filippelli et al., 2021) and warning that unmitigated climate change poses the 21 st -century "greatest threat to global public health" (Atwoli et al., 2021). These appeals align with the commitments under the Paris Agreement adopted at the United Nations Conference of the Parties (COP21) in 2015 (Paris Agreement, 2016). Despite claims of scientific consensus, skepticism persists among scientists and the public due to unresolved uncertainties and concerns surrounding these assertions. Studies suggesting overwhelming agreement among climate scientists -often citing figures such as 97% or even 99% (Cook et al., 2013;Lynas et al., 2021) are frequently interpreted as evidence of consensus on AGWT. However, critics (e.g. : Montford, 2013;Legates et al., 2015) challenge such conclusions, pointing to statistical flaws and misleading interpretations, which rely on selective datasets and imprecise survey methodologies. A case in point is the ambiguous nature of the statement "humans are causing global warming" (Cook et al., 2013). This statement can be interpreted to mean that nearly all observed warming (100%) is due to anthropogenic activities, as asserted by the AGWT and explicitly claimed by the IPCC (AR6, 2021). However, the same statement could also mean that only a portion (~50%) of the warming is attributable to human influence, reflecting earlier IPCC reports such as FAR and SAR (1990;1995). Reliable surveys must, therefore, distinguish between these divergent attribution scenarios, as their implications for climate science and assessments of future risks differ significantly. When surveys adopt clearer attribution distinctions, the diversity of scientific opinions on climate change becomes evident. Broadly, there is general agreement among scientists on qualitative aspects such as that global warming has occurred since 1850-1900, the greenhouse effect is real, anthropogenic emissions have exacerbated it, and this has contributed to warming over the past century. However, there is no consensus on the precise quantitative attribution of natural versus anthropogenic factors to the observed warming. For example, Figure 4 summarizes a national survey by the American Meteorological Society revealing a wide range of opinions among respondents about the causes of climate change (Maibach et al., 2016, pp. 5 and 8). While 96% agreed that climate change is occurring, only 29% attributed it mainly or entirely to human activities (81% to 100%), while 38% attributed it mostly to human activities (60% to 80%). Fourteen percent attributed climate change equally to human and natural causes (40% to 60%), and smaller percentages leaned toward predominantly natural drivers or expressed uncertainty. This means that at least 71% of the respondents disagree with the IPCC AR6's claim that humans are responsible for 100% of the observed global warming. Additionally, two-thirds of respondents believed that aggressive mitigation policies would only moderately or slightly reduce future warming (Maibach et al., 2016, ).
This diversity of opinions is not unexpected because it well aligns with the range of views documented in the IPCC assessment reports from FAR (1990) to AR6 (2021). Inded, although the IPCC findings have evolved to assert in AR6 that anthropogenic factors are the sole cause of climate change since 1850-1900, shifts in scientific conclusions do not necessarily reflect genuine progress, which requires reducing underlying uncertainties. In science, new findings that appear to challenge earlier results must rigorously justify the invalidation of previous conclusions, not just propose an alternative viewpoint. However, in geosciences, conflicting studies often coexist, with earlier findings persisting despite newer apparently contradictory research. One prominent example of persistent uncertainty in climate science is the assessment of the equilibrium climate sensitivity (ECS), a crucial metric for gauging the response of the climate system to radiative forcing. From Charney et al. (1979) to AR6 (IPCC, 2021), the ECS uncertainty range has remained relatively unchanged -if not broadened (Undorf et al., 2022) -as illustrated in Figure 5A. This persistent uncertainty underscores unresolved methodological and scientific challenges. Consensus-based arguments also risk logical fallacies, such as appeals to popularity or authority. In fact, many individuals, including researchers in climate-related fields, may accept the IPCC conclusions without critically engaging with the uncertainties highlighted within the IPCC's own reports. Consequently, reliance on such forms of "consensus" does little to resolve the crux of the climate debate regarding whether human activity accounts for 100% of observed warming between 1850-1900 and 2011-2020, as asserted by AR6 (IPCC, 2021), or whether a more moderate contribution (50%) might better align with actual scientific findings. If the latter opinion holds, the projected 21 st -century warming based on the CMIP5 and CMIP6 GCMs would be substantially lower, mitigating assessments of future climate change risks and hazards (Scafetta, 2013a(Scafetta, , 2024)). The following subsections delve into key scientific open issues that question the reliability of the GCMs and directly challenge the AGWT. These open key issues continue to be debated in the scientific literature. The scientific method necessitates that the hypotheses, including physical models, are rigorously tested to ensure that their predictions align with empirical evidence. When evidence supports a hypothesis, the model may be retained; however, when results challenge it, the model must be discarded or revised, requiring the formulation of a new hypothesis or model. As discussed in Section 2 and illustrated in Figures 1B and 1D, the claim that human activity accounts for approximately 100% of the observed warming from 1850-1900 to 2011-2020 is derived solely from the climate simulations of the existing GCMs. These simulations rely on specific radiative forcing functions, which are assumed to be complete and accurate. However, the only tangible evidence is that there is no data fully confirming the main GCM prediction. More specifically, no data exists to demonstrate that, absent anthropogenic forcing, the Earth's climate would have remained stable from 1850-1900 to the present. Thus, such critical prediction of the GCMs cannot be empirically validated, as there is no "twin Earth" devoid of human influence from which to obtain the necessary data. Consequently, the absence of such data -as also reflected in Figures 1B and 1D -highlights the key limitation in the validation of these models. Therefore, the AGWT remains a hypothesis contingent upon the reliability of the current GCMs, which can only be "assumed" but not "proven" correct. The fact that the primary prediction of these models has neither been experimentally validated nor is it likely to be so in the future, leaves open the possibility that the present GCMs and their climate attribution assessments (as depicted in Figures 1 and 2) may be fundamentally flawed. While certain qualitative arguments, g., the hockey-stick temperature reconstruction by Mann et al., 1999) suggest that the current warm period surpasses past warm peri- (1979) to AR6 (IPCC, 2021) (adapted from Undorf et al., 2022), with the addition of the recent estimates from Lewis (2023) and Scafetta (2022Scafetta ( , 2023a,b),b). The lowest estimate -ECS = 1.1 +/- 0.4 deg C -assumes that the climate system is hypersensitive to solar activity variations; see Sections 4.2.3 and 4.3.2. [B] The equilibrium climate sensitivity (ECS) and [C] the transient climate response (TCR) of the CMIP6 GCMs (IPCC, 2021;Hausfather et al., 2022;Scafetta, 2024). ods such as the Medieval Warm Period on a global scale, such evidence does not confirm the validity of the AGWT's climate attribution assessments proposed in the latest IPCC assessment report (AR6, 2021). Hockeystick reconstructions, as Crowley (2000) posited, may "enhance confidence" that anthropogenic forcings significantly contributed to current warming but cannot definitively prove they are its sole (100%) cause. Crowley (2000), for example, used hockey-stick temperature records and still estimated that "about 25% of the 20 thcentury temperature increase can be attributed to natural variability", which was primarily due to increased solar activity during the 19 th and 20 th centuries. Importantly, the GCMs themselves may be based on assumptions, mechanisms, and specific forcings that could inadequately capture the complexity of the Earth's climate system. Such limitations can result in overestimated projections of anthropogenic warming and its impacts. For instance, natural processes like solar forcing and internal climate variability may not be accurately represented in the GCMs, a fact that could have yielded to potentially underestimate their contributions to observed climate variability. Indeed, natural processes may have had a larger influence on climate variability than the 0 deg C warming predicted by the CMIP6 models . Additionally, many GCM mechanisms depend on parameterization, calibration, and tuning of free parameters. These parameters are adjusted to enable the GCMs to approximate the observed climate system as closely as possible (McSweeney and Hausfather, 2018). Tuning involves iterative tests to identify parameter values that produce optimal agreement between model outputs and observational data. However, such tuning does not guarantee that the models are physically accurate. Parameter values can vary significantly when model mechanisms and forcings are altered, as demonstrated in various studies (e.g. : Golaz et al., 2019;Ma et al., 2022;Mauritsen et al., 2019).
Thus, the apparent agreement between the GCM predictions and observed warming from 1850-1900 to 2011-2020 under specific radiative forcing functions, as shown in Figure 1, may stem more from tuning methodologies rather than from intrinsic physical accuracy. In scientific literature and in the same IPCC assessment reports there is growing evidence of physical uncertainty that raises significant questions about the reliability of the GCMs. A key challenge lies in the inherent difficulty of rigorously validating these models as also argued above. This section explores some of the most pressing open questions currently under debate, including the pervasive issue of the GCMs' inability to reconstruct major patterns in natural climate variability. As shown in Figure 5, the CMIP6 GCMs predict a wide and inconsistent range of equilibrium climate sensitivity (ECS) and transient climate response (TCR) values, spanning from 1.8 deg C to 5.6 deg C and from 1.33 deg C to 2.79 deg C, respectively (cf. Hausfather et al., 2022;IPCC, 2021, The ECS uncertainty is attributed to variations in the mechanisms, parameterizations, and free parameter settings employed by different GCMs. These differences significantly influence the modeling of key climate feedbacks, such as water vapor and cloud formation (Lutsko et al., 2022). The discrepancies among GCM projections become especially pronounced outside the historical period, where internal free parameter tuning is not possible. For instance, under the same Shared Socioeconomic Pathways (SSPs), the GCMs display divergent climate projections for the 21 st century, as illustrated in Figure 3 A major unresolved question in climate science is how to constrain the ECS and TCR values to better approximate the true sensitivity and response times of the climate system. While the GCMs estimate ECS to fall within 1.8 deg C to 5.6 deg C, broader literature reports ranges as wide as 0.5 deg C to 10 deg C (Knutti et al., 2017). IPCC (AR6, 2021) proposed a likely ECS range from 2.5 deg C to 4.0 deg C (17-83% probability) and a very likely range from 2.0 deg C to 5.0 deg C (5-95% probability), based on the assessment by Sherwood et al. (2020), who used multiple lines of evidence by emerging constraints and paleoclimate data. However, such assessment remains contentious. For example, the reanalysis of the same line of evidences proposed by Lewis (2023) directly challenged the assessment by Sherwood et al. ( 2020) by highlighting a num- ber of possible statistical errors and other shortcomings. Lewis (2023) estimated that the same evidence could suggest a significantly lower ECS range, with a median of 2.16 deg C (17-83% range: 1.75-2.7 deg C; 5-95% range: 1.55-3.2 deg C). Other studies propose even lower values centered between 0.5 deg C to 2 deg C (Forster and Gregory, 2006;Lindzen and Choi, 2011;Harde, 2014;Monckton et al., 2015). Similar low ECS estimates (ECS < 3.0 deg C) have been reported by Scafetta (2013aScafetta ( , 2022Scafetta ( , 2023a,b,c),b,c). See Figure 5A. In general, Gervais (2016), Knutti et al. (2017) and Rugenstein et al. (2023) highlighted that already in the early 2010s, several authors noted that climate sensitivity estimates from climate models differed significantly from those based on observed warming and radiative balance studies, with the latter suggesting much lower values. Attempts to solve this conundrum -for example by assessing whether ECS may be variable under different time-scales and/or physical conditions (Rugenstein et al., 2023) -are under investigation. ECS and TCR values are critical for understanding the climate's sensitivity to radiative forcing and for estimating expected future warming under different SSP scenarios (as shown in Figure 3). Notably, the GCMderived ECS and TCR estimates cannot be treated as repeated measurements of a single observable but rather as diverse theoretical estimates. Consequently, the true ECS and TCR values may fall either within or outside the ranges reported by the GCMs. For example, the actual ECS and TCR values could potentially be lower than those predicted by any GCM, as suggested in multiple studies (cf. : Knutti et al., 2017;Harde, 2014;Lewis, 2023;Lindzen and Choi, 2011;Monckton et al., 2015;Scafetta, 2023aScafetta, ,b, 2024)). Several lines of evidence indicate that the GCMs may inadequately represent key aspects of climate change. For example, the CMIP6 GCMs -particularly those with ECS values exceeding 3 deg C -tend to overestimate the warming observed from 1980 to 2022 (Scafetta, 2022(Scafetta, , 2023b(Scafetta, , 2024)). This persistent bias, which is undisputed for high-ECS GCMs, is widely referred to as the "hot model" problem (Hausfather et al., 2022). Furthermore, the GCMs significantly overestimate warming trends between 1979 and 2014 across the vertical atmospheric profile, particularly in the upper tropical troposphere (Mitchell et al., 2020;McKitrick andChristy, 2018, 2020; Hudson, 2023). This problem is documented by the IPCC (AR6, 2021, its The analysis shown in Figure 6 does not consider the years 2023-2024, which were marked by a sudden temperature spike possibly linked to the Hunga Tonga-Hunga Ha'apai eruption in 2022. This submarine volcanic eruption significantly increased stratospheric water vapor by ~10%, potentially exerting a positive radiative forcing effect (Jenkins et al., 2023;Schoeberl et al., 2024). However, even accounting for this anomaly, the warming estimates for 2014-2024 (0.70 deg C for HadCRUT5 and 0.54 deg C for UAH-MSU) remain below most of the GCM projections. In the future, the rapid cooling that has been observing since the mid-2024 peak could further exacerbate the "hot model" problem rather than mitigate it. The CMIP3 and CMIP5 GCMs have been identified as failing to adequately reconstruct observed climate oscillations on decadal to multidecadal timescales (Scafetta, 2012a(Scafetta, , 2013a)). Specifically, the climate system exhibits a distinct quasi-60-year oscillation that these models fail to replicate. This natural oscillation has resulted in alternating warming (w) and cooling (c) phases superimposed on a long-term secular trend: 1850-1880 (w), 1880-1910 (c), 1910-1940 (w), 1940-1970 (c), 1970-2000 (w), and possibly 2000-2030? (c) (e.g. : Scafetta, 2014a; Wyatt and Curry, 2014). Evidence of this oscillation has also been recently observed in sunshine hours over central Europe (Ludecke et al., 2024), suggesting a direct link between surface temperatures and cloud cover.
A prominent manifestation of a quasi-60-year oscillation can be seen in the Atlantic Multidecadal Oscillation (AMO) index, which tracks the variability of the North Atlantic sea surface temperatures (SSTs) (Schlesinger and Ramankutty, 1994). The AMO index is calculated by removing the linear trend from the SSTs observed within the region [0 deg N-70 deg N, 80 deg W-20 deg E], thereby isolating the oscillation from the broader secular warming trend that may still have both natural and anthropogenic origins. This oscillation has been documented in climate data since 1700 (e.g. Scafetta, 2014a), it is recognized as a defining feature of the North Atlantic ocean-atmosphere variability over the past 8,000 years (Knudsen et al., 2011), and it is found also in the paleoclimatic reconstruction of the Northern Hemisphere by Mann et al. (1999) during the last millennium, as well as in other long-term climate records (Black et al., 1999;Davis and Bohling, 2001;Neff, 2001;Agnihotri and Dutta, 2003;Patterson et al., 2004;Klyashtorin et al., 2008;Camuffo, 2010.;Chambers et al., 2012;Mazzarella and Scafetta, 2012;Scafetta, 2012c;Wyatt and Curry, 2014). For instance, Figure 7 compares the global sea level record (Jevrejeva et al, 2008) and the North Atlantic Oscillation (NAO) record (Luterbacher et al., 2002) since 1700. The right panels feature their multi-scale acceleration analysis (MSAA) plot introduced by Scafetta (2014a). The analyses highlight the 50-70-year oscillation evident in both datasets. Such spectral coherence between two independent yet physically coupled indices -AMO and NAO -underscores the assertion that this quasi-60-year oscillation is a genuine dynamical feature of the climate system, rather than an artifact. The warming observed from the 1910s to the 1940s, followed by the cooling from the 1940s to the 1970s, clearly appeared in some figures , its The existence of a natural 50-70-year cycle not modeled by the CMIP6 GCMs challenges the AGWT (cf. Gervais, 2016;Scafetta, 2013a), particularly because the observed warming from the 1970s to 2000s is strikingly similar to the warming from the 1910s to 1940s. This suggests that natural variability, such as the quasi-60-year oscillation, may have contributed to the recent warming trends (cf. : Scafetta, 2012a: Scafetta, , 2013a)), contrary to the finding of the GCMs attributing all 1970-2000 warming solely to anthropogenic forces. Figures 8A-B estimate the warming from 1940 to 2020 as +0.8 deg C for the GCMs and +0.4 deg C for the observed data, which further demonstrates the discrepancy between the GCM simulations and the empirical records. Discrepancies between model predictions and observations spanning decadals and longer timescales are significant. Indeed, climate models are expected to reproduce changes occurring over periods exceeding 15 years, whether driven by external forcings or internal variability. For example, Knight et al. (2009) remarked that "simulations rule out (at the 95% level) zero trends for intervals of 15 year or more" implying that observing such prolonged data inconsistencies with the GCM expected warming rates signals physical flaws in the models. Similar multi-decadal discrepancies are evident in precipitation patterns and trends. Both CMIP5 and CMIP6 GCMs struggle to model also such climatic patterns accurately, as demonstrated by various studies (e.g. : Connolly et al., 2019;Li et al., 2022;Zhang et al., 2025;Hellmuth et al., 2025;Plavcova et al., 2025). Global climate records from the past 1500 years reveal two prominent periods of anomalous temperatures predating the 20 th century: the Medieval Warm Period (MWP), spanning approximately 900-1300 AD, and the Little Ice Age (LIA), occurring roughly from 1400 to 1850 AD (Lamb, 1965). The magnitude and spatial patterns of the MWP have been a focal point of scientific debate, particularly following the introduction of the hockey-stick temperature graph by Mann et al. (1999), which minimized its significance. A wealth of evidence, however, supports the existence of a distinct MWP and LIA (Soon and Baliunas, 2003;Soon et al., 2003;von Storch et al., 2004), with regions such as Europe, the North Atlantic (Lasher and Axford, 2019) and China (Ge et al., 2017) having potentially experienced temperatures comparable to or exceeding those of the modern warm period. While discussions continue on whether the MWP was a global or predominantly regional phenomenon : Scafetta, 2013a: Scafetta, , 2021b)). Blue and gray arrows to the side indicate the approximate average temperature during the MWP estimated by the proxy models and simulated by the GCMs, spanning approximately from 900 to 1100 AD. (Mann et al., 2009), numerous studies -including the IPCC Assessment Reports -acknowledge a Medieval Warm anomaly relative to the subsequent LIA. For instance, the "Medieval Warm Period 1000-1200 AD" project (Luning, 2022) summarizes approximately 1300 studies, which predominantly support a warm MWP. The resulting map is shown in Figure 9, which highlights that the number of studies and sites supporting a warm MWP (red points) far outnumber those supporting a medieval cool period (blue points). Climate reconstructions often rely on multi-proxy models, which are inherently accompanied by considerable uncertainties (AR5, IPCC, 2013, its The IPCC (AR6, 2021, pp. 433) explicitly acknowledges "larger disagreements between models and temperature reconstructions" before the year 1300. Notably, AR6 emphasized the PAGES-2K Consortium (2019) temperature reconstruction, which dampens the MWP anomaly compared to alternative reconstructions (e.g. IPCC, 2013, its In conclusion, the CMIP6 GCMs predict an approximately stable natural climate extending from 800 AD to the present when only natural (solar + volcanic) radiative forcings are applied . However, this prediction conflicts with extensive paleoclimatic evidence and historical records documenting pronounced cooling from the MWP to the LIA. All recent multi-proxy reconstructions (e.g., IPCC, 2013, its The limitations of the GCMs in simulating natural climate change become increasingly apparent when the entirety of the Holocene epoch is analyzed. The mismatch between data and model predictions over this prolonged period, termed the "Holocene temperature conundrum" (Liu et al., 2014), raises further substantial concerns about the reliability of the existing global climate models . The core of this conundrum lies in the gradual and near-monotonic increase in atmospheric greenhouse gases -carbon dioxide (CO 2 ) and methane (CH 4 ) -likely driven by processes such as oceanic degassing and permafrost melting .
Over this epoch, the climate models predict a quasi-monotonic warming due to the rise in the greenhouse gas concentrations . However, paleoclimatic proxies derived from natural archives -including ice cores, sediment layers, and biological indicatorsconsistently suggest the occurrence of an early-to-mid Holocene Thermal Maximum (HTM), which is also known as the "Holocene Optimum". The HTM contradicts the models' predictions. Empirical evidence supports a significantly warm HTM, with summer temperatures in East Greenland during the early Holocene estimated to be at least 3 deg C higher than present (Westhoff et al., 2022). Similar warming trends were documented in central Europe (Zander et al., 2024;Melo et al., 2022), the eastern Mediterranean (Cruz-Silva et al., 2023), in Japan (Murata et al., 2025), and in numerous other regions. Antarctica appears to be the notable exception, showing a gradual warming rather than an HTM . Nevertheless, even this claim is debated, with alternative studies suggesting the existence of the HTM in Antarctica as well (Jones et al., 2023). On a global scale, several paleoclimatic reconstructions suggest that the HTM was warmer than present-day temperatures, although some estimates, such as those in Figure 11 (Kaufman et al., 2020), appear to mitigate its intensity. Additional support for a warm HTM comes from other studies showing that global mean sea levels were higher than today during the mid-Holocene (Creel et al., 2024), and showing evidence of the African Humid Period (also known as the "Green Sahara"), which occurred between approximately 14,800 and 5,500 years ago (deMenocal and Tierney, 2012; Kaufman et al., 2020). After the HTM, the surface temperatures declined throughout the late Holocene, culminating in the minimum recorded during the Little Ice Age. This dynamics correlates strongly with the changes in mean daily insolation at 65 deg N on summer solstices due to variations in the orbital parameters of the Earth (Laskar et al., 2011; see also the Milankovitch orbital data viewer at accessed April 10, 2025). Efforts to address the Holocene temperature conundrum have included investigating seasonal temperature variations since many paleoclimatic proxies actually represent summer temperatures, as suggested by Bova et al. (2021). However, such interpretations have been challenged by other researchers confirming the HTM also in annual temperature proxies (Cartapanis et al., 2022;Dong et al., 2022;Kaufman and Broadman, 2023). The Holocene temperature conundrum ultimately casts doubt on the accuracy of either paleoclimatic reconstructions or the climate models, or both. However, the large body of empirical evidence supporting a (Luterbacher, 2016). prominent HTM implies that even the latest climate models significantly overestimate the impact of greenhouse gases relative to other factors, such as the climatic impact of variations in solar radiation input. Additionally, the Holocene epoch appears marked by pronounced quasi-millennial oscillations (Alley, 2000;Bond et al., 2001;Kerr, 2001;Kutschera et al., 2020). Current climate models lack the capability to reconstruct these quasi-periodic warm and cool periods, as evidenced by their failure to accurately simulate the latest past warm and cool periods such as the cooling from the Medieval Warm Period (MWP) to the Little Ice Age (LIA). The evidence presented above highlights critical limitations in the Global Climate Models (GCMs), casting doubts on the robustness of the (AGWT). AGWT is heavily reliant on the climate attribution analyses derived from the GCMs, yet substantial empirical data challenges the validity of these models. Specifically, the GCMs have consistently failed to reproduce natural climate variability across multiple timescales throughout the Holocene, particularly during key historical warm periods. Furthermore, their central prediction -that Earth's climate would have remained stable from 1850-1900 to 2011-2020 in the absence of anthropogenic forcing -cannot be validated because of lack of data. The notion that the discrepancies between models and observational data may arise solely from data uncertainties does not resolve the presented challenges, as scientific models must ultimately be validated through empirical evidence. Conse-quently, the AGWT promoted by the IPCC (2021) cannot be considered The inability of the CMIP3, CMIP5 and CMIP6 GCMs to accurately reconstruct natural climate variability -including the Medieval Warm Period (MWP), and other extended warm periods of the past such as the Holocene Thermal Maximum (HTM) -raises fundamental questions. Specifically, concerns arise regarding whether these models employ appropriate natural radiative forcings and incorporate essential physical mechanisms necessary to accurately simulate past warm periods. This shortcoming suggests that the observed warming from 1850-1900 to 2011-2020 may not be exclusively attributable to anthropogenic activities because it cannot be ruled out that it was partly induced by the same mechanisms responsible for the other natural warm periods observed throughout the Holocene; mechanisms that are absent or remain insufficiently implemented in the current GCMs because of the models' failure in reconstructing the warm periods of the past. For example, a significant quasi-millennial oscillation has been documented in the climate system over the past 9,000 years (e.g. : Bond et al., 2001;Kerr, 2001;Neff, 2001;Kutschera et al., 2020). This oscillation is evident in records such as the Greenland GISP2 ice core (Alley, 2000) and many others. Notably, the Medieval Warm Period (MWP) was preceded by the Roman Warm Period (RWP), which occurred roughly 900-1000 years earlier (Ljungqvist, 2010;Christiansen and Ljungqvist, 2012;Klimenko et al., 2014;Luterbacher, 2016;Kutschera et al., 2020;Li et al., 2023). The existence of this quasi-millennial cycle -expected to peak again in the second half of the 21 st century for solar and astronomical reasons (Scafetta, 2012b;Scafetta and Bianchini, 2023) -contrasts sharply with the AGWT's assertion that the warming observed since 1850-1900 is solely (~100%) due to human emissions. This section explores several critical and unresolved challenges concerning the reliability of global surface temperature records used for detecting climate change, as well as how the solar effect on the climate is addressed in modeling frameworks. The Global surface temperature anomalies are constructed from observational records collected at meteorological stations distributed worldwide and from sea surface temperature data obtained from buoys, ships, and ocean reference stations. The processing methodology involves calculating monthly mean temperatures at each station from daily maximum and minimum values, from which annual averages are derived.
These av-erages are then normalized relative to a chosen baseline period (e.g., and aggregated to produce yearly global temperature anomalies. However, significant methodological issues persist throughout these stages. A prominent concern is the uneven geographical distribution of the observational networks, such as weather stations and shipping routes. This issue is typically addressed through gridded averaging, wherein the Earth's surface is divided into cells (e.g. 0.25 deg x0.25 deg ) based on latitude and longitude, and temperature anomalies for Urban areas are consistently warmer than their rural counterparts due to several contributing factors. In cities, natural soil is frequently replaced by heat-absorbing artificial materials, while high-rise buildings disrupt airflow and create heat-trapping canyon effects. Additionally, in urban landscapes rainwater drains, unlike in rural areas where water moistens the soil and facilitates cooling via evaporation. Vegetation, which is more prevalent in rural areas, utilizes sunlight for photosynthesis rather than converting it into heat. Furthermore, anthropogenic activities, including vehicular emissions, building heating and the use of air conditioning systems, intensify urban heating. This cumulative phenomenon is widely recognized as the "urban heat island" (UHI) effect (McKitrick and Michaels, 2007;Mohajerani, 2017;Stewart and Oke, 2012). As urbanization progresses, the temperature recorded by proximate weather stations tends to increase, reflecting enhanced UHI effects. This issue is particularly significant given that urban areas account for less than 4% of the Earth's land surface, yet a substantial proportion of weather stations within the (GHCN) are situated in urban settings . Additionally, the ongoing shift of weather stations from rural to urban environments exacerbates the problem. The global marked increase in urbanization since 1900 and, in particular, after 1950 underscores the growing relevance of this issue (cf. Scafetta, 2021a). The IPCC Sixth Assessment Report (AR6, 2021, Chapter 2) asserts that global surface temperature records are minimally affected by UHI bias, estimating its contribution to global warming to be less than 10%, thereby suggesting it can be disregarded for first-order analyses. However, a study by Helbling and Meierrieks (2023), which analyzed data from 118 countries spanning from 1960 to 2016, identified a strong correlation between rising temperatures and urbanization rates. This is just one of the recent evidence that has led some researchers to argue that UHI contamination of climate records may have been underestimated, advocating for a comprehensive reevaluation of global temperature datasets and climate change mitigation strategies. For example, Soon et al. (2023) examined the Northern Hemisphere land surface temperatures from 1850 to 2018 using two distinct datasets: one encompassing both rural and urban stations and another based exclusively on verified rural stations. Their analysis revealed significant differences in the long-term warming trends. Specifically, the combined rural-urban dataset indicated a warming rate of 0.89 deg C per century since 1850, whereas the rural-only dataset showed a markedly lower rate of 0.55 deg C per century. This divergence, A similar pattern is observed in Figure 13D, which compares instrumental summer (June-August) temperatures averaged over land areas between 30 deg N and 70 deg N latitude (red) with tree-ring-based mean temperature reconstructions within the same latitudes (blue) (Esper et al., 2018). Instrumental records exhibit a pronounced warming trend post-1980, a phenomenon referred to as the "divergence problem", where instrumental temperatures and dendrochronological proxies show conflicting trends. This discrepancy raises questions about the reliability of both global surface temperature records and dendrochronological proxies as tools for reconstructing past climate variations (Buntgen et al., 2021;Cai and Lu, 2023). For instance, Moberg et al. (2005) the trees to changing climatic conditions. Nevertheless, the substantial and anomalous divergence observed after 1980 (as depicted in Figure 13D) together with the divergence observed with the record from the rural-only stations (depicted in Figure 13A), casts doubt on the accuracy of the adopted climatic temperature records. The warming of the lower troposphere should approximate the expected surface warming across sufficiently large spatial scales. However, the GCMs predict that the troposphere -particularly at its top -should exhibit greater warming than at the surface (Mitchell et al., 2020;McKitrick and Christy, 2020;Hudson, 2023), as illustrated in the IPCC (AR6, 2021, its Specifically, the surface records exhibit a excess warming of approximately 21%, which equates to a global excess warming of roughly +0.15 deg C from 1981-1990 to 2014-2024, a significant discrepancy. Furthermore, the figure indicates pronounced surface excess warming in the Northern Hemisphere: approximately 33% over land and 23% over ocean. In contrast, the surface and satellite temperature trends in the Southern Hemisphere -across both land and ocean -align well within their uncertainties . Similarly, at the upper tropical troposphere the GCMs predict a "hot-spot" that is not observed in the data (McKitrick andChristy, 2018, 2020;Mitchell et al., 2020). The compatibility of surface and satellite temperature records in the Southern Hemisphere, as evidenced in Figure 14, supports the assumption that the warming trend in the two records should be comparable, implying that the substantial warming bias observed in Northern Hemisphere land surface temperatures implies the influence of non-climatic factors, notably urban heat island (UHI) effects. Similarly, Northern Hemisphere sea surface temperatures display a slight warming bias relative to the satellite measurements, potentially attributable to inadequate monitoring of the extensive polar regions. In such areas, missing data are reconstructed using models, which may inadvertently introduce additional warming biases, as discussed by Scafetta (2021c). The reduced land area and the lower population density in the Southern Hemisphere likely mitigate the impact of non-climatic warming biases unrelated to climate change. If the warming trend indicated by the lower troposphere satellite temperature record offers a more reliable estimate of the actual global surface temperature trend, the above findings further question the accuracy of the CMIP6 GCMs, as they would all appear to overestimate the actual warming, as noted in Section 3.3.1 and in The UAH-MSU lower troposphere temperature record (Spencer et al., 2017) has been subject to debate, particularly following adjustments to the Remote Sensing System (RSS) dataset in 2014.
These revisions led to the development of an alternative satellite-based temperature record by Mears and Wentz (2016), aligning more closely with the ), seemingly in contradiction to the UAH-MSU findings. However, the UAH-MSU-lt dataset has demonstrated consistency with global warming trends derived from the (IGRA) and reanalysis datasets (Christy et al., 2018). Additionally, recent updates to the NOAA-STAR records (Zou et al., 2023) have acknowledged prior errors, culminating in the NOAA-STAR v. 5.0 dataset. This latest dataset exhibits slightly lower warming from 1981-1990 to 2014-2024 relative to UAH-MSU (refer to Table 1 and Figure 14, top panels). Collectively, these observations suggest that the global surface temperature records may be significantly warm-biased due to improper data processing and non-climatic influences at the surface, particularly over land. An alternative method for identifying non-climatic warming biases in surface temperature records has been explored by Scafetta and Ouyang (2019) and Scafetta (2021a). Their analysis revealed substantial regions within global land surface temperature datasets that exhibit anomalously high nighttime warming (Tmin) relative to daytime temperatures (Tmax). This finding diverges from the predictions of the GCMs, which typically suggest only moderate additional nighttime warming in comparison to daytime warming. Daytime surface warming is effectively mitigated by convective vertical air movements, facilitated by a relatively elevated boundary inversion layer. In contrast, nighttime warming forces a horizontal dispersion of warm air near the surface due to the typically lower position of the boundary inversion layer (cf. Figure 15, top). In urban environments, this meteorological behavior expands urban heat island (UHI) effects during night, resulting in a stronger influence on nearby thermometers at night compared to during day. Consequently, as urban areas continue to grow, urbanization amplifies the warming trend in Tmin compared to Tmax (cf. Kim and Christy, 2022). Interestingly, even small urban centers can produce significant UHI effects (Cardoso et al., 2017;Pinho and Orgaz, 2000). The darkest wide areas may reflect anomalies in the dataset, which could be potentially linked to regions exhibiting minimal warming in Tmin or influenced by phenomena such as urban cool island (UCI) effects as in arid areas, in deforestation zones, or in regions where permafrost may be melting (cf. : Scafetta and Ouyang, 2019; Scafetta, 2021a). The above findings appear to underscore critical failures of the adopted "homogenization" processes. These are designed to correct biases in meteorological records caused by environmental, instrumental, or methodological changes (Menne et al., 2018). Homogenization algorithms aim to enhance the accuracy of temperature datasets by identifying and rectifying such inconsistencies, thus enabling accurate assessment of true climate trends. For instance, the (GHCN) provides temperature datasets in both nonhomogenized and homogenized formats, with the latter used for constructing global climate records. Despite their intended function, these algorithms exhibit limitations. Studies have highlighted inconsistencies in detecting breakpoints within temperature records (Squintu, 2020). Global temperature datasets used for climate change analyses are subject to continuous updates, often reflecting substantial changes between successive versions. For example, the transition from HadCRUT3 (discontinued in 2014) (Brohan et al., 2006) to HadCRUT4 (discontinued in 2021) (Morice et al., 2012) and subsequently to HadCRUT5 (Morice et al., 2021) resulted in notable temperature alterations during the overlapping periods. In particular, Figure 16 highlights increasingly larger warming trends in these updated records after 2000, with increases up to 20% by 2014 (cf. Scafetta, 2023c). These modifications are likely to be attributable to the incorporation of additional input data, adjustments in homogenization algorithms, and the inclusion of model-based synthetic data to fill uncovered regions. Such changes may have been also influenced by efforts to mitigate the discrepancies observed between the GCM predictions and earlier datasets like HadCRUT3, where the data-model divergences had become increasingly apparent (Scafetta, 2012a;Fyfe et al., 2013). In fact, the latest revisions of the global surface temperature records effectively removed the well documented temperature "pause" or "hiatus" observed from 2000 to 2014 in several records, whose existence was acknowledged also by the IPCC (AR5, 2013). However, this 2000-2014 hiatus is still well visible in the latest ocean and satellite-based temperature records , raising further questions about the reliability of the latest global surface temperature records, in particular, over land. In summary, while substantial evidence supports a global surface warming between 1850-1900 and 2011-2020, several factors -including comparisons of sea and land records with model projections (Scafetta, 2021a) -indicate that the actual global surface temperature warming may be 15-25% lower than what reported by the IPCC (2021). The concerns surrounding the reliability of current temperature records merit further investigation and scrutiny. (Lamb, 1965); (W) winter-severity index for the Paris-London region (Lamb, 1972). The left top side shows a list of documented warm and cold periods in human history. (Adapted from Eddy, 1977). [D, E] Comparison of the 14 C cosmogenic record with two climate records (adapted from Bond et al., 2001;Neff, 2001;Kirkby, 2007). [F] Correlation between reconstructed total solar irradiance (Wu et al., 2018) and the flood layer frequency in central Europe (30-year moving window) (Czymzik et al., 2016: r = 0.4, p < 0.0001). The correlation between solar activity and climate change throughout the Holocene has been extensively documented in scientific literature, particularly since the second half of the 20 th century. This period marked the availability of several long-term solar and climate proxy records (e.g. : Bray, 1968;Eddy, 1976Eddy, , 1977)). Figures 17A, 17B and 17C illustrate this foundational understanding by reproducing, with adaptations, a figure pub-lished by Eddy (1977), which demonstrates that the Holocene 14 C cosmogenic record -a widely used proxy for variations in solar activity -is significantly correlated with multiple climatic indices. These include: (1) the timing of Alpine and global glacier advances and retreats (Le Roy Ladurie, 1967;Denton and Karlen, 1973); (2) an estimated mean annual temperature in England; and (3) reconstructed winter severity indices for the Paris-London region, dating back to the Medieval Warm Period (Lamb, 1965(Lamb, , 1972)). Notably, historical records extensively document similar alternating warm and cold periods, which are closely linked to the rise and decline of civilizations (Diamond, 2005;Fagan, 2008).
For instance, the Viking settlements in Greenland thrived during the Medieval Warm Period -a time of prolonged high solar activity -but were abandoned with the onset of the Little Ice Age, which was characterized by long periods of low solar activity (Eddy, 1976;Lasher and Axford, 2019). Recent studies have further substantiated the presence of a significant correlation between solar and climate variability throughout the Holocene, including in the last century (e.g. : Hoyt and Schatten, 1997;Kerr, 2001;Bond et al., 2001;Neff, 2001;Scafetta et al., 2004;Kirkby, 2007;Steinhilber et al., 2012;Scafetta, 2012b;Soon and Legate, 2013;Czymzik et al., 2016;Connolly et al., 2023;Scafetta and Bianchini, 2023;Scafetta, 2023a;Xiao et al., 2024, and numerous others). Figures 17D-F provide examples of this connection. However, as discussed in Section 2, the IPCC (AR6, 2021) aligns with the CMIP6 GCMs' prediction that the solar influence on climate change is negligible, particularly from 1850-1900 to the present (Figures 2 and 3). This raises a fundamental question: how can the IPCC's claim be reconciled with the paleoclimate evidence that indicates a robust correlation between solar activity and climatic records throughout the Holocene? This apparent paradox has been addressed in numerous recent studies (Scafetta et al., 2019;Scafetta, 2023a;Connolly et al., 2024Connolly et al., , 2023)). One key finding suggests that the CMIP6 GCMs underestimate the solar contribution to climate change by a large factor because these models rely only on a specific TSI forcing function characterized by a very low secular variability (Matthes et al., 2017), which, however, may be fundamentally flawed. Additionally, these models may likely exclude significant solar-climate physical mechanisms simply because they are still poorly understood. The CMIP6 GCMs primarily attribute the Sun's influence on climate to variations in its luminosity, as reported in the total solar irradiance (TSI) and solar spectral irradiance (SSI) forcings. While orbital changes also affect solar irradiance reaching Earth, these factors are categorized as orbital forcings and are predominantly relevant on multi-millennial or longer timescales. The issues related to these topics warrant further examination, as outlined below. Decadal and long-term variations in solar activity remain largely uncertain due to the limitations in the available data, as solar irradiance can only be accurately measured by satellites since 1978 (Hoyt and Schatten, 1997;Willson and Mordvinov, 2003). Satellite-based total solar irradiance (TSI) records have been the subject of considerable debate, as their composites are not unique; for a detailed discussion, see Scafetta et al. (2019) and Connolly et al. (2024). Scientific studies highlight that, in addition to the well-documented 11-year solar cycle, various TSI composites are possible, and they exhibit distinct multidecadal trends. Such differences depend on the specific TSI satellite records utilized, the adjustments applied to the officially to 2023, others suggest a modest positive trend, and others even suggest a pronounced increase from 1980 to 2000 followed by a stabilization or slight decline from 2000 to 2023. Figure 18A illustrates examples of these TSI satellite composites; additional details are provided in Connolly et al. (2024). Notably, the TSI composites constructed using the 1979-2013 ACRIM TSI satellite composite (Willson and Mordvinov, 2003) to climate change (Scafetta and West, 2008). By contrast, the CMIP6 GCMs adopted the solar forcing model proposed by Matthes et al. (2017), which averages two distinct TSI models: NRLTSI2 (an empirical model proposed by Kopp et al., 2016, andCoddington, 2016) and SATIRE (a semi-empirical model proposed by Yeo et al., 2014). The TSI forcing proposed by Matthes et al. (2017) posits a slight decline in TSI from 1980 to 2020, which explains why the CMIP6 GCMs attribute no significant warming to solar influence during this period. A critical scientific question arises regarding the accuracy of the TSI forcing proposed by Matthes et al. (2017), as its declining trend from 1980 to the present conflicts with the ACRIM-based TSI satellite composites, which is a composite constructed directly from the ). However, even the PMOD-adjusted composites fail to align with the substantial downward trends in successive solar minima predicted by the SATIRE model, as highlighted also by de Wit et al. (2017). The modifications applied by PMOD to the ) and Connolly et al. (2024). The PMOD adjustments of the published TSI satellite records remain contentious and were never endorsed by the TSI satellite experimental teams led by other researchers such as Drs. Richard C. Willson for ACRIM and Douglass V. Hoyt for Nimbus7 (Scafetta et al., 2019). Consequently, the unresolved ACRIM-PMOD controversy continues to carry significant implications for both solar and climate science (Con-nolly et al., 2024). Despite this, the IPCC (AR6, 2021) has recently chosen to disregard this debate, even though it was acknowledged in the earlier IPCC assessment reports. To investigate long-term variations in solar activity, researchers employ proxies such as sunspot numbers and cosmogenic isotope records of 14 C and 10 Be. In the absence of direct Total Solar Irradiance (TSI) measurements prior to 1978 and due to uncertainties in the TSI satellite composites after 1980, modeling TSI changes over centuries and millennia remains highly challenging. While sunspot numbers and cosmogenic records can identify alternating periods of high and low solar activity, the precise magnitude of solar luminosity variations remains unknown, rendering rigorous validation of solar models unfeasible. Consequently, various TSI proxy models have been proposed in recent decades. Selected examples of these models are illustrated in Figures 18B, 18C, and 18D. Notably, the TSI model developed by Matthes et al. (2017), which underpins the TSI forcing for the CMIP6 GCMs, shows minimal secular variability, as it remains nearly constant from 1850 to 2020 . For millennial-scale simulations, the CMIP6 GCMs use the TSI forcing derived from the SATIRE-M model (Wu et al., 2018), which similarly shows a modest secular modulation . Consequently, it is unsurprising that the CMIP6 GCMs attribute negligible climate effects to solar forcing (Figures 1, 2, and 10). In contrast, other TSI proxy models documented in the literature display significantly larger secular variability (Figures 18B and 18D) (e.g. : Hoyt and Schatten, 1993;Bard et al., 2000;Egorova et al., 2018;Penza et al., 2022).
These models suggest a marked increase in solar activity since the 17 th century and continuing during the 20 th century, which correlates well with the observed global warming since the Little Ice Age. An increase of solar activity during the 19 th and 20 th century is also , 1999). This solar-climate correlation is evident over centuries and millennia and is particularly robust when specific TSI models are chosen (Scafetta and West, 2007;Scafetta, 2023a;Soon et al., 2023). Given the absence of direct measurements of TSI secular variability, the proposed TSI proxy models derived also from assumptions that changed over time. For instance, Lean et al (1995) estimated that at the Maunder Minimum (1645-1715) TSI could have been 0.25% (approximately 3.5 W/m 2 ) lower than the contemporary TSI satellite baseline, currently estimated at approximately 1361 W/m 2 . However, this same estimate was later reduced by Lean et al. (2005) to 0.1% (approximately 1.3 W/m 2 ). More recently, Yeo et al. (2020) asserted that the Sun's darkest possible state could not exceed a reduction of 2.0 +/- 0.7 W/m 2 relative to the modern baseline. These revised claims, supported partly by the low multi-decadal variability of the TSI composites constructed using the modified PMOD TSI satellite records underlie, the modest secular variability presented by TSI models such as NRLTSI2 and SATIRE . However, indirect evidence from luminosity changes in Sunlike stars (Judge et al., 2020) and the direct correlation between climate records and the TSI models with large secular variability suggest a much larger TSI variability over multi-centennial timescales (Schmutz, 2021). In fact, Schmutz (2021) presented a compelling argument: the strong correlation between climate and solar activity records over secular and millennial scales convincingly challenges the assumption that solar activity has been characterized by a minimal multidecadal and secular variability, as those manifested by the NRLTSI2 and SATIRE TSI models. As illustrated in Figure 10, when the GCMs are driven by TSI proxy models with low multidecadal and secular variability, they just fail to capture the cooling transition from the Medieval Warm Period to the Little Ice Age, casting doubt on both the validity of these GCMs and their underlying solar forcing assumptions. Schmutz (2021) estimated that the emergence of the Maunder Minimum-type cold climate excursions characteristic of the Little Ice Age necessitates a total solar irradiance (TSI) reduction of approximately 10 W/m 2 from present. This magnitude of TSI change exceeds by more than a factor of 10 the variability of the TSI functions employed in the CMIP6 GCMs, but it also is two to three times greater than TSI variability of the high-variability TSI proxy models . Consequently, the climate system appears to exhibit an amplified sensitivity to TSI variations (Soon et al., 2000;Ziskin and Shaviv, 2012). This over-sensitivity is observed also in analyses of the climate response to the 11-year solar cycle, whose amplitude is more reliably constrained than the TSI long-term secular modulation (e.g. : Shaviv, 2008;Scafetta, 2023a). This raises a critical question: what accounts for the observed amplification of the solar signal within the climate system? The resolution of this puzzle may require acknowledging the existence of additional mechanisms through which solar activity influences climate beyond direct TSI forcing. In particular, a still poorly understood form of solar corpuscular forcing has been hypothesized as a potential major contributor. More specifically, cosmogenic isotope records ( 14 C and 10 Be), which serve as proxies for reconstructing past solar activity, are generated by cosmic rays interacting with the Earth's atmosphere. Solar magnetic activity modulates the cosmic ray flux, which increases during periods of low solar activity and decreases when solar activity is high. Additionally, cosmic ray flux may experience episodic enhancements due to supernova events. Several studies have proposed that atmospheric ionization induced by cosmic rays plays a significant role in the formation of cloud condensation nuclei, thereby exerting a direct influence on the cloud formation processes (Shaviv and Veizer, 2003;Svensmark and Friis-Christensen, 1997;Kirkby, 2007). A comprehensive review of these findings is available in Svensmark (2019). A decline in solar activity is associated with increased cosmic ray flux, which in turn leads to a greater atmosphere ionization and, therefore, a more extended cloud cover, which increases the Earth's albedo by reflecting more incoming shortwave radiation back into space. This mechanism could results in a notable surface cooling since also a very small increase in cloud cover may reduce the solar forcing at the surface by several watts per square meter (van Wijngaarden and Happer, 2025). Conversely, higher solar activity reduces cosmic ray flux, diminishing cloud cover and thereby lowering the albedo of the Earth, leading to a surface warming. The cosmic ray hypothesis posits that variations in solar magnetic activity may be even more significant than changes in solar luminosity in driving climate changes via direct corpuscular forcing of the cloud system (Easterbrook, 2019). Empirical evidence supporting this hypothesis includes findings by Czymzik et al. (2016), who identified a robust correlation between cosmogenic records and flood frequency across Europe during the Holocene, suggesting a direct link to cloud formation 17F. Despite its potential explanatory power, the cosmic ray hypothesis remains controversial. The CLOUD experiment at CERN, designed to investigate this mechanism, concluded that cosmic rays are insufficient to induce significant nucleation of cloud condensation nuclei (Kirkby et al., 2011). However, observations of strong correlations between cosmic ray flux and cloud cover during Forbush decreases -which are sudden drops in cosmic ray flux typically caused by solar coronal mass ejections -suggest that the conclusions of the CLOUD experiment may require re-evaluation (Svensmark et al., 2016;Svensmark, 2019;Matsumoto et al., 2022). Additional recent evidence for a pronounced 11-year solar cycle signature in cloud records has been documented by Miyahara et al. (2023). Moreover, Svensmark (2022) showed that the correlation between the cosmic ray flux record and climatic data extend over the last 3.5 billion years, where ocean nutrient proxy data were found to covary with supernova frequency.
These latest results further reinforce the hypothesis that cosmic rays can significantly drive climate changes. Another potential corpuscular forcing mechanism involves interplanetary dust falling on Earth, which remains an area requiring further investigation. Ionized interplanetary dust may exert direct influence on cloud nucleation and contribute to climate variability. For instance, Scafetta et al. (2020) identified a 60-year cycle in meteorite fall frequency that exhibits strong coherence with the 60-year climatic oscillation (Figures 7 and 8). This cycle, which has persisted for centuries, appears to be linked to the 60-year modulation of Jupiter's orbital eccentricity, primarily governed by its gravitational interaction with Saturn. The prospect that solar activity could indirectly influence the climate through corpuscular forcings and space weather dynamics -processes which are not accounted for in the current GCMs -merits further scrutiny. Variability in cloud cover has been proposed as a primary driver of climate change, as illustrated (inverted scale). Both curves exhibit a well-correlated increase from 1980 to 2000 followed by a more constant level from 2000 to 2010. In contrast, Figure 19B juxtaposes the same temperature record with the total anthropogenic effective radiative forcing function adopted by the CMIP6 GCMs , which exhibits a continuous linear increase from 1980 to 2010. This comparison underscores that global temperature records align more closely with variations in cloud cover than with the anthropogenic forcing record. As discussed above, several fundamental challenges complicate the detection, attribution, and modeling of climate change. Global surface temperature records may exhibit significant warming biases, while numerical climate models may contain unresolved physical uncertainties. Additionally, the actual solar forcing -recog-nized as the dominant natural driver of climate change on millennial timescales -remains highly uncertain because the long-term solar activity variability is not well known and the mechanisms through which solar phenomena influence climate may include both radiative forcing and a poorly understood corpuscular forcing. In response to these concerns, various proposals advocate for the use of empirical and semi-empirical models to enhance climate change modeling. The Intergovernmental Panel on (2007;2013;2021) have relied predominantly on computer-based global climate models (GCMs). These models exemplify a reductionist scientific approach, which seeks to simplify complex systems by focusing on individual components. While reductionism can offer valuable insights, it also presents several limitations such as: 1. loss of context -by isolating individual components, reductionist methodologies may overlook critical interactions, leading to an incomplete understanding of the climate system. 2. oversimplification -certain essential processes, such as cloud formation, remain poorly represented in the GCMs, affecting their accuracy in climate simulation and projections; 3. emergent properties -some systemic properties only manifest when the climate system is analyzed holistically and cannot be discerned by examining isolated components; 4. interconnected systems -complex climate feedback mechanisms and relationships may be inadequately captured by reductionist models, leading to misleading conclusions; 5. application limits -insights derived from reductionist models may not be readily transferable to realworld applications where a broader, integrative understanding is required. Given these constraints, a more comprehensive approach incorporating holistic methodologies is necessary to achieve an improved understanding of complex climate dynamics. Holistic models, particularly empirical and semi-empirical frameworks, offer several advantages such as: 1. reliance on observational data -these models emphasize empirical data, enabling a direct modeling of real-world climate patterns, thereby addressing limitations present in numerical climate models such as the CMIP6 GCMs; 2. simplicity and transparency -compared to complex GCMs, empirical models tend to be more accessible and interpretable; 3. robust statistical frameworks -these models apply rigorous statistical techniques to quantify the influence of both anthropogenic and natural climate drivers; 4. direct attribution of climate signals -by comparing observed trends with historical records, empirical models help to link specific climate variations to their underlying physical drivers; 5. policy and adaptation applications -empirical models provide clear evidence that can inform climate policy and adaptation strategies more effectively than complex numerical models. However, empirical models also have inherent limitations: 1. data dependence -the reliability of empirical models is contingent on the quality and completeness of the available data, which can sometimes lead to inaccuracies; 2. simplification assumptions -while useful, empirical models may overlook complex climate interactions, leading to potential misrepresentations; 3. nonlinearity and complexity -the inherently nonlinear behavior of the climate system poses challenges for empirical modeling, sometimes resulting in oversimplifications; 4. regional specificity -findings based on one geographic region may not be universally applicable, complicating broader climate assessments. 5. attribution uncertainty -while empirical models provide valuable insights into climate change attribution, they may be less robust than physically based models for quantifying the contributions of individual factors. Thus, the study of complex natural systems necessitates the integration of both holistic and reductionist methodologies. Since each approach has inherent strengths and limitations, comparing numerical and empiric models should allow for a more accurate representation of climate processes. Let us now discuss some of the empirical modeling of the climate system proposed in scientific literature. Several types of empirical and semi-empirical models are commonly employed in climate science such as: 1. Empirical Climate Models (ECMs) -ECMs use statistical techniques such as multiple linear regression and spectral analysis to identify relationships between climate change and external drivers. Examples include models assessing El Nino-Southern Oscillation dynamics, volcanic aerosol contributions, solar radiation, and anthropogenic influences. These models provide preliminary attribution of observed temperature changes. 2. Energy Balance Models (EBMs) -EBMs represent climate dynamics by balancing incoming solar radiation with outgoing thermal emissions. They range from simple zero-dimensional (0-D) models, which treat the Earth as a single thermal unit, to more complex one-dimensional (1-D) and multi-dimensional formulations that account for latitudinal and spatial variations. Each of these modeling approaches possesses unique strengths and limitations, and they are frequently utilized in combination to provide a more comprehensive understanding of climate change. The following subsections provide an overview of empirical models presented in the literature that attribute climate change to both anthropogenic and natural drivers by directly analyzing the climatic records. This discussion will primarily focus on studies ).
These models employ diverse mathematical methodologies and underscore the significant role of solar influences in climate change, though the extent of this contribution varies depending on underlying assumptions and the specific methodologies used by model developers. For further technical details and statistical validation, readers are encouraged to consult the cited references. Regression models are widely regarded as the simplest and most used analytical tools in climate change attribution studies. They rely on statistical techniques such as multi-linear regression analysis to explore relationships between climate variables and a set of predictors. For instance, Chylek et al. (2014) utilized this approach to model the global surface temperature record by incorporating known radiative forcing functions of greenhouse gases (GHG), aerosols (AER), solar irradiance (SOL), volcanic activity (VOLC), as well as indices for the (ENSO) and the Atlantic Multidecadal Oscillation (AMO) as explanatory variables. They proposed the following multi-linear regression model: where the free parameters a 0...6 represent six regression coefficients. In their analysis, Chylek et al. (2014) adopted the climate forcin g functions derived from the GISS ModelE (Hansen et al., 2007), which utilizes an earlier version of the NRLTSI TSI model based on the PMOD-modified TSI satellite composite (Lean, 2000). They concluded that anthropogenic forcing and the positive phase of the AMO collectively accounted for approximately two-thirds and one-third, respectively, of the global warming observed since 1975. Despite its apparent utility, the proposed model has several potential limitations. First, its reliance on a potentially inaccurate solar forcing function likely skewed the results because as the post-1975 warming, which the model attributed to AMO, could have also be partially attributed to solar activity variations if TSI records compatible with the ACRIM TSI satellite composite were used (Scafetta and West, 2008;Scafetta, 2009;Connolly et al., 2023). Second, the climate system processes the radiative forcing functions through inherently non-linear mechanisms, and thus the assumed linear relationships between T(t) and its radiative forcing components may oversimplify reality. Furthermore, ENSO and AMO are subsystems of the climate system and, therefore, they cannot necessarily be considered physically independent of the forcing functions. Multi-linear regression analysis can also produce ambiguous or erroneous results when significant collinearity exists among predictors or when non-linear effects are neglected. For example, Benestad and Schmidt (2009) applied a multilinear regression model to global surface temperatures incorporating the ten radiative forcing functions used by the GISS model. Their result suggested that solar forcing may have contributed approximately 0.1 deg C to the 0.65 deg C warming observed from 1900 to 2000, but they acknowledged the lack of robustness for such finding. In fact, Scafetta (2013b) demonstrated that multilinear regression models using GISS forcing functions could effectively reproduce global temperature records even if the well-mixed GHG forcing function were ignored because of its collinearity with the other predictors. Additional analyses by Scafetta andWest (2006a,b, 2007) and later Scafetta (2009) indicated that the solar influences may have contributed to 50% or more of the warming between 1900 and 2000. These findings were derived using analytical methods such as wavelet frequency decomposition and simplified zero-dimensional energy balance models, which account for some non-linear effects. Models such as the one proposed in Eq. 1 may underestimate solar contributions due to the climate system's tendency to attenuate high-frequency signals while amplifying low-frequency components, a consequence of its thermal inertia. In this context, Benestad and Schmidt (2009) identified a significant solar signature, though it was attenuated relative to the finding of Scafetta andWest (2006a,b, 2007) largely due to methodological errors in wavelet analysis, as later highlighted by Scafetta (2013b). Empirical regression models are most effective when they balance simplicity and complexity. They should incorporate the minimum number of physically relevant predictors, chosen carefully to avoid collinearity. Additionally, these models should simulate some non-linearity. For example, radiative forcing functions are not inherently linear predictors of climate behavior, as the climate system processes them by attenuating the high-frequency components relative to the low-frequency ones due to its thermal capacity. Taking into account the above considerations, Scafetta (2023a) proposed modeling the global surface tem-perature record, T(t), using the following equation: where T(t) is derived from four components: anthropogenic, T A (t); volcano, T V (t); solar, T S (t); plus a fastfluctuating component, (t), simulating fast fluctuations such as the ENSO signal. The three main contributors can be estimated using a 0-D energy balance equation with a given time response, which can be generally modeled as: On the left side, T(t) is the mean global temperature in Kelvin, C represents the effective heat capacity of the Earth's surface and atmosphere. On the right side, in the first term a denotes the albedo, S is the incoming solar radiation adjusted for the planetary sphericity (the denominator "4"); the second term represents the outgoing longwave radiation, which depends on the temperature itself; finally, G incorporates the effect of the greenhouse gases. By redefining the constants and simplifying the formulation, the above equation can be approximated for each forcing component using differential equations based on the temperature anomalies, T(t) = T(t)  T 0 , which are relative to a given mean temperature T 0 : Here, F A (t), F V (t), and F S (t) represent the anthropogenic, volcanic, and solar effective forcings, respectively. The coefficients  A = k A /,  V = k V /,  S = k S /, and  = 1/ depend on the sensitivity parameters k A , k V and k S and the characteristic time response , which is directly related to the Earth's effective heat capacity. For simplicity,  is assumed constant for all forcings.
Under equilibrium conditions, the temperature change T 2xCO 2 , with corresponds to doubling atmospheric CO 2 concentration (for example, from 280 ppm to 560 ppm), is the equilibrium climate sensitivity For the transient climate response (TCR), which is defined as the temperature average over 20 years around the time of CO 2 doubling under a linear increase scenario (1% annually over 70 years), the calculation gives: By iterating, these equations can be reformulated as: where t is set to 1 year. Using multilinear regression analysis, the regression coefficients ( A ,  V ,  S and ) can be evaluated, and can be used to reconstruct the climatic signatures of each forcing and compute T AVS (t), the deterministic component of T(t). Unlikely Eq. 1, the model expressed by Eq. 9 accounts for the climate system's non-linear processing of the forcing functions due to its heat capacity, which leads to a timescaledependent amplitude and time-lag in the response of the climate system to the input forcing function. Scafetta (2023a) demonstrated that if the climate sensitivity is set to be equal for all forcings ( A =  V =  S ) -indicating that the climate is equally sensitive to all types of forcings -the solar contribution to the global warming from 1850-1900 to 2011-2022 is minimal, particularly when it is adopted the low-secular-variability TSI model used by the CMIP6 GCMs. Under this scenario, the ECS was estimated to be 2.1 +/- 0.7 deg C (17-83% range), which is consistent with the low ECS range proposed by the IPCC (2021). In this circumstance, the result suggests that most of the observed warming could be attributable to the anthropogenic forcing, although the ECS was estimated to be lower than 3.0 deg C. This empirically derived ECS range optimally fits the findings of Lewis (2023) and (Scafetta, 2021c(Scafetta, , 2022(Scafetta, , 2023b)). Alternatively, assuming  A =  V  =  S , which means that the climate sensitivity to solar variations can be different from that to the other radiative forcings, Scafetta (2023a) found that the climate could be also 4 to 6 times more responsive to TSI forcing than to anthropogenic or volcanic forcing. In this circumstance, the result implies an ECS that could be as low as 1.1 +/- 0.4 deg C (17-83% range), suggesting that solar activity may directly influence the Earth's albedo by physical mechanisms currently absent in the GCMs. Under this scenario, if high-secular-variability TSI models are adopted, solar activity changes could explain 50% or more of the observed warming from 1850-1900 to 2011-2022. Figures 20A and 20B, respectively, compare the HadCRUT5 global surface temperature record (black) against the CMIP6 GCM ensemble mean simulation and the ECM output (red) curves. The latter was obtained using high-secular-variability TSI models under the condition  A =  V  =  S . The green curves in both panels A and B highlight the distinct multi-decadal modulation pattern evident in the red curves of the two types of models. In panel A, the red curve demonstrates a steadily increasing trend, reflecting the anthropogenic forcing function. In contrast, the red curve in panel B exhibits a quasi 60-year oscillatory behavior that closely resembles the observed climate temperature record. This distinction becomes more evident in panels A' and B', which display the detrended curves. In Figure 20A', the correlation coefficient for the period 1850 to 1950 is r = 0.45, whereas in Figure 20B', the correlation coefficient is notably higher at r = 0.55. More specifically, the CMIP6 GCM ensemble average simulation inadequately captures the warming observed from the 1900s to the 1940s and the subsequent cooling from the 1940s to the 1970s. In contrast, these patterns are more accurately reproduced by the ECM derived from Eq. 2 under the outlined assumptions. There is another widely utilized empirical approach to model climate change, which again considers three primary components: anthropogenic, volcanic, and other natural factors. However, the latter component is conceptualized as made of a complex harmonic signal. The modeling is based on the hypothesis that these other natural climatic components derive from astronomical, solar and lunar forcings that could exhibit approximate harmonic behavior. This empirical methodology involves analyzing the periodogram of the climate records, identifying relevant frequencies, and generating constituent harmonic functions using multiple linear regression analysis. The harmonics are subsequently combined to empirically reconstruct the hypothesized harmonic natural patterns of the climate system. One notable advantage of this approach lies in its ability to extend harmonics for forecasting purposes. For example, it is well known that seasonal variations can be approximately modeled using annual and semiannual cycles. However, issues arise when the selected frequencies lack physical justification. Misinterpretation of artifacts in the periodogram as genuine physical frequencies may lead to harmonic models that perform well within the calibration interval, and yet they could diverge substantially from reality outside the regression period. Furthermore, periodograms of climatic records may only approximate true physical frequencies, particularly in the low-frequency domain where error margins are larger. To mitigate the possibility of using non-physical frequencies, Scafetta (2010Scafetta ( , 2013aScafetta ( , 2021b) ) proposed selecting only those frequencies that can be theoretically derived from astronomical considerations, which is a methodology analogous to that employed in ocean tidal modeling since the seminal work of Sir William Thomson (Lord Kelvin) in 1879 (for a recent application of this methodology see Ardalan and Hashemi-Farahani, 2007). This approach appears justified, because temperature records present periodograms spectrally coherent with planetary, solar, and lunar harmonics (Scafetta, 2010(Scafetta, , 2014b(Scafetta, , 2021b)). Solar records themselves exhibit planetary harmonics across annual to multi-millennial scales (Scafetta, 2012b(Scafetta, , 2014b;;Scafetta et al., 2016;Scafetta, 2020;Scafetta and Bianchini, 2023). These harmonics include the 11-year solar cycle, the Eddy quasi-millennial cycle, and the Bray-Hallstatt oscillation (~2318 years) identified in radiocarbon and climate records spanning the Holocene. In general, it appears that the natural harmonics of the solar system synchronize both solar and climate oscillations, although the mechanisms underlying such planetary synchronization phenomenon remain under investigation (e.g.
: Scafetta, 2012d;Scafetta and Bianchini, 2022;Stefani et al., 2024). Scafetta (2021b) extended the model first proposed in Scafetta (2010) by hypothesizing that the climate system incorporates 13 major harmonics with periods spanning approximately 3 to 1000 years. Anthropogenic and volcanic contributions were derived from the ensemble mean of the CMIP6 GCM simulations, utilizing half the climate sensitivity estimated by these models, which means that the ECS was estimated to be about 1.5-2.0 deg C, to incorporate a natural variability described by the adopted harmonic constituent model. The proposed equation is as follows where A i , f i and  i per i = 1, ..., 13 are coefficients discussed in detail by Scafetta (2021bScafetta ( , 2024)). 1. using only the CMIP6 GCMs with ECS < 3 deg C; 2. rescaling the GCMs to fit the satellite low troposphere temperature record; 3. using the empirical model expressed by Eq. 11. Detailed explanations are found in Scafetta (2024). Let us finally discuss a synthetic empirical climate model spanning from 1 to 2017 AD that may provide relevant insights into climate patterns over the past two millennia. Figure 22 illustrates an ideal application of the empirical climate model (ECM) formulated in Eq. 2, which integrates the following components:  anthropogenic effective radiative forcing -zero anthropogenic forcing is applied from 1 to 1749 AD, while the forcing values adopted by the CMIP6 GCMs are utilized for the period from 1750 to 2017;  volcanic effective radiative forcing -the volcanic forcing values used in the CMIP6 GCMs for the period 1750-2017 are extended back from 1 to 1749 AD using the volcanic aerosol optical depth records provided by Toohey and Sigl (2017);  solar effective radiative forcing -this forcing is derived from a synthetic total solar irradiance (TSI) model that simulates the major features of the proxy solar records . The function is calculated by subtracting 1361 W/m2 and dividing by 8 to align with the TSI effective forcing scale used in the CMIP6 GCMs. The synthetic TSI model serves as an idealized representation of solar activity variations by reproducing five long harmonics whose frequencies and phases are strictly based on astronomical considerations (Scafetta, 2012b(Scafetta, , 2014b(Scafetta, , 2020;;Scafetta and Bianchini, 2023;Scafetta and Milani, 2025). Additionally, the model incor- et al., 2016). The amplitude of these harmonics approximates the variability found in high-secular-variability TSI models, as those shown in Figure 18 (Bard et al., 2000;Egorova et al., 2018). The ECM model (Eq. 2) used the following parameters, estimated by Scafetta (2023a) using a solar variability model and the HadSST4 record:  A =  V = 0.083 deg Cm 2 /yW,  S = 0.427 deg Cm 2 /yW, and  = 0.303 1/y. These values imply an ECS equal to 1 +/- 0.3 deg C, and an enhanced sensitivity to TSI forcing by a factor of 5.1 +/- 1.5. A quasi-millennial cycle is evident, with amplitudes smaller during the first millennium compared to the second. This asymmetry, observed in many paleoclimatic reconstructions, is attributable to the Bray-Hallstatt cycle (~2318 years), which is characterized by grand solar minima occurring around 370 and 1530 AD, respectively. The maxima of the quasi-millennial cycle occur around 95, 1175, and 2060 AD, producing warm and cold periods such as the Roman Warm Period (RWP), the Little Ice Age (LIA), and the Current Warm Period (CWP), as shown in the TSI model depicted in Figure 12A and in the climate record plotted in Figure 12B and 12C. The model suggests that approximately 50% of the warming between 1850-1900 and 2011-2020 could be attributed to solar activity, which is consistent with other studies (e.g. : Scafetta and West, 2006b;Scafetta, 2009;Scafetta and West, 2007). This numerical experiment highlights the importance of utilizing solar records with a substantial multisecular variability and, in addition, including mechanisms that render the climate hypersensitive to solar activity changes when this is represented by TSI changes alone. The proposed toy-model suggests that TSI alone is unlikely to represent the primary solar forcing agent of the climate system and suggests the involvement of alternative mechanisms, which are still poorly understood, such as a direct solar modulation of the cloud/albedo system. Consequently, the actual ECS values may be relatively low, also ranging approximately from 1.0 deg C to 1.5 deg C. The interpretation of the climate system derived from the above empirical evidences significantly differs from that derived from the CMIP6 GCMs and promoted by the IPCC (2001,2007,2013,2023), and suggest that climate science is still unsettled in many key issues. The findings outlined above underscore significant uncertainties in climate modeling, climate data, solar records, and solar-climate interactions, leaving unresolved the key question of whether observed warming is primarily driven by anthropogenic factors, natural processes, or their interplay. Empirical methodologies, such as those utilized by Scafetta (2023aScafetta ( , 2024) ) and Connolly et al. (2023), highlight this ongoing ambiguity. Concerns are mounting regarding the limitations of the CMIP GCMs employed by the IPCC in its assessment reports from 2007, 2013, and 2021. These models appear unable to accurately replicate natural climate variability across different timescales, highlighting critical unresolved issues in fundamental climate dynamics. Also the magnitude of solar variability across temporal scales requires further investigation, particularly given the strong correlations identified between solar proxy records and climate patterns throughout the Holocene. Schmutz (2021) argued that such strong correlations challenge the validity of the low-variability TSI models, such as those proposed by Matthes et al. (2017), Kopp et al., 2016 andWu et al. (2018). Since these models serve as solar forcing inputs for the CMIP6 GCMs, their choice needs to be reconsidered. Additionally, the climate system's apparent oversensitivity to solar activity, as observed by various researchers (Soon et al., 2000;Shaviv, 2008;Scafetta, 2009, and many others), complicates the physical issue. Empirical estimates indicate that the climate's sensitivity to solar activity fluctuations may be four to six times greater than its sensitivity to radiative forcings (Scafetta, 2023a), raising important questions about the physical mechanisms underlying the solar-climate interactions.
The climate system's oversensitivity to changes in TSI implies that radiative solar forcing alone may not be the primary mechanism driving climate dynamics. Alternative processes, including solar-based corpuscular forcing mechanisms, warrant further exploration. Such mechanisms may significantly influence cloud formation via cosmic ray flux (cf. Svensmark, 2019) or other interplanetary particle interactions with the Earth's atmosphere (Scafetta et al., 2016(Scafetta et al., , 2020)). Cloud-climate interactions introduce additional complexities to attribution analyses, emphasizing the need for precise modeling of cloud processes. Several key observations highlight their critical role: 1. cloud feedback uncertainty -variability among climate models in representing cloud feedback significantly contributes to the wide range of ECS estimates derived from the GCMs. The (IPCC, 2021) acknowledges "medium" to "low" confidence in GCM representations of cloud dynamics; 2. impact of cloudiness on heat transfer -even minor changes in cloud cover can drastically affect atmospheric heat transfer for a much greater extent than significant shifts in CO 2 concentrations. For example, on heavily overcast days, surface irradiance can drop from 1,000 W/m 2 to 200 W/m 2 (van Wijngaarden and 5. flood frequencies and solar proxy records -Holocene flood frequency records, likely influenced by variations in cloud cover, exhibit significant correlations with solar proxies . The high equilibrium climate sensitivity (ECS) estimates of the GCMs primarily stem from assumptions about a strong water vapor positive feedback mechanism, initially hypothesized by Manabe and Wetherald (1975). This hypothesis suggests that warming leads to increased evaporation, further amplifying warming. However, uncertainties surrounding water vapor and cloud feedbacks have been acknowledged since the 1960s (Moller, 1963) and are still unresolved . Recent analyses indicate that numerical climate models may overestimate evaporation rates (Ma et al., 2025;Simpson et al., 2024). Additionally, top-of-atmosphere (TOA) radiative flux measurements suggest that long-wave feedbacks -such as those associated with cirrus cloud formations -may be negative rather than positive (Lindzen and Choi, 2011). Schildknecht (2020) also argues that a proper accounting of cloud-cover dynamics brings ECS estimates closer to or below 1 deg C. A number of empirical analyses suggest a moderate climate sensitivity to radiative forcing, with ECS estimates varying depending on the data and assumptions considered. Realistic ECS values could be approximated at least at 2.1 +/- 0.7 deg C (17-83% range) by combining several lines of climate sensitivity evidence (e.g. : Lewis, 2023;Scafetta, 2022Scafetta, , 2023b)). However, significantly lower ECS estimates -even as low as 1.1 +/- 0.4 deg C (17-83% range) -are also possible if solar variability plays a dominant role through mechanisms alternative to the TSI forcing (cf. Scafetta, 2013a). Such low ECS estimates indicate the existence of balanced positive and negative feedback mechanisms, and have been Lindzen and Choi, 2011;Harde, 2014;Monckton et al., 2015;Bates, 2016;Knutti et al., 2017;Schildknecht, 2020;Stefani, 2021). In any case, it is also possible that feedback mechanisms could exhibit scale-dependent behavior. Over short and decadal timescales, they may tend to show slightly negative responses, primarily due to rapid cloudrelated adjustments. Over longer timescales, however, feedback responses may become increasingly positive as surface albedo effects linked to glacial melting and vegetation expansion gain influence. Consequently, ECS values around 1.1 +/- 0.4 deg C or slightly larger may be realistic when climate change is assessed over interannual to millennial timescales. Biases in global surface temperature records, particularly over land areas, raise concerns about the reported global and regional warming trends. The urban heat island effect, exacerbated by increasing urbanization, likely contributes to these biases (Scafetta, 2021a;Soon et al., 2023). Temperature homogenization algorithms, designed to correct non-climatic warm biases in the meteorological records, appear to blend them into surrounding rural and suburban stations rather than fully removing urban-induced thermal anomalies (D'Aleo, 2016;O'Neill et al., 2022;Katata et al., 2023). Consequently, land surface temperature records -particularly those from the Northern Hemisphere -tend to show exaggerated warming trends compared to alternative datasets , including the satellite-based lower troposphere temperature measurements and sea surface temperature records . warm bias -around 20% of reported global warming may stem from non-climatic warm biases in the global surface temperature records;  solar contribution -natural solar-induced variability, operating through multiple mechanisms, could account for up to 50% of the reported warming;  anthropogenic contribution -human activities are estimated to contribute roughly 30% to the reported warming. The above empirical assessment is compatible with a low climate sensitivity to anthropogenic emissions, where ECS estimates are as low as 1.1 +/- 0.4 deg C. If correct, climate risks may be significantly lower than those projected by the CMIP6 GCMs, supporting adaptive strategies and cost-effective mitigation technologies instead of expensive Net-Zero policies (Scafetta, 2024). Climate science remains far from settled, yet trillions of dollars continue to be allocated toward policies aimed at mitigating extreme hypothetical warming scenarios based on potentially flawed GCM outputs. Historically, atmospheric CO 2 levels have been 10 to 20 times higher than current concentrations during approximately 95% of Earth's history since complex life emerged 600 million years ago (Davis, 2017). Notably, CO 2 concentrations often lag temperature changes across different timescales, suggesting temperature fluctuations may drive CO 2 variations rather than vice versa (Shakun et al., 2012;Koutsoyiannis, 2024). Since 1900, atmospheric CO 2 concentrations have increased from 295 ppm to 425 ppm, primarily due to human emissions and, to a lesser extent, rising sea surface temperatures. While higher CO 2 levels can negatively impact marine biodiversity by lowering the pH of the sea surface microlayer (Davis, 2023), they also contribute to planetary greening through fertilization effects, enhancing crop yields (Zhu et al., 2016;Piao et al., 2019;McKitrick, 2025). As CO 2 is a greenhouse gas, climate hazard assessments related to its future emissions depend largely on the Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) values of the climate system, as well as on the projected emission scenarios. Notably, extreme concentration pathways -such as SSP3-7.0 and SSP5-8.5 -are increasingly regarded as "unlikely" and "very unlikely", respectively, and emphasizing climate model simulation based on such scenarios could be misleading (Ritchie and Dowlatabadi, 2017;Hausfather and Peters, 2020;Pielke et al., 2022;Burgess et al., 2023).
This fact should be taken into account when developing climate related policies for not harming the economy with unnecessary mitigation policies, which should be based on realistic risks. In this regards, the meta economic analysis by Tol (2023) appears particularly compelling when comparing the costs and benefits of the Paris climate targets. Advancing climate science requires directly confronting uncertainties in detection, attribution, and modeling. Further research on unresolved issues is critical for improving climate risk assessment and developing more effective strategies for addressing future environmental challenges. Data Availability (accessed on April 10, 2025)  The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The author is a Guest Editor of this journal and was not involved in the editorial review or the decision to publish this article.