
On June 18, 2025 Nicola Scafetta published Detection, attribution, and modeling of climate change: key open issues. Excerpts in italics with my bolds and added images.
Abstract
The Coupled Model Intercomparison Project (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.

Figure 1. Anthropgenic and natural contributions. (a) Locked scaling factors, weak Pre Industrial Climate Anomalies (PCA). (b) Free scaling, strong PCA Source: Larminat, P. de (2023)
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°C and possibly even closer to 1.1 ± 0.4 °C. Empirical models incorporating natural variability suggest that the 21st-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.
Discussion
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.

Figure 1:
(A) Compilation of the radiative forcing functions utilized in the CMIP5 GCMs (adapted from IPCC,2013, Figure 8.18).
(B) Variations in observed global surface temperature (black) alongside the CMIP3 and CMIP5 model simulations incorporating only natural forcing and combined natural-anthropogenic forcing (adapted from IPCC, 2013, FAQ 10.1, Figure 1).
(C) Compilation of the radiative forcing functions utilized in the CMIP6 GCMs (adapted from IPCC, 2021, Figure 2.10).
(D) Observed global surface temperature variations (black) alongside the CMIP6 model simulations incorporating only natural forcing and combined naturalanthropogenic forcing (adapted from IPCC, 2021, Figure SPM.1).
Notably, in both (B) and (D), the observational data necessary
to validate the GCM predictions that consider only natural forcings
are not reported because they do not exist.
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.
Feedback mechanisms include:
• Water Vapor Feedback — A positive feedback governed by the Clausius-Clapeyron law, which links ocean vaporation rates to temperature increases;
• Albedo Feedback — A positive feedback arising from changes in surface reflectivity due to ice and snow
cover variations;
• 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 CO2 release from soils and oceans (per Henry’s law), further increasing atmospheric CO2 concentrations;
• Vegetation Feedback — Temperature and precipitation changes alter vegetation cover, which influences
carbon storage and surface albedo.
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 CO2 emissions between 2050–2075;
• SSP2-4.5 — intermediate emissions, where CO2 levels remain near current levels until 2050 and subsequently decline without achieving Net-Zero by 2100;
• SSP3-7.0 — high emissions, with CO2 concentrations doubling by 2100 under minimal policyintervention;
• SSP5-8.5 — very high emissions, with CO2 levels tripling by 2075 under a worst-case scenario devoid of
mitigation measures.

Figure 3: CMIP6 GCM ensemble mean simulations spanning from 1850 to 2100, employing historical effective radiative forcing functions from 1850 to 2014 (see Figure 1C) and the forcing functions based on the SSP scenarios 1-2.6, 2-4.5, 3-7.0, and 5-8.5. Curve colors are scaled according to the equilibrium climate sensitivity (ECS) of the models. The right panels depict the risks and impacts of climate change in relation to various global Reasons for Concern (RFCs) (IPCC, 2023). (Adapted from Scafetta, 2024).
Conclusion
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 marked up 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 20th 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 (2021, 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°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).

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 (2023a, 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 and Wu et al. (2018). Since these models serve as solar forcing inputs for the CMIP6 GCMs, their choice needs to be reconsidered.

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 CO2 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, CO2 concentrations often lag temperature changes across different timescales, suggesting temperature fluctuations may drive CO2 variations rather than vice versa (Shakun et al., 2012; Koutsoyiannis, 2024).

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.