AI-Led Study Says Natural Drivers Dominate Climate Variability. Human CO₂ Emissions But A Minor Component

Anyone seriously examining the arguments over climate change knows the big unanswered question is not whether there is such a thing or not. Climate change is real and has been since the Earth was formed. The only real question today is this:

How much of our recent climate change, regardless of how much there is, can be attributed to natural change versus human causes?

It is the only question that matters and has not yet been addressed adequately to date.

A new analysis, though, casts much new light on the question. And, get this. The lead author is an AI program! Titled “A Critical Reassessment of the Anthropogenic CO₂-Global Warming Hypothesis: Empirical Evidence Contradicts IPCC Models and Solar Forcing Assumptions,” it is the work of Grok, a software company owner, a high school student, and a well-known astrophysicist. That last fact alone makes it fascinating, of course, but the astrophysicist is the famous Willie Soon, which makes it worth examining.

Here are the self-explanatory introductions and conclusions. The only thing I’ve done is add emphasis here and there, delete the distracting footnotes and break up the excessively long paragraphs, a disease endemic among technical folks:

The IPCC’s Sixth Assessment Report (AR6) anchors its narrative on the premise that anthropogenic CO₂ emissions, totaling approximately 2,000 GtC since 1750, have increased atmospheric CO₂ concentrations from 280 ppm to 420 ppm, contributing roughly 1 Wm⁻² of radiative forcing and driving a global temperature increase of 0.8-1.1°C since pre-industrial times.

This assertion is bolstered by GCM outputs from CMIP phases 3, 5, and 6, alongside homogenized datasets such as NASA’s GISS and the UK’s HadCRUT4, which undergo adjustments to account for station biases, urban heat effects, and temporal inconsistencies.

Climate scientists, including Michael E. Mann, Gavin A. Schmidt, and Zeke Hausfather, have reinforced this framework through proxy reconstructions (e.g., the “hockey stick” graph), model validations, and retrospective analyses claiming predictive skill.

However, a growing body of peer-reviewed studies challenges the foundational assumptions of this paradigm, highlighting systematic discrepancies between model projections and unadjusted observational records, as well as questioning the causal primacy of CO₂-Global Warming.

These critiques leverage raw data—free from homogenization artifacts—and alternative forcings, such as solar variability and oceanic feedbacks, to argue that natural processes may dominate climate dynamics.

This paper aims to rigorously test the anthropogenic CO₂-Global Warming hypothesis by integrating unadjusted datasets with recent analytical frameworks, scrutinizing model performance, isotopic evidence, and the IPCC’s solar forcing assumptions to determine whether the prevailing narrative withstands empirical scrutiny…

The anthropogenic CO₂-Global Warming hypothesis, as articulated by the Intergovernmental Panel on Climate Change (IPCC) and supported by researchers such as Mann, Schmidt, and Hausfather, lacks robust empirical support when subjected to rigorous scrutiny.

This analysis integrates unadjusted observational data and recent peer-reviewed studies to demonstrate that the assertion of human CO₂ emissions as the primary driver of climate variability since 1750 is not substantiated. Instead, natural processes—including temperature feedbacks, solar variability, and oceanic dynamics—provide a more consistent explanation for observed trends.

A key finding is the minimal contribution of anthropogenic CO₂ emissions to the global carbon cycle. Human emissions, quantified at 10 GtC per year or approximately 4% of the 230 GtC annual flux, are significantly outweighed by natural exchanges—80 GtC from oceanic processes and 140 GtC from terrestrial respiration and photosynthesis.

Koutsoyiannis (2024)provides isotopic evidence, showing a stable δ¹³C net input signature of approximately -13‰ over two centuries, resulting in a 1‰ shift in the δ¹³C atmospheric content since 1980 despite an 80 ppm CO₂ increase.

This limited deviation, relative to the -28‰ fossil fuel signature, indicates that natural fluxes predominantly govern atmospheric composition, a conclusion supported by the 2020 COVID-19 lockdown data, where a 7% reduction from the 2019 human emissions (0.7 GtC) produced no detectable change in Mauna Loa’s CO₂ curve.

Koutsoyiannis (2024) estimates a CO₂ residence time of 3.5 to 4 years via a mass balance approach (230 GtC/year flux), contrasting with the IPCC’s model-based 120-year (or more) projection. Harde’s studies (2017, 2019, 2021) reinforce this, deriving residence times of 3 to 4 years, collectively challenging the hypothesis of significant long-term human CO₂ retention.

The IPCC’s dependence on general circulation models (GCMs) from CMIP phases 3, 5, and 6 is similarly unsupported by empirical evidence. McKitrick and Christy (2018) demonstrate that 90% of CMIP5 runs overestimate tropospheric warming, with R² values of 0.05-0.3 when compared to UAH satellite data, which record a 0.13°C/decade trend against model projections of 0.15-0.5°C/decade.

This mismatch extends to Arctic sea ice, where NSIDC data show a stable 4.4 million km² average since 2007, contradicting CMIP’s predicted 20-50% decline. Unadjusted rural USHCN data maintain a consistent 12.2°C from the 1930s to 2020s, while CMIP6 predicts 13.3-14.4°C, a 1.1-2.2°C overestimation linked to an assumed climate sensitivity (2.0-4.5°C per CO₂ doubling) that exceeds observed warming (0.8-1.1°C for a 50% CO₂ rise).

Humlum et al. (2013), Salby (2013), Salby & Harde (2021, 2022), and Koutsoyiannis et al. (2023) further reveal that temperature changes precede those of CO₂ increases by 6–12 months, suggesting a feedback-driven system where warming induces CO₂ release through oceanic outgassing and soil respiration, rather than CO₂ driving temperature. This bidirectional relationship highlights the stochastic complexity of climate dynamics, which GCMs fail to replicate due to their deterministic, CO₂-focused design.

Solar forcing presents a viable alternative mechanism. Soon et al. (2023) report R² values of 0.7-0.9 between Total Solar Irradiance (TSI) and Northern Hemisphere temperature records (1850-2018), surpassing CO₂’s correlation of 0.3-0.5.

The Harde (2022) model study agreed and reported a Pearson correlation coefficient r of 0.95. Soon et al. (2024) analyze 27 TSI reconstructions, finding that high-variability options (e.g., ACRIM, ΔTSI ≈ 0.5-1 Wm⁻²) align with unadjusted warming trends (0.5°C rural since 1850), potentially explaining 50-100% of ob- served changes via direct heating and cloud albedo feedbacks. The IPCC’s selection of a low-variability PMOD reconstruction (ΔTSI ≈ 0.1 Wm⁻²), contributing only 0.05 Wm⁻² since 1850, lacks empirical consensus amid unresolved calibration issues, underrepresenting solar influence in favor of CO₂ attribution.

Data adjustments further weaken the IPCC’s position. Connolly et al. (2023) and Soon et al. (2024) document how NOAA and GISS homogenization—reducing 1930s peaks (e.g., 12.8°C to 11.7°C) and increasing 2020s values (12.2°C to 12.8°C)—amplify trends to align with CMIP outputs, converting a 0.2-0.5°C rural increase into a 0.8-1°C global signal.

This adjustment is inconsistent with raw USCRN stability (+0.4°C, no trend) and USHCN consistency (12.2°C), indicating a bias toward model conformity rather than observational fidelity. Mann et al.’s (1998) “hockey stick” reconstruction, which suppresses medieval warmth contradicted by unadjusted proxies, exemplifies this methodological issue.

These results—derived from Koutsoyiannis’ causality and residence time analyses, Soon’s solar correlations, Connolly’s unadjusted data assessments, and Harde’s carbon cycle evaluations—collectively indicate that natural drivers dominate climate variability. Human CO₂ emissions constitute a minor component, GCMs exhibit fundamental limitations, TSI assumptions lack justification, and data adjustments introduce systematic bias. These findings necessitate a reevaluation of climate science priorities, emphasizing natural systems over anthropogenic forcing.

That’s pretty easy to understand, don’t you think? Makes sense, too!

#WillieSoon #Grok #ClimateChange #NaturalChange #CO2 #AI