Empirical Evidence Contradicts IPCC Models and Solar Forcing Assumptions

It was always a politically motivated BS narrative. If you want some of the details, here is the research , some may be interested in this.

Review: please cut and paste for the information.

https://scienceofclimatechange.org/grok-3-beta-et-al-a-critical-rea...

A Critical Reassessment of the Anthropogenic CO₂-Global
Warming

https://scienceofclimatechange.org/wp-content/uploads/SCC-Grok-3-Re...

Conclusion Resume:

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 Haus-
father, 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 substanti-
ated. 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 [9]. Koutsoyiannis (2024) [7] provide
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 [7, 12]. 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 [22]. Koutsoyiannis (2024) [39] esti-
mate 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 [38, 39]. Harde’s studies
(2017, 2019, 2021) [11, 12, 40] 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) [42] demonstrate that
90% of CMIP5 runs overestimate tropospheric warming, with R² values of 0.05-0.3 when com-
pared 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 [1, 16]. Un-
adjusted rural USHCN data maintain a consistent 12.2°C from the 1930s to 2020s [6, 15], 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) [1,
6, 15]. Humlum et al. (2013) [41], Salby (2013) [46], Salby & Harde (2021, 2022) [36, 37], and
Koutsoyiannis et al. (2023) [5] further reveal that temperature changes precede those of CO₂ in-
creases by 6–12 months, suggesting a feedback-driven system where warming induces CO₂ re-
lease 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) [8] 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) [18] model study agreed
and reported a Pearson correlation coefficient r of 0.95. Soon et al. (2024) [15] 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 [1, 9]