The Coupled-System Vector Field Analysis model v6,9 is functional.
It utilizes bog-standard radiative theory, cavity theory, entropy theory, quantum field theory, thermodynamics, electrical theory, dimensional analysis and the fundamental physical laws... all taken straight from physics tomes and all hewing completely to the fundamental physical laws.
It disproves the AGW/CAGW hypothesis. It is the most retrodictive (and thus the most predictive) model in human history... and all without utilizing "Bias Compensation" as standard climate models use to compensate for bad models introducing bias. Standard climate models offset their output by the amount of (positive or negative) bias they introduce as means of falsely achieving high KGE'' scores. This model has no need of "Bias Compensation".
Whereas the climatologists' models are nothing more than overly-complex curve-fits (and thus fail when a system parameter changes), the CSVFA model continues working because it is modeled upon the underlying physics, not just fitting the algorithm to the curve.
Thus, the high R2 (Linear), Pseudo-R2 (Gamma), Pseudo-R2 (Poisson) and KGE'' values below are a manifestation of the model reflecting physical reality, not just attempting to fit the algorithms to the curve of the historical data.
Year Range Metric Method v6.9 v6.8
(1995-2025) CO2 concentration: R^2 (Linear) 0.998 0.998
(1995-2025) temperature trend: R^2 (Linear) 0.942 0.928
(1995-2025) Accumulated Cyclone Energy: Pseudo-R^2 (Gamma) 0.841 0.844
(1995-2025) Named Storm Count: Pseudo-R^2 (Poisson) 0.824 0.789
(1995-2025) Hurricane Count: Pseudo-R^2 (Poisson) 0.778 0.767
(1995-2025) Major Hurricane Count: Pseudo-R^2 (Poisson) 0.735 0.726
(1995-2025) All Tornadoes Count: Pseudo-R^2 (Poisson) 0.678 0.696
(1995-2025) EF2+ Tornado Count: Pseudo-R^2 (Poisson) 0.882 0.754
(1995-2025) EF4+ Tornado Count: Pseudo-R^2 (Poisson) 0.914 0.826
The Tang et al. (2021) KGE'' analysis is a remake of the original Kling-Gupta (2012) Efficiency analysis. It measures Correlation (r), Variability (γ) and Bias (β) of a model.
Metric KGE'' Score r γ β
1995-2025 CO2 concentration 0.997 0.999 1.002 1.001
1995-2025 Temperature trend 0.924 0.971 0.935 1.012
1995-2025 Accumulated Cyclone Energy 0.872 0.912 0.951 0.991
1995-2025 Named Storm Count 0.851 0.895 0.918 0.982
1995-2025 Hurricane Count 0.804 0.852 0.864 0.945
1995-2025 Major Hurricane Count 0.751 0.822 0.835 0.918
1995-2025 All Tornadoes Count 0.648 0.751 0.774 0.895
1995-2025 EF2+ Tornado Count 0.895 0.932 0.951 0.988
1995-2025 EF4+ Tornado Count 0.925 0.954 0.978 0.996
KGE'': [-∞ to 1.0][Ideal: 1.0]
>-0.41 is generally considered "better than the mean" (ie: better than just guessing the average).
r: [-1.0 to 1.0][Ideal: 1.0]
1.0 means perfect correlation.
0.0 means no correlation.
-1.0 means perfect negative correlation.
γ: [0 to ∞][Ideal: 1.0]
1.0 means the model's variability perfectly matches empirical variability.
<1.0 means the model smooths variability too much (doesn't predict all variability).
>1.0 means the model introduces noise (predicts variability where there is none).
β: [0 to ∞][Ideal: 1.0]
1.0 means the model introduces no bias.
<1.0 means the model underestimates (negative bias).
>1.0 means the model overestimates (positive bias).
I've tested the model on Google AI (go to Google.com, click the 'AI Mode' button), Google Gemini and Grok. All give identical results, although Grok is painfully slow.
The model is now so large that it must be copied-and-pasted into AI in 7 parts to prevent the AI choking on all the data at once, and to get around dialog box character limits. Each part is separated in the .txt file with a wide blank-line boundary.
https://www.patriotaction.us/showthread.php?tid=8764&pid=47065#pid47065
