Editing Openai/6938382f-f9e4-8003-a158-6d812c1bce84 (section)

===== 4.2 Challenges to consensus datasets / models =====

The paper doesn’t attack specific global-temperature datasets (HadCRUT, GISTEMP, Berkeley Earth); it attacks the interpretation of model comparisons with those datasets. It argues:
* Model hindcasts and projections are so uncertain that any apparent agreement with observed trends is coincidental. Propagation_of_Error_and_the_Re…

But mainstream:
* Uses multiple independent lines of evidence (energy balance constraints, paleoclimate, emergent constraints) to constrain climate sensitivity and anthropogenic contribution, including but not limited to GCM hindcasts. IPCC<ref>{{cite web|title=IPCC|url=https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter09_FINAL.pdf|publisher=ipcc.ch|access-date=2025-12-10}}</ref>
* Detection & attribution results are robust across different models and observational datasets; removing or altering cloud feedback details does change best-estimate sensitivity but does not erase the anthropogenic signal completely.

Frank’s conclusion that “an anthropogenic air temperature signal cannot have been, nor presently can be, evidenced in climate observables” Propagation_of_Error_and_the_Re…

 directly contradicts a large detection-and-attribution literature and is not supported by his narrow cloud-error analysis.