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

==== 1. Big-picture assessment ====

===== Main claims (in my own words) =====

The paper argues that:
* GCM global-mean temperature projections are basically linear extrapolations of greenhouse-gas (GHG) forcing. This is represented by an emulation equation (Eq. 1) relating projected ΔT to fractional change in “water-vapor-enhanced” GHG forcing. Propagation_of_Error_and_the_Re…
* Cloud simulations in CMIP5 models have a large systematic error in global total cloud fraction (TCF), about ±12.1% RMS. This is mapped to an error of ±4 W/m² in long-wave cloud forcing (LWCF). Propagation_of_Error_and_the_Re…
* That ±4 W/m² per year of “thermal flux uncertainty” is ~114× larger than the annual increase in GHG forcing (~0.035 W/m²/yr). Propagation_of_Error_and_the_Re…
* Treating GCM projections as linear in forcing and then linearly propagating that ±4 W/m² error every year leads to an uncertainty of about ±15°C by the end of a century-scale projection. Propagation_of_Error_and_the_Re…
* Therefore, no anthropogenic warming signal can be detected in climate observations, and all past and present GCM air-temperature projections are claimed to be physically unreliable. Propagation_of_Error_and_the_Re…

===== What problem he says he is solving / novelty =====

Frank claims:
* Climate projections usually quantify “uncertainty” as model spread or ensemble variability (precision), but do not propagate physical calibration errors through the time-stepping of the model. Propagation_of_Error_and_the_Re…
* He proposes to “rectify” this by: 1. Showing that GCMs are effectively linear in forcing (via Eq. 1 emulations). Propagation_of_Error_and_the_Re… 2. Using observed cloud-simulation errors as a calibration metric. 3. Applying standard error-propagation formulas through time to get “physically valid” error bars on projections. Propagation_of_Error_and_the_Re…

===== Bottom-line conclusions about temperature records & models =====
* Projected global surface air temperature (GASAT) trajectories from SRES, RCP, and early GISS scenarios are claimed to be “fully embedded” within enormous uncertainty envelopes and thus non-unique and untestable (e.g., Figure 6B, Figure 7B, Figure 8B). Propagation_of_Error_and_the_Re…
* Frank concludes that neither historical nor future anthropogenic temperature signals can be discerned from observations, because model uncertainty overwhelms any GHG forcing effect. Propagation_of_Error_and_the_Re…
* He also implies that standard claims about model “skill” (e.g., for Hansen’s 1988 scenarios) are meaningless once his propagated error is considered. Propagation_of_Error_and_the_Re…