No response. Yet you state in the OP that they had ALL failed. How difficult can it be to find an example and, perhaps, explain to us how it failed and why
"...GCMs are not sufficiently reliable to distinguish between natural and man-made causes of the temperature increase in the 20th century. Some of the predictions from GCMs are accompanied by standard errors, as in statistical analysis. But since the GCMs are deterministic models one cannot interpret these standard errors in the same way as in statistics. GCMs are typically evaluated applying the same observations used to calibrate the model parameters. In an article in Science, Voosen (2016) writes; “Indeed, whether climate scientists like to admit it or not, nearly every model has been calibrated precisely to the 20th century climate records – otherwise it would have ended up in the trash”. Unfortunately,models that match 20th century data as a result of calibration using the same 20th century data are of dubious quality for determining the causes of the 20th century temperature variability. The problem is that some of the variables representing sources of climate variability other than greenhouse gases are not properly controlled for during the calibrations. The resulting calibration of the climate sensitivity may therefore be biased. Further critical evaluations are given by several authors, such as Essex (2022)..."
"...As mentioned in the previous section climate can also change owing to internal processes within the climate system even without any variations in external forcings (chaos). In the GCMs the source of chaos is the nonlinearity of the Navier-Stokes equations. If the initial conditions are not known exactly for a dynamic model based on the Navier-Stokes relations the forecast trajectory will diverge from the actual one, and it is not necessarily the case that small perturbations have small effects. In fact, slightly different initial conditions can yield wildly different outputs..."
"...In order to assess the uncertainty due to internal variability, researchers use so-called ICE (Initial Condition Ensembles) simulations. This means that outputs of GCMs are simulated starting from slightly different initial conditions. As the climate system is chaotic, slightly different initial conditions lead to different trajectories..."
"...Subsequently, we have summarized recent work on statistical analyses on the ability ofthe GCMs to track historical temperature data. These studies have demonstrated that the timeseries of the difference between the global temperature and the corresponding hindcast from theGCMs is non-stationary. Thus, these studies raise serious doubts about whether the GCMs are able to distinguish natural variations in temperatures from variations caused by man-made emissions of CO2..."
"...Next, we have updated the statistical time series analysis of Dagsvik et al. (2020) based on observed temperature series recorded during the last 200 years and further back in time. Despite long trends and cycles in these temperature series, we have found that the hypothesis of stationarity was not rejected, apart from a few cases. These results are therefore consistent with the results obtained by Dagsvik et al. (2020). In other words, the results imply that the effect of man-made CO2 emissions does not appear to be sufficiently strong to cause systematic changes in the pattern of the temperature fluctuations. In other words, our analysis indicates that with the current level of knowledge, it seems impossible to determine how much of the temperature increase is due to emissions of CO2..."
https://www.ssb.no/en/natur-og-milj...594b9225f9d7dc458b0b70a646baec3339/DP1007.pdf
