Uncertainty and errors in predictions made using tools calibrated with ground facility data for conditions outside of the testing envelope in charring ablator scenarios are investigated in this work. Goals of the present analysis are accomplished with a novel approach where Bayesian inference procedure for calibration and uncertainty quantification is applied given HyMETS ground arc-jet facility test and MSL flight data for the PICA material. Obtained results consisting of quantified uncertainty due to parametric, modeling, and experimental sources of error are subsequently leveraged in analysis of calibrated model predictions and posterior predictive distributions. Uncertain parameters in this work are defined prior to the procedure and sensitivity analysis performed to screen for influential inputs where material response reconstruction is performed using NASA’s PATO toolbox in combination with the TACOT material model. Results of the investigation show that propagated quantified parametric uncertainty extracted from test facility data, as is typically done, underestimate total uncertainty quantified using flight data and additional modeling uncertainty extrapolation approaches must be developed. Moreover, uncertainty in extrapolated model predictions for flight conditions is insufficient and does not adequately capture recorded flight data without the use of ad hoc solutions.