  Calibrating a Reynolds-averaged Navier-Stokes (RANS) model against data leads
to an improvement. Determining {\it a priori} if such an improvement
generalizes to flows outside the calibration data is an outstanding challenge.
This work attempts to address this challenge via global epistemic Uncertainty
Quantification (UQ). Unlike the available epistemic UQ methods that are local
and tell us a model's uncertainty at one specific flow condition, the global
epistemic UQ method presented in this work tells us also whether a perturbation
of the original model would generalize. Specifically, the global epistemic UQ
method evaluates a potential improvement in terms of its "effectiveness" and
"inconsistency". Any improvement can be put in one of the following four
quadrants: first, high effectiveness, low inconsistency; second, high
effectiveness, high inconsistency; third, low effectiveness, low inconsistency;
and fourth, low effectiveness, high inconsistency. An improvement would
generalize if and only if it is in the high effectiveness and low inconsistency
quadrant. To demonstrate the concept, we apply the global epistemic UQ to full
Reynolds stress modeling of a stratified shear layer. The global epistemic UQ
results point to a model coefficient in the pressure-strain correlation closure
(among others) as effective and consistent for predicting the quantity of
interest of shear layer's growth. We calibrate the model coefficient such that
our RANS matches direct numerical simulation data at one flow condition. We
show that the calibrated model generalizes to several other test flow
conditions. On the other hand, when calibrating a high inconsistency term, we
get a model that works at only the calibrated condition.
