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We derive universal upper estimates for model-prediction error under moderatebut otherwise unknown model uncertainty. Our estimates give upper bounds on theleading order trajectory-uncertainty arising along model trajectories, solelyas functions of the invariants of the known Cauchy-Green strain tensor of themodel. Our bounds turn out to be optimal, which means that they cannot beimproved for general systems. The quantity relating the leading-ordertrajectory-uncertainty to the model uncertainty is the Model Sensitivity, whichwe find to be a useful tool for a quick global assessment of the impact ofmodeling uncertainties in various domains of the phase space. Examining theexpectation that Finite-Time Lyapunov Exponents capture sensitivity to modelingerrors, we show that this does not generally follow. However, we find thatcertain important features of the FTLE persist in the MS field.

Universal Upper Estimate for Prediction Errors under Moderate Model Uncertainty