  In model comparisons with observational data, not all data contain
information that is useful for answering a specific science question. If
non-relevant or highly uncertain data are included in a comparison metric, they
can reduce the significance of other observations that matter for the
scientific process of interest. Sources of noise and correlations among summed
quantities within a comparison metric affect the significance of a signal that
is useful for testing model skill. In the setting of the tropical Pacific, we
introduce an "inquiry dependent" (ID) metric of model-data comparison that
determines the relative importance of the TOGA-TAO buoy observations of the
ocean temperature, salinity, and horizontal currents for influencing
upper-ocean vertical turbulent mixing as represented by the K-Profile
Parameterization (KPP) embedded in the MIT general circulation model (MITgcm)
for the 2004-2007 time period. The ID metric addresses a challenge that the
wind forcing is likely a more significant source of uncertainty for the ocean
state than the turbulence itself, and that the observations are correlated in
time, space, and across ocean state variables. In this approach the MITgcm is
used to infer variability and relationships in and among the data, and to
determine the response structures that are most relevant for constraining
uncertain parameters. We demonstrate that the ID metric is able to distinguish
the effects due to parameter perturbations from those due to uncertain winds
and that it is important to include multiple kinds of data in the comparison,
suggesting that the ID metric is appropriate for use in a calibration of the
KPP model parameters using mooring observations of the ocean state.
