  We use numerical simulations to characterize the performance of a
clustering-based method to calibrate photometric redshift biases. In
particular, we cross-correlate the weak lensing (WL) source galaxies from the
Dark Energy Survey Year 1 (DES Y1) sample with redMaGiC galaxies (luminous red
galaxies with secure photometric redshifts) to estimate the redshift
distribution of the former sample. The recovered redshift distributions are
used to calibrate the photometric redshift bias of standard photo-$z$ methods
applied to the same source galaxy sample. We apply the method to three
photo-$z$ codes run in our simulated data: Bayesian Photometric Redshift (BPZ),
Directional Neighborhood Fitting (DNF), and Random Forest-based photo-$z$ (RF).
We characterize the systematic uncertainties of our calibration procedure, and
find that these systematic uncertainties dominate our error budget. The
dominant systematics are due to our assumption of unevolving bias and
clustering across each redshift bin, and to differences between the shapes of
the redshift distributions derived by clustering vs photo-$z$'s. The systematic
uncertainty in the mean redshift bias of the source galaxy sample is $\Delta z
\lesssim 0.02$, though the precise value depends on the redshift bin under
consideration. We discuss possible ways to mitigate the impact of our dominant
systematics in future analyses.
