  As the statistical power of galaxy weak lensing reaches percent level
precision, large, realistic and robust simulations are required to calibrate
observational systematics, especially given the increased importance of object
blending as survey depths increase. To capture the coupled effects of blending
in both shear and photometric redshift calibration, we define the effective
redshift distribution for lensing, $n_{\gamma}(z)$, and describe how to
estimate it using image simulations. We use an extensive suite of tailored
image simulations to characterize the performance of the shear estimation
pipeline applied to the Dark Energy Survey (DES) Year 3 dataset. We describe
the multi-band, multi-epoch simulations, and demonstrate their high level of
realism through comparisons to the real DES data. We isolate the effects that
generate shear calibration biases by running variations on our fiducial
simulation, and find that blending-related effects are the dominant
contribution to the mean multiplicative bias of approximately $-2\%$. By
generating simulations with input shear signals that vary with redshift, we
calibrate biases in our estimation of the effective redshfit distribution, and
demonstrate the importance of this approach when blending is present. We
provide corrected effective redshift distributions that incorporate statistical
and systematic uncertainties, ready for use in DES Year 3 weak lensing
analyses.
