  CMB mapmaking relies on a data model to solve for the sky map, and this
process is vulnerable to bias if the data model cannot capture the full
behavior of the signal. We demonstrate that this bias is not just limited to
small-scale effects in high-contrast regions of the sky, but can manifest as
$\mathcal{O}(1)$ power loss on large scales in the map under conditions and
assumptions realistic for ground-based CMB telescopes. This bias is invisible
to simulation-based tests that do not explicitly model them, making it easy to
miss. We identify two different mechanisms that both cause suppression of
long-wavelength modes: sub-pixel errors and detector gain calibration mismatch.
We show that the specific case of subpixel bias can be eliminated using
bilinear pointing matrices, but also provide simple methods for testing for the
presence of large-scale model error bias in general.
