  This paper presents the first numerical implementation and tests of the
Lagrangian-averaged Navier-Stokes-alpha (LANS-alpha) turbulence model in a
primitive equation ocean model. The ocean model in which we work is the Los
Alamos Parallel Ocean Program (POP); we refer to POP and our implementation of
LANS-alpha as POP-alpha. Two versions of POP-alpha are presented: the full
POP-alpha algorithm is derived from the LANS-alpha primitive equations, but
requires a nested iteration that makes it too slow for practical simulations; a
reduced POP-alpha algorithm is proposed, which lacks the nested iteration and
is two to three times faster than the full algorithm. The reduced algorithm
does not follow from a formal derivation of the LANS-alpha model equations.
Despite this, simulations of the reduced algorithm are nearly identical to the
full algorithm, as judged by globally averaged temperature and kinetic energy,
and snapshots of temperature and velocity fields. Both POP-alpha algorithms can
run stably with longer timesteps than standard POP.
  Comparison of implementations of full and reduced POP-alpha algorithms are
made within an idealized test problem that captures some aspects of the
Antarctic Circumpolar Current, a problem in which baroclinic instability is
prominent. Both POP-alpha algorithms produce statistics that resemble
higher-resolution simulations of standard POP.
  A linear stability analysis shows that both the full and reduced POP-alpha
algorithms benefit from the way the LANS-alpha equations take into account the
effects of the small scales on the large. Both algorithms (1) are stable; (2)
make the Rossby Radius effectively larger; and (3) slow down Rossby and gravity
waves.
