  We study Euler-Maruyama numerical schemes of stochastic differential
equations driven by stable L\'{e}vy processes with i.i.d. stable components. We
obtain a uniform-in-time approximation error in Wasserstein distance. Our
approximation error has a linear dependence on the stepsize, which is expected
to be tight, as can be seen from an explicit calculation for the case of an
Ornstein-Uhlenbeck process. We also obtain a uniform-in-time approximation
error when Pareto noises are used in the discretization scheme.
