In reply to tim.

I suggest that you read the article "An introduction to simulating correlated data by using copulas."
The main differences are:

PROC SIMNORMAL will simulate multivariate normal (MVN) variates from the sample PEARSON correlation. That implies that the marginal distribution of each variable (X1-X3) will be univariate normal.
PROC COPULA will create simulated data base on the sample SPEARMAN (rank) correlation of the data. The marginal distribution of each variable will be transformed to match the empirical distributions of the original data.

If the original data are MVN, then these are equivalent. However, for non-normal data, a copula is powerful because it preserves the distributions of each variable and also preserves the joint relationships between pairs of variables.