Modes of convergence of Markov chain transition probabilities

Suppose {Pⁿ(x, A)} denotes the transition law of a general state space Markov chain {X_n}. We find conditions under which weak convergence of {X_n} to a random variable X with law L (essentially defined by ∝ Pⁿ(x, dy) g(y) → ∝ L(dy) g(y) for bounded continuous g) implies that {X_n} tends to X in total variation (in the sense that $\text{∥ P}{}^{\mathrm{n}}\text{(x,}{}^{\mathrm{.}}\text{) ? L ∥ → 0)}$, which then shows that L is an invariant measure for {X_n}. The conditions we find involve some irreducibility assumptions on {X_n} and some continuity conditions on the one-step transition law {P(x, A)}.