This talk presents methods to estimate the number of changepoint times and their locations in time-ordered (correlated) data sequences. A penalized likelihood objective function is developed from minimum description length information theory principles. Optimizing the objective function yields estimates of the changepoint numbers and location time(s). Our model penalty incorporates information on where the changepoint(s) lie, but is not solely based on the total number of model parameters (such as classical AIC and BIC penalties). Specifically, changepoints that occur relatively closely are penalized more heavily. The methods are used to analyze two climate series. The first is a time series of annual precipitations from New Bedford, Massachusetts. The second is our North Atlantic Basin tropical cyclone record. In the latter data set, we find that the US entered a period of enhanced tropical cyclone activity circa 1993 that prevails today.