Data assimilation for a climate system model is the process of combining model forecasts with observations to produce improved estimates of the model state. Ensemble filter data assimilation algorithms attempt to provide a discrete sample of model state estimates that are consistent with observations and model constraints. A practical introduction to ensemble filters is presented emphasizing empirically motivated aspects of the algorithms that are in need of theoretical understanding from the mathematical community. Results of applying ensemble filters to the atmosphere and ocean component models of a climate modeling system provide examples of current capabilities and challenges. Dealing with systematic model biases is one of the most serious challenges and can be addressed by improving the models, by extending the data assimilation system, and by including stochastic variability in the models. The possibility of using data assimilation to improve models by estimating the values of uncertain model parameters is also discussed.