 One way to remedy spatial and temporal gaps in observations is to perform statistical interpolation. Beginning in the 1990s, however, climate scientists also began to employ a different type of methodology, known as data assimilation (Kalnay 2003). First developed in the context of weather forecasting, data assimilation estimates the three-dimensional state of the atmosphere or ocean using not only available observations but also one or more forecasts from a physics-based simulation model. The forecast constitutes a first-guess estimate of the atmosphere or ocean state at the time of interest; this is updated in light of observational data collected around that time. In daily weather forecasting, the resulting best estimate is known as the analysis, and it provides initial conditions for weather prediction models. To produce long-term datasets for climate research, data assimilation is performed iteratively for a sequence of past times (e.g., every 12 hours over several decades), producing a retrospective analysis or reanalysis for each time in the sequence (Bengtsson &amp; Shukla 1988; Edwards 2010).