  We propose a new method for combining in situ buoy measurements with Earth
system models (ESMs) to improve the accuracy of temperature predictions in the
ocean. The technique utilizes the dynamics \textit{and} modes identified in
ESMs alongside buoy measurements to improve accuracy while preserving features
such as seasonality. We use this technique, which we call Dynamic Basis
Function Interpolation, to correct errors in localized temperature predictions
made by the Model for Prediction Across Scales Ocean component (MPAS-O) with
the Global Drifter Program's in situ ocean buoy dataset.
