• Earth system models (ESMs) have many tunable parameters that are difficult to estimate and weakly constrained by theory. • Kalman filter-based approaches are attractive options, but existing implementations require expensive offline hyperparameter selection. • We propose a new Kalman filter algorithm that estimates model parameters and its hyperparameter simultaneously.
