Toward reliable ensemble Kalman filter estimates of CO₂ fluxes

The examined case studies demonstrate that adopting state-of-the-art covariance inflation and localization schemes is a necessary but not sufficient condition for ensuring good filter performance, as defined by its ability to yield reliable flux estimates and uncertainties across a range of resolutions. Observational density is found to be another critical factor for stabilizing the ensemble performance, which is attributed to the lack of a dynamical model for evolving the ensemble between assimilation times.

This and other results point to key differences between the applicability of ensemble approaches to carbon cycle science relative to its use in meteorological applications where these tools were originally developed.