Carbon-Weather Data Assimilation

Eugenia Kalnay pioneered carbon-weather data assimilation to derive surface CO2 fluxes and their uncertainties at gridbox resolution. Previously, the paucity of atmospheric CO2 observations has been limited “top-down” inference of the surface fluxes to continental-scale regions. Her innovations include simultaneous assimilation of raw meteorological and carbon observations into an atmospheric circulation model, using the Local Ensemble Transform Kalman Filter (LETKF). Also, by including surface fluxes as a state variable, her group is able to derive the fluxes without priors. This paper presents the progress in carbon-weather data assimilation as well as the implications of the results for atmospheric model development, for carbon cycle studies, and for climate treaty verification.