The authors reconstruct global CO2 fluxes by modeling them as GMRFs with Matérn-like spatial covariance and autoregressive seasonal temporal covariance, using integrated fluxes over a continuous domain instead of discrete grid points.
Moreover, the columns representing sensitivities to well mixed fluxes are the same for all observations
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Spatio-Temporal Reconstructions of Global CO2-Fluxes using Gaussian Markov Random Fields
The authors reconstruct global CO2 fluxes by modeling them as GMRFs with Matérn-like spatial covariance and autoregressive seasonal temporal covariance, using integrated fluxes over a continuous domain instead of discrete grid points.