Advisor: Ken Davis

Zoom Meeting ID: 937 7620 4657
Password: 895802

Current CO2 flux inversion systems use dense representations of the spatial correlations and have not investigated the correlations between the parts of the daily cycle. I set up a framework to test these assumptions and compare stochastic and deterministic representations of the posterior flux uncertainty. Producing deterministic posterior uncertainty matrices at reduced resolution with the same transport error as the full inversion produced lower-quality uncertainty estimates, with the quality becoming worse with coarsening resolution. Stochastic estimates of the posterior flux uncertainty are similar in value to the deterministic estimates in the ideal case, and become more variable as the number of ensemble members used to construct the stochastic estimate decreases. I then investigate the temporal correlations using the difference between eddy covariance and terrestrial carbon cycle estimates of the CO2 flux, construct a family of temporal correlation functions to describe these data, and recommend a member of that family for inversions. The new temporal correlation function performs as well as two correlation functions used in previous regional inversions at matching previously-published estimates of the uncertainties in the hourly fluxes at the site level and in the annual fluxes at the continental-average level at the same time. However, neither the new nor the existing correlations were able to match previously-published estimates of the uncertainty in the monthly flux at the continental-average scale. I integrate the new correlations into a pseudo-data experiment to see whether the new correlations perform better than previously-used correlations from the literature in a best case scenario. The new correlations recover the "true" continental-average flux than the existing correlations better than the correlations from previous inversions in the ideal case.