Representing Model Uncertainty for Global Atmospheric CO 2 Flux Inversions Using ECMWF-IFS-46R1

Abstract. Atmospheric flux inversions use observations of atmospheric CO2 to provide anthropogenic and biogenic CO2 flux estimates at a range of spatiotemporal scales. Inversions require prior flux, forward model and observation errors to estimate posterior fluxes and uncertainties. We use a numerical weather prediction model to diagnose the global forward model error associated with uncertainties in the initial meteorological state, physical parameterisations and in-model biogenic response to meteorological uncertainty. We then compare the error with the atmospheric response to uncertainty in the prior anthropogenic emissions. Although transport errors are variable, average total column CO2 (XCO2) transport errors over anthropogenic emission hotspots (0.1–0.8 ppm) are comparable to, and often exceed prior monthly anthropogenic flux uncertainties project onto the same space (0.1–1.4 ppm). Average near-surface transport error at 3 sites (Paris, Caltech and Tsukuba) range from 1.7–7.2 ppm. The global average XCO2 transport error standard deviation plateaus at ~0.1 ppm after 2–3 days, after which atmospheric mixing significantly dampens the concentration gradients. Error correlations are found to be highly flow-dependent, with XCO2 spatiotemporal correlation length scales ranging from 0 km to 700 km and 0 to 260 minutes. Globally, the average model error caused by the biogenic response to atmospheric meteorological uncertainties is small (