Dominant Time Scales of CO Variability From Satellites and Models Using Power Spectral Analysis

Understanding spatiotemporal patterns in trace gases aids in separating sources and sinks and reducing model biases. As a proof-of-concept, we apply power spectral analysis to monthly total-column CO from satellites (MOPITT and IASI) and models (CAMS reanalysis and CAM-chem), along with tagged-tracer simulations, to disentangle dominant patterns of CO. The dominant time scales (seasonal, interannual, and transport modes) derived from power distributions are used to attribute model and satellite biases to physical processes driving CO variability. We show that the interannual modes reflect interhemispheric exchange while seasonal modes reflect regional transport. A combined analysis of the dominant scales of CAM-Chem tags showed that IASI and CAMS are able to detect more interannual variability due to biomass burning (BB) plumes in the extratropics compared to MOPITT and CAM-Chem. IASI also shows stronger interannual variability that is associated with biogenic and non-methane volatile organic compounds (NMVOCs) in the tropics. CO-tagged sources indicate that anthropogenic CO is dominated by an interannual mode tied to seasonal shifts in the Intertropical Convergence Zone (ITCZ), whereas CO from fires shows pronounced zonal gradients in both hemispheres during boreal fall and winter, consistent with tropical BB plume transport to extratropics. Dominant time-scale analysis of total-column CO, together with tagged model tracers, allows an alternative diagnostic of models and satellite retrievals by decomposing source variability into periodic modes. By considering bias terms in frequency space, we visualize model and instrument bias as a convolution of the biases of each source term acting at their respective time scale.