Exploring the sensitivity of interannual basin-scale air-sea CO2 fluxes to variability in atmospheric dust deposition using ocean carbon cycle models and atmospheric CO2 inversions

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 112 (2007): G02012, doi:10.1029/2006JG000236.Estimates of sources/sinks of carbon dioxide (CO2) at the Earth's surface are commonly made using atmospheric CO2 inverse modeling, terrestrial and oceanic biogeochemical modeling, and inventory-based studies. In this study, we compare sea-air CO2 fluxes from the Time-Dependent Inverse (TDI) atmosphere model and the marine Biogeochemical Elemental Cycling (BEC) model to study the processes involved in ocean carbon cycling at subbasin scales. A dust generation and transport model, based on analyzed meteorology and terrestrial vegetation cover, is also used to estimate the interannual variability in dust and iron deposition to different ocean basins. Overall, a fairly good agreement is established between the TDI and BEC model results for the net annual patterns and seasonal cycle of sea-air CO2 exchange. Sensitivity studies with the ocean biogeochemical model using increased or reduced atmospheric iron inputs indicate the relative sensitivity of air-sea CO2 exchange. The simulated responses to changes in iron inputs are not instantaneous (peak response after ∼2−3 years). The TDI model derived seasonal cycles for the Southern Ocean (South Atlantic) are better matched by the BEC model by increasing (decreasing) iron inputs through atmospheric aerosols. Our results suggest that some of the interannual variability in TDI model air-sea CO2 fluxes during the past decade may be explainable by dust variability that relaxes/increases iron limitation in high-nitrate, low-chlorophyll (HNLC) ocean regions.S. C. Doney and N. Mahowald acknowledge support from NASA grant NNG05GG30G. J. K. Moore was funded by NSF grant OCE-0452972