[M-GI33] Data assimilation: A fundamental approach in geosciences

[MGI33-P01] Oceanic iron distribution of the global ocean estimated by data assimilation approach

★Invited Papers

*Toshimasa Doi¹, Satoshi Osafune¹, Shuhei Masuda¹, Hajime Obata⁴, Kazuhiro Misumi³, Jun Nishioka² (1.Japan Agency for Marine-Earth Science and Technology, 2.Hokkaido University, 3.Central Research Institute of Electric Power Industry, 4. The University of Tokyo)

Keywords:oceanic iron, data assimilation

Information on the distribution of dissolved iron (dFe) concentrations in many parts of the ocean has been provided by recent ocean observation programs. However, a knowledge is still limited concerning the rates of processes that control the concentrations and cycling of dFe in the ocean. In this study, we constructed a three-dimensional gridded dataset of oceanic dFe concentrations by using both observations and a simple model of the iron cycle. The fluxes of dFe from the sea surface associated with the falling aeolian dust and from the dissolution of sediments on the seafloor of the continental shelves were set as the external flux. We used a Green's function approach to integrate the observations and a simple model. The control variables, in addition to model parameters, were the flux of dFe from the dissolution of sediments on the seafloor of the continental shelves. The three-dimensional distribution of dFe that we obtained was in basic agreement with observations. These estimates imply large differences in the cycling of dFe between the two basins that would need to be taken into consideration in projections of future scenarios. Although there is some uncertainty in our estimates, global estimates of iron cycle characteristics based on this approach can be expected to enhance understanding of the iron cycle process in the ocean.