How deep Argo will improve the deep ocean in an ocean reanalysis

Global ocean sampling with autonomous floats going to 4,000 m/6,000 m, known as the deep Argo array, constitutes one of the next challenges for tracking climate change. The question here is how such global deep array will impact on ocean reanalyses. Based on the different behavior of four ocean reanalyses, we first identified that large uncertainty exist in current reanalyses in representing local heat and freshwater fluxes in the deep ocean (1 W/m2 and 10 cm/yr regionally). Additionally, temperature and salinity comparison with deep Argo observations demonstrates that reanalysis error in the deep ocean are of the same size, or even stronger, than the deep ocean signal. An experimental approach, using the 1/4◦ GLORYS2V4 system, is then presented to anticipate how the evolution of the global ocean observing system (GOOS), with the advent of deep Argo, would contribute to ocean reanalyses. Based on Observing System Simulation Experiments (OSSE), which consist in extracting observing system data sets from a realistic simulation to be subsequently assimilated in an experimental system, this study suggests that a global deep Argo array of 1,200 floats will significantly constrain the deep ocean by reducing temperature and salinity errors by around 50%. Our results also show that such deep global array will help ocean reanalyses to reduce error in temperature changes below 2,000 m, equivalent to global ocean heat fluxes from 0.15 to 0.07 W/m2, and from 0.26 to 0.19 W/m2 for the entire water column. This work exploits the capabilities of operational systems to provide comprehensive information for the evolution of the GOOS