The Amazon River strongly modifies the biogeochemistry of the Western Tropical Atlantic (WTA). To disentangle the different mechanisms driving these modifications, we conduct a series of modeling experiments with a high‐resolution regional ocean model (ROMS) coupled to a biogeochemical/ecological model (BEC) that we augmented to include Diatom‐Diazotroph‐Assemblages (DDAs). In our model, the Amazon River increases net primary production (NPP) in the WTA by almost 10%, exceeding the stimulation expected from the supplied inorganic nitrogen and phosphorus by a factor of two. This amplification is fueled by new nitrogen stemming from DDA‐driven N2 fixation in the plume region, supported, in part, by the consumption of riverine dissolved organic phosphorus. The vertical export of organic carbon is enhanced by a shift of the phytoplankton community toward diatoms induced by the large amount of Si(OH)4 delivered by the Amazon. These changes in NPP and export production induce a strong uptake of atmospheric CO2. In contrast, the remineralization of the river‐delivered terrestrial organic matter leads to a release of CO2 over the WTA, which is partially offset by a net uptake induced by the riverine dissolved inorganic carbon and alkalinity. Overall, the Amazon reduces the strong outgassing of the WTA in our simulations by more than 50%. Our study demonstrates how rivers modify the marine biological pump and the air‐sea CO2 fluxes in the downstream ocean through a myriad of cascading effects, highlighting the need to fully consider the land‐ocean aquatic continuum in the modeling of the Earth System.

Long-term flood risk management often relies on future sea-level projections. Projected uncertainty ranges are however widely divergent as a result of different methodological choices. The IPCC has condensed this deep uncertainty into a single uncertainty range covering 66% probability or more. Alternatively, structured expert judgment summarizes divergent expert opinions in a single distribution. Recently published uncertainty ranges that are derived from these Bconsensus^assessments appear to differ by up to a factor four. This might result in overconfidence or overinvestment in strategies to cope with sea-level change. Here we explore possible reasons for these different interpretations. This is important for (i) the design of robust strategies and (ii) the exploration of pathways that may eventually lead to some kind of consensus distributions that are relatively straightforward to interpret.