Towards constraining the drivers of variability and trends in subantarctic productivity

The subantarctic Southern Ocean is a climatically important region, where primary production largely drives the seasonal uptake of atmospheric CO 2 , contributing to the sequestration of anthropogenic carbon emissions. Seasonal iron and light limitation control annual net primary production (NPP) in this region, but the explicit mechanisms that drive differences in NPP between years (NPP interannual variability) remain elusive due to sparse observations. This uncertainty is reflected in inconsistent interannual variability and trend estimates of remotely-sensed NPP algorithms. Without clear mechanistic underpinning, confidence in remotely-sensed NPP trends remains low and hinders predictive capability. To overcome observational limitations and better understand the drivers of interannual NPP variability, we analysed the explicit bottom-up and top-down controls of depth integrated NPP in a biogeochemical ocean model historical run (PISCES-QUOTA-FE; 1958–2022) in the subantarctic zone south of Tasmania, Australia. The highest NPP years were primarily driven by increased relief of iron limitation, with iron supplied from both deeper mixing in winter/spring and enhanced remineralisation in summer. In spring, higher phytoplankton growth rates were decoupled from surface biomass, such that years with higher NPP were due to faster growth in the mixed layer. Faster growth rates emerged following deeper winter mixed layers, driving phytoplankton distributions deeper in winter and reducing mixed layer grazing loss rates in spring. This generated a predator-prey dynamic favouring surface biomass accumulation moving into summer. Thus, inconsistent remote-sensing NPP estimates may derive from how algorithms link biomass (rather than growth rates) to NPP. We applied our analysis to a subset of CMIP6 models, and while all historical simulations converged with respect to positive trends in NPP, bias from sea surface temperature trends influenced the mechanisms driving interannual NPP variability. These findings show that interacting top-down and bottom-up processes can decouple changes in NPP with respect to phytoplankton biomass, which has important implications for remote sensing NPP estimates based on biomass. Therefore, the need for cautionary approaches to NPP trend interpretation is highlighted. Additionally, further observational data, including zooplankton grazing and phytoplankton biomass distributions over depth are needed to ground truth mechanistic understanding of NPP drivers and develop models through mechanistic inclusion.