Simulating enhanced ocean alkalinity experiments in a high-latitude fjord using nested ROMS simulations coupled with MARBL biogeochemistry

Abstract. Ocean-based carbon dioxide removal (CDR) technologies have the potential to make significant contributions to climate change mitigation, yet more research is needed to deepen our understanding of their effectiveness and safety. ​​One proposed method, ocean alkalinity enhancement (OAE), involves increasing seawater alkalinity to promote additional carbon uptake and long-term storage in the ocean. Ocean models are crucial tools to accompany OAE field trials and research, as alkalinity signals are rapidly diluted, and observations alone cannot capture the spatiotemporal scales at which interventions evolve. The C-Star open source regional ocean-biogeochemical modeling system is designed to support OAE research and quantification. Here, we present results from deploying C-Star in a nested regional modeling configuration established for Hvalfjörður, a fjord located in western Iceland. We compare the model solution with observations collected during a 2024 field campaign. These include repeated measurements of the fjord’s physical and chemical state, as well as a tracer release and sampling program used to assess the model’s ability to reproduce tracer transport and dispersal. We find that the model captures key features of the circulation in the fjord, including tidally driven currents and sea-surface height variations, tracer dispersal, and seasonal stratification. Next, we use C-Star to simulate six 96 hr OAE releases in the fjord under varying seasonal, tidal, and weather conditions. The OAE experiments produce a ~1 km² plume with detectable anomalies in alkalinity, pH and pCO₂ during the release. The uptake of CO₂ from the atmosphere is 0.05 to 0.15 mol of carbon absorbed per mol of added alkalinity during and four days following the release, and surface winds and seasonal stratification are key for both alkalinity dispersal and air-sea gas exchange. The model exhibits background biases in alkalinity, dissolved inorganic carbon (DIC), and nutrients, arising from limitations in initial and boundary conditions and representation of in situ biogeochemical processes. While these biases are a target for improvement, we show that they do not significantly degrade the model’s ability to simulate OAE-relevant anomalies. Overall, this work enhances confidence in the applicability of C-Star nested model domains as OAE research and field-trial support tools in fjord and estuary systems.