Turbulence-driven nutrient supply sustains ice-algal growth in the Arctic: a modeling approach with CICE 6.1 coupled to Icepack 1.2, and with SIMBA 2.0

Abstract. Fluxes of nutrients at the ice-ocean interface are affected by the smooth or turbulent nature of the flow under the ice. The nature of the flow depends on the friction velocity, which determines the thickness of the molecular sublayer, and on the roughness of the ice bottom. Based on in situ boundary layer studies, the range of variability of the thickness of the molecular sublayer and that of bottom roughness suggest that the flow under sea ice may easily shift from smooth to turbulent. This transition enhances nutrient exchanges at the ice-ocean interface. Despite the importance of such turbulent nutrient exchanges for sea-ice algae, no current biogeochemical model accounts for the dependence of fluxes on the nature of the flow, while different approaches were previously implemented to compensate for the perceived overestimation of nutrient limitation on ice algal growth. In the present study, we implement and test a Reynolds number-based parameterization that accounts for shifts between smooth and turbulent flow, weighting the contributions of viscosity and turbulence, in two sea-ice biogeochemical models. The results of three different case studies show that with increasing roughness, the turbulent nature of the flow contributes to larger fluxes of nutrients from the ocean to the ice. Nutrients accumulate during the winter, up to concentrations comparable to surface waters of the ocean. When light levels are sufficient to initiate algal growth, enhanced fluxes can support higher total production over a longer period, resulting in biomass accumulation more than twice that achieved under smooth flow conditions. In nutrient-rich waters, turbulence can supply sufficient nutrients to bring model outputs closer to observations. Our parameterization provides a more realistic representation of nutrient exchange at the sea ice–ocean interface, avoiding the need to “overtune” other model processes to reproduce observations. However, other processes – particularly the representation of light limitation – play a critical role in determining the timing of algal growth onset and strongly affect the agreement with observations.