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The Ross Sea is a region characterized by high primary productivity in\udcomparison to other Antarctic coastal regions, and its productivity is marked\udby considerable variability both spatially (1–50 km) and temporally\ud(days to weeks). This variability presents a challenge for inferring\udphytoplankton dynamics from observations that are limited in time or space,\udwhich is often the case due to logistical limitations of sampling. To better\udunderstand the spatiotemporal variability in Ross Sea phytoplankton dynamics\udand to determine how restricted sampling may skew dynamical interpretations,\udhigh-resolution bio-optical glider measurements were assimilated into\uda one-dimensional biogeochemical model adapted for the Ross Sea. The\udassimilation of data from the entire glider track using the micro-genetic and\udlocal search algorithms in the Marine Model Optimization Testbed improves the\udmodel–data fit by  ∼ 50 %, generating rates of integrated primary\udproduction of 104 g C m−2 yr−1 and export at 200 m\udof 27 g C m−2 yr−1. Assimilating glider data from three\uddifferent latitudinal bands and three different longitudinal bands results in\udminimal changes to the simulations, improves the model–data fit with respect\udto unassimilated data by  ∼ 35 %, and confirms that analyzing these\udglider observations as a time series via a one-dimensional model is\udreasonable on these scales. Whereas assimilating the full glider data set\udproduces well-constrained simulations, assimilating subsampled glider data at\uda frequency consistent with cruise-based sampling results in a wide range of\udprimary production and export estimates. These estimates depend strongly on\udthe timing of the assimilated observations, due to the presence of high\udmesoscale variability in this region. Assimilating surface glider data\udsubsampled at a frequency consistent with available satellite-derived data\udresults in 40 % lower carbon export, primarily resulting from optimized\udrates generating more slowly sinking diatoms. This analysis highlights the\udneed for the strategic consideration of the impacts of data frequency,\udduration, and coverage when combining observations with biogeochemical\udmodeling in regions with strong mesoscale variability

Assimilating bio-optical glider data during a phytoplankton bloom in the southern Ross Sea