Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Journal topic
Volume 15, issue 1
Biogeosciences, 15, 73–90, 2018
https://doi.org/10.5194/bg-15-73-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Biogeosciences, 15, 73–90, 2018
https://doi.org/10.5194/bg-15-73-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Jan 2018

Research article | 04 Jan 2018

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

Daniel E. Kaufman1, Marjorie A. M. Friedrichs1, John C. P. Hemmings2,a, and Walker O. Smith Jr.1 Daniel E. Kaufman et al.
  • 1Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA, USA
  • 2Wessex Environmental Associates, Salisbury, UK
  • anow at: Met Office, Exeter, UK

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

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Computer simulations of the highly variable phytoplankton in the Ross Sea demonstrated how incorporating data from different sources (satellite, ship, or glider) results in different system interpretations. For example, simulations assimilating satellite-based data produced lower carbon export estimates. Combining observations with models in this remote, harsh, and biologically variable environment should include consideration of the potential impacts of data frequency, duration, and coverage.
Computer simulations of the highly variable phytoplankton in the Ross Sea demonstrated how...
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