assimilating bio-optical glider data during a phytoplankton bloom in the southern ross sea
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2018
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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.
| Reference Key |
kaufman2018biogeosciencesassimilating
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|---|---|
| Authors | ;D. E. Kaufman;M. A. M. Friedrichs;J. C. P. Hemmings;J. C. P. Hemmings;W. O. Smith Jr. |
| Journal | tetrahedron letters |
| Year | 2018 |
| DOI |
10.5194/bg-15-73-2018
|
| URL | |
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