@Article{FordTSKLSPSLCBLKB:2021:WiSpMe,
author = "Ford, Daniel and Tilstone, Gavin H. and Shutler, Jamie D. and
Kitidis, Vassilie and Lobanova, Polina and Schwarz, Jill and
Poulton, Alex J. and Serret, Pablo and Lamont, Tarron and Chuqui,
Mateus and Barlow, Ray and Lozano, Jose and Kampel, Milton and
Brandini, Frederico",
affiliation = "{Plymouth Marine Laboratory} and {Plymouth Marine Laboratory} and
{University of Exeter} and {Plymouth Marine Laboratory} and {St.
Petersburg State University} and {University of Plymouth} and
{Heriot-Watt University} and {Universidad de Vigo} and {Oceans \&
Coasts Research} and {Universidade de S{\~a}o Paulo (USP)} and
{Bayworld Centre for Research \& Education} and {Universidad de
Vigo} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade de S{\~a}o Paulo (USP)}",
title = "Wind speed and mesoscale features drive net autotrophy in the
South Atlantic Ocean",
journal = "Remote Sensing of Environment",
year = "2021",
volume = "260",
pages = "e112435",
month = "July",
keywords = "MODIS-A, in situ uncertainty, Ocean colour, Environmental drivers,
South Atlantic Ocean, Ocean metabolism.",
abstract = "A comprehensive in situ dataset of chlorophyll a (Chl a; N =
18,001), net primary production (NPP; N = 165) and net community
production (NCP; N = 95), were used to evaluate the performance of
Moderate Resolution Imaging Spectroradiometer on Aqua (MODIS-A)
algorithms for these parameters, in the South Atlantic Ocean, to
facilitate the accurate generation of satellite NCP time series.
For Chl a, five algorithms were tested using MODIS-A data, and
OC3-CI performed best, which was subsequently used to compute NPP.
Of three NPP algorithms tested, a Wavelength Resolved Model (WRM)
was the most accurate, and was therefore used to estimate NCP with
an empirical relationship between NCP with NPP and sea surface
temperature (SST). A perturbation analysis was deployed to
quantify the range of uncertainties introduced in satellite NCP
from input parameters. The largest reductions in the uncertainty
of satellite NCP came from MODIS-A derived NPP using the WRM (40%)
and MODIS-A Chl a using OC3-CI (22%). The most accurate NCP
algorithm, was used to generate a 16 year time series (2002 to
2018) from MODIS-A to assess climate and environmental drivers of
NCP across the South Atlantic basin. Positive correlations between
wind speed anomalies and NCP anomalies were observed in the
central South Atlantic Gyre (SATL), and the Benguela Upwelling
(BENG), indicating that autotrophic conditions may be fuelled by
local wind-induced nutrient inputs to the mixed layer. Sea Level
Height Anomalies (SLHA), used as an indicator of mesoscale eddies,
were negatively correlated with NCP anomalies offshore of the BENG
upwelling fronts into the SATL, suggesting autotrophic conditions
are driven by mesoscale features. The Agulhas bank and
Brazil-Malvinas confluence regions also had a strong negative
correlation between SLHA and NCP anomalies, similarly indicating
that NCP is forced by mesoscale eddy generation in this region.
Positive correlations between SST anomalies and the Multivariate
ENSO Index (MEI) in the SATL, indicated the influence of El Niņo
events on the South Atlantic Ocean, however the plankton community
response was less clear.",
doi = "10.1016/j.rse.2021.112435",
url = "http://dx.doi.org/10.1016/j.rse.2021.112435",
issn = "0034-4257",
language = "en",
targetfile = "ford_wind.pdf",
urlaccessdate = "03 jun. 2024"
}