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 Niño events on the South Atlantic Ocean, however the plankton community response was less clear.
Highlights
Autotrophic plankton produce up to 50% of the net organic carbon on our planet (Field et al, 1998), as they draw down carbon dioxide (CO2) from the atmosphere into the ocean
N indicates the number of matchups used to compute statistics
The most accurate algorithm for each statistic is highlighted in bold
Summary
Autotrophic plankton produce up to 50% of the net organic carbon on our planet (Field et al, 1998), as they draw down carbon dioxide (CO2) from the atmosphere into the ocean. Measurements of NCP made on research ships, though essential to un derstanding the dynamics of NCP, provides only a snapshot of the system rather than broader temporal-spatial dynamics Such measurements can be estimated by ocean colour from space (Tilstone et al, 2015a). The use of in situ data to identify the most accurate ocean colour satellite algo rithms, will facilitate the generation of reliable synoptic-scale NCP time series. Such data are needed for identify trends in the metabolic balance of the oceans, and understand the biological draw down and release of CO2 from the oceans
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