Optimum satellite remote sensing of the marine carbonate system using empirical algorithms in the Global Ocean, the Greater Caribbean, the Amazon Plume and the Bay of Bengal
Land, PE; Findlay, H; Shutler, J; et al.Ashton, I; Holding, T; Grouazel, A; GIrard-Ardhuin, F; Reul, N; Piolle, J-F; Chapron, B; Quilfen, Y; Bellerby, R; Bhadury, P; Salisbury, J; Vandemark, D; Sabia, R
Date: 8 November 2019
Article
Journal
Remote Sensing of Environment
Publisher
Elsevier
Publisher DOI
Abstract
Improving our ability to monitor ocean carbonate chemistry has become a priority as the
ocean continues to absorb carbon dioxide from the atmosphere. This long-term uptake is
reducing the ocean pH; a process commonly known as ocean acidification. The use of
satellite Earth Observation has not yet been thoroughly explored as an ...
Improving our ability to monitor ocean carbonate chemistry has become a priority as the
ocean continues to absorb carbon dioxide from the atmosphere. This long-term uptake is
reducing the ocean pH; a process commonly known as ocean acidification. The use of
satellite Earth Observation has not yet been thoroughly explored as an option for routinely
observing surface ocean carbonate chemistry, although its potential has been highlighted. We
demonstrate the suitability of using empirical algorithms to calculate total alkalinity (AT) and
total dissolved inorganic carbon (CT), assessing the relative performance of satellite,
interpolated in situ, and climatology datasets in reproducing the wider spatial patterns of
these two variables. Both AT and CT in situ data are reproducible, both regionally and
globally, using salinity and temperature datasets, with satellite observed salinity from
Aquarius and SMOS providing performance comparable to other datasets for the majority of
case studies. Global root mean squared difference (RMSD) between in situ validation data
and satellite estimates is 17 µmol kg-1 with bias < 5 µmol kg-1 for AT and 30 µmol kg-1 45 with
bias < 10 µmol kg-1 46 for CT. This analysis demonstrates that satellite sensors provide a
credible solution for monitoring surface synoptic scale AT and CT. It also enables the first
demonstration of observation-based synoptic scale AT and CT temporal mixing in the
Amazon plume for 2010-2016, complete with a robust estimation of their uncertainty.
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