FN Archimer Export Format
PT J
TI Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A Novel Approach Based on Neural Networks
BT 
AF SAUZEDE, Raphaelle
   CLAUSTRE, Hervé
   BITTIG, Henry
   PASQUERON DE FOMMERVAULT, Orens
   GATTUSO, Jean-Pierre
   LEGENDRE, Louis
   JOHNSON, Kenneth S
AS 1:1,2;2:1;3:1;4:1,3;5:5;6:1;7:4;
FF 1:;2:;3:;4:;5:;6:;7:;
C1 Observatoire Océanologique de Villefranche, Laboratoire d'Océanographie de Villefranche, France
   Écosystèmes Insulaires Océaniens (EIO, UMR-241), IRD, Ifremer, UPF and ILM, French Polynesia
   Departamento de Oceanografìa Fisica, Centro de Investigacion Cientìfica y de Educacion Superior de Ensenada, Mexico
   Institute for Sustainable Development and International Relations, Sciences Po, France
   Monterey Bay Aquarium Research Institute, USA
C2 UNIV PARIS 06, FRANCE
   IRD, FRANCE
   CICESE, MEXICO
   IDDRI, FRANCE
   MONTEREY BAY AQUARIUM RES INST, USA
IN DOAJ
IF 5.247
TC 70
UR https://archimer.ifremer.fr/doc/00383/49467/49952.pdf
   https://archimer.ifremer.fr/doc/00383/49467/49953.jpeg
   https://archimer.ifremer.fr/doc/00383/49467/49954.jpeg
   https://archimer.ifremer.fr/doc/00383/49467/49955.jpeg
   https://archimer.ifremer.fr/doc/00383/49467/49956.jpeg
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
DE ;neural network;nutrients;carbonate system;global ocean;GLODAPv2 database;profiling floats
AB A neural network-based method (CANYON: CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) was developed to estimate water-column biogeochemically relevant variables in the Global Ocean. These are the concentrations of 3 nutrients [nitrate (NO3−), phosphate (PO43−) and silicate (Si(OH)4)] and 4 carbonate system parameters [total alkalinity (AT), dissolved inorganic carbon (CT), pH (pHT) and partial pressure of CO2 (pCO2)], which are estimated from concurrent in situ measurements of temperature, salinity, hydrostatic pressure and oxygen (O2) together with sampling latitude, longitude and date. Seven neural-networks were developed using the GLODAPv2 database, which is largely representative of the diversity of open-ocean conditions, hence making CANYON potentially applicable to most oceanic environments. For each variable, CANYON was trained using 80 % randomly chosen data from the whole database (after eight 10° x 10° zones removed providing an “independent data-set” for additional validation), the remaining 20 % data were used for the neural-network test of validation. Overall, CANYON retrieved the variables with high accuracies (RMSE): 0.93 mol kg-1 (NO3−), 0.07 mol kg-1 (PO43-), 3.0 mol kg-1 (Si(OH)4), 0.019 (pHT), 7 mol kg-1 (AT), 10 mol kg-1 (CT) and 28 atm (pCO2). This was confirmed for the 8 independent zones not included in the training process. CANYON was also applied to the Hawaiian Time Series site to produce a 22-years long simulated time series for the above 7 variables. Comparison of modeled and measured data was also very satisfactory (RMSE in the order of magnitude of RMSE from validation test). CANYON is thus a promising method to derive distributions of key biogeochemical variables. It could be used for a variety of global and regional applications ranging from data quality control to the production of datasets of variables required for initialization and validation of biogeochemical models but difficult to obtain. In particular, combining the increased coverage of the global Biogeochemical-Argo program, where O2 is one of the core variables now very accurately measured, with the CANYON approach offers the fascinating perspective of obtaining large-scale estimates of key biogeochemical variables with unprecedented spatial and temporal resolutions. 
PY 2017
SO Frontiers In Marine Science
SN 2296-7745
VL 4
IS 128
UT 000457690600128
BP 1
EP 17
DI 10.3389/fmars.2017.00128
ID 49467
ER

EF