FN Archimer Export Format
PT J
TI A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach
BT 
AF BROULLON, Daniel
   PEREZ, iz F
   VELO, Anton
   HOPPEMA, Mario
   OLSEN, Are
   TAKAHASHI, Taro
   KEY, Robert M.
   TANHUA, Toste
   MAGDALENA SANTANA-CASIANO, J.
   KOZYR, Alex
AS 1:1;2:1;3:1;4:2;5:3,4;6:5;7:6;8:7;9:8;10:9;
FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;10:;
C1 CSIC, Inst Invest Marinas, Eduardo Cabello 6, Vigo 36208, Spain.
   Helmholtz Ctr Polar & Marine Res, Alfred Wegener Inst, Postfach 120161, D-27515 Bremerhaven, Germany.
   Univ Bergen, Geophys Inst, Allegaten 70, N-5007 Bergen, Norway.
   Bjerknes Ctr Climte Res, Allegaten 70, N-5007 Bergen, Norway.
   Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA.
   Princeton Univ, Atmospher & Ocean Sci, 300 Forrestal Rd,Sayre Hall, Princeton, NJ 08544 USA.
   GEOMAR Helmholtz Ctr Ocean Res Kiel, Dusternbrooker Weg 20, D-24105 Kiel, Germany.
   Univ Las Palmas Gran Canaria, IOCAG, Inst Oceanog & Cambio Global, Las Palmas Gran Canaria, Spain.
   NOAA, Natl Ctr Environm Informat, 1315 East West Hwy, Silver Spring, MD 20910 USA.
C2 CSIC, SPAIN
   INST A WEGENER, GERMANY
   UNIV BERGEN, NORWAY
   BJERKNES CTR CLIMTE RES, NORWAY
   UNIV COLUMBIA, USA
   UNIV PRINCETON, USA
   IFM GEOMAR, GERMANY
   UNIV LAS PALMAS GRAN CANARIA, SPAIN
   NOAA, USA
IN DOAJ
IF 11.333
TC 21
UR https://archimer.ifremer.fr/doc/00676/78830/81115.pdf
   https://archimer.ifremer.fr/doc/00676/78830/81116.pdf
   https://archimer.ifremer.fr/doc/00676/78830/81117.pdf
   https://archimer.ifremer.fr/doc/00676/78830/81118.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
AB Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont-Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to the former's variability. The global root mean square error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 mu molkg(-1); LDEO: 11.4 mu molkg(-1)) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO to capture the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in 10 time series stations spread over different regions of the ocean (RMSE: 3.6 to 13.2 mu molkg(-1)). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity, and oxygen from the World Ocean Atlas 2013 and phosphate, nitrate, and silicate computed from a neural network fed with the previous fields. The resolution is 1 degrees x 1 degrees in the horizontal, 102 depth levels (0-5500 m), and monthly (0-1500 m) to annual (1550-5500 m) temporal resolution, and it is centered around the year 1995. The uncertainty of the climatology is low when compared with climatological values derived from measured TCO2 in the largest time series stations. Furthermore, a computed climatology of partial pressure of CO2 ( pCO(2)) from a previous climatology of total alkalinity and the present one of TCO2 supports the robustness of this product through the good correlation with a widely used pCO(2) climatology (Landschutzer et al., 2017). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551, Broullon et al., 2020). 
PY 2020
PD AUG
SO Earth System Science Data
SN 1866-3508
PU Copernicus Gesellschaft Mbh
VL 12
IS 3
UT 000558762600001
BP 1725
EP 1743
DI 10.5194/essd-12-1725-2020
ID 78830
ER

EF