A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

Type Article
Date 2020-08
Language English
Author(s) Broullon DanielORCID1, Perez Iz F1, Velo AntonORCID1, Hoppema MarioORCID2, Olsen AreORCID3, 4, Takahashi Taro5, Key Robert M.6, Tanhua Toste7, Magdalena Santana-Casiano J.8, Kozyr Alex9
Affiliation(s) 1 : CSIC, Inst Invest Marinas, Eduardo Cabello 6, Vigo 36208, Spain.
2 : Helmholtz Ctr Polar & Marine Res, Alfred Wegener Inst, Postfach 120161, D-27515 Bremerhaven, Germany.
3 : Univ Bergen, Geophys Inst, Allegaten 70, N-5007 Bergen, Norway.
4 : Bjerknes Ctr Climte Res, Allegaten 70, N-5007 Bergen, Norway.
5 : Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA.
6 : Princeton Univ, Atmospher & Ocean Sci, 300 Forrestal Rd,Sayre Hall, Princeton, NJ 08544 USA.
7 : GEOMAR Helmholtz Ctr Ocean Res Kiel, Dusternbrooker Weg 20, D-24105 Kiel, Germany.
8 : Univ Las Palmas Gran Canaria, IOCAG, Inst Oceanog & Cambio Global, Las Palmas Gran Canaria, Spain.
9 : NOAA, Natl Ctr Environm Informat, 1315 East West Hwy, Silver Spring, MD 20910 USA.
Source Earth System Science Data (1866-3508) (Copernicus Gesellschaft Mbh), 2020-08 , Vol. 12 , N. 3 , P. 1725-1743
DOI 10.5194/essd-12-1725-2020
WOS© Times Cited 19

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).

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Broullon Daniel, Perez Iz F, Velo Anton, Hoppema Mario, Olsen Are, Takahashi Taro, Key Robert M., Tanhua Toste, Magdalena Santana-Casiano J., Kozyr Alex (2020). A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach. Earth System Science Data, 12(3), 1725-1743. Publisher's official version : https://doi.org/10.5194/essd-12-1725-2020 , Open Access version : https://archimer.ifremer.fr/doc/00676/78830/