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Discussion papers
https://doi.org/10.5194/essd-2020-37
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-37
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 11 Mar 2020

Submitted as: data description paper | 11 Mar 2020

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This preprint is currently under review for the journal ESSD.

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

Daniel Broullón1, Fiz F. Pérez1, Antón Velo Lanchas1, Mario Hoppema2, Are Olsen3, Taro Takahashi4,, Robert M. Key5, Toste Tanhua6, J. Magdalena Santana-Casiano7, and Alex Kozyr8 Daniel Broullón et al.
  • 1Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
  • 2Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Postfach 120161, 27515 Bremerhaven, Germany
  • 3Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Allégaten 70, 5007 Bergen, Norway
  • 4Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA
  • 5Atmospheric and Oceanic Sciences, Princeton University, 300 Forrestal Road, Sayre Hall, Princeton, NJ 08544, USA
  • 6GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20D-24105 Kiel, Germany
  • 7Instituto de Oceanografía y Cambio Global, IOCAG, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
  • 8NOAA National Centers for Environmental Information, 1315 East-West Hwy Silver Spring, MD 20910 USA
  • deceased

Abstract. Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than two 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-squared error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 µmol kg−1; LDEO: 11.4 µmol kg−1) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO in capturing the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in ten time-series stations spread over different regions of the ocean (RMSE: 3.6 to 13.1 µmol kg−1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity and oxygen from World Ocean Atlas 2013, and phosphate, nitrate and silicate computed from a neural network fed with the previous fields. The resolution is 1º x 1º in the horizontal, 102 depth levels (0–5500 m) and monthly (0–1500 m) to annual (1550–5500 m), and it is centered in 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 (pCO2) 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 pCO2 climatology (Landschützer et al., 2016). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; http://dx.doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020).

Daniel Broullón et al.

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Status: open (until 14 May 2020)
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Daniel Broullón et al.

Data sets

Monthly climatology of total dissolved inorganic carbon (TCO2_NNGv2LDEO_climatology.nc) D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, T. Tanhua, J. M. Santana-Casiano and A. Kozyr https://doi.org/10.20350/digitalCSIC/10551

Monthly climatology of partial pressure of CO2 (pCO2_NNGv2LDEO_climatology.nc) Daniel Broullón, Fiz F. Pérez, Antón Velo, Mario Hoppema, Are Olsen, Taro Takahashi, Robert M. Key, Toste Tanhua, J. Magdalena Santana-Casiano and Alex Kozyr https://doi.org/10.20350/digitalCSIC/10551

Model code and software

Neural Network Object (NNGv2LDEO.mat) D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, T. Tanhua, J. M. Santana-Casiano and A. Kozyr https://doi.org/10.20350/digitalCSIC/10551

Video supplement

Video of the monthly climatology of total dissolved inorganic carbon D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, T. Tanhua, J. M. Santana-Casiano and A. Kozyr https://doi.org/10.20350/digitalCSIC/10551

Daniel Broullón et al.

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Short summary
This work offers a vision of the global ocean regarding the carbon cycle and the implications of ocean acidification through a climatology of a changing variable in the context of climate change: total dissolved inorganic carbon. The climatology was designed through artificial intelligence techniques to represent the mean state of the present ocean. It is very useful to introduce in models to evaluate the state of the ocean from different perspectives.
This work offers a vision of the global ocean regarding the carbon cycle and the implications of...
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