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Discussion papers
https://doi.org/10.5194/essd-2018-111
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2018-111
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Oct 2018

26 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

A global monthly climatology of total alkalinity: a neural network approach

Daniel Broullón1, Fiz F. Pérez1, Antón Velo1, Mario Hoppema2, Are Olsen3, Taro Takahashi4, Robert M. Key5, Melchor González-Dávila6, Toste Tanhua7, Emil Jeansson8, Alex Kozyr9, and Seven M. A. C. van Heuven10 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, Allègaten 70, 5007 Bergen, Norway
  • 4Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA
  • 5Atmospheric and Oceanic Sciences, Princeton Universi ty, 300 Forrestal Road, Sayre Hall, Princeton, NJ 08544, USA
  • 6Instituto de Oceanografía y Cambio Global, IOCAG, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
  • 7GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker Weg 20, 24105 Kiel, Germany
  • 8Uni Research Climate, Bjerknes Centre for Climate Research, Jahnebakken 5, 5007 Bergen, Norway
  • 9NOAA National Centers for Environmental Information, 1315 East-West Hwy Silver Spring, MD 20910 USA
  • 10Faculty of Science and Enginee ring, Isotope Research – Energy and Sustainability Research Institute Groningen, University of Groningen, Nijenborgh 6, 9747 AG Groningen, the Netherlands

Abstract. Global climatologies of the seawater CO2 chemistry variables are necessary to assess the marine carbon cycle in depth. The seasonal variability should be adequately captured in them to properly address issues such as ocean acidification. Total alkalinity (AT) is one variable of the seawater CO2 chemistry system involved in ocean acidification and frequently measured during campaigns assessing the marine carbon cycle. We took advantage of the data product Global Ocean Data Analysis Project version 2 (GLODAPv2) to extract the relations between the drivers of the AT variability and this variable using a neural network to generate a monthly climatology. 99% of the GLODAPv2 dataset used was modelled by the network with a root-mean-squared error (RMSE) of 5.1 µmol kg-1. The validation carried out using independent datasets revealed the good generalization of the network. Five ocean time-series stations used as an independent test showed an acceptable RMSE in the range of 3.1-6.2 µmol kg-1. The successful modeling of the monthly variability of AT in the time-series makes our network a good candidate to generate a monthly climatology. It was obtained passing the climatologies of the World Ocean Atlas 2013 (WOA13) through the network. The spatiotemporal resolution of the climatology is determined by the one of WOA13: 1ºx1º in the horizontal, 102 depth levels (0-5500m) in the vertical, and 12 months. We offer the product as a service to the scientific community at the data repository of the Spanish National Research Council (CSIC; doi: http://dx.doi.org/10.20350/digitalCSIC/8564) with the purpose to contribute to a continuous improvement of the understanding of the global carbon cycle.

Daniel Broullón et al.
Interactive discussion
Status: open (until 21 Dec 2018)
Status: open (until 21 Dec 2018)
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Daniel Broullón et al.
Data sets

Climatology of total alkalinity D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, M. González-Dávila, T. Tanhua, E. Jeansson, A. Kozyr, and S. M. A. C. van Heuven https://doi.org/10.20350/digitalCSIC/8564

Model code and software

Neural networks to compute total alkalinity D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, M. González-Dávila, T. Tanhua, E. Jeansson, A. Kozyr, and S. M. A. C. van Heuven https://doi.org/10.20350/digitalCSIC/8564

Video supplement

Video of the climatology of total alkalinity D. Broullón, F. F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R. M. Key, M. González-Dávila, T. Tanhua, E. Jeansson, A. Kozyr, and S. M. A. C. van Heuven https://doi.org/10.20350/digitalCSIC/8564

Daniel Broullón et al.
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Short summary
In this work, we are contributing to the knowledge of the consequences of climate change in the ocean. We have focused on a variable related to this process: total alkalinity. We have designed a monthly climatology of total alkalinity using artificial intelligence techniques, that is, a representation of the average capacity of the ocean in the last decades to decelerate the consequences of climate change. The climatology is especially useful to infer the evolution of the ocean through models.
In this work, we are contributing to the knowledge of the consequences of climate change in the...
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