Articles | Volume 11, issue 3
https://doi.org/10.5194/essd-11-1109-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-11-1109-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
31 Jul 2019
| 31 Jul 2019
A global monthly climatology of total alkalinity: a neural network approach
Daniel Broullón
CORRESPONDING AUTHOR
Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
Fiz F. Pérez
Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
Antón Velo
Instituto de Investigaciones Marinas, CSIC, Eduardo Cabello 6, 36208 Vigo, Spain
Mario Hoppema
Alfred Wegener Institute Helmholtz Centre for Polar and Marine
Research, Postfach 120161, 27515 Bremerhaven, Germany
Are Olsen
Geophysical Institute, University of Bergen and Bjerknes Centre for
Climate Research, Allégaten 70, 5007 Bergen, Norway
Taro Takahashi
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA
Robert M. Key
Atmospheric and Oceanic Sciences, Princeton University, 300 Forrestal Road, Sayre Hall, Princeton, NJ 08544, USA
Toste Tanhua
GEOMAR Helmholtz Centre for Ocean Research Kiel, Düsternbrooker
Weg 20, 24105 Kiel, Germany
Melchor González-Dávila
Instituto de Oceanografía y Cambio Global, IOCAG, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
Emil Jeansson
Uni Research Climate, Bjerknes Centre for Climate Research,
Jahnebakken 5, 5007 Bergen, Norway
Alex Kozyr
NOAA National Centers for Environmental Information, 1315 East-West
Hwy Silver Spring, MD 20910, USA
Steven M. A. C. van Heuven
Faculty of Science and Engineering, Isotope Research – Energy and
Sustainability Research Institute Groningen, University of Groningen,
Nijenborgh 6, 9747 AG Groningen, the Netherlands
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Latest update: 24 Apr 2024
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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