2024-03-19T12:28:23Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1572062022-06-24T10:31:05Zcom_10261_47com_10261_8col_10261_300
DIGITAL.CSIC
author
Broullón, Daniel
author
Pérez, Fiz F.
author
Velo, A.
author
Suzuki, Toru
funder
Ministerio de Economía y Competitividad (España)
funder
European Commission
2017-11-10T13:17:59Z
2017-11-10T13:17:59Z
2017
International Carbon Dioxide Conference (2017)
http://hdl.handle.net/10261/157206
http://dx.doi.org/10.13039/501100000780http://dx.doi.org/10.13039/501100003329
For decades, the anthropogenic modification of the carbon cycle has been widely studied. More
recently, ocean acidification studies have increased significantly. Establishing robust climatologies of
seawater CO2 chemistry variables and building models are a key point for a better understanding of
the associated processes. The availability and quality of data is crucial for the evaluation of climate
models and, consequently, to improve their predictions.
Version 2 of the Global Ocean Data Analysis Project (GLODAPv2) is an internally consistent data
product composed of data from 724 scientific cruises covering the entire global ocean. Among others,
it contains seawater CO2 chemistry variables such as total alkalinity (AT), total dissolved inorganic
carbon (TCO2) and pH. This sparse dataset has been subjected to extensive quality control and
different interpolation techniques have been applied to extend the data coverage on a homogeneous
grid (Lauvset et al. 2016). We propose a novel neural network approach to generate annual and
monthly climatologies of AT, TCO2, pH and both calcite and aragonite saturation state from the
GLODAPv2 dataset for the preindustrial and current periods. These climatologies are fitted to the
World Ocean Atlas 2013 version 2 (WOA13v2) grid. WOA13v2 is a set of objectively analyzed (1°
grid) climatological fields of different oceanographic variables (but not CO2 system) at standard depth
levels for annual, seasonal, and monthly compositing periods for the World Ocean.
A feed-forward neural network was chosen in a multi-layer architecture, which allows linear and nonlinear
variability to be assimilated by the network. The proposed configuration is able to approximate
most functions arbitrarily well (Hagan et al., 2014). We have tested different neural network designs
and sizes to obtain the minimum error. For that, the number of neurons in the network was varied and
different training techniques were used. The input variables introduced in the network, which must be
related to AT and TCO2 variability, were latitude, longitude, depth, potential temperature, phosphate,
nitrate, silicate, year, month and atmospheric pCO2. First, the network was trained with GLODAPv2
data and then AT and TCO2 fields were derived from WOA13v2 data. Saturation states and pH were
computed from these two variables.
The monthly pre-industrial climatology will be generated by eliminating anthropogenic carbon from the
ocean.
eng
openAccess
Ocean
Carbonate system
Climatologies
Neural network
Climatologies of seawater CO2 chemistry variables: A neural network approach
póster de congreso
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URL
https://digital.csic.es/bitstream/10261/157206/1/Climatologies_seawater_poster.pdf
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https://digital.csic.es/bitstream/10261/157206/2/Climatologies_seawater_abstract.pdf
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