Abstract
Abstract. Global climatologies of the seawater CO2 chemistry variables are necessary to assess the marine carbon cycle in depth. The climatologies should adequately capture seasonal variability to properly address ocean acidification and similar issues related to the carbon cycle. Total alkalinity (AT) is one variable of the seawater CO2 chemistry system involved in ocean acidification and frequently measured. We used the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2) to extract relationships among the drivers of the AT variability and AT concentration using a neural network (NNGv2) to generate a monthly climatology. The GLODAPv2 quality-controlled dataset used was modeled by the NNGv2 with a root-mean-squared error (RMSE) of 5.3 µmol kg−1. Validation tests with independent datasets revealed the good generalization of the network. Data from five ocean time-series stations showed an acceptable RMSE range of 3–6.2 µmol kg−1. Successful modeling of the monthly AT variability in the time series suggests that the NNGv2 is a good candidate to generate a monthly climatology. The climatological fields of AT were obtained passing through the NNGv2 the World Ocean Atlas 2013 (WOA13) monthly climatologies of temperature, salinity, and oxygen and the computed climatologies of nutrients from the previous ones with a neural network. The spatiotemporal resolution is set by WOA13: 1∘ × 1∘ in the horizontal, 102 depth levels (0–5500 m) in the vertical and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution. The product is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/8644, Broullón et al., 2019).
Highlights
Because of its interaction with the atmospheric carbon dioxide, the marine carbon cycle has fundamental significance for the Earth’s climate (Tanhua et al, 2013)
A monthly analysis in the previously indicated ranges shows that the largest number of samples with residuals beyond ± 3 root-mean-squared error (RMSE) are from the summer months
We have demonstrated that the AT computed by NNGv2 agrees reasonably with the measured AT when the inputs associated with it are passed through the network; i.e., the relations obtained from GLODAPv2 in the training stage are robust
Summary
Because of its interaction with the atmospheric carbon dioxide, the marine carbon cycle has fundamental significance for the Earth’s climate (Tanhua et al, 2013). The oceanic capacity to dissolve and store atmospheric CO2 and the subsequent chemical speciation have resulted in approximately 30 % less anthropogenic CO2 in the atmosphere (Le Quéré et al, 2018) than it would otherwise have. One unfortunate byproduct of this process is ocean acidification (Doney et al, 2009). As the ocean absorbs anthropogenic CO2, the seawater pH decreases – being the main change in the ocean chemistry which defines ocean acidification. There are four variables of the seawater CO2 chemistry more frequently measured in carbon chemistry campaigns: total alkalinity (AT), total dissolved inorganic carbon (TCO2, known as DIC or CT), partial pressure of CO2 (pCO2) and pH. AT is a key variable in the framework of ocean acidification because of what it is associated with: the oceanic capacity to buffer pH changes. Dickson (1981) defined AT as AT =[HCO−3 ] + 2[CO23−] + [B(OH)−4 ] + [OH−]
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