Abstract
Abstract. Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 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 square 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 to capture the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in 10 time series stations spread over different regions of the ocean (RMSE: 3.6 to 13.2 µmol kg−1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity, and oxygen from the World Ocean Atlas 2013 and phosphate, nitrate, and silicate computed from a neural network fed with the previous fields. The resolution is 1∘×1∘ in the horizontal, 102 depth levels (0–5500 m), and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution, and it is centered around 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., 2017). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020).
Highlights
The ocean is the major carbon reservoir of the Earth
Because in Gv2QC, both NNGv2LDEO and NNGv2QCLDEO produce the same global root mean square error (RMSE) (6.1 μmol kg−1), it is likely that the Gv2 dataset contains high-quality measurements, and the possible errors in the non-QC data of this dataset are clearly avoided by the network; otherwise NNGv2LDEO should have a higher RMSE in the test dataset than NNGv2QCLDEO because of an overfitting of the errors in the Gv2 dataset
The network properly fitted TCO2 derived from the Lamont–Doherty Earth Observatory (LDEO) dataset since it does not significatively increase the global RMSE relative to a network only trained with Gv2
Summary
The ocean is the major carbon reservoir of the Earth. Most of this carbon occurs as dissolved inorganic carbon (TCO2, known as DIC or CT; Ciais et al, 2013; Tanhua et al, 2013). Accompanying the change in TCO2, the pH and carbonate ion concentration have been declining because of the anthropogenic process previously mentioned, these changes being reflected in the proportions of the chemical species of TCO2 (Kleypas and Langdon, 2000; Orr et al, 2005) These changes in seawater chemistry framed in the ocean acidification process can negatively influence various processes involving marine organisms such as calcification, growth, and survival (Orr et al, 2005; Fabry et al, 2008; Hendriks et al, 2010; HoeghGuldberg and Bruno, 2010; Kroeker et al, 2013)
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