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).

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).

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.

Abstract. Short and long-term variability of seawater carbon dioxide (CO2) system shows large differences between different ecosystems which are derived from the characteristic processes of each area. The high variability of coastal ecosystems, their ecological and economic significance, the anthropogenic influence on them and their behavior as sources or sinks of atmospheric CO2, highlight the relevance to better understand the processes that underlie the variability and the alterations of the CO2 system at different spatiotemporal scales. To confidently achieve this purpose, it is necessary to have high-frequency data sustained over several years in different regions. In this work, we contribute to this need by configuring and training two neural networks with the capacity to model the weekly variability of pH and total alkalinity (AT) in the upper 50 m of the water column of the Ría de Vigo (NW Spain), with an error of 0.031 pH units and 10.9 µmol kg−1 respectively. With these networks, we generated weekly time series of pH and AT in seven locations of the Ría de Vigo in three depth ranges (0–5 m, 5–10 m and 10–15 m), which adequately represent independent discrete measurements. In a first analysis of the time series, a high short-term variability is observed, being larger for the inner stations of the Ría de Vigo. The lowest values of pH and AT were obtained for the inner zone, showing a progressive increase towards the outer/middle zone of the ría. The mean seasonal cycle also reflects the gradient between both zones, with a larger amplitude and variability for both variables in the inner zone. On the other hand, the long-term trends derived from the time series of pH show a higher acidification than that obtained for the open ocean, with surface trends ranging from −0.020 pH units per year in the outer/middle zone to −0.032 pH units per year in the inner zone. In addition, positive long-term trends of AT were obtained ranging from 0.39 µmol kg−1 per year in the outer/middle zone to 2.86 µmol kg−1 per year in the inner zone. The results presented in this study show the changing conditions both in the short and long-term variability as well as the spatial differentiation between the inner and outer/middle zone to which the organisms of the Ría de Vigo are subjected. The neural networks and the database provided in this study offer the opportunity to evaluate the CO2 system in an environment of high ecological and economic relevance, to validate high-resolution regional biogeochemical models and to evaluate the impacts on organisms of the Ría de Vigo by refining the ranges of the biogeochemical variables included in experiments.

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