SO-CHIC Broullón, D., Pérez, F. F., Velo, A., Hoppema, M., Olsen, A., Takahashi, T., Key, R. M., Tanhua, T., Santana-Casiano, J. M., and Kozyr, A.: A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach, Earth Syst. Sci. Data, 12, 1725–1743

May 8, 2020 D. Broullón, F.F. Pérez, A. Velo, M. Hoppema, A. Olsen, T. Takahashi, R.M. Key, T. Tanhua, J.M. Santana-Casiano, A. Kozyr

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).

Publication DOI: https://doi.org/10.5194/essd-12-1725-2020