A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

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).

Hydrogeochemical Characters of Karst Aquifers in Central Italy and Relationship with Neotectonics

Groundwater from karst circulation systems of Central Italy were sampled and analyzed, in 2018, for delineating a preliminary, general geochemical framework of their relationship with neotectonics, in an area characterized by a frequent and often destructive seismicity. We determined field physical-chemical parameters, concentrations of main dissolved ions and gases and isotopic composition of water (δ18O, δD) and total dissolved inorganic carbon (δ13C TDIC). We discriminated between “normal” hydro-karst systems and multi-component aquifers, composed of meteoric groundwater that have also interacted with rocks of different lithological nature and/or deep fluids. These multicomponent aquifers are of potential interest in the monitoring of neotectonics activity, because changes in the stress field associated with the preparatory phase of an earthquake may affect the permeability of rocks, in turn causing variation of their chemical-isotopic character. The geographical distribution of these aquifers seems to be controlled by tectonics. In fact, the Olevano–Antrodoco–Sibillini thrust separates the more anomalous sites, located westwards of it, from the groundwater bodies at its eastern side, showing a more typical karst character.

A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than two 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-squared 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 in capturing the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in ten time-series stations spread over different regions of the ocean (RMSE: 3.6 to 13.1 µmol kg−1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity and oxygen from World Ocean Atlas 2013, and phosphate, nitrate and silicate computed from a neural network fed with the previous fields. The resolution is 1º x 1º in the horizontal, 102 depth levels (0–5500 m) and monthly (0–1500 m) to annual (1550–5500 m), and it is centered in 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., 2016). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; http://dx.doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020).

Climatological variations of total alkalinity and total dissolved inorganic carbon in the Mediterranean Sea surface waters

A compilation of data from several cruises between 1998 and 2013 was used to derive polynomial fits that estimate total alkalinity (AT) and total dissolved inorganic carbon (CT) from measurements of salinity and temperature in the Mediterranean Sea surface waters. The optimal equations were chosen based on the 10-fold cross-validation results and revealed that second- and third-order polynomials fit the AT and CT data respectively. The AT surface fit yielded a root mean square error (RMSE) of ± 10.6 μmol kg−1, and salinity and temperature contribute to 96 % of the variability. Furthermore, we present the first annual mean CT parameterization for the Mediterranean Sea surface waters with a RMSE of ± 14.3 μmol kg−1. Excluding the marginal seas of the Adriatic and the Aegean, these equations can be used to estimate AT and CT in case of the lack of measurements. The identified empirical equations were applied on the 0.25° climatologies of temperature and salinity, available from the World Ocean Atlas 2013. The 7-year averages (2005–2012) showed that AT and CT have similar patterns with an increasing eastward gradient. The variability is influenced by the inflow of cold Atlantic waters through the Strait of Gibraltar and by the oligotrophic and thermohaline gradient that characterize the Mediterranean Sea. The summer–winter seasonality was also mapped and showed different patterns for AT and CT. During the winter, the AT and CT concentrations were higher in the western than in the eastern basin. The opposite was observed in the summer where the eastern basin was marked by higher AT and CT concentrations than in winter. The strong evaporation that takes place in this season along with the ultra-oligotrophy of the eastern basin determines the increase of both AT and CT concentrations.

Total dissolved inorganic carbon and physicochemical characteristics of surface microlayer and upper mixed layer water from Lagos Lagoon, Nigeria

<p>The carbonate and physicochemical characteristics of the surface microlayer and upper mixed layer of a tropical coastal lagoon were investigated. Data on the physicochemical parameters generally indicated a moderately polluted ecosystem. The influence of the ocean environment over the Lagoon system was evident by elevated salinity levels. The mean total dissolved inorganic carbon (DIC) for the surface microlayer (SML) and subsurface water (SSW) samples were 2626.6 and 2550.9 &micro;mol/kg SW respectively. The dominant inorganic form of DIC in the lagoon water samples was HCO₃^- with a calculated average abundance &gt;95.4% in the SML and &gt;94% in the SSW. The bicarbonate species derived abundance varied between 1.6% (SML) and 8.4% (SSW), while the aqueous carbon dioxide were generally low in percentages ranging from 0.4 in SSW to 1.5 in SML water samples. In general, the occurrence of the carbonate species was in the order HCO₃^- &gt; CO₃^2- &gt; CO₂. Results showed that total alkalinity (A_T) was relatively greater than the DIC. Long term monitoring studies in the coastal lagoon systems is needed to understand the coastal water chemistry and pollution status.</p>