This work presents two new methods to estimate oceanic alkalinity (A_T), dissolved inorganic carbon (C_T), pH, and pCO₂ from temperature, salinity, oxygen, and geolocation data. “CANYON-B” is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. “CONTENT” combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As “dynamic climatologies” they show comparable performance to classical climatologies on large scales but a much better representation on smaller scales (40–120 d, 500–1,500 km) compared to in situ data. The limits of these mappings are explored with pCO₂ estimation in surface waters, i.e., at the edge of the domain with high intrinsic variability. In highly productive areas, there is a tendency for pCO₂ overestimation due to decoupling of the O₂ and C cycles by air-sea gas exchange, but global surface pCO₂ estimates are unbiased compared to a monthly climatology. CANYON-B and CONTENT are highly useful as transfer functions between components of the ocean observing system (GO-SHIP repeat hydrography, BGC-Argo, underway observations) and permit the synergistic use of these highly complementary systems, both in spatial/temporal coverage and number of observations. Through easily and robotically-accessible observations they allow densification of more difficult-to-observe variables (e.g., 15 times denser A_T and C_T compared to direct measurements). At the same time, they give access to the complete carbonate system. This potential is demonstrated by an observation-based global analysis of the Revelle buffer factor, which shows a significant, high latitude-intensified increase between +0.1 and +0.4 units per decade. This shows the utility that such transfer functions with realistic uncertainty estimates provide to ocean biogeochemistry and global climate change research. In addition, CANYON-B provides robust and accurate estimates of nitrate, phosphate, and silicate. Matlab and R code are available at https://github.com/HCBScienceProducts/.

A neural network-based method (CANYON: CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) was developed to estimate water-column biogeochemically relevant variables in the Global Ocean. These are the concentrations of 3 nutrients [nitrate (NO3−), phosphate (PO43−) and silicate (Si(OH)4)] and 4 carbonate system parameters [total alkalinity (AT), dissolved inorganic carbon (CT), pH (pHT) and partial pressure of CO2 (pCO2)], which are estimated from concurrent in situ measurements of temperature, salinity, hydrostatic pressure and oxygen (O2) together with sampling latitude, longitude and date. Seven neural-networks were developed using the GLODAPv2 database, which is largely representative of the diversity of open-ocean conditions, hence making CANYON potentially applicable to most oceanic environments. For each variable, CANYON was trained using 80 % randomly chosen data from the whole database (after eight 10° x 10° zones removed providing an “independent data-set” for additional validation), the remaining 20 % data were used for the neural-network test of validation. Overall, CANYON retrieved the variables with high accuracies (RMSE): 0.93 mol kg-1 (NO3−), 0.07 mol kg-1 (PO43-), 3.0 mol kg-1 (Si(OH)4), 0.019 (pHT), 7 mol kg-1 (AT), 10 mol kg-1 (CT) and 28 atm (pCO2). This was confirmed for the 8 independent zones not included in the training process. CANYON was also applied to the Hawaiian Time Series site to produce a 22-years long simulated time series for the above 7 variables. Comparison of modeled and measured data was also very satisfactory (RMSE in the order of magnitude of RMSE from validation test). CANYON is thus a promising method to derive distributions of key biogeochemical variables. It could be used for a variety of global and regional applications ranging from data quality control to the production of datasets of variables required for initialization and validation of biogeochemical models but difficult to obtain. In particular, combining the increased coverage of the global Biogeochemical-Argo program, where O2 is one of the core variables now very accurately measured, with the CANYON approach offers the fascinating perspective of obtaining large-scale estimates of key biogeochemical variables with unprecedented spatial and temporal resolutions.

The distribution of the chlorophyll a concentration ([Chl a]) in the Mediterranean Sea, mainly obtained from satellite surface observations or from scattered in situ experiments, is updated by analyzing a database of fluorescence profiles converted into [Chl a]. The database, which includes 6790 fluorescence profiles from various origins, was processed with a specific quality control procedure. To ensure homogeneity between the different data sources, 65 % of fluorescence profiles have been intercalibrated on the basis of their concomitant satellite [Chl a] estimation. The climatological pattern of [Chl a] vertical profiles in four key sites of the Mediterranean Sea has been analyzed. Climatological results confirm previous findings over the range of existing [Chl a] values and throughout the principal Mediterranean trophic regimes. They also provide new insights into the seasonal variability in the shape of the vertical [Chl a] profile, inaccessible through remote-sensing observations. An analysis based on the recognition of the general shape of the fluorescence profile was also performed. Although the shape of [Chl a] vertical distribution characterized by a deep chlorophyll maximum (DCM) is ubiquitous during summer, different forms are observed during winter, thus suggesting that factors affecting the vertical distribution of the biomass are complex and highly variable. The [Chl a] spatial distribution in the Mediterranean Sea mimics, on smaller scales, what is observed in the global ocean. As already evidenced by analyzing satellite surface observations, midlatitude- and subtropical-like phytoplankton dynamics coexist in the Mediterranean Sea. Moreover, the Mediterranean DCM variability appears to be characterized by patterns already observed on the global scale.

The present study proposes a novel method that merges satellite ocean color bio‐optical products with Argo temperature‐salinity profiles to infer the vertical distribution of the particulate backscattering coefficient (b_bp)_.This neural network‐based method (SOCA‐BBP for Satellite Ocean‐Color merged with Argo data to infer the vertical distribution of the Particulate Backscattering coefficient) uses three main input components: (1) satellite‐based surface estimates of b_bpand chlorophyllaconcentration matched up in space and time with (2) depth‐resolved physical properties derived from temperature‐salinity profiles measured by Argo profiling floats and (3) the day of the year of the considered satellite‐Argo matchup. The neural network is trained and validated using a database including 4725 simultaneous profiles of temperature‐salinity and bio‐optical properties collected by Bio‐Argo floats, with concomitant satellite‐derived products. The Bio‐Argo profiles are representative of the global open‐ocean in terms of oceanographic conditions, making the proposed method applicable to most open‐ocean environments. SOCA‐BBP is validated using 20% of the entire database (global error of 21%). We present additional validation results based on two other independent data sets acquired (1) by four Bio‐Argo floats deployed in major oceanic basins, not represented in the database used to train the method; and (2) during an AMT (Atlantic Meridional Transect) field cruise in 2009. These validation tests based on two fully independent data sets indicate the robustness of the predicted vertical distribution of b_bp. To illustrate the potential of the method, we merged monthly climatological Argo profiles with ocean color products to produce a depth‐resolved climatology of b_bpfor the global ocean.

A neural network‐based method is developed to assess the vertical distribution of (1) chlorophyllaconcentration ([Chl]) and (2) phytoplankton community size indices (i.e., microphytoplankton, nanophytoplankton, and picophytoplankton) from in situ vertical profiles of chlorophyll fluorescence. This method (FLAVOR for Fluorescence to Algal communities Vertical distribution in the Oceanic Realm) uses as input only the shape of the fluorescence profile associated with its acquisition date and geo‐location. The neural network is trained and validated using a large database including 896 concomitant in situ vertical profiles of High‐Performance Liquid Chromatography (HPLC) pigments and fluorescence. These profiles were collected during 22 oceanographic cruises representative of the global ocean in terms of trophic and oceanographic conditions, making our method applicable to most oceanic waters. FLAVOR is validated with respect to the retrieval of both [Chl] and phytoplankton size indices using an independent in situ data set and appears to be relatively robust spatially and temporally. To illustrate the potential of the method, we applied it to in situ measurements of the BATS (Bermuda Atlantic Time Series Study) site and produce monthly climatologies of [Chl] and associated phytoplankton size indices. The resulting climatologies appear very promising compared to climatologies based on available in situ HPLC data. With the increasing availability of spatially and temporally well‐resolved data sets of chlorophyll fluorescence, one possible global‐scale application of FLAVOR could be to develop 3‐D and even 4‐D climatologies of [Chl] and associated composition of phytoplankton communities. The Matlab and R codes of the proposed algorithm are provided as supporting information.

Numerical models of ocean biogeochemistry are becoming the major tools used to detect and predict the impact of climate change on marine resources and to monitor ocean health. However, with the continuous improvement of model structure and spatial resolution, incorporation of these additional degrees of freedom into fidelity assessment has become increasingly challenging. Here, we propose a new method to provide information on the model predictive skill in a concise way. The method is based on the conjoint use of a k-means clustering technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-means algorithm and the assessment metrics reduce the number of model data points to be evaluated. The metrics evaluate either the model state accuracy or the skill of the model with respect to capturing emergent properties, such as the deep chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo observations as the sole evaluation data set ensures the accuracy of the data, as it is a homogenous data set with strict sampling methodologies and data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine Service. The model performance is evaluated using the model efficiency statistical score, which compares the model–observation misfit with the variability in the observations and, thus, objectively quantifies whether the model outperforms the BGC-Argo climatology. We show that, overall, the model surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and the mixed layers as well as silicate in the mesopelagic layer. However, there are still areas for improvement with respect to reducing the model–data misfit for certain variables such as silicate, pH, and the partial pressure of CO₂ in the mixed layer as well as chlorophyll-a-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed here can also aid in refining the design of the BGC-Argo network, in particular regarding the regions in which BGC-Argo observations should be enhanced to improve the model accuracy via the assimilation of BGC-Argo data or process-oriented assessment studies. We strongly recommend increasing the number of observations in the Arctic region while maintaining the existing high-density of observations in the Southern Oceans. The model error in these regions is only slightly less than the variability observed in BGC-Argo measurements. Our study illustrates how the synergic use of modeling and BGC-Argo data can both provide information about the performance of models and improve the design of observing systems.