This work presents two new methods to estimate oceanic alkalinity (A T), dissolved inorganic carbon (C T), pH, and pCO 2 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 pCO 2 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 pCO 2 overestimation due to decoupling of the O 2 and C cycles by air-sea gas exchange, but global surface pCO 2 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, Bittig et al. Robust Estimation of CO 2 Variables and Nutrients 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/.

Abstract. The distribution of the chlorophyll a concentration ([Chla

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 (i.e., from surface to 8,000 m depth) biogeochemically relevant variables in the Global Ocean. These are the concentrations of three nutrients [nitrate (NO 3 − ), phosphate (PO 4 3− ), and silicate (Si(OH) 4 )] and four carbonate system parameters [total alkalinity (A T ), dissolved inorganic carbon (C T ), pH (pH T ), and partial pressure of CO 2 (pCO 2 )], which are estimated from concurrent in situ measurements of temperature, salinity, hydrostatic pressure, and oxygen (O 2 ) 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 • × 10 • zones removed providing an "independent dataset" 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): 1.04 µmol kg −1 (NO 3 − ), 0.074 µmol kg −1 (PO 4 3− ), 3.2 µmol kg −1 (Si(OH) 4 ), 0.020 (pH T ), 9 µmol kg −1 (A T ), 11 µmol kg −1 (C T ) and 7.6 % (pCO 2 ) (30 µatm at 400 µatm). This was confirmed for the eight 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 seven 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 Sauzède et al. Nutrients and Carbonate System from T/S/O2validation of biogeochemical models that are difficult to obtain. In particular, combining the increased coverage of the global Biogeochemical-Argo program, where O 2 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 Matlab and R codes of the proposed algorithms are provided as Supplementary Material.