Search

The partial pressure of carbon dioxide (pCO2) in surface seawater is an important biogeochemical variable because, together with the pCO2 in the atmosphere, it determines the direction of air–sea carbon dioxide exchange. Large-scale observations of pCO2 are facilitated by Ships-of-Opportunity (SOOP-CO2) equipped with underway measuring instruments. The need for expanding the observation capacity and the challenges involving the sustainability and maintenance of traditional equilibrator systems led the community toward developing simpler and more autonomous systems. Here we performed a comparison between a membrane-based sensor and a showerhead equilibration sensor installed on two SOOP-CO2 between 2013 and 2018. We identified time- and space-adequate crossovers in the Skagerrak Strait, where the two ship routes often crossed. We found a mean total difference of 1.5 ± 10.6 μatm and a root mean square error of 11 μatm. The pCO2 values recorded by the two instruments showed a strong linear correlation with a coefficient of 0.91 and a slope of 1.07 (± 0.14), despite the dynamic nature of the environment and the difficulty of comparing measurements from two different vessels. The membrane-based sensor was integrated with a FerryBox system on a ship with a high sampling frequency in the study area. We showed the strength of having a sensor-based network with a high spatial coverage that can be validated against conventional SOOP-CO2 methods. Proving the validity of membrane-based sensors in coastal and continental shelf seas and using the higher frequency measurements they provide can enable a thorough characterization of pCO2 variability in these dynamic environments. publishedVersion

We present an application of generative adversarial networks (GANs) to reconstruct the sea level of the North Sea using a limited amount of data from tidal gauges (TGs). The application of this technique, which learns how to generate datasets with the same statistics as the training set, is explained in detail to ensure that interested scientists can implement it in similar or different oceanographic cases. Training is performed for all of 2016, and the model is validated on data from 3 months in 2017 and compared against reconstructions using the Kalman filter approach. Tests with datasets generated by an operational model (“true data”) demonstrated that using data from only 19 locations where TGs permanently operate is sufficient to generate an adequate reconstruction of the sea surface height (SSH) in the entire North Sea. The machine learning approach appeared successful when learning from different sources, which enabled us to feed the network with real observations from TGs and produce high‐quality reconstructions of the basin‐wide SSH. Individual reconstruction experiments using different combinations of training and target data during the training and validation process demonstrated similarities with data assimilation when errors in the data and model were not handled appropriately. The proposed method demonstrated good skill when analyzing both the full signal and the low‐frequency variability only. It was demonstrated that GANs are also skillful at learning and replicating processes with multiple time scales. The different skills in different areas of the North Sea are explained by the different signal‐to‐noise ratios associated with differences in regional dynamics., Plain Language Summary: The variability of sea level is one of the most important elements of the ocean dynamics. Basin‐wide observations are due to satellite altimeters, observations in coastal stations are provided by tidal gauges. The first are not very accurate in the coastal areas, the second do not provide basin‐wide coverage. The task in the present work is to use machine learning to reconstruct the sea‐level variability in the North Sea, which is an almost enclosed ocean region, using observations only. Using data from 19 coastal stations and data from numerical models as a representation of the true ocean (synthetic observations), we demonstrated that the generative adversarial networks reconstruct almost perfectly the sea level of the North Sea. The application of this technique, which learns how to generate datasets with the same statistics as the training set, is explained in detail to ensure that interested scientists can implement it in similar or different oceanographic cases., Key Points: Generative Adversarial Networks successfully reconstruct basin‐wide sea level in the North Sea using data from tidal gauges. Machine learning appeared successful when learning from different data sources. The proposed method is skillful at learning and replicating processes with multiple time scales.

Narrow baroclinic fronts are observed in the surface mixed layer (SML) of the Baltic Sea following an autumn storm. The fronts are subjected to hydrodynamic instabilities that lead to submesoscale and turbulent motions while restratifying the SML. We describe observations from an ocean glider that combines currents, stratification, and turbulence microstructure in a high horizontal resolution (150–300 m) to analyze such fronts. The observations show that SML turbulence is strongly modulated by frontal activity, acting as both source and sink for turbulent kinetic energy. In particular, a direct route to turbulent dissipation within the front is linked to shear instability caused by elevated nongeostrophic shear. The turbulent dissipation of frontal kinetic energy is large enough that it could be a significant influence in the evolution of the front and demonstrates that small‐scale turbulence can act as a significant sink of submesoscale kinetic energy., Key Points: An autonomous ocean glider observed turbulence, currents, and stratification in surface mixed layer submesoscale fronts following a storm. Submesoscale fronts provide both a damping and generation of surface mixed layer turbulence. Shear instability within the front could represent a significant energy transfer in frontal evolution., Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659, Helmholtz Association http://dx.doi.org/10.13039/501100001656