We have extended a classical quality control (QC) algorithm by integrating a deep learning neural network, resulting in SalaciaML-2-Arctic , a tool for automated QC of Arctic Ocean temperature and salinity profile data. The neural network component was trained on the Unified Database for Arctic and Subarctic Hydrography (UDASH), which has been quality-controlled and labeled by expert oceanographers. SalaciaML-2-Arctic successf...

By mid-2023, the international GEOTRACES program had released three intermediate data products (IDP2014, IDP2017, and IDP2021), and in July 2023, an update of the latest product, IDP2021v2, was issued. All IDPs consist of two parts: (1) a compilation of digital data for large numbers of trace elements and isotopes (TEIs), and (2) the eGEOTRACES Electronic Atlas containing almost 1,500 pre-​created section plots and 269 animate...

The Mediterranean Sea is very sensitive to climatic changes due to its semi-enclosed nature and is therefore defined as one of the hotspots in future climate change projections. In this study, we use Argo float data to assess climatologies and trends in temperature and Ocean Heat Content (OHC) throughout the Mediterranean Sea and for specific sub-basins (e.g. Western and Eastern Mediterranean, Gulf of Lion, South Adriatic). Th...

We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliab...