Climatological distribution of dissolved inorganic nutrients in the western Mediterranean Sea (1981–2017)

The Western MEDiterranean Sea BioGeochemical Climatology (BGC-WMED, https://doi.org/10.1594/PANGAEA.930447) (Belgacem et al., 2021) presented here is a product derived from quality-controlled in situ observations. Annual mean gridded nutrient fields for the period 1981–2017 and its sub-periods 1981–2004 and 2005–2017 on a horizontal 1/4∘ × 1/4∘ grid have been produced. The biogeochemical climatology is built on 19 depth levels and for the dissolved inorganic nutrients nitrate, phosphate and orthosilicate. To generate smooth and homogeneous interpolated fields, the method of the variational inverse model (VIM) was applied. A sensitivity analysis was carried out to assess the comparability of the data product with the observational data. The BGC-WMED was then compared to other available data products, i.e., the MedBFM biogeochemical reanalysis of the Mediterranean Sea and the World Ocean Atlas 2018 (WOA18) (its biogeochemical part). The new product reproduces common features with more detailed patterns and agrees with previous records. This suggests a good reference for the region and for the scientific community for the understanding of inorganic nutrient variability in the western Mediterranean Sea, in space and in time, but our new climatology can also be used to validate numerical simulations, making it a reference data product.

An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021

The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2021 is an update of the previous version, GLODAPv2.2020 (Olsen et al., 2020). The major changes are as follows: data from 43 new cruises were added, data coverage was extended until 2020, all data with missing temperatures were removed, and a digital object identifier (DOI) was included for each cruise in the product files. In addition, a number of minor corrections to GLODAPv2.2020 data were performed. GLODAPv2.2021 includes measurements from more than 1.3 million water samples from the global oceans collected on 989 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For this annual update, adjustments for the 43 new cruises were derived by comparing those data with the data from the 946 quality controlled cruises in the GLODAPv2.2020 data product using crossover analysis. Comparisons to estimates of nutrients and ocean CO2 chemistry based on empirical algorithms provided additional context for adjustment decisions in this version. The adjustments are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent with to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments. The original data, their documentation, and DOI codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/GLODAPv2_2021/, last access: 7 July 2021). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/ttgq-n825 (Lauvset et al., 2021). These bias-adjusted product files also include significant ancillary and approximated data and can be accessed via https://www.glodap.info (last access: 29 June 2021). These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2021 methods and provides a broad overview of the secondary quality control procedures and results.

A monthly surface pCO₂ product for the California Current Large Marine Ecosystem

To calculate the direction and rate of carbon dioxide gas (CO2) exchange between the ocean and atmosphere, it is critical to know the partial pressure of CO2 in surface seawater (pCO2(sw)). Over the last decade, a variety of data products of global monthly pCO2(sw) have been produced, primarily for the open ocean on 1° latitude by 1° longitude grids. More recently, monthly products of pCO2(sw) that are more finely spatially resolved in the coastal ocean have been made available. A remaining challenge in the development of pCO2(sw) products is the robust characterization of seasonal variability, especially in nearshore coastal environments. Here we present a monthly data product of pCO2(sw) at 0.25° latitude by 0.25° longitude resolution in the Northeast Pacific Ocean, centered around the California Current System (CCS). The data product (RFR-CCS; Sharp et al., 2021; https://doi.org/10.5281/zenodo.5523389) was created using the most recent (2021) version of the Surface Ocean CO2 Atlas (Bakker et al., 2016) from which pCO2(sw) observations were extracted and fit against a variety of satellite- and model-derived surface variables using a random forest regression (RFR) model. We validate RFR-CCS in multiple ways, including direct comparisons with observations from moored autonomous surface platforms, and find that the data product effectively captures seasonal pCO2(sw) cycles at nearshore mooring sites. This result is notable because alternative global products for the coastal ocean do not capture local variability effectively in this region. We briefly review the physical and biological processes — acting across a variety of spatial and temporal scales — that are responsible for the latitudinal and nearshore-to-offshore pCO2(sw) gradients seen in RFR-CCS reconstructions of pCO2(sw).

How to Land Modern Data Science in Petroleum Engineering

Digitalization and intelligence are attracting increasing attention in petroleum engineering. Amounts of published research indicates modern data science has been applied in almost every corner of petroleum engineering where data generates, however, mature products are few or the performance are not up to peoples’ expectations. Despite the great success in other industries (internet, transportation, and finance, etc.), the "amazing" data science algorithms seem to be challenged when "landing" in petroleum engineering. It is time to calmly analyze current situations and discuss the methodology to apply modern data science in petroleum engineering, for safety ensuring, efficiency improvement and cost saving. Based on the experiences of several data products in petroleum engineering and wide investigation of literatures, the methodology is summarized by answering some important questions: what is the difference between petroleum engineering and other industries and what are the greatest challenges for algorithms "landing"? how could we build a data product development team? why the machine learning models didn't work well in real world, which are derived by typical procedures in textbooks? are current artificial intelligent algorithms perfect and is there any limit? how could we deal with the relationship between prior knowledge and data-driven methods? what is the key point to keep data product competitive? Several specific scenarios are introduced as examples, such as ROP modelling, drilling parameters optimization, text mining of drilling reports and well production prediction, etc. where deep learning, traditional machine learning, incremental learning and natural language processing methods, etc. are used. Besides detailed discussions in the paper, conclusions are summarized as: 1) the strengths and weakness of current artificial intelligence should be viewed objectively, practical suggestions to make up the weakness are provided; 2) the combination of prior knowledge (from lab tests or expert experiences) and data-driven methods are always necessary and methods for the combination are summarized; 3) data volume and solution portability are the key points to improve data product competitiveness; 4) suggestions on how to build a multi-disciplinary R&D team and how to plan a product are provided. This paper conducts an objective analysis on challenges for modern data science applying in petroleum engineering and provides a clear methodology and specific suggestions on how to improve the success rate of R&D projects which apply data science to solve problems in petroleum engineering.

An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021

The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2021 is an update of the previous version, GLODAPv2.2020. The major changes are: data from 43 new cruises were added, data coverage extended until 2020, removal of all data with missing temperatures, and the inclusion of a digital object identifier (doi) for each cruise in the product files. In addition, a number of minor corrections to GLODAPv2.2020 data were performed. GLODAPv2.2021 includes measurements from more than 1.3 million water samples from the global oceans collected on 989 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to WOCE exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For this annual update, adjustments for the 43 new cruises were derived by comparing those data with the data from the 946 quality-controlled cruises in the GLODAPv2.2020 data product using crossover analysis. Comparisons to estimates of nutrients and ocean CO2 chemistry based on empirical algorithms provided additional context for adjustment decisions in this version. The adjustments are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg-1 in dissolved inorganic carbon, 4 µmol kg-1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments. The original data, their documentation and doi codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/GLODAPv2_2021/, last access: 07 July 2021). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/ttgq-n825 (Lauvset et al., 2021). These bias-adjusted product files also include significant ancillary and approximated data, and can be accessed via www.glodap.info (last access: 29 June 2021). These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2021 methods and provides a broad overview of the secondary quality control procedures and results.