• European Marine Science
• Open Access close
• Publications close
• Other research products close
• DK close
• Earth System Science Data (ESSD) close

Marine diazotrophs convert dinitrogen (N2) gas into bioavailable nitrogen (N), supporting life in the global ocean. In 2012, the first version of the global oceanic diazotroph database (version 1) was published. Here, we present an updated version of the database (version 2), significantly increasing the number of in situ diazotrophic measurements from 13 565 to 55 286. Data points for N2 fixation rates, diazotrophic cell abundance, and nifH gene copy abundance have increased by 184 %, 86 %, and 809 %, respectively. Version 2 includes two new data sheets for the nifH gene copy abundance of non-cyanobacterial diazotrophs and cell-specific N2 fixation rates. The measurements of N2 fixation rates approximately follow a log-normal distribution in both version 1 and version 2. However, version 2 considerably extends both the left and right tails of the distribution. Consequently, when estimating global oceanic N2 fixation rates using the geometric means of different ocean basins, version 1 and version 2 yield similar rates (43–57 versus 45–63 Tg N yr−1 ; ranges based on one geometric standard error). In contrast, when using arithmetic means, version 2 suggests a significantly higher rate of 223±30 Tg N yr−1(mean ± standard error; same hereafter) compared to version 1 (74 ± 7 Tg N yr−1). Specifically, substantial rate increases are estimated for the South Pacific Ocean (88±23 versus 20±2 Tg N yr−1), primarily driven by measurements in the southwestern subtropics, and for the North Atlantic Ocean (40 ± 9 versus 10 ±2 Tg N yr−1). Moreover, version 2 estimates the N2 fixation rate in the Indian Ocean to be 35 ± 14 Tg N yr−1,which could not be estimated using version 1 due to limited data availability. Furthermore, a comparison of N2 fixation rates obtained through different measurement methods at the same months, locations, and depths reveals that the conventional 15N2 bubble method yields lower rates in 69 % cases compared to the new 15N2 dissolution method. This updated version of the database can facilitate future studies in marine ecology and biogeochemistry. The database is stored at the Figshare repository (https://doi.org/10.6084/m9.figshare.21677687; Shao et al., 2022). Peer reviewed

One of the key components of this research has been the mapping of Antarctic bed topography and ice thickness parameters that are crucial for modelling ice flow and hence for predicting future ice loss and the ensuing sea level rise. Supported by the Scientific Committee on Antarctic Research (SCAR), the Bedmap3 Action Group aims not only to produce new gridded maps of ice thickness and bed topography for the international scientific community, but also to standardize and make available all the geophysical survey data points used in producing the Bedmap gridded products. Here, we document the survey data used in the latest iteration, Bedmap3, incorporating and adding to all of the datasets previously used for Bedmap1 and Bedmap2, including ice bed, surface and thickness point data from all Antarctic geophysical campaigns since the 1950s. More specifically, we describe the processes used to standardize and make these and future surveys and gridded datasets accessible under the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles. With the goals of making the gridding process reproducible and allowing scientists to re-use the data freely for their own analysis, we introduce the new SCAR Bedmap Data Portal (https://bedmap.scar.org, last access: 1 March 2023) created to provide unprecedented open access to these important datasets through a web-map interface. We believe that this data release will be a valuable asset to Antarctic research and will greatly extend the life cycle of the data held within it. Data are available from the UK Polar Data Centre: https://data.bas.ac.uk (last access: 5 May 2023​​​​​​​). See the Data availability section for the complete list of datasets.

Abstract. Between 2018 and 2022, four pearl farming Tuamotu atolls of French Polynesia were monitored with autonomous oceanographic instruments to measure the hydrodynamics of atoll lagoons and the ocean–lagoon water exchanges. These surveys were conducted within the framework of the ANR-funded MANA (Management of Atolls) project and its extensions to additional sites. The overarching goal was to improve knowledge on the processes influencing the spat collection of the pearl oyster Pinctada margaritifera, the oyster species used to produce black pearls. These data sets are also critical for the calibration and validation of 3D high spatial resolution hydrodynamic models used to study oyster larval dispersal within lagoons. The observational strategies focused on the characterization of ocean–lagoon exchanges through passes and hoa (i.e., shallow reef flats), lagoon circulation, incident waves breaking on the forereef, water elevation inside the lagoon as well as spatial temperature variability. Chronologically, the investigated atolls were first Raroia Atoll with 9 months of measurements between May 2018 and March 2019 during which the MALIS1 and MALIS2 cruises on-board the R/V ALIS took place. It was followed by a 4 month deployment in Takapoto Atoll (November 2021 to March 2022). In late April 2022, Apataki Atoll was instrumented until end of July, followed by Takaroa measurements between July and October 2022. Apataki (Leg2) and Takaroa Atoll were conjointly instrumented during the MALIS 3 oceanographic cruise. Altogether, those multi-atoll data bring a worldwide unique oceanographic atoll data set that is useful for addressing local pearl farming questions but potentially beneficial for other fundamental and applied investigations. Each data set was post-processed, quality controlled and converted into NetCDF format. Files are available in open source into dedicated repositories in the SEANOE marine data platform. Links (DOI) of individual data sets are provided in Table 2.

The Greenland Climate Network (GC-Net) consists of 31 automatic weather stations (AWS) at 30 sites across the Greenland ice sheet. The first site was initiated in 1990, and the project has operated almost continuously since 1995 under the leadership of the late Prof. Konrad Steffen. The GC-Net AWS measured air temperature, relative humidity, wind speed, atmospheric pressure, downward and reflected shortwave irradiance, net radiation, ice and firn temperatures. The majority of the GC-Net sites were located in the ice sheet accumulation area (17 AWS), while 11 AWS were located in the ablation area and two sites (three AWS) were located close to the equilibrium line altitude. Additionally, three AWS of similar design to the GC-Net AWS were installed by Prof. K. Steffen’s team on the Larsen C ice shelf, Antarctica. After more than three decades of operation, the GC-Net AWS are being decommissioned and replaced by new AWS operated by the Geological Survey of Denmark and Greenland (GEUS). Therefore, making a reassessment of the historical GC-Net AWS data is necessary. We present a full reprocessing of the historical GC-Net AWS dataset with increased attention to the filtering of erroneous measurements, data correction, and derivation of additional variables: continuous surface height, instrument heights, surface albedo, turbulent heat fluxes, 10 m ice and firn temperatures. This new augmented GC-Net Level 1 (L1) AWS dataset is now available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al. 2022) and will continue to be refined. The processing scripts, latest data and a data-user forum are available at https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing. In addition to the AWS data, a comprehensive compilation of valuable metadata is provided: maintenance reports, yearly pictures of the stations and the station positions through time. This unique dataset provides more than 320 station-years of high quality atmospheric data and is available following FAIR data and code practices.

Deep learning (DL) algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale datasets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database (RMD) with a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. Potentially, the presented database can be used to build surrogate models of well-known processes and to aid in labour-intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, incorporate consistency and credibility into deep learning models. We show the effectiveness of the presented database by surrogating the forward-modelling process, and we urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database. The dataset is publicly available at https://doi.org/10.5281/zenodo.7260886 (Asif et al., 2022a).

Arctic soils store large amounts of organic carbon and other elements, such as amorphous silicon, silicon, calcium, iron, aluminum, and phosphorous. Global warming is projected to be most pronounced in the Arctic, leading to thawing permafrost which, in turn, changes the soil element availability. To project how biogeochemical cycling in Arctic ecosystems will be affected by climate change, there is a need for data on element availability. Here, we analyzed the amorphous silicon (ASi) content as a solid fraction of the soils as well as Mehlich III extractions for the bioavailability of silicon (Si), calcium (Ca), iron (Fe), phosphorus (P), and aluminum (Al) from 574 soil samples from the circumpolar Arctic region. We show large differences in the ASi fraction and in Si, Ca, Fe, Al, and P availability among different lithologies and Arctic regions. We summarize these data in pan-Arctic maps of the ASi fraction and available Si, Ca, Fe, P, and Al concentrations, focusing on the top 100 cm of Arctic soil. Furthermore, we provide element availability values for the organic and mineral layers of the seasonally thawing active layer as well as for the uppermost permafrost layer. Our spatially explicit data on differences in the availability of elements between the different lithological classes and regions now and in the future will improve Arctic Earth system models for estimating current and future carbon and nutrient feedbacks under climate change (https://doi.org/10.17617/3.8KGQUN, Schaller and Goeckede, 2022). Arctic soils store large amounts of organic carbon and other elements, such as amorphous silicon, silicon, calcium, iron, aluminum, and phosphorous. Global warming is projected to be most pronounced in the Arctic, leading to thawing permafrost which, in turn, changes the soil element availability. To project how biogeochemical cycling in Arctic ecosystems will be affected by climate change, there is a need for data on element availability. Here, we analyzed the amorphous silicon (ASi) content as a solid fraction of the soils as well as Mehlich III extractions for the bioavailability of silicon (Si), calcium (Ca), iron (Fe), phosphorus (P), and aluminum (Al) from 574 soil samples from the circumpolar Arctic region. We show large differences in the ASi fraction and in Si, Ca, Fe, Al, and P availability among different lithologies and Arctic regions. We summarize these data in pan-Arctic maps of the ASi fraction and available Si, Ca, Fe, P, and Al concentrations, focusing on the top 100 cm of Arctic soil. Furthermore, we provide element availability values for the organic and mineral layers of the seasonally thawing active layer as well as for the uppermost permafrost layer. Our spatially explicit data on differences in the availability of elements between the different lithological classes and regions now and in the future will improve Arctic Earth system models for estimating current and future carbon and nutrient feedbacks under climate change (, Schaller and Goeckede, 2022).