Geoscience Data Journal最新文献

Understanding regional sea level variations is crucial for assessing coastal vulnerability, with accurate sea level data playing a pivotal role. Utilizing historical sea level marigrams can enhance datasets, but current digitization techniques face challenges such as bends and skews in paper charts, impacting sea level values. This study explores often-overlooked issues during marigram digitization, focusing on the case study of Dún Laoghaire in Ireland (1925–1931). The methodology involves digitizing the original marigram trace and underlying grid to assess offsets at the nearest ft (foot) interval on the paper chart, corresponding to changes in the water level trace for each hour interval. Subtracting the digitized value from the known value (the actual measurement) allows for the determination of differences, which are then subtracted from each hourly trace value. After adjusting for offsets ranging from −3.962 to 13.716 mm (millimetres), the study improves the final accuracy of sea level data to approximately the 10 mm level. Notably, data from 1926 and 1931 exhibit modest offsets (<7 mm), while other years show more substantial offsets (>9–14 mm), emphasizing the importance of adjustments for accuracy. Such 10 mm accuracy is compatible with requirements of the Global Sea Level Observing System. Comparing the adjusted digitized data with other survey data shows similar amplitudes and phases for Dún Laoghaire in both the historical and modern datasets, and there is an overall mean sea level rise of 1.5 mm/year when combined with the available data from the Dublin region.

The distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large-scale IHSs. First, a density segmentation method based on an improved K-means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high-resolution remote-sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform-related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.

This article describes a novel dataset on non-diamondiferous eclogite and garnet pyroxenite xenoliths from four kimberlite pipes of the Ekati Diamond Mine (Central Slave Craton, Canada). Xenoliths brought to the surface by kimberlite eruptions are direct sources of information on the composition and evolution of the Earth's mantle. Eclogite and garnet pyroxenite xenoliths, specifically, are testimony of subduction into, and metasomatism of, the mantle beneath cratons. Furthermore, these rocks are major hosts for diamond and thus an important part of the deep carbon cycle. The sample suite consists of 41 small xenoliths (2–5 cm) recovered from drill cores. The dataset includes major and trace element concentrations for garnet, clinopyroxene and ilmenite, as well as stable oxygen isotope compositions of garnets. Strontium and neodymium isotopic compositions are reported for garnet and clinopyroxene for four samples which were large enough to allow for analysis. Overall, this dataset significantly expands and complements existing datasets on diamondiferous and non-diamondiferous xenoliths from the Slave Craton in Canada, furthering our understanding of the composition of the Slave subcratonic lithosphere. The dataset includes several samples with rare mineral assemblages, including an olivine-bearing eclogite as well as ilmenite and apatite-bearing garnet-pyroxenites, and thus provides data shedding light on rarely reported compositional nuances in xenolith suites found in kimberlites.

Lack of a long-term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC-Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC-Argo has shorter time series coverage and fewer profiles compared to Core-Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core-Argo and BGC-Argo profiles and then developing a spatial, temporal and depth-specific gridded DOC dataset, named G4D-DOC. Validation results demonstrate that G4D-DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D-DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D-DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D-DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D-DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022.

The majority of available climate data in global digital archives consist of data only from the 1940s or 1950s onwards, and many of these series have gaps and/or are available for only a subset of the variables which were actually observed. However, there exist billions of historical weather observations from the 1700s, 1800s, and early 1900s that are still in hard-copy form and are at risk of being lost forever due to deterioration. An assessment of changes in climate extremes in several IPCC regions was not possible in IPCC AR6 WGI owing, in many cases, to the lack of available data. One such region is Africa, where the climate impact research and the ability to predict climate change impacts are hindered by the paucity of access to consistent good-quality historical observational data. The aim of this innovative project was to use classroom-based participatory learning to help transcribe some of the many meteorological observations from Africa that are thus far unavailable to researchers. This project transcribed quickly and effectively station series by enrolling the help of second-year undergraduate students at Maynooth University in Ireland. The newly digitized African data will increase the temporal and spatial coverage of data in this important data-sparse region. Students gained new skills while helping the global scientific community unearth new insight into past African climate. The project managed to transcribe 79 months of data at Andapa in Madagascar and 56 months of data for Macenta in Guinea. The digitized data will be openly and freely shared with the scientific and wider community via the Pangaea data repository, the C3S Climate Data Store, and the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) data centre in the US. The project model has the potential for a broader roll-out to other educational contexts and there is no shortage of data to be rescued. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organizations to instigate similar programs.

In this discourse, we present the unveiling of an open-source software package designed to facilitate engagement with the atmospheric model, Aeolus 2.0. This particular iteration stands as a self-contained model of intermediate complexity. The model's dynamical core is underpinned by a multi-layer pseudo-spectral moist-convective Thermal Rotating Shallow Water (mcTRSW) model. The pseudo-spectral problem-solving tasks are handled by the Dedalus algorithm, acknowledged for its spin-weighted spherical harmonics. The model captures the temporal and spatial evolution of vertically integrated potential temperature, thickness, water vapour, precipitation, and the intricate influence of bottom topography. It comprehensively characterizes velocity fields in both the lower and upper troposphere, employing resolutions spanning a spectrum from the smooth to the coarse, enabling the exploration of a wide range of dynamic phenomena with varying levels of detail and precision.

Downbursts are severe wind systems originating from thunderstorm clouds, and their strong horizontal outflows can pose serious hazards to natural and built environments. In the context of the activities of the European project THUNDERR—Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures—a comprehensive database of full-scale downburst measurements was built. All records were acquired by bi- or tri-axial ultrasonic anemometers installed in the main ports of the High Tyrrhenian Sea, namely Genova, Livorno and La Spezia, within the European projects ‘Wind and Ports’ and ‘Wind, Ports and Sea’. The very limited space and time structure of downburst outflows makes the available records in nature inadequate for developing models that could be used in the atmospheric science and engineering communities. The database described herein represents a step forward in attempting to fill this gap. The downburst nature of all events contained in the dataset was verified through detailed meteorological analyses, including comparisons with radar and satellite images and lightning recordings. The wind speed records associated with the events detected by the anemometric network are made publicly available through the online repository Zenodo and can be reused for multiple purposes. The dataset is expected to convey an important impulse towards the physical characterization and modelling of downburst winds and their codification into design tools for the assessment of wind loading and its effects on structures and infrastructure. Furthermore, it could serve as a promising, essential tool for researchers and risk-related insurance companies.

The Qinling Mountains in China frequently experience geological disasters, with large-scale landslides being particularly prominent, causing severe economic losses to the local area. To gain a comprehensive understanding of the geological disaters distribution in the region, we conducted extensive research on the entire Qinling Mountains, covering an area of approximately 380,000 km². By employing methods such as literature review, data collection, and interpretation of remote sensing images, we have successfully created a database of landslides. The inventory of landslides includes a total of 169,888 large-scale landslides, covering a combined area of approximately 1575 km². The average size of these landslides is approximately 92,734 m². The scale of these landslides varies widely, with the smallest individual landslide covering an area of 166.25 m² and the largest reaching 12.9 km². Upon examining areas with frequent landslides, it was observed that landslides are usually densely distributed along riverbanks or within valleys. Landslide development is also dense in areas prone to frequent historical earthquakes. This comprehensive database provides essential data to support the analysis of spatial distribution patterns of large-scale landslides in the Qinling Mountains. It also facilitates landslide assessments and serves as a reference for the prevention and control of landslide disasters in the area.

Deep-time Digital Earth (DDE) is an innovative international big science program, focusing on scientific propositions of earth evolution, changing Earth Science by coordinating global geoscience data, and sharing global geoscience knowledge. To facilitate the DDE program with recent advances in computer science, the geoscience knowledge graph plays a key role in organizing the data and knowledge of multiple geoscience subjects into Knowledge Graphs (KGs), which enables the calculation and inference over geoscience KGs for data mining and knowledge discovery. However, the construction of geoscience KGs is challenging. Though there have been some construction tools, they commonly lack collaborative editing and peer review for building high-quality large-scale geoscience professional KGs. To this end, a data service system or tool, DDE KG Editor, is developed to construct geoscience KGs. Specifically, it comes with several distinctive features such as collaborative editing, peer review, contribution records, intelligent assistance, and discussion forums. Currently, global geoscientists have contributed over 60,000 ontologies for 22 subjects. The stability, scalability, and intelligence of the system are regularly improving as a public online platform to better serve the DDE program.