The core partner data centres that are integrated in NorDataNet are listed in https://www.nordatanet.no/en/node/69. In addition to this NorDataNet harvests information on relevant datasets from a number of other data centres. The data centre responsible for the data presented is usually (but not always) listed in the discovery metadata. In essence NorDataNet is an aggregating service that combines information from a number of existing data centres.
Citation of data and service
If you use data retrieved through this portal, please acknowledge our funding source:
Research Council of Norway, project number 245967/F50, Norwegian Scientific Data Network.
Always remember to cite data when used!
Citation information for individual datasets is often provided in the metadata. However, not all datasets have this information embedded in the discovery metadata. On a general basis a citation of a dataset include the same components as any other citation:
author, title,
year of publication,
publisher (for data this is often the archive where it is housed),
edition or version,
access information (a URL or persistent identifier, e.g. DOI if provided)
All partner repositories of NorDataNet support Digital Object Identifiers (DOI), but not all datasets are minted. Whether or not minted depends often on source of the data (e.g. operational data are often yet not minted). However, all data centres support persistent identifiers according to local systems. The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
Brief user guide
The Data Access Portal has information in 3 columns. An outline of the content in these columns is provided above. When first entering the search interface, all potential datasets are listed. Datasets are indicated in the map and results tabulation elements which are located in the middle column. The order of results can be modified using the "Sort by" option in the left column. On top of this column is normally relevant guidance information to user presented as collapsible elements.
If the user want to refine the search, this can be done by constraining the bounding box search. This is done in the map - the listing of datasets is automatically updated. Date constraints can be added in the left column. For these to take effect, the user has to push the button marked search. In the left column it is also possible to specific text elements to search for in the datasets. Again pushing the button marked "Search" is necessary for these to take action. Complex search patterns can be constructed using logical operators identified in the drop down menu with and phrases embedded in quotation marks. Prefixing a phrase with '-' negates the phrase (i.e. should not occur in the results). Searches are case insensitive.
Other elements indicated in the left and right columns are facet searches, i.e. these are keywords that are found in the datasets and all datasets that contain these specific keywords in the appropriate metadata elements are listed together. Further refinement can be done using full text, date or bounding box constraints. Individuals, organisations and data centres involved in generating or curating the datasets are listed in the facets in the right column. The combination of search fields (including facets) is based on a logical "AND" combination of the fields, i.e. all conditions are fulfilled for the results provided.
Seismic data recorded by a permanent seismological array located in Spitsbergen. Seismic records can be used for seismological and cryoseismological studies, data is gathered continuously and access is open.
This data was collected during a AB-202 Helmer Hanssen field cruise in the spring of 2023. The cruise lasted from 26.04-01.05. The stations were Kongsfjorden (KB3), Magdalenafjorden (MAG), marginal ice zone (MIZ) and Billefjorden (BAB). The data was collected with a benthic trawl at each of the stations.
Institutions: British Antarctic Survey, British Antarctic Survey, NERC EDS UK Polar Data Centre
Last metadata update: 2022-05-19T00:00:00Z
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Abstract:
This dataset comprises summary statistics regarding historical and projected Southern Hemisphere total sea ice area (SIA) and 21st century global temperature change (dTAS), evaluated from the multi-model ensembles contributing to CMIP5 and CMIP6 (Coupled Model Intercomparison Project phases 5 and 6). The metrics are evaluated for two climatological periods (1979-2014 and 2081-2100) from a number of CMIP experiments; historical, and ScenarioMIP or RCP runs. These metrics were calculated to calculate projections of future Antarctic sea ice loss, and drivers of ensemble spread in this variable, for Holmes et al. (2022) "Antarctic sea ice projections constrained by historical ice cover and future global temperature change".
Funding was provided by the British Antarctic Survey Polar Science for Planet Earth Programme and under NERC large grant NE/N01829X/1
Institutions: British Antarctic Survey, British Antarctic Survey, UK Polar Data Centre, Natural Environment Research Council, UK Research & Innovation
Last metadata update: 2021-03-29T00:00:00Z
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Abstract:
The output of a 40-year coupled ice-ocean run of Smith Glacier, the adjoining Dotson and Crosson ice shelves, and the nearby continental shelf, with ocean boundary conditions forced with a climatology downscaled from a regional model of the Amundsen Sea.
Funding was provided by the NERC Standard Grant NE/M003590/1 - Is ice loss from West Antarctica driven by ocean forcing or ice and ocean feedbacks?
Institutions: British Antarctic Survey, British Antarctic Survey, NERC EDS UK Polar Data Centre
Last metadata update: 2023-06-13T00:00:00Z
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Abstract:
Based on the bias-corrected WRF data and the statistically downscaled CMIP5 data (see related datasets), six climate change detection indices are calculated, based on the Expert Team on Climate Change Detection and Indices (ETCCDI). Each index is calculated for the control period (1980-2018) from the bias-corrected WRF data, and the future (2019-2100) for each of the 30 CMIP5 models. Six of the ETCCDI climate indices are calculated here (taken from Zhang (2011)): the simple precipitation intensity index describing the total annual precipitation on wet days; the annual total precipitation falling on days where precipitation is above the 95th percentile of the 1980-2018 period; the number of dry days (precipitation under 1 mm) in a year (a variation on "continuous dry days" given in Zhang (2011); the annual average monthly maximum temperature; the warm spell duration index describing the annual count of days with at least 6 consecutive days above the 90th percentile of daily maximum temperature from 1980-2018; the number of frost days (minimum daily temperature below 0 deg C). These data were corrected as part of the PEGASUS (Producing EnerGy and preventing hAzards from SUrface water Storage in Peru) and Peru GROWS (Peruvian Glacier Retreat and its Impact on Water Security) projects. The datasets were created to assess future climate in the Peruvian Andes. The data were created on the JASMIN supercomputer.
The creation of this data was conducted under the Peru GROWS and PEGASUS projects, which were both funded by NERC (grants NE/S013296/1 and NE/S013318/1, respectively) and CONCYTEC through the Newton-Paulet Fund. The Peruvian part of the Peru GROWS project was conducted within the framework of the call E031-2018-01-NERC "Glacier Research Circles", through its executing unit FONDECYT (Contract No. 08-2019-FONDECYT).
The Climate Change Tower Integrated Project (CCT-IP) represents the guide lines of the italian research in the arctic and aims to study the interaction between all the components of the climate system in the Arctic. The Amundsen-Nobile Climate Change Tower (CCT) is the key infrastructure of the project, and provides continuous acquisition of the atmospheric parameters at different heights as well as at the interface between the surface and the atmosphere.
Turbulent parameters are measured at the Amundsen-Nobile Climate Change Tower (CCT) by means of a Gill R3 sonic anemometer installed at 7.5 m from the ground since 2010. It measures the three components of the wind (u, v and w) and the sonic temperature at a rate of 20 Hz. These micro-meteorological measurements are complemented by standard meteorological ones at 4 levels: 2, 5, 10 and 33 m (acquisition time step equal to 1 minute). From these measurements, sensible heat flux, friction velocity and roughness length are calculated.
Wind components and sonic temperature measurements were used to estimate friction velocity and kinematic heat flux. Before computing the micrometeorological parameters, a preliminary analysis is applied in order to assess the data quality and to remove low quality records. After the quality analysis application, mean values of the turbulence statistics were computed following two coordinate rotations to ensure the mean lateral and vertical velocities were zero (McMillen, 1988). Half-hour turbulent statistics (heat fluxes and friction velocity) were derived using two time-scales: a standard averaging time of 30 min and a reduced one (2 min) necessary for filtering out submeso motions contributions that can greatly alter the estimation of turbulent fluxes in a strong and long-lived stable BL. The short averaging time scale was evaluated on the basis of spectral analysis of data in order to include all turbulent scales, but excluding submeso motions (larger than turbulence). The turbulent statistics evaluated over the short subsets and then re-averaged over 30 min following Vickers and Mahrt (2006).
Turbulent parameter relative to unfavorable wind direction ([150÷270] degrees) for which the tower was upwind of the sonic anemometer were not discarded but are flagged (flagdir=1) in the final dataset. More, the percentage of NaNs relative to each run is indicated.
The wind speed vertical profile measured by slow response standard meteorological anemometers at 2, 5, 10 and 33 m was used for estimating the roughness length assuming a typical log wind profile under statically neutral conditions.
Mahrt, L., 1998. Flux Sampling Errors for aircraft and towers. J. Atmos. Ocean. Technol. 15, 416-429.
Mc Millen, R.T., 1988. An Eddy correlation technique with extended applicability to non-simple terrain. Boundary-Layer Meteorol. 43, 231-245.
Vickers D, Mahrt L. 2006. A solution for flux contamination by mesoscale motions with very weak turbulence. Boundary-Layer Meteorol. 118: 431–447. https://doi.org/10.1007/s10546-005-9003-y.
Zahn, E., Chor, T.L., Dias, N. L., 2016. A Simple Methodology for Quality Control of Micrometeorological Datasets. American Journal of Environmental Engineering 6(4A): 135-142 DOI: 10.5923/s.ajee.201601.20.
Observing earth critical zone processes in the bayelva basin (CZO@Bayelva)
Data represents the average values and the corresponding standard deviation obtained from each plots at different site along the transect CCT-airport. Each average value is obtained as a mean over a set of more than 20 point measures for each plot and each sampling date. Flux data are complemented by measurements of soil temperature and volumetric water content. data obtain using accumulation chamber and portable probe.
The Climate Change Tower Integrated Project (CCT-IP) represents the guide lines of the italian research in the arctic and aims to study the interaction between all the components of the climate system in the Arctic. The Amundsen-Nobile Climate Change Tower (CCT) is the key infrastructure of the project, and provides continuous acquisition of the atmospheric parameters at different heights as well as at the interface between the surface and the atmosphere.
The sensor used to measure the radiation budget and energy fluxes is a CNR1 net radiometer at 33 m of height and a CM11 and a CG4 at 25 m to measure the upwelling radiation from the surface.
30 minutes average (μ) and standard deviation (σ) of radiation data as well as products such as total net radiation average and shortwave albedo are available for the download.
Data at resolution of 1 minute are available for online visualization and downloadable under request.
The automated nivological station was installed in November 2020 in a flat area over the tundra about 80 meters far from the Gruvebadet Atmospheric Laboratory and nearby a snow sampling site from where weekly snow samples are collected for chemical analysis. Sensors have been calibrated by their companies before installation and are connected to a datalogger for continuous acquisition. For all the parameters, data are logged with 10-minute time resolution and then averaged over 1 hour. This activity is carried out by the Aldo Pontremoli Centre part of the Joint Research Agreement ENI-CNR.
The Climate Change Tower Integrated Project (CCT-IP) represents the guide lines of the italian research in the arctic and aims to study the interaction between all the components of the climate system in the Arctic. The Amundsen-Nobile Climate Change Tower (CCT) is the key infrastructure of the project, and provides continuous acquisition of the atmospheric parameters at different heights as well as at the interface between the surface and the atmosphere.
30 minutes average (μ) and standard deviation (σ) of meteorological data are available for the download.
Data at resolution of 1 minute are available for online visualization and downloadable under request.
The dataset includes Institute of Marine Research data from trawl stations in Nordfjord, Norway in 2023
Post-smolts were collected in the outer parts of the fjord systems in late spring and early summer with a specialized surface trawl for live fish sampling, towed behind a suitable fishing vessel (15-25 m). The trawl is 5 m deep and up to 35 m wide depending on speed through water.
The trawl is mounted to a system where smaller catch, such as post-smolts are separated from other catch, and remain free swimming in low turbulence in a hydrodynamic aquarium. The separation takes place when the current flow through a net tunnel and over two 45 degrees racks. The first rack (10 mm between bars) lifts the catch to the next (20 mm between bars) where the separation is done. Everything wider than 20 mm will continue to the cod end of the trawl. Details of the post-smolt trawl are described in Holst and McDonald (2000), Fisheries Research 48, pp 87-91. Typical trawling speed is 2-3 knots (STW) with a duration of 2-4 hours.
Lice counts on post-smolts were performed on the ship as soon as possible after they were captured. The fish were killed using an overdose of Benzocaine 200 mg/ml. Lice counts were performed with the fish submerged in a white plastic tub (5-10 l) using a strong headlamp (>500 lumen). The counts were only performed by personnel with special training in identification of all salmon lice stages. The following categories were recorded: copepodite, chalimus 1, chalimus 2, pre-adult, adult male and adult female. Fish length in mm and mass in gram were recorded. All post-smolts were then frozen for subsequent analysis.