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)
The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
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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 through the drop down menu above the text field. Text strings that are not quoted are treated as separate words and will match any of the words (i.e. assuming the OR operator). Phrases may be prefixed with '-' to indicate no occurence of the phrase in the results.
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.
Institutions: Norwegian Meteorological Institute / Arctic Data Centre, AWI
Last metadata update: 2023-06-29T11:12:36Z
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Abstract:
These CMIP5 model data show interpolated results in Arctic only. Original data were cut and interpolated for internal use of the EU funded project ACCESS.
The Norwegian Polar Institute measures mass balance on three glaciers, all in the Kongsfjorden area of north-western Spitsbergen, Svalbard. They are: Austre Brøggerbreen (data since 1967, Midtre Lovénbreen (since 1968) and Kongsvegen (since 1987). The first two are among the longest continuous high arctic glacier mass balance time-series. The Norwegian Polar Institute uses the so-called “combined method”, a mixture of the fixed-date and the stratigraphic methods, and comprises sounding of winter snow depth and repeated measurement of heights of an array of 8-10 stakes along the glacier centerline. Winter balance is obtained by snow-depth soundings over much of the glacier, an estimate of the autumn superimposed ice by shallow ice-cores along the longitudinal axis or at least by a measurement at the bottom of snow pits, stake height measurements, and snow density measurements. The work is carried out at the end of the accumulation period, in May. Stake positions are measured using differential GPS every year to monitor long-term velocity and elevation changes, both of which respond to the yearly mass fluctuations. Summer balance is obtained directly by comparing stake heights made in spring to fall stake measurements. The latter work is usually done at the end of the ablation period (in September and sometimes in October). Balance estimates are extrapolated over the entire glacier basin by using the distribution of glacier area per 50-m elevation band (hypsometry) obtained from maps or digital elevation models (DEMs). Net, winter and summer mass balance values are reported each year to MOSJ and as well to the World Glacier Monitoring Service.
The facility in Adventdalen can determine atmospheric parameters such as winds and turbulence from a few km altitude to over 100km and at a wide variety of spatial and temporal resolutions (which parameters are derived depends on altitude of the measurement).
The Sousy Svalbard Radar (SSR), is a so-called "mesosphere-stratosphere-troposphere" (MST) radar, operates at 53.5 MHz and is located in Adventdalen approximately 10km SW of Longyearbyen. The system is of the phased array type and as such has a low visual impact on the environment. Typical average power is only 200W - and thus a negligible radiation hazard (think of looking at 2 or 3 lightbulbs from several kilometers away). The MST radar is complemented with a meteor detection system extending the set of parameters.
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, 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.
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.
These datasets contain 100 continuous grids of ice thickness, bed topography, and topographic adjustments to geothermal heat flow in the region surrounding Dome Fuji, East Antarctica. Continuous ice thickness grids were created using publicly available measurement data and sequential gaussian simulation to generate statistically likely values between measurements. The simulation was run 100 times to create the ice thickness grids, which were then used to calculate bed elevation by subtracting from REMA ice surface elevations, and the local topographic impacts on background geothermal heat flow. The results are in raster format (.tif) and are projected in an EPSG: 3031 Antarctic Polar Stereographic coordinate system. The spatial extent is 596000 m to 1020000 m Easting and 816000 m to 1240000 m Northing with cell size 500 m x 500 m (848 columns, 848 rows). Ice-thicknesses are provided in meters and bed elevations are in meters referenced to the WGS84 Ellipsoid.
This High Mountain Asia (HMA) data set contains hydrological flow directions at 5 arc-minute resolution for the headwaters of the Amu Darya and Indus River basins. The domain spans parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan. Flow directions are reported in deterministic eight (D8) format.
The data were developed to support the University of New Hampshire Water Balance Model and the "High Mountain Asia CMIP6 Monthly and Yearly Water Balance Projections, 2016-2099 for Parts of Afghanistan, Tajikistan, Kyrgyzstan, and Pakistan, Version 1" data set.
This data set provides tracer parcels to simulate sea ice advection paths in the Northern Hemisphere combined with sea ice concentration data. Sea ice parcels are identified in 25 km grid cells where sea ice concentrations are greater than 15% and tracked daily using Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors (NSIDC-0116). Locations are updated weekly to determine whether an ice parcel has melted and should be tracked. Columns represent the EASE-grid x, y, and sea ice concentration values for each week over the 12 month tracking period and rows depict distinct simulated ice parcels.
Institutions: CH01L, Swiss Federal Laboratories for Materials Science and Technology, EMPA, Section Air Pollution, Überlandstrasse 133, 8600, Dübendorf, Switzerland, CH01L, Swiss Federal Laboratories for Materials Science and Technology, EMPA, Section Air Pollution, Überlandstrasse 133, 8600, Dübendorf, Switzerland
Last metadata update: 2023-10-16T00:00:00Z
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Abstract:
Ground based in situ observations of nitrogen_dioxide at Rigi (CH0005R) using CAPS. These measurements are gathered as a part of the following projects EMEP, GAW-WDCRG and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: nitrogen_dioxide in air (mass_concentration_of_nitrogen_dioxide_expressed_as_nitrogen_in_air)
Institutions: NO01L, Norwegian Institute for Air Research, NILU, Instituttveien 18, 2007, Kjeller, Norway, NO01L, Norwegian Institute for Air Research, NILU, Instituttveien 18, 2007, Kjeller, Norway
Last metadata update: 2024-02-07T00:00:00Z
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Abstract:
Ground based in situ observations of online_crds at Birkenes II (NO0002R). These measurements are gathered as a part of the following projects NILU_NRT and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: carbon_dioxide in air (mass_fraction_of_carbon_dioxide_in_air), carbon_dioxide in air (mass_fraction_of_carbon_dioxide_in_air), carbon_monoxide in air (mass_fraction_of_carbon_monoxide_in_air), carbon_monoxide in air (mass_fraction_of_carbon_monoxide_in_air), methane in air (mole_fraction_of_methane_in_air), methane in air (mole_fraction_of_methane_in_air), water_vapor in air (mole_fraction_of_water_vapor_in_air), water_vapor in air (mole_fraction_of_water_vapor_in_air)