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 and phrases embedded in quotation marks. Logical operators include AND, OR and NOT. Remember to add space around operators. Text strings that are not quoted are trated as separate words and will match any of the words (i.e. assuming the OR operator). E.g. in order to find WMO synoptic weather station data from Verlegenhuken use the search phrase: [synop AND verlegenhuken]. 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.
Collections
Collections allows the user to search in subsets of the existing catalogue. The collections are primarily data management projects that have been incorporated in the ADC catalogue after the project has ended. In this context the ADC is the long term access solution for these data. The collections currently served through ADC include (datasets may belong to multiple data collections):
ADC is the full collection of this service CC is the CryoClim collection
In order to search a specific data collection select that collection. If no data collection is selected all collections are searched.
AeN are data related to the Nansen Legacy project and are better explored through the SIOS Data Access Point using the collection defined there which is available through this URL.
SIOS, InfraNOR, SIOSCD, SIOSAP, SESS_* are collections related to SIOS. These are better explored through the SIOS Data Access Portal
Some cleaning is pending between InfraNOR and SIOSIN, for some of the SESS collections.
Citation of data and service
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)
The information required to properly cite a dataset is normally provided in the discovery metadata the datasets.
If you use data retrieved through this portal, please acknowledge the Norwegian Meteorological Institute/Arctic Data Centre.
Spectral slope of the coloured dissolved organic matter (CDOM) in the Arctic ocean surface layer estimated from satellite remote sensing reflectance observed by NASA AQUA/MODIS, using the method described by Fichot et al. 2013. Monthly spectral slope is calculated from the Level 3 monthly remote sensing reflectance at 443, 488, 531, 555 and 667 nm. Temporal resolution of the satellite data is monthly, and nominal spacial resolution is 4km. Data period covers July 2002 to December 2018.
Fichot et al., Pan-Arctic distributions of continental runoff in the Arctic Ocean, Scientific Report, 3, 1053, doi: 10.1038/srep01053, 2013.
Green Network of Excellence Program - Arctic Climate Change Research Project
Last metadata update: 2015-05-14T00:00:00Z
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New and improved estimates of sea ice production in the Arctic Ocean are derived from AMSRE satellite and atmospheric reanalysis data for the period 2002-2011, at a spatial resolution of 6.25 km and using a newly developed fast-ice mask. High ice production in the major coastal polynyas is well demonstrated.
Green Network of Excellence Program - Arctic Climate Change Research Project
Last metadata update: 2015-05-14T00:00:00Z
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New and improved estimates of thin sea-ice thickness in the Arctic Ocean are derived from AMSR-E satellite for the period 2002-2011, at a spatial resolution of 6.25 km and using a newly developed fast-ice mask. The algorithm is based on comparisons between the polarization ratio (PR) of AMSR-E brightness temperatures from the 89 and 36 GHz channels (PR89 and PR36) and the thermal ice thickness. The thermal ice thickness is estimated from a heat budget calculation using the ice surface temperature from clear-sky Moderate-Resolution Imaging Spectroradiometer (MODIS) infrared data. Whereas coastal polynyas have been the main target of previous algorithms, this algorithm is also applicable for marginal ice zones. AMSR-E has twice the spatial resolution of Special Sensor Microwave/Imager (SSM/I) data and can therefore resolve polynyas at a smaller scale. Although the spatial resolution of the 89 GHz data (6.25 km) is twice that of the 36 GHz data (12.5 km), the 89 GHz data can be contaminated by atmospheric water vapor. We propose an exclusion method of data affected by water vapour to resolve this issue.
The exponent of the chlorophyll-a (Chla) size distribution (CSD slope), which is an index of synoptic size structure of a phytoplankton community, was estimated based on spectral shape of the phytoplankton absorption coefficient using satellite ocean color data (MODIS/Aqua).
This dataset includes Geophysical Institute (GFI), University of Bergen mooring SVI200207_B data. The mooring consists of 4 inst. (instrument type:instrument number(instrument depth)):RCM7:2862(100m) RCM7:12349(300m) RCM7:12347(500m) RCM7:11064(680m) . Metadata and raw/processed time series are presented as NetCDF, text and pdf(plots with statistics, calibration sheets, mooring drawing) files.
This dataset includes Geophysical Institute (GFI), University of Bergen mooring SVI200207_A data. The mooring consists of 4 inst. (instrument type:instrument number(instrument depth)):RCM7:2863(100m) RCM7:8438(300m) RCM7:12318(420m) RCM7:4040(495m) . Metadata and raw/processed time series are presented as NetCDF, text and pdf(plots with statistics, calibration sheets, mooring drawing) files.
Intra-specific and intra-population variation inmovement tactics have been observed in many species,sometimes in association with alternative foraging techniquesor large-scale habitat selection. However, whetheranimals adjust their small-scale habitat selection accordingto their large-scale tactics has rarely been studied. Thisstudy identified two large-scale movement tactics in ringedseals (Phoca hispida) during their non-breeding, post-moultingperiod. First-passage times (FPT) were used toexplore these large-scale patterns. Subsequently, habitatselection was quantified by modelling the FPTs as afunction of habitat attributes using Cox proportional hazardsmodels. Some seals moved far offshore into areaspreferentially containing 40–80% ice coverage, while otherindividuals spread along the coasts of Svalbard concentratingtheir time near glacier fronts. Both tactics resultedin ringed seals being in highly productive areas where theyhad access to ice-platforms to rest. When offshore, habitatselection was influenced mainly by sea ice concentrationand season. Late in the season (autumn), increased risk ofleaving an area was identified, even when ice conditionswere still favourable, reflecting their need to return to overwintering/breeding areas before the fjords of the archipelagofreeze. For ringed seals that remained inshore, habitatuse intensities were influenced mainly by the distance toglacier fronts and season. These animals were already closeto their over-wintering habitat and hence their risk ofleaving an area decreased as winter approached. This studyof ringed seals habitat selection reveals how they fulfil theirbiological requirements in this dynamic, heterogeneoushabitat. Individuals within the same population employedtwo distinct large-scale movement tactics, adjusting theirdecisions for small-scale habitat selection accordingly.This flexibility in ringed seal spatial ecology during summerand fall is expected to result in increased populationviability in this high Arctic environment.Methods and data filteringA total of 22 animals, 11 caught in 2002 and 11 caught in 2003, were equipped with satellite-relayed data loggers (SRDLs). These SRDLs collect and relay information on movements (geographical locations), time spent dry (haulout behaviour) and aspects of diving behaviour (ascent rate, descent rate, time at depth, etc.) of marine mammals. In addition, they are equipped with a fast-response temperature sensor (micro-BetaCHIP probe; Betatherm, Ireland), with an accuracy of 0.1°C and a resolution of < 0.01°C. SRDLs were programmed to send data whenever possible, with no duty cycling.Location data obtained from Argos satellites were filtered using an algorithm based on swimming speed, distance between successive locations and turning angles. The filter removed all data points for which the Argos location process failed. It also removed all locations requiring swimming speeds higher than 2 m/s, unless the data point in question was located less than 5,000 m from the previous location. The latter condition enabled the retention of good-quality locations for which high swimming speeds resulted from locations being taken very close in time. Additionally, the filter excluded sudden deviations away from the general path’s track if the off-track spike extended greater than 2,500 or 5,000 m, with angles smaller than 15° and 25° between consecutive locations, respectively. Distance and angle limits were chosen empirically, based on the measurement of the angles and lengths of the most conspicuous, abrupt deviations from the principal path of the tracks, which are unlikely to correspond to real locations.
Free-living ringed seals (N = 11) equipped with satellite-relayed data loggers (SRDLs) with incorporated oceanographic-quality temperature sensors were used to collect data from a large sector of the northern Barents Sea during the autumn andearly winter. A total of 2346 temperature profiles were collected over a 4-month period from Norwegian and Russian arcticwaters in areas that were at times 90–100% ice-covered. Temperature distributions at different depths from northeastern parts ofSvalbard, Norway show warm North Atlantic water (NAW) flowing along the continental slope and gradually cooling at alldepths as it flows eastwards. The data suggest that most of the cooling takes place west of 30jE. Vertical temperature profilesfrom the area between Svalbard and Franz Josef Land, Russia show how the surface water cools during freeze-up anddemonstrate a warm water flow, which is probably NAW, coming in from the north through a deep trench west of Franz JosefLand. Global oceanographic and climate models require improved oceanographic databases from crucial areas where importanthydrological phenomena occur. Such areas in arctic waters are often inaccessible during winter and logistically difficult to reacheven in summer. The present study demonstrates how large amounts of oceanographic information can be collected andretrieved in a cost-efficient manner using ice-associated marine mammals as carrier of oceanographic sampling equipment. Inaddition to the oceanographic value of the data collected by marine mammals in this manner, a vast amount of informationregarding the habitat of these animals is concomitantly sampled.
Institutions: NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway, NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway
Last metadata update: 2021-02-11T00:00:00Z
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Ground based in situ observations of high_vol_sampler at Waldhof (DE0002R). These measurements are gathered as a part of the following projects EMEP, CAMPAIGN and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: elemental_carbon in pm10 (mass_concentration_of_elemental_carbon_in_pm10_in_air), organic_carbon in pm10 (mass_concentration_of_organic_carbon_in_pm10_in_air), total_carbon in pm10 (mass_concentration_of_total_carbon_in_pm10_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., CAMPAIGN: For scientific purposes, access to the data is limited. Data will be given access to after approval from the data producer or owner.
Institutions: NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway, NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway
Last metadata update: 2021-02-11T00:00:00Z
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Ground based in situ observations of low_vol_sampler at Stará Lesná (SK0004R). These measurements are gathered as a part of the following projects EMEP, CAMPAIGN and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: elemental_carbon in pm10 (mass_concentration_of_elemental_carbon_in_pm10_in_air), organic_carbon in pm10 (mass_concentration_of_organic_carbon_in_pm10_in_air), pm10_mass in pm10 (mass_concentration_of_pm10_ambient_aerosol_particles_in_air), total_carbon in pm10 (mass_concentration_of_total_carbon_in_pm10_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., CAMPAIGN: For scientific purposes, access to the data is limited. Data will be given access to after approval from the data producer or owner.
Institutions: NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway, NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway
Last metadata update: 2021-02-11T00:00:00Z
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Ground based in situ observations of low_vol_sampler at Kosetice (NOAK) (CZ0003R). These measurements are gathered as a part of the following projects EMEP, CAMPAIGN and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: elemental_carbon in pm10 (mass_concentration_of_elemental_carbon_in_pm10_in_air), organic_carbon in pm10 (mass_concentration_of_organic_carbon_in_pm10_in_air), pm10_mass in pm10 (mass_concentration_of_pm10_ambient_aerosol_particles_in_air), total_carbon in pm10 (mass_concentration_of_total_carbon_in_pm10_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., CAMPAIGN: For scientific purposes, access to the data is limited. Data will be given access to after approval from the data producer or owner.
Institutions: NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway, NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway
Last metadata update: 2021-02-11T00:00:00Z
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Ground based in situ observations of total_carbon at CEH Edingburgh (GB0046R) using low_vol_sampler. These measurements are gathered as a part of the following projects EMEP, CAMPAIGN and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: total_carbon in pm10 (mass_concentration_of_total_carbon_in_pm10_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., CAMPAIGN: For scientific purposes, access to the data is limited. Data will be given access to after approval from the data producer or owner.
Institutions: NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway, NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway
Last metadata update: 2021-02-11T00:00:00Z
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Ground based in situ observations of low_vol_sampler at Mace Head (IE0031R). These measurements are gathered as a part of the following projects EMEP, CAMPAIGN and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: elemental_carbon in pm10 (mass_concentration_of_elemental_carbon_in_pm10_in_air), organic_carbon in pm10 (mass_concentration_of_organic_carbon_in_pm10_in_air), pm10_mass in pm10 (mass_concentration_of_pm10_ambient_aerosol_particles_in_air), total_carbon in pm10 (mass_concentration_of_total_carbon_in_pm10_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., CAMPAIGN: For scientific purposes, access to the data is limited. Data will be given access to after approval from the data producer or owner.
Institutions: NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway, NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway
Last metadata update: 2021-02-11T00:00:00Z
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Ground based in situ observations of low_vol_sampler at Ispra (IT0004R). These measurements are gathered as a part of the following projects EMEP, CAMPAIGN and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: elemental_carbon in pm10 (mass_concentration_of_elemental_carbon_in_pm10_in_air), organic_carbon in pm10 (mass_concentration_of_organic_carbon_in_pm10_in_air), pm10_mass in pm10 (mass_concentration_of_pm10_ambient_aerosol_particles_in_air), total_carbon in pm10 (mass_concentration_of_total_carbon_in_pm10_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., CAMPAIGN: For scientific purposes, access to the data is limited. Data will be given access to after approval from the data producer or owner.
Institutions: NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway, NO01L, Norwegian Institute for Air Research, NILU, Atmosphere and Climate Department, Instituttveien 18, 2007, Kjeller, Norway
Last metadata update: 2021-02-11T00:00:00Z
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Abstract:
Ground based in situ observations of low_vol_sampler at ISAC Bologna (IT0008R). These measurements are gathered as a part of the following projects EMEP, CAMPAIGN and they are stored in the EBAS database (http://ebas.nilu.no/). Parameters measured are: elemental_carbon in pm10 (mass_concentration_of_elemental_carbon_in_pm10_in_air), organic_carbon in pm10 (mass_concentration_of_organic_carbon_in_pm10_in_air), pm10_mass in pm10 (mass_concentration_of_pm10_ambient_aerosol_particles_in_air), total_carbon in pm10 (mass_concentration_of_total_carbon_in_pm10_in_air)
License : EMEP: Public open access. We encourage contacting data originators if substatial use of individual time series is planned (fair use data policy)., CAMPAIGN: For scientific purposes, access to the data is limited. Data will be given access to after approval from the data producer or owner.