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.
NDVI, GCC, soil and surface temperature, and soil water content data from Adventdalen, Svalbard. This data was collected with a time-lapse RGB camera and NDVI sensor installed on a two meter high metal rack to monitor tundra vegetation. The time-lapse photos have gone through a manual quality check and were automatically adjusted with an algorithm to correct for lateral and rotational movements. A mask was used to calculate Green Chromatic Channel (GCC) from the photos. The NDVI data was quality controlled by removing outliers that were two standard deviations removed from the mean value of the growing season, and by removing dates where there was snow on the ground (as indicated by the time-lapse photos). In addition, soil and surface temperature and soil moisture were measured to facilitate the interpretation of shifts in the vegetation indices.
This collection contains a high-resolution (2.5 km) dataset of glacier mass balance and runoff in Franz Josef Land and Novaya Zemlya from 1991-2022, situated in one of the fastest warming regions in the Arctic. The dataset is created using a full energy balance model (the CryoGrid community model) forced by the Copernicus Arctic Regional ReAnalysis (CARRA) dataset (1991-2022). Each variable is available at both a daily and monthly resolution.
This collection contains a high-resolution (2.5 km) dataset of glacier mass balance, runoff and snow conditions in Svalbard from 1991-2022, one of the fastest warming regions in the Arctic. The dataset is created using a full energy balance model (the CryoGrid community model) forced by both the Copernicus Arctic Regional ReAnalysis (CARRA) dataset (1991-2021) and AROME-ARCTIC forecasts (2016-2022). Each variable is available at both a daily and monthly resolution.
This dataset provides satellite-detected surface features in Dronning Maud Land Ice Shelves that have been digitized. We used RADARSAT-2 imagery (Wide fine mode with approximate resolutions of 5-8 m) collected between 13 November 2014 and 4 December 2014. These features were categorized into 7 primary groups: (1) longitudinal stripes, (2) crevasses, (3) rifts, (4) ice rises and ice rumples, (5) blocks, (6) areas rich with above-mentioned features, (7) calving front. Because of high population of individual crevasses and rifts, we did not digitize all of them; we marked such high population areas and digitized some typical features to show their typical shapes and orientations.
Feature Groups
- Longitudinal stripes
- Longitudinal stripe
- Crevasses
- Individual crevasses
- Crevassed area
- Rifts
- Rift
- Rifted Area
- Ice rises and ice rumples
- Isle-type ice rise and ice rumple
- Promontory-type ice rise
- Stripe on isle-type ice rise and ice rumple
- Stripe on promontory-type ice rise
- Cluster of small isle-type ice rises and ice rumples
- Blocks
- Iceberg
- Area with many icebergs
- Block within ice shelf
- Areas rich with above-mentioned features
- Area with crevasses and blocks
- Area with crevasses and rifts
- Area with crevasses and longitudinal stripes
- Area with crevasses, rifts, and blocks
- Onset area of short longitudinal stripes
- Calving front
- Calving front
Date Formats
The dataset has two separate shape files, (1) line features and (2) area (polygon) features. We also provide QGIS’s project file and style files with which these features in the two shape files are visualized together.
Digital geological map of Svalbard at the scale of 1:750000.
Subdivision of geology is according to stratigraphic group, subgroup or formation, depending on which is best applicable to the given scale. Where no formations are defined in parts of the geological basement, lithological units are defined instead
Quality
Spatial Reference: WGS84/UTM zone 33N (EPSG: 32633)
Upwelling and downwelling longwave and shortwave radiation and shortwave albedo from station deployed out on the ice floe, nearby surface meteorology observations.
WP2
Quality
Albedo data is on a different time step and is a heavily processed version of a subset of the the radiation data, see attributes in the NetCDF files and the READMEs:
Surface mass balance (SMB) for varying time periods in the past three decades were obtained by dating tracked radar reflectors. These estimated cover two ice rises (Djupranen and Leningradkollen) and the Nivlisen Ice Shelf between them in central Dronning Maud Land. This dataset consists of (i) processed radargrams used to obtain the SMB (ii) tracked reflectors with age, two-way travel time, depth, and estimated SMB and, (iii) period wise surface mass balance estimates. The data are presented as:
(i) Daily retrieved (year/month/date) processed radargrams as HDF structure files consisting of following variables: • lon: longitudes in decimal degrees • lat: latitudes in decimal degrees • x: x coordinate (m) in a polar stereographic projection EPSG:3031 • y: y coordinate (m) in a polar stereographic projection EPSG:3031 • z: elevations (m) above the WGS84 ellipsoid • wfm: matrix with radar amplitude data. • fwfm: filtered wave form i.e. matrix with radar amplitude data. • dist: distance (m) from the starting point along the profile • twt: two-way-travel time (ns) vector
(ii) Each ‘reflectors_age_depth_SMB’ files consisting of tracked reflectors and associated SMB (meter ice equivalent per year or m i.e. a-1). Each file has following variables: • name: name of the reflector tracked • lon: longitudes in decimal degrees • lat: latitudes in decimal degrees • x: x coordinate (m) in a polar stereographic projection • y: y coordinate (m) in a polar stereographic projection • twt: two-way-travel time (ns) • age (years): age of reflector (with reference to year 2016/2017 surface) • SMB: surface mass balance (m i.e a-1) • depth: Ice equivalent depth (m i.e.) from the surface 2016/2017
(iii) ESRI shapefiles consisting of SMB in meter ice equivalent (m i.e. a-1) for a specific period. Each file has following variables: • name: name of the reflector tracked with the years and period • x: x coordinate (m) in a polar stereographic projection • y: y coordinate (m) in a polar stereographic projection • SMB: surface mass balance (m i.e a-1)
Quality
If you use the dataset in presentations and publications please also refer to the peer-reviewed paper (Pratap others, 2021, accepted), where the data is described in more detail.
Contact person: Kenichi Matsuoka (kenichi.matsuoka@npolar.no) This work was part of the MADICE (Mass balance, dynamics, and climate of the central Dronning Maud Land coast, East Antarctica) project co-led by the Norwegian Polar Institute in Norway and National Centre for Polar and Ocean Research in India (https://www.npolar.no/prosjekter/madice/).
Dataset of annual mass balances of Svenbreen, a small valley glacier in Central Spitsbergen, 2010/2011 - 2017/2018
To date (31st Jan 2020), the data have not been published in an article in a peer-reviewed journal, which is planned for 2021 or 2022, following the completion of ten years of measurements. It is possible that the exact values might differ slightly between this dataset and the planned paper due to differences in methodology, eg. updated glacier hypsometries. If this dataset is of your interest, please check Jakub Malecki’s publication record for the most up-to-date data..
Quality
Annual mass balance of Svenbreen has been measured with a glaciological method since 2010/2011, typically between 1st and 15th day of September every year. Ablation stake network comprises 12-16 stakes distributed along the glacier tongue and in two (out of three) high-elevation sections, i.e. in the cirque and along an ice patch leading towards neighbouring glacier Hoelbreen.
The Nansen Legacy (Arven etter Nansen), The Nansen Legacy
Last metadata update: 2023-02-06T15:15:09Z
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Abstract:
The Nansen Legacy cruise Q1 was part of the seasonal investigation of the northern Barents Sea and adjacent Arctic Basin. The cruise was conducted in 2-24 March 2021 onboard R/V Kronprins Haakon, and focused on studying the physical, chemical and biological conditions along the Nansen Legacy main transect in open waters and within the sea ice. While in sea ice we conducted ten regional scale sea ice helicopter-borne surveys of ice conditions along the Nansen Legacy transect using a helicopter-borne electromagnetic instrument (HEM) EM-bird. This dataset presents processed EM-bird data on total snow and sea-ice thickness along the flight tracks.
This is a contribution to the Research Council of Norway project “Nansen Legacy” (https://arvenetternansen.com/), WP RF-1 “Physical drivers”.
Quality
See the attached docuement “AeN_Q1_202103_HEM_icethickness_metadata_v1.0.pdf” for details on the data acqusition, processing and structure.
Kartdata tilpasset målestokksområdet 1:100 000 til 1:300 000 for landarealet av Svalbard. Kartdataene er en generalisering av S100 Kartdata.
Map data adapted for the scale range 1:100 000 to 1:300 000 for the land area of Svalbard. The map data is a generalization of S100 Kartdata.
Quality
Deler av kartgrunnlaget er av eldre dato og ikke egnet for navigasjon. Dataene er generalisert fra S100 Kartdata. —– Parts of the map data are of older dates and not suited for navigation. The data are generalized from S100 Kartdata.
This dataset provides ice thickness calculated using deep-sounding (2 MHz antenna frequency) radar profiles over three ice rises Blåskimen Island, Kupol Ciolkovskogo and Kupol Moskovskij, Fimbul Ice Shelf, western Dronning Maud land. Ice thickness was estimated by converting the two way travel time of the radar signal from surface to the bed. The calculation takes into account the increased propagation speed in the top surface firn layer (method described in Goel et. al., 2017). The dataset also provides the bed elevation along the same profiles, obtained by subtracting ice thickness from surface elevation data.
The data are provided in MATLAB (.mat) files named:
- ‘LAT’ and ‘LON’ shows the coordinates as per WGS84 ellipsoid in decimal degrees
- ‘X’ and ‘Y’ shows the coordinates in polar stereographic projection parallel to 71°S (EPSG 3031)
- ‘IT’ and ‘BE’ shows the ice thickness and bed elevation in meters
- ‘XX’, ‘YY’, ‘TT’ and ‘BB’ are matrices with interpolated gridded x-coordinate, y-coordinate, ice thickness and bed elevation data respectively
See the following manuscript for method details: Goel, V., Brown, J., and Matsuoka, K.: Glaciological settings and recent mass balance of Blåskimen Island in Dronning Maud Land, Antarctica, The Cryosphere, 11, 2883-2896, https://doi.org/10.5194/tc-11-2883-2017, 2017.