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.
a) Sea bed mapping and b) Glacial geological and paleo climatic research.Acustical profile data from seismic, penetration echo sounder and side-scanning sonar.
MAREANO, GEOS Oslofjorden, Marine grunnkart i Astafjord, fase III, ICZPM – AquaReg pilotprosjekt, Marine grunnkart i Sør Sunnmøre, Marine Grunnkart i Sore Sunnmore, Kartlegging av Saltstraumen marine verneomtåde, Frisk Oslofjord (MAREANO, GEOS Oslofjorden, AstafjordIII, AQUAREG, MGG, MG Sore Sunnmore, Saltstraumen MVO, Frisk Oslofjord)
Last metadata update: 2010-04-07T12:00:00Z
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Abstract:
Anchoring and mooring conditions in some coastal areas with detailed data coverage, as interpreted from bottom type (hard or soft bottom) and depth. It is distinguished between anchoring and mooring conditions. In this context mooring means the possibility for divers to mount bolts into exposed bedrock (to fasten marine installations), usually at depths less than 30m. Anchoring conditions mean the anticipated relative hold of anchors in the substrate.
The dataset provides an overview of modern sedimentary environment and processes on the seabed in terms of deposition, transportation and erosion of sediments.
The data on this theme is based on the content of the grain size map. Regional mapping on Norwegian continental shelf by MAREANO.
Modelled distribution of marine biotopes in the Barents Sea, which reflects the regional variation in species composition and the physical environment. This biotope map, covering the entire Barents Sea, has been compiled in collaboration between the Geological Survey of Norway, the Norwegian Institute of Marine Research (IMR) and the Russian Polar Research Institute of Marine Fisheries and Oceanography (PINRO) in the frame of the Norwegian-Russian Environmental Commission Workplan for 2011-2013 and 2013-2015.
Marine Light-Mixed Layer Experiment 89, MAREANO, Marine grunnkart i Sør Sunnmøre, Marine Grunnkart i fem kommuner i Oforten (MLML89, MAREANO, MGG, MG Ofoten)
Last metadata update: 2010-04-06T12:00:00Z
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Abstract:
The map service shows the distribution of sediments, classified by grain size and genesis; Included in the service are layers og sediment thickness and sedimentation environment. Bacscatter data from multibeam echsounder measurements provides information about the relative harness of the bottom (hard or soft bottom).
MAREANO, Marine grunnkart i Astafjord, fase III, Marine grunnkart i Sør Sunnmøre, Marine Grunnkart i fem kommuner i Oforten (MAREANO, AstafjordIII, MGG, MG Ofoten)
Last metadata update: 2010-04-07T12:00:00Z
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Abstract:
The map service shows levels of heavy metals and other inorganic environmental indicators in surface sediments in Norwegian fjords and on the Norwegian continental shelf.
The map service shows the bottom of the North-East Atlantic and the Barents Sea divided into large geographical areas with a visually homogeneous character (marine landscapes).
MAREANO, AQUREG, GEOS Oslofjorden, Marine grunnkart i Soer-Troms, Marine grunnkart i Astafjord, fase III, Marine grunnkart i Sør Sunnmøre, Marine grunnkart i Sogn og Fjordane, Marine Grunnkart i fem kommuner i Oforten (MAREANO, AQUREG, GEOS Oslofjorden, Astafjorprosjektet, AstafjordIII, MGG, MG_SFJ, MG Ofoten)
Last metadata update: 2010-09-28T12:00:00Z
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Abstract:
This geologically interpreted data is based on grain size distribution, and indicates how easy it would be to dig into the sea floor, and how stable the dugout depression would be. Sandy sediments, for example, will collapse more quickly after digging a trench than the sediments with finer grain size.
Subsea landscapes of Norwegian ocean areas based on low resolution bathymetry. The map covers areas of 2.4 mill. km2 and is made for presentation in small scale; 1:500 000 for the Barents Sea and the Mid-Norwegian shelf, and 1:1 000 000 for other Norwegian ocean areas. The map gives a good regional picture of terrain variations in areas that have so far been little studied.
Marine grunnkart i Sør-Troms, Biologisk mangfold, Havbruk, areal, samordning og utvikling i Trøndelag (Astafjordprosjektet, Biologisk mangfold, HASUT)
Last metadata update: 2019-09-03T12:00:00Z
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Abstract:
The datasett is a geographic representation of over 800 ice marginal deposits in Norwegian fjords and coastal areas. The dataset is compiled of data from several mapping projects, litteratur and other sources. The main compilation was made in cooperation between NGU and Miljødirektoratet (Biomangfoldprosjektet). The interpretation of ice marginal deposits is based on detailed bathymetry data.
Quantarctica is a collection of Antarctic geographical datasets which works with the free, cross-platform, open-source software QGIS. It includes community-contributed, peer-reviewed data from ten different scientific themes and a professionally-designed basemap.
Quality
The Quantarctica Editorial Board selects peer-reviewed datasets for a wide range of Antarctic users, including over 150 basemap and scientific data layers and thematic coverage from Glaciology and Geophysics to other themes such as Atmospheric Science, Biology, Oceanography, Social Sciences, and more. The Quantarctica project team at the Norwegian Polar Institute then incorporates these layers into the Quantarctica data package by importing, reprojecting, styling, labeling, and organizing them for user-friendly presentation.
Output from model runs to examine potential for snow ice formation in the Arctic Ocean over the period 1980-2016
Quality
Output from model runs to examine the potential for snow ice formation in the Arctic Ocean over the period 1980-2016. We used a 1-D, high resolution thermodynamic ice and snow model HIGHTSI [Launiainen and Cheng, 1998], to simulate sea ice thickness, snow-ice thickness and snow depth in the Arctic Ocean. HIGHTSI is designed to resolve the evolution of snow and ice thickness, and temperature profiles. It has been widely used in process studies and validated extensively against observations.
We implemented HIGHTSI in a Lagrangian framework to examine Arctic snow-ice distributions. Ice motion vectors are derived by satellite products, and are provided from the National Snow and Ice Data Center (NSIDC) [Tschudi et al., 2016]. Based on the motion vectors we performed Lagrangian tracking of ice parcels over the Arctic Ocean and its marginal seas from 1980 to 2016. This resulted in a daily sea ice motion product of 25 km spatial resolution. Throughout this period ice parcels disappear and new parcels are being generated. At any given time, the Arctic simulation domain can hold a total of 60000 individual ice parcels. At each time step the MicroMet meteorological preprocessor [Liston and Elder, 2006] was used to extract the atmospheric forcing based on the position of each ice parcel. Ice concentration data from Cavalieri et al. [1996] were used to initialize an ice parcel. We considered ice parcels initialized when ice concentration exceeded a 15% concentration threshold.
We used atmospheric data from reanalyses to force HIGHTSI, including 10 m wind speed, 2 m air temperature and relative humidity, and total precipitation, while MicroMet provided the solid precipitation, downwelling shortwave and longwave radiation. We used ERA-I and MERRA-2 atmospheric reanalyses [Dee et al., 2011; Gelaro et al., 2017] in order to examine the snow-ice sensitivity to the magnitude of precipitation over sea ice. These reanalyses have shown relatively good agreement for air temperature and timing of precipitation events [Merkouriadi et al., 2017b], although there is a warm bias in both products during the lowest temperatures in winter [Graham et al., 2019]. But especially, they exhibit significant differences in the magnitude of precipitation [Chaudhuri et al., 2014; Merkouriadi et al., 2017b; Boisvert et al., 2018] with ERA-I producing relatively low and MERRA-2 producing relatively high precipitation amounts [Merkouriadi et al., 2017b; Boisvert et al., 2018].
HIGHTSI simulations began each year on 1 August (1980-2016), and run through one full year at a time, using a 3-hour time step. Based on the ice motion and concentration information, existing ice parcels on 1 August were considered SYI/MYI. On 1 August we assumed that there is no snow on SYI/MYI. We performed model experiments with 4 different initial thicknesses for the existing SYI/MYI parcels on 1 August (h0=0.5, 1, 1.5 and 2 m). Thus, we conducted 8 experiments in total, 4 with ERA-I and 4 with MERRA-2 forcing. Initial ice thickness of 2 m was likely more common in 1980’s and 1990’s, whereas thicknesses of 1.5 m and less is becoming more typical in recent years [Kwok and Untersteiner, 2011]. We acknowledge that a uniform initial SYI/MYI thickness over the entire ice-covered Arctic Ocean is not realistic. However, our purpose is to examine the inter-decadal sensitivity of snow-ice formation to the regional patterns and trends of weather conditions and sea ice motion. For the same reason we chose a constant, low ocean heat flux (Fw = 1 W m-2). In a similar study we carried out north of Svalbard, in a region where ocean heat flux is of greatest importance due to the proximity to the North Atlantic, we concluded that the choice of ocean heat flux did not significantly affect the results [Merkouriadi et al., 2017b]. These simplifications allow us to examine the sensitivity of snow-ice formation to a limited number of factors, keeping in mind our level ice assumption.
The outputs of the HIGHTSI model experiments for each ice parcel at each time-step are: snow-ice layer thickness, thermal ice thickness (i.e. total ice thickness minus snow-ice thickness) and snow depth. After we conducted the simulations, the model output was gridded to the 25x25 km Equal-Area Scalable Earth Grid (EASE-Grid), provided by NSIDC. At each time step, the parcels’ location was used to calculate the overlap between the parcel and the EASE grid cell. The overlap is calculated as fractional area of the EASE grid cell. The fractional area was then multiplied by the sea ice concentration of the parcel, and the result was used to weigh the parcels’ contribution to each EASE grid cell. This procedure of area- and concentration-weighted averages within the EASE grid cells, conserves the examined parameters. In order to look separately into FYI and SYI/MYI, existing parcels on 1 August were considered to be SYI/MYI. New parcels that appear after 1 August each year were considered to be FYI.
This data set includes the results of total 8 model experiments: 4 with ERA-I and 4 with MERRA-2 atmospheric reanalysis forcing. The 4 experiments from each reanalysis correspond to different initial SYI/MYI thickness (h0 = 0.5, 1, 1.5 and 2 m). In each experiment 3 variables are produced by the HIGHTSI model: snow-ice thickness (sice), thermal ice thickness (tice) and snow depth (snod). The data format is GrADS (.gdat). The time step is 3-hours, for the period 1980-2016. The initial date and time of the data set is 1 August 1980, 00:00. Each result has 3 dimensions: the ice parcels (total number = 70000), the variable value in meters [m], and time (total number of steps = 105192).
The model outputs (sice, tice and snod) for each model experiment (ERA_0.5, ERA_1.0, ERA_1.5, ERA_2.0, MERRA_0.5, MERRA_1.0, MERRA_1.5 and MERRA_2.0,) are in the folder output/’name of model experiment’. The process scripts (Fortran files) that put the parcel data on the EASE-grid and separate between FYI and SYI/MYI, are provided in the folder ‘EASE-grid_process’. The ice parcel tracks and concentration data from NSIDC are in the folder ‘parcel_tracks’. Finally, a python script for reading Grads files is provided: ‘import_grads.py’.
References
- Boisvert, L. N., M. A. Webster, A. A. Petty, T. Markus, D. H. Bromwich, and R. I. Cullather (2018), Intercomparison of precipitation estimates over the Arctic ocean and its peripheral seas from reanalyses, J. Clim., 31(20), 8441–8462, doi:10.1175/JCLI-D-18-0125.1.
- Cavalieri, D. J., C. L. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated yearly. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/8GQ8LZQVL0VL. - Chaudhuri, A. H., R. M. Ponte, and A. T. Nguyen (2014), A comparison of atmospheric reanalysis products for the Arctic Ocean and implications for uncertainties in air-sea fluxes, J. Clim., 27(14), 5411–5421, doi:10.1175/JCLI-D-13-00424.1.
- Dee, D. P. et al. (2011), The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137(656), 553–597, doi:10.1002/qj.828.
- Gelaro, R. et al. (2017), The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Clim., 30(14), 5419–5454, doi:10.1175/JCLI-D-16-0758.1.
- Graham, R. M., L. Cohen, N. Ritzhaupt, B. Segger, R. G. Graversen, A. Rinke, V. P. Walden, M. A. Granskog, and S. R. Hudson (2019), Evaluation of six atmospheric reanalyses over Arctic sea ice from winter to early summer, J. Clim., 32(14), 4121–4143, doi:10.1175/JCLI-D-18-0643.1.
- Kwok, R., and N. Untersteiner (2011), The thinning of Arctic ice, in AIP Conference Proceedings, vol. 1401, pp. 220–231. Launiainen, J., and B. Cheng (1998), Modelling of ice thermodynamics in natural water bodies, Cold Reg. Sci. Technol., 27(3), 153–178, doi:10.1016/S0165-232X(98)00009-3.
- Liston, G. E., and K. Elder (2006), A meteorological distribution system for high-resolution terrestrial modeling (MicroMet), J. Hydrometeorology, 7, 217–234.
- Merkouriadi, I., B. Cheng, R. M. Graham, A. Rösel, and M. A. Granskog (2017b), Critical Role of Snow on Sea Ice Growth in the Atlantic Sector of the Arctic Ocean, Geophys. Res. Lett., 44(20), 10,479–10,485, doi:10.1002/2017GL075494.
- Tschudi, M., C. Fowler, J. Maslanik, J. S. Stewart, and W. Meier. (2016), Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 3. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi:10.5067/O57VAIT2AYYY.