machguth_etal_massbalance_obs_locations = Dataset(
id='machguth_etal_massbalance_obs_locations',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""Accessed the Greenland Mass Balance database on Dec. 12, 2020
from PROMICE (https://promice.org/PromiceDataPortal/api/download
/1198f862-4afa-4862-952f-acd9129d790d/greenland_SMB_database_v20
20/greenland_SMB_database_v2020.xlsx)
See:
`scripts/private-archive-preprocess/machguth_etal_massbalan
ce_obs_locations/README.md` for preprocessing steps."""),
),
],
metadata={
'title':
'Greenland surface mass-balance observations from the ice-sheet ablation area and local glaciers',
'abstract':
("""These are historical surface mass balance measurement locations
from Greenland Ice Sheet ablation area and surrounding local
glaciers. There are approximately 3000 unique measurements from 46
sites. The earliest measurements are from 1892. Each measurement is
accompanied with position and date information, as well as quality
and source flags. Users can look to the citation URL for additional
data."""),
'citation': {
'text':
("""Machgruth, H. et al (2016). Greenland surface mass-balance
observations from the ice-sheet ablation area and local
glaciers. Journal of Glaciology, 62(235), 861-887.
doi:10.1017/jog.2016.75"""),
'url':
'https://www.doi.org/10.1017/jog.2016.75',
},
},
)
velocity_mosaic = Dataset(
id='velocity_mosaic',
assets=[
HttpAsset(
id='only',
urls=[
'http://its-live-data.jpl.nasa.gov.s3.amazonaws.com/velocity_mosaic/landsat/v00.0/static/GRE_G0120_0000.nc',
],
),
],
metadata={
'title':
'Regional Glacier and Ice Sheet Surface Velocities',
'abstract':
("""The Inter-mission Time Series of Land Ice Velocity and Elevation
(ITS_LIVE) project facilitates ice sheet, ice shelf and glacier
research by providing a globally comprehensive and temporally dense
multi-sensor record of land ice velocity and elevation with low
latency."""),
'citation': {
'text':
("""Velocity data generated using auto-RIFT (Gardner et al.,
2018) and provided by the NASA MEaSUREs ITS_LIVE project
(Gardner et al., 2019).
Gardner, A. S., M. A. Fahnestock, and T. A. Scambos, 2019
[Accessed on {{date_accessed}}]: ITS_LIVE Regional Glacier and
Ice Sheet Surface Velocities. Data archived at National Snow and
Ice Data Center; doi:10.5067/6II6VW8LLWJ7.
Gardner, A. S., G. Moholdt, T. Scambos, M. Fahnstock, S.
Ligtenberg, M. van den Broeke, and J. Nilsson, 2018: Increased
West Antarctic and unchanged East Antarctic ice discharge over
the last 7 years, Cryosphere, 12(2): 521–547,
doi:10.5194/tc-12-521-2018."""),
'url':
'https://its-live.jpl.nasa.gov/#documentation',
},
},
)
from qgreenland.models.config.asset import RepositoryAsset
from qgreenland.models.config.dataset import Dataset
arctic_circle = Dataset(
id='arctic_circle',
assets=[
RepositoryAsset(
id='only',
filepath='{assets_dir}/arctic_circle.geojson',
),
],
metadata={
'title': "Arctic Circle (66° 34' North)",
'abstract': ("""Arctic Circle."""),
'citation': {
'text':
("""Generated by QGreenland based on the definition of the Arctic
Circle given by
https://nsidc.org/cryosphere/arctic-meteorology/arctic.html"""
),
'url':
'https://nsidc.org/cryosphere/arctic-meteorology/arctic.html',
},
},
)
macferrin_etal_firn_ice_layer_thicknesses = Dataset(
id='macferrin_etal_firn_ice_layer_thicknesses',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""Available via the publication website, and contained in the
Source Data Fig. 2 file."""
),
),
],
metadata={
'title': 'Rapid expansion of Greenland’s low-permeability ice slabs',
'abstract': (
"""Citation publication abstract: In recent decades, meltwater
runoff has accelerated to become the dominant mechanism for mass
loss in the Greenland ice sheet. In Greenland’s high-elevation
interior, porous snow and firn accumulate; these can absorb surface
meltwater and inhibit runoff, but this buffering effect is limited
if enough water refreezes near the surface to restrict percolation.
However, the influence of refreezing on runoff from Greenland
remains largely unquantified. Here we use firn cores, radar
observations and regional climate models to show that recent
increases in meltwater have resulted in the formation of
metres-thick, low-permeability ‘ice slabs’ that have expanded the
Greenland ice sheet’s total runoff area by 26 ± 3 per cent since
2001. Although runoff from the top of ice slabs has added less than
one millimetre to global sea-level rise so far, this contribution
will grow substantially as ice slabs expand inland in a warming
climate. Runoff over ice slabs is set to contribute 7 to 33
millimetres and 17 to 74 millimetres to global sea-level rise by
2100 under moderate- and high-emissions scenarios,
respectively—approximately double the estimated runoff from
Greenland’s high-elevation interior, as predicted by surface mass
balance models without ice slabs. Ice slabs will have an important
role in enhancing surface meltwater feedback processes,
fundamentally altering the ice sheet’s present and future
hydrology."""
),
'citation': {
'text': (
"""MacFerrin, M., Machguth, H., As, D.v. et al. Rapid expansion
of Greenland’s low-permeability ice slabs. Nature 573, 403–407
(2019). https://doi.org/10.1038/s41586-019-1550-3"""
),
'url': 'https://doi.org/10.1038/s41586-019-1550-3',
},
},
)
racmo_qgreenland_jan2021 = Dataset(
id='racmo_qgreenland_jan2021',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""RACMO data were obtained via a private data transer by Brice
Noël. See the `scripts/preprocess-private-archive/racmo_qgreenla
nd_jan2021/README.md` for additional information."""),
),
],
metadata={
'title':
'Regional Atmospheric Climate Model (RACMO)',
'abstract':
("""The Regional Atmospheric Climate Model (RACMO) data included use
a new run at 5.5-km horizontal resolution of the polar (p) version
of RACMO2.3p2 for the period 1958–2017. RACMO2.3p2 incorporates the
dynamical core of the High Resolution Limited Area Model and the
physics from the European Centre for Medium-Range Weather
Forecasts–Integrated Forecast System (ECMWF-IFS cycle CY33r1).
RACMO2.3p2 includes a multilayer snow module that simulates melt,
water percolation, and retention in snow, refreezing, and runoff.
The model also accounts for dry snow densification, and drifting
snow erosion and sublimation. Snow albedo is calculated on the basis
of snow grain size, cloud optical thickness, solar zenith angle, and
impurity concentration in snow. As compared to the model described
in Noel et al. (2018) (reference below), no model physics have been
changed. However, increased horizontal resolution of the host model,
i.e., 5.5 km instead of 11 km, better resolves gradients in SMB
components over the topographically complex ice sheet margins and
neighboring peripheral glaciers and ice caps. Some data output has
also been downscaled to 1-km resolution. QGreenland currently
displays data that describes annual mean values over 1958-2019 for
wind speed, total precipitation, snowfall, snowmelt, runoff,
sublication, snow drift erosion, and 2-m temperature. Input
topography data is also included, along with the PROMICE ice mask
and grounded ice mask.
For a detailed description of the RACMO model and recent updates,
refer to: B. Noël, W. J. van de Berg, J. M. van Wessem, E. van
Meijgaard, D. van As, J. T. M. Lenaerts, S. Lhermitte, P. Kuipers
Munneke, C. J. P. P. Smeets, L. H. van Ulft, R. S. W. van de Wal, M.
R. van den Broeke, Modelling the climate and surface mass balance of
polar ice sheets using RACMO2, Part 1: Greenland (1958-2016).
Cryosphere 12, 811–831 (2018)."""),
'citation': {
'text':
("""Noël, B., W. J. van de Berg, S. Lhermitte, and M. R. van den
Broeke (2019), Rapid ablation zone expansion amplifies north
Greenland mass loss, Science Advances, 5(9), eaaw0123."""),
'url':
'https://advances.sciencemag.org/content/5/9/eaaw0123',
},
},
)
pangaea_ground_temperature = Dataset(
id='pangaea_ground_temperature',
assets=[
HttpAsset(
id='25km',
urls=[
'http://store.pangaea.de/Publications/ObuJ-etal_2018/UiO_PEX_5.0_20181127_2000_2016_25km.nc',
],
),
HttpAsset(
id='10km',
urls=[
'http://store.pangaea.de/Publications/ObuJ-etal_2018/UiO_PEX_5.0_20181127_2000_2016_10km.nc',
],
),
HttpAsset(
id='5km',
urls=[
'http://store.pangaea.de/Publications/ObuJ-etal_2018/UiO_PEX_5.0_20181127_2000_2016_5km.nc',
],
),
],
metadata={
'title':
'Ground Temperature Map, 2000-2016, Northern Hemisphere Permafrost',
'abstract':
("""Original data information: The product provides modeled mean
annual ground temperatures (MAGT) at the top of the permafrost for
the Northern Hemisphere at 1 km spatial resolution. Permafrost
probability (fraction values from 0 to 1) is assigned to each grid
cell with MAGT < 0°C. Based on its permafrost probability each grid
cell is classified as continuous, discontinuous and sporadic
permafrost. The processing extent covers exposed land areas of the
Northern Hemisphere down to 25° latitude. The mean MAGT was
validated with GTN-P and TSP borehole ground temperature data and
yielded a RMS of 2.0 °C. According to the results, permafrost (MAGT
< 0 °C) covers 15 % of exposed land of the Northern Hemisphere.
The map was produced within the project ESA Data User Element
GlobPermafrost.
QGreenland displays data at 10 km resolution.
Dataset update: 2019-04-01."""),
'citation': {
'text':
("""Obu, Jaroslav; Westermann, Sebastian; Kääb, Andreas; Bartsch,
Annett (2018): Ground Temperature Map, 2000-2016, Northern
Hemisphere Permafrost. Alfred Wegener Institute, Helmholtz
Centre for Polar and Marine Research, Bremerhaven, PANGAEA"""),
'url':
'https://doi.org/10.1594/PANGAEA.888600',
},
},
)
undersea_features = Dataset(
id='undersea_features',
assets=[
HttpAsset(
id='only',
urls=[
'https://www.ngdc.noaa.gov/gazetteer/feature/export?aoi=POLYGON%28%28-162.82882+39.14360%2C+120.79574+45.17504%2C+-1.80233+31.69553%2C+-68.42289+32.69612%2C+-162.82882+39.14360%29%29&name=&featureType=&proposerId=&discovererId=&meeting=&status=&format=shapefile',
],
),
],
metadata={
'title':
'IHO-IOC GEBCO Gazetteer of Undersea Feature Names',
'abstract':
("""The General Bathymetric Chart of the Oceans (GEBCO) Sub-Committee
on Undersea Feature Names (SCUFN) maintains and makes available a
digital gazetteer of the names, generic feature type, and geographic
position of features on the seafloor. The gazetteer is available to
view and download (http://www.ngdc.noaa.gov/gazetteer/) via a web
map application, hosted by the International Hydrographic
Organization Data Centre for Digital Bathymetry (IHO DCDB)
co-located with the US National Centers for Environmental
Information (NCEI)."""),
'citation': {
'text': ("""IHO-IOC GEBCO Gazetteer of Undersea Feature Names,
www.gebco.net"""),
'url':
'https://www.gebco.net/data_and_products/undersea_feature_names/',
},
},
)
continental_shelf = Dataset(
id='continental_shelf',
assets=[
HttpAsset(
id='north_points',
urls=[
'http://tuvalu.grida.no/ecs/dnk_76_2014_points.zip',
],
),
HttpAsset(
id='north_lines',
urls=[
'http://tuvalu.grida.no/ecs/dnk_76_2014_lines.zip',
],
),
HttpAsset(
id='north_polygons',
urls=[
'http://tuvalu.grida.no/ecs/dnk_76_2014_polygons.zip',
],
),
HttpAsset(
id='northeast_points',
urls=[
'http://tuvalu.grida.no/ecs/dnk_68_2013_points.zip',
],
),
HttpAsset(
id='northeast_lines',
urls=[
'http://tuvalu.grida.no/ecs/dnk_68_2013_lines.zip',
],
),
HttpAsset(
id='northeast_polygons',
urls=[
'http://tuvalu.grida.no/ecs/dnk_68_2013_polygons.zip',
],
),
HttpAsset(
id='south_points',
urls=[
'http://tuvalu.grida.no/ecs/dnk_61_2012_points.zip',
],
),
HttpAsset(
id='south_lines',
urls=[
'http://tuvalu.grida.no/ecs/dnk_61_2012_lines.zip',
],
),
HttpAsset(
id='south_polygons',
urls=[
'http://tuvalu.grida.no/ecs/dnk_61_2012_polygons.zip',
],
),
],
metadata={
'title':
'Continental Shelf Programme',
'abstract':
("""The Kingdom of Denmark's submission to the Commission on the Limits of the Continental
Shelf. This is in accordance with Article 76, paragraph 8 of the United Nations Convention
on the Law of the Sea of 10 December 1982, which represents information on the limits of the
continental shelf beyond 200 nautical miles from the baselines from which the breadth of its
territorial sea is measured in respect of the Continental Shelf of Greenland."""
),
'citation': {
'text':
("""Continental Shelf Programme. Denmark - in respect of the continental shelf
of Greenland. 2012-2015. GRID-Arendal. Retrieved December 2021
(http://www.continentalshelf.org/onestopdatashop/6350.aspx). """
),
'url':
'http://continentalshelf.org/onestopdatashop/6350.aspx',
},
},
)
seaice_index = Dataset(
id='seaice_index',
assets=[
*[
HttpAsset(
id=f'median_extent_line_{month:02d}',
urls=[(
'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/shapefiles/shp_median'
f'/median_extent_N_{month:02d}_1981-2010_polyline_v3.0.zip'
)],
) for month in range(1, 12 + 1)
],
*[
HttpAsset(
id=f'minimum_concentration_{year}',
urls=[(
'ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/north/monthly/geotiff'
f'/09_Sep/N_{year}09_concentration_v3.0.tif'
)],
) for year in MIN_CONCENTRATION_YEARS
],
*[
concentration_maximum_asset_for_year(year) for year in MAX_CONCENTRATION_YEARS
],
],
metadata={
'title': 'Sea Ice Index, Version 3',
'abstract': (
"""The Sea Ice Index provides a quick look at Arctic- and
Antarctic-wide changes in sea ice. It is a source for consistent,
up-to-date sea ice extent and concentration images, in PNG format,
and data values, in GeoTIFF and ASCII text files, from November 1978
to the present. Sea Ice Index images also depict trends and
anomalies in ice cover calculated using a 30-year reference period
of 1981 through 2010.
The images and data are produced in a consistent way that makes the
Index time-series appropriate for use when looking at long-term
trends in sea ice cover. Both monthly and daily products are
available. However, monthly products are better to use for long-term
trend analysis because errors in the daily product tend to be
averaged out in the monthly product and because day-to-day
variations are often the result of short-term weather."""
),
'citation': {
'text': (
"""Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K.
Windnagel. 2017, updated daily. Sea Ice Index, Version 3.
Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center.
doi: https://doi.org/10.7265/N5K072F8. 2020-08-06."""
),
'url': 'https://nsidc.org/data/g02135',
},
},
)
from qgreenland.models.config.asset import HttpAsset
from qgreenland.models.config.dataset import Dataset
# TODO: How to create a layer using this data? The NETCDF files are composed of
# daily runoff amounts predicted by a climate model for each basin in the
# streams_outlets_basins dataset.
promice_runoff = Dataset(
id='promice_runoff',
assets=[
HttpAsset(
id=f'land_mar_{year}',
urls=[
f'https://promice.org/PromiceDataPortal/api/download/0f9dc69b-2e3c-43a2-a928-36fbb88d7433/version_01/runoff/coast/runoff_land_MAR_{year}.nc',
],
) for year in range(2010, 2017 + 1)
],
metadata={
'title':
'Map of GC-Net and PROMICE locations',
'abstract':
("""High resolution map of Greenland hydrologic outlets, basins, and
streams, and a 1979 through 2017 time series of Greenland liquid
water runoff for each outlet."""),
'citation': {
'text': ("""Mankoff et al. - submitted to ESSD."""),
'url':
'https://doi.org/10.22008/promice/data/freshwater_runoff/v01',
},
},
)
nunagis_pop2019_municipalities = Dataset(
id='nunagis_pop2019_municipalities',
assets=[
ogr_remote_asset(
asset_id='only',
output_file='{output_dir}/fetched.geojson',
url=
'https://kort.nunagis.gl/server/rest/services/Hosted/POP2019_Municipalities/FeatureServer/0/query/?f=json&where=true&outFields=*&orderByFields=pop_municipality_2019_objectid+ASC',
),
],
metadata={
'title':
'Municipalities with Population',
'abstract':
("""Greenland municipality boundaries. Data includes information on
2019 municipality population and the municipality population as a
percent of total Greenland population."""),
'citation': {
'text':
("""NunaGIS (2020). Municipalities by population numbers in 2019,
Greenland. Web:
https://kort.nunagis.gl/portal/home/item.html?id=b70a43b814e84
78c9514208548ca5f61.
Date accessed: {{date_accessed}}."""),
'url':
'https://kort.nunagis.gl/portal/home/item.html?id=b70a43b814e8478c9514208548ca5f61',
},
},
)
gshhg_coastlines = Dataset(
id='gshhg_coastlines',
assets=[
HttpAsset(
id='only',
urls=[
'http://www.soest.hawaii.edu/pwessel/gshhg/gshhg-shp-2.3.7.zip',
],
),
],
metadata={
'title':
('GSHHG: A Global Self-consistent, Hierarchical, High-resolution'
' Geography Database'),
'abstract':
("""We present a high-resolution geography data set amalgamated from
three data bases in the public domain:
World Vector Shorelines (WVS).
CIA World Data Bank II (WDBII).
Atlas of the Cryosphere (AC).
The WVS is our basis for shorelines except for Antarctica while the
WDBII is the basis for lakes, although there are instances where
differences in coastline representations necessitated adding WDBII
islands to GSHHG. The WDBII source also provides all political
borders and rivers. The addition of AC since 2.3.0 allows us to
offer two choices for Antarctica coastlines: Ice-front or Grounding
line. These are encoded as levels 5 and 6, respectively and users of
GSHHG can choose which set to use. GSHHG data have undergone
extensive processing and should be free of internal inconsistencies
such as erratic points and crossing segments. The shorelines are
constructed entirely from hierarchically arranged closed polygons. A
modified version of GSHHG is used by GMT, the Generic Mapping Tools.
Starting with version 2.2.2, GSHHG has been released under the GNU
Lesser General Public License."""),
'citation': {
'text':
("""Wessel, P., and W. H. F. Smith, A Global Self-consistent,
Hierarchical, High-resolution Shoreline Database, J. Geophys.
Res., 101, 8741-8743, 1996"""),
'url': ('https://www.soest.hawaii.edu/pwessel/gshhg/'
'Wessel+Smith_1996_JGR.pdf'),
},
},
)
nga_arctic_sea_routes = Dataset(
id='nga_arctic_sea_routes',
assets=[
HttpAsset(
id='only',
urls=[
('https://opendata.arcgis.com/datasets/67760a7f85614902ac19fa6ff643b9fa_0.zip?'
'outSR=%7B%22latestWkid%22%3A102018%2C%22wkid%22%3A102018%7D'
),
],
),
],
metadata={
'title':
'Arctic Sea Routes',
'abstract':
("""Arctic lines of general transportation. In the case of the
Northern Sea Route, the route is based on the actual route used by
Russian icebreakers and cargo ships. The Northwest Passage is based
on the channels that would be able to support large cargo ships. The
transpolar route is a hypothetical route that could be used either
as a result of ice-free summers or the extensive use of icebreakers
and ice-hardened ships."""),
'citation': {
'text': ("""National Geospatial-Intelligence Agency (NGA)"""),
'url':
'https://arctic-nga.opendata.arcgis.com/datasets/67760a7f85614902ac19fa6ff643b9fa_0',
},
},
)
wdmam = Dataset(
id='wdmam',
assets=[
CommandAsset(
id='only',
args=[
'wget',
'http://wdmam.org/file/wdmam.asc',
'-O',
'{output_dir}/full_wdmam.xyz',
],
),
],
metadata={
'title':
'World Digital Magnetic Anomaly Map',
'abstract': ("""The WDMAM (World Digital Magnetic Anomaly Map) is an
international scientific project under the auspices of IAGA
(International Association of Geomagnetism and Aeronomy) and CGMW
(Commission for the Geological Map of the World), aiming to compile
and make available magnetic anomalies caused by the Earth
lithosphere, on continental and oceanic areas, in a comprehensive
way, all over the World.
The project started in 2003 and resulted in a first version of the
map (Korhonen et al., 2007). A call for candidates initiated in 2010
led to the building of a new map which, after evaluation and
correction, was released at the IUGG General Assembly of Prag in
June 2015 as WDMAM version 2.0. This web site aims to distribute
freely and as widely as possible a provisional version of the map
(in jpeg format), the full grid (in ASCII format) or parts of the
grid (in ASCII or GMT grd formats) to interested scientists and the
general public. A printed version of the map will be released by
CGMW in a very near future. A paper describing the building of the
map will be published soon (Lesur et al., 2016, in press)."""),
'citation': {
'text':
("""Dyment, J., Lesur, V., Hamoudi, M., Choi, Y., Thebault, E.,
Catalan, M., the WDMAM Task Force*, the WDMAM Evaluators**, and
the WDMAM Data Providers**, World Digital Magnetic Anomaly Map
version 2.0, map available at http://www.wdmam.org."""),
'url':
'http://www.wdmam.org',
},
},
)
gc_net_promice_stations = Dataset(
id='gc_net_promice_stations',
assets=[
HttpAsset(
id='promice',
urls=[
'https://raw.githubusercontent.com/GEUS-PROMICE/map_GC-Net_PROMICE_kml/59455ddb50f7eeb1b8c5a5fdd7f80bfd548a0c92/input_data/PROMICE_info_from_GPS_data_2017-2018.csv',
],
),
HttpAsset(
id='promice_former',
urls=[
'https://raw.githubusercontent.com/GEUS-PROMICE/map_GC-Net_PROMICE_kml/59455ddb50f7eeb1b8c5a5fdd7f80bfd548a0c92/input_data/PROMICE_info_from_GPS_data_2017-2018_former_sites.csv',
],
),
HttpAsset(
id='gc_net',
urls=[
'https://raw.githubusercontent.com/GEUS-PROMICE/map_GC-Net_PROMICE_kml/59455ddb50f7eeb1b8c5a5fdd7f80bfd548a0c92/input_data/GCN%20info%20ca.2000.csv',
],
),
],
metadata={
'title':
'Map of GC-Net and PROMICE locations',
'abstract':
("""GitHub data description (creator: jasonebox): For PROMICE, I use
a 2017-2018 average of station GPS data. I added an updated position
for THU-U2. An improvement would be to have the .kml add the
position date to the description and how it was obtained. For
GC-Net, I use a table from Konrad Steffen. I added an updated
position for PET ELA from 2016.
Accuracy of positions is very important to avoid arriving in the
field at an old location. In no cases, do I transcribe positions by
hand as that can cause expensive problems not finding stations
because of bad coordinates.
QGreenland team notes - Source file indicating PROMICE GPS data from
2017-2018 and GC-NET GPS data from 2000. See note from data creator
above."""),
'citation': {
'text':
("""PROMICE, (2020). Map of GC-Net and PROMICE station locations.
Web: https://github.com/GEUS-PROMICE. Date accessed:
{{date_accessed}}."""),
'url':
'https://github.com/GEUS-PROMICE/map_GC-Net_PROMICE_kml/tree/59455ddb50f7eeb1b8c5a5fdd7f80bfd548a0c92',
},
},
)
future_icesheet_coverage = Dataset(
id='future_icesheet_coverage',
assets=[
HttpAsset(
id='rcp_26',
urls=[
'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3A61ff2294-4734-46ba-a0b0-845d69298131',
],
),
HttpAsset(
id='rcp_45',
urls=[
'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3Aed2d7235-2193-4ba3-a98f-f09d871199a1',
],
),
HttpAsset(
id='rcp_85',
urls=[
'https://arcticdata.io/metacat/d1/mn/v2/object/urn%3Auuid%3Aecec7b68-a544-4575-8731-47d60b73215f',
],
),
],
metadata={
'title':
'Contribution of the Greenland Ice Sheet to sea-level over the next millennium using Large Ensemble Simulations, spatial time series, 2008-3007.',
'abstract':
("""Citation publication abstract: The Greenland Ice Sheet holds
around 7.2 meters of sea-level equivalent. In recent decades rising
atmosphere and ocean temperatures have led to an acceleration in
mass loss, adding an average of about 0.5 millimeters per year to
global mean sea-level between 1991 and 2015. Current ice margin
recession in Greenland is led by the retreat of outlet glaciers, the
large rivers of ice ending in narrow fjords that drain the ice sheet
interior. Recent progress in measuring ice thickness is enabling
models to reproduce the complex flow patterns found in outlet
glaciers, a key step towards realistic projections. Here we pair an
outlet glacier resolving ice sheet model with a comprehensive
uncertainty quantification to estimate Greenland's contribution to
sea-level over the next millennium under different climate forcings.
We find that Greenland could contribute 5-33 centimeters to
sea-level by 2100 and 11-155 centimeters by 2200, with discharge
from outlet glaciers contributing 6-45% of the total mass loss. Our
analysis shows that uncertainties in projecting mass loss are
dominated by uncertainties in climate scenarios and surface
processes, followed by ice dynamics, whereas uncertainties in ocean
conditions play a minor role, particularly in the long term. We
project that Greenland will very likely become ice-free within a
millennium without significant reductions in greenhouse gas
emissions.
This dataset compilation contains the simulations for the manuscript
"Contribution of the Greenland Ice Sheet to sea-level over the next
millennium" prepared with the Parallel Ice Sheet Model (PISM).
This dataset provides the likelihood of a pixel being ice covered at
the year 3007 for the RCPs (Representative Concentration Pathways)
2.6, 4.5, and 8.5 for LES (Large Ensemble Simulations)."""),
'citation': {
'text':
("""Andy Aschwanden. 2019. Contribution of the Greenland Ice
Sheet to sea-level over the next millennium using Large Ensemble
Simulations, spatial time series, 2008-3007. Arctic Data Center.
doi:10.18739/A29G5GD39."""),
'url':
'https://doi.org/10.18739/A29G5GD39',
},
},
)
r'(?P<prefix>longitudes|latitudes)_(?P<res_id>.*)_degree.geojson', )
geojson_files = ASSETS_DIR.glob('*.geojson')
lonlat_files = {}
for f in geojson_files:
m = lonlat_regex.match(f.name)
if not m:
continue
lonlat_files[f.name] = {
'path': '{assets_dir}/' + str(f.relative_to(ASSETS_DIR)),
'shortname': m.groupdict()['prefix'][0:3],
'res_id': m.groupdict()['res_id'],
}
lonlat = Dataset(
id='lonlat',
assets=[
RepositoryAsset(
id=f"{params['shortname']}_{params['res_id']}_deg",
filepath=params['path'],
) for params in lonlat_files.values()
],
metadata={
'title': 'Longitude and Latitude Lines',
'abstract': 'Longitude and Latitude Lines.',
'citation': {
'text': 'Generated by QGreenland.',
'url': '',
},
},
)
monthly_albedo = Dataset(
id='monthly_albedo',
assets=[
HttpAsset(
id='2018_07',
urls=[
'https://dataverse01.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/URJ2VK/XXQUSC'
],
),
HttpAsset(
id='2019_07',
urls=[
'https://dataverse01.geus.dk/api/access/datafile/:persistentId?persistentId=doi:10.22008/FK2/URJ2VK/6YZNSZ'
],
),
],
metadata={
'title':
'SICE 1 km broadband albedo monthly averages and visualisations',
'abstract':
("""We present a simplified atmospheric correction algorithm for
snow/ice albedo retrievals using single view satellite
measurements. The validation of the technique is performed using
Ocean and Land Colour Instrument (OLCI) on board Copernicus
Sentinel-3 satellite and ground spectral or broadband albedo
measurements from locations on the Greenland ice sheet and in the
French Alps. Through comparison with independent ground
observations, the technique is shown to perform accurately in a
range of conditions from a 2100 m elevation mid-latitude location in
the French Alps to a network of 15 locations across a 2390 m
elevation range in seven regions across the Greenland ice
sheet. Retrieved broadband albedo is accurate within 5% over a wide
(0.5) broadband albedo range of the (N = 4155) Greenland
observations and with no apparent bias."""),
'citation': {
'text':
("""Kokhanovsky A, Box JE, Vandecrux B, Mankoff KD, Lamare M,
Smirnov A and Kern M (2020) The Determination of Snow Albedo
from Satellite Measurements Using Fast Atmospheric Correction
Technique. Remote Sensing 12(2), 234 doi: 10.3390/rs12020234
https://www.mdpi.com/2072-4292/12/2/234"""),
'url':
'doi.org/10.22008/FK2/URJ2VK',
},
},
)
asiaq_private_placenames = Dataset(
id='asiaq_private_placenames',
assets=[
ManualAsset(
id='only',
access_instructions="""Provided by Eva Mätzler via email as a zipped
collection of data '20201112_Oqaasileriffik_place-name register.zip'. See
scripts/private-archive-preprocess/eva_placenames/README.md (at QGreenland
GitHub: https://github.com/nsidc/qgreenland) for preprocessing steps.""",
),
],
metadata={
'title': 'Place names',
'abstract': """Place names as provided by Asiaq Greenland Survey,
December 2020. Translation for data fields provided by Arnaq B. Johansen,
Greenland Project Manager in Collection of Place Names (January 2021).
QGreenland Team - Noted Data Issues:
* East Greenland: Ikkatteq (correctly spelled ‘Ikateq’) is an abandoned airstrip
and is not populated.
* Northeast coast of Greenland: Longyearbyen is incorrectly placed -
Longyearbyen is a town on Svalbard.
* Near Ittoqqortoormiit: Uunartoq and Ittaajimmiit are both abandoned.
* West coast of Greenland, near Paamiut: Ivittuut is abandoned, Narsalik
was abandoned in 1997.
* South Greenland: Qaqortoq is placed twice - two dots next to each other.
* Near Qaanaaq: Qeqertarsuaq and Moriusaq are abandoned.
* Near Upernavik: Tussaaq is an abandoned settlement.
* Near Uummannaq: Illorsuit and Nuugaatsiaq are two recently abandoned
settlements (2017) due a massive landslide and subsequent tsunami.
* Kangerlussuaq (west coast): Is not indicated on the map.
* West: Aasiaat is placed twice - two dots next to each other.""",
'citation': {
'text': 'Place names, Asiaq Greenland Survey, December 2020',
'url': '',
},
},
)
arctic_vegetation_biomass_2010 = Dataset(
id='arctic_vegetation_biomass_2010',
assets=[
CmrAsset(
id='only',
granule_ur=
'Arctic_Vegetation_Maps.aga_circumpolar_avhrr_biomass_2010.tif',
collection_concept_id='C2170968604-ORNL_CLOUD',
),
],
metadata={
'title':
'Circumpolar Arctic Vegetation, Geobotanical, Physiographic Maps, 1982-2003',
'abstract':
("""The broader data set provides the spatial distributions of
vegetation types, geobotanical characteristics, and physiographic
features for the circumpolar Arctic tundra biome for the period
1982-2003. Specific attributes include dominant vegetation,
bioclimate subzones, floristic subprovinces, landscape types, lake
coverage, Arctic treeline, elevation, and substrate chemistry data.
Vegetation indices, trends, and biomass estimate products for the
circumpolar Arctic through 2010 are also provided. QGreenland
displays the 2010 vegetation biomass in kilograms per square meter.
Users can look to the source information for additional data."""),
'citation': {
'text':
("""Walker, D.A., and M.K. Raynolds. 2018. Circumpolar Arctic
Vegetation, Geobotanical, Physiographic Maps, 1982-2003. ORNL
DAAC, Oak Ridge, Tennessee, USA.
https://doi.org/10.3334/ORNLDAAC/1323"""),
'url':
'https://doi.org/10.3334/ORNLDAAC/1323',
},
},
)
image_mosaic = Dataset(
id='image_mosaic',
assets=[
OnlineAsset(
id='2019',
provider='gdal',
url=(
'/vsicurl/http://its-live-data.jpl.nasa.gov.s3.amazonaws.com/'
'rgb_mosaics/GRE2/Greenlandmedian_Aug_2019.vrt'
),
),
OnlineAsset(
id='2015',
provider='gdal',
url=(
'/vsicurl/http://its-live-data.jpl.nasa.gov.s3.amazonaws.com/'
'rgb_mosaics/GRE/GRE_L8_Aug_2015_on_S3.vrt'
),
),
],
# TODO: Switch to class instantiation. Makes it easier to differentiate keys
# from values in this big wall-of-string.
metadata={
'title': 'Sentinel-2 Imagery Mosaics',
# Editability matters most, so we use """triple-quote strings""".
'abstract': (
"""Abstract for reference publication: Each summer, surface melting
of the margin of the Greenland Ice Sheet exposes a distinctive
visible stratigraphy that is related to past variability in
subaerial dust deposition across the accumulation zone and
subsequent ice flow toward the margin. Here we map this surface
stratigraphy along the northern margin of the ice sheet using
mosaicked Sentinel-2 multispectral satellite imagery from the end of
the 2019 melt season and finer-resolution WorldView-2/3 imagery for
smaller regions of interest. We trace three distinct transitions in
apparent dust concentration and the top of a darker basal layer. The
three dust transitions have been identified previously as
representing late-Pleistocene climatic transitions, allowing us to
develop a coarse margin chronostratigraphy for northern Greenland.
Substantial folding of late-Pleistocene stratigraphy is observed but
uncommon. The oldest conformal surface-exposed ice in northern
Greenland is likely located adjacent to Warming Land and may be up
to ~55 thousand years old. Basal ice is commonly exposed hundreds of
metres from the ice margin and may indicate a widespread frozen
basal thermal state. We conclude that the ice margin across northern
Greenland offers multiple opportunities to recover paleoclimatically
distinct ice relative to previously studied regions in southwestern
Greenland.
QGreenland displays 2015 and 2019 Sentinel-2 mosaics as online-only
access layers."""
),
'citation': {
'text': (
"""MacGregor JA, Fahnestock MA, Colgan WT, Larsen NK, Kjeldsen
KK, Welker JM (2020). The age of surface-exposed ice along the
northern margin of the Greenland Ice Sheet. Journal of
Glaciology 66(258), 667–684.
https://doi.org/10.1017/jog.2020.62"""
),
'url': 'https://doi.org/10.1017/jog.2020.62',
},
},
)
tectonic_plates = Dataset(
id='tectonic_plates',
assets=[
HttpAsset(
id='only',
urls=[
'https://github.com/fraxen/tectonicplates/archive/339b0c5.zip',
],
),
],
metadata={
'title':
'World tectonic plates and boundaries',
'abstract':
("""As per data source - This dataset is a conversion of the dataset
originally published in the paper 'An updated digital model of plate
boundaries' by Peter Bird (Geochemistry Geophysics Geosystems, 4(3),
1027, doi:10.1029/2001GC000252
[http://scholar.google.se/scholar?cluster=1268723667321132798],
2003). To bring this dataset into the modern age, the original data
has been parsed, cleaned, and verified using ArcGIS 10.2 and
converted to shape files. The dataset presents tectonic plates and
their boundaries, and in addition orogens and information about the
boundaries. The data is useful for geological applications, analysis
and education, and should be easy to use in any modern GIS software
application. For information on the fields and values, please refer
to the [original](http://peterbird.name/oldFTP/PB2002/2001GC000252_r
eadme.txt) documentation and the scientific article.
Dataset credit should acknowledge Hugo Ahlenius, Peter Bird, and
Nordpil, with an additional suggested citation included."""),
'citation': {
'text':
("""Ahlenius, H. (2014). World tectonic plates and boundaries.
Data available from https://github.com/fraxen/tectonicplates."""
),
'url':
'https://github.com/fraxen/tectonicplates',
},
},
)
mineral_and_hydrocarbon_licenses = Dataset(
id='mineral_and_hydrocarbon_licenses',
assets=[
HttpAsset(
id='mcas_mlsa_public_all',
urls=[
'https://gis.govmin.gl/geoserver/MLSA/ows?service=WFS&version=1.0.0&request=GetFeature&outputFormat=shape-zip&typeNames=MLSA:mcas_mlsa_public_all',
],
),
HttpAsset(
id='mcas_mlsa_public_historic',
urls=[
'https://gis.govmin.gl/geoserver/MLSA/ows?service=WFS&version=1.0.0&request=GetFeature&outputFormat=shape-zip&typeNames=MLSA:mcas_mlsa_public_historic',
],
),
],
metadata={
'title': 'Mineral and hydrocarbon licenses',
'abstract': (
"""Mineral and hydrocarbon license data, including historic public
licenses and public licenses. License data is sourced from the
Mineral Resource Authority, Government of Greenland using their
GeoServer version 2.14.1 (https://gis.govmin.gl/geoserver/web/)."""
),
'citation': {
'text': 'Government of Greenland/GINR',
'url': '',
},
},
)
woa2018_temperature = Dataset(
id='woa2018_temperature',
assets=[
HttpAsset(
id='seasonal_winter',
urls=[
f'{BASE_URL}/0.25/woa18_decav_t13_04.nc',
],
),
HttpAsset(
id='seasonal_summer',
urls=[
f'{BASE_URL}/0.25/woa18_decav_t15_04.nc',
],
),
],
metadata={
'title': 'WORLD OCEAN ATLAS 2018 Volume 1: Temperature',
'abstract': (
"""From the World Ocean Atlas: This atlas consists of a description
of data analysis procedures and horizontal maps of climatological
distribution fields of temperature at selected standard depth levels
of the World Ocean on one-degree and quarter-degree
latitude-longitude grids. The aim of the maps is to illustrate
large-scale characteristics of the distribution of ocean
temperature. The fields used to generate these climatological maps
were computed by objective analysis of all scientifically
quality-controlled historical temperature data in the World Ocean
Database 2018. Maps are presented for climatological composite
periods (annual, seasonal, monthly, seasonal and monthly difference
fields from the annual mean field, and the number of observations)
at 102 standard depths."""
),
'citation': {
'text': (
"""Locarnini, R. A., A. V. Mishonov, O. K. Baranova, T. P.
Boyer, M. M. Zweng, H. E. Garcia, J. R. Reagan, D. Seidov, K. W.
Weathers, C. R. Paver, I. V. Smolyar, 2019: World Ocean Atlas
2018, Volume 1: Temperature. A. V. Mishonov, Technical Ed.,
NOAA Atlas NESDIS 81"""
),
'url': 'https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18_vol1.pdf',
},
},
)
greenland_territorial_waters = Dataset(
id='greenland_territorial_waters',
assets=[
ManualAsset(
id='only',
access_instructions=(
"""Dataset provided by Karl Zinglersen of the Greenland
Institude for Natural Resources via a private one-time FTP
transfer on Nov. 30, 2020. This dataset is not expected to be
publicily archived outside of QGreenland."""),
),
],
metadata={
'title':
'Greenland Territorial Waters',
'abstract':
("""Datasets provided by the Greenland Institute for Natural
Resources (from Karl Zinglersen) via a private one-time FTP transfer
on November 30, 2020. These data are not expected to be publicly
archived outside of QGreenland."""),
'citation': {
'text': ("""Vector data from the Danish Geodata Agency. Attributes
processed by Karl Brix Zinglersen, Greenland Institute of
Natural Resources (2020)."""),
'url':
'',
},
},
)
ne_timezones = Dataset(
id='ne_timezones',
assets=[
HttpAsset(
id='only',
urls=[
'https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_time_zones.zip',
],
),
],
metadata={
'title':
'Timezones',
'abstract':
("""Time zones primarily derive from the Central Intelligence Agency
map of Time Zones, downloaded from the World Factbook website May
2012. Boundaries were adjusted to fit the Natural Earth line work at
a scale of 1:10 million and to follow twelve nautical mile
territorial sea boundary lines when running along coasts. Additional
research was performed based on recent news to update several areas
including the international dateline and time zone adjustments for
Samoa and Tokelau and the discarding of daylight savings time in
Russia.
Data attributes include time offset from Coordinated Universal Time
(UTC, aka “zulu” time) and map color codes for a 6-up and 8-up
styling."""),
'citation': {
'text': ("""Made with Natural Earth"""),
'url':
'https://github.com/nvkelso/natural-earth-vector/blob/master/LICENSE.md',
},
},
)
from qgreenland.config.project import project
from qgreenland.models.config.asset import RepositoryAsset
from qgreenland.models.config.dataset import Dataset
qgr_bounds = Dataset(
id='qgr_bounds',
assets=[
RepositoryAsset(
id='data',
filepath=project.boundaries['data'].filepath,
),
RepositoryAsset(
id='background',
filepath=project.boundaries['background'].filepath,
),
],
metadata={
'title':
'QGreenland boundaries',
'abstract':
("""This boundary is used to subset many datasets for QGreenland."""),
'citation': {
'text': ("""Generated by QGreenland."""),
'url': '',
},
},
)
geothermal_heat_flux = Dataset(
id='geothermal_heat_flux',
assets=[
HttpAsset(
id='only',
urls=[
'https://ads.nipr.ac.jp/api/v1/metadata/A20180227-001/2.00/data/DATA?path=GHF_Greenland_Ver2.0_GridEPSG3413_05km.nc',
],
),
],
metadata={
'title':
'Geothermal heat flux distribution for the Greenland ice sheet, derived by combining a global representation and information from deep ice cores',
'abstract':
("""The data present a distribution of the geothermal heat flux (GHF)
for Greenland, which is an update of two earlier versions by Greve
(2005, Ann. Glaciol. 42) and Greve and Herzfeld (2013, Ann. Glaciol.
54). The GHF distribution is constructed in two steps. First, the
global representation by Pollack et al. (1993, Rev. Geophys. 31) is
scaled for the area of Greenland. Second, by means of a
paleoclimatic simulation carried out with the ice sheet model
SICOPOLIS, the GHF values for five deep ice core locations are
modified such that observed and simulated basal temperatures match
closely. The resulting GHF distribution generally features low
values in the south and the north-west, whereas elevated values
prevail in central North Greenland and towards the north-east. The
original source data are provided as NetCDF files on two different
grids (EPSG:3413 grid, Bamber grid) that have frequently been used
in modelling studies of the Greenland ice sheet, and for the three
different resolutions of 5 km, 10 km and 20 km."""),
'citation': {
'text':
("""Greve, R., 2018, Geothermal heat flux distribution for the
Greenland ice sheet, derived by combining a global
representation and information from deep ice cores, 2.00, Arctic
Data archive System (ADS), Japan,
http://doi.org/10.17592/001.2018022701"""),
'url':
'http://doi.org/10.17592/001.2018022701',
},
},
)
caff_murre_colonies = Dataset(
id='caff_murre_colonies',
assets=[
HttpAsset(
id='only',
urls=[
'https://abds.is/index.php/publications/the-distribution-of-thick-billed-and-common-murre-colonies-in-the-north/download',
],
),
],
metadata={
'title':
'The distribution of thick-billed and common murre colonies in the North.',
'abstract':
("""Murres are among the most abundant seabirds in the Northern
Hemisphere with a population in excess of ten million adults. No
obvious global trend has been identified but the majority of
regional populations have shown declines over the past three
decades. While they are currently abundant, climate change is
projected to pose problems to murres in the future, especially for
the more northern species, the thick-billed murre, which is strongly
associated with sea ice. Other threats include fisheries
interactions, over-exploitation, contaminants, and oil spills, the
latter becoming more important if climate change expands shipping
and hydrocarbon development in the Arctic."""),
'citation': {
'text':
("""Arctic Biodiversity Trends 2010 – Selected indicators of
change. CAFF International Secretariat, Akureyri, Iceland.May
2010."""),
'url':
'https://abds.is/index.php/publications/species/the-distribution-of-thick-billed-and-common-murre-colonies-in-the-north',
},
},
)
bas_coastlines = Dataset(
id='bas_coastlines',
assets=[
HttpAsset(
id='only',
urls=[
'https://ramadda.data.bas.ac.uk/repository/entry/get/Greenland_coast.zip?entryid=synth:8cecde06-8474-4b58-a9cb-b820fa4c9429:L0dyZWVubGFuZF9jb2FzdC56aXA=',
],
),
],
metadata={
'title': (
'The coastline of Kalaallit Nunaat/ Greenland available as a shapefile'
' and geopackage, covering the main land and islands, with glacier'
' fronts updated as of 2017.'
),
'abstract': (
"""A coastline of Kalaallit Nunaat/ Greenland covering all land and
islands, produced in 2017 for the BAS map 'Greenland and the
European Arctic'. The dataset was produced by extracting the land
mask from the Greenland BedMachine dataset and manually editing
anomalous data. Some missing islands were added and glacier fronts
were updated using 2017 satellite imagery. The dataset can be used
for cartography, analysis and as a mask, amongst other uses. At very
large scales, the data will appear angular due to the nature of
being extracted from a raster with 150 m cell size, but the dataset
should be suitable for use at most scales and can be edited by the
user to exclude very small islands if required. The projection of
the dataset is WGS 84 NSIDC Sea Ice Polar Stereographic North, EPSG
3413. The dataset does not promise to cover every island and
coastlines were digitised using the data creator's interpretation of
the landforms from the images."""
),
'citation': {
'text': (
"""Gerrish, L. (2020). The coastline of Kalaallit Nunaat/
Greenland available as a shapefile and geopackage, covering the
main land and islands, with glacier fronts updated as of 2017.
(Version 1.0) [Data set]. UK Polar Data Centre, Natural
Environment Research Council, UK Research & Innovation."""
),
'url': 'https://doi.org/10.5285/8cecde06-8474-4b58-a9cb-b820fa4c9429',
},
},
)