Circum-Antarctic landfast sea ice extent, 2000-2018 Annually from March 2000 to March 2017, we interpolate ice-sheet areas in a 1-km-resolution optical- and thermal-band Moderate Resolution Imaging Spectroradiometer (MODIS)-based fast-ice dataset to our 240-m-resolution grid.
Sentinel 1a Annually from March 2015 to March 2021, we digitize coastlines using the same methods that were used to generate the annual MODIS-based fast-ice dataset, but we apply the methods to Sentinel 1a radar image mosaics from http://seaice.dk/ that were collected concurrently with the MODIS data each March, thus providing two independent datasets obtained from different types of sensor.
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Description: This data product uses observed ice velocity to constrain the growth rate of ice shelf extents. Each snapshot is constrained by observations taken years before and after the snapshot to ensure that ice never magically appears out of nowhere. The processing combines multiple datasets from multiple years and seeks a compromise agreement between datasets. The text below is copied and pasted from the methods section from our paper:
All coastlines were masked to the 240-m-resolution ITS_LIVE Antarctic mosaic grid. We immediately found that the uncorrected masks from each dataset are not directly intercomparable, as different research groups sometimes differ in how they designate ice types, different sensors vary in their ability to differentiate shelf ice from sea ice, and certain islands are included in some coastline datasets but neglected in others. To ensure that any changes we see from one coastline mapping to the next reflect true changes in coastline rather than changes in sensors or methodology, we create a composite dataset by adjusting each contributing dataset as follows. After each adjustment described in the following, any potential new holes in any ice mask are filled to preserve a continuous ice sheet. (1) Remove the attached iceberg D15 from all datasets, because D15 calved from West Ice Shelf in 1992, then abutted the coast for decades after62 and appears in some coastline products but not others. (2) Remove any islands that are not present in all datasets. (3) Remove from the MODIS-based data any ice that is present in all 18 MODIS-based mappings but does not appear in any other datasets. Add to all MODIS-based data any ice that is present in all other datasets but does not appear in any of the MODIS-based data. (4) Remove from the Sentinel 1a-based data any ice that is present in all seven years of Sentinel 1a mappings but does not appear in any other datasets. Add to all Sentinel 1a data any ice that is present in all other datasets but does not appear in any of the Sentinel 1a data. (5) Remove from the MOA-based data any ice that is present in all three MOA-based mappings but does not appear in any other datasets. Add to all MOA data any ice that is present in all other datasets but does not appear in any of the MOA-based data. (6) Add to all RAMP-based data any ice that is not present in either of the RAMP mappings but is present in all other datasets. (7) Using the years 2015, 2016, 2017 and 2021 when the same techniques were applied to MODIS and Sentinel 1a data collected concurrently, we remove from both datasets any ice that does not appear at least once in each dataset. (8) Add to all datasets any ice that is present within all three MOA coastlines, but is not present in any of the three concurrent MODIS-based mappings. (9) Use projected x and y velocity components vx and vy to calculate grid-cell centre displacements that should occur, in increments of six months, up to six years for the entire grid. For this step we multiply vx and vy by 1.1, which allows some tolerance for variations in velocity, which we describe below. (10) It is evident by inspection that the 1997 RAMP coastline was digitized at higher resolution and with greater care than the 2000 RAMP coastline. Therefore, we put some faith in the 1997 coastline as a more reliable reference. Using the expected displacements from the velocity fields to tell us the maximum amount of coastline growth that could possibly occur from 1997 to 2000, including the 10% velocity tolerance, any ice that is present in the 2000 mask but is missing from the 1997 mask, and could not have advected to the new location in just three years, is removed from the 2000 mask. (11) Adjust the MOA2004 mask following the same logic described in the previous step, but tie MOA2004 to RAMP2000 and MOA2009. Any pixels in MOA2004 that could have advected from ice that is present in RAMP2000 and will advect to a location that will appear as ice in MOA2009, then must be ice in MOA2004. Similarly, any ice that is present in MOA2004, but could not have advected there from RAMP2000 and will not advect to an ice location in MOA2009, cannot be ice in MOA2004. Adjust MOA2004 accordingly. (12) Follow the previous step to tie MOA2009 to the adjusted MOA2004 and MOA2014. (13) Tie MOA2014 to MOA2009 and any ice that is present in both the Sentinel 1a- and MODIS-based mappings for 2021. (14) The adjusted RAMP, MOA and combined 2021 mappings now serve as anchors to tie the MODIS- and Sentinel 1a-based mappings. Following the same method described above, tie each annual MODIS- and Sentinel 1a-based mapping to the closest past and future anchor mappings.
The resulting 24 ice masks achieve higher resolution than the underlying MODIS or Sentinel 1a mosaics because we exploit the offset of the MODIS and Sentinel 1a mosaic grids, and because we use known velocity to interpolate coastline migration between coarse-resolution grid postings to create our 240-m grid.
We note that some islands do not appear in every constituent dataset, so through the methods described above, we have constrained them to a constant area, or a nearly constant area in some cases where the algorithm introduces or otherwise allows a small amount of noise. As a result, if any bias exists in our overall estimates of ice-sheet-area change, we suspect it would be towards underestimation of area reduction, because most islands are small, susceptible to changes in their environment and located around the Antarctic Peninsula, where major reductions in ice-shelf area are known to have occurred.
We have the most confidence in our ice-shelf-area time series that show the largest amplitudes of change, so we recommend considering the range of values that are presented in Supplementary Information and Supplementary Table 1 when interpreting the time series of smaller ice shelves or ice shelves that are adjacent to any islands that may not appear in all contributing datasets.
Basins:
To quantify changes in the area and mass of each ice shelf, we create a gridded mask of 181 ice-shelf names based on the MEaSUREs Antarctic Boundaries for IPY 2007–2009 from Satellite Radar, Version 258. We dilate the boundaries of each ice shelf by 100 km in all directions, then use constant extrapolation along flowlines following the procedure described above for velocity and thickness. The result shown in Extended Data Fig. 3 is a set of masks that cover areas much larger than any observed ice-shelf extents, but are certain to fully capture changes at the ice front while providing extra tolerance in the grounding zone, which is beneficial when working with multiple datasets that may have been created with different grounding-line masks. The ice-shelf mask in Extended Data Fig. 3 shows the fully dilated ice-shelf areas; although, to be clear, we limit analysis of the area and mass of each ice shelf to pixels where ice is observed and the BedMachine mask indicates ice shelf or ocean.
Type: raster
Resolution if raster: 240 m
Projection information: EPSG 3031
Downstream
Raster data available here for now. The dataset is currently in review at NSIDC. We've submitted a revised version of the NetCDF which will be posted to NSIDC soon. Only the metadata have been changed in the revisions, and the ice mask data itself will not change from the original version here.
A vectorized version of the data is available here.
Overview
Here's an animation of the final product:
Upstream
Radarsat Antarctic Mapping Project (RAMP) Antarctic Mapping Mission-1 coastline used
after_coast_continuous.shpfile for late 1997 and the RAMP Modified Antarctic Mapping Missioncst2000line.shp, which corresponds to late 2000.MODIS Mosaic of Antarctica coastlines We use coastlines provided by the MODIS Mosaic of Antarctica (MOA) project for the years 2004, 2009 and 2014.
Circum-Antarctic landfast sea ice extent, 2000-2018 Annually from March 2000 to March 2017, we interpolate ice-sheet areas in a 1-km-resolution optical- and thermal-band Moderate Resolution Imaging Spectroradiometer (MODIS)-based fast-ice dataset to our 240-m-resolution grid.
Sentinel 1a Annually from March 2015 to March 2021, we digitize coastlines using the same methods that were used to generate the annual MODIS-based fast-ice dataset, but we apply the methods to Sentinel 1a radar image mosaics from http://seaice.dk/ that were collected concurrently with the MODIS data each March, thus providing two independent datasets obtained from different types of sensor.
Internal
Type: raster
Resolution if raster: 240 m
Projection information: EPSG 3031
Downstream
Raster data available here for now. The dataset is currently in review at NSIDC. We've submitted a revised version of the NetCDF which will be posted to NSIDC soon. Only the metadata have been changed in the revisions, and the ice mask data itself will not change from the original version here.
A vectorized version of the data is available here.