MATLAB Examples

rtopo_data documentation

The rtopo2_data function loads data from RTopo-2 (Schaffer et al. 2016, see reference below).

Contents

Syntax

[lat,lon] = rtopo2_data('coast')
[lat,lon] = rtopo2_data('isf')
[lat,lon,Z] = rtopo2_data(gridded_dataset)
[lat,lon,Z] = rtopo2_data(...,lati,loni)
[lat,lon,Z] = rtopo2_data(...,xi,yi)
[lat,lon,Z] = rtopo2_data(...,extra_degrees)

Description

[lat,lon] = rtopo2_data('coast') returns the coast line dataset. NOTE: This is called the coast line in the RTopo-2 documentation, but it more closely matches the GROUNDING LINE of most other datasets (Bedmap2, ASAID, MODIS MOA, MEaSUREs).

[lat,lon] = rtopo2_data('isf') returns the ice shelf front locations.

[lat,lon,Z] = rtopo2_data(gridded_dataset) loads gridded elevation, mask, or source data. This syntax may take 10 seconds or more because it's a HUGE grid, so you may wish to subset by specifying the spatial extents you're interested in by including lati,loni or xi,yi as described below. Options for gridded_dataset names are:

  • 'bed' bed elevations relative to the geoid.
  • 'base' ice base elevations relative to the geoid.
  • 'surface' ice surface elevations relative to the geoid.
  • 'wct' water column thickness taken as base-bed.
  • 'mask' mask values numbered 0 through 3 as described below.
  • 'sourcedraft' source of ice draft data as described below.
  • 'sourcebathy' source of elevation data as described below.

[lat,lon,Z] = rtopo2_data(...,lati,loni) loads only enough data to encompass the geo coordinates lati,loni using inpsquad to determine extents of data.

[lat,lon,Z] = rtopo2_data(...,xi,yi) loads only enough data to encompass polar stereographic meters xi,yi.

[lat,lon,Z] = rtopo2_data(...,extra_degrees) loads extra rows and columns around the extents of data coordinates to allow interpolation. The RTopo-2 dataset is natively at 30 second resolution, so you may wish to load an extra 2 or 3 rows and columns of data by specifing extra_degrees = 0.017.

Data sources and mask values

Mask values:

  • 0. ocean
  • 1. grounded ice
  • 2. floating ice
  • 3. bare land

Data source values:

  1. World Ocean bathymetry GEBCO_2014 (Weatherall et al., 2015)
  2. Southern Ocean bathymetry IBCSO (Arndt et al., 2013)
  3. Arctic Ocean bathymetry IBCAOv3 (Jakobsson et al., 2012)
  4. Antarctic ice sheet/shelf surface height Bedmap2 and thickness and bedrock topography (Fretwell et al., 2013)
  5. Greenland ice sheet/glacier surface height Morlighem et al. (2014) (M-2014) and thickness and bedrock topography
  6. Fjord and shelf bathymetry close to the Bamber et al. (2013) (B-2013) Greenland coast
  7. Bathymetry on Northeast Greenland Arndt et al. (2015) (NEG_DBM) continental shelf
  8. Bathymetry in several narrow Greenland fjords artificial, see Merging strategy and Data and on parts of the Greenland continental shelf corrections in Sect. 2.2.3 for details
  9. Bathymetry for Getz and western Abbot Ice ALBMAP (Le Brocq et al., 2010) Shelf cavities
  10. Bathymetry for Fimbulisen cavity Nøst (2004), Smedsrud et al. (2006)
  11. Ice thickness for Nioghalvfjerdsfjorden Glacier DTU (Seroussi et al., 2011) and Zachariæ Isstrøm Operation Icebridge (Allen et al., 2010, updated 2015) Alfred Wegener Institute (AWI) Mayer et al. (2000)
  12. Contour of iceberg A-23A in Weddell Sea Paul et al. (2015)

Example 1: Grounding line and ice shelf front

Loading and plotting the grounding line (called the 'coast' by Schaffer et al.) is easy:

[lat,lon] = rtopo2_data('coast');
plotps(lat,lon,'k.')

Overlaying the ice shelf front dataset is similar:

[lat,lon] = rtopo2_data('isf');
plotps(lat,lon,'b.')

Example 2: Figure 6

Figure 6b of the Schaffer et al. 2016 paper shows unprojected coordinates of Getz Ice Shelf from about 72S to 76S and longitude range is 140W to 105 E. We can load and plot the bathymetry data like this:

[lat,lon,bed] = rtopo2_data('bed',[-72 -76],[-105 -140]);

figure('position',[100 100 470 270])
imagesc(lon,lat,bed')
axis xy

We can make that look a slight bit more like Figure 6b by piecing together a couple of cmocean (Thyng et al., 2016) colormaps:

caxis([-1200 1200])
colormap([cmocean('-deep');cmocean('-turbid')])

Example 3: Masking

Suppose you're interested in the water cavities under Fimbul Ice Shelf. Initialize a map of the area using mapzoomps, and if you happen to have the IBCSO toolbox, you can lay down a base map like this:

figure
mapzoomps('fimbul ice shelf','mapwidth',[800 400],...
   'inset','nw','frame','off')
ibcso('xy')
axis image

Load the water column thickness data from RTopo-2. We can use xlim and ylim to only load the RTopo-2 data within the spatial extents of the current map axis limits:

[lat,lon,wct] = rtopo2_data('wct',xlim,ylim);

Here's where we have to get fancy. The RTopo-2 dataset is distributed in regular 30 second intervals in geographic coordinates. But for our purposes we typically want to talk in terms of physical space, rather than spherical coordinates. So we'll need to turn our 1D lat,lon arrays into 2D grids before plotting in polar stereographic coordinates. Use meshgrid to do the trick:

[LAT,LON] = meshgrid(lat,lon);

Now we have a big grid of wct values with corresponding grids of LAT and LON values. If we're only interested in water column thickness under the ice shelf we can mask-out the open ocean and grounded ice by setting non-ice-shelf values to NaN. The RTopo-2 mask indicates ice shelves by a value of 2:

[~,~,mask] = rtopo2_data('mask',xlim,ylim);
wct(mask~=2) = NaN;

Plot water column thickness with pcolorps. Note the pcolor function wants double precision:

pcolorps(LAT,LON,double(wct))
caxis([0 1000])

Overlay a grounding line and an ice shelf front line like this:

[gllat,gllon] = rtopo2_data('coast',xlim,ylim);
[isflat,isflon] = rtopo2_data('isf',xlim,ylim);

plotps(gllat,gllon,'k.')
plotps(isflat,isflon,'r.')

scalebarps('location','se')

Citing these datasets

If you use this dataset and Antarctic Mapping Tools, please cite the following:

Schaffer, Janin; Timmermann, Ralph; Arndt, Jan Erik; Kristensen, Steen Savstrup; Mayer, Christoph; Morlighem, Mathieu; Steinhage, Daniel (2016): A global, high-resolution data set of ice sheet topography, cavity geometry, and ocean bathymetry. Earth System Science Data, 8(2), 543-557, doi:10.5194/essd-8-543-2016 and doi:10.1594/PANGAEA.856844

Greene, C.A., Gwyther, D.E. and Blankenship, D.D., 2016. Antarctic Mapping Tools for Matlab. Computers & Geosciences. http://dx.doi.org/10.1016/j.cageo.2016.08.003

Author Info

This function and supporting documentation were written by Chad A. Greene in November 2016. The University of Texas at Austin Institute for Geophysics.