Supplementary data for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
Supplementary datasets for: Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature. Abstract: The melting of glaciers and ice caps accounts for about one-third of current sea-...
| Main Authors: | , , , , |
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| Format: | Dataset |
| Language: | unknown |
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Zenodo
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.5644414 https://zenodo.org/record/5644414 |
| Summary: | Supplementary datasets for: Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature. Abstract: The melting of glaciers and ice caps accounts for about one-third of current sea-level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal climate shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Here we link historical and modern glacier observations to predict that twenty-first century glacier thinning rates will more than double those from 1936 to 2010. Making use of an archive of historical aerial imagery from 1936 and 1938, we use structure-from-motion photogrammetry to reconstruct the three-dimensional geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a more than 70-year timespan, enabling us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr -1 of water equivalent. Finally, we design a space-for-time substitution8 to combine our historical glacier observations with climate projections and make first-order predictions of twenty-first century glacier change across Svalbard. Dataset description: This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder). ------------------------------------------------------------------------------------- Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]: 1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file] 2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file] 3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file] 4. Shapefile of 1936 glacier extents [ESRI .shp file] 5. Glacier-by-glacier statistics [.xlsx file] ------------------------------------------------------------------------------------- Regional-datasets: Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d: Zone 1 - South Spitsbergen Zone 2 - Barentsoya-Edgeoya Zone 3 - Austfonna Zone 4 - Vestfonna Zone 5 - Northeast Spitsbergen Zone 6 - Central Spitsbergen Zone 7 - Northwest Spitsbergen Zone 8 - North Spitsbergen 6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files] 7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files] 8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files] 9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3. 10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph. |
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