Twelvefold acceleration in Antarctic shelf ice loss over two decades

The story of Antarctic ice shelf melt continues to develop. A new report measures ice loss over the entire two Antarctic continents, finding a twelvefold acceleration in ice loss comparing the interval 2003-2012 to the interval 1994-2003.

This is from a paper in Science by F. S. Paolo, H. A. Fricker, and L. Padman, “Volume loss from Antarctic ice shelves is accelerating”, Science, DOI: 10.1126/science.aaa0940, 26th March 2015.


The floating ice shelves surrounding the Antarctic Ice Sheet restrain the grounded ice-sheet flow. Thinning of an ice shelf reduces this effect, leading to an increase in ice discharge to the ocean. Using eighteen years of continuous satellite radar altimeter observations we have computed decadal-scale changes in ice-shelf thickness around the Antarctic continent. Overall, average ice-shelf volume change accelerated from negligible loss at 25 ± 64 km3 per year for 1994-2003 to rapid loss of 310 ± 74 km3 per year for 2003-2012. West Antarctic losses increased by 70% in the last decade, and earlier volume gain by East Antarctic ice shelves ceased. In the Amundsen and Bellingshausen regions, some ice shelves have lost up to 18% of their thickness in less than two decades.

Some details:

Our technique for ice-shelf thickness change detection is based on crossover analysis of satellite radar altimeter data, where time-separated height estimates are differenced at orbit intersections (13, 16, 17). To cross-calibrate measurements from the different satellite altimeters we used the roughly one-year overlap between consecutive missions. The signal-to-noise ratio of altimeter-derived height differences for floating ice in hydrostatic equilibrium is roughly an order of magnitude smaller than over grounded ice, requiring additional data averaging to obtain comparable statistical significance. We aggregated observations in time (3-month bins) and space (~30-km cells). Because the spatial distribution of crossovers changes with time (e.g., non-exact repeat tracks, nadir mispointing) we constructed several records at each cell location and stacked them to produce a mean time series with reduced statistical error (18). We converted our height-change time series and rates to thickness changes by assuming that observed losses occurred predominantly at the density of solid ice (i.e., basal melting) (4, 5, 17). This is further justified by the relative insensitivity of radar measurements to fluctuations in surface mass balance (18). For volume changes we tracked the minimum (fixed) area of each ice shelf (18). We assessed uncertainties for all estimates using the bootstrap approach (resampling with replacement of the residuals of the fit) (19), which allows estimation of formal confidence intervals. All our uncertainties are stated at the 95% confidence level [see discussion of uncertainties in 18, and the several corrections applied in (20)].

We estimated 18-year trends in ice-shelf thickness by fitting low-order polynomials (n ≤ 3) to the data using a combination of lasso-regularized regression (21) and cross-validation for model-parameter selection (i.e., the shape of the fit is determined by the data). This combined approach allows us to minimize the effect of short-term variability on the 18-year trends. Relative to previous studies (4, 5, 13, 22) we have improved estimations by (i) using 18-year continuous records, (ii) implementing a time-series averaging scheme to enhance the signal-to-noise ratio, and (iii) using a robust approach to trend extraction.

The details of the methods are documented. Here are some figures.



The authors are from Scripps Institution of Oceanography, University of California, San Diego, California, USA, and Earth & Space Research, Oregon, USA.

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This entry was posted in Antarctica, Anthropocene, carbon dioxide, civilization, climate, climate change, climate education, environment, geophysics, oceanography, physics, rationality, science, sea level rise, spatial statistics, statistics, WAIS, WHOI. Bookmark the permalink.

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