Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys
We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery acquired in 2014, 2016, and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88 ∘ S, an elevat...
| Published in: | The Cryosphere |
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| Main Authors: | , , , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2020
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/tc-14-3287-2020 https://doaj.org/article/bc397f91697e4b768dcc92818e0b5de6 |
| Summary: | We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery acquired in 2014, 2016, and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88 ∘ S, an elevation bias validation site for ICESat-2 and potential validation site for CryoSat-2. We find significant small-scale variability ( <10 km) in snow accumulation based on the snow radar subsurface stratigraphy, indicating areas of strong wind redistribution are prevalent at 88 ∘ S. In general, highs in snow accumulation rate correspond with topographic lows, resulting in a negative correlation coefficient of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mi>r</mi><mn mathvariant="normal">2</mn></msup><mo>=</mo><mo>-</mo><mn mathvariant="normal">0.32</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="54pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="5ed32971497199b4f64d23960fca493d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-14-3287-2020-ie00001.svg" width="54pt" height="14pt" src="tc-14-3287-2020-ie00001.png"/></svg:svg> between accumulation rate and MSWD (mean slope in the mean wind direction). This relationship is strongest in areas where the dominant wind direction is parallel to the survey profile, which is expected as the geophysical surveys only capture a two-dimensional cross section of snow redistribution. Variability in snow accumulation appears to correlate with variability in MSWD. The correlation coefficient between the standard deviations of accumulation rate and MSWD is r 2 =0.48 , indicating a stronger link between the standard deviations than the actual parameters. Our analysis shows that there is no simple relationship between surface slope, wind ... |
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