Short-scale variations in high-resolution crystal-preferred orientation
data in an alpine ice core - do we need a new statistical approach?
- Johanna Kerch,
- Olaf Eisen,
- Jan Eichler,
- Tobias Binder,
- Johannes Freitag,
- Pascal Bohleber,
- Paul Bons,
- Ilka Weikusat
Olaf Eisen
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research
Author ProfileJan Eichler
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research
Author ProfileTobias Binder
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research
Author ProfileJohannes Freitag
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research
Author ProfileIlka Weikusat
Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research
Author ProfileAbstract
We analysed crystal-preferred orientation of c-axis and microstructure
data from the Alpine ice core KCC at an unprecedented resolution and
coverage of any Alpine ice core. We find that an anisotropic
single-maximum fabric develops as early as 25 m depth in firn under
vertical compression and strengthens under simple shear conditions
towards the bedrock at 72 m depth. The analysis of continuously measured
intervals with subsequent thin section samples from several depths of
the ice core reveals a high spatial variability in the crystal
orientation and crystal size on the 10 cm-scale as well as within a few
centimeters. We quantify the variability and investigate the possible
causes and links to other microstructural properties. Our findings
support the hypothesis that the observed variability is a consequence of
strain localisation on small spatial scales with influence on fabric and
microstructure. From a methodological perspective, the results of this
study lead us to challenge whether single thin sections from ice cores
provide representative parameters for their depth to be used to infer
the fabric development in a glacier on the large scale. Previously
proposed uncertainty estimates for fabric and grain size parameters do
not capture the observed variability. This might therefore demand a new
scale-sensitive statistical approach.