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understanding of how impurities are redistributed and how fabric forms in the shallow layers of ice sheets. EXPERIMENTAL We examined six firn specimens from depths of 9.71 m, 19.545 m and 34.465 m from core 02- SP, and depths of 13.019 m, 23.028 m and 38.533 m, from core 02-5 obtained on the United States International Trans-Antarctic Scientific Expedition (ITASE). Core 02-SP is at the South Pole while core 02-5 is the next core along the route, see the route map at http://www2.umaine.edu/USITASE/html/map.html for details. For examination in the SEM, ~25-mm- × 25-mm- × 10-mm-thick specimens were cut perpendicular to the core axis, shaved flat with a razor blade under a HEPA-filtered, laminar-flow hood at -10°C and frozen onto a brass plate. Specimens were then either sealed in a small container for later examination or mounted onto a cold stage for immediate observation. For imaging and microanalysis, the uncoated specimens were examined at -80°C ± 10°C using a JEOL 5310 low-vacuum SEM operated at 10 kV equipped with a Princeton Gamma-Tech IMIX EDS system utilizing a lithium-drifted silicon thin-window detector. For orientation determination, specimens were frozen to a purpose-built pre-tilted brass sled that was then mounted on a cold stage in a field emission gun FEI XL-30 environmental SEM. The SEM was operated at 15 kV with a beam current of 0.15 nA, and the ice was again examined at temperatures between -80°C ± 10°C at a pressure of approximately 5 × 10 –4 Pa. Secondary electron imaging was used on both SEMs. EBSPs were captured using the techniques described by Iliescu and others (Iliescu et al. 2004; 2005) and indexed using HKL Technologies’ CHANNEL 5 software. RESULTS AND DISCUSSION Figure 1 shows low-magnification secondary electron images of firn from three different depths. The images were obtained as soon as possible after the specimens were inserted into the SEM to reduce the degree of sublimation, which can affect the appearance of the specimens. Several features are worth noting. First, shaving the ice produced some debris, which fell into the pores in the ice. This was much more noticeable in the higher porosity shallow ice, presumably because the grains were less strongly bonded together and the pore volume is larger. Examples of such debris are labeled “D” in Figure 1(a). Second, grain boundaries grooves were observed in some images. These indicate where the snow crystals are joined together, see Figure 1 (a). By allowing the specimens to sublimate in the SEM it is possible to see grain boundary grooves between all the individual ice crystals and, hence, it should be possible to determine how the grain size changes as a function of depth in the firn. However, one needs to be aware that longer sublimation times, due to loss of mass, drastically affect the appearance of the microstructure and hence a delicate compromise needs to be achieved between the need to depict the microstructure in an accurate unadulterated manner and the desired to better visualize the details within the grain boundary region. Third, it is straightforward to determine the % areal porosity from these images by dividing the area occupied by the shaved flat regions by the area of the image. The pore area and length of boundary area were measured on the SEM images using Image SXM (Barrett 2005), a derivative of NIH Image (Rasband 1997). The boundaries of pore areas were traced with the drawing tool, and the perimeter and enclosed area measured with the pixel-counting measurement utility, see Figure 2. As is quite common in the SEM, the edges of the flat surface tended to charge. This analysis was performed from multiple images at each depth (except for the 9.71 m specimen, where only one image was available). The porosity values obtained at each depth are shown in Table 1. As might be expected, (except for the 9.71 m specimen), the porosity at a particular site decreases with increasing depth. 212
A B Figure 1. Low-magnification secondary electron images of firn samples from depths of (a) 9.71 m, (b) 23.028 m, and (c) 34.465 m. The features labeled “D” in (a) are debris produced by shaving. Some grain boundaries where the original snow crystals joined together, as indicated by grooves, are labeled “GB”. Some air bubbles are indicated by “A”. Table 1. Porosity, measured length of internal surface around the pores in 16 mm2 areas, length of (internal surface) line per unit area, LA, and internal pore surface area per unit volume, SV, for various depths at two sites from the US ITASE measured from SEM images. Site Depth (m) % Porosity C Measured length of surface (mm) 213 LA (mm -1 ) SV (mm -1 ) 02-SP 9.71 80 33.2 2.08 2.65 02-5 13.019 85 28.7 1.79 2.28 02-SP 19.545 87 44.5 2.78 3.54 02-5 23.028 58 30.4 1.98 2.52 02-SP 34.465 36 40.7 2.54 3.23 02-5 38.533 38 40.6 2.54 3.23
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Proceedings of the 63rd ANNUAL EAST
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FOREWORD T his proceedings volume c
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CONTENTS Foreword..................
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STATEMENT OF PURPOSE The Eastern Sn
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EXECUTIVES FOR THE 63rd EASTERN SNO
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THE PRESIDENT’S PAGE The 63rd ann
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Weisnet Medal for Best Student Pape
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3 63 rd EASTERN SNOW CONFERENCE New
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Figure 1. Layer 1 represents the so
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Figure 4. The general layout of the
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lue color represented no convection
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Total Density of Water Profiles The
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Figure 11(a). Simulated snow grain
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Jordan R, 1991. A one-dimensional t
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Campbell Scientific Award for Best
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19 63 rd EASTERN SNOW CONFERENCE Ne
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R ∑i ∑ n 2 = 1 NS = − n i = 1
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Snow and Climate 37
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Spatial distributions of changes in
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Snow and Climate Posters 43
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Figure 2 presents correlation maps
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CONCLUSIONS Three regional-continen
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Discharge (mm) 20 18 16 14 12 10 8
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ABSTRACT 55 63 rd EASTERN SNOW CONF
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This paper analyzes the synoptic pa
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Cyclones that track west of the App
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The synoptic-scale atmospheric circ
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CONCLUSIONS The record snowfall in
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correlations between April-May snow
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Figure 3. Linear correlations betwe
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winter/early spring could contribut
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Snow Remote Sensing 73
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height averaged 11.4 m with an aver
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strings were buried 20 - 30 cm belo
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Although the λE/Rn fraction was on
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estimated snow depth based on the 2
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important to note that the average
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Schmidt, R. A., C. A. Troendle, and
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METHODS The IMS Product The IMS was
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Figure 1. Microwave spectral charac
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snow depth and SWE on a weekly basi
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Figure 4. Example of blended SWE pr
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Evaluation of the global distributi
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Figure 9. Inter-comparison plots of
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Helfrich, S.R., D. McNamara, B.H.Ra
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105 63 rd EASTERN SNOW CONFERENCE N
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and validated regionally so they ca
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109 Test Site Figure 2. Variation o
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Correlation Coe. Correlation Coe. C
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Figure 8. SSM/I scattering signatur
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Considering the facts mentioned abo
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Besides the temporal validation, th
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RMSE Non linear Azar Chang Goodison
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63 nd EASTERN SNOW CONFERENCE Newar
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http://www.ccin.ca); from simulatio
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over the period 1988-2000. With the
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Figure 3: Explained variance (R 2 )
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correspondence between simulated an
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values. Continued development of ne
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National Climatic Data Center (2005
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Snow Remote Sensing Posters 135
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ABSTRACT 63 rd EASTERN SNOW CONFERE
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day and has a mean pixel resolution
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Table 1. Wheaton River basin EASE-G
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The SSM/I snowmelt onset algorithm
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coarse-resolution pixel with the hi
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Figure 5. (a) Scatterplot of snowpa
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For both 2004 and 2005, the AMSR-E
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threshold of ±18 K that reflects t
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ABSTRACT 153 63 rd EASTERN SNOW CON
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DATA USED SSM/I & QuikSCAT Coverage
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Page 173 and 174: 161 63 rd EASTERN SNOW CONFERENCE N
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Page 179 and 180: independent of regions. This greatl
Page 181 and 182: slightly vary the IMS with this pro
Page 183 and 184: imagery, the higher latitudes rely
Page 185 and 186: MODIS Land Science Team, NASA Godda
Page 187 and 188: FUTURE OF IMS Enhancements to the I
Page 189 and 190: information never present before on
Page 191 and 192: Brubaker, K.L., R. Pinker and E. De
Page 195 and 196: The Sierra Nevada del Cocuy region
Page 197 and 198: Colombia Venezuela Glacier Series R
Page 199 and 200: Table 4. Glacier Areas of the Ruiz-
Page 201 and 202: Pico Bonpland Massif-Sinigüis Glac
Page 203 and 204: Linder W, Jordan E, 1991. Ice-mass
Page 205 and 206: Snowpack Processes 193
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Page 209 and 210: In the following paragraph the main
Page 211 and 212: Figure 1: Variation of the season a
Page 213 and 214: Figure 3: 8-day composite maps (24
Page 215 and 216: The third pair (Figure 5) represent
Page 217 and 218: Figure 9: Time evolution of SWE ave
Page 219 and 220: melting occurs, whereas at the high
Page 221 and 222: Colbeck SC, Anderson EA. 1982. The
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Page 227 and 228: GB ridge GB groove Figure 3. Higher
Page 229 and 230: CONCLUSION Figure 5. Indexed electr
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Page 233 and 234: Meteorological data for the winter
Page 235 and 236: monthly probability adjusted data 4
Page 237 and 238: 225 Pullman WA Rawlins WY Leadville
Page 239 and 240: The daily wind speed can exceed 6.5
Page 241 and 242: NCDC. 2006. National Climate Data C
Page 243 and 244: ABSTRACT 231 63 rd EASTERN SNOW CON
Page 245 and 246: and mass balances for 540 environme
Page 247 and 248: Temperature, precipitation, windspe
Page 249 and 250: the left in response to snow compac
Page 251 and 252: Pinched-cone dimensions for each sl
Page 253 and 254: Terrain slope (degrees) Terrain slo
Page 255 and 256: ACKNOWLEDGEMENTS This work was fund
Page 257 and 258: Glacial and Periglacial Processes 2
Page 261 and 262: From linear regression the constant
Page 265 and 266: DATA COLLECTION Figure 1. Location
Page 267 and 268: Table 1. Comparison of GPS measured
Page 269 and 270: Transverse Velocity Profiles Surfac
Page 271 and 272: is shown in Figure 4. This variatio
Page 273 and 274: Table 4. The calculated volume flux
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Figure 1. Cordillera Blanca regiona
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MATERIALS AND METHODS Field Measure
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Data analysis techniques We synthes
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season, but unacceptable for the dr
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significant, although more informat
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(a) Liquid Water (mm) Dry Period: M
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Future Work We are installing addit
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Shuttleworth, W.J., and J.S. Wallac
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Snow and Periglacial Processes Post
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No one to date has shown why snowfl
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APPENDIX A Northern Hemisphere Year
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62nd ESC Papers 291
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62 nd EASTERN SNOW CONFERENCE Water
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each other; and, they both decrease
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297 62 nd EASTERN SNOW CONFERENCE W
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comes in the form of precipitation.
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averaged to reduce bias in this var
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RESULTS Figure 2. Flow chart of reg
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Table 6: Actual-Conditions SWE, dep
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307 Table 9: Regression results bet
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Goita, K., A. Walker, B. Goodison,
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TO ERR IS HUMAN, TO FORGET IT WOULD
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