Articles | Volume 15, issue 4
https://doi.org/10.5194/tc-15-1931-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-1931-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spectral attenuation coefficients from measurements of light transmission in bare ice on the Greenland Ice Sheet
Department of Geography, University of California, Los Angeles, Los
Angeles, California, 90027, USA
Pacific Northwest National Laboratory, Richland, Washington, 99354,
USA
Laurence C. Smith
Institute at Brown for Environment and Society, Brown University,
Providence, Rhode Island, 02912, USA
Department of Earth, Environmental and Planetary Sciences, Brown
University, Providence, Rhode Island, 02912, USA
Department of Geography, University of California, Los Angeles, Los
Angeles, California, 90027, USA
Asa K. Rennermalm
Department of Geography, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, 08854, USA
Marco Tedesco
NASA Goddard Institute for Space Studies, New York, New York, 10025, USA
Lamont-Doherty Earth Observatory, Columbia University, New York, New York, 10964, USA
Rohi Muthyala
Department of Geography, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, 08854, USA
Sasha Z. Leidman
Department of Geography, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, 08854, USA
Samiah E. Moustafa
Institute at Brown for Environment and Society, Brown University,
Providence, Rhode Island, 02912, USA
Jessica V. Fayne
Department of Geography, University of California, Los Angeles, Los
Angeles, California, 90027, USA
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Colin J. Gleason, Kang Yang, Dongmei Feng, Laurence C. Smith, Kai Liu, Lincoln H. Pitcher, Vena W. Chu, Matthew G. Cooper, Brandon T. Overstreet, Asa K. Rennermalm, and Jonathan C. Ryan
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We apply first-principle hydrology models designed for global river routing to route flows hourly through 10 000 individual supraglacial channels in Greenland. Our results uniquely show the role of process controls (network density, hillslope flow, channel friction) on routed meltwater. We also confirm earlier suggestions that large channels do not dewater overnight despite the shutdown of runoff and surface mass balance runoff being mistimed and overproducing runoff, as validated in situ.
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Claire E. Simpson, Christopher D. Arp, Yongwei Sheng, Mark L. Carroll, Benjamin M. Jones, and Laurence C. Smith
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This study compares hourly supraglacial moulin discharge simulations from three surface meltwater routing models. Results show that these models are superior to simply using regional climate model runoff without routing, but different routing models, different-spatial-resolution DEMs, and parameterized seasonal evolution of supraglacial stream and river networks induce significant variability in diurnal moulin discharges and corresponding subglacial effective pressures.
Cited articles
Askebjer, P., Barwick, S. W., Bergström, L., Bouchta, A., Carius, S.,
Coulthard, A., Engel, K., Erlandsson, B., Goobar, A., Gray, L., Hallgren,
A., Halzen, F., Hulth, P. O., Jacobsen, J., Johansson, S., Kandhadai, V.,
Liubarsky, I., Lowder, D., Miller, T., Mock, P. C., Morse, R., Porrata, R.,
Price, P. B., Richards, A., Rubinstein, H., Schneider, E., Sun, Q., Tilav,
S., Walck, C., and Yodh, G.: Optical Properties of the South Pole Ice at
Depths Between 0.8 and 1 Kilometer, Science, 267, 1147–1150,
https://doi.org/10.1126/science.267.5201.1147, 1995.
Askebjer, P., Barwick, S. W., Bergström, L., Bouchta, A., Carius, S.,
Dalberg, E., Erlandsson, B., Goobar, A., Gray, L., Hallgren, A., Halzen, F.,
Heukenkamp, H., Hulth, P. O., Hundertmark, S., Jacobsen, J., Kandhadai, V.,
Karle, A., Liubarsky, I., Lowder, D., Miller, T., Mock, P., Morse, R.,
Porrata, R., Price, P. B., Richards, A., Rubinstein, H., Schneider, E.,
Spiering, C., Streicher, O., Sun, Q., Thon, T., Tilav, S., Wischnewski, R.,
Walck, C., and Yodh, G.: UV and optical light transmission properties in deep
ice at the South Pole, Geophys. Res. Lett., 24, 1355–1358,
https://doi.org/10.1029/97GL01246, 1997.
Bintanja, R. and Van Den Broeke, M. R.: The Surface Energy Balance of
Antarctic Snow and Blue Ice, J. Appl. Meteor., 34, 902–926,
https://doi.org/10.1175/1520-0450(1995)034<0902:TSEBOA>2.0.CO;2, 1995.
Bøggild, C. E., Oerter, H., and Tukiainen, T.: Increased ablation of
Wisconsin ice in eastern north Greenland: observations and modelling, Ann. Glaciol., 23, 144–148, https://doi.org/10.3189/S0260305500013367,
1996.
Bøggild, C. E., Brandt, R. E., Brown, K. J. and Warren, S. G.: The
ablation zone in northeast Greenland: ice types, albedos and impurities,
J. Glaciol., 56, 101–113,
https://doi.org/10.3189/002214310791190776, 2010.
Bohren, C. F.: Colors of snow, frozen waterfalls, and icebergs, J. Opt. Soc.
Am., J. Opt. Soc. Am., 73, 1646–1652, https://doi.org/10.1364/JOSA.73.001646, 1983.
Bohren, C. F.: Multiple scattering of light and some of its observable
consequences, Am. J. Phys., 55, 524–533,
https://doi.org/10.1119/1.15109, 1987.
Bohren, C. F. and Barkstrom, B. R.: Theory of the optical properties of
snow, J. Geophys. Res., 79, 4527–4535,
https://doi.org/10.1029/JC079i030p04527, 1974.
Brandt, R. E. and Warren, S. G.: Solar-heating rates and temperature
profiles in Antarctic snow and ice, J. Glaciol., 39, 99–110,
https://doi.org/10.3189/S0022143000015756, 1993.
Briegleb, B. P. and Light, B.: A Delta-Eddington Mutiple Scattering
Parameterization for Solar Radiation in the Sea Ice Component of the
Community Climate System Model, Technical Note, National Center for
Atmospheric Research, Boulder, Colorado, https://doi.org/10.5065/D6B27S71, 2007.
Brunt, K. M., Neumann, T. A., Amundson, J. M., Kavanaugh, J. L., Moussavi, M. S., Walsh, K. M., Cook, W. B., and Markus, T.: MABEL photon-counting laser altimetry data in Alaska for ICESat-2 simulations and development, The Cryosphere, 10, 1707–1719, https://doi.org/10.5194/tc-10-1707-2016, 2016.
Cantrell, C. A.: Technical Note: Review of methods for linear least-squares fitting of data and application to atmospheric chemistry problems, Atmos. Chem. Phys., 8, 5477–5487, https://doi.org/10.5194/acp-8-5477-2008, 2008.
Cooper, M. G.: Ice Monte Carlo Radiative Transfer Model v1.0 (Version v1.0), Zenodo, https://doi.org/10.5281/zenodo.4579073, 2021.
Cooper, M. G., Smith, L. C., Rennermalm, A. K., Miège, C., Pitcher, L. H., Ryan, J. C., Yang, K., and Cooley, S. W.: Meltwater storage in low-density near-surface bare ice in the Greenland ice sheet ablation zone, The Cryosphere, 12, 955–970, https://doi.org/10.5194/tc-12-955-2018, 2018.
Cooper, M. G., Smith, L. C., Rennermalm, A. K., Tedesco, M., Muthyala, R., Leidman, S. Z., Moustafa, S. E., and Fayne, J. V.: Optical attenuation coefficients of glacier ice from 350–700 nm and raw irradiance values from 350–900 nm, PANGAEA, https://doi.org/10.1594/PANGAEA.930278, 2021.
Dadic, R., Mullen, P. C., Schneebeli, M., Brandt, R. E., and Warren, S. G.:
Effects of bubbles, cracks, and volcanic tephra on the spectral albedo of
bare ice near the Transantarctic Mountains: Implications for sea glaciers on
Snowball Earth, J. Geophys. Res.-Earth Surf., 118,
1658–1676, https://doi.org/10.1002/jgrf.20098, 2013.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479,
https://doi.org/10.3189/2013JoG12J154, 2013.
Di Mauro, B., Baccolo, G., Garzonio, R., Giardino, C., Massabò, D., Piazzalunga, A., Rossini, M., and Colombo, R.: Impact of impurities and cryoconite on the optical properties of the Morteratsch Glacier (Swiss Alps), The Cryosphere, 11, 2393–2409, https://doi.org/10.5194/tc-11-2393-2017, 2017.
Doherty, S. J., Warren, S. G., Grenfell, T. C., Clarke, A. D., and Brandt, R. E.: Light-absorbing impurities in Arctic snow, Atmos. Chem. Phys., 10, 11647–11680, https://doi.org/10.5194/acp-10-11647-2010, 2010.
Fisher, F. N., King, M. D., and Lee-Taylor, J.: Extinction of
UV-visible radiation in wet midlatitude (maritime) snow: Implications for
increased NOx emission, J. Geophys. Res., 110, D21301,
https://doi.org/10.1029/2005JD005963, 2005.
France, J. L., King, M. D., Frey, M. M., Erbland, J., Picard, G., Preunkert, S., MacArthur, A., and Savarino, J.: Snow optical properties at Dome C (Concordia), Antarctica; implications for snow emissions and snow chemistry of reactive nitrogen, Atmos. Chem. Phys., 11, 9787–9801, https://doi.org/10.5194/acp-11-9787-2011, 2011.
Frey, K. E., Perovich, D. K., and Light, B.: The spatial distribution of
solar radiation under a melting Arctic sea ice cover, Geophys. Res. Lett., 38, L22501, https://doi.org/10.1029/2011GL049421, 2011.
Gardner, A. S. and Sharp, M. J.: A review of snow and ice albedo and the
development of a new physically based broadband albedo parameterization, J.
Geophys. Res., 115, F01009, https://doi.org/10.1029/2009JF001444, 2010.
Gardner, A. S., Smith, B. E., Brunt, K. M., Harding, D. J., Neumann, T., and
Walsh, K.: ICESat2 subsurface-scattering biases estimated based on the
2015 SIMPL/AVRIS campaign, in AGU Fall Meeting Abstracts, vol. 41,
C41C-0710, http://adsabs.harvard.edu/abs/2015AGUFM.C41C0710G (last
access: 25 January 2019), 2015.
Gerland, S., Liston, G. E., Winther, J.-G., Ørbæk, J. B., and
Ivanov, B. V.: Attenuation of solar radiation in Arctic snow: field
observations and modelling, Ann. Glaciol., 31, 364–368,
https://doi.org/10.3189/172756400781820444, 2000.
Goelles, T. and Bøggild, C. E.: Albedo reduction of ice caused by dust
and black carbon accumulation: a model applied to the K-transect, West
Greenland, J. Glaciol., 63, 1063–1076,
https://doi.org/10.1017/jog.2017.74, 2017.
Goelles, T., Bøggild, C. E., and Greve, R.: Ice sheet mass loss caused by dust and black carbon accumulation, The Cryosphere, 9, 1845–1856, https://doi.org/10.5194/tc-9-1845-2015, 2015.
Gow, A. J., Meese, D. A., Alley, R. B., Fitzpatrick, J. J., Anandakrishnan,
S., Woods, G. A., and Elder, B. C.: Physical and structural properties of the
Greenland Ice Sheet Project 2 ice core: A review, J. Geophys. Res.,
102, 26559–26575, https://doi.org/10.1029/97JC00165, 1997.
Greeley, A., Kurtz, N. T., Neumann, T., and Markus, T.: Estimating Surface
Elevation Bias Due to Subsurface Scattered Photons from Visible Wavelength
Laser Altimeters, in AGU Fall Meeting Abstracts, vol. 51,
http://adsabs.harvard.edu/abs/2017AGUFM.C51A0961G (last access: 25 January 2019), 2017.
Grenfell, T. C.: The Effects of Ice Thickness on the Exchange of Solar
Radiation Over the Polar Oceans, J. Glaciol., 22, 305–320,
https://doi.org/10.3189/S0022143000014295, 1979.
Grenfell, T. C.: A radiative transfer model for sea ice with vertical
structure variations, J. Geophys. Res.-Oceans, 96,
16991–17001, https://doi.org/10.1029/91JC01595, 1991.
Grenfell, T. C. and Maykut, G. A.: The Optical Properties of Ice and Snow in
the Arctic Basin, J. Glaciol., 18, 445–463,
https://doi.org/10.3189/S0022143000021122, 1977.
Grenfell, T. C. and Perovich, D. K.: Radiation absorption coefficients of
polycrystalline ice from 400–1400 nm, J. Geophys. Res., 86, 7447–7450,
https://doi.org/10.1029/JC086iC08p07447, 1981.
Grenfell, T. C. and Warren, S. G.: Representation of a nonspherical ice
particle by a collection of independent spheres for scattering and
absorption of radiation, J. Geophys. Res., 104, 31697–31709,
https://doi.org/10.1029/1999JD900496, 1999.
Grenfell, T. C., Light, B., and Perovich, D. K.: Spectral transmission and
implications for the partitioning of shortwave radiation in arctic sea ice,
Ann. Glaciol., 44, 1–6, https://doi.org/10.3189/172756406781811763, 2006.
He, C. and Flanner, M.: Snow Albedo and Radiative Transfer: Theory,
Modeling, and Parameterization, in: Springer Series in Light Scattering,
edited by A. Kokhanovsky, Springer International Publishing,
Cham, 67–133, https://doi.org/10.1007/978-3-030-38696-2_3, 2020.
He, C., Takano, Y., Liou, K.-N., Yang, P., Li, Q., and Chen, F.: Impact of
Snow Grain Shape and Black Carbon–Snow Internal Mixing on Snow Optical
Properties: Parameterizations for Climate Models, J. Climate, 30,
10019–10036, https://doi.org/10.1175/JCLI-D-17-0300.1, 2017.
Hoffman, M. J., Fountain, A. G., and Liston, G. E.: Near-surface internal
melting: a substantial mass loss on Antarctic Dry Valley glaciers, J. Glaciol., 60, 361–374, https://doi.org/10.3189/2014JoG13J095,
2014.
Holland, M. M., Bailey, D. A., Briegleb, B. P., Light, B., and Hunke, E.:
Improved Sea Ice Shortwave Radiation Physics in CCSM4: The Impact of Melt
Ponds and Aerosols on Arctic Sea Ice, J. Climate, 25, 1413–1430,
https://doi.org/10.1175/JCLI-D-11-00078.1, 2012.
Järvinen, O. and Leppäranta, M.: Solar radiation transfer in the
surface snow layer in Dronning Maud Land, Antarctica, Polar Science, 7,
1–17, https://doi.org/10.1016/j.polar.2013.03.002, 2013.
Joseph, J. H., Wiscombe, W. J., and Weinman, J. A.: The Delta-Eddington
Approximation for Radiative Flux Transfer, J. Atmos. Sci., 33,
2452–2459, https://doi.org/10.1175/1520-0469(1976)033<2452:TDEAFR>2.0.CO;2, 1976.
King, M. D. and Simpson, W. R.: Extinction of UV radiation in Arctic snow at
Alert, Canada (82∘ N), J. Geophys. Res., 106, 12499–12507,
https://doi.org/10.1029/2001JD900006, 2001.
Kokhanovsky, A. A. and Zege, E. P.: Scattering optics of snow, Appl. Opt.,
43, 1589, https://doi.org/10.1364/AO.43.001589, 2004.
Kuipers Munneke, P., van den Broeke, M. R., Reijmer, C. H., Helsen, M. M., Boot, W., Schneebeli, M., and Steffen, K.: The role of radiation penetration in the energy budget of the snowpack at Summit, Greenland, The Cryosphere, 3, 155–165, https://doi.org/10.5194/tc-3-155-2009, 2009.
Leathers, R. A., Downes, T. V., Davis, C. O., and Mobley, C. D.: Monte Carlo
Radiative Transfer Simulations for Ocean Optics: A Practical Guide,
Memorandum, Naval Research Laboratory, Washington, DC, available at:
https://www.oceanopticsbook.info/packages/iws_l2h/conversion/files/Leathersetal_NRL2004.pdf (last access:
11 October 2020), 2004.
Libois, Q., Picard, G., France, J. L., Arnaud, L., Dumont, M., Carmagnola, C. M., and King, M. D.: Influence of grain shape on light penetration in snow, The Cryosphere, 7, 1803–1818, https://doi.org/10.5194/tc-7-1803-2013, 2013.
Light, B., Maykut, G. A., and Grenfell, T. C.: A two-dimensional Monte
Carlo model of radiative transfer in sea ice, J. Geophys. Res.-Oceans, 108, 3219, https://doi.org/10.1029/2002JC001513, 2003.
Light, B., Maykut, G. A., and Grenfell, T. C.: A temperature-dependent,
structural-optical model of first-year sea ice, J. Geophys. Res.,
109, C06013, https://doi.org/10.1029/2003JC002164, 2004.
Light, B., Grenfell, T. C., and Perovich, D. K.: Transmission and absorption
of solar radiation by Arctic sea ice during the melt season, J. Geophys.
Res., 113, C03023, https://doi.org/10.1029/2006JC003977, 2008.
Liston, G. E. and Winther, J.-G.: Antarctic Surface and Subsurface Snow
and Ice Melt Fluxes, J. Climate, 18, 1469–1481,
https://doi.org/10.1175/JCLI3344.1, 2005.
Liston, G. E., Bruland, O., Elvehøy, H., and Sand, K.: Below-surface
ice melt on the coastal Antarctic ice sheet, J. Glaciology, 45,
273–285, https://doi.org/10.3189/002214399793377130, 1999a.
Liston, G. E., Bruland, O., Winther, J.-G., Elvehøy, H., and Sand, K.:
Meltwater production in Antarctic blue-ice areas: sensitivity to changes
in atmospheric forcing, Polar Res., 18, 283–290,
https://doi.org/10.1111/j.1751-8369.1999.tb00305.x, 1999b.
Malinka, A., Zege, E., Heygster, G., and Istomina, L.: Reflective properties of white sea ice and snow, The Cryosphere, 10, 2541–2557, https://doi.org/10.5194/tc-10-2541-2016, 2016.
Malinka, A. V.: Light scattering in porous materials: Geometrical optics and
stereological approach, J. Quant. Spectrosc. Ra., 141, 14–23, https://doi.org/10.1016/j.jqsrt.2014.02.022, 2014.
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B.,
Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R.,
Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R.,
Neuenschwander, A., Palm, S., Popescu, S., Shum, C., Schutz, B. E., Smith,
B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation
Satellite-2 (ICESat-2): Science requirements, concept, and
implementation, Remote Sens. Environ., 190, 260–273,
https://doi.org/10.1016/j.rse.2016.12.029, 2017.
Mätzler, C.: MATLAB Functions for Mie Scattering and Absorption, Version
2, Research Report, Institut für Angewandte Physik, Bern, Switzerland,
https://doi.org/10.7892/boris.146550, 2002.
Maykut, G. A. and Untersteiner, N.: Some results from a time-dependent
thermodynamic model of sea ice, J. Geophys. Res., 76, 1550–1575, https://doi.org/10.1029/JC076i006p01550,
1971.
Meirold-Mautner, I. and Lehning, M.: Measurements and model calculations
of the solar shortwave fluxes in snow on Summit, Greenland, Ann. Glaciol., 38, 279–284, https://doi.org/10.3189/172756404781814753, 2004.
Muhs, D. R.: The geologic records of dust in the Quaternary, Aeolian
Res., 9, 3–48, https://doi.org/10.1016/j.aeolia.2012.08.001, 2013.
Mullen, P. C. and Warren, S. G.: Theory of the optical properties of lake
ice, J. Geophys. Res., 93, 8403–8414,
https://doi.org/10.1029/JD093iD07p08403, 1988.
Pegau, W. S. and Zaneveld, J. R. V.: Field measurements of in-ice
radiance, Cold Reg. Sci. Technol., 31, 33–46,
https://doi.org/10.1016/S0165-232X(00)00004-5, 2000.
Perovich, D. K.: The Optical Properties of Sea Ice., U.S. Army Cold Regions
Research and Engineering Laboratory, Hanover, NH,
https://apps.dtic.mil/dtic/tr/fulltext/u2/a310586.pdf, 1996.
Perovich, D. K. and Govoni, J. W.: Absorption coefficients of ice from 250
to 400 nm, Geophys. Res. Lett., 18, 1233–1235,
https://doi.org/10.1029/91GL01642, 1991.
Petit, J. R., Jouzel, J., Raynaud, D., Barkov, N. I., Barnola, J.-M.,
Basile, I., Bender, M., Chappellaz, J., Davis, M., Delaygue, G., Delmotte,
M., Kotlyakov, V. M., Legrand, M., Lipenkov, V. Y., Lorius, C., PÉpin,
L., Ritz, C., Saltzman, E., and Stievenard, M.: Climate and atmospheric
history of the past 420,000 years from the Vostok ice core, Antarctica,
Nature, 399, 429–436, https://doi.org/10.1038/20859, 1999.
Petrenko, V. V., Severinghaus, J. P., Brook, E. J., Reeh, N., and Schaefer,
H.: Gas records from the West Greenland ice margin covering the Last Glacial
Termination: a horizontal ice core, Quatern. Sci. Rev., 25,
865–875, https://doi.org/10.1016/j.quascirev.2005.09.005, 2006.
Picard, G., Libois, Q., and Arnaud, L.: Refinement of the ice absorption spectrum in the visible using radiance profile measurements in Antarctic snow, The Cryosphere, 10, 2655–2672, https://doi.org/10.5194/tc-10-2655-2016, 2016.
Price, P. B. and Bergström, L.: Enhanced Rayleigh scattering as a
signature of nanoscale defects in highly transparent solids, Philos. Mag. A, 75, 1383–1390, https://doi.org/10.1080/01418619708209861, 1997a.
Price, P. B. and Bergström, L.: Optical properties of deep ice at the
South Pole: scattering, Appl. Opt., 36, 4181–4194,
https://doi.org/10.1364/AO.36.004181, 1997b.
Reeh, N., Oerter, H., and Thomsen, H. H.: Comparison between Greenland
ice-margin and ice-core oxygen-18 records, Ann. Glaciol.,
35, 136–144, https://doi.org/10.3189/172756402781817365, 2002.
Ridley, J. K. and Partington, K. C.: A model of satellite radar altimeter
return from ice sheets, International J. Remote Sens., 9,
601–624, https://doi.org/10.1080/01431168808954881, 1988.
Rignot, E., Echelmeyer, K., and Krabill, W.: Penetration depth of
interferometric synthetic-aperture radar signals in snow and ice,
Geophys. Res. Lett., 28, 3501–3504,
https://doi.org/10.1029/2000GL012484, 2001.
Ruth, U., Wagenbach, D., Steffensen, J. P., and Bigler, M.: Continuous record
of microparticle concentration and size distribution in the central
Greenland NGRIP ice core during the last glacial period, J. Geophys. Res.-Atmos., 108, 4098,
https://doi.org/10.1029/2002JD002376, 2003.
Ryan, J. C., Hubbard, A. L., Stibal, M., Irvine-Fynn, T. D., Cook, J.,
Smith, L. C., Cameron, K., and Box, J. E.: Dark zone of the Greenland Ice
Sheet controlled by distributed biologically-active impurities, Nat.
Commun., 9, 1065,
https://doi.org/10.1038/s41467-018-03353-2, 2018.
Schuster, A.: Radiation through a foggy atmosphere, Astrophys.
J., 21, 1–22, 1905.
Schuster, C.: Weathering crust processes on melting glacier ice (Alberta,
Canada), Theses and Dissertations (Comprehensive), no. 489, available at:
http://scholars.wlu.ca/etd/489 (last access: 3 December 2016), 2001.
Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D., and DiMarzio, J.
P.: Overview of the ICESat Mission, Geophys. Res. Lett., 32, L21S01,
https://doi.org/10.1029/2005GL024009, 2005.
Smith, B. E., Gardner, A., Schneider, A., and Flanner, M.: Modeling biases in
laser-altimetry measurements caused by scattering of green light in snow,
Remote Sens. Environ., 215, 398–410,
https://doi.org/10.1016/j.rse.2018.06.012, 2018.
Stibal, M., Box, J. E., Cameron, K. A., Langen, P. L., Yallop, M. L.,
Mottram, R. H., Khan, A. L., Molotch, N. P., Chrismas, N. A. M., Quaglia, F.
C., Remias, D., Smeets, C. J. P. P., Broeke, M. R. van den, Ryan, J. C.,
Hubbard, A., Tranter, M., As, D., van and Ahlstrøm, A. P.: Algae Drive
Enhanced Darkening of Bare Ice on the Greenland Ice Sheet, Geophys. Res. Lett., 44, 11463–11471,
https://doi.org/10.1002/2017GL075958, 2017.
Takeuchi, N.: Optical characteristics of cryoconite (surface dust) on
glaciers: the relationship between light absorbency and the property of
organic matter contained in the cryoconite, Ann. Glaciol., 34,
409–414, https://doi.org/10.3189/172756402781817743, 2002.
Taylor, B. N. and Kuyatt, C. E.: Guidelines for Evaluating and Expressing
the Uncertainty of NIST Measurement Results (NIST Technical Note vol. 1297),
National Institue of Standards and Technology, Gaithersburg, MD,
http://physics.nist.gov/Pubs/guidelines/TN1297/tn1297s.pdf (last access: 18 January 2021), 1994.
Tuzet, F., Dumont, M., Arnaud, L., Voisin, D., Lamare, M., Larue, F., Revuelto, J., and Picard, G.: Influence of light-absorbing particles on snow spectral irradiance profiles, The Cryosphere, 13, 2169–2187, https://doi.org/10.5194/tc-13-2169-2019, 2019.
van de Hulst, H. C.: Multiple light scattering: tables, formulas, and
applications, Academic Press, New York, 1980.
van den Broeke, M., Smeets, P., Ettema, J., van der Veen, C., van de Wal, R., and Oerlemans, J.: Partitioning of melt energy and meltwater fluxes in the ablation zone of the west Greenland ice sheet, The Cryosphere, 2, 179–189, https://doi.org/10.5194/tc-2-179-2008, 2008.
Wang, L., Jacques, S. L., and Zheng, L.: MCML – Monte Carlo modeling of light
transport in multi-layered tissues, Comput. Meth. Prog. Bio., 47, 131–146,
https://doi.org/10.1016/0169-2607(95)01640-F, 1995.
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67–89,
https://doi.org/10.1029/RG020i001p00067, 1982.
Warren, S. G.: Optical constants of ice from the ultraviolet to the
microwave, Appl. Opt., 23, 1206–1225,
https://doi.org/10.1364/AO.23.001206, 1984.
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the
ultraviolet to the microwave: A revised compilation, J. Geophys. Res.,
113, D14220, https://doi.org/10.1029/2007JD009744, 2008.
Warren, S. G., Brandt, R. E., Grenfell, T. C., and McKay, C. P.: Snowball
Earth: Ice thickness on the tropical ocean, J. Geophys. Res.-Oceans, 107, 31-1–31-18, https://doi.org/10.1029/2001JC001123,
2002.
Warren, S. G., Brandt, R. E., and Grenfell, T. C.: Visible and
near-ultraviolet absorption spectrum of ice from transmission of solar
radiation into snow, Appl. Opt., 45, 5320–5334,
https://doi.org/10.1364/AO.45.005320, 2006.
Wientjes, I. G. M., Van de Wal, R. S. W., Reichart, G. J., Sluijs, A., and Oerlemans, J.: Dust from the dark region in the western ablation zone of the Greenland ice sheet, The Cryosphere, 5, 589–601, https://doi.org/10.5194/tc-5-589-2011, 2011.
Wientjes, I. G. M., De Van Wal, R. S. W., Schwikowski, M., Zapf, A., Fahrni,
S., and Wacker, L.: Carbonaceous particles reveal that Late Holocene dust
causes the dark region in the western ablation zone of the Greenland ice
sheet, J. Glaciol., 58, 787–794,
https://doi.org/10.3189/2012JoG11J165, 2012.
Wiscombe, W. J. and Warren, S. G.: A Model for the Spectral Albedo of Snow.
I: Pure Snow, J. Atmos. Sci., 37, 2712–2733,
https://doi.org/10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2, 1980.
Woschnagg, K. and Price, P. B.: Temperature dependence of absorption in ice
at 532 nm, Appl. Opt., 40, 2496–2500,
https://doi.org/10.1364/AO.40.002496, 2001.
Yallop, M. L., Anesio, A. M., Perkins, R. G., Cook, J., Telling, J., Fagan,
D., MacFarlane, J., Stibal, M., Barker, G., Bellas, C., Hodson, A., Tranter,
M., Wadham, J., and Roberts, N. W.: Photophysiology and albedo-changing
potential of the ice algal community on the surface of the Greenland ice
sheet, ISME J., 6, 2302–2313, https://doi.org/10.1038/ismej.2012.107,
2012.
Yang, Y., Marshak, A., Han, M., Palm, S. P., and Harding, D. J.: Snow grain
size retrieval over the polar ice sheets with the Ice, Cloud, and land
Elevation Satellite (ICESat) observations, J. Quant. Spectrosc. Ra., 188, 159–164,
https://doi.org/10.1016/j.jqsrt.2016.03.033, 2017.
York, D., Evensen, N. M., Martınez, M. L., and De Basabe Delgado, J.:
Unified equations for the slope, intercept, and standard errors of the best
straight line, Am. J. Phys., 72, 367–375,
https://doi.org/10.1119/1.1632486, 2004.
Zebker, H. A. and Weber Hoen, E.: Penetration depths inferred from
interferometric volume decorrelation observed over the Greenland Ice Sheet,
IEEE T. Geosci. Remote Sens., 38, 2571–2583,
https://doi.org/10.1109/36.885204, 2000.
Zhang, X., Qiu, J., Li, X., Zhao, J., and Liu, L.: Complex refractive indices
measurements of polymers in visible and near-infrared bands, Appl. Opt.,
59, 2337, https://doi.org/10.1364/AO.383831, 2020.
Zieger, P., Weingartner, E., Henzing, J., Moerman, M., de Leeuw, G., Mikkilä, J., Ehn, M., Petäjä, T., Clémer, K., van Roozendael, M., Yilmaz, S., Frieß, U., Irie, H., Wagner, T., Shaiganfar, R., Beirle, S., Apituley, A., Wilson, K., and Baltensperger, U.: Comparison of ambient aerosol extinction coefficients obtained from in-situ, MAX-DOAS and LIDAR measurements at Cabauw, Atmos. Chem. Phys., 11, 2603–2624, https://doi.org/10.5194/acp-11-2603-2011, 2011.
Short summary
We measured sunlight transmitted into glacier ice to improve models of glacier ice melt and satellite measurements of glacier ice surfaces. We found that very small concentrations of impurities inside the ice increase absorption of sunlight, but the amount was small enough to enable an estimate of ice absorptivity. We confirmed earlier results that the absorption minimum is near 390 nm. We also found that a layer of highly reflective granular "white ice" near the surface reduces transmittance.
We measured sunlight transmitted into glacier ice to improve models of glacier ice melt and...