Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.

Two important factors that control snow albedo are snow grain growth and presence of light‐absorbing impurities (aerosols) in snow. However, current regional climate models do not include such processes in a physically based manner in their land surface models. We improve snow albedo calculations in...

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Published in:	Scientia Marina
Main Authors:	Oaida, Catalina M., Xue, Yongkang, Flanner, Mark G., Skiles, S. McKenzie, De Sales, Fernando, Painter, Thomas H.
Format:	Article in Journal/Newspaper
Language:	unknown
Published:	Cambridge Univ. Press 2015
Subjects:	WRF snow regional climate aerosols dust black carbon Atmospheric and Oceanic Sciences Science Arctic
Online Access:	https://hdl.handle.net/2027.42/111782 https://doi.org/10.1002/2014JD022444

id	ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/111782
record_format	openpolar
institution	Open Polar
collection	University of Michigan: Deep Blue
op_collection_id	ftumdeepblue
language	unknown
topic	WRF snow regional climate aerosols dust black carbon Atmospheric and Oceanic Sciences Science
spellingShingle	WRF snow regional climate aerosols dust black carbon Atmospheric and Oceanic Sciences Science Oaida, Catalina M. Xue, Yongkang Flanner, Mark G. Skiles, S. McKenzie De Sales, Fernando Painter, Thomas H. Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.
topic_facet	WRF snow regional climate aerosols dust black carbon Atmospheric and Oceanic Sciences Science
description	Two important factors that control snow albedo are snow grain growth and presence of light‐absorbing impurities (aerosols) in snow. However, current regional climate models do not include such processes in a physically based manner in their land surface models. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the physically based SNow ICe And Radiative (SNICAR) scheme. SNICAR simulates snow albedo evolution due to snow aging and presence of aerosols in snow. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. This paper presents model development technique, validation with in situ observations, and preliminary results from RCM simulations investigating the impact of such impurities in snow on surface energy and water budgets. By including snow‐aerosol interactions, the new land surface model is able to realistically simulate observed snow albedo, snow grain size, dust in snow, and surface water and energy balances in offline simulations for a location in western U.S. Preliminary results with the fully coupled RCM show that over western U.S., realistic aerosol deposition in snow induces a springtime average radiative forcing of 16 W/m2 due to a 6% albedo reduction, a regional surface warming of 0.84°C, and a snowpack reduction of 11 mm.Key PointsIncluding snow aging and aerosols in snow improves offline and WRF snow simulationsDust and black/organic carbon exerts nontrivial radiative forcing in western U.S.RCM simulation shows temperature increase and snow mass loss from aerosols in snow Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/111782/1/jgrd52045.pdf
format	Article in Journal/Newspaper
author	Oaida, Catalina M. Xue, Yongkang Flanner, Mark G. Skiles, S. McKenzie De Sales, Fernando Painter, Thomas H.
author_facet	Oaida, Catalina M. Xue, Yongkang Flanner, Mark G. Skiles, S. McKenzie De Sales, Fernando Painter, Thomas H.
author_sort	Oaida, Catalina M.
title	Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.
title_short	Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.
title_full	Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.
title_fullStr	Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.
title_full_unstemmed	Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.
title_sort	improving snow albedo processes in wrf/ssib regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western u.s.
publisher	Cambridge Univ. Press
publishDate	2015
url	https://hdl.handle.net/2027.42/111782 https://doi.org/10.1002/2014JD022444
genre	Arctic
genre_facet	Arctic
op_relation	Oaida, Catalina M.; Xue, Yongkang; Flanner, Mark G.; Skiles, S. McKenzie; De Sales, Fernando; Painter, Thomas H. (2015). "Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S." Journal of Geophysical Research: Atmospheres 120(8): 3228-3248. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/111782 doi:10.1002/2014JD022444 Journal of Geophysical Research: Atmospheres Shamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.‐Y. Huang, W. Wang, and J. G. Powers ( 2008 ), A description of the advanced research WRF version 3, NCAR Tech. Note. [Available at www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.] Qian, Y., M. G. Flanner, L. R. Leung, and W. Wang ( 2011 ), Sensitivity studies on the impacts of Tibetan Plateau snowpack pollution on the Asian hydrological cycle and monsoon climate, Atmos. Chem. Phys., 11, 1929 – 1948, doi:10.5194/acp-11-1929-2011. Qian, Y., H. Wang, R. Zhang, M. G. Flanner, and P. J. Rasch ( 2014 ), A sensitivity study on modeling black carbon in snow and its radiative forcing over the Arctic and Northern China, Environ. Res. Lett., 9 ( 6 ), 064001, doi:10.1088/1748-9326/9/6/064001. Qu, X., and A. Hall ( 2007 ), What controls the strength of snow‐albedo feedback?, J. Clim., 20, 3971 – 3981, doi:10.1175/JCLI4186.1. Rangwala, I., and J. R. Miller ( 2012 ), Climate change in mountains: A review of elevation‐dependent warming and its potential causes, Clim. Change, 114, 527 – 547, doi:10.1007/s10584-012-0419-3. Rasmussen, R. M., et al. ( 2011 ), High‐resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate, J. Clim., 24, 3015 – 3048, doi:10.1175/2010JCLI3985.1. Reid, J. S., et al. ( 2003 ), Comparison of size and morphological measurements of dust particles from Africa, J. Geophys. Res., 108, 8593, doi:10.1029/2002JD002485. Rutter, N., et al. ( 2009 ), Evaluation of forest snow processes models (SnowMIP2), J. Geophys. Res., 114, D06111, doi:10.1029/2008JD011063. Sellers, P. J., et al. ( 1996 ), A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation, J. Clim., 9, 676 – 705, doi:10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2. Shrestha, M., L. Wang, T. Koike, Y. Xue, and Y. Hirabayashi ( 2012 ), Modeling the spatial distribution of snow cover in the Dudhkoshi region of the Nepal Himalaya, J. Hydrometeorol., 13, 204 – 222, doi:10.1175/JHM-D-10-05027.1. Skiles, S. M. ( 2014 ), Dust and Black Carbon Radiative Forcing Controls on Snowmelt in the Colorado River Basin (Doctoral Dissertation), Univ. of California‐Los Angeles, Los Angeles, Publisher: ProQuest, document ID# 3637640. Skiles, S. M., T. H. Painter, J. S. Deems, A. C. Bryant, and C. C. Landry ( 2012 ), Dust radiative forcing in snow of the Upper Colorado River Basin: 2. Interannual variability in radiative forcing and snowmelt rates, Water Resour. Res., 48, W07522, doi:10.1029/2012WR011986. Sterle, K. M., J. R. McConnell, J. Dozier, R. Edwards, and M. G. Flanner ( 2013 ), Retention and radiative forcing of black carbon in eastern Sierra Nevada snow, Cryosphere, 7, 365 – 374, doi:10.5194/tc-7-365-2013. Sturm, M., and C. S. Benson ( 1997 ), Vapor transport, grain growth and depth‐hoar development in the subarctic snow, J. Glaciol., 43 ( 143 ), 42 – 59. Sun, S., J. Jin, and Y. Xue ( 1999 ), A simple snow‐atmosphere‐soil transfer model, J. Geophys. Res., 104 ( D16 ), 19,587 – 19,597, doi:10.1029/1999JD900305. Thompson, L. G., T. Yao, E. Mosley‐Thompson, K. A. Henderson, and P. N. Lin ( 2000 ), A high‐resolution millennial record of the south Asian monsoon from Himalayan ice cores, Science, 289, 1916 – 1919, doi:10.1126/science.289.5486.1916. Toon, O. B., C. P. McKay, T. P. Ackerman, and K. Santhanam ( 1989 ), Rapid calculation of radiative heating rates and photodissociation rates in inhomogeneous multiple scattering atmospheres, J. Geophys. Res., 94 ( D13 ), 16,287 – 16,301, doi:10.1029/JD094iD13p16287. Warren, S. G., and W. J. Wiscombe ( 1980 ), A model for the spectral albedo of snow. II: Snow containing atmospheric aerosols, J. Atmos. Sci., 37, 2734 – 2745, doi:10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2. Wiscombe, W. J., and S. G. Warren ( 1980 ), A model for the spectral albedo of snow. I: Pure snow, J. Atmos. Sci., 37, 2712 – 2733, doi:10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2. Xu, B. Q., et al. ( 2009 ), Black soot and the survival of Tibetan glaciers, Proc. Natl. Acad. Sci. U.S.A., 106 ( 52 ), 22,114 – 22,118, doi:10.1073/pnas.0910444106. Xue, Y., P. J. Sellers, J. L. Kinter, and J. Shukla ( 1991 ), A simplified model for global climate studies, J. Clim., 4, 345 – 364, doi:10.1175/1520-0442(1991)004<0345:ASBMFG>2.0.CO:2. Xue, Y., F. J. Zeng, K. Mitchell, Z. Janjic, and E. Rogers ( 2001 ), The impact of land surface processes on the simulation of the U.S. hydrological cycle: A case study of 1993 US flood using the Eta/SSiB regional model, Mon. Weather Rev., 129, 2833 – 2860, doi:10.1175/1520-0493(2001)129<2833:TIOLSP>2.0.CO;2. Xue, Y., S. Sun, D. S. Kahan, and Y. Jiao ( 2003 ), Impact of parameterizations in snow physics and interface processes on the simulation of snow cover and runoff at several cold region sites, J. Geophys. Res., 108 ( D22 ), 8859, doi:10.1029/2002JD003174. Xue, Y., R. Vasic, Z. Janjic, F. Mesinger, and K. E. Mitchell ( 2007 ), Assessment of dynamic downscaling of the continental U.S. regional climate using the Eta/SSiB Regional Climate Model, J. Clim., 20, 4172 – 4193, doi:10.1175/JCLI4239.1. Yang, F., A. Kumar, W. Wang, H.‐M. H. Juang, and M. Kanamitsu ( 2001 ), Snow‐albedo feedback and seasonal climate variability over North America, J. Clim., 14, 4245 – 4248, doi:10.1175/1520-0442(2001)014<4245:SAFASC>2.0.CO;2. Zhang, W., and J. H. Scneibel ( 1995 ), The sintering of two particles by surface diffusion and grain boundary diffusion—A two‐dimensional numerical study, Acta Metall. Mater., 43 ( 12 ), 4377 – 4386, doi:10.1016/0956-7151(95)00115-C. Zhao, C., et al. ( 2014 ), Simulating black carbon and dust and their radiative forcing in seasonal snow: A case study over North China with field campaign measurements, Atmos. Chem. Phys., 14, 11,475 – 11,491, doi:10.5194/acp-14-11475-2014. Belnap, J., and D. A. Gillette ( 1998 ), Vulnerability of desert biological soil crusts to wind erosion: The influences of crust development, soil texture, and disturbance, J. Arid Environ., 39, 133 – 142, doi:10.1006/jare.1998.0388. Belnap, J., R. L. Reynolds, M. C. Reheis, S. L. Phillips, F. E. Urban, and H. L. Goldstein ( 2009 ), Sediment losses and gains across a gradient of livestock grazing and plant invasion in a cool, semi‐arid grassland, Colorado Plateau, USA, Aeolian Res., 1 ( 1–2 ), 27 – 43, doi:10.1016/j.aeolia.2009.03.001. Blackford, J. R. ( 2007 ), Sintering and microstructure of ice: A review, J. Phys. D: Appl. Phys., 40, R355 – R385, doi:10.1088/0022-3727/40/21/R02. Bond, T. C., E. Bhardwaj, R. Dong, R. Jogani, S. Jung, C. Roden, D. G. Streets, and N. M. Trautmann ( 2007 ), Historical emissions of black and organic carbon aerosol from energy‐related combustion, 1850–2000, Global Biogeochem. Cycles, 21, GB2018, doi:10.1029/2006GB002840. Brun, E. ( 1989 ), Investigation of wet‐snow metamorphism in respect of liquid‐water content, Ann. Glaciol., 13, 22 – 26. Budyko, M. I. ( 1969 ), The effect of solar radiation variations on the climate of the Earth, Tellus, 21, 611 – 619, doi:10.1111/j.2153-3490.1969.tb00466.x. Chin, M., R. B. Rood, S.‐J. Lin, J.‐F. Müller, and A. Thompson ( 2000 ), Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties, J. Geophys. Res., 105 ( D20 ), 24,671 – 24,687, doi:10.1029/2000JD900384. Chin, M., P. Ginoux, S. Kinne, O. Torres, B. Holben, B. N. Duncan, R. V. Martin, J. Logan, A. Higurashi, and T. Nakajima ( 2002 ), Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements, J. Atmos. Phys., 59, 461 – 483, doi:10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2. Christensen, J. H., et al. ( 2007 ), Regional climate projections, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., Cambridge Univ. Press, Cambridge, U. K., and New York. Colbeck, S. C. ( 2001 ), Sintering of unequal grains, J. Appl. Phys., 89 ( 8 ), 4612 – 4618, doi:10.1063/1.1356427. Conway, H., A. Gades, and C. F. Raymond ( 1996 ), Albedo of dirty snow during conditions of melt, Water Resour. Res., 32 ( 6 ), 1713 – 1718, doi:10.1029/96WR00712. De Sales, F., and Y. Xue ( 2012 ), Dynamic downscaling of 22‐year CFS winter seasonal forecasts with the UCLA‐ETA regional climate model over the United States, Clim. Dyn., 41, 255 – 275, doi:10.1007/s00382-012-1567-x. Doherty, S. J., T. C. Grenfell, S. Forsström, D. L. Hegg, R. E. Brandt, and S. G. Warren ( 2013 ), Observed vertical redistribution of black carbon and other insoluble light‐absorbing particles in melting snow, J. Geophys. Res. Atmos., 118, 5553 – 5569, doi:10.1002/jgrd.50235. Flanner, M. G., and C. S. Zender ( 2005 ), Snowpack radiative heating: Influence on Tibetan Plateau climate, Geophys. Res. Lett., 32, L06501, doi:10.1029/2004GL022076. Flanner, M. G., and C. S. Zender ( 2006 ), Linking snowpack microphysics and albedo evolution, J. Geophys. Res., 111, D12208, doi:10.1029/2005JD006834. Flanner, M. G., C. S. Zender, J. T. Randerson, and P. J. Rasch ( 2007 ), Present‐day climate forcing and response from black carbon in snow, J. Geophys. Res., 112, D11202, doi:10.1029/2006JD008003. Flanner, M. G., C. S. Zender, P. G. Hess, N. M. Mahowald, T. H. Painter, V. Ramanathan, and P. J. Rasch ( 2009 ), Springtime warming and reduced snow cover from carbonaceous particles, Atmos. Chem. Phys., 9, 2481 – 2497, doi:10.5194/acp-9-2481-2009.
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spelling	ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/111782 2023-08-20T04:03:12+02:00 Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S. Oaida, Catalina M. Xue, Yongkang Flanner, Mark G. Skiles, S. McKenzie De Sales, Fernando Painter, Thomas H. 2015-04-27 application/pdf https://hdl.handle.net/2027.42/111782 https://doi.org/10.1002/2014JD022444 unknown Cambridge Univ. Press Wiley Periodicals, Inc. Oaida, Catalina M.; Xue, Yongkang; Flanner, Mark G.; Skiles, S. McKenzie; De Sales, Fernando; Painter, Thomas H. (2015). "Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S." Journal of Geophysical Research: Atmospheres 120(8): 3228-3248. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/111782 doi:10.1002/2014JD022444 Journal of Geophysical Research: Atmospheres Shamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.‐Y. Huang, W. Wang, and J. G. Powers ( 2008 ), A description of the advanced research WRF version 3, NCAR Tech. Note. [Available at www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.] Qian, Y., M. G. Flanner, L. R. Leung, and W. Wang ( 2011 ), Sensitivity studies on the impacts of Tibetan Plateau snowpack pollution on the Asian hydrological cycle and monsoon climate, Atmos. Chem. Phys., 11, 1929 – 1948, doi:10.5194/acp-11-1929-2011. Qian, Y., H. Wang, R. Zhang, M. G. Flanner, and P. J. Rasch ( 2014 ), A sensitivity study on modeling black carbon in snow and its radiative forcing over the Arctic and Northern China, Environ. Res. Lett., 9 ( 6 ), 064001, doi:10.1088/1748-9326/9/6/064001. Qu, X., and A. Hall ( 2007 ), What controls the strength of snow‐albedo feedback?, J. Clim., 20, 3971 – 3981, doi:10.1175/JCLI4186.1. Rangwala, I., and J. R. Miller ( 2012 ), Climate change in mountains: A review of elevation‐dependent warming and its potential causes, Clim. Change, 114, 527 – 547, doi:10.1007/s10584-012-0419-3. Rasmussen, R. M., et al. ( 2011 ), High‐resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate, J. Clim., 24, 3015 – 3048, doi:10.1175/2010JCLI3985.1. Reid, J. S., et al. ( 2003 ), Comparison of size and morphological measurements of dust particles from Africa, J. Geophys. Res., 108, 8593, doi:10.1029/2002JD002485. Rutter, N., et al. ( 2009 ), Evaluation of forest snow processes models (SnowMIP2), J. Geophys. Res., 114, D06111, doi:10.1029/2008JD011063. Sellers, P. J., et al. ( 1996 ), A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation, J. Clim., 9, 676 – 705, doi:10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2. Shrestha, M., L. Wang, T. Koike, Y. Xue, and Y. Hirabayashi ( 2012 ), Modeling the spatial distribution of snow cover in the Dudhkoshi region of the Nepal Himalaya, J. Hydrometeorol., 13, 204 – 222, doi:10.1175/JHM-D-10-05027.1. Skiles, S. M. ( 2014 ), Dust and Black Carbon Radiative Forcing Controls on Snowmelt in the Colorado River Basin (Doctoral Dissertation), Univ. of California‐Los Angeles, Los Angeles, Publisher: ProQuest, document ID# 3637640. Skiles, S. M., T. H. Painter, J. S. Deems, A. C. Bryant, and C. C. Landry ( 2012 ), Dust radiative forcing in snow of the Upper Colorado River Basin: 2. Interannual variability in radiative forcing and snowmelt rates, Water Resour. Res., 48, W07522, doi:10.1029/2012WR011986. Sterle, K. M., J. R. McConnell, J. Dozier, R. Edwards, and M. G. Flanner ( 2013 ), Retention and radiative forcing of black carbon in eastern Sierra Nevada snow, Cryosphere, 7, 365 – 374, doi:10.5194/tc-7-365-2013. Sturm, M., and C. S. Benson ( 1997 ), Vapor transport, grain growth and depth‐hoar development in the subarctic snow, J. Glaciol., 43 ( 143 ), 42 – 59. Sun, S., J. Jin, and Y. Xue ( 1999 ), A simple snow‐atmosphere‐soil transfer model, J. Geophys. Res., 104 ( D16 ), 19,587 – 19,597, doi:10.1029/1999JD900305. Thompson, L. G., T. Yao, E. Mosley‐Thompson, K. A. Henderson, and P. N. Lin ( 2000 ), A high‐resolution millennial record of the south Asian monsoon from Himalayan ice cores, Science, 289, 1916 – 1919, doi:10.1126/science.289.5486.1916. Toon, O. B., C. P. McKay, T. P. Ackerman, and K. Santhanam ( 1989 ), Rapid calculation of radiative heating rates and photodissociation rates in inhomogeneous multiple scattering atmospheres, J. Geophys. Res., 94 ( D13 ), 16,287 – 16,301, doi:10.1029/JD094iD13p16287. Warren, S. G., and W. J. Wiscombe ( 1980 ), A model for the spectral albedo of snow. II: Snow containing atmospheric aerosols, J. Atmos. Sci., 37, 2734 – 2745, doi:10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2. Wiscombe, W. J., and S. G. Warren ( 1980 ), A model for the spectral albedo of snow. I: Pure snow, J. Atmos. Sci., 37, 2712 – 2733, doi:10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2. Xu, B. Q., et al. ( 2009 ), Black soot and the survival of Tibetan glaciers, Proc. Natl. Acad. Sci. U.S.A., 106 ( 52 ), 22,114 – 22,118, doi:10.1073/pnas.0910444106. Xue, Y., P. J. Sellers, J. L. Kinter, and J. Shukla ( 1991 ), A simplified model for global climate studies, J. Clim., 4, 345 – 364, doi:10.1175/1520-0442(1991)004<0345:ASBMFG>2.0.CO:2. Xue, Y., F. J. Zeng, K. Mitchell, Z. Janjic, and E. Rogers ( 2001 ), The impact of land surface processes on the simulation of the U.S. hydrological cycle: A case study of 1993 US flood using the Eta/SSiB regional model, Mon. Weather Rev., 129, 2833 – 2860, doi:10.1175/1520-0493(2001)129<2833:TIOLSP>2.0.CO;2. Xue, Y., S. Sun, D. S. Kahan, and Y. Jiao ( 2003 ), Impact of parameterizations in snow physics and interface processes on the simulation of snow cover and runoff at several cold region sites, J. Geophys. Res., 108 ( D22 ), 8859, doi:10.1029/2002JD003174. Xue, Y., R. Vasic, Z. Janjic, F. Mesinger, and K. E. Mitchell ( 2007 ), Assessment of dynamic downscaling of the continental U.S. regional climate using the Eta/SSiB Regional Climate Model, J. Clim., 20, 4172 – 4193, doi:10.1175/JCLI4239.1. Yang, F., A. Kumar, W. Wang, H.‐M. H. Juang, and M. Kanamitsu ( 2001 ), Snow‐albedo feedback and seasonal climate variability over North America, J. Clim., 14, 4245 – 4248, doi:10.1175/1520-0442(2001)014<4245:SAFASC>2.0.CO;2. Zhang, W., and J. H. Scneibel ( 1995 ), The sintering of two particles by surface diffusion and grain boundary diffusion—A two‐dimensional numerical study, Acta Metall. Mater., 43 ( 12 ), 4377 – 4386, doi:10.1016/0956-7151(95)00115-C. Zhao, C., et al. ( 2014 ), Simulating black carbon and dust and their radiative forcing in seasonal snow: A case study over North China with field campaign measurements, Atmos. Chem. Phys., 14, 11,475 – 11,491, doi:10.5194/acp-14-11475-2014. Belnap, J., and D. A. Gillette ( 1998 ), Vulnerability of desert biological soil crusts to wind erosion: The influences of crust development, soil texture, and disturbance, J. Arid Environ., 39, 133 – 142, doi:10.1006/jare.1998.0388. Belnap, J., R. L. Reynolds, M. C. Reheis, S. L. Phillips, F. E. Urban, and H. L. Goldstein ( 2009 ), Sediment losses and gains across a gradient of livestock grazing and plant invasion in a cool, semi‐arid grassland, Colorado Plateau, USA, Aeolian Res., 1 ( 1–2 ), 27 – 43, doi:10.1016/j.aeolia.2009.03.001. Blackford, J. R. ( 2007 ), Sintering and microstructure of ice: A review, J. Phys. D: Appl. Phys., 40, R355 – R385, doi:10.1088/0022-3727/40/21/R02. Bond, T. C., E. Bhardwaj, R. Dong, R. Jogani, S. Jung, C. Roden, D. G. Streets, and N. M. Trautmann ( 2007 ), Historical emissions of black and organic carbon aerosol from energy‐related combustion, 1850–2000, Global Biogeochem. Cycles, 21, GB2018, doi:10.1029/2006GB002840. Brun, E. ( 1989 ), Investigation of wet‐snow metamorphism in respect of liquid‐water content, Ann. Glaciol., 13, 22 – 26. Budyko, M. I. ( 1969 ), The effect of solar radiation variations on the climate of the Earth, Tellus, 21, 611 – 619, doi:10.1111/j.2153-3490.1969.tb00466.x. Chin, M., R. B. Rood, S.‐J. Lin, J.‐F. Müller, and A. Thompson ( 2000 ), Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties, J. Geophys. Res., 105 ( D20 ), 24,671 – 24,687, doi:10.1029/2000JD900384. Chin, M., P. Ginoux, S. Kinne, O. Torres, B. Holben, B. N. Duncan, R. V. Martin, J. Logan, A. Higurashi, and T. Nakajima ( 2002 ), Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and sun photometer measurements, J. Atmos. Phys., 59, 461 – 483, doi:10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2. Christensen, J. H., et al. ( 2007 ), Regional climate projections, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon et al., Cambridge Univ. Press, Cambridge, U. K., and New York. Colbeck, S. C. ( 2001 ), Sintering of unequal grains, J. Appl. Phys., 89 ( 8 ), 4612 – 4618, doi:10.1063/1.1356427. Conway, H., A. Gades, and C. 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Randerson, and P. J. Rasch ( 2007 ), Present‐day climate forcing and response from black carbon in snow, J. Geophys. Res., 112, D11202, doi:10.1029/2006JD008003. Flanner, M. G., C. S. Zender, P. G. Hess, N. M. Mahowald, T. H. Painter, V. Ramanathan, and P. J. Rasch ( 2009 ), Springtime warming and reduced snow cover from carbonaceous particles, Atmos. Chem. Phys., 9, 2481 – 2497, doi:10.5194/acp-9-2481-2009. IndexNoFollow WRF snow regional climate aerosols dust black carbon Atmospheric and Oceanic Sciences Science Article 2015 ftumdeepblue https://doi.org/10.1002/2014JD02244410.5194/acp-11-1929-201110.1088/1748-9326/9/6/06400110.1007/s10584-012-0419-310.1175/2010JCLI3985.110.1029/2002JD00248510.1029/2008JD01106310.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;210.1175/JHM-D-10-05027.110.1029/2 2023-07-31T20:42:35Z Two important factors that control snow albedo are snow grain growth and presence of light‐absorbing impurities (aerosols) in snow. However, current regional climate models do not include such processes in a physically based manner in their land surface models. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the physically based SNow ICe And Radiative (SNICAR) scheme. SNICAR simulates snow albedo evolution due to snow aging and presence of aerosols in snow. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. This paper presents model development technique, validation with in situ observations, and preliminary results from RCM simulations investigating the impact of such impurities in snow on surface energy and water budgets. By including snow‐aerosol interactions, the new land surface model is able to realistically simulate observed snow albedo, snow grain size, dust in snow, and surface water and energy balances in offline simulations for a location in western U.S. Preliminary results with the fully coupled RCM show that over western U.S., realistic aerosol deposition in snow induces a springtime average radiative forcing of 16 W/m2 due to a 6% albedo reduction, a regional surface warming of 0.84°C, and a snowpack reduction of 11 mm.Key PointsIncluding snow aging and aerosols in snow improves offline and WRF snow simulationsDust and black/organic carbon exerts nontrivial radiative forcing in western U.S.RCM simulation shows temperature increase and snow mass loss from aerosols in snow Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/111782/1/jgrd52045.pdf Article in Journal/Newspaper Arctic University of Michigan: Deep Blue Scientia Marina 80 S1 173 193

Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.

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