• Barros, A. P., , and D. P. Lettenmaier, 1993: Dynamic modeling of the spatial distribution of precipitation in remote mountainous areas. Mon. Wea. Rev., 121, 11951214, doi:10.1175/1520-0493(1993)121<1195:DMOTSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Benestad, R. E., , I. Hanssen-Bauer, , and D. Chen, 2008: Empirical-Statistical Downscaling. World Scientific, 228 pp.

  • Berg, N., , A. Hall, , F. Sun, , S. Capps, , D. Walton, , B. Langenbrunner, , and D. Neelin, 2015: Twenty-first-century precipitation changes over the Los Angeles region. J. Climate, 28, 401421, doi:10.1175/JCLI-D-14-00316.1.

    • Search Google Scholar
    • Export Citation
  • Braganza, K., , D. J. Karoly, , A. C. Hirst, , M. E. Mann, , P. Stott, , R. J. Stouffer, , and S. F. B. Tett, 2003: Simple indices of global climate variability and change: Part I—Variability and correlation structure. Climate Dyn., 20, 491502, doi:10.1007/s00382-002-0286-0.

    • Search Google Scholar
    • Export Citation
  • Braganza, K., , D. J. Karoly, , A. C. Hirst, , P. Stott, , R. J. Stouffer, , and S. F. B. Tett, 2004: Simple indices of global climate variability and change: Part II: Attribution of climate change during the twentieth century. Climate Dyn., 22, 823838, doi:10.1007/s00382-004-0413-1.

    • Search Google Scholar
    • Export Citation
  • Cubasch, U., and et al. , 2001: Projections of future climate change. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 525–582.

  • Daly, C., , M. Halbleib, , J. I. Smith, , W. P. Gibson, , M. K. Doggett, , G. H. Taylor, , J. Curtis, , and P. P. Pasteris, 2008: Physiographically sensitive mapping of temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 20312064, doi:10.1002/joc.1688.

    • Search Google Scholar
    • Export Citation
  • Dayon, G., , J. Boé, , and E. Martin, 2015: Transferability in the future climate of a statistical downscaling method for precipitation in France. J. Geophys. Res. Atmos., 120, 10231043, doi:10.1002/2014JD022236.

    • Search Google Scholar
    • Export Citation
  • Dixon, K. W., , J. R. Lanzante, , M. J. Nath, , K. Hayhoe, , A. Stoner, , A. Radhakrishnan, , V. Balaji, , C. F. Gaitán, 2016: Evaluating the stationarity assumption in statistically downscaled climate projections: Is past performance an indicator of future results? Climatic Change, 135, 395408, doi:10.1007/s10584-016-1598-0.

    • Search Google Scholar
    • Export Citation
  • Dong, B., , J. M. Gregory, , and R. T. Sutton, 2009: Understanding land–sea warming contrast in response to increasing greenhouse gases. Part I: Transient adjustment. J. Climate, 22, 30793097, doi:10.1175/2009JCLI2652.1.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fasullo, J. T., 2010: Robust land–ocean contrasts in energy and water cycle feedbacks. J. Climate, 23, 46774693, doi:10.1175/2010JCLI3451.1.

    • Search Google Scholar
    • Export Citation
  • Georgakakos, K. P., , N. E. Graham, , T. M. Carpenter, , and H. Yao, 2005: Integrating climate–hydrology forecasts and multi-objective reservoir management for northern California. Eos, Trans. Amer. Geophys. Union, 86, 122127, doi:10.1029/2005EO120002.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , and R. Francisco, 2000: Evaluating uncertainties in the prediction of regional climate change. Geophys. Res. Lett., 27, 12951298, doi:10.1029/1999GL011016.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , J. W. Hurrell, , M. R. Marinucci, , and M. Beniston, 1997: Elevation dependency of the surface climate change signal: A model study. J. Climate, 10, 288296, doi:10.1175/1520-0442(1997)010<0288:EDOTSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and et al. , 2001: Regional climate information—Evaluation and projections. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 583–638.

  • Guilyardi, E., , A. Wittenberg, , A. Fedorov, , M. Collins, , C. Wang, , A. Capotondi, , G. van Oldenborgh, , and T. Stockdale, 2009: Understanding El Niño in ocean–atmosphere general circulation models: Progress and challenges. Bull. Amer. Meteor. Soc., 90, 325340, doi:10.1175/2008BAMS2387.1.

    • Search Google Scholar
    • Export Citation
  • Gutiérrez, J. M., , D. San-Martín, , S. Brands, , R. Manzanas, , and S. Herrera, 2013: Reassessing statistical downscaling techniques for their robust application under climate change conditions. J. Climate, 26, 171188, doi:10.1175/JCLI-D-11-00687.1.

    • Search Google Scholar
    • Export Citation
  • Gutmann, E., , I. Barstad, , M. Clark, , J. Arnold, , and R. Rasmussen, 2016: The Intermediate Complexity Atmospheric Research Model (ICAR). J. Climate, 17, 957973, doi:10.1175/JHM-D-15-0155.1.

    • Search Google Scholar
    • Export Citation
  • Hall, D. K., , V. V. Salomonson, , and G. A. Riggs, 2006: MODIS/Terra snow cover monthly L3 global 0.05 deg CMG, version 5 (April 2000–December 2006 subset). National Snow and Ice Data Center, accessed 31 July 2015, doi:10.5067/IPPLURB6RPCN.

  • Hidalgo, H. G., , M. D. Dettinger, , and D. R. Cayan, 2008: Downscaling with constructed analogues: Daily precipitation and temperature fields over the United States. California Energy Commission Tech. Rep. CEC-500-2007-123, 48 pp.

  • Joshi, M. M., , J. M. Gregory, , M. J. Webb, , D. M. Sexton, , and T. C. Johns, 2008: Mechanisms for the land/sea warming contrast exhibited by simulations of climate change. Climate Dyn., 30, 455465, doi:10.1007/s00382-007-0306-1.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., , and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Amer. Sci., 47, 27842802.

    • Search Google Scholar
    • Export Citation
  • Kawase, H., , T. Yoshikane, , M. Hara, , F. Kimura, , T. Yasunari, , B. Ailikun, , H. Ueda, , and T. Inoue, 2009: Intermodel variability of future changes in the Baiu rainband estimated by the pseudo global warming downscaling method. J. Geophys. Res., 114, D24110, doi:10.1029/2009JD011803.

    • Search Google Scholar
    • Export Citation
  • Kim, J., 2001: A nested modeling study of elevation-dependent climate change signals in California induced by increased atmospheric CO2. Geophys. Res. Lett., 28, 29512954, doi:10.1029/2001GL013198.

    • Search Google Scholar
    • Export Citation
  • Letcher, T. W., , and J. R. Minder, 2015: Characterization of the simulated regional snow albedo feedback using a regional climate model over complex terrain. J. Climate, 28, 75767595, doi:10.1175/JCLI-D-15-0166.1.

    • Search Google Scholar
    • Export Citation
  • Manabe, S., , R. J. Stouffer, , M. J. Spelman, , and K. Bryan, 1991: Transient responses of a coupled ocean–atmosphere model to gradual changes of atmospheric CO2. Part I: Annual mean response. J. Climate, 4, 785818, doi:10.1175/1520-0442(1991)004<0785:TROACO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maraun, D., and et al. , 2010: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys., 48, RG3003, doi:10.1029/2009RG000314.

    • Search Google Scholar
    • Export Citation
  • Mass, C. F., , D. Ovens, , K. Westrick, , and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407430, doi:10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., 2007: Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two emissions scenarios. Climatic Change, 82, 309325, doi:10.1007/s10584-006-9180-9.

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., , and H. G. Hidalgo, 2008: Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Sci., 12, 551563, doi:10.5194/hess-12-551-2008.

    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and et al. , 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, doi:10.1175/BAMS-87-3-343.

    • Search Google Scholar
    • Export Citation
  • Minder, J., 2010: The sensitivity of mountain snowpack accumulation to climate warming. J. Climate, 23, 26342650, doi:10.1175/2009JCLI3263.1.

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., , S. J. Taubman, , P. D. Brown, , M. J. Iacono, , and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave. J. Geophys. Res., 102, 16 66316 682, doi:10.1029/97JD00237.

    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., , and H. Niino, 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397407, doi:10.1007/s10546-005-9030-8.

    • Search Google Scholar
    • Export Citation
  • Niu, G.-Y., and et al. , 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J. Geophys. Res., 116, D12109, doi:10.1029/2010JD015139.

    • Search Google Scholar
    • Export Citation
  • Pepin, N., and et al. , 2015: Elevation-dependent warming in mountain regions of the world. Nat. Climate Change, 5, 424430, doi:10.1038/nclimate2563.

    • Search Google Scholar
    • Export Citation
  • Pierce, D. W., , and D. R. Cayan, 2013: The uneven response of different snow measures to human-induced climate warming. J. Climate, 26, 41484167, doi:10.1175/JCLI-D-12-00534.1.

    • Search Google Scholar
    • Export Citation
  • Pierce, D. W., and et al. , 2013: Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling. Climate Dyn., 40, 839856, doi:10.1007/s00382-012-1337-9.

    • Search Google Scholar
    • Export Citation
  • Plummer, D. A., and et al. , 2006: Climate and climate change over North America as simulated by the Canadian RCM. J. Climate, 19, 31123132, doi:10.1175/JCLI3769.1.

    • Search Google Scholar
    • Export Citation
  • Qu, X., , and A. Hall, 2007: What controls the strength of snow-albedo feedback? J. Climate, 20, 39713981, doi:10.1175/JCLI4186.1.

  • Qu, X., , and A. Hall, 2014: On the persistent spread of snow-albedo feedback. Climate Dyn., 42, 6981, doi:10.1007/s00382-013-1774-0.

  • Rangwala, I., , and J. R. Miller, 2012: Climate change in mountains: A review of elevation-dependent warming and its possible causes. Climatic Change, 114, 527547, doi:10.1007/s10584-012-0419-3.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, R., and et al. , 2011: High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate. J. Climate, 24, 30153048, doi:10.1175/2010JCLI3985.1.

    • Search Google Scholar
    • Export Citation
  • Reclamation, 2013: Downscaled CMIP3 and CMIP5 climate and hydrology projections: Release of downscaled CMIP5 climate projections, comparison with preceding information, and summary of user needs. Prepared by the U.S. Department of the Interior, Bureau of Reclamation, Technical Services Center, Denver, CO, 47 pp. [Available online at http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html.]

  • Rhea, J. O., 1978: Orographic precipitation model for hydrometeorological use. Atmospheric Science Paper 287, Colorado State University, 198 pp. [Available online at https://dspace.library.colostate.edu/handle/10217/169958.]

  • Riahi, K., and et al. , 2011: RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109, 3357, doi:10.1007/s10584-011-0149-y.

    • Search Google Scholar
    • Export Citation
  • Sanford, T., , P. C. Frumhoff, , A. Luers, , and J. Gulledge, 2014: The climate policy narrative for a dangerously warming world. Nat. Climate Change, 4, 164166, doi:10.1038/nclimate2148.

    • Search Google Scholar
    • Export Citation
  • Sarker, R. P., 1966: A dynamical model of orographic rainfall. Mon. Wea. Rev., 94, 555572, doi:10.1175/1520-0493(1966)094<0555:ADMOOR>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sato, T., , F. Kimura, , and A. Kitoh, 2007: Projection of global warming onto regional precipitation over Mongolia using a regional climate model. J. Hydrol., 333, 144154, doi:10.1016/j.jhydrol.2006.07.023.

    • Search Google Scholar
    • Export Citation
  • Schär, C., , C. Frie, , D. Lüthi, , and H. C. Davies, 1996: Surrogate climate-change scenarios for regional climate models. Geophys. Res. Lett., 23, 669672, doi:10.1029/96GL00265.

    • Search Google Scholar
    • Export Citation
  • Sinclair, M. R., 1994: A diagnostic model for estimating orographic precipitation. J. Appl. Meteor., 33, 11631175, doi:10.1175/1520-0450(1994)033<1163:ADMFEO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and et al. , 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

  • Sun, F., , D. B. Walton, , and A. Hall, 2015: A hybrid dynamical–statistical downscaling technique. Part II: End-of-century warming projections predict a new climate state in the Los Angeles region. J. Climate, 28, 46184636, doi:10.1175/JCLI-D-14-00197.1.

    • Search Google Scholar
    • Export Citation
  • Sutton, R. T., , B. Dong, , and J. M. Gregory, 2007: Land/sea warming ratio in response to climate change: IPCC AR4 model results and comparison with observations. Geophys. Res. Lett., 34, L02701, doi:10.1029/2006GL028164.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., , R. J. Stouffer, , and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Tebaldi, C., , R. L. Smith, , D. Nychka, , and L. O. Mearns, 2005: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles. J. Climate, 18, 15241540, doi:10.1175/JCLI3363.1.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., , P. R. Field, , R. M. Rasmussen, , and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, doi:10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Wakazuki, Y., , and R. Rasmussen, 2015: Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections. Geophys. Res. Lett., 42, 10 84710 855, doi:10.1002/2015GL066242.

    • Search Google Scholar
    • Export Citation
  • Walton, D. B., , F. Sun, , A. Hall, , and S. Capps, 2015: A hybrid dynamical–statistical downscaling technique. Part I: Development and validation of the technique. J. Climate, 28, 45974617, doi:10.1175/JCLI-D-14-00196.1.

    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., , and T. M. L. Wigley, 1997: Downscaling general circulation model output: A review of methods and limitations. Prog. Phys. Geogr., 21, 530548, doi:10.1177/030913339702100403.

    • Search Google Scholar
    • Export Citation
  • Wood, A. W., , E. P. Maurer, , A. Kumar, , and D. P. Lettenmaier, 2002: Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.

    • Search Google Scholar
    • Export Citation
  • Wood, A. W., , L. R. Leung, , V. Sridhar, , and D. P. Lettenmaier, 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189216, doi:10.1023/B:CLIM.0000013685.99609.9e.

    • Search Google Scholar
    • Export Citation
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Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada

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  • 1 Department of Atmospheric and Oceanic Sciences, and Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, California
  • | 2 Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
  • | 3 Department of Geosciences, University of Missouri–Kansas City, Kansas City, Missouri, and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, California
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Abstract

California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems.

Corresponding author e-mail: Daniel B. Walton, waltond@atmos.ucla.edu

Abstract

California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems.

Corresponding author e-mail: Daniel B. Walton, waltond@atmos.ucla.edu
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