• Antic, S., , R. Laprise, , B. Denis, , and R. de Elía, 2006: Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography. Climate Dyn., 26, 305325, doi:10.1007/s00382-005-0046-z.

    • Search Google Scholar
    • Export Citation
  • Arakawa, A., , J.-H. Jung, , and C.-M. Wu, 2011: Toward unification of the multiscale modeling of the atmosphere. Atmos. Chem. Phys., 11, 37313742, doi:10.5194/acp-11-3731-2011.

    • Search Google Scholar
    • Export Citation
  • Bacmeister, J. T., , M. F. Wehner, , R. B. Neale, , A. Gettelman, , C. Hannay, , P. H. Lauritzen, , J. M. Caron, , and J. E. Truesdale, 2014: Exploratory high-resolution climate simulations using the Community Atmosphere Model (CAM). J. Climate, 27, 30733099, doi:10.1175/JCLI-D-13-00387.1.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., , J. Chen, , F. R. Robertson, , and R. F. Adler, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 22792299, doi:10.1175/2008JAMC1921.1.

    • Search Google Scholar
    • Export Citation
  • Boville, B. A., 1991: Sensitivity of simulated climate to model resolution. J. Climate, 4, 469485, doi:10.1175/1520-0442(1991)004<0469:SOSCTM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Boyle, J., , and S. A. Klein, 2010: Impact of horizontal resolution on climate model forecasts of tropical precipitation and diabatic heating for the TWP-ICE period. J. Geophys. Res., 115, D23113, doi:10.1029/2010JD014262.

    • Search Google Scholar
    • Export Citation
  • Castro, C. L., , H.-I. Chang, , F. Dominguez, , C. Carrillo, , J.-K. Schemm, , and H.-M. H. Juang, 2012: Can a regional climate model improve the ability to forecast the North American monsoon? J. Climate, 25, 82128237, doi:10.1175/JCLI-D-11-00441.1.

    • Search Google Scholar
    • Export Citation
  • Collins, W. D., and et al. , 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCAR/TN-464+STR, 214 pp.

  • Community Earth System Model, 2014: CESM 1.0 experiments, data and diagnostics. UCAR, accessed 23 August 2014. [Available online at http://www.cesm.ucar.edu/experiments/cesm1.0/.]

  • Craig, P., , M. Vertenstein, , and R. Jacob, 2012: A new flexible coupler for Earth system modeling developed for CCSM4 and CESM1. Int. J. High Perform. Comput. Appl., 26, 3142, doi:10.1177/1094342011428141.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • de Elía, R., , R. Laprise, , and B. Denis, 2002: Forecasting skill limits of nested, limited-area models: A perfect-model approach. Mon. Wea. Rev., 130, 20062023, doi:10.1175/1520-0493(2002)130<2006:FSLONL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ding, Q., , E. J. Steig, , D. S. Battisti, , and J. M. Wallace, 2012: Influence of the tropics on the southern annular mode. J. Climate, 25, 63306348, doi:10.1175/JCLI-D-11-00523.1.

    • Search Google Scholar
    • Export Citation
  • Edwards, T. L., and et al. , 2014: Effect of uncertainty in surface mass balance–elevation feedback on projections of the future sea level contribution of the Greenland ice sheet. Cryosphere, 8, 195208, doi:10.5194/tc-8-195-2014.

    • Search Google Scholar
    • Export Citation
  • Evans, K. J., , P. H. Lauritzen, , S. K. Mishra, , R. B. Neale, , M. A. Taylor, , and J. J. Tribbia, 2013: AMIP simulation with the CAM4 spectral element dynamical core. J. Climate, 26, 689709, doi:10.1175/JCLI-D-11-00448.1.

    • Search Google Scholar
    • Export Citation
  • Flato, G., and et al. , 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. T. F. Stocker et al., Eds., Cambridge University Press, 741–866.

  • Fox-Rabinovitz, M. S., , E. H. Berbery, , L. L. Takacs, , and R. C. Govindaraju, 2005: A multiyear ensemble simulation of the U.S. climate with a stretched-grid GCM. Mon. Wea. Rev., 133, 25052525, doi:10.1175/MWR2956.1.

    • Search Google Scholar
    • Export Citation
  • Fox-Rabinovitz, M. S., , J. Côté, , B. Dugas, , M. Déqué, , and J. L. McGregor, 2006: Variable resolution general circulation models: Stretched-grid model intercomparison project (SGMIP). J. Geophys. Res., 111, D16104, doi:10.1029/2005JD006520.

    • Search Google Scholar
    • Export Citation
  • Gao, Y., , L. R. Leung, , J. Lu, , Y. Liu, , M. Huang, , and Y. Qian, 2014: Robust spring drying in the southwestern U.S. and seasonal migration of wet/dry patterns in a warmer climate. Geophys. Res. Lett., 41, 17451751, doi:10.1002/2014GL059562.

    • Search Google Scholar
    • Export Citation
  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73, 19621970, doi:10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gesch, D. B., , and K. S. Larson, 1998: Techniques for development of global 1-kilometer digital elevation models. Proc. Pecora 13th Symp., Sioux Falls, SD, American Society for Photogrammetry and Remote Sensing.

  • Ghan, S. J., , X. Bian, , and L. Corsetti, 1996: Simulation of the Great Plains low-level jet and associated clouds by general circulation models. Mon. Wea. Rev., 124, 13881408, doi:10.1175/1520-0493(1996)124<1388:SOTGPL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447462, doi:10.1002/qj.49710644905.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , and L. O. Mearns, 1991: Approaches to the simulation of regional climate change: A review. Rev. Geophys., 29, 191216, doi:10.1029/90RG02636.

    • Search Google Scholar
    • Export Citation
  • Hack, J. J., 1994: Parameterization of moist convection in the National Center for Atmospheric Research community climate model (CCM2). J. Geophys. Res., 99, 55515568, doi:10.1029/93JD03478.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , R. Leung, , S. A. Rauscher, , and T. Ringler, 2013: Error characteristics of two grid refinement approaches in aquaplanet simulations: MPAS-A and WRF. Mon. Wea. Rev., 141, 30223036, doi:10.1175/MWR-D-12-00338.1.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , R. Leung, , W. I. Gustafson, , and B. Singh, 2014: Eddy fluxes and sensitivity of the water cycle to spatial resolution in idealized regional aquaplanet model simulations. Climate Dyn., 42, 931940, doi:10.1007/s00382-013-1857-y.

    • Search Google Scholar
    • Export Citation
  • Harris, L. M., , and S.-J. Lin, 2013: A two-way nested global-regional dynamical core on the cubed-sphere grid. Mon. Wea. Rev., 141, 283306, doi:10.1175/MWR-D-11-00201.1.

    • Search Google Scholar
    • Export Citation
  • Harris, L. M., , and S.-J. Lin, 2014: Global-to-regional nested grid climate simulations in the GFDL high resolution atmospheric model. J. Climate, 27, 48904910, doi:10.1175/JCLI-D-13-00596.1.

    • Search Google Scholar
    • Export Citation
  • Hawkins, E., , and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90, 10951107, doi:10.1175/2009BAMS2607.1.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., , M. Ting, , and H. Wang, 2002: Northern winter stationary waves: Theory and modeling. J. Climate, 15, 21252144, doi:10.1175/1520-0442(2002)015<2125:NWSWTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hewitson, B., and et al. , 2014: Regional context. Climate Change 2014: Impacts, Adaptation, and Vulnerability, C. B. Field et al., Eds., Cambridge University Press, 1133–1197.

  • Holtslag, A. A. M., , and B. A. Boville, 1993: Local versus nonlocal boundary-layer diffusion in a global climate model. J. Climate, 6, 18251842, doi:10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., , and T. Ambrizzi, 1993: Rossby wave propagation on a realistic longitudinally varying flow. J. Atmos. Sci., 50, 16611671, doi:10.1175/1520-0469(1993)050<1661:RWPOAR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., , R. F. Adler, , M. M. Morrissey, , D. T. Bolvin, , S. Curtis, , R. Joyce, , B. McGavock, , and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 3650, doi:10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and et al. , 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., , R. F. Adler, , D. T. Bolvin, , and G. Gu, 2009: Improving the global precipitation record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.

    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., , J. J. Hack, , D. Shea, , J. M. Caron, , and J. Rosinski, 2008: A new sea surface temperature and sea ice boundary dataset for the Community Atmosphere Model. J. Climate, 21, 51455153, doi:10.1175/2008JCLI2292.1.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., , and B. J. Hoskins, 2004: The zonal asymmetry of the Southern Hemisphere winter storm track. J. Climate, 17, 48824892, doi:10.1175/JCLI-3232.1.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., , and M. Kimoto, 2009: A scale interaction study on East Asian cyclogenesis using a general circulation model coupled with an interactively nested regional model. Mon. Wea. Rev., 137, 28512868, doi:10.1175/2009MWR2825.1.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., , H. Mukougawa, , and S.-P. Xie, 2002: Stationary eddy response to surface boundary forcing: Idealized GCM experiments. J. Atmos. Sci., 59, 18981915, doi:10.1175/1520-0469(2002)059<1898:SERTSB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., , Y. Satake, , M. Kimoto, , and N. Yasutomi, 2012: GCM bias of the western Pacific summer monsoon and its correction by two-way nesting system. J. Meteor. Soc. Japan, 90B, 110, doi:10.2151/jmsj.2012-B01.

    • Search Google Scholar
    • Export Citation
  • Iorio, J. P., , P. B. Duffy, , B. Govindasamy, , S. L. Thompson, , M. Khairoutdinov, , and D. Randall, 2004: Effects of model resolution and subgrid-scale physics on the simulation of precipitation in the continental United States. Climate Dyn., 23, 243258, doi:10.1007/s00382-004-0440-y.

    • Search Google Scholar
    • Export Citation
  • Jiang, X., , N.-C. Lau, , I. M. Held, , and J. J. Ploshay, 2007: Mechanisms of the Great Plains low-level jet as simulated in an AGCM. J. Atmos. Sci., 64, 532547, doi:10.1175/JAS3847.1.

    • Search Google Scholar
    • Export Citation
  • Jones, P. W., 1999: First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Wea. Rev., 127, 22042210, doi:10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ju, L., , T. Ringler, , and M. Gunzburger, 2011: Voronoi tessellations and their application to climate and global modeling. Numerical Techniques for Global Atmospheric Models, P. Lauritzen et al., Eds., Lecture Notes in Computational Science and Engineering, Vol. 80, Springer, 313–342.

  • Kinter, J. L., and et al. , 2013: Revolutionizing climate modeling with Project Athena: A multi-institutional, international collaboration. Bull. Amer. Meteor. Soc., 94, 231245, doi:10.1175/BAMS-D-11-00043.1.

    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., , W. C. Skamarock, , and J. Dudhia, 2007: Conservative split-explicit time integration methods for the compressible nonhydrostatic equations. Mon. Wea. Rev., 135, 28972913, doi:10.1175/MWR3440.1.

    • Search Google Scholar
    • Export Citation
  • Landu, K., , L. R. Leung, , S. Hagos, , V. Vinoj, , S. A. Rauscher, , T. Ringler, , and M. Taylor, 2014: The dependence of ITCZ structure on model resolution and dynamical core in aquaplanet simulations. J. Climate, 27, 23752385, doi:10.1175/JCLI-D-13-00269.1.

    • Search Google Scholar
    • Export Citation
  • Lauritzen, P. H., , R. D. Nair, , and P. A. Ullrich, 2010: A conservative semi-Lagrangian multi-tracer transport scheme (CSLAM) on the cubed-sphere grid. J. Comput. Phys., 229, 14011424, doi:10.1016/j.jcp.2009.10.036.

    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and et al. , 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst., 3, M03001, doi:10.1029/2011MS000045.

    • Search Google Scholar
    • Export Citation
  • Lee, S., , and H.-K. Kim, 2003: The dynamical relationship between subtropical and eddy-driven jets. J. Atmos. Sci., 60, 14901503, doi:10.1175/1520-0469(2003)060<1490:TDRBSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , Y. Qian, , X. Bian, , W. M. Washington, , J. Han, , and J. O. Roads, 2004: Mid-century ensemble regional climate change scenarios for the western United States. Climatic Change, 62, 75113, doi:10.1023/B:CLIM.0000013692.50640.55.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , Y.-H. Kuo, , and J. Tribbia, 2006: Research needs and directions of regional climate modeling using WRF and CCSM. Bull. Amer. Meteor. Soc., 87, 17471751, doi:10.1175/BAMS-87-12-1747.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , T. D. Ringler, , W. D. Collins, , M. A. Taylor, , and M. Ashfaq, 2013: A hierarchical evaluation of regional climate simulations. Eos, Trans. Amer. Geophys. Union, 94, 297298, doi:10.1002/2013EO340001.

    • Search Google Scholar
    • Export Citation
  • L’Heureux, M., , and D. W. J. Thompson, 2006: Observed relationships between the El Niño–Southern Oscillation and the extratropical zonal-mean circulation. J. Climate, 19, 276287, doi:10.1175/JCLI3617.1.

    • Search Google Scholar
    • Export Citation
  • Lin, S.-J., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 22932307, doi:10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lorenz, P., , and D. Jacob, 2005: Influence of regional scale information on the global circulation: A two-way nesting climate simulation. Geophys. Res. Lett., 32, L18706, doi:10.1029/2005GL023351.

    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., 2005: Characteristics and spatio-temporal variability of the Amazon River basin water budget. Climate Dyn., 24, 1122, doi:10.1007/s00382-004-0461-6.

    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., , W. R. Soares, , C. Saulo, , and M. Nicolini, 2004: Climatology of the low-level jet east of the Andes as derived from the NCEP–NCAR reanalyses: Characteristics and temporal variability. J. Climate, 17, 22612280, doi:10.1175/1520-0442(2004)017<2261:COTLJE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McGregor, J. L., 2015: Recent developments in variable-resolution global climate modelling. Climatic Change, 129, 369380, doi:10.1007/s10584-013-0866-5.

    • Search Google Scholar
    • Export Citation
  • Mearns, L. O., and et al. , 2012: The North American Regional Climate Change Assessment Program: Overview of phase I results. Bull. Amer. Meteor. Soc., 93, 13371362, doi:10.1175/BAMS-D-11-00223.1.

    • Search Google Scholar
    • Export Citation
  • Medvigy, D., , R. L. Walko, , M. J. Otte, , and R. Avissar, 2013: Simulated changes in northwest U.S. climate in response to Amazon deforestation. J. Climate, 26, 91159136, doi:10.1175/JCLI-D-12-00775.1.

    • Search Google Scholar
    • Export Citation
  • Mo, K. C., , and R. W. Higgins, 1998: The Pacific–South American modes and tropical convection during the Southern Hemisphere winter. Mon. Wea. Rev., 126, 15811596, doi:10.1175/1520-0493(1998)126<1581:TPSAMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., , and B. J. Hoskins, 2000: A standard test for AGCMs including their physical parametrizations: I: The proposal. Atmos. Sci. Lett., 1, 101107, doi:10.1006/asle.2000.0022.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., , J. H. Richter, , and M. Jochum, 2008: The impact of convection on ENSO: From a delayed oscillator to a series of events. J. Climate, 21, 59045924, doi:10.1175/2008JCLI2244.1.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., and et al. , 2010: Description of the NCAR Community Atmosphere Model (CAM 4.0). NCAR Tech. Note NCAR/TN-485+STR, 212 pp.

  • Neale, R. B., and et al. , 2012: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 274 pp.

  • Neale, R. B., , J. H. Richter, , S. Park, , P. H. Lauritzen, , S. J. Vavrus, , P. J. Rasch, , and M. Zhang, 2013: The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J. Climate, 26, 51505168, doi:10.1175/JCLI-D-12-00236.1.

    • Search Google Scholar
    • Export Citation
  • O’Brien, T. A., , F. Li, , W. D. Collins, , S. A. Rauscher, , T. D. Ringler, , M. Taylor, , S. M. Hagos, , and L. R. Leung, 2013: Observed scaling in clouds and precipitation and scale incognizance in regional to global atmospheric models. J. Climate, 26, 93139333, doi:10.1175/JCLI-D-13-00005.1.

    • Search Google Scholar
    • Export Citation
  • Orlanski, I., 2008: The rationale for why climate models should adequately resolve the mesoscale. High Resolution Numerical Modelling of the Atmosphere and Ocean, K. Hamilton and W. Ohfuchi, Eds., Springer, 29–44.

  • Park, S.-H., , W. C. Skamarock, , J. B. Klemp, , L. D. Fowler, , and M. G. Duda, 2013: Evaluation of global atmospheric solvers using extensions of the Jablonowski and Williamson baroclinic wave test case. Mon. Wea. Rev., 141, 31163129, doi:10.1175/MWR-D-12-00096.1.

    • Search Google Scholar
    • Export Citation
  • Pope, V. D., , and R. A. Stratton, 2002: The processes governing horizontal resolution sensitivity in a climate model. Climate Dyn., 19, 211236, doi:10.1007/s00382-001-0222-8.

    • Search Google Scholar
    • Export Citation
  • Rasch, P. J., , and J. E. Kristjánsson, 1998: A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J. Climate, 11, 15871614, doi:10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rauscher, S. A., , and T. D. Ringler, 2014: Impact of variable-resolution meshes on midlatitude baroclinic eddies using CAM-MPAS-A. Mon. Wea. Rev., 142, 42564268, doi:10.1175/MWR-D-13-00366.1.

    • Search Google Scholar
    • Export Citation
  • Rauscher, S. A., , A. Seth, , J.-H. Qian, , and S. J. Camargo, 2006: Domain choice in an experimental nested modeling prediction system for South America. Theor. Appl. Climatol., 86, 229246, doi:10.1007/s00704-006-0206-z.

    • Search Google Scholar
    • Export Citation
  • Rauscher, S. A., , T. D. Ringler, , W. C. Skamarock, , and A. A. Mirin, 2013: Exploring a global multiresolution modeling approach using aquaplanet simulations. J. Climate, 26, 24322452, doi:10.1175/JCLI-D-12-00154.1.

    • Search Google Scholar
    • Export Citation
  • Richter, J. H., , and P. J. Rasch, 2008: Effects of convective momentum transport on the atmospheric circulation in the Community Atmosphere Model, Version 3. J. Climate, 21, 14871499, doi:10.1175/2007JCLI1789.1.

    • Search Google Scholar
    • Export Citation
  • Ringler, T. D., , L. Ju, , and M. Gunzburger, 2008: A multiresolution method for climate system modeling: Application of spherical centroidal Voronoi tessellations. Ocean Dyn., 58, 475498, doi:10.1007/s10236-008-0157-2.

    • Search Google Scholar
    • Export Citation
  • Ringler, T. D., , J. Thuburn, , J. B. Klemp, , and W. C. Skamarock, 2010: A unified approach to energy conservation and potential vorticity dynamics for arbitrarily-structured C-grids. J. Comput. Phys., 229, 30653090, doi:10.1016/j.jcp.2009.12.007.

    • Search Google Scholar
    • Export Citation
  • Ringler, T. D., , D. Jacobsen, , M. Gunzburger, , L. Ju, , M. Duda, , and W. C. Skamarock, 2011: Exploring a multiresolution modeling approach within the shallow-water equations. Mon. Wea. Rev., 139, 33483368, doi:10.1175/MWR-D-10-05049.1.

    • Search Google Scholar
    • Export Citation
  • Ringler, T. D., , M. Petersen, , R. L. Higdon, , D. Jacobsen, , P. W. Jones, , and M. Maltrud, 2013: A multi-resolution approach to global ocean modeling. Ocean Modell., 69, 211232, doi:10.1016/j.ocemod.2013.04.010.

    • Search Google Scholar
    • Export Citation
  • Seth, A., , S. A. Rauscher, , S. J. Camargo, , J.-H. Qian, , and J. S. Pal, 2007: RegCM3 regional climatologies for South America using reanalysis and ECHAM global model driving fields. Climate Dyn., 28, 461480, doi:10.1007/s00382-006-0191-z.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2011: Kinetic energy spectra and model filters. Numerical Techniques for Global Atmospheric Models, P. Lauritzen et al., Eds., Lecture Notes in Computational Science and Engineering, Vol. 80, Springer, 495–512.

  • Skamarock, W. C., , and A. Gassmann, 2011: Conservative transport schemes for spherical geodesic grids: High-order flux operators for ODE-based time integration. Mon. Wea. Rev., 139, 29622975, doi:10.1175/MWR-D-10-05056.1.

    • 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. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3_bw.pdf.]

  • Skamarock, W. C., , J. B. Klemp, , M. G. Duda, , L. D. Fowler, , S.-H. Park, , and T. D. Ringler, 2012: A multiscale nonhydrostatic atmospheric model using centroidal Voronoi tessellations and C-grid staggering. Mon. Wea. Rev., 140, 30903105, doi:10.1175/MWR-D-11-00215.1.

    • Search Google Scholar
    • Export Citation
  • St-Cyr, A., , C. Jablonowski, , J. M. Dennis, , H. M. Tufo, , and S. J. Thomas, 2008: A comparison of two shallow-water models with nonconforming adaptive grids. Mon. Wea. Rev., 136, 18981922, doi:10.1175/2007MWR2108.1.

    • Search Google Scholar
    • Export Citation
  • Takahashi, Y. O., , K. Hamilton, , and W. Ohfuchi, 2006: Explicit global simulation of the mesoscale spectrum of atmospheric motions. Geophys. Res. Lett., 33, L12812, doi:10.1029/2006GL026429.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 71837192, doi:10.1029/2000JD900719.

    • 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
  • Taylor, M. A., 2011: Conservation of mass and energy for the moist atmospheric primitive equations on unstructured grids. Numerical Techniques for Global Atmospheric Models, P. Lauritzen et al., Eds., Lecture Notes in Computational Science and Engineering, Vol. 80, Springer, 357–380.

  • Thuburn, J., 2008: Numerical wave propagation on the hexagonal C-grid. J. Comput. Phys., 227, 58365858, doi:10.1016/j.jcp.2008.02.010.

    • Search Google Scholar
    • Export Citation
  • Thuburn, J., , T. D. Ringler, , W. C. Skamarock, , and J. B. Klemp, 2009: Numerical representation of geostrophic modes on arbitrarily structured C-grids. J. Comput. Phys., 228, 83218335, doi:10.1016/j.jcp.2009.08.006.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1981: Observed Southern Hemisphere eddy statistics at 500 mb: Frequency and spatial dependence. J. Atmos. Sci., 38, 25852605, doi:10.1175/1520-0469(1981)038<2585:OSHESA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., , J. W. Hurrell, , and D. P. Stepaniak, 2006: The Asian monsoon: Global perspectives. The Asian Monsoon, B. Wang, Ed., Springer, 67–87.

  • Tripathi, O. P., , and F. Dominguez, 2013: Effects of spatial resolution in the simulation of daily and subdaily precipitation in the southwestern US. J. Geophys. Res. Atmos., 118, 75917605, doi:10.1002/jgrd.50590.

    • Search Google Scholar
    • Export Citation
  • Vallis, G. K., 2006: Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation. Cambridge University Press, 745 pp.

  • van der Linden, P., , and J. F. B. Mitchell, 2009: ENSEMBLES: Climate change and its impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre Tech. Rep., 160 pp. [Available online at http://www.ecad.eu/documents/Ensembles_final_report_Nov09.pdf.]

  • Vavrus, S., , and D. Waliser, 2008: An improved parameterization for simulating Arctic cloud amount in the CCSM3 climate model. J. Climate, 21, 56735687, doi:10.1175/2008JCLI2299.1.

    • Search Google Scholar
    • Export Citation
  • Walko, R. L., 2011: Challenges of modeling convection on grids with spatially varying resolution. Global to Regional Climate Simulation Workshop, Santa Fe, NM, Los Alamos National Laboratory Energy Security Center, 27 pp. [Available online at http://public.lanl.gov/ringler/BDBS/2011/talks/walko.pdf.]

  • Walko, R. L., , and R. Avissar, 2011: A direct method for constructing refined regions in unstructured conforming triangular–hexagonal computational grids: Application to OLAM. Mon. Wea. Rev., 139, 39233937, doi:10.1175/MWR-D-11-00021.1.

    • Search Google Scholar
    • Export Citation
  • Wan, H., , P. J. Rasch, , M. A. Taylor, , and C. Jablonowski, 2015: Short-term time step convergence in a climate model. J. Adv. Model. Earth Syst.,7, 215225, doi:10.1002/2014MS000368.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., , L. R. Leung, , J. L. McGregor, , D.-K. Lee, , W.-C. Wang, , Y. Ding, , and F. Kimura, 2004: Regional climate modeling: Progress, challenges, and prospects. J. Meteor. Soc. Japan, 82, 15991628, doi:10.2151/jmsj.82.1599.

    • Search Google Scholar
    • Export Citation
  • Warner, T. T., , R. A. Peterson, , and R. E. Treadon, 1997: A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Amer. Meteor. Soc., 78, 25992617, doi:10.1175/1520-0477(1997)078<2599:ATOLBC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Webster, S., , A. R. Brown, , D. R. Cameron, , and C. P. Jones, 2003: Improvements to the representation of orography in the Met Office Unified Model. Quart. J. Roy. Meteor. Soc., 129, 19892010, doi:10.1256/qj.02.133.

    • Search Google Scholar
    • Export Citation
  • Wehner, M. F., and et al. , 2014: The effect of horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1. J. Adv. Model. Earth Syst., 6, 980997, doi:10.1002/2013MS000276.

    • Search Google Scholar
    • Export Citation
  • Werth, D., , R. Kurzeja, , N. L. Dias, , G. Zhang, , H. Duarte, , M. Fischer, , M. Parker, , and M. Leclerc, 2011: The simulation of the southern Great Plains nocturnal boundary layer and the low-level jet with a high-resolution mesoscale atmospheric model. J. Appl. Meteor. Climatol., 50, 14971513, doi:10.1175/2011JAMC2272.1.

    • Search Google Scholar
    • Export Citation
  • Wicker, L., , and W. Skamarock, 2002: Time-splitting methods for elastic models using forward time schemes. Mon. Wea. Rev., 130, 20882097, doi:10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Williamson, D. L., 2007: The evolution of dynamical cores for global atmospheric models. J. Meteor. Soc. Japan, 85B, 241269, doi:10.2151/jmsj.85B.241.

    • Search Google Scholar
    • Export Citation
  • Williamson, D. L., 2008: Convergence of aqua-planet simulations with increasing resolution in the Community Atmospheric Model, Version 3. Tellus, 60A, 848862, doi:10.1111/j.1600-0870.2008.00339.x.

    • Search Google Scholar
    • Export Citation
  • Williamson, D. L., 2013: The effect of time steps and time-scales on parametrization suites. Quart. J. Roy. Meteor. Soc., 139, 548560, doi:10.1002/qj.1992.

    • Search Google Scholar
    • Export Citation
  • Yang, Q., , L. R. Leung, , S. A. Rauscher, , T. D. Ringler, , and M. A. Taylor, 2014: Atmospheric moisture budget and spatial resolution dependence of precipitation extremes in aquaplanet simulations. J. Climate, 27, 35653581, doi:10.1175/JCLI-D-13-00468.1.

    • Search Google Scholar
    • Export Citation
  • Zarzycki, C. M., , and C. Jablonowski, 2014: A multidecadal simulation of Atlantic tropical cyclones using a variable-resolution global atmospheric general circulation model. J. Adv. Model. Earth Syst., 6, 805826, doi:10.1002/2014MS000352.

    • Search Google Scholar
    • Export Citation
  • Zarzycki, C. M., , M. N. Levy, , C. Jablonowski, , J. R. Overfelt, , M. A. Taylor, , and P. A. Ullrich, 2014: Aquaplanet experiments using CAM’s variable-resolution dynamical core. J. Climate, 27, 54815503, doi:10.1175/JCLI-D-14-00004.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., , and N. A. McFarlane, 1995: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model. Atmos.–Ocean, 33, 407446, doi:10.1080/07055900.1995.9649539.

    • Search Google Scholar
    • Export Citation
  • Zhang, H., , K. Fraedrich, , R. Blender, , and X. Zhu, 2013: Precipitation extremes in CMIP5 simulations on different time scales. J. Hydrometeor., 14, 923928, doi:10.1175/JHM-D-12-0181.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, M., , W. Lin, , C. S. Bretherton, , J. J. Hack, , and P. J. Rasch, 2003: A modified formulation of fractional stratiform condensation rate in the NCAR Community Atmospheric Model (CAM2). J. Geophys. Res., 108, 4035, doi:10.1029/2002JD002523.

    • Search Google Scholar
    • Export Citation
  • Zwiers, F. W., , and H. von Storch, 1995: Taking serial correlation into account in tests of the mean. J. Climate, 8, 336351, doi:10.1175/1520-0442(1995)008<0336:TSCIAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    (a) The 200-hPa eddy streamfunction (m2 s−1/10 × 106, contours) and precipitation deviations from the zonal mean (mm day−1, shaded) for the CAM–MPAS VR simulation. The 200-hPa eddy velocity potential (m2 s−1/10 × 106) for the (b) variable-resolution aquaplanet simulation and (c) Held–Suarez simulation. These are climatological means over 4.5 yr reproduced from Fig. 14 in Rauscher et al. (2013; used with permission).The high-resolution region of the VR mesh is outlined by the gray circle in the center of the panels.

  • View in gallery

    The two variable-resolution grid setups in this study: refinement over (a) South America and (b) North America. The solid and dashed circles represent approximate boundaries enclosing the domain with ~30-km grid and the transition to 120-km grid domain, respectively., These are also reflected as the vertical lines in (c) where the gridcell size is plotted as a function of radial distance from the center of the refined domain. The trapezoid in (a) and (b) represents the area used to calculate the area-average and performance statistics for the high-resolution area.

  • View in gallery

    Gridcell structures across the transition from the coarse-to-fine domains over (a) South America and (b) North America, as shown in Fig. 2. The approximate boundaries for the high-resolution and transition regions are depicted by red lines. The color shading shows surface geopotential height (m).

  • View in gallery

    Taylor diagrams summarizing the climatology of different variables over the global domain. Variables are denoted by the numbers shown in the bottom legend. Horizontal wind is evaluated as (u2 + υ2)1/2. (a) MPAS quasi-uniform low- (120-km grid cell; UR120) and high-resolution (30-km grid cell; UR30) and FV-CAM4 (Neale et al. 2013) are compared to ERA-Interim. (b) VR-SA, VR-NA, and UR120 are compared to UR30. The size and shape of the symbol represent mean bias after being normalized by the mean value of the reference data and is shown as a percentage (legend given in the center). Note that in (a) the vertical velocities for UR120 and UR30 are shown outside the diagram because their normalized standard deviations (shown near the symbol by the number on top; the bottom value is anomaly correlation) are outside the range of the diagram.

  • View in gallery

    Zonal-mean zonal velocity (color, m s−1) and meridional–vertical wind (vectors) from UR30 for (a) DJF and (b) JJA mean, and their differences from UR30 in (c),(d) UR120, (e),(f) VR-SA, and (g),(h) VR-NA for each season. The vectors are scaled arbitrarily and differently for (a) and (b) and (c)–(h) because of the difference in the units and typical magnitude between the meridional and vertical (pressure) velocity. The left ordinate shows pressure height (hPa) and the right ordinate shows approximate height (km).

  • View in gallery

    Maps showing zonal wind (m s−1) at 200-hPa level for the UR30 for the (a) DJF and (b) JJA mean, and the difference between UR30 and (c),(d) UR120, (e),(f) VR-SA, and (g),(h) VR-NA for each season. All the data are remapped to a 0.9° × 1.25° grid. The crosshatch in (b)–(d) indicates the grid cells with statistical significance at α = 0.05 level based on two-sided Student’s t tests against the null hypothesis of no difference.

  • View in gallery

    Spatial maps for DJF mean precipitation (mm day−1) over the South America region. Two observational datasets: (a) GPCP v1.1 on 1° grid and (b) TRMM-TMPA on 0.25° grid. And the MPAS simulation outputs: (c) UR120, (d) UR30, and (e) VR-SA.

  • View in gallery

    (top) DJF mean moisture convergence in color (mm day−1) and moisture flux with vectors integrated from the surface to 50-hPa level (kg m−1 s−1) in (a) UR120, (b) the difference between UR120 and UR30, and (c) the difference between UR120 and VR-SA. (bottom) DJF mean evapotranspiration (mm day−1) in (d) UR120 and their differences in (e) UR30 and (f) VR-SA. The crosshatch indicates the grid cells with statistical significance at α = 0.05 level (for shaded quantities).

  • View in gallery

    Zonal-mean sea level pressure difference (hPa) from UR30 (blue), VR-SA (red), and VR-NA (yellow) compared to UR120, averaged over (a) DJF and 40°W to 0°E (corresponding to South Atlantic), and (b) JJA and 70°W to 20°W (corresponding to North Atlantic).

  • View in gallery

    (a)–(e) JJA mean precipitation over the North America region. (f) Evapotranspiration (mm day−1) in UR120 and the difference from UR120 in (g) UR30 and (h) VR-NA. The crosshatch in (g) and (h) indicates the grid cells with statistical significance at α = 0.05 level. Note that in (e) the transition region appears noisy because of the imprint of the native (hexagonal) grid by conservative remapping to the high-resolution (0.23° × 0. 31°) regular grid.

  • View in gallery

    Time series of monthly precipitation (mm day−1) averaged over the area within the high-resolution area of the VR simulations (40° × 40° trapezoidal area in Fig. 2) for (a) South America, (b) North America, and the ratio of grid-scale precipitation to total precipitation (from the model simulations only) for (c) South America and (d) North America. The numbers in the upper-right corner of each panel are the mean values of the time series, shown in the corresponding colors.

  • View in gallery

    Taylor diagrams showing performance statistics of (a)–(c) UR120 and VR-SA compared to UR30 within the refined grid over South America (40° × 40° rectangular area in Fig. 2) for DJF and (d)–(f) UR120 and VR-NA compared to UR30 over North America. The top and bottom panels each show the different groups of variables that are represented by the numbers shown in the middle. The size and shape of the symbol indicate mean bias after normalized by the mean value of the reference data and shown as a percentage (legend given in the center). Horizontal wind is evaluated as (u2 + υ2)1/2.

  • View in gallery

    Cumulative distributions of daily precipitation within the high-resolution regions (40° × 40° trapezoidal area in Fig. 2) in (a) South America (DJF) and (b) North America (JJA). The distributions are obtained after all the data are regridded to a 0.9° × 1.25° grid (same as FV-CAM4) by conservative remapping.

  • View in gallery

    (left) The difference between UR-30 and UR120 in the 10-yr JJA mean of (a) temperature at 500-hPa level (K), (b) 200-hPa velocity potential (m2 s−1/106, shading) and divergent wind (m s−1, vectors), and (c) 200-hPa streamfunction (m2 s−1/107, shading) and rotational wind (m s−1, vectors). (middle) As in (left), but for (d)–(f) UR-30 and VR-SA. (right) As in (left), but for (g)–(i) UR-30 and VR-NA. Grid cells with statistically significant difference (quantities shown by shading, based on Student’s t tests) at α = 0.05 level are crosshatched. The solid and dashed circles in (d)–(i) represent approximate boundaries enclosing the domain with 30-km grid and the transition to 120-km grid domain, respectively. The yellow boxes in (h) and (i) represent the regions analyzed for Fig. 15.

  • View in gallery

    (a)–(c) Mean JJA bias relative to UR120 over three regions (boxes are shown in Figs. 14h,i) for the variables shown along the x axis. The MPAS-A bias is normalized by the standard deviation of the biases calculated between 15 unique combinations of the six members of the FV-CAM4 ensemble. Some values for UR30 extend outside the range shown [e.g., cloud cover fraction (CLDTOT) in (a) and PRECT in (c)]. Gray horizontal lines represent a 95% interval (based on the t distribution with 14 degrees of freedom) for a difference expected from the internal variability of the FV-CAM4 ensemble.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 284 284 21
PDF Downloads 80 80 5

Exploring a Multiresolution Approach Using AMIP Simulations

View More View Less
  • 1 Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
  • | 2 Department of Geography, University of Delaware, Newark, Delaware
  • | 3 School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama
  • | 4 Theoretical Division, Climate, Ocean, and Sea Ice Modeling Group, Los Alamos National Laboratory, Los Alamos, New Mexico
  • | 5 Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado
© Get Permissions
Full access

Abstract

This study presents a diagnosis of a multiresolution approach using the Model for Prediction Across Scales–Atmosphere (MPAS-A) for simulating regional climate. Four Atmospheric Model Intercomparison Project (AMIP) experiments were conducted for 1999–2009. In the first two experiments, MPAS-A was configured using global quasi-uniform grids at 120- and 30-km grid spacing. In the other two experiments, MPAS-A was configured using variable-resolution (VR) mesh with local refinement at 30 km over North America and South America and embedded in a quasi-uniform domain at 120 km elsewhere. Precipitation and related fields in the four simulations are examined to determine how well the VRs reproduce the features simulated by the globally high-resolution model in the refined domain. In previous analyses of idealized aquaplanet simulations, characteristics of the global high-resolution simulation in moist processes developed only near the boundary of the refined region. In contrast, AMIP simulations with VR grids can reproduce high-resolution characteristics across the refined domain, particularly in South America. This finding indicates the importance of finely resolved lower boundary forcings such as topography and surface heterogeneity for regional climate and demonstrates the ability of the MPAS-A VR to replicate the large-scale moisture transport as simulated in the quasi-uniform high-resolution model. Upscale effects from the high-resolution regions on a large-scale circulation outside the refined domain are observed, but the effects are mainly limited to northeastern Asia during the warm season. Together, the results support the multiresolution approach as a computationally efficient and physically consistent method for modeling regional climate.

Corresponding author address: L. Ruby Leung, Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division, 902 Battelle Blvd., Richland, WA 99352. E-mail: ruby.leung@pnnl.gov

Abstract

This study presents a diagnosis of a multiresolution approach using the Model for Prediction Across Scales–Atmosphere (MPAS-A) for simulating regional climate. Four Atmospheric Model Intercomparison Project (AMIP) experiments were conducted for 1999–2009. In the first two experiments, MPAS-A was configured using global quasi-uniform grids at 120- and 30-km grid spacing. In the other two experiments, MPAS-A was configured using variable-resolution (VR) mesh with local refinement at 30 km over North America and South America and embedded in a quasi-uniform domain at 120 km elsewhere. Precipitation and related fields in the four simulations are examined to determine how well the VRs reproduce the features simulated by the globally high-resolution model in the refined domain. In previous analyses of idealized aquaplanet simulations, characteristics of the global high-resolution simulation in moist processes developed only near the boundary of the refined region. In contrast, AMIP simulations with VR grids can reproduce high-resolution characteristics across the refined domain, particularly in South America. This finding indicates the importance of finely resolved lower boundary forcings such as topography and surface heterogeneity for regional climate and demonstrates the ability of the MPAS-A VR to replicate the large-scale moisture transport as simulated in the quasi-uniform high-resolution model. Upscale effects from the high-resolution regions on a large-scale circulation outside the refined domain are observed, but the effects are mainly limited to northeastern Asia during the warm season. Together, the results support the multiresolution approach as a computationally efficient and physically consistent method for modeling regional climate.

Corresponding author address: L. Ruby Leung, Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division, 902 Battelle Blvd., Richland, WA 99352. E-mail: ruby.leung@pnnl.gov

1. Introduction

Reliable projections of the future climate at regional scales are necessary to inform decision-making regarding mitigation and adaptation strategies in response to global warming. However, the confidence level associated with currently available information is still limited (Hewitson et al. 2014). Coupled general circulation (or global earth system) models are tools used to make such projections, but the current generation of models have difficulty simulating climate at the regional scale (Flato et al. 2013), partly from computational constraints that limit the spatial resolution feasible for long-term integration. Finer grid spacing is particularly important for regional climate, which is often tied to surface forcings such as topography and land-cover types. For instance, resolving complex terrains plays a critical role in simulating regional energy and water cycles in mountainous regions (Leung et al. 2004). Higher resolution also enables models to directly simulate features at smaller scales (e.g., mesoscale eddies, fronts, gravity waves) and their interactions with flows at larger scales (Orlanski 2008). Experimental global high-resolution simulations have shown some merit of finer grid spacing in simulating regional phenomena (Kinter et al. 2013; Bacmeister et al. 2014; Wehner et al. 2014). At the regional scale, however, the greater noise from natural variability may dominate the signals from certain climate forcing such as greenhouse gases (Hawkins and Sutton 2009). Hence, larger ensembles or longer integrations are necessary for the detection, attribution, or projection of climate change for regional applications, which further increase the computational burden and limit the use of global high-resolution models.

Given the above challenges, dynamical downscaling has been used in regional climate assessments as a more computationally feasible approach (Giorgi and Mearns 1991; van der Linden and Mitchell 2009; Mearns et al. 2012). A series of idealized experiments using the Big Brother Experiment protocol supports the idea that regional climate models (RCMs) can produce small-scale features that are absent in the driving boundary conditions (de Elía et al. 2002; Antic et al. 2006). Castro et al. (2012) showed some evidence of RCMs adding value from representing mesoscale processes for monsoon precipitation compared to a general circulation model (GCM) at T62 resolution. Dynamical downscaling is done primarily using “one-way nesting” in which an RCM obtains boundary conditions from offline GCM simulations, but treatments of the lateral boundaries (e.g., interpolation of the GCM output data in space and time) could be a source of model errors (Warner et al. 1997). It is also possible that the lack of two-way interactions overlooks important upscaled influences on the regional simulations (Inatsu and Kimoto 2009). Lorenz and Jacob (2005) showed that two-way nesting of an RCM over the Maritime Continent within a coupled GCM reduces some large-scale biases of the host GCM. Inatsu et al. (2012) found that two-way interactions between the nested RCM and host GCM can improve the model intrinsic modes of variability over the subtropical North Pacific by better resolving the Himalayas and the Maritime Continent. However, two-way nesting faces similar boundary condition issues of one-way nesting, and global conservation of mass and energy must be addressed to increase the credibility of long-term simulations (Wang et al. 2004; Leung et al. 2006; Inatsu and Kimoto 2009). Furthermore, in both one-way and two-way nesting, inconsistencies between the GCM and RCM model formulations may have undesirable effects, such as cancelling errors or artificial gradients near the lateral boundaries, because of differences in model resolution, physics parameterizations, dynamical cores, and interactions among them (Giorgi and Mearns 1991). A more recent two-way nesting system by Harris and Lin (2013, 2014) bypasses these issues by implementing the nesting within a single modeling framework, but the nested and coarse grids are marked by a sharp boundary between them; therefore, some challenges associated with nested modeling remain.

An alternative approach for regional climate modeling that has gained visibility in the last decade is global variable-resolution (VR) models with unstructured grids (St-Cyr et al. 2008; Ringler et al. 2011; Taylor 2011; Walko and Avissar 2011; Zarzycki et al. 2014). Unlike models that use stretching to refine grid resolution in areas of interest while reducing resolution in the rest of the globe (Fox-Rabinovitz et al. 2006; McGregor 2015), thus potentially sacrificing the fidelity of the large-scale circulation, VR GCMs with unstructured grids can be applied at quasi-uniform resolution globally or configured to include one or more high-resolution regions that transition smoothly from the quasi-uniform coarse-resolution grid. In this approach, finescale features are better resolved in the regions of interest, two-way interactions are achieved without artificial boundaries, parameterization schemes are consistently applied globally, and conservation consideration is more straightforward. Recently, Medvigy et al. (2013) used a VR model to explore possible teleconnections between the Amazon and western North America, and Zarzycki and Jablonowski (2014) showed some promising results in simulating Atlantic tropical cyclones using a GCM with a regionally refined grid.

The purpose of this study is to evaluate a global VR model called the Model for Prediction Across Scales–Atmosphere (MPAS-A) through simulations with realistic surface boundary conditions. This work follows earlier efforts at developing and testing MPAS-A, particularly its local grid refinement capabilities (Ringler et al. 2011; Skamarock et al. 2012; Park et al. 2013; Rauscher et al. 2013, hereafter RA13; Hagos et al. 2013; Rauscher and Ringler 2014; M. N. Martini et al. 2015, manuscript submitted to J. Adv. Model. Earth Syst.). More specifically, it is motivated by earlier studies of VR MPAS-A in idealized aquaplanet experiments. Figure 1 reproduces one of the previous findings, namely the undesirable zonal asymmetry created by introducing local grid refinement in an otherwise zonally symmetric aquaplanet (Fig. 14 in RA13). In RA13, MPAS-A was coupled with the physics parameterizations of the Community Atmosphere Model version 4 (CAM4; Neale et al. 2010), and the simulations were run for 5 yr forced by the “control” sea surface temperature (SST) distribution proposed in Neale and Hoskins (2000). The result shown in Fig. 1 is obtained from a VR simulation with 8 times grid refinement over the tropics in which grid spacing smoothly varies from ~30 km in the refinement center to ~240 km outside the refinement. Enhanced precipitation is seen on the downwind side of the refined domain along the equator, and the associated diabatic heating excite equatorial waves as expected based on the linear theory of Gill (1980) for localized heating in the tropics (Fig. 1a). The rotational and divergent components of the stationary wave response are shown in Fig. 1a (contours) and Fig. 1b, respectively. RA13 performed Held–Suarez tests with no physics (Fig. 1c), which confirmed that the artifacts of grid refinement in the aquaplanet simulations are largely attributable to the resolution-dependent physics parameterizations most likely related to the moist processes. In another simulation with the higher-resolution mesh placed over the midlatitudes, baroclinic eddy activity is locally enhanced in the vicinity of the refined mesh (Rauscher and Ringler 2014).

Fig. 1.
Fig. 1.

(a) The 200-hPa eddy streamfunction (m2 s−1/10 × 106, contours) and precipitation deviations from the zonal mean (mm day−1, shaded) for the CAM–MPAS VR simulation. The 200-hPa eddy velocity potential (m2 s−1/10 × 106) for the (b) variable-resolution aquaplanet simulation and (c) Held–Suarez simulation. These are climatological means over 4.5 yr reproduced from Fig. 14 in Rauscher et al. (2013; used with permission).The high-resolution region of the VR mesh is outlined by the gray circle in the center of the panels.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

These earlier results have motivated a continued and rigorous examination of the MPAS-A VR approach, extending from aquaplanet to real-world simulations to address two issues. First, we are attempting to determine whether surface characteristics such as topography, which provide important forcing to regional climate in real-world simulations, enable MPAS-A VR to reproduce the features of the global high-resolution simulations in the high-resolution domain. Our primary interest is precipitation because of its high spatial variability that is directly linked to model horizontal resolution. Second, we are examining the MPAS-A VR to determine if it exhibits remote upscale effects from the refined grid to the coarse grid in real-world simulations. One hypothesis is that multiple and complex forcing such as zonally nonuniform SST, land–sea contrast, and land-cover and topographical heterogeneity (e.g., Inatsu et al. 2002; Held et al. 2002; Inatsu and Hoskins 2004) would dominate the unphysical signals produced by local grid refinement. On the other hand, one could conjecture that the real-world forcing may amplify such signals. Our overarching goal based on the above two challenges is to evaluate the VR model as a feasible, promising approach for regional climate modeling that is computationally efficient as well as consistent in model physics, dynamics, and engineering.

The structure of this paper is as follows. We begin with describing the model and experiment design in section 2. Section 3 presents the results, first focusing on the large-scale aspect, followed by the model behavior in the high-resolution regions and an evaluation of upscale effects. In section 4, we discuss our findings compared with previous studies and questions left for future work. Section 5 summarizes this study.

2. Methods

a. Model description: MPAS-A with CAM4 physics

MPAS is a framework for building dynamical cores of the atmosphere (MPAS-A), land ice (Edwards et al. 2014), and ocean systems (Ringler et al. 2013) based on unstructured meshes. The MPAS grid is based on spherical centroidal Voronoi tessellations (SCVTs) that discretize a sphere into highly uniform mesh (Ringler et al. 2008, 2011; Ju et al. 2011) and avoid the issues with polar singularity in regular latitude–longitude grids (Williamson 2007). SCVT is also capable of local grid refinement with remarkably smooth transition and quasi-uniform cell shape, minimizing unphysical grid imprinting, wave distortions, and difficulty in numerical diffusion and filtering. In MPAS-A the conservation equations are spatially discretized over SCVT to simulate faithfully regional-scale atmospheric motions with a high-resolution grid (i.e., less than ~1°) while achieving several conservation properties desirable for long-term climate simulations (Ringler et al. 2010, 2011; Skamarock et al. 2012). C-grid staggering is adopted for its superior performance in simulating divergent motions dominant in the mesoscale or smaller scales (Skamarock 2011; Thuburn 2008; Thuburn et al. 2009; Ringler et al. 2010). The conservation equations are solved by a finite-volume method with a third-order transport scheme developed for unstructured grids by Skamarock and Gassmann (2011) and temporally integrated by the explicit time-splitting approach as in the WRF Model (Skamarock et al. 2008). A forward–backward method is used for fast modes (e.g., gravity waves), and the third-order Runge–Kutta scheme is used for the other terms such as advection and physics (Wicker and Skamarock 2002; Klemp et al. 2007; Park et al. 2013).

As both hydrostatic and nonhydrostatic solvers are available in MPAS-A as described in Park et al. (2013), we used the hydrostatic version, which has been coupled to a suite of physics parameterizations from CAM4. The vertical coordinate is a hybrid sigma pressure with 26 levels with a model top at 2.9 mb similar to the default CAM4, except that the MPAS-A uses dry instead of full pressure (Park et al. 2013). RA13 documented in detail the application of MPAS-A using CAM4 physics for aquaplanet simulations, which have been examined by several studies in comparison with other dynamical cores (Hagos et al. 2013; O’Brien et al. 2013; Landu et al. 2014; Yang et al. 2014) in the coordinated project “Development of Frameworks for Robust Regional Climate Modeling” (Leung et al. 2013). The CAM4 physics package was chosen for this study for consistency with previously reported aquaplanet experiments, although a newer generation package (CAM5) is available (Neale et al. 2012). The components of the CAM4 parameterization are shown in Table 1, and readers are referred to references in Neale et al. (2013) for details, who documented the general performance of CAM4 with its default finite-volume dynamical core (FV-CAM4; Lin 2004). RA13 compared MPAS-A (-CAM4) and FV-CAM4 in aquaplanet simulations and found weaker general circulation, smaller tropical precipitation rates, and less frequent extreme precipitation in MPAS-A than FV-CAM4.

Table 1.

CAM4 physics parameterizations and their main references.

Table 1.

b. Experimental setup

Our experiment consisted of four global Atmospheric Model Intercomparison Project (AMIP) style simulations (Gates 1992) with different SCVT grids (Table 2). Each simulation spanned 11 yr from 1999 to 2009, with the first year excluded as spinup. The first simulation was run at quasi-uniform resolution with cell-center size of about 120 km (UR120). The second simulation also used a quasi-uniform grid but with 30-km cells (UR30). The other two simulations employed VR meshes as shown in Fig. 2. The coarse-resolution domain in these VR simulations had 120-km cells, while the circular finer-resolution domain was composed of 30-km cells. In one VR simulation, we placed the finer grid domain over South America (VR-SA) centered at 10°S, 60°W; in the other, the refinement was located over North America (VR-NA) centered at 30°N, 90°W. The radius of the high-resolution domain is ~30° and the width of the transitional zone is the same, providing a smooth change in grid spacing (Fig. 2c). The high-resolution domain covers only ~6% of the surface area of the earth, and the transition zone occupies another 6%. We focused on 4 times refinement from 120 to 30 km, such that the coarse domain corresponds with typical resolutions used in the current generation of global models for long-term simulations [e.g., in the Coupled Model Intercomparison Project (CMIP5); Taylor et al. 2012]. The finer mesh was comparable to the resolution commonly employed in dynamical downscaling studies using RCMs (e.g., Castro et al. 2012; Gao et al. 2014). With this VR configuration, the number of horizontal grid cells is reduced by more than a factor of 6 compared to the quasi-uniform high-resolution simulation (i.e., 655 362 grid cells in UR30 versus 102 402 in VR simulations Table 2) and computational time scales almost linearly with the gridcell numbers (a factor of 6 shorter for the VR simulations) in our typical configuration using 1024 nodes (24 576 cores) on Hopper at the National Energy Research Scientific Computing Center.

Table 2.

MPAS-A experiments.

Table 2.
Fig. 2.
Fig. 2.

The two variable-resolution grid setups in this study: refinement over (a) South America and (b) North America. The solid and dashed circles represent approximate boundaries enclosing the domain with ~30-km grid and the transition to 120-km grid domain, respectively., These are also reflected as the vertical lines in (c) where the gridcell size is plotted as a function of radial distance from the center of the refined domain. The trapezoid in (a) and (b) represents the area used to calculate the area-average and performance statistics for the high-resolution area.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Following the perfect model approach adopted in the aquaplanet experiments, the global high-resolution simulation (UR30) was used as the reference to evaluate the VR simulations. The error (VR − UR30) is partitioned as follows after Hagos et al. (2013):
e1
The first term on the right-hand side of Eq. (1) represents errors due to the resolution difference; the second refers to the influence of regional grid refinement that would manifest as downscale effects in the high-resolution domain (e.g., expected improvement or unphysical artifacts) and upscale effects in the coarse-resolution domain.

All simulations used the same time steps of 900 s for physics and 100 s for dynamics, which is constrained by the high-resolution UR30. The same physics time step enables us to isolate the sensitivity of parameterizations to horizontal resolution from that to time step, but it is shorter than the assumed time scales in the deep (~1 h) and shallow convection (~30 min) schemes designed for coarse-resolution global models (Williamson 2013). In this experiment, only the coefficient for numerical diffusion is adjusted at each resolution, while all the other model parameters are kept the same to isolate the effects of changing the mesh. There is no attempt to tune each configuration to match the observations more closely or to produce the same net radiation balance at the top of the atmosphere. Nevertheless, as shown later, the overall quality of the simulations is reasonable. Fourth-order hyperviscosity was used for numerical diffusion, and the diffusion coefficient was tuned based on the quasi-uniform 60-km aquaplanet simulation by RA13. The coefficient is then adjusted for different grid spacing following the empirical scaling from Boville (1991) and Takahashi et al. (2006). We have examined the kinetic energy spectrum for the horizontal wind at 250 hPa from the MPAS-A real-world simulations, and they are visually indistinguishable from the corresponding aquaplanet simulations (Fig. 2 in RA13), except for the smallest wavenumbers that are presumably affected by the land and topography distribution (not shown). In the VR simulations, the diffusion coefficient is a function of cell size, varying from 5 × 1012 m4 s−1 at the finest cell (same as UR30) to 5 × 1014 m4 s−1 at the coarsest cell (same as UR120). Following RA13, the MPAS-A output data are conservatively remapped to the regular latitude–longitude grids used in FV-CAM4 (0.9° × 1.25° and 0.23° × 0.31°) by Spherical Coordinate Remapping and Interpolation Package (Jones 1999).

Topography inputs for all the simulations are produced using the software described in P. H. Lauritzen et al. (2015, manuscript submitted to Geosci. Model. Dev.). This software remaps the GTOPO30 global elevation dataset on an ~1-km grid (Gesch and Larson 1998) to any structured or unstructured grid, including the VR mesh, using the Conservative Semi-Lagrangian Multitracer Transport Scheme (CSLAM) remapping technology (Lauritzen et al. 2010). The topography field remapped onto a VR grid varies smoothly with the gridcell size as shown in Fig. 3. Subgrid-scale variance of topography (SGH) is also calculated consistently with the resolved surface elevation (SGH is used for the gravity wave drag parameterization). No smoothing is applied to the topography, and as shown later the variability of vertical velocity in our MPAS simulations is overpredicted in mountainous regions compared to reference datasets, though its effect is localized (e.g., no Gibbs oscillations in the MPAS finite-volume model). Optimal smoothing would suppress such small-scale noises (Webster et al. 2003; Evans et al. 2013), so the sensitivity of VR MPAS simulations to the resolution of topography is being analyzed in a separate study.

Fig. 3.
Fig. 3.

Gridcell structures across the transition from the coarse-to-fine domains over (a) South America and (b) North America, as shown in Fig. 2. The approximate boundaries for the high-resolution and transition regions are depicted by red lines. The color shading shows surface geopotential height (m).

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Our simulations closely follow the AMIP implementation in the Community Earth System Model (CESM) as in Neale et al. (2013), which in turn is compatible with the AMIP protocol in CMIP5 (Taylor et al. 2012). Over land, the lower boundary conditions are simulated by the Community Land Model (CLM), version 4 (Lawrence et al. 2011). CLM4 was run on a 0.23° × 0.31° latitude–longitude grid for all simulations and conservatively remapped to different MPAS-A grids by the flux coupler (Craig et al. 2012). Over the ocean the SST and sea ice concentrations are prescribed based on observational data on a 1° × 1° grid (Hurrell et al. 2008). The topography and land–sea mask are independently provided to the atmospheric model at their own grid resolution. Specification of other forcings (greenhouse gases, aerosols, etc.) also follows the AMIP protocol, which is the same as the historical experiment of CMIP5.

c. Reference data

Although we adopted the perfect model framework for our analysis [Eq. (1)], we evaluated the general realism of the simulated climate by comparing the global climatological field in MPAS-A to ERA-Interim (Dee et al. 2011). We also used the six-member ensemble of the FV-CAM4 AMIP simulations (Neale et al. 2013) available from the CESM experiment (Community Earth System Model 2014). This ensemble simulation was run with a well-tested, default CAM4 configuration at a comparable resolution (0.9° in latitude, 1.25° in longitude) to UR120. The intermember difference of the FV-CAM4 ensemble was used to infer the uncertainty due to internal variability in assessing the upscale effects in the MPAS-A VR simulations.

In addition, we used two observational datasets for precipitation because of the high uncertainty in this field in reanalysis products and climate models (Bosilovich et al. 2008; Neale et al. 2013): the Global Precipitation Climatology Project (GPCP) version 1.1 daily products on a 1° × 1° latitude–longitude grid (Huffman et al. 2001, 2009) and the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) on a 0.25° × 0.25° latitude–longitude grid (Huffman et al. 2007). Both products were produced from multiple remote sensing and gauge measurements, but they reveal different estimates even at annual time scales (Huffman et al. 2007), thus providing a range of observational uncertainty.

3. Results

a. Large-scale features

We begin with the description of large-scale features in the uniform low- and high-resolution runs followed by the VR simulations. Figure 4a compares FV-CAM4, UR120, and UR30 to ERA-Interim for eight variables: surface pressure, horizontal wind speed (both 200 and 850 hPa), geopotential height (500 hPa), vertical velocity (500 hPa), cloud cover fraction, precipitable water, and surface air temperature. The mean bias, Pearson spatial pattern correlation, and spatial variability were calculated for each month’s climatology and averaged over 12 months for display using Taylor diagrams (Taylor 2001). For most of the variables considered, FV-CAM4 and the two MPAS-A simulations are clustered tightly together, and their mean biases are also similar (represented by the size and shape of the symbols). Most of the variables show a correlation higher than 0.9 and variability within 25% of that in ERA-Interim. A notable exception is vertical velocity at 500 hPa; FV-CAM4 shows reasonable correlation (~0.7) and similar variability to ERA-Interim, but MPAS-A shows much smaller correlation (~0.2) and higher variability (about 3 times as high as ERA-Interim; symbols shown outside Fig. 4a in the lower left). Although MPAS-A and FV indicated some distinct characteristics in vertical velocity in aquaplanet simulations (RA13), the difference is mainly due to the topography in our simulations as discussed in section 2b.

Fig. 4.
Fig. 4.

Taylor diagrams summarizing the climatology of different variables over the global domain. Variables are denoted by the numbers shown in the bottom legend. Horizontal wind is evaluated as (u2 + υ2)1/2. (a) MPAS quasi-uniform low- (120-km grid cell; UR120) and high-resolution (30-km grid cell; UR30) and FV-CAM4 (Neale et al. 2013) are compared to ERA-Interim. (b) VR-SA, VR-NA, and UR120 are compared to UR30. The size and shape of the symbol represent mean bias after being normalized by the mean value of the reference data and is shown as a percentage (legend given in the center). Note that in (a) the vertical velocities for UR120 and UR30 are shown outside the diagram because their normalized standard deviations (shown near the symbol by the number on top; the bottom value is anomaly correlation) are outside the range of the diagram.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Having confirmed that our MPAS-A simulations agreed reasonably well with the ERA-Interim and FV-CAM4 climate, we looked into the sensitivity of MPAS-A to grid resolution in greater detail. In Fig. 4b, the two VR simulations and UR120 are compared against UR30 over the whole globe, including both the high- and low-resolution domains in the VR simulations. In general, the global errors for the VR and UR120 simulations are similar. The greater deviations in cloud cover and vertical velocity of all the three simulations from UR30 reflect the strong sensitivity of the CAM4 physics package to grid resolution (Williamson 2008; O’Brien et al. 2013; RA13; Zarzycki et al. 2014) and the finer topography representation in UR30. These features are similar for the June–August (JJA) and December–February (DJF) means (not shown).

The zonal-mean circulation in and the errors relative to UR30 in the other three simulations are depicted in Fig. 5. The spatial average is taken over all longitudes, including both the high- and low-resolution domains in the VR simulations. It is common to see changes in the tropospheric westerly jet with changing horizontal resolution (e.g., Pope and Stratton 2002), as this is the case with UR120 and UR30 (Figs. 5c,d). The sensitivity of the jet to the resolution in MPAS-A is also observed in the aquaplanet experiments by RA13. Specifically, the strength of the jet core decreases and its location moves poleward with finer grid spacing. Similar changes in the jet are seen in the AMIP simulations, which appear more clearly in the Southern Hemisphere (SH) than in the Northern Hemisphere (NH). Looking at the error of VR-SA (i.e., VR-SA−UR30) in comparison to UR120 (i.e., UR120−UR30; Figs. 5e,f), notable features include reduced error in the SH jet in DJF, somewhat increased error in the NH mid- and high latitudes in DJF, and a greater difference from UR30 in the SH jet in JJA. For VR-NA−UR30, the error is generally reduced over all latitudes in DJF compared to JJA (Figs. 5g,h). It is intriguing to note that the mid- to high-latitude circulations in the NH (SH) are affected by the grid refinement centered in the SH (NH).

Fig. 5.
Fig. 5.

Zonal-mean zonal velocity (color, m s−1) and meridional–vertical wind (vectors) from UR30 for (a) DJF and (b) JJA mean, and their differences from UR30 in (c),(d) UR120, (e),(f) VR-SA, and (g),(h) VR-NA for each season. The vectors are scaled arbitrarily and differently for (a) and (b) and (c)–(h) because of the difference in the units and typical magnitude between the meridional and vertical (pressure) velocity. The left ordinate shows pressure height (hPa) and the right ordinate shows approximate height (km).

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

To examine the longitude-dependent error in the zonal wind, we plotted the zonal wind at the 200-hPa level from UR30 and the model error in UR120 and the VR simulations (VR-SA and VR-NA) relative to UR30 in Fig. 6. For the baseline (UR30) distribution, the zonal wind in the winter hemisphere in general has a more zonal asymmetry than the summer hemisphere (Figs. 6a,b). Contrasting UR120 with UR30 in the NH in DJF, the zonal wind shifts equatorward in UR120 in both hemispheres and in both the Pacific and Atlantic basins (Fig. 6c). This jet shift-related bias in the NH exhibits considerable dependence on longitude, leading to a cancellation of errors and small biases in the zonal averages shown in Fig. 5c. In JJA, the wind bias is characterized more by intensification than a shift in latitude (Fig. 6d). Relative to the UR120−UR30 zonal wind error, the errors in the VR runs are generally reduced (Figs. 6e–h), with an exception in the SH midlatitudes near South America during austral winter, where and when the westerly jet is intensified and displaced to the south, resulting in a longitudinally coherent error in the 45°–60°S band (Figs. 6f,h). According to a Student’s t test, the difference is statistically significant at the 5% level over most of the longitudes, although the statistical confidence should be interpreted with care because of the length of the simulation (10 yr) and high interannual variability of the SH circulation (Trenberth 1981).1

Fig. 6.
Fig. 6.

Maps showing zonal wind (m s−1) at 200-hPa level for the UR30 for the (a) DJF and (b) JJA mean, and the difference between UR30 and (c),(d) UR120, (e),(f) VR-SA, and (g),(h) VR-NA for each season. All the data are remapped to a 0.9° × 1.25° grid. The crosshatch in (b)–(d) indicates the grid cells with statistical significance at α = 0.05 level based on two-sided Student’s t tests against the null hypothesis of no difference.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

The westerly jet response to resolutions exhibits a vertically coherent structure in the midlatitudes (Figs. 5c–h), suggesting that it is the eddy-driven jet, rather than the subtropical jet driven by the mean angular momentum transport (Lee and Kim 2003; chapter 12 in Vallis 2006), that is sensitive to the regional grid refinement. Indeed, eddy momentum flux in the SH midlatitudes is enhanced in VR-SA over most of the longitudes and in VR-NA over the longitude range corresponding to its grid refinement, and the associated increase in the momentum flux convergence spatially coincides with the stronger jet (not shown). Rauscher and Ringler (2014) found that grid refinement enhances baroclinic eddy activity inside and downwind of the high-resolution domain in their aquaplanet experiments. Therefore, grid refinement might energize the wintertime eddy activity development in the SH storm track, which maintains a stronger jet (discussed further in section 4). However, the exact dynamical mechanisms linking the grid refinement to midlatitude circulation can be complicated and is beyond the scope of this study. In section 3c, we assess the actual impact of these changes to regional scale climate in the areas remote from the grid refinement (i.e., upscale effects).

b. Refined domain

We examined the high-resolution domain in VR to address our first question: whether the MPAS-A VR simulations can reproduce the features in the global high-resolution MPAS-A in the refined region. Only results from the warm season are shown because differences between high and low resolutions over the United States (VR-NA) are more notable in JJA. For South America (VR-SA), the following findings generally apply to all seasons.

DJF mean precipitation over South America is shown in Fig. 7. Compared to the two observational estimates, gross underestimation by UR120 is evident (Figs. 7a–c). This deficiency is partly from the lack of tuning toward observations as discussed in section 2b. Still, UR120 reasonably simulates the moisture flux throughout the region as suggested by observational studies (e.g., Marengo 2005), including the moist northeasterly trade winds from the equatorial Atlantic Ocean that converge and produce precipitation in the central Amazon basin (Fig. 8a). The remaining moisture is carried southward by the South American low-level jet (SALLJ; Marengo et al. 2004). The SALLJ converges with the inflow from the subtropical Atlantic high and creates another precipitation peak near the coastal area in the La Plata basin (centered at 25°S, 55°W).

Fig. 7.
Fig. 7.

Spatial maps for DJF mean precipitation (mm day−1) over the South America region. Two observational datasets: (a) GPCP v1.1 on 1° grid and (b) TRMM-TMPA on 0.25° grid. And the MPAS simulation outputs: (c) UR120, (d) UR30, and (e) VR-SA.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Fig. 8.
Fig. 8.

(top) DJF mean moisture convergence in color (mm day−1) and moisture flux with vectors integrated from the surface to 50-hPa level (kg m−1 s−1) in (a) UR120, (b) the difference between UR120 and UR30, and (c) the difference between UR120 and VR-SA. (bottom) DJF mean evapotranspiration (mm day−1) in (d) UR120 and their differences in (e) UR30 and (f) VR-SA. The crosshatch indicates the grid cells with statistical significance at α = 0.05 level (for shaded quantities).

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

With the higher horizontal resolution of UR30, the intensity of precipitation is dramatically increased, and the precipitation swath running across South America from the northwest to southeast [as seen in the observations and known as the South Atlantic convergence zone (SACZ)], is more clearly defined in UR30 (Fig. 7d). Notable increases occur over the central Amazon, at the mouth of the Amazon River, in the La Plata basin, and in the oceanic portion of the SACZ (30°S, 37°W). The former two areas are associated with greater moisture convergence as a result of slightly increased flux from the Atlantic Ocean near the equator and reduced flux out from the Amazon to the south (Fig. 8b). Somewhat unexpectedly, the finer topography of the Andes in UR30 does not necessarily increase the northerly moisture flux with the SALLJ. For the La Plata basin, the flux from the ocean is higher in UR30 near 16°S, but the main source of the additional moisture seems to be provided by the enhanced surface evapotranspiration (Figs. 8b,e).

Notably, VR-SA captures all of the above features of precipitation in UR30 over the South American continent (Fig. 7e). The same applies to the horizontal moisture flux (Fig. 8c) and surface evaporation (Fig. 8f), indicating that the hydrological cycle of South America in VR-SA is quite close to that of UR30. The enhanced moisture flux is associated with the stronger easterly wind at lower levels in both UR30 and VR-SA, which is linked to the stronger meridional pressure gradient over the South Atlantic Ocean when compared to UR120 (Fig. 9a). This dependence of meridional pressure distribution on grid resolution is clearly seen in aquaplanet simulations not only in MPAS-A (RA13) but also in other dynamical cores in CAM (e.g., Williamson 2008). In addition, the importance of the pressure distribution over the Atlantic Ocean for modeling South American climate was shown in previous studies (Rauscher et al. 2006; Seth et al. 2007). The climatology of the monsoon season is dependent on several feedbacks; therefore, the stronger precipitation in South America at higher resolution also contributes to the stronger South Atlantic high. This resemblance of VR-SA to UR30 in the surface pressure pattern demonstrates the ability of MPAS-A to simulate faithfully the dynamics in the coarse-to-fine mesh transition that coincides with most of the Atlantic Ocean (Fig. 2).

Fig. 9.
Fig. 9.

Zonal-mean sea level pressure difference (hPa) from UR30 (blue), VR-SA (red), and VR-NA (yellow) compared to UR120, averaged over (a) DJF and 40°W to 0°E (corresponding to South Atlantic), and (b) JJA and 70°W to 20°W (corresponding to North Atlantic).

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Although the above results are very promising, some undesirable effects of grid refinement on the precipitation are noted in the intertropical convergence zone (ITCZ) over the ocean. While the ITCZ precipitation in UR120 and UR30 is similar in the equatorial Atlantic (0°N, 30°–50°W), it is substantially reduced in VR-SA (Figs. 7c–e). On the other hand, near the western boundary of the high-resolution area in the eastern equatorial Pacific (5°N, 85°W), the precipitation peak is increased from UR120 to UR30 and even more so in VR-SA, leading to a greater deviation of VR-SA from the observed value. This enhancement of the ITCZ precipitation is presumably similar in nature to that of the aquaplanet MPAS-A (cf. Fig. 1a), but it is obscured by the response to the zonally asymmetric forcings and natural variability in VR-SA, all of which warrant further investigation.

Over North America, the influence of horizontal resolution does not appear as clearly as over South America, but there are notable differences. Comparing Figs. 10a–c, UR120 again underestimates the warm-season (JJA) precipitation: the precipitation tongue of ~5 mm day−1 in the Great Plains is lacking, the gradient across the central United States is shifted to the east, and the precipitation band is weaker off the east coast in the Atlantic storm track. In both UR30 and VR-NA, there are visible increases in precipitation over the southeastern United States and along the Atlantic storm track (Figs. 10d,e). The precipitation gradient across the Great Plains is somewhat improved, but the observed precipitation tongue is still lacking in UR30 and VR-NA. UR30 and VR-NA also extend the precipitation band northwest along the Sierra Madre Occidental, but this increase as well as that over the southern Appalachian Mountains may be related to the lack of topographic smoothing in addition to the resolution change. The response of precipitation to grid resolution is similar to that in CAM4- and CAM5-FV reported by Bacmeister et al. (2014; their Fig. 13, right column). It is interesting that they found a stronger response to grid resolution in winter rather than in summer, which is the opposite of our result (the winter result not shown). The focus remains on JJA because the contrasting seasonal response may be related to different experimental design (e.g., ice-cloud effective radius is modified for the high-resolution simulation in Bacmeister et al. 2014). The improvement of VR-NA is less substantial over the ocean, which indicates the dominant role of land surface forcings that both UR30 and VR-NA are better able to capture compared to UR120.

Fig. 10.
Fig. 10.

(a)–(e) JJA mean precipitation over the North America region. (f) Evapotranspiration (mm day−1) in UR120 and the difference from UR120 in (g) UR30 and (h) VR-NA. The crosshatch in (g) and (h) indicates the grid cells with statistical significance at α = 0.05 level. Note that in (e) the transition region appears noisy because of the imprint of the native (hexagonal) grid by conservative remapping to the high-resolution (0.23° × 0. 31°) regular grid.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Moisture convergence into the central, south-central, and southeastern United States is similar among the three simulations (not shown), but evapotranspiration is elevated in UR30 and VR-NA (Figs. 10g,h). The increase in surface evaporation with higher horizontal resolution was analyzed in detail by Hagos et al. (2014). It is expected that as resolution is refined, gustiness increases and the newly resolved eddies transport moisture out of the boundary layer, further enhancing the surface fluxes. CAM4 physics was shown to underestimate the eddy moisture flux when compared to explicitly resolved eddy mixing at higher resolution (Hagos et al. 2014). Thus, in the high-resolution domain (and in UR30), we see higher surface fluxes compared to UR120. Clearly it is desirable to have the parameterizations be more scale aware and minimize such differences. The meridional surface pressure gradient over the North Atlantic is similar across the three simulations (i.e., not so sensitive to resolution; Fig. 9b), and the low-level easterly flow into the Gulf of Mexico is similar regardless of model resolution. In addition, the poleward moisture flux through the low-level jet (LLJ) across the central United States also does not show sensitivity to grid resolution, similar to South America (Figs. 8a–c). The lack of strong sensitivity of the LLJ may indicate that the resolution of UR120 is adequate for simulating the general features of the LLJ. Several previous studies suggest that the structure of the LLJ can be simulated reasonably by GCMs of 1° or even 2° gridcell size (Ghan et al. 1996; Jiang et al. 2007) as well as by a stretched-grid model with refined mesh regions of 100- and 60-km grid spacing (Fox-Rabinovitz et al. 2005), although the variance or other higher-moment statistics may require higher resolution (Werth et al. 2011). Over eastern Canada where moisture convergence is greater in UR30 and VR-NA, precipitation is also greater in UR30 and VR-NA than UR120.

Following the diagnosis on the spatial pattern of precipitation, we briefly show the monthly time series of the simulated precipitation averaged over the high-resolution area (the rectangles in Figs. 2a,b). Over South America, both UR30 and VR-SA consistently predict higher precipitation than UR120, and they are quite close to each other (Fig. 11a). It appears that the systematic underestimation of precipitation by UR120 relative to the observations is almost removed by switching to finer mesh in UR30 and VR, although the dry-season precipitation becomes slightly overestimated in UR30 and VR-SA. Over North America, sensitivity of simulated precipitation to horizontal resolution is blurred by the interannual variability (Fig. 11b), but overall, UR30 and VR-NA tend to simulate more precipitation than UR120 (see the mean values shown in each panel). Figures 11c,d present the partitioning of modeled precipitation as the ratio of the resolved component (from grid-scale saturation) to the total precipitation. Over South America, this ratio consistently increases in UR30 and VR-SA (Fig. 11c) and reflects that the greater precipitation in the two simulations results mainly from the increase in grid-scale precipitation rather than convective precipitation consistent with previous studies using CAM3 and CAM4 physics for regions where deep convection is common (e.g., Williamson 2008; Boyle and Klein 2010; Hagos et al. 2014; Bacmeister et al. 2014). Over North America, this ratio is unchanged between the low and high resolutions, except for the warm seasons in some years (Fig. 11d). The characteristic partitioning in each region is consistent throughout the seasons and years, thus providing another illustration that precipitation over South America is more sensitive to horizontal grid resolution.

Fig. 11.
Fig. 11.

Time series of monthly precipitation (mm day−1) averaged over the area within the high-resolution area of the VR simulations (40° × 40° trapezoidal area in Fig. 2) for (a) South America, (b) North America, and the ratio of grid-scale precipitation to total precipitation (from the model simulations only) for (c) South America and (d) North America. The numbers in the upper-right corner of each panel are the mean values of the time series, shown in the corresponding colors.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Figure 12 summarizes the comparison of our MPAS-A simulations on other variables (see the legend) inside the two high-resolution areas. The statistics are calculated within the rectangular areas defined in Fig. 2 using UR30 as the reference. First focusing on UR120, pattern correlations of 0.5–0.9 and mean biases of 10%–20% are generally found for variables related to the moist parameterizations and those directly influenced by them (Figs. 12a,b,d,e).2 The error of UR120 relative to UR30 is generally larger over South America than over North America. In both domains, the high fidelity of the VR runs to UR30 is obvious. Such distinct performance statistics are seen only in March–May (MAM; not shown) and JJA over North America; for South America, the results are similar in all seasons. This seasonal dependence in North America indicates the dominance of large-scale processes during winter and fall.3

Fig. 12.
Fig. 12.

Taylor diagrams showing performance statistics of (a)–(c) UR120 and VR-SA compared to UR30 within the refined grid over South America (40° × 40° rectangular area in Fig. 2) for DJF and (d)–(f) UR120 and VR-NA compared to UR30 over North America. The top and bottom panels each show the different groups of variables that are represented by the numbers shown in the middle. The size and shape of the symbol indicate mean bias after normalized by the mean value of the reference data and shown as a percentage (legend given in the center). Horizontal wind is evaluated as (u2 + υ2)1/2.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Our last diagnosis for the high-resolution areas is the probability distribution of daily precipitation. The high sensitivity of this quantity to grid resolution is seen in previous studies (e.g.,Tripathi and Dominguez 2013; Zhang et al. 2013; Yang et al. 2014; Wehner et al. 2014), and it is desirable that a VR simulation reproduces the global quasi-uniform high-resolution simulation with respect to these statistics. Figure 13 confirms that this is indeed the case with our MPAS-A simulations. The cumulative distribution functions in this figure were constructed from the days with ≥0.1 mm precipitation following Iorio et al. (2004), after all the data were conservatively remapped to the same grid as UR120. In both regions, the curves from UR30 and the VR simulations are nearly identical. The distributions of UR30 and VR remain different from those in the observational data as already indicated by the above analysis (Figs. 7, 10, and 11), although the uncertainty in the observational data is quite high in the extreme events (the difference between GPCP and TRMM-TMPA).

Fig. 13.
Fig. 13.

Cumulative distributions of daily precipitation within the high-resolution regions (40° × 40° trapezoidal area in Fig. 2) in (a) South America (DJF) and (b) North America (JJA). The distributions are obtained after all the data are regridded to a 0.9° × 1.25° grid (same as FV-CAM4) by conservative remapping.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

c. Remote upscale effects

The second goal of this study was to examine the upscale effects from regional grid refinement [the second term on the right-hand side of Eq. (1) in the VR low-resolution domain using UR120 as a reference]. As mentioned in the introduction, earlier studies using MPAS-A in aquaplanet simulations found that regional refinement produces erroneous upscale effects (Hagos et al. 2013; RA13). Conversely, previous studies using two-way nested models with real-world boundary conditions have observed positive upscale effects manifested as the reduced model bias outside of the nested regions (Lorenz and Jacob 2005; Inatsu et al. 2012).

We present the result for the JJA mean because we found the upscale effect on the circulation being the strongest during this season. The differences between UR120 and the other three simulations in the midlevel temperature (500 hPa) and upper-level velocity potential and streamfunction (200 hPa) are shown in Fig. 14. Divergent and rotational components of the horizontal wind vectors are also shown with velocity potential and streamfunction, respectively. The difference between UR30 and UR120 is characterized by cooling in the tropics and warming in the mid- and high latitudes at the midtroposphere (Fig. 14a), stronger divergence field in UR30 with a wavenumber 1 pattern (Fig. 14b), and zonally oriented structure in the streamfunction corresponding to the changes of tropospheric jets (Fig. 14c). The difference in the temperature is consistent with the overall weaker westerlies in UR30 (Fig. 5d) through thermal wind balance. The spatial pattern in the divergence difference is similar to the pattern of the divergence field itself, which is associated with the large-scale monsoonal overturning circulations (Trenberth et al. 2006), indicating enhanced monsoonal circulations in UR30. Upscale effects are seen in VR-SA in all three fields (Figs. 14d–f). Focusing on the differences outside of the mesh refinement or remote upscale effects, the most striking difference from UR120 appears in the southern mid- and high latitudes. The zonally coherent pattern centered at 40°S in the streamfunction indicates significant changes in the jet stream (Fig. 14f) as already noted in section 3a. The mostly zonal spatial structure does not resemble the Rossby wave propagation patterns that the teleconnection mechanism normally depicts (e.g., Hoskins and Ambrizzi 1993; Mo and Higgins 1998; Ding et al. 2012). This scenario suggests that the broad influence of the regional refinements in VR-SA and VR-NA may involve strong feedbacks from the transient eddies in the midlatitudes. A relatively strong upscale effect is also observed in the velocity potential over the western Pacific and Australia, likely a response to the enhanced convergence over South America and the Atlantic Ocean in the refined and transitioning domains (Fig. 14e). The upscale effects in VR-NA exhibit similar patterns to VR-SA but with somewhat stronger (weaker) response in the NH (SH; Figs. 14g–i). A notable feature in VR-NA is the enhanced streamfunction across the North Pacific storm track. While differing slightly in the latitudinal location, this pattern looks similar to the vorticity anomaly pattern in response to a disturbance located in the northeastern United States simulated by a linearized barotropic model in Hoskins and Ambrizzi (1993), thus implying possible sensitivity of circulation in parts of Asia to grid refinement over North America.

Fig. 14.
Fig. 14.

(left) The difference between UR-30 and UR120 in the 10-yr JJA mean of (a) temperature at 500-hPa level (K), (b) 200-hPa velocity potential (m2 s−1/106, shading) and divergent wind (m s−1, vectors), and (c) 200-hPa streamfunction (m2 s−1/107, shading) and rotational wind (m s−1, vectors). (middle) As in (left), but for (d)–(f) UR-30 and VR-SA. (right) As in (left), but for (g)–(i) UR-30 and VR-NA. Grid cells with statistically significant difference (quantities shown by shading, based on Student’s t tests) at α = 0.05 level are crosshatched. The solid and dashed circles in (d)–(i) represent approximate boundaries enclosing the domain with 30-km grid and the transition to 120-km grid domain, respectively. The yellow boxes in (h) and (i) represent the regions analyzed for Fig. 15.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Presumably, the upscale effects on large-scale circulation would cause changes to other aspects of regional climate away from the refined region. Therefore an important consideration is how much impact regional refinement may have on other variables representing “regional climate” in remote areas. We chose three regions outside the high-resolution domains mentioned above (indicated by yellow boxes in Figs. 14h,i): northeast Asia (centered at 60°N, 120 °E), western Pacific (22°N, 140°E), and south Indian Ocean (55°S, 95°E), and calculated the area-averaged difference (“bias” or “upscale effect”) between the VR simulations and UR120. To assess the uncertainty due to internal variability, we also calculated the difference between each pair of the six-member FV-CAM4 AMIP ensemble, assuming that FV-CAM4 and MPAS-A simulate comparable internal variability. This situation resulted in 15 unique combinations4 of ensemble member pairs for estimating internal variability. The standard deviation of the differences between ensemble members of the 15 pairs was then used to normalize the bias of the VR simulations (i.e., upscale effect). A 95% confidence interval based on Student’s t distribution is shown by the gray lines to denote the range of the difference expected from internal variability.

Figure 15 shows the bias of each MPAS-A simulation against UR120 for JJA. The difference between UR30 and UR120 is also displayed. In the other seasons [MAM, September–November (SON), and DJF], upscale effects are weaker in all three regions (not shown). Using the confidence interval as a threshold, significant upscale effects were detected in several variables over northeast Asia (Fig. 15a). Particularly strong signals were found in precipitable water, evapotranspiration, and surface air temperature. It is notable that compared to UR120, the changes in these variables from VR were in the same direction as UR30, suggesting that regional refinement partly captures the impacts of high resolution in NA/SA on climate in remote areas, or positive upscale effects. We further suggest that VR and UR30 agree on the sign of the changes for all other variables (except for small changes in sensible heat flux) shown in Fig. 15a, even though the changes are not statistically significant. The suite shows some consistency between atmospheric and surface changes; warmer tropospheric temperature (Figs. 14d,g) and higher precipitable water are consistent with the increased 500-hPa geopotential height, which may reduce cloud cover, therefore increasing net solar radiation at the surface and leading to warmer surface temperature and increased evapotranspiration.

Fig. 15.
Fig. 15.

(a)–(c) Mean JJA bias relative to UR120 over three regions (boxes are shown in Figs. 14h,i) for the variables shown along the x axis. The MPAS-A bias is normalized by the standard deviation of the biases calculated between 15 unique combinations of the six members of the FV-CAM4 ensemble. Some values for UR30 extend outside the range shown [e.g., cloud cover fraction (CLDTOT) in (a) and PRECT in (c)]. Gray horizontal lines represent a 95% interval (based on the t distribution with 14 degrees of freedom) for a difference expected from the internal variability of the FV-CAM4 ensemble.

Citation: Journal of Climate 28, 14; 10.1175/JCLI-D-14-00729.1

Over the western Pacific, a strong signal is detected only for the midlevel geopotential height and marginally for precipitation, surface evaporation, surface net shortwave radiation, and low-level wind (Fig. 15b). The lower geopotential height is accompanied by stronger low-level convergence in the VR simulations (not shown but reflected in the response in the 850-hPa wind and stronger upper-level divergence in Fig. 14h), which may increase precipitation and evaporation. Positive upscale effects are seen for the southern Indian Ocean (Fig. 15c) in a few variables, while negative upscale effects (directed in the opposite way from UR30) are found for the upper- and lower-level winds consistent with the result on the westerly jet presented in section 3a. We are unable to find the exact process for how the negative upscale effects in the circulation lead to positive influence on the other variables, but we note that the upscale effects in the VR simulations are generally small.

Although we highlighted some consistency among changes in atmospheric and surface variables in the three regions, the causes for the large-scale circulation changes that initiated the suite of changes should ultimately be linked dynamically to diabatic heating changes in the refined region. The latter needs to be further investigated using numerical experiments. Finally, we note that comparing the changes among the three regions, there is greater consistency among variables and between VR and UR30 in northeast Asia than the western Pacific and the southern Indian Ocean. This situation may be related to the stronger coupling between the atmosphere and surface over land than ocean, and the use of prescribed SST further limits air–sea coupling and alters the relationship between changes in large-scale circulation and surface climate.

4. Discussion

For the fidelity of the MPAS-A VR inside of the high-resolution area, results suggest that the MPAS-A VR is adequate for simulating regional climate, provided that strong surface forcing exists. In the aquaplanet experiment without land surface forcing and zonally asymmetric SST, the grid refinement over the equator creates a feedback loop among the persistent easterly wind, resolution-dependent parameterizations, and changes in the resolved dynamics across the transition zone, and leads to a local moisture convergence and precipitation anomaly in the downwind edge of the high-resolution region (cf. Fig. 1 in RA13; Hagos et al. 2013). A similar result is found in another aquaplanet experiment by Zarzycki et al. (2014), who used a different dynamical core (spectral element on a cubed-sphere unstructured grid) but the same CAM4 physics parameterizations.

It was expected that this scenario might change with the real-world surface conditions. For instance, topographic lift would directly translate finer grid information to the atmosphere, and land surface would respond to radiative heating in a shorter time scale and with a stronger magnitude than ocean surface, thus making convection and associated processes more locally tied to the surface. This expectation is confirmed in our study, a specific example of which includes the South American monsoon circulation that brings moisture from the Atlantic Ocean into the South American continent, induced mainly by the land–sea contrast. The Andes Mountains help moisture convergence over the Amazon forest and steer the moisture flow southward into the La Plata basin, within which another moisture convergence occurs. However, the error of precipitation over the ocean is not as substantially reduced as over land, and minor artifacts similar to the aquaplanet experiments are seen over the oceanic ITCZ (i.e., VR-SA precipitation is reduced in the northeast and enhanced in the northwest in the refined domain near the grid refinement boundary). Both the weak surface forcing and prescribed SST likely contribute to the muted benefits of regional refinement over the oceans.

As for the second issue of remote upscale effects, more uncertainty remains in our conclusion. Lorenz and Jacob (2005) reported a reduction of model bias at global scale in their coupled GCM–RCM simulation in which a regional model is interactively nested over the Maritime Continent. Inatsu et al. (2012) found that two-way interaction positively affects the dominant mode of variability in the Asia–western Pacific region in which they nested an RCM over East Asia and the Maritime Continent. Harris and Lin (2014) found little influence of two-way nesting on the large-scale fields in their experiments in which a nested grid is located over the Maritime Continent and North America. The magnitude, rather than the locations, of the remote upscale effect on temperature in our VR simulations (Figs. 14d,g) is similar to those shown in Lorenz and Jacob (2005), and we see some positive remote upscale effects depending on the variables, seasons, and regions. We also see a nonnegligible influence of the grid refinement on the zonal wind in the SH especially during austral winter (Figs. 5 and 6), which seems to be a negative impact, using UR30 as the reference. The overall influence on regional-scale climate in remote locations is found to be limited, however, with a possible exception over northeastern Asia in the warm season (Fig. 15a). Furthermore, the global-scale climate in the VR simulations (including both the high- and low-resolution domains) is close to that of UR120 (Fig. 4). Therefore, even though a statistically significant remote upscale effect is detected in some large-scale circulation variables that can be dynamically linked to the refined domain, their physical influence on the surface climate is generally small regionally and is negligible on the global scale. However, the muted upscale effects on the surface climate could be largely due to the experimental setup using prescribed SST, which strongly constrains the changes in the surface energy and water fluxes, and complicates their relationships with the large-scale circulation changes.

There is clear sign showing that regional grid refinement affects the zonal momentum budget at a much broader scale in the SH. The exact mechanisms remain unknown, and we can offer only a speculation. The grid refinement in the VR simulation introduces several features that might alter the regional wave source. Those features could include the enhanced monsoonal rain, modified cyclone genesis due to the refined topography, and the zonal asymmetry introduced by the grid refinement itself as demonstrated by Rauscher and Ringler (2014). These zonally asymmetric wave sources may be teleconnected to the SH midlatitude storm track to excite annular-mode-like wind structure through the processes whereby a negative cold tongue SST anomaly in the eastern equatorial Pacific drives a positive phase of the southern annular mode (L’Heureux and Thompson 2006).

The mechanisms suggested above undoubtedly depend on many factors such as the resolution dependency of the physics parameterizations as well as the geometry, location, and ratio of the grid refinement. In another VR simulation with 8 times refinement over North America, the magnitude of the upscale effects in the upper-level circulation is found to be stronger than VR-NA with 4 times refinement, and the emergent pattern is more consistent with the aquaplanet experiments by Rauscher and Ringler (2014), particularly the anomaly of the annual mean upper-level circulation extending from the high- to low-resolution region in a similar pattern to the aquaplanet VR simulation (Fig. 3 in Rauscher and Ringler 2014). This situation implies that the upscale effects and their underlying processes in the idealized aquaplanet experiments exist in the AMIP experiments, in addition to the complexities introduced by the real-world forcings. We take this large-scale influence as a potentially serious aspect of the regional grid refinement and will continue the effort to understand the contributing processes.

Future efforts will examine the sensitivity of physics parameterizations to gridcell size and time step. The intimate interaction between dynamics and physics parameterizations revealed points to the importance of scale-aware parameterizations (Arakawa et al. 2011) and physics–dynamics coupling in VR modeling (Walko 2011). In particular and beyond the perfect model framework, it becomes much more critical that simulations converge to a realistic atmospheric state with increasing horizontal resolution. However, the recent generations of CAM physics indicated rather unsatisfactory behavior in this respect (Williamson 2008; O’Brien et al. 2013; Wan et al. 2015). For instance, Williamson (2008) found a lack of convergence in several large-scale fields in his aquaplanet experiments with CAM3. He showed that the global mean cloud cover increasingly differs between adjacent resolutions with higher resolutions, which is unexpected if there is a convergence. Although parameter tuning at each resolution could partially alleviate the situation, it is more challenging with the VR model since one needs to determine the optimal parameters empirically or theoretically over a range of grid spacing. Therefore, a set of physics parameterizations and dynamical core that together exhibit consistent convergence behavior without tuning is of fundamental importance for the VR approach. Using a different dynamical core with regional grid refinement similar to RA13, Zarzycki et al. (2014) reported that the CAM5 physics partly weaken the artifacts near the refinement boundaries. We are currently conducting aquaplanet experiments with MPAS-A coupled to CAM5 physics to investigate its scale awareness or lack thereof. More detailed analyses on the reported simulations are ongoing as well, through which we anticipate revealing scale-dependence issues in multiresolution modeling.

5. Conclusions

A VR approach by the MPAS-A was tested using a series of real-world simulations with realistic surface boundary conditions following the common AMIP protocol. Because the grid refinement was made within a single modeling framework using unstructured grids, no complications arose from different dynamical cores, physical parameterizations, and boundary condition treatment. However, previous studies using aquaplanet simulations showed that VR reproduced the precipitation in a globally high-resolution simulation only near the boundary of the high-resolution domain. It was also found that the VR simulations produced upscale effects that are absent in the globally uniform resolution simulations. Extending this previous work, we conducted four global experiments in an AMIP setting. By comparing VR with quasi-uniform resolution simulations, we found that strong lower boundary forcings over land during warm season or in the tropics enable the MPAS-A VR simulations to replicate quasi-uniform high-resolution simulation features in the high-resolution domain much more uniformly. Another reason for the successful simulation is the ability of MPAS-A VR to replicate the large-scale moisture transport from the ocean to the continent as in the quasi-uniform high-resolution simulation, which coincides with the transition zone from the high- to low-resolution domains. Some upscale effects were detected in the large-scale circulation that warrant future investigations, particularly on the zonal momentum budget. Their impacts on regional-scale climate are further investigated through several physical variables in the areas remote from the grid refinement. Additionally, the overall effect is not significant except for northeastern Asia during summer, where several variables show detectable, positive upscale effects. Although unanswered questions remain, the results from this study support the multiresolution approach as a computationally efficient, physically consistent framework for modeling regional climate.

Acknowledgments

This study was supported by the U.S. Department of Energy (DOE) Office of Science Biological and Environmental Research as part of the Regional and Global Climate Modeling program. The research used computational resources from the National Energy Research Scientific Computing Center (NERSC), a DOE User Facility supported by the Office of Science under Contract DE-AC02-05CH11231. Additional computational resources were provided by the Pacific Northwest National Laboratory (PNNL) Institutional Computing program. The FV-CAM4 data were provided by the Earth system grid data portal from the National Center for Atmospheric Research (NCAR), which is supported by grants from the National Science Foundation. The authors wish to thank Dr. Travis O’Brien of Lawrence Berkeley National Laboratory for facilitating the use of the model data archive at NERSC. We also thank Drs. Jin-Ho Yoon, Matus Martini, Phil Rasch, and Hailong Wang for their insights provided through discussions. The thorough review and constructive comments by two anonymous reviewers are also greatly appreciated. PNNL is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830.

REFERENCES

  • Antic, S., , R. Laprise, , B. Denis, , and R. de Elía, 2006: Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography. Climate Dyn., 26, 305325, doi:10.1007/s00382-005-0046-z.

    • Search Google Scholar
    • Export Citation
  • Arakawa, A., , J.-H. Jung, , and C.-M. Wu, 2011: Toward unification of the multiscale modeling of the atmosphere. Atmos. Chem. Phys., 11, 37313742, doi:10.5194/acp-11-3731-2011.

    • Search Google Scholar
    • Export Citation
  • Bacmeister, J. T., , M. F. Wehner, , R. B. Neale, , A. Gettelman, , C. Hannay, , P. H. Lauritzen, , J. M. Caron, , and J. E. Truesdale, 2014: Exploratory high-resolution climate simulations using the Community Atmosphere Model (CAM). J. Climate, 27, 30733099, doi:10.1175/JCLI-D-13-00387.1.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M. G., , J. Chen, , F. R. Robertson, , and R. F. Adler, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 22792299, doi:10.1175/2008JAMC1921.1.

    • Search Google Scholar
    • Export Citation
  • Boville, B. A., 1991: Sensitivity of simulated climate to model resolution. J. Climate, 4, 469485, doi:10.1175/1520-0442(1991)004<0469:SOSCTM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Boyle, J., , and S. A. Klein, 2010: Impact of horizontal resolution on climate model forecasts of tropical precipitation and diabatic heating for the TWP-ICE period. J. Geophys. Res., 115, D23113, doi:10.1029/2010JD014262.

    • Search Google Scholar
    • Export Citation
  • Castro, C. L., , H.-I. Chang, , F. Dominguez, , C. Carrillo, , J.-K. Schemm, , and H.-M. H. Juang, 2012: Can a regional climate model improve the ability to forecast the North American monsoon? J. Climate, 25, 82128237, doi:10.1175/JCLI-D-11-00441.1.

    • Search Google Scholar
    • Export Citation
  • Collins, W. D., and et al. , 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCAR/TN-464+STR, 214 pp.

  • Community Earth System Model, 2014: CESM 1.0 experiments, data and diagnostics. UCAR, accessed 23 August 2014. [Available online at http://www.cesm.ucar.edu/experiments/cesm1.0/.]

  • Craig, P., , M. Vertenstein, , and R. Jacob, 2012: A new flexible coupler for Earth system modeling developed for CCSM4 and CESM1. Int. J. High Perform. Comput. Appl., 26, 3142, doi:10.1177/1094342011428141.

    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • de Elía, R., , R. Laprise, , and B. Denis, 2002: Forecasting skill limits of nested, limited-area models: A perfect-model approach. Mon. Wea. Rev., 130, 20062023, doi:10.1175/1520-0493(2002)130<2006:FSLONL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ding, Q., , E. J. Steig, , D. S. Battisti, , and J. M. Wallace, 2012: Influence of the tropics on the southern annular mode. J. Climate, 25, 63306348, doi:10.1175/JCLI-D-11-00523.1.

    • Search Google Scholar
    • Export Citation
  • Edwards, T. L., and et al. , 2014: Effect of uncertainty in surface mass balance–elevation feedback on projections of the future sea level contribution of the Greenland ice sheet. Cryosphere, 8, 195208, doi:10.5194/tc-8-195-2014.

    • Search Google Scholar
    • Export Citation
  • Evans, K. J., , P. H. Lauritzen, , S. K. Mishra, , R. B. Neale, , M. A. Taylor, , and J. J. Tribbia, 2013: AMIP simulation with the CAM4 spectral element dynamical core. J. Climate, 26, 689709, doi:10.1175/JCLI-D-11-00448.1.

    • Search Google Scholar
    • Export Citation
  • Flato, G., and et al. , 2013: Evaluation of climate models. Climate Change 2013: The Physical Science Basis. T. F. Stocker et al., Eds., Cambridge University Press, 741–866.

  • Fox-Rabinovitz, M. S., , E. H. Berbery, , L. L. Takacs, , and R. C. Govindaraju, 2005: A multiyear ensemble simulation of the U.S. climate with a stretched-grid GCM. Mon. Wea. Rev., 133, 25052525, doi:10.1175/MWR2956.1.

    • Search Google Scholar
    • Export Citation
  • Fox-Rabinovitz, M. S., , J. Côté, , B. Dugas, , M. Déqué, , and J. L. McGregor, 2006: Variable resolution general circulation models: Stretched-grid model intercomparison project (SGMIP). J. Geophys. Res., 111, D16104, doi:10.1029/2005JD006520.

    • Search Google Scholar
    • Export Citation
  • Gao, Y., , L. R. Leung, , J. Lu, , Y. Liu, , M. Huang, , and Y. Qian, 2014: Robust spring drying in the southwestern U.S. and seasonal migration of wet/dry patterns in a warmer climate. Geophys. Res. Lett., 41, 17451751, doi:10.1002/2014GL059562.

    • Search Google Scholar
    • Export Citation
  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73, 19621970, doi:10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gesch, D. B., , and K. S. Larson, 1998: Techniques for development of global 1-kilometer digital elevation models. Proc. Pecora 13th Symp., Sioux Falls, SD, American Society for Photogrammetry and Remote Sensing.

  • Ghan, S. J., , X. Bian, , and L. Corsetti, 1996: Simulation of the Great Plains low-level jet and associated clouds by general circulation models. Mon. Wea. Rev., 124, 13881408, doi:10.1175/1520-0493(1996)124<1388:SOTGPL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447462, doi:10.1002/qj.49710644905.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , and L. O. Mearns, 1991: Approaches to the simulation of regional climate change: A review. Rev. Geophys., 29, 191216, doi:10.1029/90RG02636.

    • Search Google Scholar
    • Export Citation
  • Hack, J. J., 1994: Parameterization of moist convection in the National Center for Atmospheric Research community climate model (CCM2). J. Geophys. Res., 99, 55515568, doi:10.1029/93JD03478.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , R. Leung, , S. A. Rauscher, , and T. Ringler, 2013: Error characteristics of two grid refinement approaches in aquaplanet simulations: MPAS-A and WRF. Mon. Wea. Rev., 141, 30223036, doi:10.1175/MWR-D-12-00338.1.

    • Search Google Scholar
    • Export Citation
  • Hagos, S., , R. Leung, , W. I. Gustafson, , and B. Singh, 2014: Eddy fluxes and sensitivity of the water cycle to spatial resolution in idealized regional aquaplanet model simulations. Climate Dyn., 42, 931940, doi:10.1007/s00382-013-1857-y.

    • Search Google Scholar
    • Export Citation
  • Harris, L. M., , and S.-J. Lin, 2013: A two-way nested global-regional dynamical core on the cubed-sphere grid. Mon. Wea. Rev., 141, 283306, doi:10.1175/MWR-D-11-00201.1.

    • Search Google Scholar
    • Export Citation
  • Harris, L. M., , and S.-J. Lin, 2014: Global-to-regional nested grid climate simulations in the GFDL high resolution atmospheric model. J. Climate, 27, 48904910, doi:10.1175/JCLI-D-13-00596.1.

    • Search Google Scholar
    • Export Citation
  • Hawkins, E., , and R. Sutton, 2009: The potential to narrow uncertainty in regional climate predictions. Bull. Amer. Meteor. Soc., 90, 10951107, doi:10.1175/2009BAMS2607.1.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., , M. Ting, , and H. Wang, 2002: Northern winter stationary waves: Theory and modeling. J. Climate, 15, 21252144, doi:10.1175/1520-0442(2002)015<2125:NWSWTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hewitson, B., and et al. , 2014: Regional context. Climate Change 2014: Impacts, Adaptation, and Vulnerability, C. B. Field et al., Eds., Cambridge University Press, 1133–1197.

  • Holtslag, A. A. M., , and B. A. Boville, 1993: Local versus nonlocal boundary-layer diffusion in a global climate model. J. Climate, 6, 18251842, doi:10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., , and T. Ambrizzi, 1993: Rossby wave propagation on a realistic longitudinally varying flow. J. Atmos. Sci., 50, 16611671, doi:10.1175/1520-0469(1993)050<1661:RWPOAR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., , R. F. Adler, , M. M. Morrissey, , D. T. Bolvin, , S. Curtis, , R. Joyce, , B. McGavock, , and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 3650, doi:10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and et al. , 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., , R. F. Adler, , D. T. Bolvin, , and G. Gu, 2009: Improving the global precipitation record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.

    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., , J. J. Hack, , D. Shea, , J. M. Caron, , and J. Rosinski, 2008: A new sea surface temperature and sea ice boundary dataset for the Community Atmosphere Model. J. Climate, 21, 51455153, doi:10.1175/2008JCLI2292.1.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., , and B. J. Hoskins, 2004: The zonal asymmetry of the Southern Hemisphere winter storm track. J. Climate, 17, 48824892, doi:10.1175/JCLI-3232.1.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., , and M. Kimoto, 2009: A scale interaction study on East Asian cyclogenesis using a general circulation model coupled with an interactively nested regional model. Mon. Wea. Rev., 137, 28512868, doi:10.1175/2009MWR2825.1.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., , H. Mukougawa, , and S.-P. Xie, 2002: Stationary eddy response to surface boundary forcing: Idealized GCM experiments. J. Atmos. Sci., 59, 18981915, doi:10.1175/1520-0469(2002)059<1898:SERTSB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Inatsu, M., , Y. Satake, , M. Kimoto, , and N. Yasutomi, 2012: GCM bias of the western Pacific summer monsoon and its correction by two-way nesting system. J. Meteor. Soc. Japan, 90B, 110, doi:10.2151/jmsj.2012-B01.

    • Search Google Scholar
    • Export Citation
  • Iorio, J. P., , P. B. Duffy, , B. Govindasamy, , S. L. Thompson, , M. Khairoutdinov, , and D. Randall, 2004: Effects of model resolution and subgrid-scale physics on the simulation of precipitation in the continental United States. Climate Dyn., 23, 243258, doi:10.1007/s00382-004-0440-y.

    • Search Google Scholar
    • Export Citation
  • Jiang, X., , N.-C. Lau, , I. M. Held, , and J. J. Ploshay, 2007: Mechanisms of the Great Plains low-level jet as simulated in an AGCM. J. Atmos. Sci., 64, 532547, doi:10.1175/JAS3847.1.

    • Search Google Scholar
    • Export Citation
  • Jones, P. W., 1999: First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Wea. Rev., 127, 22042210, doi:10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ju, L., , T. Ringler, , and M. Gunzburger, 2011: Voronoi tessellations and their application to climate and global modeling. Numerical Techniques for Global Atmospheric Models, P. Lauritzen et al., Eds., Lecture Notes in Computational Science and Engineering, Vol. 80, Springer, 313–342.

  • Kinter, J. L., and et al. , 2013: Revolutionizing climate modeling with Project Athena: A multi-institutional, international collaboration. Bull. Amer. Meteor. Soc., 94, 231245, doi:10.1175/BAMS-D-11-00043.1.

    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., , W. C. Skamarock, , and J. Dudhia, 2007: Conservative split-explicit time integration methods for the compressible nonhydrostatic equations. Mon. Wea. Rev., 135, 28972913, doi:10.1175/MWR3440.1.

    • Search Google Scholar
    • Export Citation
  • Landu, K., , L. R. Leung, , S. Hagos, , V. Vinoj, , S. A. Rauscher, , T. Ringler, , and M. Taylor, 2014: The dependence of ITCZ structure on model resolution and dynamical core in aquaplanet simulations. J. Climate, 27, 23752385, doi:10.1175/JCLI-D-13-00269.1.

    • Search Google Scholar
    • Export Citation
  • Lauritzen, P. H., , R. D. Nair, , and P. A. Ullrich, 2010: A conservative semi-Lagrangian multi-tracer transport scheme (CSLAM) on the cubed-sphere grid. J. Comput. Phys., 229, 14011424, doi:10.1016/j.jcp.2009.10.036.

    • Search Google Scholar
    • Export Citation
  • Lawrence, D. M., and et al. , 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Model. Earth Syst., 3, M03001, doi:10.1029/2011MS000045.

    • Search Google Scholar
    • Export Citation
  • Lee, S., , and H.-K. Kim, 2003: The dynamical relationship between subtropical and eddy-driven jets. J. Atmos. Sci., 60, 14901503, doi:10.1175/1520-0469(2003)060<1490:TDRBSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , Y. Qian, , X. Bian, , W. M. Washington, , J. Han, , and J. O. Roads, 2004: Mid-century ensemble regional climate change scenarios for the western United States. Climatic Change, 62, 75113, doi:10.1023/B:CLIM.0000013692.50640.55.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , Y.-H. Kuo, , and J. Tribbia, 2006: Research needs and directions of regional climate modeling using WRF and CCSM. Bull. Amer. Meteor. Soc., 87, 17471751, doi:10.1175/BAMS-87-12-1747.

    • Search Google Scholar
    • Export Citation
  • Leung, L. R., , T. D. Ringler, , W. D. Collins, , M. A. Taylor, , and M. Ashfaq, 2013: A hierarchical evaluation of regional climate simulations. Eos, Trans. Amer. Geophys. Union, 94, 297298, doi:10.1002/2013EO340001.

    • Search Google Scholar
    • Export Citation
  • L’Heureux, M., , and D. W. J. Thompson, 2006: Observed relationships between the El Niño–Southern Oscillation and the extratropical zonal-mean circulation. J. Climate, 19, 276287, doi:10.1175/JCLI3617.1.

    • Search Google Scholar
    • Export Citation
  • Lin, S.-J., 2004: A “vertically Lagrangian” finite-volume dynamical core for global models. Mon. Wea. Rev., 132, 22932307, doi:10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lorenz, P., , and D. Jacob, 2005: Influence of regional scale information on the global circulation: A two-way nesting climate simulation. Geophys. Res. Lett., 32, L18706, doi:10.1029/2005GL023351.

    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., 2005: Characteristics and spatio-temporal variability of the Amazon River basin water budget. Climate Dyn., 24, 1122, doi:10.1007/s00382-004-0461-6.

    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., , W. R. Soares, , C. Saulo, , and M. Nicolini, 2004: Climatology of the low-level jet east of the Andes as derived from the NCEP–NCAR reanalyses: Characteristics and temporal variability. J. Climate, 17, 22612280, doi:10.1175/1520-0442(2004)017<2261:COTLJE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McGregor, J. L., 2015: Recent developments in variable-resolution global climate modelling. Climatic Change, 129, 369380, doi:10.1007/s10584-013-0866-5.

    • Search Google Scholar
    • Export Citation
  • Mearns, L. O., and et al. , 2012: The North American Regional Climate Change Assessment Program: Overview of phase I results. Bull. Amer. Meteor. Soc., 93, 13371362, doi:10.1175/BAMS-D-11-00223.1.

    • Search Google Scholar
    • Export Citation
  • Medvigy, D., , R. L. Walko, , M. J. Otte, , and R. Avissar, 2013: Simulated changes in northwest U.S. climate in response to Amazon deforestation. J. Climate, 26, 91159136, doi:10.1175/JCLI-D-12-00775.1.

    • Search Google Scholar
    • Export Citation
  • Mo, K. C., , and R. W. Higgins, 1998: The Pacific–South American modes and tropical convection during the Southern Hemisphere winter. Mon. Wea. Rev., 126, 15811596, doi:10.1175/1520-0493(1998)126<1581:TPSAMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., , and B. J. Hoskins, 2000: A standard test for AGCMs including their physical parametrizations: I: The proposal. Atmos. Sci. Lett., 1, 101107, doi:10.1006/asle.2000.0022.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., , J. H. Richter, , and M. Jochum, 2008: The impact of convection on ENSO: From a delayed oscillator to a series of events. J. Climate, 21, 59045924, doi:10.1175/2008JCLI2244.1.

    • Search Google Scholar
    • Export Citation
  • Neale, R. B., and et al. , 2010: Description of the NCAR Community Atmosphere Model (CAM 4.0). NCAR Tech. Note NCAR/TN-485+STR, 212 pp.

  • Neale, R. B., and et al. , 2012: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, 274 pp.

  • Neale, R. B., , J. H. Richter, , S. Park, , P. H. Lauritzen, , S. J. Vavrus, , P. J. Rasch, , and M. Zhang, 2013: The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J. Climate, 26, 51505168, doi:10.1175/JCLI-D-12-00236.1.

    • Search Google Scholar
    • Export Citation
  • O’Brien, T. A., , F. Li, , W. D. Collins, , S. A. Rauscher, , T. D. Ringler, , M. Taylor, , S. M. Hagos, , and L. R. Leung, 2013: Observed scaling in clouds and precipitation and scale incognizance in regional to global atmospheric models. J. Climate, 26, 93139333, doi:10.1175/JCLI-D-13-00005.1.

    • Search Google Scholar
    • Export Citation
  • Orlanski, I., 2008: The rationale for why climate models should adequately resolve the mesoscale. High Resolution Numerical Modelling of the Atmosphere and Ocean, K. Hamilton and W. Ohfuchi, Eds., Springer, 29–44.

  • Park, S.-H., , W. C. Skamarock, , J. B. Klemp, , L. D. Fowler, , and M. G. Duda, 2013: Evaluation of global atmospheric solvers using extensions of the Jablonowski and Williamson baroclinic wave test case. Mon. Wea. Rev., 141, 31163129, doi:10.1175/MWR-D-12-00096.1.

    • Search Google Scholar
    • Export Citation
  • Pope, V. D., , and R. A. Stratton, 2002: The processes governing horizontal resolution sensitivity in a climate model. Climate Dyn., 19, 211236, doi:10.1007/s00382-001-0222-8.

    • Search Google Scholar
    • Export Citation
  • Rasch, P. J., , and J. E. Kristjánsson, 1998: A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J. Climate, 11, 15871614, doi:10.1175/1520-0442(1998)011<1587:ACOTCM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rauscher, S. A., , and T. D. Ringler, 2014: Impact of variable-resolution meshes on midlatitude baroclinic eddies using CAM-MPAS-A. Mon. Wea. Rev., 142, 42564268, doi:10.1175/MWR-D-13-00366.1.

    • Search Google Scholar
    • Export Citation
  • Rauscher, S. A., , A. Seth, , J.-H. Qian, , and S. J. Camargo, 2006: Domain choice in an experimental nested modeling prediction system for South America. Theor. Appl. Climatol., 86, 229246, doi:10.1007/s00704-006-0206-z.

    • Search Google Scholar
    • Export Citation
  • Rauscher, S. A., , T. D. Ringler, , W. C. Skamarock, , and A. A. Mirin, 2013: Exploring a global multiresolution modeling approach using aquaplanet simulations. J. Climate, 26, 24322452, doi:10.1175/JCLI-D-12-00154.1.

    • Search Google Scholar
    • Export Citation
  • Richter, J. H., , and P. J. Rasch, 2008: Effects of convective momentum transport on the atmospheric circulation in the Community Atmosphere Model, Version 3. J. Climate, 21, 14871499, doi:10.1175/2007JCLI1789.1.

    • Search Google Scholar
    • Export Citation
  • Ringler, T. D., , L. Ju, , and M. Gunzburger, 2008: A multiresolution method for climate system modeling: Application of spherical centroidal Voronoi tessellations. Ocean Dyn., 58, 475498, doi:10.1007/s10236-008-0157-2.

    • Search Google Scholar
    • Export Citation
  • Ringler, T. D., , J. Thuburn, , J. B. Klemp, , and W. C. Skamarock, 2010: A unified approach to energy conservation and potential vorticity dynamics for arbitrarily-structured C-grids. J. Comput. Phys., 229, 30653090, doi:10.1016/j.jcp.2009.12.007.

    • Search Google Scholar
    • Export Citation
  • Ringler, T. D., , D. Jacobsen, , M. Gunzburger, , L. Ju, , M. Duda, , and W. C. Skamarock, 2011: Exploring a multiresolution modeling approach within the shallow-water equations. Mon. Wea. Rev., 139, 33483368, doi:10.1175/MWR-D-10-05049.1.

    • Search Google Scholar
    • Export Citation
  • Ringler, T. D., , M. Petersen, , R. L. Higdon, , D. Jacobsen, , P. W. Jones, , and M. Maltrud, 2013