• Bierdel, L., C. Snyder, S.-H. Park, and W. C. Skamarock, 2016: Accuracy of rotational and divergent kinetic energy spectra diagnosed from flight-track winds. J. Atmos. Sci., 73, 32733286, https://doi.org/10.1175/JAS-D-16-0040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bierdel, L., T. Selz, and G. Craig, 2017: Theoretical aspects of upscale error growth through the mesoscales: An analytical model. Quart. J. Roy. Meteor. Soc., 143, 30483059, https://doi.org/10.1002/qj.3160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Blunden, J., and D. S. Arndt, 2013: State of the Climate in 2012. Bull. Amer. Meteor. Soc., 94 (8), S1S258, https://doi.org/10.1175/2013BAMSStateoftheClimate.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boer, G. J., 1994: Predictability regimes in atmospheric flow. Mon. Wea. Rev., 122, 22852295, https://doi.org/10.1175/1520-0493(1994)122<2285:PRIAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boffetta, G., and S. Musacchio, 2017: Chaos and predictability of homogeneous-isotropic turbulence. Phys. Rev. Lett., 119, 054102, https://doi.org/10.1103/PhysRevLett.119.054102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boffetta, G., A. Celani, A. Crisanti, and A. Vulpiani, 1997: Predictability in two-dimensional decaying turbulence. Phys. Fluids, 9, 724734, https://doi.org/10.1063/1.869227.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and M. F. Khairoutdinov, 2015: Convective self-aggregation feedbacks in near-global cloud-resolving simulations of an aquaplanet. J. Adv. Model. Earth Syst., 7, 17651787, https://doi.org/10.1002/2015MS000499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., and M. Leutbecher, 2015: The forecast skill horizon. Quart. J. Roy. Meteor. Soc., 141, 33663382, https://doi.org/10.1002/qj.2619.

  • Dalcher, A., and E. Kalnay, 1987: Error growth and predictability in operational ECMWF forecasts. Tellus, 39A, 474491, https://doi.org/10.1111/j.1600-0870.1987.tb00322.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and M. Gingrich, 2014: Atmospheric predictability: Why butterflies are not of practical importance. J. Atmos. Sci., 71, 24762488, https://doi.org/10.1175/JAS-D-14-0007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and J. A. Weyn, 2016: Thunderstorms do not get butterflies. Bull. Amer. Meteor. Soc., 97, 237243, https://doi.org/10.1175/BAMS-D-15-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • ECMWF, 2016: ECMWF strategy 2016–2025: The strength of a common goal. European Centre for Medium-Range Weather Forecasts Tech. Rep., 32 pp., https://www.ecmwf.int/sites/default/files/ECMWF_Strategy_2016-2025.pdf.

  • Errico, R., and D. Baumhefner, 1987: Predictability experiments using a high-resolution limited-area model. Mon. Wea. Rev., 115, 488504, https://doi.org/10.1175/1520-0493(1987)115<0488:PEUAHR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fowler, L. D., W. C. Skamarock, G. A. Grell, S. R. Freitas, and M. G. Duda, 2016: Analyzing the Grell–Freitas convection scheme from hydrostatic to nonhydrostatic scales within a global model. Mon. Wea. Rev., 144, 22852306, https://doi.org/10.1175/MWR-D-15-0311.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grell, G. A., and S. R. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 52335250, https://doi.org/10.5194/acp-14-5233-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harlim, J., M. Oczkowski, J. A. Yorke, E. Kalnay, and B. R. Hunt, 2005: Convex error growth patterns in a global weather model. Phys. Rev. Lett., 94, 228501, https://doi.org/10.1103/PhysRevLett.94.228501.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawblitzel, D. P., F. Zhang, Z. Meng, and C. A. Davis, 2007: Probabilistic evaluation of the dynamics and predictability of the mesoscale convective vortex of 10–13 June 2003. Mon. Wea. Rev., 135, 15441563, https://doi.org/10.1175/MWR3346.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heinzeller, D., M. G. Duda, and H. Kunstmann, 2016: Towards convection-resolving, global atmospheric simulations with the Model for Prediction Across Scales (MPAS) v3.1: An extreme scaling experiment. Geosci. Model Dev., 9, 77110, https://doi.org/10.5194/gmd-9-77-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., and C. Schär, 2007: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88, 17831793, https://doi.org/10.1175/BAMS-88-11-1783.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Judt, F., S. S. Chen, and J. Berner, 2016: Predictability of tropical cyclone intensity: Scale-dependent forecast error growth in high-resolution stochastic kinetic-energy backscatter ensembles. Quart. J. Roy. Meteor. Soc., 142, 4357, https://doi.org/10.1002/qj.2626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., 2011: A terrain-following coordinate with smoothed coordinate surfaces. Mon. Wea. Rev., 139, 21632169, https://doi.org/10.1175/MWR-D-10-05046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leith, C. E., and R. H. Kraichnan, 1972: Predictability of turbulent flows. J. Atmos. Sci., 29, 10411058, https://doi.org/10.1175/1520-0469(1972)029<1041:POTF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci., 20, 130141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289307, https://doi.org/10.3402/tellusa.v21i3.10086.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1982: Atmospheric predictability experiments with a large numerical model. Tellus, 34, 505513, https://doi.org/10.3402/tellusa.v34i6.10836.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1996: Predictability—A problem partly solved. Proc. Seminar on Predictability, Reading, United Kingdom, ECMWF, 1–18.

  • Mapes, B., S. Tulich, T. Nasuno, and M. Satoh, 2008: Predictability aspects of global aqua-planet simulations with explicit convection. J. Meteor. Soc. Japan, 86, 175185, https://doi.org/10.2151/jmsj.86A.175.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Métais, O., and M. Lesieur, 1986: Statistical predictability of decaying turbulence. J. Atmos. Sci., 43, 857870, https://doi.org/10.1175/1520-0469(1986)043<0857:SPODT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miyamoto, Y., Y. Kajikawa, R. Yoshida, T. Yamaura, H. Yashiro, and H. Tomita, 2013: Deep moist atmospheric convection in a subkilometer global simulation. Geophys. Res. Lett., 40, 49224926, https://doi.org/10.1002/grl.50944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morss, R. E., C. Snyder, and R. Rotunno, 2009: Spectra, spatial scales, and predictability in a quasigeostrophic model. J. Atmos. Sci., 66, 31153130, https://doi.org/10.1175/2009JAS3057.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912, https://doi.org/10.2151/jmsj.87.895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nastrom, D. G., and K. S. Gage, 1985: A climatology of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci., 42, 950960, https://doi.org/10.1175/1520-0469(1985)042<0950:ACOAWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ngan, K., and G. E. Eperon, 2012: Middle atmosphere predictability in a numerical weather prediction model: Revisiting the inverse error cascade. Quart. J. Roy. Meteor. Soc., 138, 13661378, https://doi.org/10.1002/qj.984.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ngan, K., P. Bartello, and D. N. Straub, 2009: Predictability of rotating stratified turbulence. J. Atmos. Sci., 66, 13841400, https://doi.org/10.1175/2008JAS2799.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., A. Döring, and G. Seregin, 2014: The real butterfly effect. Nonlinearity, 27, R123R141, https://doi.org/10.1088/0951-7715/27/9/R123.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potvin, C. K., E. M. Murillo, M. L. Flora, and D. M. Wheatley, 2017: Sensitivity of supercell simulations to initial-condition resolution. J. Atmos. Sci., 74, 526, https://doi.org/10.1175/JAS-D-16-0098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Putman, W. M., and M. Suarez, 2011: Cloud-system resolving simulations with the NASA Goddard Earth Observing System global atmospheric model (GEOS-5). Geophys. Res. Lett., 38, L16809, https://doi.org/10.1029/2011GL048438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotunno, R., and C. Snyder, 2008: A generalization of Lorenz’s model for the predictability of flows with many scales of motion. J. Atmos. Sci., 65, 10631076, https://doi.org/10.1175/2007JAS2449.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, and S. Iga, 2008: Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations. J. Comput. Phys., 227, 34863514, https://doi.org/10.1016/j.jcp.2007.02.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Selz, T., and G. C. Craig, 2015: Upscale error growth in a high-resolution simulation of a summertime weather event over Europe. Mon. Wea. Rev., 143, 813827, https://doi.org/10.1175/MWR-D-14-00140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, J., 1981: Dynamical predictability of monthly means. J. Atmos. Sci., 38, 25472572, https://doi.org/10.1175/1520-0469(1981)038<2547:DPOMM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and A. Hollingsworth, 2002: Some aspects of the improvement in skill of numerical weather prediction. Quart. J. Roy. Meteor. Soc., 128, 647677, https://doi.org/10.1256/003590002321042135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., R. Mureau, and T. Petroliagis, 1995: Error growth and estimates of predictability from the ECMWF forecasting system. Quart. J. Roy. Meteor. Soc., 121, 17391771, https://doi.org/10.1002/qj.49712152711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sippel, J. A., and F. Zhang, 2008: A probabilistic analysis of the dynamics and predictability of tropical cyclogenesis. J. Atmos. Sci., 65, 34403459, https://doi.org/10.1175/2008JAS2597.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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 tesselations and C-grid staggering. Mon. Wea. Rev., 140, 30903105, https://doi.org/10.1175/MWR-D-11-00215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., S.-H. Park, J. B. Klemp, and C. Snyder, 2014: Atmospheric kinetic energy spectra from global high-resolution nonhydrostatic simulations. J. Atmos. Sci., 71, 43694381, https://doi.org/10.1175/JAS-D-14-0114.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Y. Q., and F. Zhang, 2016: Intrinsic versus practical limits of atmospheric predictability and the significance of the butterfly effect. J. Atmos. Sci., 73, 14191438, https://doi.org/10.1175/JAS-D-15-0142.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tennant, W., 2009: The butterfly effect—Myth or reality? Weather, 64, 299, https://doi.org/10.1002/wea.464.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, P. D., 1957: Uncertainty of initial state as a factor in the predictability of large scale atmospheric flow patterns. Tellus, 9, 275295, https://doi.org/10.1111/j.2153-3490.1957.tb01885.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tribbia, J. J., and D. P. Baumhefner, 2004: Scale interactions and atmospheric predictability: An updated perspective. Mon. Wea. Rev., 132, 703713, https://doi.org/10.1175/1520-0493(2004)132<0703:SIAAPA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vukicevic, T., and R. M. Errico, 1990: The influence of artificial and physical factors upon predictability estimates using a complex limited-area model. Mon. Wea. Rev., 118, 14601482, https://doi.org/10.1175/1520-0493(1990)118<1460:TIOAAP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waite, M. L., and C. Snyder, 2013: Mesoscale energy spectra of moist baroclinic waves. J. Atmos. Sci., 70, 12421256, https://doi.org/10.1175/JAS-D-11-0347.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weyn, J. A., and D. R. Durran, 2017: The dependence of the predictability of mesoscale convective systems on the horizontal scale and amplitude of initial errors in idealized simulations. J. Atmos. Sci., 74, 21912210, https://doi.org/10.1175/JAS-D-17-0006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Z.-L., and Coauthors, 2011: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins. J. Geophys. Res., 116, D12110, https://doi.org/10.1029/2010JD015140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Žagar, N., M. Horvat, Ž. Zaplotnik, and L. Magnusson, 2017: Scale-dependent estimates of the growth of forecast uncertainties in a global prediction system. Tellus, 69A, 1287492, https://doi.org/10.1080/16000870.2017.1287492.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and R. Rotunno, 2002: Mesoscale predictability of the surprise snowstorm of 24–25 January 2000. Mon. Wea. Rev., 130, 16171632, https://doi.org/10.1175/1520-0493(2002)130<1617:MPOTSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 11731185, https://doi.org/10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., N. Bei, R. Rotunno, C. Snyder, and C. C. Epifanio, 2007: Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J. Atmos. Sci., 64, 35793594, https://doi.org/10.1175/JAS4028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., F. Zhang, D. J. Stensrud, and Z. Meng, 2015: Practical predictability of the 20 May 2013 tornadic thunderstorm event in Oklahoma: Sensitivity to synoptic timing and topographical influence. Mon. Wea. Rev., 143, 29732997, https://doi.org/10.1175/MWR-D-14-00394.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 27 27 27
PDF Downloads 27 27 27

Insights into Atmospheric Predictability through Global Convection-Permitting Model Simulations

View More View Less
  • 1 Advanced Study Program, National Center for Atmospheric Research, Boulder, Colorado
Restricted access

Abstract

Global convection-permitting models enable weather prediction from local to planetary scales and are therefore often expected to transform the weather prediction enterprise. This potential, however, depends on the predictability of the atmosphere, which was explored here through identical twin experiments using the Model for Prediction Across Scales. The simulations were produced on a quasi-uniform 4-km mesh, which allowed the illumination of error growth from convective to global scales. During the first two days, errors grew through moist convection and other mesoscale processes, and the character of the error growth resembled the case of turbulence. Between 2 and 13 days, errors grew with the background baroclinic instability, and the character of the error growth mirrored the case of turbulence. The existence of an error growth regime with properties similar to turbulence confirmed the radical idea of E. N. Lorenz that the atmosphere has a finite limit of predictability, no matter how small the initial error. The global-mean predictability limit of the troposphere was estimated here to be around 2–3 weeks, which is in agreement with previous work. However, scale-dependent predictability limits differed between the divergent and rotational wind component and between vertical levels, indicating that atmospheric predictability is a more complex problem than that of homogeneous, isotropic turbulence. The practical value of global cloud-resolving models is discussed in light of the various aspects of atmospheric predictability.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAS-D-17-0343.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Falko Judt, fjudt@ucar.edu

Abstract

Global convection-permitting models enable weather prediction from local to planetary scales and are therefore often expected to transform the weather prediction enterprise. This potential, however, depends on the predictability of the atmosphere, which was explored here through identical twin experiments using the Model for Prediction Across Scales. The simulations were produced on a quasi-uniform 4-km mesh, which allowed the illumination of error growth from convective to global scales. During the first two days, errors grew through moist convection and other mesoscale processes, and the character of the error growth resembled the case of turbulence. Between 2 and 13 days, errors grew with the background baroclinic instability, and the character of the error growth mirrored the case of turbulence. The existence of an error growth regime with properties similar to turbulence confirmed the radical idea of E. N. Lorenz that the atmosphere has a finite limit of predictability, no matter how small the initial error. The global-mean predictability limit of the troposphere was estimated here to be around 2–3 weeks, which is in agreement with previous work. However, scale-dependent predictability limits differed between the divergent and rotational wind component and between vertical levels, indicating that atmospheric predictability is a more complex problem than that of homogeneous, isotropic turbulence. The practical value of global cloud-resolving models is discussed in light of the various aspects of atmospheric predictability.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAS-D-17-0343.s1.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Falko Judt, fjudt@ucar.edu

Supplementary Materials

    • Supplemental Materials (ZIP 274.61 MB)
Save