The Relationship between Horizontal and Vertical Velocity Wavenumber Spectra in Global Storm-Resolving Simulations

Yanmichel A. Morfa aMax Planck Institute for Meteorology, Hamburg, Germany

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Claudia C. Stephan aMax Planck Institute for Meteorology, Hamburg, Germany

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Abstract

Several studies have reported vertical kinetic energy spectra almost white in horizontal wavenumber space with evidence of two maxima at synoptic scales and mesoscales, leaving the explanation of these maxima open. Processes known to influence the shape of the horizontal kinetic energy spectra include the superposition of quasi-linear inertia–gravity waves (IGWs), quasigeostrophic turbulence, and moist convection. In contrast, vertical kinetic energy has been discussed much less, as measuring vertical velocity remains challenging. This study compares the horizontal and vertical kinetic energy spectra and their relationships in global storm-resolving simulations from the DYAMOND experiment. The consistency of these relationships with linear IGW theory is tested by diagnosing horizontal wind fluctuations associated with IGW modes. Furthermore, it is shown that hydrostatic IGW polarization relations provide a quantitative prediction of the spectral slopes of vertical kinetic energy at large scales and mesoscales, where the intrinsic frequencies are inferred from the linearized vorticity equation. Our results suggest that IGW modes dominate the vertical kinetic energy spectra at most horizontal scales, whereas an incompressible, isotropic scaling of the continuity equation captures the relationship between horizontal and vertical kinetic energy spectra at small scales.

This article is included in the Special Collection.

© 2023 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: Yanmichel A. Morfa, yanmichel.morfa-avalos@mpimet.mpg.de

Abstract

Several studies have reported vertical kinetic energy spectra almost white in horizontal wavenumber space with evidence of two maxima at synoptic scales and mesoscales, leaving the explanation of these maxima open. Processes known to influence the shape of the horizontal kinetic energy spectra include the superposition of quasi-linear inertia–gravity waves (IGWs), quasigeostrophic turbulence, and moist convection. In contrast, vertical kinetic energy has been discussed much less, as measuring vertical velocity remains challenging. This study compares the horizontal and vertical kinetic energy spectra and their relationships in global storm-resolving simulations from the DYAMOND experiment. The consistency of these relationships with linear IGW theory is tested by diagnosing horizontal wind fluctuations associated with IGW modes. Furthermore, it is shown that hydrostatic IGW polarization relations provide a quantitative prediction of the spectral slopes of vertical kinetic energy at large scales and mesoscales, where the intrinsic frequencies are inferred from the linearized vorticity equation. Our results suggest that IGW modes dominate the vertical kinetic energy spectra at most horizontal scales, whereas an incompressible, isotropic scaling of the continuity equation captures the relationship between horizontal and vertical kinetic energy spectra at small scales.

This article is included in the Special Collection.

© 2023 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: Yanmichel A. Morfa, yanmichel.morfa-avalos@mpimet.mpg.de
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  • Allen, S. J., and R. A. Vincent, 1995: Gravity wave activity in the lower atmosphere: Seasonal and latitudinal variations. J. Geophys. Res., 100, 13271350, https://doi.org/10.1029/94JD02688.

    • Search Google Scholar
    • Export Citation
  • Bacmeister, J. T., S. D. Eckermann, P. A. Newman, L. Lait, K. R. Chan, M. Loewenstein, M. H. Proffitt, and B. L. Gary, 1996: Stratospheric horizontal wavenumber spectra of winds, potential temperature, and atmospheric tracers observed by high-altitude aircraft. J. Geophys. Res., 101, 94419470, https://doi.org/10.1029/95JD03835.

    • Search Google Scholar
    • Export Citation
  • Baer, F., 1972: An alternate scale representation of atmospheric energy spectra. J. Atmos. Sci., 29, 649664, https://doi.org/10.1175/1520-0469(1972)029<0649:AASROA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Bony, S., and B. Stevens, 2019: Measuring area-averaged vertical motions with dropsondes. J. Atmos. Sci., 76, 767783, https://doi.org/10.1175/JAS-D-18-0141.1.

    • Search Google Scholar
    • Export Citation
  • Burgess, B. H., A. R. Erler, and T. G. Shepherd, 2013: The troposphere-to-stratosphere transition in kinetic energy spectra and nonlinear spectral fluxes as seen in ECMWF analyses. J. Atmos. Sci., 70, 669687, https://doi.org/10.1175/JAS-D-12-0129.1.

    • Search Google Scholar
    • Export Citation
  • Businger, J. A., 1973: Turbulence transfer in the atmospheric surface layer. Workshop on Micrometeorology, Boston, MA, Amer. Meteor. Soc., 67–100.

  • Callies, J., R. Ferrari, and O. Bühler, 2014: Transition from geostrophic turbulence to inertia–gravity waves in the atmospheric energy spectrum. Proc. Natl. Acad. Sci. USA, 111, 17 03317 038, https://doi.org/10.1073/pnas.1410772111.

    • Search Google Scholar
    • Export Citation
  • Callies, J., O. Bühler, and R. Ferrari, 2016: The dynamics of mesoscale winds in the upper troposphere and lower stratosphere. J. Atmos. Sci., 73, 48534872, https://doi.org/10.1175/JAS-D-16-0108.1.

    • Search Google Scholar
    • Export Citation
  • Charney, J. G., 1971: Geostrophic turbulence. J. Atmos. Sci., 28, 10871095, https://doi.org/10.1175/1520-0469(1971)028<1087:GT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chen, T.-C., and A. Wiin-Nielsen, 1976: On the kinetic energy of the divergent and nondivergent flow in the atmosphere. Tellus, 28A, 486498, https://doi.org/10.3402/tellusa.v28i6.11317.

    • Search Google Scholar
    • Export Citation
  • Craig, G. C., and T. Selz, 2018: Mesoscale dynamical regimes in the midlatitudes. Geophys. Res. Lett., 45, 410417, https://doi.org/10.1002/2017GL076174.

    • Search Google Scholar
    • Export Citation
  • Dewan, E. M., 1979: Stratospheric wave spectra resembling turbulence. Science, 204, 832835, https://doi.org/10.1126/science.204.4395.832.

    • Search Google Scholar
    • Export Citation
  • Dewan, E. M., 1997: Saturated-cascade similitude theory of gravity wave spectra. J. Geophys. Res., 102, 29 79929 817, https://doi.org/10.1029/97JD02151.

    • Search Google Scholar
    • Export Citation
  • Dewan, E. M., and R. E. Good, 1986: Saturation and the “universal” spectrum for vertical profiles of horizontal scalar winds in the atmosphere. J. Geophys. Res., 91, 27422748, https://doi.org/10.1029/JD091iD02p02742.

    • Search Google Scholar
    • Export Citation
  • Dritschel, D. G., and W. J. McKiver, 2015: Effect of Prandtl’s ratio on balance in geophysical turbulence. J. Fluid Mech., 777, 569590, https://doi.org/10.1017/jfm.2015.348.

    • Search Google Scholar
    • Export Citation
  • ECMWF, 2021: IFS documentation CY47R3—Part III: Dynamics and numerical procedures. ECMWF IFS Doc. 3, 31 pp., https://www.ecmwf.int/sites/default/files/elibrary/2021/81270-ifs-documentation-cy47r3-part-iii-dynamics-and-numerical-procedures_1.pdf.

  • Fritts, D. C., 1984: Gravity wave saturation in the middle atmosphere: A review of theory and observations. Rev. Geophys., 22, 275308, https://doi.org/10.1029/RG022i003p00275.

    • Search Google Scholar
    • Export Citation
  • Gage, K. S., 1979: Evidence for a k−5/3 law inertial range in mesoscale two-dimensional turbulence. J. Atmos. Sci., 36, 19501954, https://doi.org/10.1175/1520-0469(1979)036<1950:EFALIR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gao, X., and J. W. Meriwether, 1998: Mesoscale spectral analysis of in situ horizontal and vertical wind measurements at 6 km. J. Geophys. Res., 103, 63976404, https://doi.org/10.1029/97JD03074.

    • Search Google Scholar
    • Export Citation
  • Gardner, C. S., 1996: Testing theories of atmospheric gravity wave saturation and dissipation. J. Atmos. Terr. Phys., 58, 15751589, https://doi.org/10.1016/0021-9169(96)00027-X.

    • Search Google Scholar
    • Export Citation
  • Gardner, C. S., S. J. Franke, W. Yang, X. Tao, and J. R. Yu, 1998: Interpretation of gravity waves observed in the mesopause region at Starfire optical range, New Mexico: Strong evidence for nonseparable intrinsic (m, ω) spectra. J. Geophys. Res., 103, 86998713, https://doi.org/10.1029/97JD03428.

    • Search Google Scholar
    • Export Citation
  • Geller, M. A., and J. Gong, 2010: Gravity wave kinetic, potential, and vertical fluctuation energies as indicators of different frequency gravity waves. J. Geophys. Res., 115, D11111, https://doi.org/10.1029/2009JD012266.

    • Search Google Scholar
    • Export Citation
  • Hamilton, K., Y. O. Takahashi, and W. Ohfuchi, 2008: Mesoscale spectrum of atmospheric motions investigated in a very fine resolution global general circulation model. J. Geophys. Res., 113, D18110, https://doi.org/10.1029/2008JD009785.

    • Search Google Scholar
    • Export Citation
  • Kasahara, A., 2020: 3D normal mode functions (NMFs) of a global baroclinic atmospheric model. Modal View of Atmospheric Variability, N. Žagar and J. Tribbia, Eds., Mathematics of Planet Earth Series, Vol. 8, Springer, 1–62.

  • Kasahara, A., and K. Puri, 1981: Spectral representation of three-dimensional global data by expansion in normal mode functions. Mon. Wea. Rev., 109, 3751, https://doi.org/10.1175/1520-0493(1981)109<0037:SROTDG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kitamura, Y., and Y. Matsuda, 2010: Energy cascade processes in rotating stratified turbulence with application to the atmospheric mesoscale. J. Geophys. Res., 115, D11104, https://doi.org/10.1029/2009JD012368.

    • Search Google Scholar
    • Export Citation
  • Lambert, S. J., 1984: A global available potential energy-kinetic energy budget in terms of the two-dimensional wavenumber for the FGGE year. Atmos.-Ocean, 22, 265282, https://doi.org/10.1080/07055900.1984.9649199.

    • Search Google Scholar
    • Export Citation
  • Li, Q., and E. Lindborg, 2018: Weakly or strongly nonlinear mesoscale dynamics close to the tropopause? J. Atmos. Sci., 75, 12151229, https://doi.org/10.1175/JAS-D-17-0063.1.

    • Search Google Scholar
    • Export Citation
  • Lindborg, E., 1999: Can the atmospheric kinetic energy spectrum be explained by two-dimensional turbulence? J. Fluid Mech., 388, 259288, https://doi.org/10.1017/S0022112099004851.

    • Search Google Scholar
    • Export Citation
  • Lindborg, E., 2006: The energy cascade in a strongly stratified fluid. J. Fluid Mech., 550, 207242, https://doi.org/10.1017/S0022112005008128.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1960: Energy and numerical weather prediction. Tellus, 12A, 364373, https://doi.org/10.3402/tellusa.v12i4.9420.

  • Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289307, https://doi.org/10.1111/j.2153-3490.1969.tb00444.x.

    • Search Google Scholar
    • Export Citation
  • Malardel, S., and N. P. Wedi, 2016: How does subgrid-scale parametrization influence nonlinear spectral energy fluxes in global NWP models? J. Geophys. Res. Atmos., 121, 53955410, https://doi.org/10.1002/2015JD023970.

    • Search Google Scholar
    • Export Citation
  • McIntyre, M. E., 2015: Dynamical meteorology: Balanced flow. Encyclopedia of Atmospheric Sciences, 2nd ed. G. R. North, J. Pyle, and F. Zhang, Eds., Academic Press, 298–303, https://doi.org/10.1016/B978-0-12-382225-3.00484-9.

  • Müller, S. K., E. Manzini, M. Giorgetta, K. Sato, and T. Nasuno, 2018: Convectively generated gravity waves in high resolution models of tropical dynamics. J. Adv. Model. Earth Syst., 10, 25642588, https://doi.org/10.1029/2018MS001390.

    • Search Google Scholar
    • Export Citation
  • Nastrom, G. D., 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.

    • Search Google Scholar
    • Export Citation
  • Nastrom, G. D., K. S. Gage, and W. H. Jasperson, 1984: Kinetic energy spectrum of large-and mesoscale atmospheric processes. Nature, 310, 3638, https://doi.org/10.1038/310036a0.

    • Search Google Scholar
    • Export Citation
  • Peltier, L. J., J. C. Wyngaard, S. Khanna, and J. O. Brasseur, 1996: Spectra in the unstable surface layer. J. Atmos. Sci., 53, 4961, https://doi.org/10.1175/1520-0469(1996)053<0049:SITUSL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Polichtchouk, I., N. Wedi, and Y.-H. Kim, 2022: Resolved gravity waves in the tropical stratosphere: Impact of horizontal resolution and deep convection parametrization. Quart. J. Roy. Meteor. Soc., 148, 233251, https://doi.org/10.1002/qj.4202.

    • Search Google Scholar
    • Export Citation
  • Schulzweida, U., 2022: CDO user guide, version 2.1. Zenodo, https://doi.org/10.5281/zenodo.7112925.

  • Schumann, U., 2019: The horizontal spectrum of vertical velocities near the tropopause from global to gravity wave scales. J. Atmos. Sci., 76, 38473862, https://doi.org/10.1175/JAS-D-19-0160.1.

    • Search Google Scholar
    • Export Citation
  • Selz, T., L. Bierdel, and G. C. Craig, 2019: Estimation of the variability of mesoscale energy spectra with three years of COSMO-DE analyses. J. Atmos. Sci., 76, 627637, https://doi.org/10.1175/JAS-D-18-0155.1.

    • Search Google Scholar
    • Export Citation
  • Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 30793102, https://doi.org/10.1256/qj.04.106.

    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and D. M. Burridge, 1981: An energy and angular-momentum conserving vertical finite-difference scheme and hybrid vertical coordinates. Mon. Wea. Rev., 109, 758766, https://doi.org/10.1175/1520-0493(1981)109<0758:AEAAMC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032, https://doi.org/10.1175/MWR2830.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, https://doi.org/10.1016/j.jcp.2007.01.037.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., C. Snyder, J. B. Klemp, and S.-H. Park, 2019: Vertical resolution requirements in atmospheric simulation. Mon. Wea. Rev., 147, 26412656, https://doi.org/10.1175/MWR-D-19-0043.1.

    • Search Google Scholar
    • Export Citation
  • Smith, S. A., D. C. Fritts, and T. E. VanZandt, 1987: Evidence for a saturated spectrum of atmospheric gravity waves. J. Atmos. Sci., 44, 14041410, https://doi.org/10.1175/1520-0469(1987)044<1404:EFASSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stephan, C. C., and A. Mariaccia, 2021: The signature of the tropospheric gravity wave background in observed mesoscale motion. Wea. Climate Dyn., 2, 359372, https://doi.org/10.5194/wcd-2-359-2021.

    • Search Google Scholar
    • Export Citation
  • Stephan, C. C., and Coauthors, 2022: Atmospheric energy spectra in global kilometre-scale models. Tellus, 74A, 280299, https://doi.org/10.16993/tellusa.26.

    • Search Google Scholar
    • Export Citation
  • Stevens, B., and Coauthors, 2019: DYAMOND: The Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains. Prog. Earth Planet. Sci., 6, 61, https://doi.org/10.1186/s40645-019-0304-z.

    • Search Google Scholar
    • Export Citation
  • Terasaki, K., H. Tanaka, and M. Satoh, 2009: Characteristics of the kinetic energy spectrum of NICAM model atmosphere. SOLA, 5, 180183, https://doi.org/10.2151/sola.2009-046.

    • Search Google Scholar
    • Export Citation
  • Terasaki, K., H. Tanaka, and N. Žagar, 2011: Erratum: Energy spectra of Rossby and gravity waves. SOLA, 7, 4548, https://doi.org/10.2151/sola.2011-012-e1.

    • Search Google Scholar
    • Export Citation
  • Tong, C., and K. X. Nguyen, 2015: Multipoint Monin–Obukhov similarity and its application to turbulence spectra in the convective atmospheric surface layer. J. Atmos. Sci., 72, 43374348, https://doi.org/10.1175/JAS-D-15-0134.1.

    • Search Google Scholar
    • Export Citation
  • Tulloch, R., and K. S. Smith, 2006: A theory for the atmospheric energy spectrum: Depth-limited temperature anomalies at the tropopause. Proc. Natl. Acad. Sci. USA, 103, 14 69014 694, https://doi.org/10.1073/pnas.0605494103.

    • Search Google Scholar
    • Export Citation
  • Tung, K. K., and W. W. Orlando, 2003: The k−3 and k−5/3 energy spectrum of atmospheric turbulence: Quasigeostrophic two-level model simulation. J. Atmos. Sci., 60, 824835, https://doi.org/10.1175/1520-0469(2003)060<0824:TKAKES>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Untch, A., and M. Hortal, 2003: A finite-element scheme for the vertical discretization in the semi-Lagrangian version of the ECMWF model. ECMWF Tech. Memo. 382, 27 pp., https://www.ecmwf.int/sites/default/files/elibrary/2003/12873-finite-element-scheme-vertical-descretization-semi-lagrangian-version-ecmwf-model.pdf.

  • VanZandt, T. E., 1982: A universal spectrum of buoyancy waves in the atmosphere. Geophys. Res. Lett., 9, 575578, https://doi.org/10.1029/GL009i005p00575.

    • Search Google Scholar
    • Export Citation
  • Waite, M. L., 2016: Dependence of model energy spectra on vertical resolution. Mon. Wea. Rev., 144, 14071421, https://doi.org/10.1175/MWR-D-15-0316.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Wang, H., and O. Bühler, 2020: Ageostrophic corrections for power spectra and wave–vortex decomposition. J. Fluid Mech., 882, A16, https://doi.org/10.1017/jfm.2019.815.

    • Search Google Scholar
    • Export Citation
  • Žagar, N., A. Kasahara, K. Terasaki, J. Tribbia, and H. Tanaka, 2015: Normal-mode function representation of global 3-D data sets: Open-access software for the atmospheric research community. Geosci. Model Dev., 8, 11691195, https://doi.org/10.5194/gmd-8-1169-2015.

    • Search Google Scholar
    • Export Citation
  • Žagar, N., D. Jelić, M. Blaauw, and P. Bechtold, 2017: Energy spectra and inertia–gravity waves in global analyses. J. Atmos. Sci., 74, 24472466, https://doi.org/10.1175/JAS-D-16-0341.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, S. D., C. M. Huang, K. M. Huang, Y. H. Zhang, Y. Gong, and Q. Gan, 2017: Vertical wavenumber spectra of three-dimensional winds revealed by radiosonde observations at midlatitude. Ann. Geophys., 35, 107116, https://doi.org/10.5194/angeo-35-107-2017.

    • Search Google Scholar
    • Export Citation
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