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An energy consistent summation-by-parts finite difference discretization for the non-hydrostatic atmospheric dynamics equations on the cubed-sphere grid

  • Dmitry A. Markhanov EMAIL logo , Vladimir V. Shashkin and Gordey S. Goyman
Published/Copyright: April 17, 2025

Abstract

It is expected that atmospheric models with kilometer-scale horizontal resolution will become accessible for use in climate research in the near future. Such high-resolution models require efficient, accurate and conservative numerical solvers for the non-hydrostatic atmospheric dynamics equations on quasi-uniform spherical meshes. In this article, we address this problem by presenting a novel high-order accurate, energy-conserving spatial approximation for non-hydrostatic dynamics equations on the cubed-sphere mesh. The key novel feature of our work is the application of the Summation-By-Parts Finite-Difference (SBP-FD) method for horizontal approximation. SBP-FD enables high-order accurate and stable approximations on logically rectangular multiblock meshes, such as the cubed-sphere. The horizontal gradient and divergence operators constructed using SBP-FD satisfy the discrete analogue of the Ostrogradsky–Gauss theorem, which is essential for energy conservation. As compared to previous works, we explicitly present expressions for discrete vertical mass and entropy fluxes in height-based vertical coordinates, ensuring the compensation potential and thermodynamics energy tendencies with energy contributions from pressure gradient and gravity forces. The proposed spatial approximation is verified using a suite of widely accepted idealized test cases, the results are in good agreement with reference solutions. The robustness of presented spatial approximation is confirmed with the stability in long-term Held–Suarez experiment.

MSC 2010: 86-10; 86-08; 86A08

Funding statement: The work was supported by Moscow Center of Fundamental and Applied Mathematics at INM RAS.

References

[1] T. Davies, M. Cullen, A. Malcolm, M. Mawson, A. Staniforth, A. White, and N. Wood, A new dynamical core for the Met Office’s global and regional modelling of the atmosphere. Quart. J.Roy. Met.Soc. 131 (2005), 1759–1782.10.1256/qj.04.101Search in Google Scholar

[2] D. C. Del Rey Fernández, J. E. Hicken, and D. W. Zingg, Review of summation-by-parts operators with simultaneous approximation terms for the numerical solution of partial differential equations. Computers & Fluids 95 (2014), 171–196.10.1016/j.compfluid.2014.02.016Search in Google Scholar

[3] T. Dubos and M. Tort, Equations of atmospheric motion in non-Eulerian vertical coordinates: Vector-invariant form and quasi-Hamiltonian formulation. Monthly Weather Review 142 (2014), No. 10, 3860–3880.10.1175/MWR-D-14-00069.1Search in Google Scholar

[4] D. R. Durran, Lee waves and mountain waves. Encyclopedia of Atmospheric Sciences 12 (2003), 1161–1169.10.1016/B0-12-227090-8/00202-5Search in Google Scholar

[5] A. Gassmann, A global hexagonal C-grid non-hydrostatic dynamical core (ICON-IAP) designed for energetic consistency. Quart. J. Roy. Met. Soc. 139 (2012), 152–175.10.1002/qj.1960Search in Google Scholar

[6] M. A. Giorgetta, W. Sawyer, X. Lapillonne, P. Adamidis, D. Alexeev, V. Clément, R. Dietlicher, J. F. Engels, M. Esch, H. Franke, C. Frauen, W. M. Hannah, B. R. Hillman, L. Kornblueh, P. Marti, M. R. Norman, R. Pincus, S. Rast, D. Reinert, R. Schnur, U. Schulzweida, and B. Stevens, The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514). Geoscientific Model Development 15 (2022), No. 18, 6985–7016.10.5194/gmd-15-6985-2022Search in Google Scholar

[7] J. E. Guerra and P. A. Ullrich, A high-order staggered finite-element vertical discretization for non-hydrostatic atmospheric models. Geoscientific Model Development 9 (2016), No. 5, 2007–2029.10.5194/gmd-9-2007-2016Search in Google Scholar

[8] R. J. Haarsma, M. J. Roberts, P. L. Vidale, C. A. Senior, A. Bellucci, Q. Bao, P. Chang, S. Corti, N. S. Fučkar, V. Guemas, J. von Harden-berg, W. Hazeleger, C. Kodama, T. Koenigk, L. R. Leung, J. Lu, J.-J. Luo, J. Mao, M. S. Mizielinski, R. Mizuta, P. Nobre, M. Satoh, E. Scoc-cimarro, T. Semmler, J. Small, and J.-S. von Storch, High resolution model intercomparison project (HighResMIP v1.0) for CMIP6. Geoscientific Model Development 9 (2016), No. 11, 4185–4208.10.5194/gmd-9-4185-2016Search in Google Scholar

[9] I. M. Held and M. J. Suarez, A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models. Bull. Amer. Meteor. Soc. 75 (1994), 1825–1830.10.1175/1520-0477(1994)075<1825:APFTIO>2.0.CO;2Search in Google Scholar

[10] J. E. Hicken and D. W. Zingg, Summation-by-parts operators and high-order quadrature. Journal of Computational and Applied Mathematics 237 (2013), No. 1, 111–125.10.1016/j.cam.2012.07.015Search in Google Scholar

[11] S. Husain, C. Girard, A. Qaddouri, and A. Plante, A new dynamical core of the Global Environmental Multiscale (GEM) model with a height-based terrain-following vertical coordinate. Monthly Weather Review 147 (2019), No. 7. 2555–2578.10.1175/MWR-D-18-0438.1Search in Google Scholar

[12] C. Jablonowski, P. H. Lauritzen, M. Taylor, and R. D. Nair, Idealized test cases for the dynamical cores of atmospheric general circulation models. A proposal for the NCAR ASP 2008 summer colloquium, 2008.Search in Google Scholar

[13] C. Jablonowski and D. L. Williamson, A baroclinic instability test case for atmospheric model dynamical cores. Quart. J. Roy. Met. Soc. 132 (2006), 2943–2975.10.1256/qj.06.12Search in Google Scholar

[14] C. Jablonowski and D. L. Williamson, A baroclinic wave test case for dynamical cores of general circulation models: Model intercom-parisons. NCAR technical note NCAR/TN-469+STR, 2006.Search in Google Scholar

[15] J. B. Klemp, W. C. Skamarock, and S.-H. Park, Idealized global nonhydrostatic atmospheric test cases on a reduced-radius sphere. Journal of Advances in Modeling Earth Systems 7 (2015), No. 3, 1155–1177.10.1002/2015MS000435Search in Google Scholar

[16] D. Lee and A. Palha, Exact spatial and temporal balance of energy exchanges within a horizontally explicit/vertically implicit non-hydrostatic atmosphere. Journal of Computational Physics 440 (2021), 110432.10.1016/j.jcp.2021.110432Search in Google Scholar

[17] E. N. Lorenz, Energy and numerical weather prediction. Tellus 12 (1960), 364–373.10.1111/j.2153-3490.1960.tb01323.xSearch in Google Scholar

[18] G. Mengaldo, A. Wyszogrodzki, M. Diamantakis, S.-J. Lock, F. X. Giraldo, and N. P. Wedi, Current and emerging time-integration strategies in global numerical weather and climate prediction. Archives of Computational Methods in Engineering 26 (2019), 663–684.10.1007/s11831-018-9261-8Search in Google Scholar

[19] P. Olsson, Summation by parts, projections, and stability, I. Mathematics of Computation 64 (1995), No. 211, 1035–1065.10.1090/S0025-5718-1995-1297474-XSearch in Google Scholar

[20] O. O’Reilly and N. A. Petersson, Energy conservative SBP discretizations of the acoustic wave equation in covariant form on staggered curvilinear grids. Journal of Computational Physics 411 (2020), 109386.10.1016/j.jcp.2020.109386Search in Google Scholar

[21] T. Rackow, X. Pedruzo-Bagazgoitia, T. Becker, S. Milinski, I. Sandu, R. Aguridan, P. Bechtold, S. Beyer, J. Bidlot, S. Boussetta, W. De-coninck, M. Diamantakis, P. Dueben, E. Dutra, R. Forbes, R. Ghosh, H. F. Goessling, I. Hadade, J. Hegewald, T. Jung, S. Keeley, L. Kluft, N. Koldunov, A. Koldunov, T. Kölling, J. Kousal, C. Kühnlein, P. Maciel, K. Mogensen, T. Quintino, I. Polichtchouk, B. Reuter, D. Sár-mány, P. Scholz, D. Sidorenko, J. Streffing, B. Sützl, D. Takasuka, S. Tietsche, M. Valentini, B. Vannière, N. Wedi, L. Zampieri, and F. Ziemen, Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4. Geoscientific Model Development 18 (2025), No. 1, 33–69.10.5194/gmd-18-33-2025Search in Google Scholar

[22] M. Rančić, R. J. Purser, and F. Mesinger, A global shallow-water model using an expanded spherical cube: Gnomonic versus conformal coordinates. Quarterly Journal of the Royal Meteorological Society 122 (1996), No. 532, 959–982.10.1002/qj.49712253209Search in Google Scholar

[23] M. Satoh, A. Noda, T. Seiki, Y.-W. Chen, C. Kodama, Y. Yamada, N. Kuba, and Y. Sato, Toward reduction of the uncertainties in climate sensitivity due to cloud processes using a global non-hydrostatic atmospheric model. Progress in Earth and Planetary Science 5 (2018), 12.10.1186/s40645-018-0226-1Search in Google Scholar

[24] C. Schär, D. Leuenberger, O. Fuhrer, D. Lüthi, and C. Girard, A new terrain-following vertical coordinate formulation for atmospheric prediction models. Monthly Weather Review 130 (2002), No. 10, 2459–2480.10.1175/1520-0493(2002)130<2459:ANTFVC>2.0.CO;2Search in Google Scholar

[25] D. E. Sergeev, N. J. Mayne, T. Bendall, I. A. Boutle, A. Brown, I. Kavčič, J. Kent, K. Kohary, J. Manners, T. Melvin, E. Olivier, L. K. Ragta, B. Shipway, J. Wakelin, N. Wood, and M. Zerroukat, Simulations of idealised 3d atmospheric flows on terrestrial planets using LFRic-atmosphere. Geoscientific Model Development 16 (2023), No. 19, 5601–5626.10.5194/gmd-16-5601-2023Search in Google Scholar

[26] V. Shashkin, G. Goyman, and I. Tretyak, Development of the next-generation atmosphere dynamics model in Russia: Current state and prospects. Lobachevskii Journal of Mathematics 45 (2024), No. 10, 3159–3172.10.1134/S1995080224603746Search in Google Scholar

[27] V. V. Shashkin, Stability analysis of implicit semi-Lagrangian methods for numerical solution of non-hydrostatic atmospheric dynamics equations. Russian Journal of Numerical Analysis and Mathematical Modelling 36 (2021), No. 4, 239–253.10.1515/rnam-2021-0020Search in Google Scholar

[28] V. V. Shashkin, G. S. Goyman, and M. A. Tolstykh, Summation-by-parts finite-difference shallow water model on the cubed-sphere grid, part I: Non-staggered grid. Journal of Computational Physics 474 (2023), 111797.10.1016/j.jcp.2022.111797Search in Google Scholar

[29] S. C. Sherwood, M. J. Webb, J. D. Annan, K. C. Armour, P. M. Forster, J. C. Hargreaves, G. Hegerl, S. A. Klein, K. D. Marvel, E. J. Rohling, M. Watanabe, T. Andrews, P. Braconnot, C. S. Bretherton, G. L. Foster, Z. Hausfather, A. S. von der Heydt, R. Knutti, T. Mauritsen, J. R. Norris, C. Proistosescu, M. Rugenstein, G. A. Schmidt, K. B. Tokarska, and M. D. Zelinka, An assessment of Earth’s climate sensitivity using multiple lines of evidence. Reviews of Geophysics 58 (2020), No. 4, e2019RG000678.10.1029/2019RG000678Search in Google Scholar

[30] A. J. Simmons and D. M. Burridge, An energy and angular-momentum conserving vertical finite-difference scheme and hybrid vertical coordinates. Mon. Wea. Rev. 109 (1981), No. 4, 758–766.10.1175/1520-0493(1981)109<0758:AEAAMC>2.0.CO;2Search in Google Scholar

[31] A. Staniforth and J. Thuburn, Horizontal grids for global weather and climate prediction models: a review. Quart. J. Roy. Met. Soc. 138 (2012), 1–26.10.1002/qj.958Search in Google Scholar

[32] B. Strand, Summation by parts for finite difference approximations for d/dx. Journal of Computational Physics 110 (1994), No. 1, 47–67.10.1006/jcph.1994.1005Search in Google Scholar

[33] M. A. Taylor and A. Fournier, A compatible and conservative spectral element method on unstructured grids. Journal of Computational Physics 229 (2010), No. 17, 5879–5895.10.1016/j.jcp.2010.04.008Search in Google Scholar

[34] M. A. Taylor, O. Guba, A. Steyer, P. A. Ullrich, D. M. Hall, and C. Eldred, An energy consistent discretization of the nonhydrostatic equations in primitive variables. Journal of Advances in Modeling Earth Systems 12 (2020), No. 1, e2019MS001783.10.1029/2019MS001783Search in Google Scholar

[35] C. Terai, N. Keen, P. Caldwell, H. Beydoun, P. Bogenschutz, L.-W. Chao, B. Hillman, H.-Y. Ma, M. Zelinka, L. Bertagna, A. Bradley, T. Clevenger, A. Donahue, J. Foucar, J.-C. Golaz, O. Guba, W. Hannah, J. Lee, W. Lin, and Y. Zhang, Climate response to warming in Cess–Potter simulations using the global 3-km SCREAM. Preprint 01, 2025.10.22541/essoar.173655643.33295443/v1Search in Google Scholar

[36] J. Thuburn, Conservation in dynamical cores: What, how and why? In: Numerical Techniques for Global Atmospheric Models. Springer, Berlin–Heidelberg, 2011, pp. 345–355.10.1007/978-3-642-11640-7_11Search in Google Scholar

[37] M. Tolstykh, V. Shashkin, R. Fadeev, and G. Goyman, Vorticity-divergence semi-Lagrangian global atmospheric model SL-AV20: Dynamical core. Geoscientific Model Development 10 (2017), No. 5, 1961–1983.10.5194/gmd-10-1961-2017Search in Google Scholar

[38] P. A. Ullrich, C. Jablonowski, J. Kent, P. H. Lauritzen, R. D. Nair, and M. A. Taylor, Dynamical core model intercomparison project (DCMIP) test case document, 2012.Search in Google Scholar

[39] H. Wan, M. Giorgetta, and L. Bonaventura, Ensemble Held-–Suarez test with a spectral transform model: Variability, sensitivity, and convergence. Mon. Wea. Rev. 136 (2008), 1075–1092.10.1175/2007MWR2044.1Search in Google Scholar

[40] H. Wan, M. A. Giorgetta, G. Zangl, M. Restelli, D. Majewski, L. Bonaventura, K. Fruhlich, D. Reinert, P. Ripodas, L. Kornblueh, and J. Forstner, The ICON-1.2 hydrostatic atmospheric dynamical core on triangular grids – part 1: Formulation and performance of the baseline version. Geosci. Model. Dev. 6 (2013), 735–763.10.5194/gmd-6-735-2013Search in Google Scholar

[41] J. Whitaker, HIWPP non-hydrostatic dynamical core tests: Results from idealized test cases. 2014, 2005212015.Search in Google Scholar

[42] G. Zangl, D. Reinert, P. Ripodas, and M. Baldauf. The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core. Quart. J. Roy. Met. Soc. 141 (2015), No. 687, 563–579.10.1002/qj.2378Search in Google Scholar

[43] M. Zelinka, T. Myers, D. Mccoy, S. Po-Chedley, P. Caldwell, P. Ceppi, S. Klein, and K. Taylor, Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters 47 (2020), No. 1, e2019GL085782.10.1029/2019GL085782Search in Google Scholar

[44] C. Zeman, N. P. Wedi, P. D. Dueben, N. Ban, and C. Schär, Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing. Geoscientific Model Development 14 (2021), No. 7, 4617–4639.10.5194/gmd-14-4617-2021Search in Google Scholar

A Thin spherical shell mapping

Thin spherical shell domain is the Cartesian product of a sphere S ⊂3 and a section [0, H]. The points of SH = S × [0, H] are defined with 4-dimensional vectors r=(x,y,z,h), where first three are ‘horizontal’ coordinates representing a point on the sphere and the 4th coordinate is for the height. Nevertheless, the dimensionality of SH is 3, because S is two dimensional (can be represented by two coordinates, e.g., latitude and longitude λ,φ).

We consider a mapping ℳ from the model coordinates (α, β, η) to a subset of thin spherical shell domain S × [hs(α, β), H], where hs is the orography (atmosphere bottom) height. The mapping is the product (successive application) of horizontal mapping H : (α, β, η) → (S, η) and vertical mapping V : (S, η) → (x, y, z, h). The horizontal mapping is equiangular gnomonic cubed-sphere [22]. The vertical mapping depends on (α, β) or equivalently on the position of the point on the sphere (x, y, z).

The covariant basis vectors of ℳ are defined as aξ=r/ξ,ξ(α,β,η). The contravariant basis vectors are aξ=ξ(x,y,z,h), where ∇ is the gradient in (x, y, z, h) coordinates. The scalar product aξaχ=δξχ, δ is the Kronecker delta. It is also useful to define analogously covariant and contravariant basis vectors of only horizontal mapping H:aαH,aβH,aHα,aHβ. One can show that contravariant vectors of full and horizontal mappings coincide aHξ=aξ,ξ=α,β. This is not the case for the covariant vectors.

The metric tensor of ℳ is defined as 3×3 matrix Qξχ=aξaχ. The metric tensor of horizontal transformation is the 2×2 matrix Qhor,Qhorξχ=aξ,Haχ,H. The mapping Jacobian is J=detQ. One can show that the mapping Jacobian is the product of the Jacabians of the vertical and horizontal mappings: J=JVJhor=h/ηJhor. We also need the vertical unit vector k=(0,0,0,1) in the mapping 4D space.

B Auxiliary statements for energy conservation proof

Statement B.1

(B.1) ρρTApJpIwpxw=ρρTAwJwxw

where xw is an arbitrary w-points quantity column vector.

Proof. The identity follows from the vertical interpolation SBP-property (2.8) and the definition of · (2.13).

Statement B.2

(B.2) CpΠΠTApJpDq+CpqαTApJpGhor,αΠΠ+CpqβTApJpGhor,βΠΠ+ρρTApJpIwpwCpρρ1ΘΘhη1DηpwΠΠ=0.

Proof. First, using equation (B.1) and commutation of Hadamard product with the multiplication by diagonal matrix we write:

(B.3) ρρTApJpIwpwCpρρ1ΘΘhη1DηpwΠΠ=Cphη1wΘTAwJwDηpwΠΠ.

Then, using Ghor,ξ definition under equations (2.21) and (B.1) we can write

(B.4) qαTApJpGhor,αΠΠ=qαTApJpGh,αΠΠhαhη1IwpDηpwΠ=qαTApJpGh,αΠΠhαhη1qαTAwJwDηpwΠΠ

and the same for β-component.

Combining equations (B.3) and (B.4) and recalling the definition of the entropy flux (2.16) we rewrite the last three terms of (B.2) as

(B.5) qαTApJpGh,αΠΠ+qβTApJpGh,βΠΠ+qηTAwJwDηpwΠΠ

that is equal to ΠΠTApJpDq by the discrete horizontal Ostrogradsky–Gauss theorem (2.5), vertical SBP (2.9) and 3D discrete divergence D definition under equation (2.20).

Statement B.3

(B.6) ΦΦTApJpDm+mαApJpGhor,αΦΦ+mβTApJpGhor,βΦΦ+ρρTApJpIwpwhη1DηpwΦΦ=0.

Proof. The proof is exactly the same as for Statement B.2, but using ρ instead of Θ,minstead of q and Φ instead of Π.

Statement B.4

(B.7) mαTApJpvη,vαp+mβTApJpvη,vβp+ρρTApJpIwpwvη,ww=mηTAwJwDηpwK.

Proof. We first prove for the horizontal part. Given the definition of equation (2.17) and vertical interpolation SBP-property (2.8) we can write

(B.8) mαTApJpvη,vαp+mβTApJpvη,vβp=mηTAwJwIpwvαDηpwvα+IpwvβDηpwvβ.

Covariant and contravariant horizontal wind components are linked through horizontal mapping con-travariant metric tensor Qhor1 which is symmetric, positive definite and commute with vertical operators Ipw, Dηpw. Using Qhor1 properties and the discrete analogue of product rule (2.11) we rewrite (B.8) as

(B.9) mαTApJpvη,vαp+mβTApJpvη,vβp=12mηTAwJwDηpwvαvα+vβvβ.

For the vertical part of Statement B.4 we use definition of [vη , w]w (2.18) and the · property (B.1):

(B.10) ρρTApJpIwpwvη,ww=wTAwJwJw1IpwIwpJwmηDηwpw.

Then, using interpolation SBP-property (2.8) twice:

(B.11) ρρTApJpIwpwvη,ww=mηTAwJwIpwIwpwDηwpw.

Finally, we can use discrete product rule (2.10) and commutation property (2.12) to derive

(B.12) ρρTApJpIwpwvη,w=12mηTAwJwDηpwIwp(ww)

summing this with the horizontal part equation (B.9) and recalling K = (vα vα + vβ vβ + Iwpw2)/2 we get (B.7).

Statement B.5

(B.13) ρρTApJpIwp{wρρ1mαGh,αw+mβGh,βw}mαTApJpIwpwGh,αmβTApJpIwpwGh,β=0.

Proof. Using 〈·〉 property (B.1) twice: for ρ from left to right and for 〈mα〉 from right to left we can write:

(B.14) ρρTApJpIwpwρρ1mαGh,αw=1TAwJwwmαGh,αw=mαTApJpIwpwGh,α

and the same takes place for β-part of equation (B.13).

Received: 2025-03-10
Accepted: 2025-03-12
Published Online: 2025-04-17
Published in Print: 2025-04-28

© 2025 Walter de Gruyter GmbH, Berlin/Boston, Germany

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