Articles | Volume 14, issue 4
https://doi.org/10.5194/gmd-14-2011-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-2011-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Towards multiscale modeling of ocean surface turbulent mixing using coupled MPAS-Ocean v6.3 and PALM v5.0
Fluid Dynamics and Solid Mechanics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Luke Van Roekel
Fluid Dynamics and Solid Mechanics, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
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Different ocean vertical mixing schemes are usually developed in different modeling framework, making the comparison across such schemes difficult. Here, we develop a consistent framework for testing, comparing, and applying different ocean mixing schemes by integrating CVMix into GOTM, which also extends the capability of GOTM towards including the effects of ocean surface waves. A suite of test cases and toolsets for developing and evaluating ocean mixing schemes is also described.
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Wind-generated waves are an important process in the global climate system. They mediate many interactions between the ocean, atmosphere, and sea ice. Models which describe these waves are computationally expensive and have often been excluded from coupled Earth system models. To address this, we have developed a capability for the WAVEWATCH III model which allows model resolution to be varied globally across the coastal open ocean. This allows for improved accuracy at reduced computing time.
Cited articles
Benedict, J. J. and Randall, D. A.: Structure of the Madden–Julian Oscillation in the Superparameterized CAM, J. Atmos. Sci., 66, 3277–3296, https://doi.org/10.1175/2009JAS3030.1, 2009. a
Brenowitz, N. D. and Bretherton, C. S.: Prognostic Validation of a Neural Network Unified Physics Parameterization, Geophys. Res. Lett., 45, 6289–6298, https://doi.org/10.1029/2018GL078510, 2018. a
Burchard, H., Craig, P. D., Gemmrich, J. R., van Haren, H., Mathieu, P.-P., Meier, H. E. M., Smith, W. A. M. N., Prandke, H., Rippeth, T. P., Skyllingstad, E. D., Smyth, W. D., Welsh, D. J. S., and Wijesekera, H. W.: Observational and Numerical Modeling Methods for Quantifying Coastal Ocean Turbulence and Mixing, Prog. Oceanogr., 76, 399–442, https://doi.org/10.1016/j.pocean.2007.09.005, 2008. a
Campin, J.-M., Hill, C., Jones, H., and Marshall, J.: Super-Parameterization in Ocean Modeling: Application to Deep Convection, Ocean Model., 36, 90–101, https://doi.org/10.1016/j.ocemod.2010.10.003, 2011. a, b, c
Damerell, G. M., Heywood, K. J., Calvert, D., M. Grant, A. L., Bell, M. J., and Belcher, S. E.: A Comparison of Five Surface Mixed Layer Models with a Year of Observations in the North Atlantic, Prog. Oceanogr., 187, 102 316, https://doi.org/10.1016/j.pocean.2020.102316, 2020. a
Fan, Y., Jarosz, E., Yu, Z., Rogers, W. E., Jensen, T. G., and Liang, J.-H.: Langmuir Turbulence in Horizontal Salinity Gradient, Ocean Model., 129, 93–103, https://doi.org/10.1016/j.ocemod.2018.07.010, 2018. a, b, c
Fox-Kemper, B., Ferrari, R., and Hallberg, R.: Parameterization of Mixed Layer Eddies. Part I: Theory and Diagnosis, J. Phys. Oceanogr., 38, 1145–1165, https://doi.org/10.1175/2007JPO3792.1, 2008. a
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a
Grabowski, W. W.: An Improved Framework for Superparameterization, J. Atmos. Sci., 61, 1940–1952, https://doi.org/10.1175/1520-0469(2004)061<1940:AIFFS>2.0.CO;2, 2004. a, b, c
Grabowski, W. W. and Smolarkiewicz, P. K.: CRCP: A Cloud Resolving Convection Parameterization for Modeling the Tropical Convecting Atmosphere, Physica D, 133, 171–178, https://doi.org/10.1016/S0167-2789(99)00104-9, 1999. a, b
Grooms, I. and Julien, K.: Multiscale Models in Geophysical Fluid Dynamics, Earth and Space Science, 5, 668–675, https://doi.org/10.1029/2018EA000439, 2018. a
Grooms, I. and Majda, A. J.: Efficient Stochastic Superparameterization for Geophysical Turbulence, Proc. Natl. Acad. Sci., 110, 4464–4469, https://doi.org/10.1073/pnas.1302548110, 2013. a, b
Harcourt, R. R.: A Second-Moment Closure Model of Langmuir Turbulence, J. Phys. Oceanogr., 43, 673–697, https://doi.org/10.1175/JPO-D-12-0105.1, 2013. a
Harcourt, R. R.: An Improved Second-Moment Closure Model of Langmuir Turbulence, J. Phys. Oceanogr., 45, 84–103, https://doi.org/10.1175/JPO-D-14-0046.1, 2015. a
Jansson, F., van den Oord, G., Pelupessy, I., Grönqvist, J. H., Siebesma, A. P., and Crommelin, D.: Regional Superparameterization in a Global Circulation Model Using Large Eddy Simulations, J. Adv. Model. Earth Sy., 11, 2958–2979, https://doi.org/10.1029/2018MS001600, 2019. a
Jung, J.-H. and Arakawa, A.: Development of a Quasi-3D Multiscale Modeling Framework: Motivation, Basic Algorithm and Preliminary Results, J. Adv. Model. Earth Sy., 2, 11, https://doi.org/10.3894/JAMES.2010.2.11, 2010. a
Khairoutdinov, M. F. and Randall, D. A.: A Cloud Resolving Model as a Cloud Parameterization in the NCAR Community Climate System Model: Preliminary Results, Geophys. Res. Lett., 28, 3617–3620, https://doi.org/10.1029/2001GL013552, 2001. a, b
Khairoutdinov, M., Randall, D., and DeMott, C.: Simulations of the Atmospheric General Circulation Using a Cloud-Resolving Model as a Superparameterization of Physical Processes, J. Atmos. Sci., 62, 2136–2154, https://doi.org/10.1175/JAS3453.1, 2005. a, b
Large, W. G., Mcwilliams, J. C., and Doney, S. C.: Oceanic Vertical Mixing: A Review and a Model with a Nonlocal Boundary Layer Parameterization, Rev. Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994. a, b, c
Li, Q. and Fox-Kemper, B.: Assessing the Effects of Langmuir Turbulence on the Entrainment Buoyancy Flux in the Ocean Surface Boundary Layer, J. Phys. Oceanogr., 47, 2863–2886, https://doi.org/10.1175/JPO-D-17-0085.1, 2017. a, b
Li, Q. and Fox-Kemper, B.: Anisotropy of Langmuir Turbulence and the Langmuir-Enhanced Mixed Layer Entrainment, Phys. Rev. Fluids, 5, 013 803, https://doi.org/10.1103/PhysRevFluids.5.013803, 2020. a
Li, Q. and Van Roekel, L.: Archived source code for “Towards Multiscale Modeling of Ocean Surface Turbulent Mixing Using Coupled MPAS-Ocean v6.3 and PALM v5.0”, Zenodo, https://doi.org/10.5281/zenodo.4131134, 2020. a
Li, Q. and Van Roekel, L.: Test cases for coupled MPAS-Ocean and PALM, Data set, Zenodo, https://doi.org/10.5281/zenodo.4680969, 2021. a
Li, Q., Reichl, B. G., Fox-Kemper, B., Adcroft, A., Belcher, S., Danabasoglu, G., Grant, A., Griffies, S. M., Hallberg, R. W., Hara, T., Harcourt, R., Kukulka, T., Large, W. G., McWilliams, J. C., Pearson, B., Sullivan, P., Van Roekel, L., Wang, P., and Zheng, Z.: Comparing Ocean Surface Boundary Vertical Mixing Schemes Including Langmuir Turbulence, J. Adv. Model. Earth Sy., 11, 3545–3592, https://doi.org/10.1029/2019MS001810, 2019. a, b, c
Maronga, B., Gryschka, M., Heinze, R., Hoffmann, F., Kanani-Sühring, F., Keck, M., Ketelsen, K., Letzel, M. O., Sühring, M., and Raasch, S.: The Parallelized Large-Eddy Simulation Model (PALM) version 4.0 for atmospheric and oceanic flows: model formulation, recent developments, and future perspectives, Geosci. Model Dev., 8, 2515–2551, https://doi.org/10.5194/gmd-8-2515-2015, 2015. a, b, c
McWilliams, J. C., Sullivan, P. P., and Moeng, C.-H.: Langmuir Turbulence in the Ocean, J. Fluid Mech., 334, 1–30, 1997. a
O'Gorman, P. A. and Dwyer, J. G.: Using Machine Learning to Parameterize Moist Convection: Potential for Modeling of Climate, Climate Change, and Extreme Events, J. Adv. Model. Earth Sy., 10, 2548–2563, https://doi.org/10.1029/2018MS001351, 2018. a
Paulson, C. A. and Simpson, J. J.: Irradiance Measurements in the Upper Ocean, J. Phys. Oceanogr., 7, 952–956, https://doi.org/10.1175/1520-0485(1977)007<0952:IMITUO>2.0.CO;2, 1977. a
Raasch, S. and Schröter, M.: PALM – A Large-Eddy Simulation Model Performing on Massively Parallel Computers, Meteorol. Z., 10, 363–372, https://doi.org/10.1127/0941-2948/2001/0010-0363, 2001. a, b
Randall, D., Khairoutdinov, M., Arakawa, A., and Grabowski, W.: Breaking the Cloud Parameterization Deadlock, B. Am. Meteor. Soc., 84, 1547–1564, https://doi.org/10.1175/BAMS-84-11-1547, 2003. a
Randall, D., DeMott, C., Stan, C., Khairoutdinov, M., Benedict, J., McCrary, R., Thayer-Calder, K., and Branson, M.: Simulations of the Tropical General Circulation with a Multiscale Global Model, Meteor. Mon., 56, 15.1–15.15, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0016.1, 2016. a
Randall, D. A.: Beyond Deadlock, Geophys. Res. Lett., 40, 5970–5976, https://doi.org/10.1002/2013GL057998, 2013. a
Reichl, B. G. and Hallberg, R.: A Simplified Energetics Based Planetary Boundary Layer (ePBL) Approach for Ocean Climate Simulations., Ocean Model., 132, 112–129, https://doi.org/10.1016/j.ocemod.2018.10.004, 2018. a, b
Reichl, B. G. and Li, Q.: A Parameterization with a Constrained Potential Energy Conversion Rate of Vertical Mixing Due to Langmuir Turbulence, J. Phys. Oceanogr., 49, 2935–2959, https://doi.org/10.1175/JPO-D-18-0258.1, 2019. a, b
Reichl, B. G., Wang, D., Hara, T., Ginis, I., and Kukulka, T.: Langmuir Turbulence Parameterization in Tropical Cyclone Conditions, J. Phys. Oceanogr., 46, 863–886, https://doi.org/10.1175/JPO-D-15-0106.1, 2016. a
Ringler, T. D., Thuburn, J., Klemp, J. B., and Skamarock, W. C.: A Unified Approach to Energy Conservation and Potential Vorticity Dynamics for Arbitrarily-Structured C-Grids, J. Comput. Phys., 229, 3065–3090, https://doi.org/10.1016/j.jcp.2009.12.007, 2010. a, b
Ringler, T., Petersen, M., Higdon, R. L., Jacobsen, D., Jones, P. W., and Maltrud, M.: A Multi-Resolution Approach to Global Ocean Modeling, Ocean Model., 69, 211–232, https://doi.org/10.1016/j.ocemod.2013.04.010, 2013. a, b
Sullivan, P. P. and McWilliams, J. C.: Langmuir Turbulence and Filament Frontogenesis in the Oceanic Surface Boundary Layer, J. Fluid Mech., 879, 512–553, https://doi.org/10.1017/jfm.2019.655, 2019.
a, b, c, d
Van Roekel, L., Adcroft, A., Danabasoglu, G., Griffies, S. M., Kauffman, B., Large, W., Levy, M., Reichl, B. G., Ringler, T., and Schmidt, M.: The KPP Boundary Layer Scheme for the Ocean: Revisiting Its Formulation and Benchmarking One-Dimensional Simulations Relative to LES, J. Adv. Model. Earth Sy., 10, 2647–2685, https://doi.org/10.1029/2018MS001336, 2018. a, b, c, d, e
Verma, V., Pham, H. T., and Sarkar, S.: The Submesoscale, the Finescale and Their Interaction at a Mixed Layer Front, Ocean Model., 140, 101 400, https://doi.org/10.1016/j.ocemod.2019.05.004, 2019. a, b
Wang, D., Kukulka, T., Reichl, B. G., Hara, T., Ginis, I., and Sullivan, P. P.: Interaction of Langmuir Turbulence and Inertial Currents in the Ocean Surface Boundary Layer under Tropical Cyclones, J. Phys. Oceanogr., 48, 1921–1940, https://doi.org/10.1175/JPO-D-17-0258.1, 2018. a
White, L. and Adcroft, A.: A High-Order Finite Volume Remapping Scheme for Nonuniform Grids: The Piecewise Quartic Method (PQM), J. Comput. Phys., 227, 7394–7422, https://doi.org/10.1016/j.jcp.2008.04.026, 2008. a, b
Wicker, L. J. and Skamarock, W. C.: Time-Splitting Methods for Elastic Models Using Forward Time Schemes, Mon. Weather Rev., 130, 2088–2097, https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2, 2002. a
Short summary
Physical processes in the ocean span multiple spatial and temporal scales. Simultaneously resolving all these in a simulation is computationally challenging. Here we develop a more efficient technique to better study the interactions across scales, particularly focusing on the ocean surface turbulent mixing, by coupling a global ocean circulation model MPAS-Ocean and a large eddy simulation model PALM. The latter is customized and ported on a GPU to further accelerate the computation.
Physical processes in the ocean span multiple spatial and temporal scales. Simultaneously...