Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-543-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-14-543-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A simplified atmospheric boundary layer model for an improved representation of air–sea interactions in eddying oceanic models: implementation and first evaluation in NEMO (4.0)
Florian Lemarié
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
Guillaume Samson
Mercator Océan, Toulouse, France
Jean-Luc Redelsperger
Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Brest, France
Hervé Giordani
Centre National de Recherches Météorologiques (CNRM), Université de Toulouse, Météo-France, CNRS, Toulouse, France
Théo Brivoal
Mercator Océan, Toulouse, France
Gurvan Madec
Sorbonne Universités (UPMC, Univ Paris 06)-CNRS-IRD-MNHN, LOCEAN Laboratory, Paris, France
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
Related authors
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Earth system models consist of many components, coupled in time and space. Standard coupling algorithms introduce a numerical error, which one can compute with iterative coupling methods. We use such a method for the EC-Earth AOSCM, which models a single vertical column of the atmosphere, ocean, and sea ice. We find that coupling errors in the atmosphere and at the ice surface can be substantial and that discontinuous physics parameterizations lead to convergence issues of the iteration.
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We show that modern concepts of non-equilibrium statistical mechanics can be applied to large-scale environmental fluid dynamics, where fluctuations are not thermal but come from turbulence. The work theorems developed by Jarzynski and Crooks are applied to air–sea interaction. Rather than looking at the average values of thermodynamic variables, their probability density functions are considered, which allows us to replace the inequalities of equilibrium statistical mechanics with equalities.
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State-of-the-art Earth system models, like the ones used in CMIP6, suffer from temporal inconsistencies at the ocean–atmosphere interface. In this study, a mathematically consistent iterative Schwarz method is used as a reference. Its tremendous computational cost makes it unusable for production runs, but it allows us to evaluate the error made when using legacy coupling schemes. The impact on the climate at longer timescales of days to decades is not evaluated.
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Earth system models consist of many components, coupled in time and space. Standard coupling algorithms introduce a numerical error, which one can compute with iterative coupling methods. We use such a method for the EC-Earth AOSCM, which models a single vertical column of the atmosphere, ocean, and sea ice. We find that coupling errors in the atmosphere and at the ice surface can be substantial and that discontinuous physics parameterizations lead to convergence issues of the iteration.
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Nicholas K.-R. Kevlahan and Florian Lemarié
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WAVETRISK-2.1 is an innovative climate model for the world's oceans. It uses state-of-the-art techniques to change the model's resolution locally, from O(100 km) to O(5 km), as the ocean changes. This dynamic adaptivity makes optimal use of available supercomputer resources, and allows two-dimensional global scales and three-dimensional submesoscales to be captured in the same simulation. WAVETRISK-2.1 is designed to be coupled its companion global atmosphere model, WAVETRISK-1.x.
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A single-column version of the global climate model CNRM-CM6-1 has been designed to ease development and validation of the model physics at the air–sea interface in a simplified environment. This model is then used to assess the ability to represent the sea surface temperature diurnal cycle. We conclude that the sea surface temperature diurnal variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m.
Joris Pianezze, Jonathan Beuvier, Cindy Lebeaupin Brossier, Guillaume Samson, Ghislain Faure, and Gilles Garric
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Most numerical weather and oceanic prediction systems do not consider ocean–atmosphere feedback during forecast, and this can lead to significant forecast errors, notably in cases of severe situations. A new high-resolution coupled ocean–atmosphere system is presented in this paper. This forecast-oriented system, based on current regional operational systems and evaluated using satellite and in situ observations, shows that the coupling improves both atmospheric and oceanic forecasts.
Achim Wirth and Florian Lemarié
Earth Syst. Dynam., 12, 689–708, https://doi.org/10.5194/esd-12-689-2021, https://doi.org/10.5194/esd-12-689-2021, 2021
Short summary
Short summary
We show that modern concepts of non-equilibrium statistical mechanics can be applied to large-scale environmental fluid dynamics, where fluctuations are not thermal but come from turbulence. The work theorems developed by Jarzynski and Crooks are applied to air–sea interaction. Rather than looking at the average values of thermodynamic variables, their probability density functions are considered, which allows us to replace the inequalities of equilibrium statistical mechanics with equalities.
Olivier Marti, Sébastien Nguyen, Pascale Braconnot, Sophie Valcke, Florian Lemarié, and Eric Blayo
Geosci. Model Dev., 14, 2959–2975, https://doi.org/10.5194/gmd-14-2959-2021, https://doi.org/10.5194/gmd-14-2959-2021, 2021
Short summary
Short summary
State-of-the-art Earth system models, like the ones used in CMIP6, suffer from temporal inconsistencies at the ocean–atmosphere interface. In this study, a mathematically consistent iterative Schwarz method is used as a reference. Its tremendous computational cost makes it unusable for production runs, but it allows us to evaluate the error made when using legacy coupling schemes. The impact on the climate at longer timescales of days to decades is not evaluated.
Cited articles
Abel, R.: Aspects of air-sea interaction in atmosphere-ocean models, PhD
thesis, Kiel University, 2018. a
Ayet, A. and Redelsperger, J.-L.: An analytical study of the atmospheric
boundary layer flow and divergence over a SST front, Q. J. Roy. Meteor.
Soc., 145, 2549–2567, https://doi.org/10.1002/qj.3578, 2019. a, b, c, d
Baklanov, A. A., Grisogono, B., Bornstein, R., Mahrt, L., Zilitinkevich, S. S.,
Taylor, P., Larsen, S. E., Rotach, M. W., and Fernando, H. J. S.: The
Nature, Theory, and Modeling of Atmospheric Planetary Boundary Layers, B.
Am. Meteorol. Soc., 92, 123–128, https://doi.org/10.1175/2010BAMS2797.1,
2011. a
Barnier, B., Siefridt, L., and Marchesiello, P.: Thermal forcing for a
global ocean circulation model using a three-year climatology of ECMWF
analyses, J. Mar. Res., 6, 363–380,
https://doi.org/10.1016/0924-7963(94)00034-9, 1995. a
Barnier, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M., Le
Sommer, J., Beckmann, A., Biastoch, A., Böning, C., Dengg, J., Derval,
C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud,
M., McClean, J., and De Cuevas, B.: Impact of partial steps and momentum
advection schemes in a global ocean circulation model at eddy-permitting
resolution, Ocean Dynam., 56, 543–567, https://doi.org/10.1007/s10236-006-0082-1,
2006. a
Bazile, E., Marquet, P., Bouteloup, Y., and Bouyssel, F.: The Turbulent Kinetic
Energy (TKE) scheme in the NWP models at Meteo France, in: Workshop on
Workshop on Diurnal cycles and the stable boundary layer, 7–10 November 2011,
ECMWF, Shinfield Park, Reading, 127–135,
available at: https://www.ecmwf.int/node/8006 (last access: 20 January 2021), 2012. a, b
Beljaars, A.: The parametrization of surface fluxes in large-scale models
under free convection, Q. J. Roy. Meteor. Soc., 121, 255–270, 1995. a
Beljaars, A., Dutra, E., Balsamo, G., and Lemarié, F.: On the numerical stability of surface–atmosphere coupling in weather and climate models, Geosci. Model Dev., 10, 977–989, https://doi.org/10.5194/gmd-10-977-2017, 2017. a, b
Bielli, S., Douville, H., and Pohl, B.: Understanding the West African monsoon
variability and its remote effects: An illustration of the grid point nudging
methodology, Clim. Dynam., 35, 159–174, https://doi.org/10.1007/s00382-009-0667-8,
2009. a
Bougeault, P. and André, J.-C.: On the Stability of the THIRD-Order
Turbulence Closure for the Modeling of the Stratocumulus-Topped Boundary
Layer, J. Atmos. Sci., 43, 1574–1581,
https://doi.org/10.1175/1520-0469(1986)043<1574:OTSOTT>2.0.CO;2, 1986. a
Bourras, D., Reverdin, G., Giordani, H., and Caniaux, G.: Response of the
atmospheric boundary layer to a mesoscale oceanic eddy in the northeast
Atlantic, J. Geophys. Res., 109, https://doi.org/10.1029/2004JD004799, 2004. a
Brivoal, T., Samson, G., Giordani, H., Bourdallé-Badie, R., Lemarié, F., and Madec, G.: Impact of the current feedback on kinetic energy over the North-East Atlantic from a coupled ocean/atmospheric boundary layer model, Ocean Sci. Discuss. [preprint], https://doi.org/10.5194/os-2020-78, in review, 2020. a
Brodeau, L., Barnier, B., Gulev, S. K., and Woods, C.: Climatologically
Significant Effects of Some Approximations in the Bulk Parameterizations of
Turbulent Air–Sea Fluxes, J. Phys. Oceanogr., 47, 5–28,
https://doi.org/10.1175/JPO-D-16-0169.1, 2017. a
Bryan, F. O., Tomas, R., Dennis, J. M., Chelton, D. B., Loeb, N. G., and
McClean, J. L.: Frontal Scale Air–Sea Interaction in High-Resolution
Coupled Climate Models, J. Climate, 23, 6277–6291,
https://doi.org/10.1175/2010JCLI3665.1, 2010. a, b, c, d
Burchard, H.: Energy-conserving discretisation of turbulent shear and buoyancy
production, Ocean Modell., 4, 347–361,
https://doi.org/10.1016/S1463-5003(02)00009-4,
2002a. a
Businger, J. and Shaw, W.: The response of the marine boundary layer to
mesoscale variations in sea-surface temperature, Dynam. Atmos. Oceans, 8,
267–281, 1984. a
Couvelard, X., Lemarié, F., Samson, G., Redelsperger, J.-L., Ardhuin, F., Benshila, R., and Madec, G.: Development of a two-way-coupled ocean–wave model: assessment on a global NEMO(v3.6)–WW3(v6.02) coupled configuration, Geosci. Model Dev., 13, 3067–3090, https://doi.org/10.5194/gmd-13-3067-2020, 2020. a
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017. a
Cuxart, J., Holtslag, A. A. M., Beare, R. J., Bazile, E., Beljaars, A., Cheng,
A., Conangla, L., Ek, M., Freedman, F., Hamdi, R., Kerstein, A., Kitagawa,
H., Lenderink, G., Lewellen, D., Mailhot, J., Mauritsen, T., Perov, V.,
Schayes, G., Steeneveld, G.-J., Svensson, G., Taylor, P., Weng, W., Wunsch,
S., and Xu, K.-M.: Single-Column Model Intercomparison for a Stably
Stratified Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 118, 273–303,
2006. a, b, c
Deardorff, J. W.: Three-dimensional numerical study of turbulence in an
entraining mixed layer, Bound.-Lay. Meteorol., 7, 199–226, 1974. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Deleersnijder, E., Beckers, J.-M., Campin, J.-M., El Mohajir, M., Fichefet, T.,
and Luyten, P.: Some mathematical problems associated with the development
and use of marine models, in: The Mathematics of Models for Climatology and
Environment, edited by: Díaz, J. I., Springer Berlin
Heidelberg, Berlin, Heidelberg, 39–86, 1997. a
Deremble, B., Wienders, N., and Dewar, W. K.: CheapAML: A Simple, Atmospheric
Boundary Layer Model for Use in Ocean-Only Model Calculations, Mon. Weather
Rev., 141, 809–821, 2013. a
Deshayes, J., Tréguier, A.-M., Barnier, B., Lecointre, A., Sommer, J. L.,
Molines, J.-M., Penduff, T., Bourdallé-Badie, R., Drillet, Y., Garric,
G., Benshila, R., Madec, G., Biastoch, A., Böning, C. W., Scheinert, M.,
Coward, A. C., and Hirschi, J. J.-M.: Oceanic hindcast simulations at high
resolution suggest that the Atlantic MOC is bistable, J. Geophys. Res., 40,
3069–3073, https://doi.org/10.1002/grl.50534, wOS:000321951300034, 2013. a
Dewar, W. K. and Flierl, G. R.: Some Effects of the Wind on Rings, J. Phys.
Oceanogr., 17, 1653–1667, 1987. a
Frenger, I., Gruber, N., Knutti, R., and Munnich, M.: Southern Ocean Eddies
Affect Local Weather, Nat. Geosci., 6, 608–612, 2013. a
Giordani, H., Planton, S., Bénech, B., and Kwon, B.-H.: Atmospheric boundary
layer response to sea surface temperatures during the Semaphore experiment,
J. Geophys. Res., 103, 25047–25060, 1998. a
Giordani, H., Caniaux, G., and Prieur, L.: A Simplified 3D Oceanic Model
Assimilating Geostrophic Currents: Application to the POMME Experiment, J.
Phys. Oceanogr., 35, 628–644, https://doi.org/10.1175/JPO2724.1, 2005. a
Haney, R. L.: Surface Thermal Boundary Condition for Ocean Circulation Models,
J. Phys. Oceanogr., 1, 241–248,
https://doi.org/10.1175/1520-0485(1971)001<0241:STBCFO>2.0.CO;2, 1971. a
Hogg, A., Dewar, W., Berloff, P., Kravtsov, S., and Hutchinson, D. K.: The
effects of mesoscale ocean-atmosphere coupling on the large-scale ocean
circulation, J. Climate, 22, 4066–4082, 2009. a
Hourdin, F., Couvreux, F., and Menut, L.: Parameterization of the Dry
Convective Boundary Layer Based on a Mass Flux Representation of Thermals, J.
Atmos. Sci., 59, 1105–1123,
https://doi.org/10.1175/1520-0469(2002)059<1105:POTDCB>2.0.CO;2, 2002. a
Kleeman, R. and Power, S.: A Simple Atmospheric Model of Surface Heat Flux for
Use in Ocean Modeling Studies, J. Phys. Oceanogr., 25, 92–105,
https://doi.org/10.1175/1520-0485(1995)025<0092:ASAMOS>2.0.CO;2, 1995. a
Lac, C., Chaboureau, J.-P., Masson, V., Pinty, J.-P., Tulet, P., Escobar, J., Leriche, M., Barthe, C., Aouizerats, B., Augros, C., Aumond, P., Auguste, F., Bechtold, P., Berthet, S., Bielli, S., Bosseur, F., Caumont, O., Cohard, J.-M., Colin, J., Couvreux, F., Cuxart, J., Delautier, G., Dauhut, T., Ducrocq, V., Filippi, J.-B., Gazen, D., Geoffroy, O., Gheusi, F., Honnert, R., Lafore, J.-P., Lebeaupin Brossier, C., Libois, Q., Lunet, T., Mari, C., Maric, T., Mascart, P., Mogé, M., Molinié, G., Nuissier, O., Pantillon, F., Peyrillé, P., Pergaud, J., Perraud, E., Pianezze, J., Redelsperger, J.-L., Ricard, D., Richard, E., Riette, S., Rodier, Q., Schoetter, R., Seyfried, L., Stein, J., Suhre, K., Taufour, M., Thouron, O., Turner, S., Verrelle, A., Vié, B., Visentin, F., Vionnet, V., and Wautelet, P.: Overview of the Meso-NH model version 5.4 and its applications, Geosci. Model Dev., 11, 1929–1969, https://doi.org/10.5194/gmd-11-1929-2018, 2018. a
Lafore, J. P., Stein, J., Asencio, N., Bougeault, P., Ducrocq, V., Duron, J.,
Fischer, C., Héreil, P., Mascart, P., Masson, V., Pinty, J. P.,
Redelsperger, J. L., Richard, E., and Vilà-Guerau de Arellano, J.: The
Meso-NH Atmospheric Simulation System. Part I: adiabatic formulation and
control simulations, Ann. Geophys., 16, 90–109, 1998. a
Lambaerts, J., Lapeyre, G., Plougonven, R., and Klein, P.: Atmospheric response
to sea surface temperature mesoscale structures, J. Geophys. Res., 118,
9611–9621, 2013. a
Large, W. G.: Surface Fluxes for Practitioners of Global Ocean Data
Assimilation, in: Ocean Weather Forecasting. An Integrated View of
Oceanography, edited by: Chassignet, E. P. and Verron, J., chap. 9,
Springer, 229–270, 2006. a
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air-sea flux data set, Clim. Dynam., 33, 341–364,
https://doi.org/10.1007/s00382-008-0441-3, 2009. a
Lemarié, F. and Samson, G.: A simplified atmospheric boundary layer model for
an improved representation of air-sea interactions in eddying oceanic models:
implementation and first evaluation in NEMO (v4.0)), Zenodo,
https://doi.org/10.5281/zenodo.3904518, 2020. a, b
Lemarié, F., Kurian, J., Shchepetkin, A. F., Molemaker, M. J., Colas, F., and
McWilliams, J. C.: Are there inescapable issues prohibiting the use of
terrain-following coordinates in climate models?, Ocean Modell., 42, 57–79, https://doi.org/10.1016/j.ocemod.2011.11.007,
2012. a
LeMone, M. A., Angevine, W. M., Bretherton, C. S., Chen, F., Dudhia, J.,
Fedorovich, E., Katsaros, K. B., Lenschow, D. H., Mahrt, L., Patton, E. G.,
Sun, J., Tjernström, M., and Weil, J.: 100 Years of Progress in Boundary
Layer Meteorology, Meteorol. Monogr., 59, 9.1–9.85,
https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0013.1, 2019. a, b
Lilly, D.: The representation of small-scale turbulence in numerical simulation
experiments, in: Proc. IBM Sci. Comput. Symp. on Environmental Sci., 14–16 November 1966, Thomas J. Watson Res. Center, Yorktown Heights, N. Y., IBM
Form 320–1951, 195–210, 1967. a
Lindzen, R. S. and Nigam, S.: On the role of sea surface temperature gradients
in forcing low-level winds and convergence in the tropics, J. Atmos. Sci.,
44, 2418–2436, 1987. a
Maisonnave, E. and Masson, S.: Ocean/sea-ice macro task parallelism in NEMO,
in: Technical report, TR/GMGC/15/54, available at:
https://www.cerfacs.fr/~maisonna/Reports/opa_sas_tr.pdf (last access: 20 January 2021),
2015. a
Maisonnave, E. and Masson, S.: NEMO 4.0 performance: how to identify and
reduce unnecessary communications, in: Technical report, TR/CMGC/19/19,
available at:
https://cerfacs.fr/wp-content/uploads/2019/01/GLOBC-TR_Maisonnave-Nemo-2019.pdf (last access: 20 January 2021),
2019. a
Marchesiello, P., Capet, X., Menkes, C., and Kennan, S.: Submesoscale dynamics
in tropical instability waves, Ocean Modell., 39, 31–46,
https://doi.org/10.1016/j.ocemod.2011.04.011, 2011. a
McWilliams, J. C., Gula, J., and Molemaker, M. J.: The Gulf Stream North Wall:
Ageostrophic Circulation and Frontogenesis, J. Phys. Oceanogr., 49,
893–916, https://doi.org/10.1175/JPO-D-18-0203.1, 2019. a
Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings,
J. A., Wallcraft, A. J., Zamudio, L., Franklin, D. S., Posey, P. G., Phelps,
M. W., Hogan, P. J., Bub, F. L., and DeHaan, C. J.: US Navy Operational
Global Ocean and Arctic Ice Prediction Systems, Oceanogr., 27, 32–43,
https://doi.org/10.5670/oceanog.2014.66, 2014. a
Meurdesoif, Y., Caubel, A., Lacroix, R., Dérouillat, J., and Nguyen, M.: XIOS
Tutorial, available at:
http://forge.ipsl.jussieu.fr/ioserver/raw-attachment/wiki/WikiStart/XIOS-tutorial.pdf (last access: 20 January 2021),
2016. a
Minobe, S., Kuwano-Yoshida, A., Komori, N., Xie, S.-P., and Small, R. J.:
Influence of the Gulf Stream on the troposphere, Nature, 452, 206–209, 2008. a
Monin, A. S. and Obukhov, A. M.: Basic laws of turbulent mixing in the surface
layer of the atmosphere, Trudy Akademii Nauk SSSR Geofizicheskogo Instituta,
24, 163–187, 1954. a
Mulholland, D. P., Laloyaux, P., Haines, K., and Balmaseda, M. A.: Origin and
Impact of Initialization Shocks in Coupled Atmosphere-Ocean Forecasts, Mon.
Weather Rev., 143, 4631–4644, https://doi.org/10.1175/MWR-D-15-0076.1, 2015. a, b
O'Neill, L. W., Esbensen, S. K., Thum, N., Samelson, R. M., and Chelton,
D. B.: Dynamical Analysis of the Boundary Layer and Surface Wind Responses
to Mesoscale SST Perturbations, J. Climate, 23, 559–581,
https://doi.org/10.1175/2009JCLI2662.1, 2010. a, b
Razavi, S., Tolson, B. A., and Burn, D. H.: Review of surrogate modeling in
water resources, Water Resour. Res., 48, W07401, https://doi.org/10.1029/2011WR011527,
2012. a
Renault, L., Molemaker, M. J., Gula, J., Masson, S., and McWilliams, J. C.:
Control and Stabilization of the Gulf Stream by Oceanic Current Interaction
with the Atmosphere, J. Phys. Oceanogr., 46, 3439–3453,
https://doi.org/10.1175/JPO-D-16-0115.1, 2016a. a
Renault, L., Lemarié, F., and Arsouze, T.: On the implementation and
consequences of the oceanic currents feedback in ocean–atmosphere coupled
models, Ocean Modell., 141, 101 423,
https://doi.org/10.1016/j.ocemod.2019.101423,
2019a. a, b, c
Renault, L., Masson, S., Oerder, V., Jullien, S., and Colas, F.: Disentangling
the Mesoscale Ocean-Atmosphere Interactions, J. Geophys. Res., 124,
2164–2178, https://doi.org/10.1029/2018JC014628, 2019b. a, b, c
Rotta, J.: Statistische theorie nichthomogener turbulenz, Z.
Physik, 129, 547–572, 1951. a
Rousset, C., Vancoppenolle, M., Madec, G., Fichefet, T., Flavoni, S., Barthélemy, A., Benshila, R., Chanut, J., Levy, C., Masson, S., and Vivier, F.: The Louvain-La-Neuve sea ice model LIM3.6: global and regional capabilities, Geosci. Model Dev., 8, 2991–3005, https://doi.org/10.5194/gmd-8-2991-2015, 2015. a, b
Schneider, N. and Qiu, B.: The Atmospheric Response to Weak Sea Surface
Temperature Fronts, J. Atmos. Sci., 72, 3356–3377,
https://doi.org/10.1175/JAS-D-14-0212.1, 2015. a, b, c
Seager, R., Blumenthal, M. B., and Kushnir, Y.: An advective atmospheric mixed
layer model for ocean modeling purposes: global simulation of surface heat
fluxes, J. Climate, 8, 1952–1964, 1995. a
Small, R. J., deSzoeke, S. P., Xie, S. P., O'Neill, L., Seo, H., Song, Q.,
Cornillon, P., Spall, M., and Minobe, S.: Air-sea interaction over ocean
fronts and eddies, Dynam. Atmos. Oceans, 45, 274–319,
https://doi.org/10.1016/j.dynatmoce.2008.01.001, 2008. a
Soares, P. M. M., Miranda, P. M. A., Siebesma, A. P., and Teixeira, J.: An
eddy-diffusivity/mass-flux parametrization for dry and shallow cumulus
convection, Q. J. Roy. Meteor. Soc., 130, 3365–3383,
https://doi.org/10.1256/qj.03.223, 2004. a
Spall, M.: Midlatitude Wind Stress–Sea Surface Temperature Coupling in the
Vicinity of Oceanic Fronts, J. Climate, 20, 3785–3801, https://doi.org/10.1175/JCLI4234.1, 2007. a, b
Takano, K., Mintz, Y., and Han, J.-Y.: Numerical simulation of the world ocean
circulation, Second Conf. on Numerical Weather Prediction, Monterey, CA,
Amer. Meteor. Soc., 121–129, 1973. a
Troen, I. B. and Mahrt, L.: A simple model of the atmospheric boundary layer;
sensitivity to surface evaporation, Bound.-Lay. Meteorol., 37, 129–148,
https://doi.org/10.1007/BF00122760, 1986. a
Wallace, J. M., Mitchell, T. P., and Deser, C.: The Influence of Sea-Surface
Temperature on Surface Wind in the Eastern Equatorial Pacific: Seasonal and
Interannual Variability, J. Climate, 2, 1492–1499,
https://doi.org/10.1175/1520-0442(1989)002<1492:TIOSST>2.0.CO;2, 1989. a
Wilson, J. M. and Venayagamoorthy, S. K.: A Shear-Based Parameterization of
Turbulent Mixing in the Stable Atmospheric Boundary Layer, J. Atmos. Sci.,
72, 1713–1726, https://doi.org/10.1175/JAS-D-14-0241.1, 2015. a
Short summary
A simplified model of the atmospheric boundary layer (ABL) of intermediate complexity between a bulk parameterization and a full three-dimensional atmospheric model has been developed and integrated to the NEMO ocean model.
An objective in the derivation of such a simplified model is to reach an apt representation of ocean-only numerical simulations of some of the key processes associated with air–sea interactions at the characteristic scales of the oceanic mesoscale.
A simplified model of the atmospheric boundary layer (ABL) of intermediate complexity between a...
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