Cloud-Wave Coupling (Minerva Fast Track RG)

We focus on internal gravity waves generated by boundary layer thermals (left) and convection (right). Internal waves can become trapped (left), or propagate vertically and horizontally inside the troposphere and stratosphere. We specifically focus on the question of how feedbacks between gravity waves and convection shape the properties of clouds and tropical precipitation clusters (middle).


Predicting how mean and extreme weather will change in the future is difficult because a large number of complex processes are involved. A particularly important process is precipitating deep convection. Atmospheric convection is called deep when it extends from near the surface to above the 500 hPa level, generally stopping at the tropopause at around 200 hPa. Especially in the tropics, it unleashes substantial amounts of energy with strong effects on global weather. Despite strong societal and climatic impacts, our knowledge of what controls tropical convection is poor. This creates considerable potential for surprises as the climate warms. Like a stone thrown into a pond, the energy release from condensation generates internal atmospheric waves that can suppress, trigger, or favor new convection in the mesoscale (20 to 500 km) environment surrounding their sources. This means that convection does not passively respond to environmental conditions imposed from some well-separated large scale (as traditional climate models are forced to assume). Rather convection and its environment evolve together as a tightly coupled system with little evidence for separation of scales. In our group, we combine new kilometer-scale global models, deep learning techniques, and novel observational methods to make the invisible waves visible in order to understand their interactions with convection and circulation, and what these interactions portend for global warming.


Our Research

Cloud scales have first-order effects on larger scales and mean aspects of climate. We study the role of waves in shaping patterns of clouds and precipitating convection, the role of convection as a source of atmospheric waves, and the coupling between cloud- and wave-fields. Convective processes, such as the condensational heating released during  cloud formation, act on scales that global models are beginning to resolve. We are well able to observe clouds, but understanding the connection between moist processes and motion fields has been a long-standing goal, as the three-dimensional wind field is difficult to observe directly [1]. Global kilometer-scale modelling makes the link between small-scale convection and the large-scale circulation a new frontier, and this frontier is the focus of our research.

Wave-Boundary Layer Interactions

Drag-Instability Waves

Satellite images frequently show mesoscale arc-shaped cloud lines with a spacing of several tens of kilometers (Fig. 1, second from left). These shallow clouds form in the layer adjacent to the ocean surface (the mixed boundary layer). Unlike other mesoscale cloud line phenomena, such as horizontal convective rolls, these cloud lines do not align with the wind direction but form roughly at a 90° angle to the near-surface wind [2]. We showed that the formation of this pattern has a similar cause as the formation of ripples in water flowing over pavement (Fig. 2).

Convection Waves

Idealized simulations have shown decades ago that shallow clouds may generate gravity waves of small horizontal scales (of the order of 10 km). We investigate if under certain atmospheric background conditions, these waves can become trapped inside the troposphere and influence the development of clouds. For a 2.5-km simulation with a domain covering the Atlantic, we showed that the coupling between shallow clouds and trapped waves is sufficiently strong to be detected [3]. With knowledge of the large-scale atmospheric state, wave theory can reliably predict the regions and times where cloud-wave feedbacks become relevant to convective organization.

Our current research extends these model-based results to observations. When clouds are present, the imprint of the waves can easily be seen in visible satellite imagery (Fig. 3).

We have recently developed a new method that allows us to detect such waves automatically in satellite imagery without relying on the presence of clouds. With the large amount of data made available by this new method, we are trying to answer how much of an active role these short waves play in shaping the properties of convection.

Mesoscale Vertical Motion

The statistical properties of cloud fields are defined on scales of several tens to hundreds of kilometers and are thus linked to vertical motion on these scales. Although the magnitude of the mean vertical motion averaged over such length scales is tiny compared to that of horizontal motions, it is key for distributing water vapor and for determining the likelihood of clouds to form. Observing the vertical ascent or descent of air averaged over areas of a few hundred kilometers diameter (which we call the mesoscale) is a great challenge and such observations are scarce. We analyzed data from two field campaigns for which suitable observations existed [4, 5, 2]. The results imply that atmospheric gravity waves of all scales greater than the considered area play an important role in defining the magnitude of mesoscale vertical motions. This underlines the importance of studying convection in global simulations, rather than in domains of limited area.

Further information:

Not only may waves affect mesoscale convection, but convection may also affect mesoscale vertical motion. In recent years, an emerging class of atmospheric circulation models has been developed that enables global simulations where deep convection is resolved (Fig. 4). We investigate how sensitive the energy spectra of horizontal and vertical motions are to the simulated convection and how the properties of resolved convection affect the relationship between horizontal and vertical spectra.

Further information:

Representing Gravity Waves For Improved Climate Simulations

Gravity waves are excited by flow over topography, convective systems, and fronts, and then propagate both vertically and horizontally into the atmosphere. Eventually, they break or dissipate at higher altitudes and deposit the momentum imparted to the waves at their generation level. For this reason, gravity waves are important for driving the global circulation in the middle atmosphere, which is relevant for the redistribution of ozone, water vapor and other trace gases. The challenge for climate simulations is that some waves have length scales as short as tens of kilometers and time scales ranging from minutes to hours. This means that a large fraction of the gravity wave spectrum is not resolved in low-resolution climate models. Instead, their effects have to be estimated from resolved flow. A major problem is the absence of observation systems that cover the full spectrum of gravity waves. This and the need for efficient computations are the main reasons for large uncertainties in gravity wave parameterizations, which limit our ability to predict how the global circulation will respond to a warming climate.

As part of the DataWave project, we collaborate with an international group of atmospheric and data scientists to design new approaches for better representing gravity waves in climate models. We combine very high-resolution position, temperature, pressure, and wind observations taken by super-pressure balloons with kilometer-scale model simulations to constrain gravity wave sources, propagation and breaking. To achieve computationally feasible representations of gravity waves, we use machine learning to compute the gravity wave momentum deposition (Fig. 5).


[1] Stephan, C.C., N. Žagar and T.G. Shepherd (2021): Waves and coherent flows in the tropical atmosphere: new opportunities, old challenges, Quart. J. Roy. Meteor. Soc., 147 (738) 2597–2624, doi: 10.1002/qj.4109 

[2] Stephan, C.C. (2021): Mechanism for the formation of arc-shaped cloud lines over the tropical oceans, J. Atmos. Sci., 78 (3), 817–824, doi: 10.1175/JAS-D-20-0129.1

[3] Stephan, C.C. (2020): Seasonal modulation of trapped gravity waves and their imprints on trade wind clouds, J. Atmos. Sci., 77, 2993–3009, doi: 10.1175/JAS-D-19-0325.1

[4] Stephan, C.C., T.P. Lane and C. Jakob (2020): Gravity wave influences on mesoscale divergence: An observational case study, Geophys. Res. Lett., 47, doi: 10.1029/2019GL086539

[5] Stephan, C.C. et al. (2021): Ship- and island-based atmospheric soundings from the 2020 EUREC4A field campaign, Earth Sys. Sci. Data, 13, 491–514, doi: 10.5194/essd-13-491-2021–514, doi: 10.5194/essd-13-491-2021

Group members and publications

Phd Candidate
  • Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll , C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger , T., Naumann, A., Paccini, L., Peinado, A., Praturi, D., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C., von Storch, J., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J. & Stevens, B. (2023). ICON-Sapphire: simulating the components of the Earth System and their interactions at kilometer and subkilometer scales. Geoscientific Model Development, 16, 779-811. doi:10.5194/gmd-16-779-2023 [ Fulltext]
  • Horváth, Á., Vadas, S., Stephan, C. & Buehler, S. (2023). One-minute resolution GOES-R observations of Lamb and gravity waves triggered by the Hunga Tonga-Hunga Ha'apai eruptions on 15 January 2022. ESS Open Archive. doi:10.22541/essoar.168565406.64574148/v1
  • Morfa Avalos, Y. & Stephan, C. (2023). The relationship between horizontal and vertical velocity wavenumber spectra in global storm-resolving simulations. Journal of the Atmospheric Sciences, 80, 1087-1105. doi:10.1175/JAS-D-22-0105.1
  • Stephan, C., Duras, J., Harris, L., Klocke, D., Putman, W., Taylor, M., Wedi, N., Žagar, N. & Ziemen , F. (2022). Atmospheric energy spectra in global kilometre-scale models. Tellus Series A-Dynamic Meteorology and Oceanography, 74, 280-299. doi:10.16993/tellusa.26 [ Fulltext]
  • Stephan, C. (2021). Mechanism for the formation of arc-shaped cloud lines over the tropical oceans. Journal of the Atmospheric Sciences, 78, 817-824. doi:10.1175/JAS-D-20-0129.1 [ Fulltext]
  • Stephan, C., Zagar, N. & Shepherd, T. (2021). Waves and coherent flows in the tropical atmosphere: new opportunities, old challenges. Quarterly Journal of the Royal Meteorological Society, 147, 2597-2624. doi:10.1002/qj.4109 [ Fulltext]
  • Stephan, C. & Mariaccia, A. (2021). The signature of the tropospheric gravity wave background in observed mesoscale motion. Weather and Climate Dynamics, 2, 359-372. doi:10.5194/wcd-2-359-2021 [ Fulltext]
  • Stephan, C., Schnitt, S., Schulz, H., Bellenger, H., de Szoeke, S., Acquistapace, C., Baier , K., Dauhut, T., Laxenaire, R., Morfa Avalos, Y., Person, R., Meléndez, E., Bagheri, G., Böck, T., Daley, A., Güttler, J., Helfer, K., Los, S., Neuberger, A., Röttenbacher, J., Raeke, A., Ringel, M., Ritschel, M., Sadoulet, P., Schirmacher, I., Stolla, M., Wright, E., Charpentier, B., Doerenbecher, A., Wilson, R., Jansen, F., Kinne, S., Reverdin, G., Speich, S., Bony, S. & Stevens, B. (2021). Ship- and island-based atmospheric soundings from the 2020 EUREC4A field campaign. Earth System Science Data, 18, 491-514. doi:10.5194/essd-13-491-2021 [ Fulltext]
  • Stevens, B., Bony, S., Farrell, D., Ament, F., Blyth, A., Fairall, C., Karstensen, J., Quinn, P., Speich, S., Acquistapace, C., Aemisegger, F., Albright, A., Bellenger, H., Bodenschatz, E., Caesar, K.-A., Chewitt-Lucas, R., de Boer, G., Delanoë, J., Denby, L., Ewald, F., Fildier, B., Forde, M., George, G., Gross, S., Hagen, M., Hausold, A., Heywood, K., Hirsch, L., Jacob, M., Jansen, F., Kinne, S., Klocke, D., Kölling, T., Konow, H., Lothon, M., Mohr, W., Naumann, A., Nuijens, L., Olivier, L., Pincus, R., Pöhlker, M., Reverdin, G., Roberts, G., Schnitt, S., Schulz, H., Siebesma, A., Stephan, C., Sullivan, P., Touzé-Peiffer, L., Vial, J., Vogel, R., Zuidema, P., Alexander, N., Alves, L., Arixi, S., Asmath, H., Bagheri, G., Baier , K., Bailey, A., Baranowski, D., Baron, A., Barrau, S., Barrett, P., Batier, F., Behrendt, A., Bendinger, A., Beucher, F., Bigorre, S., Blades, E., Blossey, P., Bock, O., Böing, S., Bosser, P., Bourras, D., Bouruet-Aubertot, P., Bower, K., Branellec, P., Branger, H., Brennek, M., Brewer, A., Brilouet, P.-E., Brügmann, B., Buehler, S., Burke, E., Burton, R., Calmer, R., Canonici, J.-C., Carton, X., Cato, G., Charles, J., Chazette, P., Chen, Y., Chilinski, M., Choularton, T., Chuang, P., Clarke, S., Coe, H., Cornet, C., Coutris, P., Couvreux, F., Crewell, S., Cronin, T., Cui, Z., Cuypers, Y., Daley, A., Damerell, G., Dauhut, T., Deneke, H., Desbios, J.-P., Dörner, S., Donner, S., Douet, V., Drushka, K., Dütsch, M., Ehrlich, A., Emanuel, K., Emmanouilidis, A., Etienne, J.-C., Etienne-Leblanc, S., Faure, G., Feingold, G., Ferrero, L., Fix, A., Flamant, C., Flatau, P., Foltz, G., Forster, L., Furtuna, I., Gadian, A., Galewsky, J., Gallagher, M., Gallimore, P., Gaston, C., Gentemann, C., Geyskens, N., Giez, A., Gollop, J., Gouirand, I., Gourbeyre, C., de Graaf, D., de Groot, G., Grosz, R., Güttler, J., Gutleben, M., Hall, K., Harris, G., Helfer, K., Henze, D., Herbert, C., Holanda, B., Ibanez-Landeta, A., Intrieri, J., Iyer, S., Julien, F., Kalesse, H., Kazil, J., Kellman, A., Kidane, A., Kirchner, U., Klingebiel, M., Körner, M., Kremper, L., Kretzschmar, J., Krüger, O., Kumala, W., Kurz, A., L'Hégaret, P., Labaste, M., Lachlan-Cope, T., Laing, A., Landschützer, P., Lang, T., Lange, D., Lange, I., Laplace, C., Lavik, G., Laxenaire, R., Le Bihan, C., Leandro, M., Lefevre, N., Lena, M., Lenschow, D., Li, Q., Lloyd, G., Los, S., Losi, N., Lovell, O., Luneau, C., Makuch, P., Malinowski, S., Manta, G., Marinou, E., Marsden, N., Masson, S., Maury, N., Mayer, B., Mayers-Als, M., Mazel, C., McGeary, W., McWilliams, J., Mech, M., Mehlmann, M., Meroni, A., Mieslinger, T., Minikin, A., Minnett, P., Möller, G., Morfa Avalos, Y., Muller, C., Musat, I., Napoli, A., Neuberger, A., Noisel, C., Noone, D., Nordsiek, F., Nowak, J., Oswald, L., Parker, D., Peck, C., Person, R., Philippi, M., Plueddemann, A., Pöhlker, C., Pörtge, V., Pöschl, U., Pologne, L., Posyniak, M., Prange, M., Meléndez, E., Radtke, J., Ramage, K., Reimann, J., Renault, L., Reus, K., Reyes, A., Ribbe, J., Ringel, M., Ritschel, M., Rocha, C., Rochetin, N., Röttenbacher, J., Rollo, C., Royer, H., Sadoulet, P., Saffin, L., Sandiford, S., Sandu, I., Schäfer, M., Schemann, V., Schirmacher, I., Schlenczek, O., Schmidt, J., Schröder, M., Schwarzenboeck, A., Sealy, A., Senff, C., Serikov, I., Shohan, S., Siddle, E., Smirnov, A., Späth, F., Spooner, B., Stolla, M., Szkółka, W., de Szoeke, S., Tarot, S., Tetoni, E., Thompson, E., Thomson, J., Tomassini, L., Totems, J., Ubele, A., Villiger, L., von Arx, J., Wagner, T., Walther, A., Webber, B., Wendisch, M., Whitehall, S., Wiltshire, A., Wing, A., Wirth, M., Wiskandt, J., Wolf, K., Worbes, L., Wright, E., Wulfmeyer, V., Young, S., Zhang, C., Zhang, D., Ziemen, F., Zinner, T. & Zöger, M. (2021). EUREC4A. Earth System Science Data, 13, 4067-4119. doi:10.5194/essd-13-4067-2021 [ Fulltext]
  • Fiedler, S., Crueger, T., D'Agostino, R., Peters, K., Becker, T., Leutwyler, D., Paccini, L., Burdanowitz, J., Buehler, S., Uribe, A., Dauhut, T., Dommenget, D., Fraedrich, K., Jungandreas, L., Maher, N., Naumann, A., Rugenstein, M., Sakradzija, M., Schmidt, H., Sielmann, F., Stephan, C., Timmreck, C., Zhu , X. & Stevens, B. (2020). Simulated tropical precipitation assessed across three major phases of the Coupled Model Intercomparison Project (CMIP). Monthly Weather Review, 148, 3653-3680. doi:10.1175/MWR-D-19-0404.1 [ Fulltext] [ Fulltext]
  • Guo, L., van der Ent, R., Klingaman, N., Demory, M.-E., Vidale, P., Turner, A., Stephan, C. & Chevuturi, A. (2020). Effects of horizontal resolution and air-sea coupling on simulated moisture source for East Asian precipitation in MetUM GA6/GC2. Geoscientific Model Development, 13, 6011-6028: 266. doi:10.5194/gmd-13-6011-2020 [ Fulltext] [ Fulltext]
  • Stephan, C. (2020). Why and how do clouds form in particular locations?. Latest Thinking. doi:10.21036/LTPUB10825
  • Stephan, C., Lane, T. & Jacob, C. (2020). Gravity wave influences on mesoscale divergence: An observational case study. Geophysical Research Letters, 47: e2019GL086539. doi:10.1029/2019GL086539 [ Fulltext] [ Fulltext]
  • Stephan, C., Schmidt, H., Zülicke, C. & Matthias, V. (2020). Oblique gravity wave propagation during sudden stratospheric warmings. Journal of Geophysical Research: Atmospheres, 125: e2019JD031528. doi:10.1029/2019JD031528 [ Fulltext] [ Fulltext]
  • Stephan, C. (2020). Seasonal modulation of trapped gravity and their imprints on trade wind clouds. Journal of the Atmospheric Sciences, 77, 2993-3009. doi:10.1175/JAS-D-19-0325.1 [ Fulltext]
  • Heale, C., Snively, J., Bhatt, A., Hoffmann, L., Stephan, C. & Kendall, E. (2019). Multilayer observations and modeling of thunderstorm-generated gravity waves over the Midwestern United States. Geophysical Research Letters, 46, 14164-14174. doi:10.1029/2019GL085934 [ Fulltext]
  • Stephan, C., Strube, C., Klocke, D., Ein, M., Hoffmann, L., Preusse, P. & Schmidt, H. (2019). Intercomparison of gravity waves in convection-permitting models. Journal of the Atmospheric Sciences, 76, 2739-2759. doi:10.1175/JAS-D-19-0040.1 [ Fulltext]


Dr. Claudia Stephan

Group leader
Phone: +49 (0)40 41173-124
claudia.stephan@we dont want

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