Articles | Volume 3, issue 4
https://doi.org/10.5194/wcd-3-1199-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-3-1199-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of grid spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt
Anubhav Choudhary
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research – Department Troposphere Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
Aiko Voigt
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Related authors
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551, https://doi.org/10.5194/gmd-16-3535-2023, https://doi.org/10.5194/gmd-16-3535-2023, 2023
Short summary
Short summary
Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Edgardo I. Sepulveda Araya, Sylvia C. Sullivan, and Aiko Voigt
Atmos. Chem. Phys., 25, 8943–8958, https://doi.org/10.5194/acp-25-8943-2025, https://doi.org/10.5194/acp-25-8943-2025, 2025
Short summary
Short summary
Clouds composed of ice crystals are key when evaluating atmospheric radiation. The morphology of the crystals found in clouds is not clear yet, and even less clear is the impact on the cloud heating rate, which is essential to describe precipitation and wind patterns. This motivated us to study how the heating rate behaves under a variety of ice complexity and environmental conditions, finding that increasing complexity in high and dense clouds weakens the heating rate.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Short summary
Short summary
The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Blaž Gasparini, Rachel Atlas, Aiko Voigt, Martina Krämer, and Peter N. Blossey
EGUsphere, https://doi.org/10.5194/egusphere-2025-203, https://doi.org/10.5194/egusphere-2025-203, 2025
Short summary
Short summary
Tropical cirrus clouds, especially their evolution, are poorly understood, contributing to uncertainty in climate projections. We address this by using novel tracers in a cloud-resolving model to track the life cycle of cirrus clouds, providing insights into cloud formation, ice crystal evolution, and radiative effects. We also improve the model's cloud microphysics with a simple, computationally efficient approach that can be applied to other models.
Aiko Voigt, Stefanie North, Blaž Gasparini, and Seung-Hee Ham
Atmos. Chem. Phys., 24, 9749–9775, https://doi.org/10.5194/acp-24-9749-2024, https://doi.org/10.5194/acp-24-9749-2024, 2024
Short summary
Short summary
Clouds shape weather and climate by interacting with photons, which changes temperatures within the atmosphere. We assess how well CMIP6 climate models capture this radiative heating by clouds within the atmosphere. While we find large differences among models, especially in cold regions of the atmosphere with abundant ice clouds, we also demonstrate that physical understanding allows us to predict the response of clouds and their radiative heating near the tropopause to climate change.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
Short summary
Short summary
Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Behrooz Keshtgar, Aiko Voigt, Bernhard Mayer, and Corinna Hoose
Atmos. Chem. Phys., 24, 4751–4769, https://doi.org/10.5194/acp-24-4751-2024, https://doi.org/10.5194/acp-24-4751-2024, 2024
Short summary
Short summary
Cloud-radiative heating (CRH) affects extratropical cyclones but is uncertain in weather and climate models. We provide a framework to quantify uncertainties in CRH within an extratropical cyclone due to four factors and show that the parameterization of ice optical properties contributes significantly to uncertainty in CRH. We also argue that ice optical properties, by affecting CRH on spatial scales of 100 km, are relevant for the large-scale dynamics of extratropical cyclones.
Johannes Hörner and Aiko Voigt
Earth Syst. Dynam., 15, 215–223, https://doi.org/10.5194/esd-15-215-2024, https://doi.org/10.5194/esd-15-215-2024, 2024
Short summary
Short summary
Snowball Earth refers to a climate in the deep past of the Earth where the whole planet was covered in ice. Waterbelt states, where a narrow region of open water remains at the Equator, have been discussed as an alternative scenario, which might explain how life was able to survive these periods. Here, we demonstrate how waterbelt states are influenced by the thermodynamical sea-ice model used. The sea-ice model modulates snow on ice, ice albedo and ultimately the stability of waterbelt states.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551, https://doi.org/10.5194/gmd-16-3535-2023, https://doi.org/10.5194/gmd-16-3535-2023, 2023
Short summary
Short summary
Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Behrooz Keshtgar, Aiko Voigt, Corinna Hoose, Michael Riemer, and Bernhard Mayer
Weather Clim. Dynam., 4, 115–132, https://doi.org/10.5194/wcd-4-115-2023, https://doi.org/10.5194/wcd-4-115-2023, 2023
Short summary
Short summary
Forecasting extratropical cyclones is challenging due to many physical factors influencing their behavior. One such factor is the impact of heating and cooling of the atmosphere by the interaction between clouds and radiation. In this study, we show that cloud-radiative heating (CRH) increases the intensity of an idealized cyclone and affects its predictability. We find that CRH affects the cyclone mostly via increasing latent heat release and subsequent changes in the synoptic circulation.
Aiko Voigt, Petra Schwer, Noam von Rotberg, and Nicole Knopf
Geosci. Model Dev., 15, 7489–7504, https://doi.org/10.5194/gmd-15-7489-2022, https://doi.org/10.5194/gmd-15-7489-2022, 2022
Short summary
Short summary
In climate science, it is helpful to identify coherent objects, for example, those formed by clouds. However, many models now use unstructured grids, which makes it harder to identify coherent objects. We present a new method that solves this problem by moving model data from an unstructured triangular grid to a structured cubical grid. We implement the method in an open-source Python package and show that the method is ready to be applied to climate model data.
Frederik Wolf, Aiko Voigt, and Reik V. Donner
Earth Syst. Dynam., 12, 353–366, https://doi.org/10.5194/esd-12-353-2021, https://doi.org/10.5194/esd-12-353-2021, 2021
Short summary
Short summary
In our work, we employ complex networks to study the relation between the time mean position of the intertropical convergence zone (ITCZ) and sea surface temperature (SST) variability. We show that the information hidden in different spatial SST correlation patterns, which we access utilizing complex networks, is strongly correlated with the time mean position of the ITCZ. This research contributes to the ongoing discussion on drivers of the annual migration of the ITCZ.
Cited articles
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.:
Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Bechtold, P., Koehler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M., Rodwell, M., Vitart, F., and Balsamo, G.:
Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, https://doi.org/10.1002/qj.289, 2008. a
Binder, H., Boettcher, M., Joos, H., and Wernli, H.:
The role of warm conveyor belts for the intensification of extratropical cyclones in Northern Hemisphere winter, J. Atmos. Sci., 73, 3997–4020, https://doi.org/10.1175/JAS-D-15-0302.1, 2016. a, b, c, d
Blanchard, N., Pantillon, F., Chaboureau, J.-P., and Delanoë, J.:
Organization of convective ascents in a warm conveyor belt, Weather Clim. Dynam., 1, 617–634, https://doi.org/10.5194/wcd-1-617-2020, 2020. a, b
Blanchard, N., Pantillon, F., Chaboureau, J.-P., and Delanoë, J.:
Mid-level convection in a warm conveyor belt accelerates the jet stream, Weather Clim. Dynam., 2, 37–53, https://doi.org/10.5194/wcd-2-37-2021, 2021. a, b
Browning, K. A.:
Organization of clouds and precipitation in extratropical cyclones, in: Extratropical cyclones, edited by: Newton, C. W., Holopainen, E. O., American Meteorological Society, Boston, MA, 129–153, https://doi.org/10.1007/978-1-944970-33-8_8, 1990. a
Carlson, T. N.:
Airflow through midlatitude cyclones and the comma cloud pattern, Mon. Weather Rev., 108, 1270–1282, https://doi.org/10.1175/1520-0493(1980)108<1498:ATMCAT>2.0.CO;2, 1980. a
Champion, A. J., Hodges, K. I., Bengtsson, L. O., Keenlyside, N. S., and Esch, M.:
Impact of increasing resolution and a warmer climate on extreme weather from Northern Hemisphere extratropical cyclones, Tellus A, 63, 893–906, https://doi.org/10.1111/j.1600-0870.2011.00538.x, 2011. a, b
Chang, E. K. and Fu, Y.:
Using mean flow change as a proxy to infer interdecadal storm track variability, J. Climate, 16, 2178–2196, https://doi.org/10.1175/2773.1, 2003. a
Choudhary, A.: Analysis scripts for “Impact of grid
spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt”, Gitlab [code],
https://gitlab.phaidra.org/climate/choudhary-vladiana-wcd-2022, last access: 24 October 2022a. a
Choudhary, A.: Data and analysis scripts for “Impact of grid spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt”, Zenodo [data set], https://doi.org/10.5281/zenodo.5921126, 2022b. a
Colle, B. A., Zhang, Z., Lombardo, K. A., Chang, E., Liu, P., and Zhang, M.:
Historical evaluation and future prediction of eastern North American and western Atlantic extratropical cyclones in the CMIP5 models during the cool season, J. Climate, 26, 6882–6903, https://doi.org/10.1175/JCLI-D-12-00498.1, 2013. a
Doms, G., Förstner, J., Heise, E., Herzog, H., Mironov, D., Raschendorfer, M., and Vogel, G.:
A description of the nonhydrostatic regional model LM Part II: Physical parameterization, Scientific documentation, Deutscher Wetterdienst, Offenbach, Germany, 2005. a
Eichler, T. P., Natalie, G., and Zaitao, P.:
Impacts of global warming on Northern Hemisphere winter storm tracks in the CMIP5 model suite, J. Geophys. Res.-Atmos., 118, 3919–3932, https://doi.org/10.1002/jgrd.50286, 2013. a
Fink, A. H., Pohle, S., Pinto, J. G., and Knippertz, P.:
Diagnosing the influence of diabatic processes on the explosive deepening of extratropical cyclones, Geophys. Res. Lett., 39, L07803, https://doi.org/10.1029/2012GL051025, 2012. a, b, c, d
Flack, D. L. A., Rivière, G., Musat, I., Roehrig, R., Bony, S., Delanoë, J., Cazenave, Q., and Pelon, J.:
Representation by two climate models of the dynamical and diabatic processes involved in the development of an explosively deepening cyclone during NAWDEX, Weather Clim. Dynam., 2, 233–253, https://doi.org/10.5194/wcd-2-233-2021, 2021. a, b, c
Grams, C. M., Wernli, H., Böttcher, M., Čampa, J., Corsmeier, U., Jones, S. C., and Wiegand, L.:
The key role of diabatic processes in modifying the upper-tropospheric wave guide: a North Atlantic case-study, Q. J. Roy. Meteor. Soc., 137, 2174–2193, https://doi.org/10.1002/qj.891, 2011. a, b
Jakob, C.:
Accelerating progress in global atmospheric model development through improved parameterizations: Challenges, opportunities, and strategies, B. Am. Meteorol. Soc., 91, 869–876, https://doi.org/10.1175/2009BAMS2898.1, 2010. a
Joos, H.:
Warm conveyor belts and their role for cloud radiative forcing in the extratropical storm tracks, J. Climate, 32, 5325–5343, https://doi.org/10.1175/JCLI-D-18-0802.1, 2019. a
Joos, H. and Forbes, R. M.:
Impact of different IFS microphysics on a warm conveyor belt and the downstream flow evolution, Q. J. Roy. Meteor. Soc., 142, 2727–2739, https://doi.org/10.1002/qj.2863, 2016. a, b
Joos, H. and Wernli, H.:
Influence of microphysical processes on the potential vorticity development in a warm conveyor belt: a case-study with the limited-area model COSMO, Q. J. Roy. Meteor. Soc., 138, 407–418, https://doi.org/10.1002/qj.934, 2012. a, b, c
Jung, T., Gulev, S. K., Rudeva, I., and Soloviov, V.:
Sensitivity of extratropical cyclone characteristics to horizontal resolution in the ECMWF model, Q. J. Roy. Meteor. Soc., 132, 1839–1857, https://doi.org/10.1256/qj.05.212, 2006. a, b, c
Jung, T., Miller, M. J., Palmer, T. N., Towers, P., Wedi, N., Achuthavarier, D., Adams, J. M., Altshuler, E. L., Cash, B. A., Kinter III, J. L., Marx, L., Stan, C., and Hodges, K. I.: High-resolution global climate simulations with the ECMWF model in Project Athena: Experimental design, model climate, and seasonal forecast skill, J. Climate, 25, 3155–3172, https://doi.org/10.1175/JCLI-D-11-00265.1, 2012. a
Knippertz, P. and Fink, A. H.:
Dry-season precipitation in tropical West Africa and its relation to forcing from the extratropics, Mon. Weather Rev., 136, 3579–3596, https://doi.org/10.1175/2008MWR2295.1, 2008. a
Knippertz, P., Fink, A. H., and Pohle, S.:
Comments on “Dry-Season Precipitation in Tropical West Africa and Its Relation to Forcing from the Extratropics” – Reply, Mon. Weather Rev., 137, 3151–3157, https://doi.org/10.1175/2009MWR3006.1, 2009. a
Madonna, E., Wernli, H., Joos, H., and Martius, O.:
Warm conveyor belts in the ERA-Interim dataset (1979–2010). Part I: Climatology and potential vorticity evolution, J. Climate, 27, 3–26, https://doi.org/10.1175/JCLI-D-12-00720.1, 2014. a, b, c
Martinez-Alvarado, O., Joos, H., Chagnon, J., Boettcher, M., Gray, S., Plant, R., and Wernli, H.:
The dichotomous structure of the warm conveyor belt, Q. J. Roy. Meteor. Soc., 140, 1809–1824, https://doi.org/10.1002/qj.2276, 2014. a, b
Mazoyer, M., Ricard, D., Rivière, G., Delanoë, J., Arbogast, P., Vié, B., Lac, C., Cazenave, Q., and Pelon, J.:
Microphysics impacts on the warm conveyor belt and ridge building of the NAWDEX IOP6 cyclone, Mon. Weather Rev., 149, 3961–3980, https://doi.org/10.1175/MWR-D-21-0061.1, 2021. a, b, c
Oertel, A., Boettcher, M., Joos, H., Sprenger, M., Konow, H., Hagen, M., and Wernli, H.:
Convective activity in an extratropical cyclone and its warm conveyor belt–a case-study combining observations and a convection-permitting model simulation, Q. J. Roy. Meteor. Soc., 145, 1406–1426, https://doi.org/10.1002/qj.3500, 2019. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Palmer, T. and Stevens, B.:
The scientific challenge of understanding and estimating climate change, P. Natl. Acad. Sci. USA, 116, 24390–24395, https://doi.org/10.1073/pnas.1906691116, 2019. a
Papavasileiou, G., Voigt, A., and Knippertz, P.:
The role of observed cloud-radiative anomalies for the dynamics of the North Atlantic Oscillation on synoptic time-scales, Q. J. Roy. Meteor. Soc., 146, 1822–1841, https://doi.org/10.1002/qj.3768, 2020. a
Pfahl, S., Madonna, E., Boettcher, M., Joos, H., and Wernli, H.:
Warm conveyor belts in the ERA-Interim dataset (1979–2010): Part II: Moisture origin and relevance for precipitation, J. Climate, 27, 27–40, https://doi.org/10.1175/JCLI-D-13-00223.1, 2014. a
Pfahl, S., Schwierz, C., Croci-Maspoli, M., Grams, C. M., and Wernli, H.:
Importance of latent heat release in ascending air streams for atmospheric blocking, Nat. Geosci., 8, 610–614, https://doi.org/10.1038/ngeo2487, 2015. a
Pohle, S.:
Synoptische und dynamische Aspekte tropisch-extratropischer Wechselwirkungen: Drei Fallstudien von Hitzetiefentwicklungen über Westafrika während des AMMA-Experiments 2006, PhD thesis, University of Cologne, Cologne, Germany, https://kups.ub.uni-koeln.de/3157/1/DissertationSusanPohle2010.pdf (last access: 24 October 2022), 2010. a, b
Prill, F., Reinert, D., Rieger, D., and Zaengl, G. (Eds.):
Working with the ICON Model, Tech. rep., DWD German Weather Service, Offenbach, Germany, https://doi.org/10.5676/dwd_pub/nwv/icon_tutorial2020, 2020. a, b, c
Randall, D., Khairoutdinov, M., Arakawa, A., and Grabowski, W.:
Breaking the cloud parameterization deadlock, B. Am. Meteorol. Soc., 84, 1547–1564, https://doi.org/10.1175/BAMS-84-11-1547, 2003. a
Rasp, S., Selz, T., and Craig, G. C.:
Convective and slantwise trajectory ascent in convection-permitting simulations of midlatitude cyclones, Mon. Weather Rev., 144, 3961–3976, https://doi.org/10.1175/MWR-D-16-0112.1, 2016. a
Rivière, G., Wimmer, M., Arbogast, P., Piriou, J.-M., Delanoë, J., Labadie, C., Cazenave, Q., and Pelon, J.:
The impact of deep convection representation in a global atmospheric model on the warm conveyor belt and jet stream during NAWDEX IOP6, Weather Clim. Dynam., 2, 1011–1031, https://doi.org/10.5194/wcd-2-1011-2021, 2021. a, b, c
Satoh, M., Stevens, B., Judt, F., Khairoutdinov, M., Lin, S.-J., Putman, W. M., and Düben, P.:
Global cloud-resolving models, Current Climate Change Reports, 5, 172–184, https://doi.org/10.1007/s40641-019-00131-0, 2019. a
Schäfer, S. and Voigt, A.:
Radiation weakens idealized mid-latitude cyclones, Geophys. Res. Lett., 45, 2833–2841, https://doi.org/10.1002/2017GL076726, 2018. a
Schäfler, A., Craig, G., Wernli, H., Arbogast, P., Doyle, J., McTaggart-Cowan, R., and Bramberger, M.:
The North Atlantic waveguide and downstream impact experiment, B. Am. Meteorol. Soc., 99, 1607–1637, https://doi.org/10.1175/BAMS-D-17-0003.1, 2018. a, b
Schulzweida, U.: CDO User Guide, Version 1.9.8, Zenodo, https://doi.org/10.5281/zenodo.3539275, 2019. a
Seifert, A. and Beheng, K. D.:
A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006. a
Sprenger, M. and Wernli, H.:
The LAGRANTO Lagrangian analysis tool – version 2.0, Geosci. Model Dev., 8, 2569–2586, https://doi.org/10.5194/gmd-8-2569-2015, 2015. a
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X., and Kodama, C.:
DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Progress in Earth and Planetary Science, 6, 61, https://doi.org/10.1186/s40645-019-0304-z, 2019. a
Stevens, B., Acquistapace, C., Hansen, A., Heinze, R., Klinger, C., Klocke, D., Rybka, H., Schubotz, W., Windmiller, J., Adamidis, P., Arka, I., Barlakas, V., Biercamp, J., Brueck, M., Brune, S., Buehler, S. A., Burkhardt, U., Cioni, G., Costa-Suros, M., Crewell, S., Crüger, T., Deneke, H., Friedrichs, P., Henken, C. C., Hohenegger, C., Jacob, M., Jakub, F., Kalthoff, N., Köhler, M., Laar, T. W. v., Li, P., Löhnert, U., Macke, A., Madenach, N., Mayer, B., Nam, C., Naumann, A. K., Peters, K., Poll, S., Quaas, J., Röber, N., Rochetin, N., Scheck, L., Schemann, V., Schnitt, S., Seifert, A., Senf, F., Shapkalijevski, M., Simmer, C., Singh, S., Sourdeval, O., Spickermann, D., Strandgren, J., Tessiot, O., Vercauteren, N., Vial, J., Voigt, A., and Zängl, G.:
The Added Value of Large-Eddy and Storm-Resolving Models for Simulating Clouds and Precipitation, J. Meteorol. Soc. Jpn. Ser. II, 98, 395–435, https://doi.org/10.2151/jmsj.2020-021, 2020. a, b
Stohl, A.:
A 1-year Lagrangian “climatology” of airstreams in the Northern Hemisphere troposphere and lowermost stratosphere, J. Geophys. Res.-Atmos., 106, 7263–7279, https://doi.org/10.1029/2000JD900570, 2001. a
Tiedtke, M.:
A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a
Trzeciak, T. M., Knippertz, P., Pirret, J. S., and Williams, K. D.:
Can we trust climate models to realistically represent severe European windstorms?, Clim. Dynam., 46, 3431–3451, https://doi.org/10.1007/s00382-015-2777-9, 2016. a
Ulbrich, U., Pinto, J. G., Kupfer, H., Leckebusch, G., Spangehl, T., and Reyers, M.:
Changing Northern Hemisphere storm tracks in an ensemble of IPCC climate change simulations, J. Climate, 21, 1669–1679, https://doi.org/10.1175/2007JCLI1992.1, 2008. a
Ulbrich, U., Leckebusch, G. C., and Pinto, J. G.:
Extra-tropical cyclones in the present and future climate: a review, Theor. Appl. Climatol., 96, 117–131, https://doi.org/10.1007/s00704-008-0083-8, 2009. a
Vergara-Temprado, J., Ban, N., Panosetti, D., Schlemmer, L., and Schär, C.:
Climate Models Permit Convection at Much Coarser Resolutions Than Previously Considered, J. Climate, 33, 1915–1933, https://doi.org/10.1175/JCLI-D-19-0286.1, 2020. a, b
Wernli, H. and Davies, H. C.:
A Lagrangian-based analysis of extratropical cyclones. I: The method and some applications, Q. J. Roy. Meteor. Soc., 123, 467–489, https://doi.org/10.1002/qj.49712353811, 1997. a, b, c
Williams, K., Bodas-Salcedo, A., Déqué, M., Fermepin, S., Medeiros, B., Watanabe, M., and Williamson, D.:
The Transpose-AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models, J. Climate, 26, 3258–3274, https://doi.org/10.1175/JCLI-D-12-00429.1, 2013. a
Willison, J., Robinson, W. A., and Lackmann, G. M.:
The importance of resolving mesoscale latent heating in the North Atlantic storm track, J. Atmos. Sci., 70, 2234–2250, https://doi.org/10.1175/JAS-D-12-0226.1, 2013. a, b
Willison, J., Robinson, W. A., and Lackmann, G. M.:
North Atlantic storm-track sensitivity to warming increases with model resolution, J. Climate, 28, 4513–4524, https://doi.org/10.1175/JCLI-D-14-00715.1, 2015. a
Wimmer, M., Rivière, G., Arbogast, P., Piriou, J.-M., Delanoë, J., Labadie, C., Cazenave, Q., and Pelon, J.:
Diabatic processes modulating the vertical structure of the jet stream above the cold front of an extratropical cyclone: sensitivity to deep convection schemes, Weather Clim. Dynam., 3, 863–882, https://doi.org/10.5194/wcd-3-863-2022, 2022. a
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.:
The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a, b
Short summary
The warm conveyor belt (WCB), which is a stream of coherently rising air parcels, is an important feature of extratropical cyclones. This work presents the impact of model grid spacing on simulation of cloud diabatic processes in the WCB of a North Atlantic cyclone. We find that the refinement of the model grid systematically enhances the dynamical properties and heat releasing processes within the WCB. However, this pattern does not have a strong impact on the strength of associated cyclones.
The warm conveyor belt (WCB), which is a stream of coherently rising air parcels, is an...