Articles | Volume 24, issue 2
Technical note
22 Jan 2024
Technical note |  | 22 Jan 2024

Technical note: Emulation of a large-eddy simulator for stratocumulus clouds in a general circulation model

Kalle Nordling, Jukka-Pekka Keskinen, Sami Romakkaniemi, Harri Kokkola, Petri Räisänen, Antti Lipponen, Antti-Ilari Partanen, Jaakko Ahola, Juha Tonttila, Muzaffer Ege Alper, Hannele Korhonen, and Tomi Raatikainen

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Technical note: Parameterising cloud base updraft velocity of marine stratocumuli
Jaakko Ahola, Tomi Raatikainen, Muzaffer Ege Alper, Jukka-Pekka Keskinen, Harri Kokkola, Antti Kukkurainen, Antti Lipponen, Jia Liu, Kalle Nordling, Antti-Ilari Partanen, Sami Romakkaniemi, Petri Räisänen, Juha Tonttila, and Hannele Korhonen
Atmos. Chem. Phys., 22, 4523–4537,,, 2022
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Understanding the surface temperature response and its uncertainty to CO2, CH4, black carbon, and sulfate
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How Asian aerosols impact regional surface temperatures across the globe
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Subject: Clouds and Precipitation | Research Activity: Machine Learning | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains
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Cited articles

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Abdul-Razzak, H., Ghan, S. J., and Rivera-carpio, C.: A parameterization of aerosol activation: 1. Single aerosol type, J. Geophys. Res., 103, 6123–6131,, 1998. a
Adler, R. F., Gu, G., and Huffman, G. J.: Estimating climatological bias errors for the Global Precipitation Climatology Project (GPCP), J. Appl. Meteorol. Clim., 51, 84–99,, 2012. a
Ahola, J., Raatikainen, T., Alper, M. E., Keskinen, J.-P., Kokkola, H., Kukkurainen, A., Lipponen, A., Liu, J., Nordling, K., Partanen, A.-I., Romakkaniemi, S., Räisänen, P., Tonttila, J., and Korhonen, H.: Technical note: Parameterising cloud base updraft velocity of marine stratocumuli, Atmos. Chem. Phys., 22, 4523–4537,, 2022. a, b, c, d, e, f, g, h, i, j, k, l, m
Besombes, C., Pannekoucke, O., Lapeyre, C., Sanderson, B., and Thual, O.: Producing realistic climate data with generative adversarial networks, Nonlin. Processes Geophys., 28, 347–370,, 2021. a
Short summary
Our results show that the global model is stable and it provides meaningful results. This way we can include a physics-based presentation of sub-grid physics (physics which happens on a 100 m scale) in the global model, whose resolution is on a 100 km scale.
Final-revised paper