Preprints
https://doi.org/10.5194/acp-2021-757
https://doi.org/10.5194/acp-2021-757

  15 Sep 2021

15 Sep 2021

Review status: this preprint is currently under review for the journal ACP.

Parameterising cloud base updraft velocity of marine stratocumuli

Jaakko Ahola1, Tomi Raatikainen1, Muzaffer Ege Alper1, Jukka-Pekka Keskinen1, Harri Kokkola2, Antti Kukkurainen2, Antti Lipponen2, Jia Liu3, Kalle Nordling1, Antti-Ilari Partanen1, Sami Romakkaniemi2, Petri Räisänen1, Juha Tonttila2, and Hannele Korhonen1 Jaakko Ahola et al.
  • 1Finnish Meteorological Institute, Helsinki, Finland
  • 2Finnish Meteorological Institute, Kuopio, Finland
  • 3Research School of Earth Sciences, College of Science and Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra, Australia

Abstract. The number of cloud droplets formed at the cloud base depends both on the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parametrisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produce on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.

Jaakko Ahola et al.

Status: open (until 27 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-757', Anonymous Referee #1, 18 Oct 2021 reply

Jaakko Ahola et al.

Data sets

Results of "Parameterising cloud base updraft velocity of marine stratocumuli" -manuscript Ahola, Jaakko and Raatikainen, Tomi and Alper, Muzaffer Ege and Keskinen, Jukka-Pekka and Kokkola, Harri and Kukkurainen, Antti and Lipponen, Antti and Liu, Jia and Nordling, Kalle and Partanen, Antti-Ilari and Romakkaniemi, Sami and Räisänen, Petri and Tonttila, Juha and Korhonen, Hannele https://doi.org/10.23728/FMI-B2SHARE.477AF35BE02F4A158E2F7E852022EC60

LES simulations of "Parameterising cloud base updraft velocity of marine stratocumuli" -manuscript hola, Jaakko and Raatikainen, Tomi and Alper, Muzaffer Ege and Keskinen, Jukka-Pekka and Kokkola, Harri and Nordling, Kalle and Partanen, Antti-Ilari and Romakkaniemi, Sami and Räisänen, Petri and Tonttila, Juha and Korhonen, Hannele https://doi.org/10.23728/FMI-B2SHARE.296483F247B1412EBD27F0B82DD1BB76

DESIGN: SALSA daytime 150 simulations Alper, Muzaffer Ege https://doi.org/10.5281/zenodo.5346794

DESIGN: SALSA nighttime 135 simulations Alper, Muzaffer Ege https://doi.org/10.5281/zenodo.5346789

DESIGN: SB 500 daytime simulations Alper, Muzaffer Ege https://doi.org/10.5281/zenodo.5346768

DESIGN: SB 500 nighttime simulations Alper, Muzaffer Ege https://doi.org/10.5281/zenodo.5346765

Model code and software

LES-emulator-03plotting Ahola, Jaakko https://doi.org/10.5281/zenodo.5385283

LES-emulator-04configFiles Ahola, Jaakko https://doi.org/10.5281/zenodo.5383581

LES-03plotting Ahola, Jaakko https://doi.org/10.5281/zenodo.5347571

GPEmulatorPython Ahola, Jaakko and Kukkurainen, Antti and Alper, Muzaffer Ege and Liu, Jia and Lipponen, Antti https://doi.org/10.5281/zenodo.5347718

LES-emulator-02postpros Ahola, Jaakko and Raatikainen, Tomi and Kukkurainen, Antti and Alper, Muzaffer Ege and Liu, Jia and Keskinen, Jukka-Pekka and Lipponen, Antti https://doi.org/10.5281/zenodo.5385251

LES-02postpros Tonttila, Juha and Ahola, Jaakko and Raatikainen, Tomi https://doi.org/10.5281/zenodo.5347269

LES-emulator-01prepros Ahola, Jaakko and Raatikainen, Tomi https://doi.org/10.5281/zenodo.5336989

UCLALES-SALSA Tonttila, Juha and Raatikainen, Tomi and Ahola, Jaakko and Kokkola, Harri and Ruuskanen, Antti and Romakkaniemi, Sami https://doi.org/10.5281/zenodo.5289397

BSP algorithm - StateSpaceDesign Alper, Muzaffer Ege and Liu, Jia https://doi.org/10.5281/zenodo.5343366

Filter Source Data Nordling, Kalle https://doi.org/10.5281/zenodo.5343428

Jaakko Ahola et al.

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Short summary
Clouds are important for the climate and cloud droplets have a significant role in cloud properties. Cloud droplets form when air rises and cools, and water vapor condenses on small particles that can be natural or human-origin. Currently, the updraft velocity, meaning how fast the air rises, is poorly represented in global climate models. In our study, we show three methods that will improve the depiction of updraft velocity and what properties are vital to updrafts.
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