Gaussian-process emulation for integrating data-driven aerosol-cloud physics from simulation, satellite, and ground-based data

Franziska Glassmeier; Fabian Hoffmann; Graham Feingold; Edward Gryspeerdt; Antoon van Hooft; Takanobu Yamaguchi; Jill S. Johnson; Ken S. Carslaw

doi:https://doi.org/10.5194/ems2022-701

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EMS Annual Meeting Abstracts

Vol. 19, EMS2022-701, 2022, updated on 15 Sep 2025

https://doi.org/10.5194/ems2022-701

EMS Annual Meeting 2022

Gaussian-process emulation for integrating data-driven aerosol-cloud physics from simulation, satellite, and ground-based data

Franziska Glassmeier

¹, Fabian Hoffmann

^2,3, Graham Feingold

⁴, Edward Gryspeerdt

⁵, Antoon van Hooft¹, Takanobu Yamaguchi

^3,4, Jill S. Johnson

⁶, and Ken S. Carslaw

⁶

Franziska Glassmeier et al.

¹Department Geoscience and Remote Sensing, TU Delft, Delft, Netherlands
²Meteorologisches Institut, Ludwig-Maximilans-Universität München, Munich, Germany
³Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder (CO), USA
⁴Chemical Sciences Laboratory, NOAA/Earth System Research Laboratories, Boulder (CO), USA
⁵Space and Atmospheric Physics Group, Imperial College London, London, UK
⁶School of Earth and Environment, University of Leeds, Leeds, UK

Data-driven quantification and parameterization of cloud physics in general, and of aerosol-cloud interactions in particular, rely on input data from observations or detailed simulations. These data sources have complementary limitations in terms of their spatial and temporal coverage and resolution; simulation data has the advantage of readily providing causality but cannot represent the full process complexity. In order to base data-driven approaches on comprehensive information, we therefore need ways to integrate different data sources.

We discuss how the classical statistical technique of Gaussian-process emulation can be combined with specifically initialized ensembles of detailed cloud simulations (large-eddy simulations, LES) to provide a framework for evaluating data-driven descriptions of cloud characteristics and processes across different data sources. We specifically illustrate this approach for integrating LES and satellite data of aerosol-cloud interactions in subtropical stratocumulus cloud decks. We furthermore explore the extension of our framework to ground-based observations of Arctic mixed-phase clouds.

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References:

Glassmeier, F., F. Hoffmann, J. S. Johnson, T. Yamaguchi, K. S. Carslaw and G. Feingold (2019): “An emulator approach to stratocumulus susceptibility”, Atmos. Chem. Phys., 19, 10191- 10203, doi: 10.5194/acp-19-10191-2019
Hoffmann, F., F. Glassmeier, T. Yamaguchi and G. Feingold (2020): “Liquid water path steady states in stratocumulus: insights from process-level emulation and mixed-layer theory”, J. Atmos. Sci., 77, 2203-2215, doi: 10.1175/JAS-D-19-0241.1
Glassmeier, F., F. Hoffmann, J.S. Johnson, T. Yamaguchi, K. S. Carslaw, and G. Feingold (2021): “Aerosol-cloud climate cooling overestimated by ship-track data”, Science 371, 485–489, doi: 10.1126/science.abd3980

How to cite: Glassmeier, F., Hoffmann, F., Feingold, G., Gryspeerdt, E., van Hooft, A., Yamaguchi, T., Johnson, J. S., and Carslaw, K. S.: Gaussian-process emulation for integrating data-driven aerosol-cloud physics from simulation, satellite, and ground-based data, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-701, https://doi.org/10.5194/ems2022-701, 2022.

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