4-9 September 2022, Bonn, Germany
EMS Annual Meeting Abstracts
Vol. 19, EMS2022-701, 2022
EMS Annual Meeting 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Franziska Glassmeier1, Fabian Hoffmann2,3, Graham Feingold4, Edward Gryspeerdt5, Antoon van Hooft1, Takanobu Yamaguchi3,4, Jill S. Johnson6, and Ken S. Carslaw6
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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  • 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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