Wellmann, C., Barrett, A.I., Johnson, J.S. orcid.org/0000-0002-4587-6722 et al. (4 more authors) (2019) Using emulators to understand the sensitivity of deep convective clouds and hail to environmental conditions. Journal of Advances in Modeling Earth Systems, 10 (12). pp. 3103-3122. ISSN 1942-2466
Abstract
This study aims to identify model parameters describing atmospheric conditions such as wind shear and cloud condensation nuclei (CCN) concentration, which lead to large uncertainties in the prediction of deep convective clouds. In an idealized setup of a cloud-resolving model including a two-moment microphysics scheme we use the approach of statistical emulation to allow for a Monte Carlo sampling of the parameter space, which enables a comprehensive sensitivity analysis. We analyze the impact of six uncertain input parameters on cloud properties (vertically integrated content of six hydrometeor classes), precipitation, and the size distribution of hail. Furthermore, we investigate whether the sensitivities are robust for different trigger mechanisms of convection. We find that the uncertainties of most cloud and precipitation outputs are dominated by the uncertainty in the temperature profile and the CCN concentration while the contributions of other input parameters to the uncertainties may vary. The temperature profile is also an important factor in determining the size distribution of surface hail. We also notice that the sensitivities of cloud water and hail to the CCN concentration depend on environmental conditions. Our results show that depending on the choice of the trigger mechanism, the contribution of the input parameters to the uncertainty varies, which means that studies with different trigger mechanisms might not be comparable. Overall, the emulator approach appears to be a powerful tool for the analysis of complex weather prediction models in an idealized setup.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | statistical emulation; deep convection; sensitivity to environmental conditions |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Jan 2022 12:11 |
Last Modified: | 18 Jan 2022 12:11 |
Status: | Published |
Publisher: | American Geophysical Union (AGU) |
Refereed: | Yes |
Identification Number: | 10.1029/2018MS001465 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182657 |