Gutenberg Open Science: Algorithmic differentiation for sensitivity analysis in cloud microphysics

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8106

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DC Field	Value	Language
dc.contributor.author	Hieronymus, Maicon	-
dc.contributor.author	Baumgartner, Manuel	-
dc.contributor.author	Miltenberger, Annette	-
dc.contributor.author	Brinkmann, André	-
dc.date.accessioned	2022-10-31T07:56:30Z	-
dc.date.available	2022-10-31T07:56:30Z	-
dc.date.issued	2022	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/8121	-
dc.description.abstract	The role of clouds for radiative transfer, precipitation formation, and their interaction with atmospheric dynamics depends strongly on cloud microphysics. The parameterization of cloud microphysical processes in weather and climate models is a well-known source of uncertainties. Hence, robust quantification of this uncertainty is mandatory. Sensitivity analysis to date has typically investigated only a few model parameters. We propose algorithmic differentiation (AD) as a tool to detect the magnitude and timing at which a model state variable is sensitive to any of the hundreds of uncertain model parameters in the cloud microphysics parameterization. AD increases the computational cost by roughly a third in our simulations. We explore this methodology as the example of warm conveyor belt trajectories, that is, air parcels rising rapidly from the planetary boundary layer to the upper troposphere in the vicinity of an extratropical cyclone. Based on the information of derivatives with respect to the uncertain parameters, the ten parameters contributing most to uncertainty are selected. These uncertain parameters are mostly related to the representation of hydrometeor diameter and fall velocity, the activation of cloud condensation nuclei, and heterogeneous freezing. We demonstrate the meaningfulness of the AD-estimated sensitivities by comparing the AD results with ensemble simulations spawned at different points along the trajectories, where different parameter settings are used in the various ensemble members. The ranking of the most important parameters from these ensemble simulations is consistent with the results from AD. Thus, AD is a helpful tool for selecting parameters contributing most to cloud microphysics uncertainty.	en_GB
dc.description.sponsorship	Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	550 Geowissenschaften	de_DE
dc.subject.ddc	550 Earth sciences	en_GB
dc.title	Algorithmic differentiation for sensitivity analysis in cloud microphysics	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-8106	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Journal of advances in modeling earth systems	de
jgu.journal.volume	14	de
jgu.journal.issue	7	de
jgu.pages.alternative	e2021MS002849	de
jgu.publisher.year	2022	-
jgu.publisher.name	John Wiley & Sons, Ltd	de
jgu.publisher.place	Fort Collins, Colo.	de
jgu.publisher.issn	1942-2466	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	550	de
jgu.publisher.doi	10.1029/2021MS002849	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Naturwissenschaften	de
Appears in collections:	DFG-491381577-G

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