001     910157
005     20260122232355.0
024 7 _ |2 doi
|a 10.5194/gmd-15-7471-2022
024 7 _ |2 ISSN
|a 1991-959X
024 7 _ |2 ISSN
|a 1991-9603
024 7 _ |2 Handle
|a 2128/32040
024 7 _ |2 WOS
|a WOS:000865444800001
037 _ _ |a FZJ-2022-03644
041 _ _ |a English
082 _ _ |a 550
100 1 _ |0 P:(DE-Juel1)129130
|a Konopka, Paul
|b 0
|e Corresponding author
245 _ _ |a Tropospheric transport and unresolved convection: numerical experiments with CLaMS 2.0/MESSy
260 _ _ |a Katlenburg-Lindau
|b Copernicus
|c 2022
336 7 _ |2 DRIVER
|a article
336 7 _ |2 DataCite
|a Output Types/Journal article
336 7 _ |0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
|a Journal Article
|b journal
|m journal
|s 1666009485_30574
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 ORCID
|a JOURNAL_ARTICLE
336 7 _ |0 0
|2 EndNote
|a Journal Article
520 _ _ |a Pure Lagrangian, i.e., trajectory-based transport models, take into account only the resolved advective part of transport. That means neither mixing processes between the air parcels (APs) nor unresolved subgrid-scale advective processes like convection are included. The Chemical Lagrangian Model of the Stratosphere (CLaMS 1.0) extends this approach by including mixing between the Lagrangian APs parameterizing the small-scale isentropic mixing. To improve model representation of the upper troposphere and lower stratosphere (UTLS), this approach was extended by taking into account parameterization of tropospheric mixing and unresolved convection in the recently published CLaMS 2.0 version. All three transport modes, i.e., isentropic and tropospheric mixing and the unresolved convection can be adjusted and optimized within the model. Here, we investigate the sensitivity of the model representation of tracers in the UTLS with respect to these three modes.For this reason, the CLaMS 2.0 version implemented within the Modular Earth Submodel System (MESSy), CLaMS 2.0/MESSy, is applied with meteorology based on the ERA-Interim (EI) and ERA5 (E5) reanalyses with the same horizontal resolution (1.0×1.0∘) but with 60 and 137 model levels for EI and E5, respectively. Comparisons with in situ observations are used to rate the degree of agreement between different model configurations and observations. Starting from pure advective runs as a reference and in agreement with CLaMS 1.0, we show that among the three processes considered, isentropic mixing dominates transport in the UTLS. Both the observed CO, O3, N2O, and CO2 profiles and CO–O3 correlations are clearly better reproduced in the model with isentropic mixing. The second most important transport process considered is convection which is only partially resolved in the vertical velocity fields provided by the analysis. This additional pathway of transport from the planetary boundary layer (PBL) to the main convective outflow dominates the composition of air in the lower stratosphere relative to the contribution of the resolved transport. This transport happens mainly in the tropics and sub-tropics, and significantly rejuvenates the age of air in this region. By taking into account tropospheric mixing, weakest changes in tracer distributions without any clear improvements were found.
536 _ _ |0 G:(DE-HGF)POF4-2112
|a 2112 - Climate Feedbacks (POF4-211)
|c POF4-211
|f POF IV
|x 0
536 _ _ |0 G:(DE-HGF)POF4-5111
|a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|c POF4-511
|f POF IV
|x 1
536 _ _ |0 G:(DE-Juel-1)SDLCS
|a Simulation and Data Lab Climate Science
|c SDLCS
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |0 P:(DE-HGF)0
|a Tao, Mengchu
|b 1
700 1 _ |0 P:(DE-Juel1)129170
|a von Hobe, Marc
|b 2
700 1 _ |0 P:(DE-Juel1)129125
|a Hoffmann, Lars
|b 3
700 1 _ |0 P:(DE-Juel1)161426
|a Kloss, Corinna
|b 4
700 1 _ |0 0000-0003-0735-9297
|a Ravegnani, Fabrizio
|b 5
700 1 _ |0 P:(DE-HGF)0
|a Volk, C. Michael
|b 6
700 1 _ |0 P:(DE-HGF)0
|a Lauther, Valentin
|b 7
700 1 _ |0 P:(DE-HGF)0
|a Zahn, Andreas
|b 8
700 1 _ |0 0000-0001-6582-6864
|a Hoor, Peter
|b 9
700 1 _ |0 P:(DE-Juel1)129141
|a Ploeger, Felix
|b 10
773 _ _ |0 PERI:(DE-600)2456725-5
|a 10.5194/gmd-15-7471-2022
|g Vol. 15, no. 19, p. 7471 - 7487
|n 19
|p 7471 - 7487
|t Geoscientific model development
|v 15
|x 1991-959X
|y 2022
856 4 _ |u https://juser.fz-juelich.de/record/910157/files/gmd-15-7471-2022.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:910157
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)129130
|a Forschungszentrum Jülich
|b 0
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)129170
|a Forschungszentrum Jülich
|b 2
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)129125
|a Forschungszentrum Jülich
|b 3
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)161426
|a Forschungszentrum Jülich
|b 4
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)129141
|a Forschungszentrum Jülich
|b 10
|k FZJ
913 1 _ |0 G:(DE-HGF)POF4-211
|1 G:(DE-HGF)POF4-210
|2 G:(DE-HGF)POF4-200
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-2112
|a DE-HGF
|b Forschungsbereich Erde und Umwelt
|l Erde im Wandel – Unsere Zukunft nachhaltig gestalten
|v Die Atmosphäre im globalen Wandel
|x 0
913 1 _ |0 G:(DE-HGF)POF4-511
|1 G:(DE-HGF)POF4-510
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5111
|a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|v Enabling Computational- & Data-Intensive Science and Engineering
|x 1
914 1 _ |y 2022
915 _ _ |0 StatID:(DE-HGF)0160
|2 StatID
|a DBCoverage
|b Essential Science Indicators
|d 2021-01-26
915 _ _ |0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
|a Creative Commons Attribution CC BY 4.0
915 _ _ |0 StatID:(DE-HGF)0113
|2 StatID
|a WoS
|b Science Citation Index Expanded
|d 2021-01-26
915 _ _ |0 StatID:(DE-HGF)0700
|2 StatID
|a Fees
|d 2021-01-26
915 _ _ |0 StatID:(DE-HGF)0510
|2 StatID
|a OpenAccess
915 _ _ |0 StatID:(DE-HGF)0561
|2 StatID
|a Article Processing Charges
|d 2021-01-26
915 _ _ |0 StatID:(DE-HGF)0300
|2 StatID
|a DBCoverage
|b Medline
|d 2022-11-25
915 _ _ |0 StatID:(DE-HGF)0501
|2 StatID
|a DBCoverage
|b DOAJ Seal
|d 2021-01-16T18:00:10Z
915 _ _ |0 StatID:(DE-HGF)0500
|2 StatID
|a DBCoverage
|b DOAJ
|d 2021-01-16T18:00:10Z
915 _ _ |0 StatID:(DE-HGF)0030
|2 StatID
|a Peer Review
|b DOAJ : Peer review
|d 2021-01-16T18:00:10Z
915 _ _ |0 StatID:(DE-HGF)0199
|2 StatID
|a DBCoverage
|b Clarivate Analytics Master Journal List
|d 2022-11-25
915 _ _ |0 StatID:(DE-HGF)0150
|2 StatID
|a DBCoverage
|b Web of Science Core Collection
|d 2022-11-25
915 _ _ |0 StatID:(DE-HGF)1150
|2 StatID
|a DBCoverage
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2022-11-25
915 _ _ |0 StatID:(DE-HGF)0100
|2 StatID
|a JCR
|b GEOSCI MODEL DEV : 2021
|d 2022-11-25
915 _ _ |0 StatID:(DE-HGF)0200
|2 StatID
|a DBCoverage
|b SCOPUS
|d 2022-11-25
915 _ _ |0 StatID:(DE-HGF)0600
|2 StatID
|a DBCoverage
|b Ebsco Academic Search
|d 2022-11-25
915 _ _ |0 StatID:(DE-HGF)0030
|2 StatID
|a Peer Review
|b ASC
|d 2022-11-25
915 _ _ |0 StatID:(DE-HGF)9905
|2 StatID
|a IF >= 5
|b GEOSCI MODEL DEV : 2021
|d 2022-11-25
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IEK-7-20101013
|k IEK-7
|l Stratosphäre
|x 0
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 1
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IEK-7-20101013
980 _ _ |a I:(DE-Juel1)JSC-20090406
981 _ _ |a I:(DE-Juel1)ICE-4-20101013


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21