guest ::
| Home > Publications database > Peroxy acetyl nitrate (PAN) measurements at northern midlatitude mountain sites in April: a constraint on continental source–receptor relationships > print |
| Home > Publications database > Peroxy acetyl nitrate (PAN) measurements at northern midlatitude mountain sites in April: a constraint on continental source–receptor relationships > print |
| 001 | 858692 | ||
| 005 | 20230127125335.0 | ||
| 024 | 7 | _ | |a 10.5194/acp-18-15345-2018 |2 doi |
| 024 | 7 | _ | |a 1680-7316 |2 ISSN |
| 024 | 7 | _ | |a 1680-7324 |2 ISSN |
| 024 | 7 | _ | |a = |2 ISSN |
| 024 | 7 | _ | |a Atmospheric |2 ISSN |
| 024 | 7 | _ | |a chemistry |2 ISSN |
| 024 | 7 | _ | |a and |2 ISSN |
| 024 | 7 | _ | |a physics |2 ISSN |
| 024 | 7 | _ | |a (Online) |2 ISSN |
| 024 | 7 | _ | |a 2128/20982 |2 Handle |
| 024 | 7 | _ | |a WOS:000448313100002 |2 WOS |
| 024 | 7 | _ | |a altmetric:50236190 |2 altmetric |
| 037 | _ | _ | |a FZJ-2018-07537 |
| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Fiore, Arlene M. |0 0000-0003-0221-2122 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Peroxy acetyl nitrate (PAN) measurements at northern midlatitude mountain sites in April: a constraint on continental source–receptor relationships |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2018 |b EGU |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1545209251_15563 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Abundance-based model evaluations with observations provide critical tests for the simulated mean state in models of intercontinental pollution transport, and under certain conditions may also offer constraints on model responses to emission changes. We compile multiyear measurements of peroxy acetyl nitrate (PAN) available from five mountaintop sites and apply them in a proof-of-concept approach that exploits an ensemble of global chemical transport models (HTAP1) to identify an observational emergent constraint. In April, when the signal from anthropogenic emissions on PAN is strongest, simulated PAN at northern midlatitude mountaintops correlates strongly with PAN source–receptor relationships (the response to 20% reductions in precursor emissions within northern midlatitude continents; hereafter, SRRs). This finding implies that PAN measurements can provide constraints on PAN SRRs by limiting the SRR range to that spanned by the subset of models simulating PAN within the observed range. In some cases, regional anthropogenic volatile organic compound (AVOC) emissions, tracers of transport from different source regions, and SRRs for ozone also correlate with PAN SRRs. Given the large observed interannual variability in the limited available datasets, establishing strong constraints will require matching meteorology in the models to the PAN measurements. Application of this evaluation approach to the chemistry–climate models used to project changes in atmospheric composition will require routine, long-term mountaintop PAN measurements to discern both the climatological SRR signal and its interannual variability. |
| 536 | _ | _ | |a 512 - Data-Intensive Science and Federated Computing (POF3-512) |0 G:(DE-HGF)POF3-512 |c POF3-512 |f POF III |x 0 |
| 536 | _ | _ | |0 G:(DE-Juel-1)ESDE |a Earth System Data Exploration (ESDE) |c ESDE |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Fischer, Emily V. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Milly, George P. |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Pandey Deolal, Shubha |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Wild, Oliver |0 0000-0002-6227-7035 |b 4 |
| 700 | 1 | _ | |a Jaffe, Daniel A. |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Staehelin, Johannes |0 0000-0001-7861-1889 |b 6 |
| 700 | 1 | _ | |a Clifton, Olivia E. |0 0000-0002-1669-9878 |b 7 |
| 700 | 1 | _ | |a Bergmann, Dan |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Collins, William |0 0000-0002-7419-0850 |b 9 |
| 700 | 1 | _ | |a Dentener, Frank |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Doherty, Ruth M. |0 0000-0001-7601-2209 |b 11 |
| 700 | 1 | _ | |a Duncan, Bryan N. |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Fischer, Bernd |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Gilge, Stefan |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Hess, Peter G. |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Horowitz, Larry W. |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Lupu, Alexandru |0 0000-0002-4520-5523 |b 17 |
| 700 | 1 | _ | |a MacKenzie, Ian A. |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Park, Rokjin |0 0000-0001-8922-0234 |b 19 |
| 700 | 1 | _ | |a Ries, Ludwig |0 P:(DE-HGF)0 |b 20 |
| 700 | 1 | _ | |a Sanderson, Michael G. |0 P:(DE-HGF)0 |b 21 |
| 700 | 1 | _ | |a Schultz, Martin |0 P:(DE-Juel1)6952 |b 22 |
| 700 | 1 | _ | |a Shindell, Drew T. |0 0000-0003-1552-4715 |b 23 |
| 700 | 1 | _ | |a Steinbacher, Martin |0 P:(DE-HGF)0 |b 24 |
| 700 | 1 | _ | |a Stevenson, David S. |0 0000-0002-4745-5673 |b 25 |
| 700 | 1 | _ | |a Szopa, Sophie |0 0000-0002-8641-1737 |b 26 |
| 700 | 1 | _ | |a Zellweger, Christoph |0 P:(DE-HGF)0 |b 27 |
| 700 | 1 | _ | |a Zeng, Guang |0 0000-0002-9356-5021 |b 28 |
| 773 | _ | _ | |a 10.5194/acp-18-15345-2018 |g Vol. 18, no. 20, p. 15345 - 15361 |0 PERI:(DE-600)2069847-1 |n 20 |p 15345 - 15361 |t Atmospheric chemistry and physics |v 18 |y 2018 |x 1680-7324 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/858692/files/acp-18-15345-2018.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://juser.fz-juelich.de/record/858692/files/acp-18-15345-2018.pdf?subformat=pdfa |
| 909 | C | O | |o oai:juser.fz-juelich.de:858692 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 22 |6 P:(DE-Juel1)6952 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-512 |2 G:(DE-HGF)POF3-500 |v Data-Intensive Science and Federated Computing |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2018 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b ATMOS CHEM PHYS : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0110 |2 StatID |b Science Citation Index |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Peer review |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b ATMOS CHEM PHYS : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|