Hauptseite > Publikationsdatenbank > The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing > print

001     1032065
005     20241028205635.0
024 7 _ |a 10.5281/ZENODO.12746362
|2 doi
037 _ _ |a FZJ-2024-05969
100 1 _ |a Platt, John
|0 P:(DE-HGF)0
|b 0
245 _ _ |a The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing
260 _ _ |c 2024
|b Zenodo
336 7 _ |a MISC
|2 BibTeX
336 7 _ |a Dataset
|b dataset
|m dataset
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|s 1730093580_25077
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336 7 _ |a Chart or Table
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336 7 _ |a Dataset
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336 7 _ |a DATA_SET
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336 7 _ |a ResearchData
|2 DINI
520 _ _ |a Previous work has shown that while the net effect of aircraft condensation trails (contrails) on theclimate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain.In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flightsegments with high contrail energy forcing. We find that skill is greater than climatologicalpredictions alone, even accounting for uncertainty in weather fields and model parameters. We estimate the uncertainty in weather by using the ensemble ERA5 weather reanalysis from the EuropeanCentre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correctunder-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humiditymeasurements taken at cruising altitude. We set aside CoCiP energy forcing estimates calculated usingone of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifyingsegments with large positive proxy energy forcing. We further estimate the uncertainty in the modelparameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn fromuncertainty distributions consistent with the literature. When CoCiP outputs are averaged over seasons to form climatological predictions, the skill inpredicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carryover to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions canreduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the costand fuel impact of contrail avoidance.
536 _ _ |a 2111 - Air Quality (POF4-211)
|0 G:(DE-HGF)POF4-2111
|c POF4-211
|f POF IV
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588 _ _ |a Dataset connected to DataCite
700 1 _ |a Shapiro, Marc
|0 0000-0003-0864-6394
|b 1
700 1 _ |a Engberg, Zeb
|0 P:(DE-HGF)0
|b 2
700 1 _ |a McCloskey, Kevin
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Geraedts, Scott
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Sankar, Tharun
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Stettler, Marc
|0 0000-0002-2066-9380
|b 6
700 1 _ |a Teoh, Roger
|0 P:(DE-HGF)0
|b 7
700 1 _ |a Schumann, Ulrich
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Rohs, Susanne
|0 P:(DE-Juel1)129146
|b 9
|u fzj
700 1 _ |a Brand, Erica
|0 P:(DE-HGF)0
|b 10
700 1 _ |a Van Arsdale, Christopher
|0 P:(DE-HGF)0
|b 11
773 _ _ |a 10.5281/ZENODO.12746362
909 C O |o oai:juser.fz-juelich.de:1032065
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910 1 _ |a Forschungszentrum Jülich
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|k FZJ
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|6 P:(DE-Juel1)129146
913 1 _ |a DE-HGF
|b Forschungsbereich Erde und Umwelt
|l Erde im Wandel – Unsere Zukunft nachhaltig gestalten
|1 G:(DE-HGF)POF4-210
|0 G:(DE-HGF)POF4-211
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-200
|4 G:(DE-HGF)POF
|v Die Atmosphäre im globalen Wandel
|9 G:(DE-HGF)POF4-2111
|x 0
914 1 _ |y 2024
920 1 _ |0 I:(DE-Juel1)ICE-3-20101013
|k ICE-3
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980 _ _ |a dataset
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)ICE-3-20101013
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
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