Long-term trend analysis of aerosol parameters at the Jungfraujoch
es1mates(of(Fgases(based(on(long ... ·...
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! Topdown emission es-mates based on in situ longterm, high frequency observa-ons combined with inverse modelling have proved to be a powerful and important tool for the quan-fica-on of emissions and the verifica-on of bo=omup inventories for many radia-vely ac-ve trace gases; ! The es-mates provided by this analysis are relevant for constraining the atmospheric budget of these gases on a regional scale, also improving the accuracy of their emissions quan-fica-on on a global scale; ! The comparison of our es-mates with bo=omup inventories revealed not negligible discrepancies, thus showing the effec-veness of this approach as a verifica-on tool for declared emissions. 16th GEIA Conference Bridging Emissions Science and Policy, Boulder, 1011 June 2014 Topdown European emission es1mates of Fgases based on long term high frequency measurements and a Bayesian inversion method M. Maione, F. Graziosi, U. Giostra, J. Arduini, F. Furlani
Transcript of es1mates(of(Fgases(based(on(long ... ·...
! Top-‐down emission es-mates based on in situ long-‐term, high-‐frequency observa-ons combined with inverse modelling have proved to be a powerful and important tool for the quan-fica-on of emissions and the verifica-on of bo=om-‐up inventories for many radia-vely ac-ve trace gases;
! The es-mates provided by this analysis are relevant for constraining the
atmospheric budget of these gases on a regional scale, also improving the accuracy of their emissions quan-fica-on on a global scale;
! The comparison of our es-mates with bo=om-‐up inventories revealed
not negligible discrepancies, thus showing the effec-veness of this approach as a verifica-on tool for declared emissions.
16th GEIA Conference Bridging Emissions Science and Policy, Boulder, 10-‐11 June 2014
Top-‐down European emission es1mates of F-‐gases based on long-‐term high frequency measurements and a Bayesian inversion method
M. Maione, F. Graziosi, U. Giostra, J. Arduini, F. Furlani
Top-‐down European emission es1mates of F-‐gases based on long-‐term high frequency measurements and a Bayesian inversion method
Ny-Alesund
(Svalbard)
Monte Cimone
(Italy)
Ragged Point
(Barbados)
Hateruma
(Japan) Cape Matatula
(American Samoa)
Cape Grim
(Tasmania)
Mace Head
(Ireland)
Trinidad Head
(California)
Jungfraujoch
(Switzerland)
AGAGE measurement stations
collaborative measurement stations
Gosan
(Korea)
Shangdianzi
(China)
Ny-Alesund
(Svalbard)
Monte Cimone
(Italy)
Ragged Point
(Barbados)
Hateruma
(Japan) Cape Matatula
(American Samoa)
Cape Grim
(Tasmania)
Mace Head
(Ireland)
Trinidad Head
(California)
Jungfraujoch
(Switzerland)
AGAGE measurement stations
collaborative measurement stations
Gosan
(Korea)
Shangdianzi
(China)
Long term high frequency measurements of selected Kyoto gases: Red: baseline " trends, lifetime, global emissions Black: pollution events" regional emissions
Mt. CIMONE (CMN) Global GAW-‐WMO sta1on " 40 halocarbons + 15 anthropogenic VOCs measured every 2 hours by GC-‐MS, SIO calibra1on scale, AGAGE QA/QC
Top-‐down European emission es1mates of F-‐gases based on long-‐term high frequency measurements and a Bayesian inversion method
• SRR (Source Receptor Rela-onship) obtained from FLEXPART 20 d backward calcula-ons averaged over two years (2008-‐2009)
• ECMWF nested data 0.25° x 0.25° resolu-on
• 40.000 par-cles released every 3 h • The FLEXPART output can be ingested directly by the inversion algorithm based on the analy-cal inversion method by Stohl et al. (2009)
• The FLEXPART output is ingested by the inversion algorithm • An a posteriori emission distribu-on leading to the best fit between the measurements and the model results is found
• Mul-plying the SRR with an emission flux taken by an appropriate a priori emission field gives the simulated mixing ra-o at the receptors to be compared with the measurements
• For es-ma-ng HFC-‐152 a emissions, we used as a priori EDGAR v42_HCFC-‐152a_2008_IPCC_2_3.txt spa-al resolu-on 0.1 x 0.1°lat long
• Emissions then grouped in cells 0.5x0.5° lat long. Emissions intensity and distribu-on are given
JFJ MHD CMN
Time series of HFC-‐154a at CMN, JFJ and MHD obtained with the standard inversion setup using the EDGAR a priori. A priori (green line), a posteriori (red line) results, and observa-ons (grey line).
Top-‐down European emission es1mates of F-‐gases based on long-‐term high frequency measurements and a Bayesian inversion method
Gg EDGAR EU UNFCCC INV err inv (+ -‐) EU 6.39 2.44 5.00 0.375Italy 0.78 0 0.88 0.12France 1.01 0.02 0.58 0.01Germany 1.20 0.38 0.47 0.01UK 0.77 0 0.19 0.04Ireland 0.08 0 0.02 0.00Spain 0.08 0.08 0.36 0.04
HFC-‐152a emissions from the European Geographic Domain (EGD) in 2008. Leb, a priori; right, a posteriori. EGD = AL, AT, BE, BG, BiH, BY, CH, CZ, DE, DK, EE, EL, ES, FI, FR, HR, HU, IE, IT, LT, LU, LV, MD, ME, NL, NO, PL, PT, RO, SE, SI, SL, UK,
Comparison among the inversion results, the EDGAR inventory and data submi=ed to UNFCCC for 2008
Top-‐down European emission es1mates of F-‐ gases based on long-‐term high frequency measurements and a Bayesian inversion method
2002 to 2009 HFC-‐152a emissions from the European Geographic Domain es-mated by the inversion (green bars), compared with the EDGAR inventory, same domain (purple bars) 2002-‐2003 HFC-‐152a emissions from a EU15 Domain es-mated by the inversion (blue bars), compared with NAME emission es-mates (Greally et al,2007) , same domain (red bars)
