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Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportATMOSPHERICMONITORINGANDINVERSE MODELLINGFORVERIFICATIONOFNATIONAL AND EUBOTTOM-UPGHG INVENTORIESreport of the workshop“Atmospheric monitoring and inverse modelling forverification of national and EU bottom-up GHGinventories”under the mandate of Climate Change CommitteeWorking Group ICasa Don Guanella, Ispra, Italy (08-09 March 2007)Editor: P. Bergamaschi2007EUR 22893 EN


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportATMOSPHERICMONITORINGANDINVERSE MODELLINGFORVERIFICATIONOFNATIONAL AND EUBOTTOM-UPGHG INVENTORIESreport of the workshop“Atmospheric monitoring and inverse modelling forverification of national and EU bottom-up GHGinventories”under the mandate of Climate Change CommitteeWorking Group ICasa Don Guanella, Ispra, Italy (08-09 March 2007)Editor: P. Bergamaschi2007EUR 22893 EN


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportThe mission of the Institute for Environment and Sustainability is to provide scientifictechnicalsupport to the European Union’s Policies for the protection and sustainabledevelopment of the European and global environment.European CommissionJoint Research CentreInstitute for Environment and SustainabilityContact informationPeter BergamaschiEuropean CommissionJoint Research CentreInstitute for Environment and SustainabilityClimate Change UnitTP 290I-21020 Ispra (Va)Tel. +39 0332 789621peter.bergamaschi@jrc.itDocument also available on the JRC/IES/CCU world wide web site at:http://ccu.jrc.it/http://ies.jrc.ec.europa.euhttp://www.jrc.ec.europa.euLegal NoticeNeither the European Commission nor any person acting on behalf of the Commission isresponsible for the use which might be made of this publication.A great deal of additional information on the European Union is available on the Internet.It can be accessed through the Europa serverhttp://europa.eu/JRC 38074EUR 22893 ENISBN 978-92-79-06621-4ISSN 1018-5593Luxembourg: Office for Official Publications of the European Communities© European Communities, 2007Reproduction is authorised provided the source is acknowledgedPrinted in Italy


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportCONTENTS1 SUMMARY AND CONCLUSIONS......................................................................................... 31 SUMMARY AND CONCLUSIONS......................................................................................................... 32 EU EU PROJECTS......................................................................................................................................... 10CarboEurope-IP - Assessment of the European Terrestrial Carbon Balance (C. Rödenbeck et al.)......... 11CarboEurope-IP - Assessment of the European Terrestrial Carbon Balance (C. Rödenbeck et al.) ......... 11CHIOTTO - Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gasesCHIOTTO - Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gases (A.(A. Vermeulen et al.)................................................................................................................. 16Vermeulen et al.) ........................................................................................................................................ 16IMECC - Infrastructure for Measurement of the European Carbon Cycle (P. Rayner)........................... 21IMECC - Infrastructure for Measurement of the European Carbon Cycle (P. Rayner)............................. 21GEMS-IP - Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in-situ dataGEMS-IP - Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in-situ(P. Rayner)............................................................................................................................... 24data (P. Rayner) ......................................................................................................................................... 24GEOMON-IP - Global Earth Observation and Monitoring (P. Rayner)................................................. 27GEOMON-IP - Global Earth Observation and Monitoring (P. Rayner) ................................................... 27NitroEurope-IPNitroEurope-IP- The- ThenitrogennitrogencyclecycleandanditsitsinfluenceinfluenceononthetheEuropeanEuropeangreenhousegreenhousegasgasbalancebalance (P.(P. Bergamaschi et al.)............................................................................................................... 30Bergamaschi et al.)..................................................................................................................................... 30HYMNHYMN- HYdrogen,- HYdrogen,MethaneMethaneandandNitrousNitrousoxide:oxide:TrendTrendvariability,variability,budgetsbudgetsand interactionsand interactionswithwiththe biosphere (P. van Velthoven and P. Bousquet)......................................................................... 33the biosphere (P. van Velthoven and P. Bousquet)..................................................................................... 33SOGESOGE- System- SystemforforObservationObservationofofHalogenatedHalogenatedGreenhouseGreenhouseGasesGasesininEuropeEurope(S.(S.Reimann)................Reimann) .................. 3535Geoland Geoland (J.-C. (J.-C. Calvet)................................................................................................................ ............................................................................................................................... 383 INVERSE INVERSE MODELLING MODELLING STUDIES........................................................................................ ...................................................................................................... 41 41Baseline trends trends and and top-down estimates of of UK UK and and NW NW European GHG GHG emissions (A. (A. Manning)........... 42Methane flux flux estimates for for Europe using tall tall tower observations and the COMET inverse model(A.(A. Vermeulen and and G. G. Pieterse)........................................................................................................................ 48New New TM5-4DVAR inverse inverse modelling modelling system system to estimate to estimate global global and European and European CH 4sources CH 4 sources (P.(P. Bergamaschi et et al.)..................................................................................................................................... 54Inverse modelling activities at at LSCE: from from global to to regional scales scales (P (P . . Bousquet)................................ 60Top-down Methods in the in the Presence of of Partial Carbon Accounting (P. (P. Rayner)..................................... ....................................... 67Data Data assimilation of of atmospheric CO CO 2: CarbonTracker 2 : (W. (W. Peters)......................................................... 69EU-LEVEL REPORTING ON SOURES AND SINKS TO UNFCCC AND BOTTOM-UP4 EU-LEVELINVENTORIESREPORTING........................................................................................................................................ON SOURES AND SINKS TO UNFCCC AND BOTTOM-UP75INVENTORIES...................................................................................................................... 75EU EU reporting on on sources sources and and sinks sinks (E. (E. Kitou) Kitou)............................................................................................ 76European greenhouse gas gas emissions (F. (F. Dejean)........................................................................... ...................................................................................... 81EDGAR EDGAR and and UNFCCC UNFCCC greenhouse greenhouse gas datasets: gas datasets: comparisons comparisons as indicator as indicator of accuracy of accuracy (J. Olivier(J. and Olivier J. van and Aardenne).................................................................................................................................. J. van 87Agriculture, Forestry Forestry and and Other Other Land Land Uses Uses (AFOLU): (AFOLU): Realities Realities and needs and needs for Kyoto for Kyoto reporting reporting (G.(G. Seufert).............................................................................................................................. ....................................................................................................................................................... 915 EUROPEAN AND INTERNATIONAL GHG MONITORING PROGRAMS .................................. 965 EUROPEAN AND INTERNATIONAL GHG MONITORING PROGRAMS.............................. 96The The AGAGE AGAGE network network for ground for ground based measurements based measurements of non-CO of non-CO 2 GHGs: Monitoring of2GHGs: Monitoring of atmosphericconcentrations atmospheric concentrations and emission estimates and emission (D. Cunnold)..................................................................... estimates (D. 97The The WMO WMO GAW GAW Global Global GHG GHG Programme Programme (L. Barrie)........................................................................ (L. ............................................................................ 102RAMCES RAMCES - The - The French French Network Network of Atmospheric of Atmospheric Greenhouse Greenhouse Gas Gas Monitoring Monitoring (M. (M. Schmidt Schmidt et al.)........... et al.) ........ 108 108MeasurementsMeasurementsofofgreenhousegreenhousegasesgasesatatthetheMediterraneanMediterraneanislandislandofofLampedusaLampedusa(A.(A.didiSarraSarraetetal.)......al.)....... 112112Long-Term Monitoring of Greenhouse Gases at Jungfraujoch (S. Reimann) .......................................... 116Long-Term Monitoring of Greenhouse Gases at Jungfraujoch (S. Reimann)........................................ 116ICOS - Integrated Carbon Observation System (P. Ciais et al.) .............................................................. 120ICOS - Integrated Carbon Observation System (P. Ciais et al.)........................................................ 120GMES and the GMES Atmospheric Service (V. Puzzolo and J. Wilson).................................................. 127GMES and the GMES Atmospheric Service (V. Puzzolo and J. Wilson)............................................... 1276 POSTER PRESENTATIONS ............................................................................................................... 1316 POSTER Greenhouse PRESENTATIONS.................................................................................................. Gas observations within the monitoring network of the German Federal 131Greenhouse Environmental Gas observations Agency (Umweltbundesamt) within the monitoring (F. Meinhardt network et of al.)........................................................... the German Federal Environmental 132AgencySet-up(Umweltbundesamt)of a continuous greenhouse(F. Meinhardtgasetmonitoringal.)...........................................................................station for CO 2 , CH 4 , N 2 O, SF 6 , and CO in132Set-up Northern of a continuous Italy (B. Scheeren greenhouse et al.)............................................................................................................ gas monitoring station for CO 2, CH 4, N 2O, SF 6, and CO in 136Northern4D-VARItalySystem(B. Scheerenfor Inverseet al.)..............................................................................................Modeling of Atmospheric CH 4 : Sensitivity Analyses using Synthetic1364D-VAR Observations System (M.G. for Inverse Villani Modeling et al.) ............................................................................................................ of Atmospheric CH 4: Sensitivity Analyses using Synthetic 142Observations (M.G. Villani et al.)................................................................................................. 1427 ANNEX1: WORKSHOP AGENDA ..................................................................................................... 14778 ANNEX1: ANNEX2: WORKSHOP WORKSHOP AGENDA.......................................................................................... PARTICIPANTS......................................................................................... 150 1478 ANNEX2: WORKSHOP PARTICIPANTS.............................................................................. 1501


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report1 SUMMARY AND CONCLUSIONSPeter Bergamaschi 1 , Erasmia Kitou 2 , Francois Dejean 3 , and Frank Raes 1(reviewed by all workshop participants)[1] European Commission Joint Research Centre, Institute for Environment andSustainability, Ispra, Italy[2] European Commission, DG Environment, Brussels, Belgium[3] European Environment Agency, Climate change and energy, Copenhagen, DenmarkThe workshop "Atmospheric monitoring and inverse modelling for verification ofnational and EU bottom-up GHG inventories" was held on 08-09 March 2007 in Ispra,Italy, under the mandate of European Climate Change Committee Working Group 1,as follow-up of a first workshop on 23-24 October 2003.Atmospheric monitoring (AM) of greenhouse gases (GHGs), combined with inversemodelling (IM), can trace back observed atmospheric concentrations of GHGs totheir origin, i.e. to the regions where they have been emitted into the atmosphere,and provide top-down estimates of the GHG emissions. This is particularly relevantin the context of the reporting of national GHG emissions to UNFCCC (which arebased on bottom-up inventories).The objective of the workshop was to bring together scientists working onatmospheric monitoring and inverse modelling and EU national experts responsiblefor official reporting of national GHG inventories to UNFCCC, in order to: assess current state of the art of AM/IM and progress since the first workshop assess the usefulness of using AM/IM for checking consistency or verifyingbottom-up GHG inventories identify further research and infrastructure needs for the application of AM/IM inthe context of national GHG inventoriesMajor developments since first workshop AM/IM studies have made substantial progress over the last years, due to theavailability of new observational data and due to further development of inversemodels. A number of new inverse modelling studies have been presented at Europeanscale and for a limited number of European countries. AM/IM studies have been further extended to new F-gases (e.g. HFC-365mfc,HFC-245fa). New inverse modelling systems now allow the estimate of emissions fromindividual model grid cells, hence better resolving the true footprints (regions ofinfluence) of individual measurement sites.3


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report Substantial efforts have been made during the last years to setup furtherEuropean monitoring stations, including 8 tall towers (within the CHIOTTOproject), most of which are now operational. However, the European monitoringprograms are still very heterogeneous. They are largely funded within differentEuropean research projects, complemented also by some national andinternational activities. This caused (and continues to cause) major data gaps. An important step towards an operational European GHG monitoring program hasnow been made with the ICOS ("Integrated Carbon Observation System")proposal. This project aims to setup an integrated European GHG monitoringnetwork.Applicability of inverse modelling for different GHGsCO 2Bottom-up estimates of fossil CO 2 emissions are assumed to be relatively accurate.They are usually used as input data in inverse modelling studies for CO 2 aiming atquantifying the net CO 2 fluxes from the terrestrial biosphere. Critical for these IMresults, however, are also the assumed spatio-temporal variations of fossil fuelemissions (for which uncertainties are larger than for national totals, and which arebased on scientific bottom-up inventories), as demonstrated in a recentCarboEurope-IP study.The reporting of LULUCF sources and sinks under UNFCCC is considered completefor those countries that don’t have significant areas of unmanaged forests. Howeverwith regard to the theoretical requirements, the actual reporting in inventories is notyet complete in practice. The reporting of LULUCF activities under the Kyoto Protocolonly covers some carbon stock changes ("partial carbon accounting"). Thus, AM/IMcan be used as check for UNFCCC GHG inventories, but not for the information onLULUCF activities under the Kyoto Protocol.IM/AM of CO 2 , however, is generally of great importance, since the carbon cycle isvery sensitive to climate. A notable example is a significant CO 2 flux anomaly overWestern Europe related to the heatwave 2003, detected consistently by 2independent inverse models (CarboEurope-IP).CH 4CH 4 is an attractive application for inverse modelling, in particular becauseuncertainties of bottom-up inventories remain considerable (especially in certainsectors as e.g. landfills, agriculture, and energy related fugitive emissions).Uncertainties of top-down estimates of national total emissions from various studiesare estimated in the order of 30-100%, but are likely to become more accurate in thefuture with improvements of inverse models and if the number of monitoring stationsis increased. On the European scale, CH 4 emissions are dominated byanthropogenic emissions (estimated natural emissions EU-15: ~5-20 %), but thereare significant differences in the relative contribution of natural CH 4 sources amongdifferent countries. Top-down estimates for European (EU-15) total CH 4 emissionsfrom various studies (including some updates presented at the workshop) showbroad consistency with values reported to UNFCCC when taking into account thecorrections for the natural sources (using scientific bottom-up estimates). Differencesfor emission estimates from individual countries (differences among top-down4


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportestimates, and between top-down and bottom-up) are still larger. Better quantificationof the uncertainties of both bottom-up and top-down emission estimates is crucial forbetter comparisons.N 2 ORelatively high uncertainties of N 2 O bottom-up inventories make N 2 O a potentiallygood candidate for inverse modelling. N 2 O top-down estimates based on a singlestation (Mace Head, Ireland) appear very promising and show broad consistency withUNFCCC estimates (within the relatively large uncertainties of both bottom-up andtop-down estimates). However, relatively small spatio-temporal gradients ofatmospheric concentrations require very careful inter-calibration of differentmonitoring stations. Very important for N 2 O is also better quantification of natural N 2 Osources and of anthropogenic, indirect N 2 O sources.HFCs, PFCs, SF 6Comparisons between top-down and bottom-up approaches for emissions offluorinated gases are most promising, because emissions of these gases are almostentirely due to anthropogenic activities. Hence, top-down estimates can be directlycompared with values reported to UNFCCC. Several studies (including updatespresented at the workshop) clearly demonstrate the usefulness of AM/IM to indicatepotential gaps in the reporting. For example, recent IM studies attribute significantHFC-152a emissions to two European countries, for which no emissions werereported to UNFCCC. Furthermore, atmospheric monitoring and inverse modelling isparticularly suited to address some F-gases which are currently not covered by theofficial bottom-up inventories (e.g. HFC-365mfc). Finally, AM/IM may provideadditional knowledge on the timing of larger releases of F-gases related to industrialprocesses.European GHG monitoring programsThe availability of spatially and temporally comprehensive atmosphericmeasurements is crucial for inverse modelling. Efforts have been made during thelast years to setup further monitoring stations (including 8 tall towers in Europe withinthe CHIOTTO project) and to better intercalibrate the different measurements.However, the European monitoring programs are still very heterogeneous (largelyfunded within different European research projects, but complemented also by somenational and international activities). This caused (and continues to cause) majordata gaps in space and time. Unfortunately, some GHG monitoring stations werediscontinued (e.g. German UBA stations Deusselbach and Zingst). In particular highaccuracyquasi-continuous surface in-situ measurements provide significantconstraints on regional emissions and should be further extended.An important step towards an operational European GHG monitoring program hasnow been made with the ICOS ("Integrated Carbon Observation System") proposal.This project aims to setup an integrated European GHG monitoring network,including existing monitoring stations and further extending the network. Such anintegrated European monitoring program appears central to ensure the availability oflong-term high-accuracy atmospheric GHG measurements. With the envisaged ~30operational 'backbone' European atmospheric monitoring sites the ICOS networkwould provide a solid basis for top-down estimates of European GHG emissions.5


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportDuring the preparatory phase of ICOS (2008-2011), the legal design and fundingstructure are to be decided. If successful, ICOS could start its operational phase in2012. Even for the positive scenario that ICOS will be setup, the availability ofatmospheric monitoring data until the operational phase of ICOS starts, i.e. for theperiod 2007-2012, relies on the support of existing and additional monitoring sites. Inparticular the monitoring of non-CO 2 GHGs, which seem to be the gases with thelargest potential for comparison between top-down and bottom-up inventories, iscurrently not satisfactorily covered by European monitoring programs.In addition to an appropriate European network, it would be important to supportfurther global monitoring stations – in particular in regions with large emissions ortrends (e.g. China, India) – and satellite retrievals – in particular for CO 2 and CH 4 , forwhich recent research demonstrated considerable progress and the potential toprovide valuable complementary information in regions with scarce in situ monitoring(e.g. tropical land masses).Inverse modelling systemsSince the previous workshop, considerable progress has been made with thedevelopment of inverse modelling systems. New systems (e.g. "4DVAR", or"Ensemble Kalman Filter") now allow the estimate of emissions from individual modelgrid cells. Thus, they better resolve the true footprints (regions of influence) ofindividual measurement sites, minimizing the so-called aggregation error.First inverse model intercomparisons have been performed for CO 2 (CarboEurope-IP) demonstrating the robustness of the top-down estimates for larger Europeanregions. Detailed model intercomparisons for European CH 4 and N 2 O inversions areplanned within NitroEurope-IP (2008-2010). These and further intercomparisons alsofor inversions of other GHGs are still needed in order to: Better quantify model errors (in particular transport errors and representativenesserrors). Ideally, top-down estimates should be generally based on severalindependent models (Ensemble inversions). Better quantify the influence of using a priori bottom-up emission inventories.Strictly speaking, only inverse modelling approaches which do not include any apriori information may be considered as truly 'independent'. On the other hand,some apparent and unquestionable a priori information helps to avoid 'unrealistic'model solutions (in particular when scarce measurements do not constrain theemissions sufficiently).Further development of atmospheric transport models (e.g. simulation of verticaltransport) and further increase of the resolution of the models would allow to bettersimulate atmospheric monitoring stations and to better resolve national boundaries.Furthermore, high-resolution mesoscale models would be useful for comparison withemissions on regional (sub-national) scales.In addition, independent validation of atmospheric models remains important (e.g.using tracers as 222 Rn and meteorological data, such as e.g. boundary layer height).6


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportBottom-up inventoriesMany IM approaches use bottom-up inventories as a priori information. Thesebottom-up inventories have to be provided to the models at the resolution of themodel grid cells. This information is not provided by the official UNFCCC inventories,but by scientific inventories such as EDGAR. Better spatial and temporaldisaggregation of such scientific bottom-up inventories would help to further improveatmospheric simulations significantly.Furthermore, also uncertainty estimates of bottom-up emissions are usually set as'boundary conditions' for the inverse modelling systems. Better estimates of theseuncertainties remain very important. While UNFCCC inventories should provideuncertainty estimates of national totals, scientific inventories should provide alsospatially and temporally disaggregated uncertainties. In this context also theinformation about (spatial and temporal) correlation of uncertainties is very important.Furthermore, scientific bottom-up inventories should provide better estimates ofnatural sources, in particular for CH 4 and N 2 O. Natural sources should have highpriority also in view of potential large feedbacks to climate change.Conclusions: Usefulness of atmospheric monitoring and inverse modelling AM/IM is most useful for those gases, for which uncertainties from bottom-upinventories are large, or for which UNFCCC data are incomplete, e.g. CH 4 , N 2 O,and fluorinated gases. Several studies based on high-resolution inverse atmospheric transport modelsdemonstrated that top-down emission estimates can be provided on variousspatial scales relevant for comparison with UNFCCC inventories, e.g. nationalscale (for larger European countries) and European scale (EU-15, and potentiallyEU-25, and EU-27). However, the number of countries for which top-downestimates are currently available at national level remains limited, mainly due tolimited observational data. Some IM approaches avoid the use of bottom-up inventories, aiming atindependent top-down emission estimates (but may include other implicit a prioriassumptions). Other IM methods use bottom-up inventories as a priori information,and should rather be considered as tools to check the consistency betweenatmospheric concentrations and bottom-up inventories, to narrow down theuncertainties and identify potential deficiencies of the bottom-up inventories. The comparison between top-down approaches (which estimate total emissions)with GHG emissions reported to UNFCCC (which cover only anthropogenicemissions) requires reliable estimates of natural GHG emissions. For severalGHGs, the natural emissions are relatively small or negligible (e.g. fluorinatedgases). AM/IM can assess reliability of emission estimates from countries for which officialbottom-up inventory data are lacking or incomplete, but for which emissions and7


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reporttrends are important for the global GHG budgets (e.g. China, India), and whichare, therefore, also of high political interest. Apart from the direct use for comparison with reported anthropogenic GHGemissions, the global and regional AM/IM is very important to quantify totalemissions into the atmosphere, including natural emissions of the major GHGsCO 2 , CH 4 and N 2 O, which may considerably increase in the future as aconsequence of climate change. The accuracy of the top-down emission estimates increases with the number ofavailable atmospheric measurements. Crucial is long-term continuity and highquality of measurements (including thorough intercalibration). A set of around 30monitoring stations across Europe, as envisaged by the ICOS proposal for anintegrated operational European network, should provide a sound basis for IM onthe European scale.Recommendations: Further steps towards enhanced use of atmosphericmonitoring and inverse modelling for verification of national and EU bottom-upGHG inventoriesIn order to develop the potential of using inverse modelling results for comparisonswith bottom-up inventories, AM/IM research projects should: Provide more detailed inverse model intercomparisons and apply different,independent models (Ensemble inversions), in order to provide more realisticuncertainty estimates. This will be addressed to some extent e.g. for CH 4 and N 2 Owithin NitroEurope-IP (2008-2010), but should be further investigated also for theother GHGs. Further identify the gases and countries for which AM/IM could provide estimateswith similar or lower uncertainty level than currently achieved in bottom-upinventories. Provide more detailed comparisons between top-down estimates and bottom-upinventory data. Comparisons in the most promising areas should be emphasised,in particular for emissions of non-CO 2 GHGs. Further investigate the possible use of additional tracers or isotopes for sectorspecifictop-down estimates. E.g. measurements of atmospheric 14 CO 2 allowindependent estimates of the fraction of fossil CO 2 emissions and may becomevery useful to check the consistency of reported fossil CO 2 emissions.In order to better use inverse modelling for improving the reliability andcomprehensiveness of bottom-up inventories submitted to the UNFCCC(independent verification and consistency checks), European Member States and theEuropean Commission should:8


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report As far as possible, secure the long-term availability of spatially and temporallycomprehensive atmospheric measurements to be used in inverse modellingsystems, by:o Supporting research projects and projects under GMES-AS, in order to ensurethe continuation of the European atmospheric monitoring until the operationalphase of ICOS could start, i.e. for the period 2007-2012.o Supporting the ICOS project which is the central prerequisite for the long-termhigh-accuracy monitoring of GHGs on the European scale. ICOS should coverall relevant GHGs. Support the further development of atmospheric monitoring and inverse modellingof GHGs, in particular non-CO 2 GHGs, which seem to be the gases with thelargest potential for comparison with bottom-up inventories. Provide further support to the set-up of operational inverse modelling facilities (e.g.pre-operational inverse modelling systems under GMES-AS). Improve the uncertainty estimates of official bottom-up inventories. Maintain and support the communication process between inventory compilersand AM/IM scientists to continuously assess the usefulness of AM/IM forimproving greenhouse gas inventories.List of AcronymsCarboEurope-IP"Assessment of the European Terrestrial Carbon Balance", EU research project (FP6)http://www.carboeurope.org/CHIOTTO"Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gases", EU research project(FP5)http://www.chiotto.org/EDGAR"Emission Database for Global Atmospheric Research"http://www.mnp.nl/edgar/GMES-AS"Global Monitoring for Environment and Security - Atmosphere Service"http://www.gmes.info/ICOS"Integrated Carbon Observation System", proposal for integrated, operational GHG monitoringsystem"http://ftp.bgc-jena.mpg.de/pub/outgoing/afreib/ICOS/ICOS_Flyer1.pdfNitroEurope-IP"The nitrogen cycle and its influence on the European greenhouse gas balance", EU research project(FP6)http://www.nitroeurope.eu/9


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report2 EU projects10


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportCarboEurope-IP - Assessment of the European Terrestrial CarbonBalanceChristian Rödenbeck 1 , Martin Heimann 1 , and Philipp Ciais 2[1] Max Planck Institute for Biogeochemistry, Jena, Germany[2] Laboratoire des Sciences du Climat et de l’Environnement LSCE/IPSL,CEA/CNRS/UVSQ, Gif-sur-Yvette, FranceThe aim of CarboEurope-IP is to estimate the European carbon balance in the recentpast and present. It will provide a scientifically sound, independent verification ofnational and European CO 2 sources and sinks over a five-years period (2004-2008)as a template for the First Commitment period and give scientific advice how to dealwith sinks in the Second Commitment Period. The strength of CarboEurope-IP isprimarily in the comprehensive experimental strategy that allows by data analysis theacross-scale validation and verification through the multiple constraint approach.Dissemination of results via publications, demonstration and training activities andadvise to policy makers and activities synthesise project results in order to supportthe implementation of observing and monitoring schemes related to the UNFCCCand the Kyoto Protocol. Training activities will increase the skills of youngresearchers for international, interdisciplinary research about the Carbon Cycle.CarboEurope-IP is organised along four "Components":Figure 1: Components and their interactions in CarboEurope-IPComponent 1: EcosystemsThe Ecosystem Component quantifies the carbon fluxes of the variety of land coverand uses of the European continent, thus providing input into the spatial scaling and11


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportbottom up modelling efforts. The flux data are verified and supported by ecosystemlevel data of carbon stock changes in biomass and soil, which also allow estimates ofthe permanence of sinks. The gathered data will also provide the basis forparameterisation of models for up-scaling of carbon fluxes to the regional andcontinental scale as well as data synthesis and modelling of effects of driving forceson the Carbon Cycle such as land management, disturbance by harvest, etc. whichare not yet included in larger scale biogeochemical models.Component 2: AtmosphereIn the Atmosphere Component, the atmospheric network spatial coverage isextended over Southern and Western Europe by adding new continuous monitoringstations, increasing the frequency of vertical profiles sampling through the PlanetaryBoundary Layer and aloft, and finally to optimise the atmospheric data selection,using in situ meteorological data and other tracers such as 222 Rn, to extract fromcontinuous CO 2 time series representative measurements of regional sources andsinks activityThe measurements at continental scale provide the boundary conditions for both theregional experiment and the integration efforts. Novel in the strategy is theincorporation of CO 2 concentration measurements at the flux tower sites tocomplement the atmospheric monitoring at free tropospheric sites, tall towers, andaircraft. This provides a strong link between the ecological and continental scaleobservations.Component 3: Regional ExperimentThe Regional Experiment Component provides a direct link between the ecology andcontinental scale measurements and models. Continental scale models provide theboundary conditions for the regional carbon balance. Upscaling of the flux towers isperformed with forward meso-scale models and calibrated biogeochemical modelsfor the long term (20 yrs). At the regional scale inverse model techniques are usedsimilar to those developed at the continental scale, thus establishing amethodological link with the larger scale inverse modelling estimates. The regionalexperiment will test and provide aggregation algorithms that will be used in theupscaling efforts in the Continental Integration Component.Component 4: Continental IntegrationThe Continental Integration Component relies on the data streams collected by theother Components of the IP, including syntheses of existing data. Conversely, itprovides guidance on how to fill in gaps in the current Observing System and helpdesign optimal observation strategies in the future. This integration is achieved bymeans of a numerical modelling framework that bridges across scales going fromprocess-studies up to the continental budget. In this framework, diverse approachesof top-down, bottom-up, sectorial, process based and extrapolation techniques have12


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportto be employed, compared for consistency and ultimately merged in a mostcomprehensive way (Fig 2).Figure 2: Data streams and modelling systems in CarboEurope-IP (Integration component)The specific objectives are to develop, test, and apply advanced modelling tools forestimating the spatially explicit continental carbon balance and its variability at aresolution of 10 to 50 km for at least the length of a Commitment Period byImplementation of nested atmospheric model hierarchy over the Europeandomain for the top-down determination of surface sources and sinks by means ofinverse methods.Implementation of bottom-up models for the extrapolation and upscaling ofsurface flux measurements and carbon inventories.Acquisition of remote sensing products for upscalingDevelopment and implementation of a carbon data assimilation system for theEuropean domain.As a test case, the summer period of 2003 is used. During this period, extremedrought and heat wave conditions over wide parts of Europe lead to strongresponses in the European carbon cycle, allowing to unravel mechanisms in theEuropean carbon balance. As an example, Figs 3 and 4 show the responses ofecosystem models and top-down inverse flux estimates to the 2003 extreme event.13


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 3: Growing season NEP anomaly in 2003, simulated by 4 prognostic and 3 diagnosticterrestrial ecosystem models [Vetter et al., in prep.]. The response to the 2003 anomalousclimate conditions are seen as reduced NEP in wide parts of Western Europe.14


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomuupGHG GHG inventories " -" - reportFigure 4: 4: Summer (JJA) anomalies of of the CO 22 flux in 5 parts of Europe as estimated by thetop-down inversion of of atmospheric data. The figure compares independent estimates by theCarboEurope-IP partners LSCE (Paris) and MPI-BGC (Jena). Strongly increased surfaceatmosphereflux flux in in `Western Europe' in in 2003 reflects the carbon cycle response as detectedby by the the atmospheric observations.15


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportCHIOTTO - Continuous HIgh-precisiOn Tall Tower Observations ofgreenhouse gasesAlex T. Vermeulen 1 , Gerben Pieterse 1 , Andrew Manning 2 , Martina Schmidt 3 , LaszloHaszpra 4 , Elena Popa 4 , Rona Thompson 5 , John Moncrieff 6 , Anders Lindroth 7 , PaoloStefani 8 , Josep Morguí 9 , Eddy Moors 10 , Rolf Neubert 11 , Manuel Gloor 12[1] Netherlands Energy Research Foundation (ECN), Petten, Netherlands[2] MPI BGC, Jena, Germany; now at: University of East Anglia[3] LSCE, Gif sur Yvette, France[4] HMS, Budapest, Hungaria[5] MPI BGC, Jena, Germany[6] Univ. Edinburgh, UK[7] Univ. Lund, Sweden[8] Univ. Tuscia, Viterbo, Italy[9] Univ. Barcelona, Spain[10] Alterra, Wageningen, Netherlands[11] Univ. Groningen, Netherlands[12] MPI BGC, Jena, Germany; now at: Univ. Leeds, UK1. Objectives of CHIOTTOThe CHIOTTO project is an EU FP5 funded collaborative project that ran fromNovember 2002 to May 2006. The CHIOTTO project objective is to build an improvedinfrastructure for the continuous monitoring of the concentrations of greenhousegases on the European continent above the surface layer using tall towers. Theproject is based on and extended existing research projects (AEROCARB, TCOSSiberia and TACOS). This project formed an important step towards a fullyoperational continuous observing system in the framework of the Kyoto Protocol forthe sources and sinks of the most important greenhouse gases (CO 2 , CH 4 , N 2 O, CO,SF 6 ) over Europe.An important aspect of the objective is the establishment of high quality calibrationsfor the existing and new atmospheric measurement stations, and the implementationof a near-online data-transmission system for tall tower measurements.An important target of the project is to make the measurements intercomparablebetween the institutes operating the air sampling networks. Quality controlledatmospheric concentration, CO 2 flux and additional meteorological data are archivedin a data centre accessible to the scientific community through the World Wide Web.In CHIOTTO we have integrated existing flux towers in the vicinity of the tall towerswith the atmospheric stations networks in a synergetic approach enabling the talltowers to become atmospheric monitoring sites for use in transport models.In the course of the project we have implemented all new and existing measurementsystems. We have worked on establishing the precision targets for themeasurements and implemented a calibration and intercomparison protocol toachieve those targets for the individual towers and between towers.In Table 1 an overview is given of the tall towers in the CHIOTTO project, theirpositions, the parameters measured and the operators. In Figure 1 their positions can16


“Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop Workshop "Atmospheric monitoring and inverse modelling for for verification verification of national of national and EU and bottomupGHG inventories " -" -EU bottomupGHG reportbe be viewed on on the the map of Europe. In Table 22 the the Precision and and accuracy accuracy targets targets for forthe the CHIOTTO measurements as a function of of measured species can can be found. be found.Table Table 1: Tall 1: Tall tower data summaryHght Position Concentration measurement (levels) (levels) Flux meas Flux measName Name (m) (m) Lon Lat CO 2 CH 4 N 2 O SF 6 CO222 2 CH 4 N 2 O SF 6 CO222 Rn Rn Flasks Flasks CO 2 CO CH 24 Operator CH 4 OperatorCabauw Cabauw NL NL 200 200 04°56’ 51°58’ 44 4 4 4 4 4 4 4 4 1 1 2 2 ECN ECNGriffin Griffin UK UK 232 232 -2°59' -2°59' 56°33’ 11 1 1 1 1 1 1 1 1 UEDIN UEDINHegyhatsal Hegyhatsal H H 117 117 1616 o 39’ o 39’ 46 o 57'57' 44 1 1 1 1 1 1 1 1 2 2 ELTE ELTEOrleans/TrainouOrleans/TrainouFF131131 2°07’2°07’46°58’46°58’ 33333333331 1 LSCELSCENorunda S 102 17°28’ 60°05’Norunda S 102 17°28’ 60°05’4 2 2 2 LUPG4 2 2 LUPGFlorence I 245 11°16’ 43°49’Florence I 245 11°16’ 43°49’1 1 1 1 1 UNITUS1 1 1 1 1 UNITUSOchsenkopf D 163 11°49’ 50°03’ 3 3 3 3 MPIBGCOchsenkopf D 163 11°49’ 50°03’ 3 3 3 3 MPIBGCBialystok PL 300 22°45’ 52°15’ 5 5 5 5 5 MPIBGCBialystok PL 300 22°45’ 52°15’ 5 5 5 5 5 MPIBGCTable 2: Precision and accuracy targets for the CHIOTTO measurements as a function ofTable measured 2: Precision species and accuracy targets for the CHIOTTO measurements as a function ofmeasured species2. Scientific progress made in CHIOTTO2. Scientific progress made in CHIOTTOIn the 1st year of the CHIOTTO project we have defined the exact requirements forIn the 1st equipment year of to the be CHIOTTO used and we project have we defined have the defined measurement, the exact calibration requirements and forthedataequipmentsubmissionto beprotocols.used andThesewearehavethe foundationdefined theof themeasurement,project. On thecalibrationbasis ofandthis information the new equipment was purchased, customized and installed. In thedata submission protocols. These are the foundation of the project. On the basis of2nd year we continued to implement these choices of the 1st year, building togetherthis information the new equipment was purchased, customized and installed. In thethe instrumentation with all modifications required to meet our specific and high2nd year we continued to implement these choices of the 1st year, building togetherdemands regarding quality and materials. In the third and last year and the followingthe6instrumentationmonth extension,withmostallof themodificationstowers haverequiredbeen equippedto meetandourstartedspecificeitherandthehighdemandsinitial orregardingoperationalqualitymode.and materials. In the third and last year and the following6 month The CHIOTTO extension, concentration most of the data towers is now have being been used widely, equipped examples and started are the either FP6 theinitial CarboEurope-IP or operational and mode. NitroEurope-IP to derive estimates for the strengths of CO 2 andThe CHCHIOTTO 4 , N 2 O sources concentration and sinks data of Europe, is now in being combination used widely, with other examples measurements are the FP6CarboEurope-IP types and other and (global NitroEurope-IP and local) networks. to derive CHIOTTO estimates will for continue the strengths as an integral of CO 2 andCHpart 4 , Nof 2 O the sources atmospheric and sinks component of Europe, of the CarboEurope-IP, in combination that with has other officially measurementsstartedtypes in January and other 2004. (global and local) networks. CHIOTTO will continue as an integralpart of the atmospheric component of the CarboEurope-IP, that has officially startedin January 2004.1717


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 1: The influence function for the year 2002 of the 8 CHIOTTO tall towers derived bythe COMET trajectory modelGas purging systemAir inletsystemF11/2"P um ps1/2"C 11/2"C 51/2"C 21/2"C 61/2"C 31/2"C 71/2"C 41/2"C 8F131/4"V 1V 5V 2V 6V 3V 7V 4V 8M 1R1R5R2R6R3R7R4R8V915L/m in15L/m in15L/m in15L/m in15L/m in15L/m in15L/m in15L/m in600mL/minR9C 9slow purgePurge panelfast purge100m L/m in500m L/m inR 11R10P11V 25V 24V 22 V 23V 48 V49 V21Pressure control systemLEG EN DV alve (one w ay)V alve (needle)V alve (three w ay)V alve (four w ay)Pressure control systemC ryogenic trapR otam eterM ass flow controllerFilterP ressure sensorC om pressorFridge trapF21/4"F31/2"F41/2"F51/2"F61/2"F71/2"F81/2"F91/4"F101/2"F111/4"1/4"1/4"1/4"1/4"1/4"F14F15F16F17F18M 2M 3M 4M 5M 6P1P2P3P4P5P6V10V11V12V13V 14D rying systemT3-80°CT21°CC 10F21V 15T11m L/m in1°CP 8C 11F201m L/m inC 11400mL/minAir dryingsystemR 19T8-80°CP 9V46T4P 12V20 -80°CV 26 V28V27F 22V30V 29P14V31P13F23N itrogenC om pressorG eneratorV 32P 15R 12LICORV 34O XZILLA(CO2) (O 2)P 16R 13V33NitrogenpurifierJunAirZero AirC om pressorGenerator200m L/m inV 35CO 2 and O 2 analysersF121/4"F19M 7P7C 12T71°CT9-80°CFID 200°CBLUE BO X1/4" V401/4"V391/4"V381/4"V371/4"V36R18R17R16R15R14C 13V 45T61m L/m inP 23P 21 V 441°CP22V 50V42C 14F24P 201m L/m inP 19V43C14V 41PR E -C O LUM NsP 18P 17A ux 5V52M AIN C OLUM N sMETHANIZER360°CH2 GeneratorV 51V 53EC D385°CO ven74°CAux 4GC80°CV16V 17V18V19M 8V 47P 10T 5-80°CM 9Ar-CH4 (5%)V541 2100m L/m inAr-CH4 (5%)GC SYSTEMGasChromatograph(CO, CH 4 , SF 6 , N 2 O)Calibration gasesFigure 2: Example of one of the complex system designs for the high resolutionmeasurements system, here for the Bialystok tower. All devices, tubings and fittings aremission critical and have to comply to high standards concerning minimisation of leakage,inertness and durability.18


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportTable 3: Implementation dates for the start of observations in the framework of the CHIOTTOproject.Concentration measurement start dateName CO 2 CH 4 N 2 O SF 6 CO222 Rn FlasksCabauw NL (1992) (1993) (2000) Nov 04 Nov 04 Nov 05 Nov 06Nov 04 Nov 04 Nov 04Griffin UK Aug 05 Aug 05 Aug 05 Aug 05 Aug 05 Sep 03 -Hegyhatsal H 1993 Jan 06 Jan 06 Jan 06 Jan 06 - (NOAA)Orleans F Oct 06 Oct 06 Oct 06 Oct 06 Oct 06 Oct 06 Jan 07Norunda S (1998) - 2007* 2007* 2007* - -Jan 05Florence I Aug 05 Aug 05 Aug 05 Aug 05 - - -Ochsenkopf D (2000) Apr 06 Apr 06 Apr 06 Apr 06 - Jan 06Jan 06Bialystok PL Sep 05 Sep 05 Sep 05 Sep 05 Sep 05 - Sep 053. Scientific achievementsMain result of the project is that 7 out of 8 towers are now operational in the new ormodified setup. In the reporting period the towers of Hegyhatsal and Ochsenkopfbecame operational (again). The only tower not operational yet is theTrainou/Orleans tower, but here the negotiations with the tower owner have finallybeen settled. All equipment for this tower has been ready for some time now. Theequipment is not yet installed, only the inlet lines to the tower. The container withinstrumentation will be shipped to the tower and installed at latest in September2006.Partner MPI-BGC invested an enormous effort in providing all partners with the highprecision calibrated Working Standards 2005. Circulation of the importantintercalibration Traveling Standards has started at the end of 2005. The firstintercomparison exercises started in the reporting periods and the first results showreasonable performance, while there is still room for improvement.Unfortunately the Florence tower will have to be relocated as the tower will bedeconstructed due to airport security reasons. UNITUS has found a new suitablelocation in the Umbria region in Italy.The work in, and results of CHIOTTO thus far have been presented at several talksand posters during scientific meetings.4. ConclusionsThe CHIOTTO project succeeded in setting up the pre-operational tall towergreenhouse gas concentration observation network of European dimension, usinghigh quality equipment, measurement procedures, calibration and intercomparisonschemes. Delays due to logistical problems have however shortened the period in19


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportwhich real operational measurements have been acquired by the 8 Tall Towers. The6-month extension granted allowed us to create a reasonable amount of intercalibratedand high quality continous measurements series at almost all towers. Mostmeasurements will continue after the end of the project in the framework of theCarboEurope IP as far as can be foreseen now.The CHIOTTO Tower data will provide a wealth of information on GHG sources andsinks, where the tall towers allow to overcome some observational problems of thesurface based measurements:- Less sensitive to very near-field (less bias), more representative on model-scale- Better estimate of Boundary Layer average concentration: full transport flux- Continuous data- Multi-componentCurrent transport models to describe the fate of atmospheric tracers folllowingemission have been improved over the last years so we can start exploiting theinformation content of the tall tower surface observations.ReferencesHaszpra, L., Barcza, Z., Davis, K., Tarczay, K., Long term tall tower carbon dioxide fluxmonitoring over an area of mixed vegetation. Agricultural and Forest Meteorology, 132,58-77, 2005:Haszpra L., Barcza, Z., Tarczay, K., National report on the Hungarian CO 2 monitoring andresearch programs. In: 12th WMO/IAEA meeting of experts on carbon dioxideconcentration and related tracer measurement techniques, Toronto, Canada, 15-18September 2003 (eds.: Worthy, D., Huang, L.). WMO GAW Report No. 161., 154-158,2005.Haszpra L., Barcza, Z., CO 2 monitoring and research programs in Hungary. In: 13thWMO/IAEA meeting of experts on carbon dioxide concentration and related tracermeasurement techniques, Boulder, Colorado, U.S.A., 19-23 September 2005 (ed.: Miller,J.). WMO GAW Report (submitted), 2006.Pieterse G., A. Bleeker, A.T. Vermeulen, Y. Wu and J.W. Erisman, High resolution modelingof atmosphere-canopy exchange of acidifying and eutrophying components and carbondioxide for European forests. Tellus B, submitted, 2006.Vermeulen A.T., G. Pieterse, A. Hensen, W.C.M. van den Bulk, and J.W. Erisman, COMET:A Lagrangian transport model for greenhouse gas emission. Forward model techniqueand performance for methane. Atmos. Chem. Phys. Discuss., 6, 8727–8779, 2006.Vermeulen A.T., L. Haszpra, A. Lindroth, A. Manning, C. Messager, E. Moors, J. Mon-crieff,G. Pieterse, E. Popa, M. Schmidt, P. Stefani, The CHIOTTO tall tower program inEurope: first results. In: 13th WMO/IAEA meeting of experts on carbon dioxideconcentration and related tracer measurement techniques, Boulder, Colorado, U.S.A.,19-23 September 2005 (ed.: Miller, J.). WMO GAW, Vienna, Report nr 168, 2006.20


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportIMECC - Infrastructure for Measurement of the European CarbonCyclePeter RaynerLaboratoire des Sciences du Climat et l'Environnement (LSCE), Gif sur Yvette, FranceIMECC is an Integrated Infrastructure Initiative (I 3 ) under the European Commission’s6th Framework Programme. IMECC aims to build the infrastructure for a coordinated,calibrated, integrated and accessible dataset for characterizing the function of theEuropean terrestrial biosphere.Web-site: www.imecc.orgIMECC’s main strategies are: Improving the comparability of atmospheric measurements of greenhouse gasesand isotopic composition Coordinating optimal development of infrastructure via comprehensiveexperimental design studies Improving access to existing and future atmospheric and ecosystem data Coordinated data delivery centre Improving access to data on ecosystem parameters Tying European terrestrial data into emerging remotely-sensed datasets onatmospheric composition.IMECC contains three classes of activities: Networking Activities: Designed to improve cohesion, comparability and accessto European carbon cycle measurements Transnational Access Activities: Designed to broaden and improve access toEuropean carbon cycle measurement facilities Joint Research Activities: Designed to support new technologies for Europeancarbon cycle measurements21


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportNetworking ActivitiesMany inferences in carbon cycle monitoring are based on either temporal or spatialgradients of measurements. For these to be reliable measurements must beprecisely comparable. Many of the networking activities in IMECC are aimed atimproving comparability of measurements made at different locations or by differentlaboratories. Network Design Tool: Provides a service in which an experimenter candetermine the impact of a potential future measurement on knowledge of carbonfluxes and accounting Network of Quality Control for Atmospheric Measurements: Establishes thedegree of interoperability of European atmospheric measurement laboratories. Isotope Standard Preparation: Establishes and circulates a new primarystandard for the isotopic composition of CO 2 in air. Network of Algorithms and Software for Flux Measurements: Intercomparisonof flux calculation methodologies in use within Europe Terrestrial Carbon Data Centre (TCDC): Establishes a data centre for all IMECCdata and other data on the terrestrial carbon cycle in Europe.Trasnational Access activitiesThe network of analysis laboratories and measurement locations throughout Europerepresents a distributed measurement infrastructure. The aim of these activities is toimprove access to this infrastructure. Note that the quality of this access is supportedby the networking activities. Access to measurement facilities: Gives external users access to a network of thehighest quality atmospheric measurement laboratories Access to CarboEurope-IP Atmospheric Network: Gives external users access toa network of atmospheric sampling stations. Access to CarboEurope-IP Terrestrial Network: Gives users broader access to anetwork of ecosystem manipulation experiments in the Mediterranean region Access to European Ecosystem Measurement Laboratories: Gives external usersaccess to a network of ecosystem measurement facilitiesResearch activitiesThe distributed measurement infrastructure is constantly growing. For exampleassimilation systems such as those provided by GEMS-IP or direct measurement bysatellite will soon provide large amounts of data. These new measurement systems22


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportmust also be linked into the total measurement infrastructure and these links musttake account of measurement and operational characteristics. The Joint ResearchActivities are designed to develop this infrastructure. Real-Time in situ Atmospheric CO 2 Data: Provides real-time data onatmospheric CO 2 composition for use in operational data assimilation systems Ground-based remote sensing of GHG: Develops and deploys a groundbasedFourier transform interferometer (FTIR) for validation of satellite CO 2measurements Real-Time Ecosystem and Flux Data: Produces real-time ecosystem fluxes andfunction for use in operational data assimilation and carbon accounting23


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportGEMS-IP - Global and regional Earth-system (Atmosphere)Monitoring using Satellite and in-situ dataPeter RaynerLaboratoire des Sciences du Climat et l'Environnement (LSCE), Gif sur Yvette, FranceGEMS is an integrated project under the European Commission’s 6th FrameworkProgramme. The GEMS project will create a new European operational system foroperational global monitoring of atmospheric chemistry and dynamics and anoperational system to produce improved medium-range & short-range air-chemistryforecasts, through much improved exploitation of satellite data.GEMS is divided into several subprojects as follows: Greenhouse Gases (GHG) Global Reactive Gases (GRG) Aerosols (AER) Regional Air Quality (RAQ) Validation (VAL) Production System (PRO)In this summary we focus on the greenhouse gas subproject. The motivation forGEMS arises because many satellite measures contain signals of atmosphericcomposition. These signals are small so can only be extracted in contexts whereother variables are well-constrained. Atmospheric data assimilation provides aframework for estimating disparate atmospheric quantities. The most efficient way toutilise these signals is to attach their retrieval to an existing state-of-art assimilationsystem such as that at ECMWF.The objective of the GHG subproject is to develop an operational system to monitorthe concentrations of greenhouse gases (CO 2 and CH 4 ), and their associated surfacesources and sinks. It’s tasks can be divided as follows: Find 4-d distributions of GHG Validate these distributions Derive surface sources and their uncertainties Improve knowledge of controlling processesThe main applications in the framework of GHG accounting are likely to be theestimate of net fluxes from a region (either geographic or political) and the calculationof lateral boundary conditions for more detailed regional inversions.Current statusGEMS is 2 years through its 4-year life. The project has constructed an assimilationsystem at ECMWF for CO 2 and CH 4 based on measurements from the AdvancedInfra-Red Sounder (AIRS). Figure 1 shows validation of the assimilation at Molokai24


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportIsland, Hawaii. The validation data is from an airborne profile (data kindly provided byNOAA/ESRL). The figure shows the observations, firstguess simulation and theassimmilated profile. AIRS is sensitive to CO 2 in the mid and upper troposphere. Wesee then that, as we move up in the atmosphere, the assimilation is pulled towardsthe validation data away from the first-guess.Figure 1: Assimilated and observed CO 2 profile over Molokai Island, Hawaii for May 112003. The blue curve shows the case with no CO 2 assimilation, the red curve the assimilatedCO 2 profile and the black curve the profile as measured by in-situ aircraft observations. Wethank NOAA/ESRL for providing this profile.AIRS is, of course, not the only instrument providing information on greenhouse gasconcentration. The SCIAMACHY instrument on board ENVISAT provides data on thecolumn-integrated concentration of various greenhouse gases (Figure 2 foratmospheric CH 4 ). These measurements provide considerable information on spatialstructures of concentration, but still require bias correction. The simultaneousassimilation of SCIAMACHY data and highly precise surface data into a flux inversionallows one to make such corrections. Along with the unknown fluxes, the inversionsolves for a series of correction factors on the SCIAMACHY data. In this way thecorrections are consistent with the physics of atmospheric transport.25


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 2: Top: SCIAMACHY retrievals of column-averaged CH 4 mixing ratios [Frankenberg,et al., 2006], corrected with bias correction from TM5-4DVAR data assimilation system (biascorrection 2nd order polynomial as function of latitude and month) including high-accuracysurface measurements from NOAA/ESRL. Bottom: Assimilated column-averaged CH 4 mixingratios based on TM5-4DVAR system [Bergamaschi et al., 2007]. We thank NOAA/ESRL forproviding the CH 4 surface data.ReferencesBergamaschi, P., J.F. Meirink, M. Krol, and G.M. Villani, New TM5-4DVAR inverse modellingsystem to estimate global and European CH 4 sources, this report, 2007.Frankenberg, C., J. F. Meirink, P. Bergamaschi, A. P. H. Goede, M. Heimann, S. Körner, U.Platt, M. van Weele, and T. Wagner, Satellite chartography of atmospheric methane fromSCIAMACHY on board ENVISAT: Analysis of the years 2003 and 2004, J. Geophys.Res., 111, D07303, doi:10.1029/2005JD006235, 200626


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportGEOMON-IP - Global Earth Observation and MonitoringPeter RaynerLaboratoire des Sciences du Climat et l'Environnement (LSCE), Gif sur Yvette, FranceGEOMON is an integrated project under the European Commission’s sixthFramework Programme. It’s main objective is to construct a prototype system foratmospheric composition monitoring for climate applications, by the combination ofground-based with satellite observations. GEOMON is a contribution to GEOSS.GEOMON is organised around three key scientific questions:1. What are the regional European trends and variability of greenhouse gases,tropospheric and stratospheric ozone, aerosols, and pollutants in relation tochanges in surface emissions?2. How to validate top-down satellite observation of the changing atmosphericcomposition, and integrate them with ground based stations and airborneobservations into a coherent picture?3. What are the global trends of atmospheric composition from ground-based andsatellite observations assimilated in modelling studies, and what keymeasurements should be added for reducing uncertainties on surface emissionsand atmospheric processes?In this summary we focus on the application to the greenhouse gases although thescope of the project includes chemically active species and aerosols.Strategy1. Quantify and understand the ongoing changes of the atmospheric composition.2. Integrate ground-based and satellite observations.3. Build an integrated pan-European atmospheric observing system of greenhousegases, reactive gases, aerosols, and stratospheric ozone.GEOMON activitiesGEOMON is divided into four data gathering activities, three of which parallel theglobal activities of GEMS. A modelling activity integrates data from the four datagathering activities and the final activity is responsible for data archiving andoutreach. The activities are briefly summarised below.27


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportActivity 1: Greenhouse gases Improve the CO 2 and CH 4 monitoring in situ programs. Add new sites in Cyprusand Africa. Support the European passenger aircraft CARIBIC program. Implement a new pilot network of near infra-red FTIR instruments. Develop Near Real Time greenhouse gas in situ data.Activity 2: Tropospheric Reactive gases Consolidate ground-based networks of composition measurements. Support measurements in the free troposphere (mainly via the Caribbic aircraftmeasurements). Improve links between ground-based and satellite measurements using models.Activity 3: Atmospheric aerosols Enhance and consolidate ground-based monitoring of aerosols and theirproperties. The development of ENAN (European network of aerosol networks). Evaluate satellite aerosol products against ENAN data. Assessment of the 4-D aerosol distribution over Europe using ENAN data.Activity 4: Stratospheric composition Integrate various measurement systems to provide multidimensionalcharacterisation of O 3 -relevant species. Development of associated observationoperators. Validate satellite data records suitable for long-term studies. Integration of ground-based with satellite data. Provision of data for modelvalidation. Evaluation of trends from data series, for O 3 , NO 2 , BrO, T. Support of Montrealand Kyoto Policies.28


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportActivity 5: Integrative modelling Use models to produce integrated data products. Improve satellite retrievals. Use models to fill gaps and analyse long-term trends. Use models to constrain budgets of atmospheric species. Evaluate model performance by comparison with GEOMON data. Optimise observing networks by comparing models and data.Activity 6: System Architecture and outreach Establish a common GEOMON Data Centre for atmospheric compositionparameters including near-real time data products, as a prototype for a Europeancontribution to GEOSS. Establish links to other GEOSS observing systems, national and internationaldatabases of past and ongoing scientific projects, and other relevant activities. Establish targeted outreach actions towards international Earth Observationcoordination bodies, programs, the scientific assessment community, and thegeneral public. Provide user friendly tools including graphical presentation of data and synthesisinformation for any interested party.29


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportNitroEurope-IP - The nitrogen cycle and its influence on theEuropean greenhouse gas balancePeter Bergamaschi 1 Alex Vermeulen 2 , Martin Heimann 3 , Philippe Bousquet 4 , PhilippeCiais 4 , Alistair Manning 5[1] European Commission DG Joint Research Centre, Institute for Environment andSustainability, Ispra, Italy[2] Netherlands Energy Research Foundation (ECN), Petten, Netherlands[3] Max Planck Institute for Biogeochemistry, Jena, Germany[4] Laboratoire des Sciences du Climat et l'Environnement (LSCE), Gif sur Yvette, France[5] Met Office, Exeter, UKNitroEurope-IP (NEU) is an integrated European research project on the nitrogencycle. It includes the setup and operation of a European network to measure nitrogenfluxes and pools, and various modelling activities, ranging from plot-scale andlandscape modelling to European wide up-scaling and European/global inversemodelling. An important issue for NEU is the coupling and interaction of the nitrogenand carbon cycles. NEU is funded under the EU's FP6, and will run for 5 years fromFebruary 2006 until 2011. Detailed information about the project can be found athttp://www.nitroeurope.eu/.Here we describe the NEU workpackage "Independent inverse modelling ofEuropean N 2 O and CH 4 emissions"The main objective of this work package is to derive European and nationalestimates of N 2 O and CH 4 emissions based on atmospheric observations andinverse modelling. A central prerequisite to improve the confidence of top-downestimates obtainted by inverse modelling is the application of different, independentmodels (e.g. Gurney et al. [2002], Bergamaschi et al. [2004]). Therefore we will apply5 independent inverse modelling systems, including Eulerian and Langrangianparticle dispersion models (see Table 1).Specific objectives areTo provide more realistic estimates of overall uncertainties of top-down emissionestimates.To trace back differences among top-down estimates, in particular related tomodel transport and inversion techniques.To investigate the difference between approaches which use and those which donot use a priori information from bottom-up inventoriesThis inverse modelling workpackage will start February 2008 and run for 3 years until2011.We plan to use continuous CH 4 and N 2 O meausurement from CHIOTTO [Vermeulen,2007a] and further European monitoring stations. This should provide a reasonable30


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportdata set for year 2006 or 2007. Nevertheless, there exist some important regionalgaps in Europe. Furthermore, good intercalibration between different monitoringstations remains an important issue, in particular for N 2 O. Directly linked to NEU, aproposal for the setup of a European non-CO 2 GHG network had been submitted(MANOMETER "Methane And Nitrous Oxide Monitoring of the EuropeanTroposphere: European and Russian sources (and sinks)"). Despite positiveevaluation, however, MANOMETER was not funded by the EU.Table 1: Inverse models foreseen in NEU inverse modelling work packagepartner modelshort descriptionJRCMPITM5-4DVARmodelTM3 ormesoscalemodelNew 4DVAR inverse modelling system [Bergamaschi et al.,2007], based on Eulerian two-way nested zoom model TM5[Krol et al., 2005]TM3: Eulerian model [Heimann, and Körner, 2003]LSCE LMDZ model Eulerian model with flexible grid size, high resolution overEurope [Bousqet, 2007]ECN COMETFLEXPARTLagrangian trajectory model [Vermeulen, 2006, 2007b]Lagrangian particle disp. model [Stohl, 1998]UKM NAME model Lagrangian particle dispersion model[Manning et al., 2003, Manning, 2007]ReferencesBergamaschi, P., J.F. Meirink, M. Krol, and G.M. Villani, New TM5-4DVAR inverse modellingsystem to estimate global and European CH 4 sources, this report, 2007.Bergamaschi, P., M. Krol, F. Dentener, A. Vermeulen, F. Meinhardt, R. Graul, M. Ramonet,W. Peters, and E.J. Dlugokencky, Inverse modelling of national and European CH 4emissions using the atmospheric zoom model TM5, Atmos. Chem. Phys., 5, 2431-2460,2005.Bergamaschi, P., Behrend, H., and Jol, A. (Eds.): Inverse modelling of national and EUgreenhouse gas emission inventories – report of the workshop “Inverse modelling forpotential verification of national and EU bottom-up GHG inventories” under the mandateof the Monitoring Mechanism Committee WG-1 23–24 October 2003, JRC, Ispra, 146pp., EUR 21099 EN/ISBN 92-894-7455-6, European Commission Joint Research Centre,Ispra, 2004.Bousquet, P., Inverse modelling activities at LSCE: from global to regional scales, this report,2007.Gurney, K.R., R.M. Law, A.S. Denning, P.J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler,Y.H. Chen, P. Ciais, S.-M. Fan, I.Y. Fung, M. Gloor, M. Heimann, K. Higuchi, J. John, T.Makl, S. Maksyutov, K. Masarie, P. Peylin, M. Prather, B.C. Pak, J. Randerson, J.Sarmiento, S. Taguchi, T. Takahashi, and C.-W. Yuen, Towards robust regionalestimates of CO 2 sources and sinks using atmospheric transport models, Nature, 415,626-630, 2002.31


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportHeimann, M. and Körner, S., The Global Atmospheric Tracer Model TM3. Model Descriptionand Users Manual Release 3.8a, No. 5, Max Planck Institute for Biogeochemistry (MPI-BGC), Jena, Germany, 2003.Krol, M.C., S. Houweling, B. Bregman, M. van den Broek, A. Segers, P. van Velthoven, W.Peters, F. Dentener, and P. Bergamaschi, The two-way nested global chemistry-transportzoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417-432, 2005.Manning, A.J., D.B. Ryall, R.G. Derwent, P.G. Simmonds, and S. O'Doherty, EstimatingEuropean emissions of ozone-depleting and greenhouse gases using observations and amodeling back-attribution technique, J. Geophys. Res., 108 (D14), 4405,doi:10.1029/2002JD002312, 2003.Manning, A., Baseline trends and top-down estimates of UK and NW European GHGemissions, this report, 2007.Stohl, A., Computation, accuracy and apllications of trajectories - a review and bibliography,Atm. Env., 32, 947-966, 1998.Vermeulen, A.T., G. Pieterse, A. Hensen, W.C.M. van den Bulk, and J.W. Erisman, COMET:a Lagrangian transport model for greenhouse gas emission estimation – forward modeltechnique and performance for methane, Atmos. Chem. Phys. Discuss., 6, 8727-8779,2006.Vermeulen, A., G. Pieterse, A. Manning, M. Schmidt, L. Haszpra, E. Popa, R. Thompson, J.Moncrieff, A. Lindroth, P. Stefani, J. Morguí, E. Moors, R. Neubert, M. Gloor, CHIOTTO -Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gases, this report,2007a.Vermeulen, A. and G. Pieterse, Methane flux estimates for Europe using tall towerobservations and the COMET inverse model, this report, 2007b.Figure 1: Comparison of various top-down estimates of CH 4 emissions from some Europeancountries (see also Bergamaschi et al. [2005]).32


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportHYMN - HYdrogen, Methane and Nitrous oxide: Trend variability,budgets and interactions with the biospherePeter van Velthoven 1 and Philippe Bousquet 2[1] Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands.[2] Laboratoire des Sciences du Climat et l'Environnement (LSCE), Gif sur Yvette, FranceThe EU HYMN project (Hydrogen, Methane and Nitrous oxide: Trend variability,budgets and interactions with the biosphere) focuses on the trends and life cycles ofmethane (CH 4 ), nitrous oxide (N 2 O), and molecular hydrogen (H 2 ). Ever-increasinghuman activities on a global scale are the cause of the rising concentrations ofvarious long-lived greenhouse gases in the atmosphere, including methane andnitrous oxide. The possible transition to a so-called ‘hydrogen economy’ in thecoming decades is likely to cause a significant increase in future atmosphericmolecular hydrogen levels. Associated with this are possible impacts on climateforcing, air quality and ozone depletion.In the Kyoto Protocol, concrete ceilings have been set for greenhouse gas emissionsof the participating countries in 2012. It addresses emissions of CO 2 , CH 4 , N 2 O, andthe fully human-made HFCs, PFCs and SF 6 . For the EU this implies a targetedreduction of 8 % in for the Kyoto commitment period 2008-2012 relative to 1990levels. A significant contribution to this target is expected to be made by reductions inCH 4 (about 32 %) and N 2 O (about 12%) emissions. For the post-Kyoto era moresevere emission reductions are being discussed. The need for substantialquantitative reductions in anthropogenic greenhouse gas emissions is recognisedmore and more internationally – less agreement exists on methods to achieve suchreductions. For the climate gases with both natural and anthropogenic components(CO 2 , CH 4 and N 2 O) a thorough assessment of their budgets could serve as astarting point for future regulations, since different abatement strategies will havedifferent potential, efficiency and impact. Finding the optimal strategy for reductionsin greenhouse gas emissions is complex, and different trade offs are possible. Forexample, the efficacy of methane emission regulation to mitigate climate forcing hasbeen reported to be relatively high, amongst others because of its impact ontropospheric ozone. Furthermore, regulation of greenhouse gas emissions may haveconsequences for air quality and ozone depletion. Methane emissions as well aspollutant emissions of NO X , CO and VOCs contribute to tropospheric O 3 , which is atoxic gas, an important oxidant, as well as a potent greenhouse gas. Finally it can benoted that the global atmospheric cycles of methane, nitrous oxide and hydrogen, arecoupled and include various interactions with the biosphere which need to be takeninto account. Some of these are still badly understood. An example is the recentfinding of large methane sources in the tropics.HYMN aims to provide information to guide policy making with respect to futureemission regulations taking into account these issues. It therefore focuses ongathering in-depth knowledge of the budgets and biogeochemical cycles of thegases, including their geographical distribution, the magnitude and variability of theirsources, sinks and dispersion, and the feedbacks that connect land-biosphere33


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportprocesses with the life cycles of these gases through atmospheric chemistry andlong-range transport. The objectives of HYMN are:1. To improve the process modelling of the land-biosphere-atmosphere exchange ofthe HYMN gases and to provide global and regional estimates of their naturalsources and sinks2. To contribute to global monitoring by provision of multi-year global satellite datasets of the CH 4 and CO distribution and long-term time series for CH 4 and N 2 O ata range of observing stations3. To provide advice on the further optimisation of monitoring networks for thesegases.4. To quantify atmospheric loss of CH 4 and H 2 and the impact of changinganthropogenic and natural (climate-induced) emissions on regional OH trendsand on current and future global CH 4 and H 2 levels.5. To quantify how the possible future change to a hydrogen economy will affect theH 2 distribution and the distribution of CH 4 and O 3 through changes in emissions ofH 2 and pollutants (NO X , CO, VOCs).6. To evaluate simulations with a coupled atmospheric chemistry-biosphere modelfor CH 4 , N 2 O and H 2 by comparison to ground based and satellite observations ona global and regional scale.7. To make new estimates of the sources and sinks of CH 4 and H 2 , including theirtemporal and spatial variabilityApart from classical surface observations that are part of the GAW and CMDLnetworks, HYMN will derive new detailed information on the regional scale aboutmethane and nitrous oxide from recently become available satellite observationsfrom SCIAMACHY and IASI, and from remote sensing observations by FTIR. Theseobservational data sets will be homogenised and evaluated against each other inorder to derive consistent long-term time series. The error statistics of theobservations will be carefully determined. By subsequently applying advancedemission inversion and data assimilation techniques to the validated observations inatmospheric chemistry models, the sources and sinks of the HYMN gases will bequantified on regional scales (up to 1x1 degree). The coupling between their lifecycles and OH will be investigated focussing on presently not well understoodrelations between their inter-annual variations and trends.The atmospheric chemistry models will furthermore be applied to investigate theeffects of a future transfer to a hydrogen economy and of the associated reduction infossil fuel burning emissions (NO X , CO, VOCs) on the coupled cycles of H 2 , CH 4 , OH,and O 3 taking into account interactions with the biosphere simulated with the LPJland-biosphere model.Three partners in HYMN will apply atmospheric chemistry models and perform dataassimilation/emission inversions: KNMI (the TM5 model), Univ. Oslo (the OsloCTM2), and CEA LPCE/CNRS (the LMDZ-INCA model). Univ. Bristol will furtherdevelop their land-biosphere model LPJ and couple its output to the atmosphericchemistry models. Univ. Heidelberg and CNRS will provide new satellite data fromSCIAMACHY resp. IASI. Partners from the FTIR network (Univ. Bremen, BIRA-IASB,Univ. Karlsruhe, Univ. Liège, Chalmers Univ., FZ Karlsruhe) will providehomogenized time series of CH 4 and N 2 O observations.34


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportSOGE - System for Observation of Halogenated Greenhouse Gasesin EuropeStefan Reimann 1 , Frode Stordal 2 , Peter Simmonds 3 , Simon O’Doherty 3 , Martin K.Vollmer 1 , Brian Greally 3 , Michela Maione 4 , Igor Arduini 4 , Chris Lunder 5 , NorbertSchmidbauer 5 , Doris Folini 1 , Alistair Manning 6[1] EMPA, Duebendorf, Switzerland[2] University of Oslo, Department of Geosciences, Norway[3] School of Chemistry, University of Bristol, Bristol, UK.[4] Istituto di Scienze Chimiche, University of Urbino, Urbino, Italy.[5] Norwegian Institute for Air Research (NILU), Kjeller, Norway.[6] Climate Research, Met Office, Exeter, UK.The System for Observation of halogenated greenhouse Gases in Europe (SOGE)provides continuous in situ measurements by gas chromatograph-mass spectrometry(GC-MS) of key halocarbon species at Mace Head (Ireland), Jungfraujoch(Switzerland), Ny-Ålesund (Spitsbergen, Norway) and Monte Cimone (Italy) (Figure1). Calibration has been of primary importance to SOGE and a rigorous andtraceable calibration system for the GC-MS’s is successfully maintained andextended. Coupled to the extended calibration, comparison of data from Mace Head,Jungfraujoch, Ny-Ålesund and Monte Cimone demonstrates good agreement forexpected baseline levels of the targeted gases. SOGE is linked to the world-widenetworks of AGAGE (Advanced Global Atmospheric Gases Experiment) and NOAA(National Oceanic & Atmospheric Administration) in terms of common standards andquality assurance tools.Figure 1: The four SOGE network stations.The SOGE system contributes to global observing networks to determine trends ofCFCs (chlorofluorocarbons), HCFCs (hydrofluorochlorocarbons), long-lived35


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportchlorinated solvents (CCl 4 , CH 3 CCl 3 ), brominated organic compounds (halons,CH 3 Br) and HFCs (hydrofluorocarbons). Due to its broad coverage of Europe itprovides an important link between the global and regionally representativebackground concentrations.As an example the data series of HFC-134a is shown in Figure 2, where a commontrend in the background concentrations is overlaid by higher peak values atJungfraujoch and Monte Cimone, which are located nearer to important Europeansource regions in comparison to the more remote site of Mace Head and the Arcticsite of Ny-Alesund.HFC 134a200150ppt10050Monte CimoneJungfraujochMace HeadNy-Alesund02000 2001 2002 2003 2004Figure 2: Continuous in-situ measurements of HFC-134a at the four SOGE sites.Thus, as European sources can be detected at the SOGE stations, they providemeasurements which can be used are in support of the Kyoto and the Montrealprotocols, in assessing the compliance of European regions with the protocolrequirements. In particular the observation system has been set up to (i) detecttrends in the concentrations of greenhouse active and ozone-destroying halocarbons,(ii) verify reported emissions and validate emission inventories for a series ofhalocarbons for Europe as a whole as well as for certain regions, (iii) developobservational capacity for all halocarbons included in the Kyoto protocol for whichthis was previously not existing, and (iv) develop a strategy for a cost-effective longtermobservation system for halocarbons in Europe. The second objective has beento predict and assess impacts of the halocarbons on the climate and on the ozonelayer. This implies extensive exploitation of existing data.The European emissions of halocarbons are regularly estimated within SOGE withinseveral publications. For example, HCFC-141b emissions have been shown to have36


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportdeclined already after its ban from usage as foam blowing agent [Derwent et al.2007]. On the other hand, emissions from its substitutes (HFC-245fa and HFC-365mfc) have been estimated to have been increased enormously over the last years[Vollmer et al. 2006; Stemmler et al. 2007]. Furthermore, HFC-134a and HFC-152aemissions have been estimated over the last years [O'Doherty et al. 2004; Greally etal. 2007].In the last years SOGE has been extended to Asia. Within SOGE-A (Asia) a newinstrument has been deployed at a station North of Beijing to perform continuous insitumeasurements of ozone-depleting substances (CFCs, HCFCs, halons and longlivedchlorinated solvents). Resulting emission estimates will be the first of its kindfrom China and will be used to verify compliance of China with the requirements ofthe Montreal protocol and its amendments.ReferencesDerwent, R. G., P. G. Simmonds, B. R. Greally, S. O'Doherty, A. McCulloch, A. Manning, S.Reimann, D. Folini and M. K. Vollmer, The phase-in and phase-out of Europeanemissions of HCFC-141b and HCFC-142b under the Montreal Protocol: Evidence fromobservations at Mace Head, Ireland and Jungfraujoch, Switzerland from 1994 to 2004,Atmospheric Environment, 41 (4): 757-767, 2007.Greally, B. R., A. J. Manning, S. Reimann, A. McCulloch, J. Huang, B. L. Dunse, P. G.Simmonds, R. G. Prinn, P. J. Fraser, D. M. Cunnold, S. O'Doherty, L. W. Porter, K.Stemmler, M. K. Vollmer, C. R. Lunder, N. Schmidbauer , O. Hermansen, J. Arduini, P. K.Salameh, P. B. Krummel, R. H. J. Wang, D. Folini, R. F. Weiss, M. Maione, G. Nickless,F. Stordal and R. G. Derwent, Observations of 1,1-difluoroethane (HFC-152a) at AGAGEand SOGE monitoring stations in 1994–2004 and derived global and regional emissionestimates, J. Geophys. Res., 112,: D06308, doi:10.1029/2006JD007527, 2007.O'Doherty, S., D. M. Cunnold, A. Manning, B. R. Miller, R. H. J. Wang, P. B. Krummel, P. J.Fraser, P. G. Simmonds, A. McCulloch, R. F. Weiss, P. Salameh, L. W. Porter, R. G.Prinn, J. Huang, G. Sturrock, D. Ryall, R. G. Derwent and S. A. Montzka, Rapid growth ofhydrofluorocarbon 134a and hydrochlorofluorocarbons 141b, 142b, and 22 fromAdvanced Global Atmospheric Gases Experiment (AGAGE) observations at Cape Grim,Tasmania, and Mace Head, Ireland, J. Geophys. Res., 109 (D6): art. no.-D06310, 2004.Stemmler, K., D. Folini, S. Ubl, M. K. Vollmer, S. Reimann, S. O'Doherty, B. Greally, P. G.Simmonds and A. Manning, European emissions of HFC-365mfc, a chlorine freesubstitute for the foam blowing agents HCFC-141b and CFC-11, Environ. Sci. Technol.41, 1145-1151, 2007.Vollmer, M. K., S. Reimann, D. Folini, L. W. Porter and L. P. Steele, First appearance andrapid growth of anthropogenic HFC-245fa (CHF 2 CH 2 CF 3 ) in the atmosphere, Geophys.Res. Lett. 33 (20), 2006.37


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportGeolandJean-Christophe CalvetMétéo-France, Toulouse, FranceThe GEOLAND projectGEOLAND is carried out in the context of Global Monitoring of Environment andSecurity (GMES), a joint initiative of European Commission (EC) and EuropeanSpace Agency (ESA), which aims to build up a European capacity for GMES.GEOLAND is designed to fundamentally support this initiative, focusing on the GMESpriorities "Land Cover Change in Europe", "Environmental Stress in Europe", and"Global Vegetation Monitoring". In FP6, the GEOLAND integrated project started in2004 and the technical work was completed in December 2006.The ambition of the GEOLAND consortium is to develop and demonstrate a range ofreliable, affordable and cost efficient European geo-information services, supportingthe implementation of European directives and their national implementation, as wellas European and International policies. Thus, the GMES initiative is considered aunique opportunity to integrate existing technology with innovative and scientificallysound elements into sustainable services.The Land Carbon component of GEOLANDThe objective of the land carbon component of GEOLAND is to develop a bottom-up(Fig. 1) multimodel carbon accounting system accounting for weather and climatevariability, coupled with an Earth Observation (EO) data assimilation system. Thisnew tool will support Kyoto (and post-Kyoto) reporting activities.The main achievement so far (Fig. 2) consisted in performing the 'greening' of theland surface operational platforms of meteorological services (ECMWF and Météo-France). Namely, a CO 2 responsive capability was introduced in the land surfacemodels and the possibility to simulate the vegetation biomass and leaf area index.ECMWF is now ready to simulate the terrestrial carbon flux at a global scale with aspatial resolution of 25 km. The modelled carbon flux is fully consistent with themodelled water flux, soil moisture, vegetation biomass and leaf area index.A demonstration EO data assimilation system was implemented over southwesternFrance (Fig. 3), and a simplified version was successfully applied at a global scale.ProspectsFuture activities will focus on the representation of carbon storage and soilrespiration in the modelling platforms of meteorological services, on the developmentof the operational use of EO data assimilation, on the improvement of the spatialresolution over Europe (1-10 km), and on linking the products with forest and soilcarbon inventory activities in Europe.38


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 1: Complementarities of the GMES atmosphere and vegetation integrated projectswithin FP6.Figure 2: A result of GEOLAND: greening of land surface models used in atmosphericmodels (Météo-France and ECMWF), and near-real time demonstration products derivedfrom the ORCHIDEE model.39


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 3: Assimilation of soil moisture and Leaf Area Index observations over southwesternFrance: example over the SMOSREX experimental site (De Rosnay et al. 2006).40


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report3 Inverse Modelling Studies41


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportBaseline trends and top-down estimates of UK and NW EuropeanGHG emissionsAlistair ManningMet Office, Exeter, UKIntroductionHigh frequency observations on the remote west coast of Ireland have been used toestimate the Northern Hemisphere background trends of methane, nitrous oxide anda range of HFCs. Subtracting the background concentration from the observationyields a quantity that represents the impact of regional pollution on the atmosphericconcentration. By understanding the recent history of the air arriving at theobservation station and importantly how emissions dilute with time it is possible,through inverse modelling, to estimate the magnitude of emissions from differentgeographical regions. Emission estimates for the UK and other European regionsover a 12 year period (1995-2006) are presented for methane, nitrous oxide and arange of important HFCs (134a, 152a, 125, 365mfc).Northern Hemisphere baseline trendsThe Mace Head observing station is situated on the west coast of Ireland and is partof the global AGAGE (Advanced Global Atmospheric Gases Experiment) network ofsites. It is ideally situated to observe air from the Atlantic that has received no landemissions for thousands of kilometres. The majority of air therefore is a goodrepresentation of mid-latitude composition, referred to as baseline air, and can beused to assess annual and seasonal trends. The Mace Head station records acomprehensive set of greenhouse gases to state-of-the-art accuracy at high timeresolution.This study attempts to isolate those times from 1995 onwards that are representativeof mid-latitude baseline air by using a sophisticated atmospheric dispersion model,NAME (Numerical Atmospheric dispersion Modelling Environment) and statisticalpost-processing. NAME is a Lagrangian model developed following the Chernobylaccident to understand the movement of material in the atmosphere and has sincebeen used in a wide range of emergency and policy-based applications. In this workthe 3D meteorology has been obtained from the UK Met Office numerical weatherprediction model, the Unified Model, with a horizontal resolution of 55km falling to40km, a vertical resolution of 12 levels in 1995 increasing to 33 levels by 2006 andan analysis output every 3 hours.NAME has been run backwards in time for ten days for each 3 hour period from 1995until the end of 2006 releasing thousands of model particles at Mace Head. For each3 hour period therefore a map is produced estimating all of the surface (0-100m)contributions within ten days of travel arriving at Mace Head during that 3 hourinterval (figure 1). By selecting only those times when the history of the air massindicates negligible impact from the European land mass, or transport from tropical42


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportlatitudes or significant local contributions, it is possible to isolate those times classedas mid-latitude baseline. For a selection of greenhouse gases measured at MaceHead these baseline observations have been selected and then further refined byapplication of a statistical filter to remove outlying data points. Monthly averages ofthe resulting data are used to assess the annual and seasonal baseline trends ineach measured gas. The methodology has been applied to methane, nitrous oxide,and a range of HFCs covering the period 1995-2006 inclusive, figure 2.Figure 1: Example of air history map produced by NAME. The darker shades indicategreater surface contributions from that area to the air arriving at Mace Head during that 3hour period.Estimating European emissions using top-down inversion methodologyThe aim is to generate regional emission distribution estimates from ‘polluted’ (abovebaseline) observations. The emissions are defined as the geographical vector e thathas n elements indicating n geographical regions. The NAME model is used topredict the concentration time-series (t elements) at each observation point (MaceHead) from each potential source region. This information is captured in the transportand dilution matrix A which has t rows and n columns. The observation time-seriesrepresenting regional pollution is determined by subtracting the estimated timevaryingbaseline concentration from each observation, this generates the observationvector m that has t elements.For methane the observations from three stations have been used to better constrainthe inversion. The stations are Mace Head in Ireland, Deuselbach in westernGermany and Neuglobsow in eastern Germany. All three are Global AtmosphericWatch (GAW) stations. Concurrent data from all three stations only exist up until theend of 2004. In order to be able to use the data from all three stations it is necessarythat all the observations are inter-comparable so that all the data can be representedon the same calibration scale. In this case the data were all converted onto theJapanese scale. As both of the German sites are within the ‘polluted’ regional domainit is difficult to estimate the baseline concentrations at these stations. As all threestations have similar latitudes it has been assumed here that the baselineconcentration estimated at Mace Head would be applicable at both of these othersstations. Also because methane has natural biogenic sources that can exist close tothe observation sites and thus dominate the signal, all observations recorded duringperiods when ‘local’ effects would be significant, e.g. low wind speed and low43


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportboundary layer periods, have been removed from the analysis. For all of the othergases investigated only Mace Head data was available and no data selection criteriawere applied.The results of the different trace gases are shown in figures 3-6, the country andregional totals have been determined by summing the emissions from the relevantgrids contained within the geographical domains of each area. The NW European(NWEU) region covers Ireland, UK, France, Belgium, the Netherlands, Luxembourg,Germany and Denmark. The average, minimum and maximum estimates indicate therange of uncertainty within the results and arise from repeating the inversionmethodology multiple times with different noise perturbations applied to theobservation time-series. The annual estimates have been calculated by averagingthe relevant six month solutions weighted by their overlap period with the given year.The UNFCCC inventory estimates for the NW European region were obtained fromthe web site (www.unfccc.int), the UK estimates were provided by the collator of theUK inventory for the UNFCCC, AEA Technology.Figure 2: Monthly-averaged baseline concentrations (blue points) for methane, nitrous oxide,HFC-134a, HFC-152a, HFC-125, HFC-365mfc covering the period 1995-2006. Red pointsshow the monthly-averaged observations.44


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 3: Annual average Methane emission estimates for the UK and northwest Europe(Mt/yr). Black line = UNFCCC and Blue line (with uncertainty range) = NAME methodologyFigure 4: Annual average Nitrous Oxide emission estimates for the UK and northwestEurope (kt/yr). Black line = UNFCCC and Blue line (with uncertainty range) = NAMEmethodology(a)(b)Figure 5: Annual average (a) HFC-134a and (b) HFC-152a emission estimates for the UK(kt/yr). Black line = UNFCCC and Blue line (with uncertainty range) = NAME methodology45


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report(a)(b)Figure 6: Annual average (a) HFC-125 and (b) HFC-365mfc emission estimates for the UK(kt/yr). Blue line (with uncertainty range) = NAME methodologyDiscussionThe mid-latitude baseline concentrations of methane increased between 1995 and2000 but since that time have remained relatively stable, the actual annual trend isdifficult to accurately estimate as the seasonal cycle, peaking in the winter, is strong.For nitrous oxide and the HFCs (134a, 152a, 125 and 365mfc) the baselineconcentrations have all increased during the measurement period.The NAME estimated methane emissions for NW Europe follow a similar trend to thatreported by through the UNFCCC (figure 3). The UK estimates follow a significantlydifferent trend. The NAME estimates show a fairly static emission whereas theUNFCCC estimates have steady declined over the same period. It must beremembered that the NAME methodology makes no distinct between anthropogenicand biogenic emissions unlike the UNFCCC which is purely anthropogenic. It wouldtherefore be expected that the NAME estimates should be similar to or larger thanthe UNFCCC estimates depending on the magnitude of the biogenic contribution.The estimated biogenic methane emissions from the UK are considered to be small.The comparison between UNFCCC nitrous oxide emissions and the NAME estimatesare in good agreement (figure 4). The UNFCCC estimates for the UK lie entirelywithin the uncertainty range of the NAME estimates. The trend in the NW Europeanestimates is very similar with the UNFCCC estimates being at the top end of theuncertainty range of the NAME estimates.For HFC-134a (figure 5a) the patterns of UK emissions are similar in that bothestimates show a strong increase in emissions over the 12 year period. However theUNFCCC estimates start increasing two years ahead of the NAME inversionestimates. In the last three years the NAME estimates have levelled off whereas theUNFCCC estimates have continued to grow.46


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe overall emission estimates of HFC-152a for the UK are similar between the twomethods (figure 5b) but profile is significantly different. The increase in emissionsoccurs two years later using the inversion method and shows a sharp decline in usein the last two years when compared to the UNFCCC estimates.The inversion estimates for UK emissions of HFC-125 and HFC-365mfc (figures 6aand 6b) show strong increases over the measurement period.The NAME inversion estimates have significant uncertainty. This uncertainty hasbeen captured by solving the inversion multiple times with randomly appliedperturbations to the observed time-series and using two different skill score (cost)functions. The noise signal applied to the observations represents the uncertainties inthe inversion assumptions, in the transport and dispersion and to a minor degree inthe observations themselves.The use of multiple observation sites better constrains the inversion equations andtherefore enables improved emission estimates to be calculated. Each observingstation is impacted by the emissions from different geographical areas to differentextents and therefore using multiple stations enables the inversion estimates to besolved on a higher resolution grid. It is assumed that the observations from thedifferent sites can be inter-compared, i.e. they are reported on the same calibrationscale or can be converted between scales.The inversion methodology reported here requires high-frequency long-term highqualitymeasurements. It also assumes that a time-varying baseline concentrationcan be estimated for each measurement site. Isotopic measurements of a trace gasmay enable the source categorisation of the emissions, as each source type emitsthe trace gas with a different isotopic signature.AcknowledgementsIt is important to recognise and acknowledge the work of the scientists developingand maintaining the observation stations that have provided data for this study,namely Mace Head, Deuselbach and Neuglobsow, and also their funders. The keyrole of GAW is also vital for enabling access to this data. Defra, UK need to berecognised for providing the resources to enable the modelling.47


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportMethane flux estimates for Europe using tall tower observationsand the COMET inverse modelAlex Vermeulen 1 , Gerben Pieterse 1,2[1] Netherlands Energy Research Foundation (ECN), Petten, Netherlands[2] Currently at: IMAU – Institute for Marine and Atmospheric Research, Utrecht, Netherlands1. Model and data description1.1 Model descriptionThe COMET (CO2 MEthane Transport) model is a Lagrangian model that can beused for both predictive and inverse modelling purposes. COMET uses backwardtrajectories to establish the source-receptor relationship, the so-called sourcereceptormatrix (SRM). The calculations described in this paper were performedusing trajectory and mixing layer height data derived from three nested grids(resolution from 2 to 0.5 degrees) with 3-hourly resolution ECMWF analysedoperational meteorological data. The vertical resolution used is L61. Using thesemeteorological data, the 3-D 144 h backward trajectories were calculated from theECMWF wind fields using the Flextra model [Stohl and Thomson, 1999].To account for mixing of the source signal in the planetary boundary layer with thefree troposphere, two vertical layers are distinguished, a mixing layer and a reservoirlayer. The initial methane concentration at the start of each trajectory is taken in thiscase from the two-weekly averages of the calculated methane concentrations of theTM2 model [Heimann, 1996] for 1995 as calculated by Houweling et al. [1999]. Theheight of the mixed layer in contact with the surface varies as a function ofatmospheric stability. All emissions are first accumulated in this mixed layer andwhen the mixed layer height changes, mass transfer takes place with the reservoirlayer.The area that influences the concentrations in the column of air in the mixed layer isassumed to be circular and the diameter of this circle is assumed to change linearlywith travel time; from large at the start of the backward trajectory to small at thedestination. This cone-shaped trajectory path defines a highly simplified parametrisedform of the real region of influence, determined by advection, convection andturbulent diffusion. Normal trajectory models only follow an infinitesimal narrow path,ignoring the effect of turbulent diffusion along this path. An alternative to the singletrajectory approach is to use an ensemble of trajectories to get at least someinformation on the accuracy of the trajectory information and the influence of forexample turbulent diffusion. More information can be found in Vermeulen et al. [1999,2006].In order to perform inversions the model can store the contributions of emissionsfrom grid cells to the concentration at a certain arrival point and time in a big matrix.The resolution of this grid can be as high as the emission data, but the model isflexible in this and in this paper we choose to select an aggregation level with aresolution of 0.2 degree. After evaluation of all arrival times the sum of thecontributions per grid cell is evaluated.Usually the cells surrounding the receptor point(s) contain very high contributions andthe cells further away shows rapidly decreasing values. Then an automated routine48


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportaggregates neighbouring grid cells in pairs to form areas with the same or higheraverage total contribution as that of the individual maximum for the whole grid. Thejoined cells with lower than the maximum contribution are then again joined pair bypair, etc. This leads to a highly linearised version of the Source Receptor Matrix, thatis more suitable for the Singular Value Decomposition inversion routine (seeVermeulen et al., [1999]).1.2 Cabauw observationCabauw tower (51 o 58' N, 4 o 55' E) is located near the centre of the Netherlands, 20km southwest of the city of Utrecht. The Royal Netherlands Meteorological Institute(KNMI) is using this tall tower for boundary layer studies. The direct surroundings ofthe Cabauw tower is just below sea level in a polder area. It consists of flat meadowsand ditches, with some scattered villages. On this site a 213m high meteorologicaltower is situated. Since 1993 ECN performs high precision measurements of verticalgradients of greenhouse gas concentrations along this tower.1.3 Emission dataMETDAT (METhane DATabase) is a high-resolution database for CH 4 emissions thathas been developed for the Netherlands and the Northwestern part of Europe. It hasa spatial resolution of 500x500 m 2 and 5x5 km 2 , respectively [Berdowski et al., 1998].The countries covered in the METDAT database are Belgium, Denmark, France,Germany, Ireland, Luxembourg, The Netherlands, Norway, Sweden and the UnitedKingdom. For the remaining regions the values for the emissions are derived fromthe EDGAR database version 2.0 [Olivier et al., 1996].Both data sets are based on the inventories in the year 1995. The following sourcecategories have been included in the database: enteric fermentation, animal waste,oceans, coastal waters, lakes, rivers, wetlands, biomass burning, rice paddies,landfills, gas and oil exploration, gas transport, gas distribution, waste watertreatment, coal mining and combustion processes.Emissions were estimated from information on emission factors and activity data.Also, data required for spatial apportioning have been applied. For each sourcecategory, an estimate was made for the temporal variation in emissions. Emissionvariation between years and months as well as within weeks and days has beendetermined [Berdowski et al., 1998].2. ResultsFigure 1 shows a small part of the measured and modelled time series for methaneat Cabauw tower. For periods of several weeks to months the model can explain upto 90% (R 2 =0.90) of the observed variability of the methane mixed layer bulkconcentration. For the whole time series of 2002 the R 2 equals 0.72. Figure 2illustrates the RMSE of the methane modelled values when evaluated against theobservations. From the figure a slight tendency of the model to underestimate theconcentrations can be deduced. For the mixed layer bulk concentration the averageerror is around zero, while for the lower observation levels the model tends toproduce too low values on average.49


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 1: Comparison of modelled and measured bulk concentration of methane in themixed layer for March to May 2002 at Cabauw tall tower.Figure 2: Density of the model error for methane at the different observation heights atCabauwFigure 3: Prior (black line) and posterior estimates (red bars, error bars indicate inversionuncertainty) of the emission of methane around Cabauw for the 25 aggregated sourceregions that could be determined through the inverse COMET calculation for observedmethane concentration at Cabauw in 2002Figure 3 shows the prior emissions (black line) of 25 aggregated regions that can beresolved from 1 year (2002) of methane observations at Cabauw. The regionnumbers (at the x-axis) grow with distance from Cabauw and with the size of the50


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportsource region. The resulting emissions are shown on the map in Figure 4. The mapalso shows how the aggregation areas get bigger with distance from the receptorpoint Cabauw. The procedure can be repeated by inverting time-series for Cabauw ofthe respective years 2000-2006. The result of this exercise is shown in Table 1. Asexpected the emissions estimated from the inversion process are higher than theprior METDAT fluxes, as the latter do not contain the natural fluxes. Interannualvariability of the fluxes is quite large. However, the overall uncertainty of the annualflux for The Netherlands is estimated by the inversion routine to be 20-30%, so theyear to year differences found here can not be seen as highly significant.Figure 4: Posterior emissions, determined by the COMET model and the SVD inversionwhen applied to the 2002 timeseries for methane concentrations at CabauwTable 2: Prior and posterior estimates of the annual mean emission of methane from theNetherlands, determined through the inverse COMET calculation for observed methaneconcentration at Cabauw for the years 2000-2006. In 2004 not enough observations wereavailable.Year Emission kTon CH 4 /yrPrior (METDAT, 1998) 10202000 16002001 20002002 13502003 16002005 13502006 195051


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe exercise using observations from one station can be repeated by using timeseries from other (tall tower) observation sites and/or by extending the analysis tomultiple years. This will allow to reduce the inversion error drastically and to extendthe spatial coverage of the area for which the fluxes can be inverted. Figure 5illustrates that about 160 individual aggregated cells can be distinguished with highaccuracy for a synthetic data experiment where forward calculated concentrations for1 year for 8 receptor points (CHIOTTO tall towers [Vermeulen et al., 2007]) havebeen inverted using the source aggregation+SVD method.Figure 5: Left: Prior (red) and Posterior (blue) methane emission estimates for aggregatedareas in Europe using the observations of the 8 CHIOTTO tall towers in a synthetic dataexperiment. The gray area indicates the uncertainty of the emission estimate. Right: Theresulting aggregated source areas for the synthetic inversion. The observation sites areindicated with red triangles.Acknowledgements.The authors would like to thank KNMI for hosting the measurements at Cabauwtower. ECMWF and KNMI are acknowledged for the access to the ECMWF MARSmeteorological archive. Financial support for this research has come from theMinistry of VROM, Novem/Senter and the European Commission (CarboEurope-IPand CHIOTTO projects).ReferencesBerdowski, J. J. M., Draaijers, G. P. J., Janssen, L. H. J. M., Hollander, J. C. T., Loon, M. v.,Roemer, M. G. M., Vermeulen, A. T., Vosbeek, M., and Visser, H., Independent Checksfor Validation of Emission Estimates: The METDAT Example for Methane, Tech. Rep.P98/037, TNO, Apeldoorn, The Netherlands, 1998.Heimann, M. and Kaminski, T., Inverse modelling approaches to infer surface trace gasfluxes from observed atmospheric mixing ratios, in: Approaches to scaling of trace gasfluxes in ecosystems, edited by: Bouwman, A. F., 277–295, Elsevier, Amsterdam, ISBN0-444-82934-2, 1999.52


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportHouweling, S., Kaminski, T., Dentener, F., Lelieveld, J., and Heimann, M., Inverse modelingof methane sources and sinks using the adjoint of a global transport model, J. Geophys.Res., 104, 26 137–26 160, 1999.Olivier, J., Bouwman, A., Van der Maas, C., Berdowski, J., Veldt, C., Bloos, J., Vissche-dijk,A., Zandveld, P., and Haverlag, J., Description of EDGAR Version 2.0. A set of globalemission inventories of greenhouse gases and ozone-depleting substances for allanthropogenic and most natural sources on a per country basis and on 1 o x1 o grid, Tech.Rep. 771060 002, RIVM, Bilthoven, 1996.Stohl, A. and Thomson, D. J., A density correction for Lagrangian particle dispersion models,Boundary-Layer Meteorology, 90, 155–167, 1999.Vermeulen, A. T., Eisma, R., Hensen, A., and Slanina, J., Transport model calcula-tions ofNW-European methane emissions, Environ. Sci. Policy, 2, 315–324, 1999.Vermeulen A.T., G. Pieterse, A. Hensen, W.C.M. van den Bulk, and J.W. Erisman, COMET:A Lagrangian transport model for greenhouse gas emission. Forward model techniqueand performance for methane. Atmos. Chem. Phys. Discuss., 6, 8727–8779, 2006.Vermeulen A.T., L. Haszpra, A. Lindroth, A. Manning, C. Messager, E. Moors, J. Mon-crieff,G. Pieterse, E. Popa, M. Schmidt, P. Stefani, The CHIOTTO tall tower program inEurope: first results. In: 13th WMO/IAEA meeting of experts on carbon dioxideconcentration and related tracer measurement techniques, Boulder, Colorado, U.S.A.,19-23 September 2005 (ed.: Miller, J.). WMO GAW, Vienna, Report nr 168, 2006.Vermeulen, A., G. Pieterse, A. Manning, M. Schmidt, L. Haszpra, E. Popa, R. Thompson, J.Moncrieff, A. Lindroth, P. Stefani, J. Morguí, E. Moors, R. Neubert, M. Gloor, CHIOTTO -Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gases, this report,2007.53


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportNew TM5-4DVAR inverse modelling system to estimate global andEuropean CH 4 sourcesPeter Bergamaschi 1 , Jan Fokke Meirink 2 , Maarten Krol 2,3 , and Maria Gabriella Villani 1[1] European Commission DG Joint Research Centre, Institute for Environment andSustainability, Ispra, Italy[2] Institute for Marine and Atmospheric Research Utrecht, University of Utrecht, TheNetherlands[3] Wageningen University and Research Centre, Wageningen, The NetherlandsIntroductionA new, 4-dimensional variational (4DVAR) inverse modelling system has beendeveloped for inverse modelling of atmospheric methane. The main advantage of thenew system is that it allows optimizing emissions of individual model grid cells. At thesame time very large sets of observational data can be used (e.g. high-frequencyin situ measurements or satellite data). In contrast, the previously widely usedsynthesis inversions were restricted to the optimization of emissions of larger, predefinedregions (e.g. continents, or countries), but could not further optimize spatialemission distributions within these pre-defined regions. Therefore, these approacheswere prone to the so-called aggregation error [Kaminski et al., 2001].4DVAR technique4DVAR techniques are widely used in numerical weather prediction in order tooptimize the initial state of the atmosphere. For application to inverse modelling, weextend the state vector, including the (1) initial 3D atmospheric mixing ratios, (2)monthly emissions per grid cell (and optionally also per emission category), and (3)further parameters as e.g. bias corrections for satellite data.The cost function:JT 11x x x B x x y H x T- R y H xn1 12 BB 2 OBS,i i i OBS,i ii1(with x : state vector; x B: a priori estimate of x ; B background error covariancematrix; y OBS: observations; H (x ) model simulations of observations; R observationserror covariance matrix; i index of assimilation time window)is minimized iteratively, by evaluating its gradient:n1 T T T T -1 Jx B x x M MM H R y H x Bi11i-1iiiOBS, i(with M: atmospheric transport model; M T : adjoint of M; H observation operator)and applying the ECMWF conjugate gradient minimization algorithm [Fisher andCourtier, 1995]. We apply the atmospheric transport TM5 [Krol et al., 2005], anddeveloped its adjoint model for this purpose. Figure 1 shows the iterativei54


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportminimization for a 1-year global 4DVAR inversion, illustrating the very rapid decreaseof the gradient norm.Figure 1: Reduction of gradient norm by iterative 4DVAR minimization using the ECMWFconjugate gradient algorithm.First global 4DVAR inversions and comparison with synthesis inversionFigure 2 shows an example of a global 4DVAR inversion, using a priori emissionsfrom 11 different source categories. We assumed constant uncertainties of monthlyemissions per grid cell, ranging between 20 and 80% for the different sourcecategories. Furthermore, a spatial decorrelation length of 500 km was assumed. Asobservational data we used the CH 4 surface measurements from the NOAA network([Dlugokencky et al., 1994], background sites only).The 4DVAR inversion leads to some redistribution between NH and SH sources,and, as large scale regional features, increased emissions over the Amazon andtropical Africa, and decreased emissions over Canada and Siberia.We compare these results with results from a recent synthesis inversion (Figure 3),for which the same a priori emission inventories and the same observational datawere used, and for which 7 big global regions were defined (this synthesis inversionis described in detail in [Bergamaschi et al., 2007]). Obviously, despite the limitationsof the synthesis inversion, the agreement between both approaches is surprisinglygood: This is attributed to the fact that the synthesis inversion with the 11 sourcecategories already provides relatively high flexibility. Furthermore, the appliedbackground observations do not provide strong constraints on emissions of individualmodel grid cells, but rather on larger scale emissions.55


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 2: Global 4DVAR inversion for year 2003 using surface background observationsfrom the NOAA network. Upper panel: a priori emissions; lower panel: Inversion increment(i.e. a posteriori - a priori emissions).Figure 3: Inversion increment for synthesis inversion [Bergamaschi et al., 2007].56


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFirst European TM5-4DVAR inversionsFigure 4 shows a coupled global - European inversion, applying the 1 o x1 o zoomingover the European domain. Observational data include a number of high-frequencyobservations over Europe (and are identical to those used in a country-basedsynthesis inversion [Bergamaschi et al., 2005]; however we use here different a prioriemission inventories for the 4DVAR inversion). The first results for a series of 4DVARscenarios yield a posteriori total emissions for the EU-15 countries close to thosederived from the synthesis inversion (20-23 Tg CH 4 /yr derived in the synthesisinversion for scenarios S1-S9 [Bergamaschi et al., 2005]). The influence of the apriori emissions and further parameters of the 4DVAR system are currently furtherinvestigated.Figure 4: Coupled global-European 4DVAR inversion for year 2001 using high-frequencyobservations from several European monitoring stations (complemented by European andglobal flask measurements). Upper panel: a priori emissions; lower panel: Inversionincrement (i.e. a posteriori - a priori emissions).57


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 5 shows the achieved uncertainty reduction in the 4DVAR inversion(approximation based on leading eigenvectors). The figure illustrates the significantuncertainty reduction in particular close to high-frequency observations.Figure 5: Uncertainty reduction achieved by 4DVAR inversion.Conclusions The new 4DVAR inverse modelling system allows flexible optimization of complexsystems with very large numbers of parameters (emissions from individual modelgrid cells) and very large numbers of observations (both in the order of 10 4 - 10 6 ). First 4DVAR results show high consistency with previous synthesis inversions(both on global and European scale). The much higher flexibility of the 4DVARsystem becomes apparent when using continental high-frequency surfaceobservations (or satellite data), minimizing the 'aggregation-error'. The first preliminary results of the European 4DVAR inversion yield total CH 4emissions from EU-15 countries close to previous synthesis inversions[Bergamaschi et al., 2005].AcknowledgmentsWe are grateful to the NOAA, AGAGE, and GAW networks for the provision of theirobservational data. Furthermore, we thank A. Vermeulen, M. Ramonet, and F.Meinhardt for delivery of high-frequency measurements at various Europeanmonitoring stations.ReferencesBergamaschi, P., C. Frankenberg, J.F. Meirink, M. Krol, F. Dentener, T. Wagner, U. Platt,J.O. Kaplan, S. Körner, M. Heimann, E.J. Dlugokencky, and A. Goede, Satellitechartography of atmospheric methane from SCIAMACHY onboard ENVISAT: (II)58


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportEvaluation based on inverse model simulations, J. Geophys. Res., 112, D02304,doi:10.1029/2006JD007268, 2007.Bergamaschi, P., M. Krol, F. Dentener, A. Vermeulen, F. Meinhardt, R. Graul, M. Ramonet,W. Peters, and E.J. Dlugokencky, Inverse modelling of national and European CH4emissions using the atmospheric zoom model TM5, Atmos. Chem. Phys., 5, 2431-2460,2005.Dlugokencky, E. J., L. P. Steele, P. M. Lang, and K. A. Masarie, The growth rate anddistribution of atmospheric methane, J. Geophys. Res., 99, 17,021– 17,043, 1994.Fisher, M. and P. Courtier, Estimating the covariance matrices of analysis and forecast errorin variational data assimilation. Technical Memorandum 220, ECMWF, Reading, U.K.,1995.Kaminski, T., P. J. Rayner, M. Heimann, and I. G. Enting, On aggregation errors inatmospheric transport inversions. J. Geophys. Res., 106, 4703–4715, 2001.Krol, M.C., S. Houweling, B. Bregman, M. van den Broek, A. Segers, P. van Velthoven, W.Peters, F. Dentener, and P. Bergamaschi, The two-way nested global chemistry-transportzoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417-432, 2005.Meirink J.F., Bergamaschi P., and Krol M.: Four-dimensional variational data assimilation forinverse modelling of methane emissions, paper in preparation, 2007,Meirink, J.F., H.J. Eskes, and A.P.H. Goede, Sensitivity analysis of methane emissionsderived from SCIAMACHY observations through inverse modelling, Atmos. Chem. Phys.,6, 1275-1292, 2006.59


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportInverse modelling activities at LSCE: from global to regional scalesPhilippe BousquetLaboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL), Gif sur Yvette,FranceAcknowledgements : C. Aulagnier, F.M. Bréon, F. Chevallier, C. Carouge, S.Houweling, T. Lauvaux, P. Peylin, P. Rayner, L. Rivier and P. Ciais.“Traditional” approachAtmospheric inverse modelling is a technique to analyse atmospheric observations ofa gas (concentrations) in terms of sources and sinks (fluxes), generally using atransport model to link fluxes and concentrations. LSCE has been involved ininversion of greenhouse gas sources and sinks since 1997. Based on the work of[Enting et al., 1995], we developed an inverse system solving for monthly sourcesand sinks of CO 2 over large regions against available observations assimilated asmonthly means from 1979 to present [Bousquet et al., 1999a; Peylin et al., 1999].Isotopic measurements can be added as constraints to partition the different types ofemissions or sinks [Bousquet et al., 1999b]. Inverse formalism is based on[Tarantola, 1987] and explicitly builds and inverts the matrices involved in the inversecalculation, producing estimates of both fluxes and their uncertainties. Recentlyinversion of long-lived reactive gas emissions has been implemented for methylchloroformand methane [Bousquet et al., 2006; Bousquet et al., 2005]. Comparisonwith land-surface models calculations were also performed systematically [Peylin etal., 2005].This so-called “traditional approach” provided a lot of scientific results from global tocontinental scales. For instance, it confirmed that a large terrestrial carbon sink existover non-tropical northern hemisphere lands [Gurney et al., 2002]. It also providedevidences that the oceanic climatology of CO 2 air-sea fluxes was overestimatingsouthern ocean sinks by a factor of 2. After integrating winter measurements in theclimatology, the inverse assessment was confirmed. Most of these results,consistently obtained by several research groups, were emphasized by theTRANSCOM inter-comparison project, which provided a very efficient and stimulatingframework to the CO 2 community since the mid 1990s [Baker et al., 2006; Denning etal., 1999; Gurney et al., 2002; Gurney et al., 2003; Gurney et al., 2004; Law et al.,2003; Law et al., 1996]. An interesting result from TRANSCOM experiment is that the2 main limitations of atmospheric inversions are the lack of observations and theerrors in atmospheric transport modelling. The former limits the spatio-temporalresolution of atmospheric inversions and the latter creates large spreads in theestimations of CO 2 sources and sinks between the different research groups. Forinstance, the partition of the northern hemispheric carbon sink between NorthAmerica, Asia and Europe is still uncertain largely because errors in transportmodelling [Peylin et al., 2002]. An important point about most CO 2 inversionsperformed so far is that fossil fuel emissions were more or less prescribed annuallyaccording to inventories in order to retrieve land and ocean sinks. A recent60


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportcomparison exercise within TRANSCOM showed that accounting for hourly to annualvariability of fossil emissions in a pixel-based inversion produces a significant effecton retrieved sinks in Europe (Figure 1 : Peylin, Houweling, pers. com.).Figure 1 : Monthly inverse fluxes (January and July) estimated with LMDZ transport modelfor a standard inversion using EDGAR annual fossil fuel emissions (lower panels) and for aninversion using the difference between IER hourly fossil emissions and EDGAR annual fossilfuel emissions (upper panels) [P. Peylin, S. Houweling, pers. com.]Interannual variabilityAfter 1999, at LSCE, we concentrated our efforts on the interannual variability (IAV)of greenhouse gas sources and sinks. IAV is less sensitive to systematic errors intransport modelling as it uses changes of concentrations to infer changes in thefluxes, thus cancelling (at least partly) systematic modelling errors. Adapting ourmethodology to analyse long time series of observations, we found that land CO 2fluxes were 2-3 times as large the air-sea fluxes [Bousquet et al., 2000]. IAV of CO 2land sinks was also successfully compared with vegetation models estimates,providing hints on the underlying processes that are not directly accessible withinverse models. Recently, we explained the small CH 4 growth rate of the early 2000sby a compensatory effect between reduced wetland emissions and increasinganthropogenic emissions, especially in Asia [Bousquet et al., 2006]. The partitionbetween natural and anthropogenic emissions is based on (1) different spatiotemporalpatterns of the emissions, (2) the use of 13 CH 4 observations, and (3) theadditional assumption that anthropogenic emissions vary more smoothly in time thannatural ones. We also found that European CH 4 emissions were decreasing onaverage since the late 1980s (Figure 2) mainly due to energy-related and ruminant61


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportanimal emissions, in good agreement with EDGAR3.2 inventory [Olivier andBerdowski, 2001].Figure 2: CH 4 emissions for Europe (Atlantic to the Ural Montains, in Tg CH 4 /yr) for differentanthropogenic sources as inferred by atmospheric inversions from Bousquet et al. [2006].Solid black line represents the standard inversion. Shaded area represents the range of the18 inversions performed. Seasonality has been removed. Black boxes (right panel) standsfor prior estimates and their uncertainties. Blue bars represent posterior uncertainties. Reddots represents estimates from EDGAR inventory (a 20% error was assumed).Recent developmentsThe traditional inverse approach has several caveats, nicely summarized byBergamaschi and Houweling in the first Inverse Modelling Workshop report[Bergamaschi et al.,2004]. Among all issues, the use of large regions was pointed outto cause the so-called aggregation error: if the prescribed flux patterns within eachregion is wrong, an error is made and carried out in the inverse procedure. Asinitiated by [Rödenbeck et al., 2003], we handled this problem by solving fluxes atmodel resolution. Such inverse system are totally underdetermined (much moreunknowns than observations) and require the use of additional constraints to linkmodel pixels, such as distance-based correlation of errors. For observations, as theknowledge of continental sources and sinks of greenhouse gas became a priority in62


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportthe mid 1990s, continuous monitoring sites largely developed, especially in Europeand North America, providing much more information at regional (space) andsynoptic (time) scales. First satellite retrievals of greenhouse gas also recentlybecame available, as SCIAMACHY for CH 4 [Frankenberg et al., 2005], largelyincreasing the number of observations constraining inverse problems. With largeobservation and flux spaces (typically 10 6 x 10 6 matrices for inversion of satellite dataat model resolution), traditional matricial approach cannot be applied anymore. Newmethods must be used that generally directly minimizes a cost function. At LSCE, wedeveloped a variational approach based on the 4D-VAR assimilation system ofECMWF [Chevallier et al., 2005]. This method was first applied to study the potentialof future OCO mission (Figure 3) to retrieve CO 2 from space [Chevallier et al., 2006].Other groups also developed variational approaches or Ensemble Kalman filters, andapplied them to CO 2 or CH 4 inverse problems [Bergamaschi et al., 2007b, Peters etal., 2005]. These methods can handle very large inverse systems and can give adirect estimate of flux uncertainties, but at a higher numerical cost than simple fluxestimates.Figure 3: Fractional error reduction of the monthly mean grid point CO 2 surface fluxes[Chevallier et al., 2006]. Error reduction is defined as 1- a / b , with a the posterior errorstandard deviation and b the prior error standard deviation.The use of satellite data and continuous surface measurements may remove thelimitation of the number of observations in the next years but the quality ofatmospheric transport modelling is still a major issue. Global models proved toproperly represent monthly concentrations at marine and remote sites. However, asland carbon budget is an important scientific and political target at regional-to-countryscales, one needs models that are capable to reproduce concentrations of63


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportgreenhouse gas close to the surface of the continental planetary boundary layer. Themajor limitations of global models are their coarse resolutions and the quality of theirvertical transport (both turbulence and convection). Several strategies weredeveloped to tackle these issues such as zoomable models [Hauglustaine et al.,2004], nested models [Krol et al., 2005] or regional models (domain-limited models).At LSCE, we are using LMDZ model [Hourdin and Armengaud, 1999; Hourdin andTalagrand, 2006; Hourdin et al., 2002] which is zoomable over 1 region (withresolution of typically 40km x 40km). We are also using regional models to evaluatetheir ability to improve the modelling of monitoring sites located in a complexenvironment: urban or industrial areas, mountains, valleys, etc. For instance, withinthe CARBOEUROPE-IP regional experiment ([Dolman et al., 2006], see alsoRödenbeck et al., this report), we will try to estimate the carbon budget of a 300km x300km region in the south west of France using a mesoscale model at 2 kmresolution coupled with a Lagrangian transport model together with intensivecampaigns with high tower and aircraft CO 2 measurements [Lauvaux et al., 2007].Finally, another path to estimate greenhouse gas sources and sinks is to optimizesome parameters of vegetation and ocean models instead of the exchange fluxesthemselves. One advantage is that there are much less parameters to estimate ascompared to flux estimate in atmospheric inversion. Such systems are called CarbonData Assimilation Systems (CCDAS, [Rayner et al., 2005]). They are based on thecoupling of one vegetation model and one atmospheric model and the use ofdifferent kind of observations: atmospheric concentrations, direct flux measurements,satellite retrievals of surface properties. LSCE is developing a CCDAS based onLMDZ and vegetation model ORCHIDEE [Krinner et al., 2005].ReferencesBaker, D. F., R.M. Law, K.R. Gurney, P. Rayner, P. Peylin, A.S. Denning, L. P. Bousquet,Bruhwiler, Y.-H. Chen, P. Ciais, I.Y. Fung, M. Heimann, J. John, T. Maki, S. Maksyutov,K. Masarie, M. Prather, B. Pak, S. Taguchi, and Z. Zhu, TransCom3 inversionintercomparison: Impact of transport model errors on the interannual variabilitof regionalCO 2 fluxes, 1988–2003, Global Biogeochem. Cycles, 20, doi:10.1029/2004GB002439,2006.Bergamaschi, P., Behrend, H., and Jol, A. (Eds.): Inverse modelling of national and EUgreenhouse gas emission inventories – report of the workshop “Inverse modelling forpotential verification of national and EU bottom-up GHG inventories” under the mandateof the Monitoring Mechanism Committee WG-1 23–24 October 2003, JRC, Ispra, 146pp., EUR 21099 EN/ISBN 92-894-7455-6, European Commission Joint Research Centre,Ispra, 2004.Bergamaschi, P., C. Frankenberg, J.F. Meirink, M. Krol, F. Dentener, T. Wagner, U. Platt,J.O. Kaplan, S. Körner, M. Heimann, E.J. Dlugokencky, and A. Goede, Satellitechartography of atmospheric methane from SCIAMACHY onboard ENVISAT: (II)Evaluation based on inverse model simulations, J. Geophys. Res., 112, D02304,doi:10.1029/2006JD007268, 2007a.Bergamaschi, P., J.F. Meirink, M. Krol, and G.M. Villani, New TM5-4DVAR inverse modellingsystem to estimate global and European CH 4 sources, this report, 2007b.Bousquet, P., P. Ciais, J. B. Miller, E. J. Dlugokencky, D. A. Hauglustaine, C. Prigent, G. R.Van der Werf, P. Peylin, E. G. Brunke, C. Carouge, R. L. Langenfelds, J. Lathiere, F.Papa, M. Ramonet, M. Schmidt, L. P. Steele, S. C. Tyler, and J. White, Contribution of64


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportanthropogenic and natural sources to atmospheric methane variability, Nature, 443, 439-443, 2006.Bousquet, P., P. Ciais, P. Peylin, M. Ramonet, and P. Monfray, Inverse modeling of annualatmospheric CO 2 sources and sinks 1. Method and control inversion, J. Geophys. Res.,104, 26161-26178, 1999a.Bousquet, P., D. A. Hauglustaine, P. Peylin, C. Carouge, and P. Ciais, Two decades of OHvariability as inferred by an inversion of atmospheric transport and chemistry of methylchloroform, Atmos Chem Phys, 5, 2635-2656, 2005.Bousquet, P., P. Peylin, P. Ciais, C. Le Quere, P. Friedlingstein, and P. P. Tans, Regionalchanges in carbon dioxide fluxes of land and oceans since 1980, Science, 290, 1342-1346, 2000.Bousquet, P., P. Peylin, P. Ciais, M. Ramonet, and P. Monfray, Inverse modeling of annualatmospheric CO 2 sources and sinks 2. Sensitivity study, J. Geophys. Res., 104, 26179-26193, 1999b.Chevallier, F., F. M. Bréon, and J. L. Rayner, The contribution of the orbiting carbonobservatory to the estimation of CO 2 sources and sinks: Theoritical study in a variationaldata assimilation framework, submitted to J. Geophys. Res.. 2006.Chevallier, F., M. Fisher, P. Peylin, S. Serrar, P. Bousquet, F.-M. Bréon, A. Chédin, and P.Ciais, Inferring CO 2 sources and sinks from satellite observations: Method andapplication to TOVS data, J. Geophys. Res., 110, D24309,doi:24310.21029/22005JD006390, 2005.Denning, A. S., M. Holzer, K. R. Gurney, M. Heimann, R. M. Law, P. J. Rayner, I. Y. Fung, S.M. Fan, S. Taguchi, P. Friedlingstein, Y. Balkanski, J. Taylor, M. Maiss, and I. Levin,Three-dimensional transport and concentration of SF 6 - A model intercomparison study(TransCom 2), Tellus B, 51, 266-297, 1999.Dolman, A. J., J. Noihlan, P. Durand, C. Sarrat, A. Brut, B. Piguet, A. Butet, N. Jarozs, Y.Brunet, D. Loustau, E. MLamaud, L. Tolk, R. Ronda, F. Miglietta, B. Gioli, V. Magliulo, M.Esposito, C. Gerbig, S. Körner, P. Galdemard, M. Ramonet, P. Ciais, B. Neininger, R. W.A. Hutjes, J. A. Elbers, R. Macatangay, O. Schrems, G. Perez-Landa, M. J. Sanz, Y.Scholz, G. Facon, E. Ceschia, and P. Beziat, CERES, the CarboEurope RegionalExperiment Strategy in Les Landes, South West France, May-June 2005, Bulletin of theAmerican Meteorological Society, 2006.Enting, I. G., C. M. Trudinger, and R. J. Francey, A Synthesis Inversion of the Concentrationand Delta-C-13 of Atmospheric CO 2 , Tellus B, 47, 35-52, 1995.Frankenberg, C., J. F. Meirink, M. van Weele, U. Platt, and T. Wagner, Assessing methaneemissions from global space-borne observations, Science, 308, 1010-1014, 2005.Gurney, K. R., R. M. Law, A. S. Denning, P. J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler,Y. H. Chen, P. Ciais, S. Fan, I. Y. Fung, M. Gloor, M. Heimann, K. Higuchi, J. John, T.Maki, S. Maksyutov, K. Masarie, P. Peylin, M. Prather, B. C. Pak, J. Randerson, J.Sarmiento, S. Taguchi, T. Takahashi, and C. W. Yuen, Towards robust regionalestimates of CO 2 sources and sinks using atmospheric transport models, Nature, 415,626-630, 2002.Gurney, K. R., R. M. Law, A. S. Denning, P. J. Rayner, D. Baker, P. Bousquet, L. Bruhwiler,Y. H. Chen, P. Ciais, S. M. Fan, I. Y. Fung, M. Gloor, M. Heimann, K. Higuchi, J. John, E.Kowalczyk, T. Maki, S. Maksyutov, P. Peylin, M. Prather, B. C. Pak, J. Sarmiento, S.Taguchi, T. Takahashi, and C. W. Yuen, TransCom 3 CO 2 inversion intercomparison: 1.Annual mean control results and sensitivity to transport and prior flux information, TellusB, 55, 555-579, 2003.Gurney, K. R., R. M. Law, A. S. Denning, P. J. Rayner, B. C. Pak, D. Baker, P. Bousquet, L.Bruhwiler, Y. H. Chen, P. Ciais, I. Y. Fung, M. Heimann, J. John, T. Maki, S. Maksyutov,P. Peylin, M. Prather, and S. Taguchi, Transcom 3 inversion intercomparison: Modelmean results for the estimation of seasonal carbon sources and sinks, Global.Biogeochem. Cycles, 18, GB1010, doi:10.1029/2003GB002111, 2004.Hauglustaine, D. A., F. Hourdin, L. Jourdain, M. A. Filiberti, S. Walters, J. F. Lamarque, andE. A. Holland, Interactive chemistry in the Laboratoire de Meteorologie Dynamique65


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportgeneral circulation model: Description and background tropospheric chemistry evaluation,J. Geophys. Res., 109, D04314, doi:04310.01029/02003JD003957, 2004.Hourdin, F., and A. Armengaud, The use of finite-volume methods for atmospheric advectionof trace species. Part I: Test of various formulations in a general circulation model,Monthly Weather Review, 127, 822-837, 1999.Hourdin, F., and O. Talagrand, Eulerian backtracking of atmospheric tracers. I: Adjointderivation and parametrization of subgrid-scale transport, Quarterly Journal of the RoyalMeteorological Society, 132, 567-583, 2006.Hourdin, F. D., F. Couvreux, and L. Menut, Parameterization of the dry convective boundarylayer based on a mass flux representation of thermals, J. Atmos. Sci, 59, 1105-1123,2002.Krinner, G., N. Viovy, N. de Noblet-Ducoudre, J. Ogee, J. Polcher, P. Friedlingstein, P. Ciais,S. Sitch, and I. C. Prentice, A dynamic global vegetation model for studies of the coupledatmosphere-biosphere system, Global. Biogeochem. Cycles, 19, GB1015,doi:1010.1029/2003GB002199, 2005.Krol, M., S. Houweling, B. Bregman, M. van den Broek, A. Segers, P. van Velthoven, W.Peters, F. Dentener, and P. Bergamaschi, The two-way nested global chemistry-transportzoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417-432, 2005.Lauvaux, T., M. Uliasz, C. Sarrat, F. Chevallier, P. Bousquet, C. Lac, K. J. Davis, P. Ciais, A.S. Denning, and P. Rayner, Mesoscale inversion: first results from the CERES campaignwith synthetic data , submitted to Atmos. Chem. Phys., 2007.Law, R. M., Y. H. Chen, K. R. Gurney, T. Modellers, TransCom 3 CO 2 inversionintercomparison: 2. Sensitivity of annual mean results to data choices, Tellus B, 55, 580-595, 2003.Law, R. M., P. J. Rayner, A. S. Denning, D. Erickson, I. Y. Fung, M. Heimann, S. C. Piper, M.Ramonet, S. Taguchi, J. A. Taylor, C. M. Trudinger, and I. G. Watterson, Variations inmodeled atmospheric transport of carbon dioxide and the consequences for CO 2inversions, Global. Biogeochem. Cycles, 10, 783-796, 1996.Olivier, J. G. J., and J. J. M. Berdowski, Global emissions sources and sinks, in The ClimateSystem, edited by J. Berdowski, et al., p. p. 33–37, 2001.Peters, W., J. B. Miller, J. Whitaker, A. S. Denning, A. Hirsch, M. C. Krol, D. Zupanski, L.Bruhwiler, and P. P. Tans, An ensemble data assimilation system to estimate CO 2surface fluxes from atmospheric trace gas observations, J. Geophys. Res., 110, D24304,doi:24310.21029/22005JD006157, 2005.Peylin, P., D. Baker, J. Sarmiento, P. Ciais, and P. Bousquet, Influence of transportuncertainty on annual mean and seasonal inversions of atmospheric CO 2 data, J.Geophys. Res., 107, 4385, doi:4310.1029/2001JD000857, 2002.Peylin, P., et al., Multiple constraints on regional CO 2 flux variations over land and oceans,Global. Biogeochem. Cycles, 19, GB1011, doi:1010.1029/2003GB002214, 2005.Peylin, P., P. Bousquet, P. Ciais, and P. Monfray, Time-Dependant vs Time-Independantinversion of the atmospheric CO 2 observations: consequences for the regional fluxes, inInverse methods in global biogeochemical cycles, Geophysical Monograph 114, editedby P. Kashibata, et al., American Geophysical Union, Washington, DC., 1999.Rayner, P. J., M. Scholze, W. Knorr, T. Kaminski, R. Giering, and H. Widmann, Two decadesof terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS),Global. Biogeochem. Cycles, 19, GB2026, doi:2010.1029/2004GB002254, 2005.Rödenbeck, C., S. Houweling, M. Gloor, and M. Heimann, CO 2 flux history 1982-2001inferred from atmospheric data using a global inversion of atmospheric transport, Atmos.Chem. Phys., 3, 1919-1964, 2003.Tarantola, A., Inverse problem theory, Amsterdam, The Netherlands, 1987.66


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportTop-down Methods in the Presence of Partial Carbon AccountingPeter RaynerLaboratoire des Sciences du Climat et l'Environnement (LSCE), Gif sur Yvette, FranceIn this brief summary I will try to describe some of the limitations of top-downmethods in the presence of partial carbon accounting and uses of these methods.This is a completely personal view.How top-down methods workTop-down methods are of many forms but they share the following common steps:1. Take the best possible current picture of fluxes. These fluxes may be describedusing regional patterns and these patterns may refer to different processesoccurring at the same place and time. (There remains a debate about startingwith such prior estimates but we can include the case with no prior information bytreating the initial uncertainty as infinite.)2. Insert these fluxes into an atmospheric transport model and compare withobservations.3. Adjust fluxes concentrating on the most uncertain regions.4. Sum all processes at a given point or over a region to infer net flux.5. Recall that the atmosphere is not the only method of transport of material from apoint or region and such lateral fluxes may be invisible to top-down methods.The problem of equifinality in which two processes have identical impact on theobservations is well-known in many inverse problems. In the context of accounting itis only a problem if one process is inside and the other outside the accountingframework. In general equifinality arises because the atmospheric datasets are toosparse but there are cases where we could not hope, even in principle, to distinguishprocesses. An example is the inadvertent “thickening” of existing forests vs.deliberate planting.The above raises the question of the exact uses of top-down methods in this context.One obvious point is that partial carbon accounts cannot be validated by top-downmethods but they can be falsified. This can be achieved by following the first twosteps of the top-down algorithm listed earlier. The comparison of atmosphericconcentrations (driven by the best estimate of fluxes) and observations provides onetest of the accounts. It requires extra work since parts of the full carbon budget notincluded in the partial account must be calculated. Such a technique works best at aregional scale where mismatches with the atmosphere cannot be blamed on errors in67


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportbudgets from other regions. Note the use of assimilated fields as boundary conditionsfor regional inversions in the GEMS summary.Another approach is to use the atmosphere to test the models that often underpincarbon accounting procedures. Such models (either empirical or mechanistic)describe quantities like the productivity of current and preexisting land cover. Asshown in various studies combining terrestrial models and atmospheric observationsin a data assimilation framework, the parameters in such models are oftenobservable, using atmospheric concentrations.68


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportData assimilation of atmospheric CO 2 : CarbonTrackerWouter Peters 1,2,3 , Maarten Krol 3 , Andy Jacobson 1,2 , Ken Masarie 1 , Pieter Tans 1 ,Arlyn Andrews 1 , Lori Bruhwiler 1 , Tom Conway 1 , Adam Hirsch 1,2 , John B. Miller 1,2 ,Gabrielle Pétron 1,2 , Colm Sweeney 1,2 , Doug Worthy 4 , Jim Randerson 5 , and Guidovan der Werf 6[1] NOAA Earth Systems Research Lab, Boulder, Colorado, USA[2] Cooperative Institute for Research in Environmental Sciences, University of Colorado,Boulder, Colorado, USA[3] Wageningen University, Wageningen, The Netherlands[4] Meteorological Service Canada, Toronto, Canada[5] University of California, Irvine, California[6] Free University, Amsterdam, The NetherlandsCarbonTracker is the name of the new data assimilation system for CO 2 developed atNOAA ESRL. Its primary purpose is to convert high precision mole fractionobservations of CO 2 into estimates of surface fluxes which are needed to improve ourunderstanding of the carbon cycle. The system design is based on ensemble Kalmanfiltering techniques that are gaining traction in the operational meteorological forecastcommunity due to its efficiency in solving large optimization problems efficiently.CarbonTracker is envisioned to handle continuous data from many sites in the nearfuture,including flux observation sites and possibly satellite observed radiances inCO 2 absorption bands.At the heart of CarbonTracker is a large set of calibrated CO 2 mole fraction observationsfrom the Cooperative Air Sampling Network, as well as from a set of instrumentedtowers that measure CO 2 continuously. Specifically, daytime average CO 2mole fractions are used from: (1) the 396m level of the WLEF tower in Wisconsin, (2)the 107m level of the AMT tower in Argyle, Maine, (3) the 251m level of the KWKTtower in Texas, (4) the 40m level of the tower in Fraserdale, Canada operated by theMeteoro-logical Service Canada (MSC), and (5) the 23m level of the tower at CandleLake, Canada operated by MSC. In total, more than 28,000 observations are used toestimate weekly global CO 2 fluxes for the period 2000-2005.Surface fluxes of CO 2 are simulated using a set of flux ‘modules’, each designed torepresent a specific process in the carbon cycle. We currently consider exchange betweenthe atmosphere and the biosphere and oceans, as well as emissions from biomassburning and fossil fuel use. The flux modules are designed to be simple yetcarry the most important variability that is hard to constrain from CO 2 observationsalone. This includes for instance the effect of sunlight and temperature onphotosynthesis and respiration, and the increasing ocean exchange as a function ofwind speed. Ocean and biospheric exchange are both optimized to fit simulated CO 2to the observed records.The biosphere model currently used in CarbonTracker is the Carnegie-AmesStanford Approach (CASA) biogeochemical model. This model calculates globalcarbon fluxes using input from weather models to drive biophysical processes, as69


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportwell as satellite observed Normalized Difference Vegetation Index (NDVI) to trackplant phenology. The version of CASA model output used so far was driven by yearspecific weather and satellite observations, and including the effects of fires onphotosynthesis and respiration (see van der Werf et al., [2006] and Giglio et al.,[2006]). This simulation gives 1x1 degree global fluxes on a monthly time resolution.Net Ecosystem Exchange (NEE) is re-created from the monthly mean CASA NetPrimary Production (NPP) and ecosystem respiration (RE). Higher frequencyvariations (diurnal, synoptic) are added to Gross Primary Production (GPP=2*NPP)and RE(=NEE-GPP) fluxes every 3 hours using a simple temperature Q10relationship assuming a global Q10 value of 1.5 for respiration, and a linear scaling ofphotosynthesis with solar radiation. The procedure is very similar, but NOT identicalto the procedure in Olsen and Randerson [2004] and based on ECMWF analyzedmeteorology. Note that the introduction of 3-hourly variability conserves the monthlymean NEE from the CASA model. Instantaneous NEE for each 3-hour interval is thuscreated as:NEE(t) = GPP(I, t) + RE(T, t)GPP(t) = I(t) * ((GPP) / (I))RE(t) = Q10(t) * ((RE) / (Q10))Q10(t) = 1.5((T2m-T0) / 10.0)where T=2 meter temperature, I=incoming solar radiation, t=time, and summationsare done over one month in time, per gridbox. The instantaneous fluxes yieldedrealistic diurnal cycles when used in the TransCom Continuous experiment.Ocean exchange of CO 2 in our system is computed using climatological air-seadifferences in partial pressure of CO 2 combined with 3-hourly wind speed andbarometric pressure from the atmospheric transport model. Seawater pCO 2 isprovided by Takahashi et al. [2002]. Atmospheric pCO 2 is, however, modulated bythe surface barometric pressure in the atmospheric transport model following theformulation of Kettle and Merchant [2005]. The gas transfer coefficient (k) isparameterized following the quadratic wind speed formulation of Wanninkhof [1992]for instantaneous winds. Gas exchange is computed every 3 hours using theEuropean Centre for Medium-Range Weather Forecasts (ECMWF) forecastmeteorology of the atmospheric transport model. The introduction of high resolutionwind speed and pressure variability on the ocean fluxes follows the methods of Batesand Merlivat [2001] and Kettle and Merchant [2005]. The latter study found a 7%decrease in annual mean ocean sink when covariations of wind and pressure aretaken into account. Air-sea transfer is inhibited by the presence of sea ice, and forthis work fluxes are scaled by the daily sea ice fraction in each gridbox provided bythe ECMWF forecast data.Fossil fuel emissions are prescribed in the system and not optimized. Withoutdetailed observations of the 14 C content of CO 2 in the atmosphere currentatmospheric based estimates can not improve on bottom-up inventories. The currentimplementation of fossil fuel emissions uses CDIAC annual global total emissions[Marland et al., 2006] to scale EDGAR 1998 spatial patterns at 1x1 degrees [Olivieret al., 2001]. Moreover, North American emissions carry a seasonal cycle derivedfrom the Blasing et al. [2005] inventory. Similarly, monthly fire emissions are70


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportprescribed to the atmosphere based on emission estimates from the Global FireEmissions Database version 2 (GFED2).During the assimilation, the difference between forecast and observed CO 2 molefractions drives changes to a set of linear scaling factors on the fluxes. The scalingfactors are estimated for each week and assumed constant over this period. Eachscaling factor is associated with a particular region of the global domain, andcurrently the geographical distribution of the regions is fixed. The choice of regions isa strong a priori constraint on the resulting fluxes and should be approached withcare to avoid so-called "aggregation errors" [Kaminsky, 1999]. We chose anapproach in which the ocean is divided up into 11 large basins encompassing largescaleocean circulation features, as in the TransCom inversion study (e.g. Gurney etal., [2002]). The terrestrial biosphere is divided up according to ecosystem type aswell as geographical location. Thereto, each of the 11 TransCom land regionscontains a maximum of 19 ecosystem types derived from the Olson ecosystemdatabase [Olson, 1992].The final product from the assimilation has been assessed rigorously from astatistical point of view. Both parameter and observation residuals were found to beGaussian and normally distributed relative to the prescribed errors.Estimated fluxes for the period 2000-2005 indicate that the North Americanbiosphere was a sink of carbon of close to 0.7 PgC/yr. This sink is geographicallylocated in Central Canada and across the East coast of the United States. It islargest in areas dominated by forest ecosystems, although a substantial part of itoccurs on grass and shrub lands that remain from agricultural practices. Stronguptake in croplands derived from the atmospheric data is somewhat misleading: theharvested goods are exported horizontally and consumed thereby releasing most ofthe carbon as a small source per unit area.Year-to-year variations show that 2002 was the lowest uptake year in the record,likely as a consequence of widespread extreme droughts over the US and Canadathat year. In contrast, 2004 was a relatively strong uptake year due to a relatively wetsummer and longer growing season. Principal component analysis did not reveal aquantitative correlation between the derived fluxes and either temperature, rainfall, ordrought indices. This lack of correlation between net-flux and climate variables wasalso seen in analyses of eddy-covariance observations of CO 2 fluxes [Zeng et al.,2005; Reichstein et al., 2007].An important test of the retrieved fluxes is their agreement with independent data.For this purpose we have compared CarbonTracker observations in the freetroposphere with a large set of CO 2 mole fractions from the NOAA ESRL AircraftSampling Program. The agreement with close to 14,000 independent samplesreveals a small seasonal cycle in their difference which is generally much smaller(


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportobservations from this location both on the seasonal cycle and synoptic variations(R2=0.93). A similar picture emerges when CarbonTracker is compared to partialcolumn CO 2 from 885 profiles across North America. Together, this suggests thatCarbonTracker is a good extrapolation of sparse CO 2 observations that can be usedto check satellite retrieved CO 2 across larger scales.All CarbonTracker results, data, and extensive documentation is available athttp://carbontracker.noaa.gov and can freely be used by other investigators. We planto update CarbonTracker once per year to extend and expand the observationsrecord and reflect the latest developments to our modeling framework.ReferencesAndres, R. J., Fielding, D. J., Marland, G., Boden, T. A., Kumar, N., & Kearney, A. T. ,Carbon dioxide emissions from fossil-fuel use, 1751-1950. Tellus B, 51 (4), 759-765,1999.Blasing, T. J., Broniak, C. T., & Marland, G.. The annual cycle of fossil-fuel carbon dioxideemissions in the United States. Tellus B, 57 (2), 107-115, 2005.Bosch, H., Toon, G. C., Sen, B., Washenfelder, R. A., Wennberg, P. O., Buchwitz, M., et al.,Space-based near-infrared CO 2 measurements: Testing the Orbiting Carbon Observatoryretrieval algorithm and validation concept using SCIAMACHY observations over ParkFalls, Wisconsin,. J. Geophys. Res., 111 (D23), D23302, 2006.Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., & Kasibhatla, P., Globalestimation of burned area using MODIS active fire observations, Atmos. Chem. Phys., 6(4), 957-974, 2006.Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D., Bousquet, P., et al..Towards robust regional estimates of CO 2 sources and sinks using atmospheric transportmodels, Nature, 415 (6872), 626-630, 2002.Kaminski, T., Heimann, M., & Giering, R. , A coarse grid three-dimensional global inversemodel of the atmospheric transport - 2. Inversion of the transport of CO 2 in the 1980s. J.Geophys. Res., 104 (D15), 18555-18581, 1999.Kettle, H. & Merchant, C. J. , Systematic errors in global air-sea CO2 flux caused bytemporal averaging of sea-level pressure, Atmos. Chem. Phys., 5 (6), 1459-1466, 2005.Marland, G., Andres, R. J., & Boden, T. A., Global CO 2 Emissions from Fossil-Fuel Burning,Cement Manufacture, and Gas Flaring: 1751-2003,http://cdiac.ornl.gov/ftp/ndp030/global.1751_2003.ems, 2006.Olivier, J. G. J., & Berdowski, J. J. M., Global emissions sources and sinks. In J. Berdowski,R. Guicherit, & B. J. Heij (pp. 33-78). Lisse, The Netherlands: A.A. BalkemaPublishers/Swets and Zeitlinger Publishers, 2001.Olsen, S. C. & Randerson, J. T.,. Differences between surface and column atmospheric CO 2and implications for carbon cycle research. J. Geophys. Res., 109 (D2), 2004.Olson, J. S., Watts, J. A., & Allison, L. J., Major world ecosystem complexes ranked bycarbon in live vegetation: A Database. Carbon Dioxide Information Center, Oak RidgeNational Laboratory, Oak Ridge, Tennessee, 1985.Reichstein, M., Papale, D., Valentini, R., Aubinet, M., Bernhofer, C., Knohl, A., et al.,Determinants of terrestrial ecosystem carbon balance inferred from European eddycovariance flux sites. Geophys. Res. Lett., 34 (1), L01402, 2007.Takahashi, T., Sutherland, S. C., Sweeney, C., Poisson, A., Metzl, N., Tilbrook, B., et al.,Global sea-air CO 2 flux based on climatological surface ocean pCO 2 , and seasonalbiological and temperature effects. Deep-Sea Research Part Ii-Topical Studies inOceanography, 49 (9-10), 1601-1622, 2002.72


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportVan Der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Kasibhatla, P. S., & Arellano,J., A.F. , Interannual variability in global biomass burning emissions from 1997 to 2004.Atmos. Chem. Phys., 6 (11), 3423-3441, 2006.Wanninkhof, R., Relationship between wind speed and gas exchange over the ocean. J.Geophys. Res., 97 (C5), 7373-7382, 1992.Washenfelder, R. A., Toon, G. C., Blavier, J. F., Yang, Z., Allen, N. T., Wennberg, P. O., etal., Carbon dioxide column abundances at the Wisconsin Tall Tower site, J. Geophys.Res., 111 (D22), D22305, 2006.Zeng, N., Qian, H. F., Roedenbeck, C., & Heimann, M.. Impact of 1998-2002 midlatitudedrought and warming on terrestrial ecosystem and the global carbon cycle, Geophy. Res.Lett., 32 (22), 2005.Figure 1: The components of CarbonTracker. Each component is built as a module andhooked to the global 2-way nested TM5 transport model.Figure 2: The five year average biological uptake pattern of CO 2 derived with Carbon-Tracker.73


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 3: The distribution of observed-minus-modeled CO 2 mole fraction for a large set offree tropospheric observations not used in the assimilation. The bars denote the averagedifference, while the whiskers reflect the standard deviation of the difference. The numberabove each bar shows the number of observations used in the average.Figure 4: (left) Comparison of CarbonTracker against column CO 2 observations from ParkFalls, Wisconsin by Paul Wennberg and colleagues. (right) Similar comparison against 885aircraft profiles reflecting partial CO 2 columns up to ~8km.74


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report4 EU-level Reporting on Soures and Sinks to UNFCCCand Bottom-up Inventories75


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportEU reporting on sources and sinksErasmia KitouEuropean Commission, DG Environment, Brussels, BelgiumGreenhouse gas (GHG) inventoryGHG inventory reports are prepared annually by all Member States (MS) and theEuropean Community (EC). The EC inventory is compiled on the basis of the bottomupinventories of the EC MS. The emissions of each source category are the sum ofthe emissions of the respective source and sink categories of the EC MS.DG Environment is responsible for the inventory submission to the UNFCCC and theoverall coordination. The EEA assisted by its Topic Center on Air Pollution andClimate Change, DG JRC and DG Eurostat are responsible for the compilation of theinformation at EC level. A comprehensive QA/QC process is in place to ensure thatall parties involved review, assess and correct as necessary the inventoryinformation.GHG inventories capture primarily information on anthropogenic emissions of carbondioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), hydrofluorocarbons (HfCs),perfluorocarbons (PFCs) and sulphur hexafluoride SF 6 from 5 sectors: energy,industial processes, agriculture, forestry, waste for the year (X-2).Key Category AnalysisThe preparation of the inventory includes a key category analysis to determine forwhich categories it will be necessary to use higher Tier methods. A key sourcecategory is defined as an emission source that has a significant influence on acountry’s GHG inventory in terms of the absolute level of emissions, the trend inemissions, or both. Depending on the tier, default, national or other emission factorsbased on modeling can be used.Uncertainty AnalysisAn uncertainty analysis is also required as part of the inventory preparation. Thetable below presents the uncertainties per level or trend at the EC level for thevarious sectors.Source category Share of emissions Level uncertainty estimates Trend uncertainty estimatesfor which MS uncertainty based on based onestimates are available MS uncertainty estimates MS uncertainty estimatesFuel combustion stationary 97% 2% 1%Transport 94% 3% 1%Fugitive emissions 92% 11% 8%Industrial processes 76% 8% 5%Agriculture 102% 41% - 104% 6% - 14%Waste 83% 18% 11%Total 94% 4% - 11% 1% - 2%76


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportHighest uncertainties both in terms of the level of emissions and the trends exist inthe area of agriculture followed by waste.Data GapsIn terms of information provided by MS, we note that most gaps exist in relation to F-gases.Fluorinated gasesFluorinated gas emissions account for 1.6% of total EU-15 GHG emissions. HFCsfrom consumption of halocarbons showed large increases between 1990 and 2004.The main reason for this was the phase-out of ozone-depleting substances such aschlorofluorocarbons under the Montreal Protocol and the replacement of thesesubstances with HFCs (mainly in refrigeration, air conditioning, foam production andas aerosol propellants). HFC emissions from production of halocarbons decreasedsubstantially.Energy sectorCRF Sector 1: ‘Energy’ contributes 80% to total GHG emissions and is the largestemitting sector in the EU-15. The most important energy-related gas is CO 2 thatmakes up 78% of the total EU-15 GHG emissions while CH 4 and N 2 O are eachresponsible for 1% of the total GHG emissions.Reference ApproachThe IPCC reference approach for CO 2 from fossil fuels for the EU-15 is based onEurostat energy data (NewCronos database). Energy statistics are submitted toEurostat which is responsible for compiling the annual energy balances which are inturn used for the estimation of CO 2 emissions from fossil fuels by MS and for the EU-15 as a whole. The Eurostat data for the EU-15 IPCC reference approach includesactivity data, net calorific values and carbon emission factors as available in theEurostat NewCronos database. For the calculation of CO 2 emissions, the IPCCdefault carbon emission factors are used.77


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportIndustrial Processes sectorCRF Sector 2 ‘Industrial processes’ is the third largest sector contributing 8% to totalEU-15 GHG emissions. The most important GHGs from ‘Industrial processes’ areCO 2 (5% of total GHG emissions), HFCs and N 2 O (1% each).UncertaintiesThe highest level uncertainty was estimated for CH 4 from chemical industry and thelowest for CO 2 from cement production. With regard to trend, SF 6 from metalproduction shows the highest uncertainty estimates while CO 2 from lime productionthe lowest.HFC emissions from the consumption of halocarbons have to be estimated based onleakage rates (obsolete equipment, use of equipment?). These assumptions areuncertain and real emissions may take place in different years than assumed.Agriculture sectorCRF Sector 4 ‘Agriculture’ contributes 9% to total EU-15 GHG emissions, making itthe second largest sector after ‘Energy’. The most important GHGs from ‘Agriculture’are N 2 O and CH 4 accounting for 5% and 4% of the total GHG emissions respectively.UncertaintiesCH 4 emissions from enteric fermentation is a key source category for cattle andsheep and are less uncertain (1%). Animal numbers are assumed to be correct witha maximum uncertainty of 10%, and also the emission factor known with a precisionbetter than 20% for most countries, with 40% being the highest uncertainty estimate.N 2 O emissions from agricultural soils belong to the most uncertain source categoriesof national GHG inventories.Table: Trend uncertainty for EU-15 emissions of N 2 O from agricultural soils by using differentassumptions of correlation estimated using Monte Carlo simulation. “YES” denotes fullcorrelation between years or Member States. Trend uncertainty is presented as percentagepoints.For direct N 2 O emissions, the highest uncertainty is attributed to the emission factor,which ranges between 48% and 400% relative uncertainty. For indirect emissions,both the activity data and the emission factors are considered equally uncertain(fraction of nitrogen leached, must be applied to determine the activity data).Uncertainties of indirect N 2 O emissions are estimated as up to 100% and 900% forthe activity data and emission factor, respectively. Compared to these values, the78


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportsub-category of animal production is less uncertain, with a maximum uncertaintyestimated (112%).Waste sectorCRF Sector 6 ‘Waste’ is the fourth largest sector in the EU-15, contributing 2.7% tototal GHG emissions.The highest level uncertainty was estimated for N 2 O from waste water handling.Land-use, land-use change and forestry (LULUCF) sectorThe main problems in the LULUCF sectors are: Lack of data Harmonization issues Uncertainties, e.g., due to high variability of emission factors Collection methods differ: design, spatial intensity, frequency of field survey, andof latest information available.It would be interesting to explore to what extent verification of greenhouse gasemissions provided by atmospheric observations could be combined with inversemodelling.Non-CO 2 emissionsMost non-CO 2 emissions are due to: CH 4 and NO 2 deriving from wildfires - especially in the Mediterranean countries N 2 O from disturbances associated with land-use conversion to croplandIn most cases these emissions appear negligible in comparison to emissions /removals of CO 2 .UncertaintiesThe uncertainties in the LULUF sector are linked to: forest area definitions activity data national forest inventories (NFI) calculation of stock increments volume stocks statistics, or harvest/drain statistics expansion and conversion factors, or biomass functionsand are estimated to be: 0.2–1.2% (3–15% for UK) for forest area (9 Member States); 0.54–5.1% (1–15% for UK) for wood volume (10 Member States); 0.4–0.8% for volume growth (3 Member States).79


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportDifficult issuesParticularly problematic for the LULUCF sector is land identification and tracking landtransitions over time for all activities such as afforestation and deforestation, forestcropland-,grassland-management and revegetation. Also reporting of soils,especially organic soils, as well as reporting on forest fires for Mediterraneancountries with regards to assessment of destroyed areas and the future of burnedland.Questions from the inventory community on inverse modeling Can IM/AM results already be used? To what extent? How? Caveats? When will the results be mature enough to be used? Level of disaggregation that can be provided? Sectoral? Spatial? National, EU-15,EU-27 levels? Level of disaggregation that is attainable? Associated uncertainties? Differences in uncertainties from one gas to the other? Gases that IM/AM can be most helpful for? Type of information to be derived? Trends? Level of emissions? Are GHG inventory-related information used for IM? To what extent? Can the inventory community help? How? Can a time series analysis be provided? How can IM/AM help policy analysis?80


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportEuropean greenhouse gas emissionsFrançois DejeanEuropean Environment Agency, Climate change and energy, Copenhagen, Denmark1. The EEA and its role in GHG inventoriesThe EEA aims to support sustainable development and to help achieve significantand measurable improvement in Europe’s environment, through the provision oftimely, targeted, relevant and reliable information to policy-making agents and thepublic. It is an EU body and has 32 member countries. The EEA coordinates theEuropean environment information and observation network (Eionet), which includesmore than 300 institutions, and delivers a wide range of information andassessments of:• The state of the environment and trends• Pressures on the environment and the driving forces behind them• Policies and their effectiveness• Outlooks/scenariosIn relation to air and climate change issues, the EEA delivers several reports,indicators, assessments and data, which are regularly updated and publicly availableon the EEA website (www.eea.europa.eu). For example: EEA reports and technical reports:o Annual European Community greenhouse gas inventory 1990–2004 andinventory report 2006 - Submission to the UNFCCC Secretariat,o Greenhouse gas emission trends and projections in Europe 2006,o The European Community's initial report under the Kyoto Protocol EEA core set indicators (CSI):o atmospheric greenhouse gas concentrations (CSI 013),o greenhouse gas emissions and removals (CSI 010),o global and European temperature (CSI 012),o projections of greenhouse gas emissions and removals (CSI 011) EEA Data Service:o EEA aggregated and gap filled air emission data,o Air Emission data set for IndicatorsThe EEA and JRC assist the European Commission for the submission of the annualEC GHG inventory to the UNFCCC:• Preparation of initial checks of Member States‘ submissions in cooperationwith Eurostat and the JRC, and circulation of the results from initial checks (statusreports, consistency and completeness reports)• Consultation with Member States in order to clarify data and other informationprovided• Preparation and circulation of the draft EC inventory and inventory reportbased on Member States' submissions (compilation work performed by ETC/ACCat UBA Vienna).81


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report• Preparation of the final EC inventory and inventory report (to be submitted bythe Commission to the UNFCCC Secretariat)• Assisting Member States in their reporting of GHG inventories by means ofsupplying software tools• Maintenance of the inventory database and of inventory archives• Implementation of QA/QC procedures for the EC inventory as outlined in the ECQA/QC programme.2. EU bottom-up inventoriesGHG emissions inventories for EU-27 and EU-15 are compiled annually, based onMember States bottom-up national inventories submitted to the UNFCCC. GHGinventories are established on an annual basis, by gas and by source and submittedaccording to a common reporting format (CRF).The reported gases for each individual source are: CO 2 , CH 4 , N 2 O, SF 6 , HFCs,PFCs. The main source categories are:CategoriesSub-categories1. Energy • Energy industries• Manufacturing industries andconstruction• Transport• Other sectors• Other• Fugitive emissions2. Industrial processes • Mineral products• Chemical industry• Metal production• Production of halocarbons and SF6• Consumption of halocarbons and SF63. Solvent and other productuse4. Agriculture • Enteric fermentation• Manure management• Rice cultivation• Agricultural soils5. LULUCF • Forest land• Cropland• Grassland6. Waste • Solid waste disposal on land• Wastewater handling• Waste incineration• Other7. OtherEmissions from international bunker fuels (aviation and marine) are not included inthese inventories.82


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportGuidance on reporting methodologies is provided in 1996 IPCC Guidelines forNational Greenhouse Gas Inventories, which comprise:- the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories,- Good Practice Guidance and Uncertainty Management in National GreenhouseGas Inventories,- Good Practice Guidance for Land Use, Land-Use Change and Forestry.3. Sectoral trends in European greenhouse emissions as estimated inbottom-up inventories and submitted to the UNFCCCAt the EU-15 level, energy-related GHG emissions represent 80% of totalanthropogenic emissions. Energy-related emissions include emissions fromtransport. The most important source categories are, by decreasing order:In the EU-15, emissions decreased in almost all sectors between 1990 and 2004,except for transport and, to a lesser extent energy industries.- CO 2 emissions from transport increased due to road transport growth.- N 2 O emissions from transport increased by more than 100 %, due to astandardized use of catalytic converters, which produce N 2 O as a by-product.83


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report- CO 2 emissions from energy industries increased due to fossil fuel consumption inpublic electricity and heat plants.In the EU-10, GHG emissions decreased between 1990 and 2004 in all the mainsectors responsible for greenhouse gas emissions, except in the transport sector(+29%).4. Trends by gasBetween 1990 and 2004, in the EU-15, the trends of GHG emissions by gas are asfollows:- CO 2 emissions increased by 4.4 %,- CH 4 emissions decreased by 26 %, mainly due to the decline of coal mining(fugitive emissions), reductions in solid waste disposal on land (waste sector) andfalling cattle population (agriculture)- N 2 O emissions decreased by 18%, mainly due to specific measures at adipic acidproduction plants (UK, Germany and France) (industrial processes) and thedecline in fertilizer and manure use (agricultural soils),- HFC, PFC and SF 6 from industrial processes decreased overall, although thisreflects large increases (expanding use of HFCs as a substitute for ozonedepleting CFCs), offset by decreases of emissions from the production ofhalocarbons and SF 6 .Emission trends by gas in the EU-15 between 1990 and 2004:kt CO2 eq50045040035030025020015010050019901991199219931994199519961997199819992000200120022003200410^-1*CO2 emissions (without LULUCF) CH4 N2O HFCs 10*PFCs 10*SF684


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFor EU-10 countries, CO 2 and CH 4 emissions decreased significantly between 1990and 2004 (-33% and -40%, respectively). N 2 O emissions were reduced by 15% whilefluorinated gases emissions increased by as much as 147% (some countries still donot report F-gases).5. Data quality and uncertainty issuesThere are currently several review processes existing for GHG bottom-upinventories:• In-country QA/QC reviews of inventories before these are actually submitted tothe European Commission (DG Environment),• Quality control of MS submissions (completeness, consistency, sector-specificchecks) at by EU experts• Internal review of EC inventory by EU and Member States experts• Official external UNFCCC review of submitted inventories by review teams formedby reviewers of other countries.In addition, Member States are performing uncertainty analyses on their ownreported emissions. In 2006, Tier 1 uncertainty analyses were available from 13 EU-15 Member States, covering 94 % of total EU-15 2004 GHG emissions. Based onthese estimates, the levels of uncertainty for the main GHG sources can be roughlyrepresented as follows:LULUCFCO 2Uncertainty levelAgriculture 41%-104%Higher uncertainty from N 2 OCH 4 also uncertainWaste 18%N 2 O and CH 4Fugitive emissions 11%CH 4 and CO 2Industrial processes 8%F-gases mostly uncertainTransport 3%Fuel combustion stationary 2%2005 estimates6. Relevancy of AM/IM studies in the context of bottom-up GHGinventories to the UNFCCCBased on currently available results from AM/IM studies, it appears that top-downestimates could provide useful sources of comparison with bottom-up estimates.For such comparisons to be relevant: The relevant gases to study through AM/IM are those:85


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report- for which anthropogenic and natural emissions can be easily be separated (inparticular fluorinated gases, N 2 O and CH 4 ),- that correspond to emissions from CRF sectors where data uncertainty iscurrently estimated to be relatively high (LULUCF, agriculture, waste, F-gasesfrom consumption of halocarbons).The relevant time scale for providing top-down estimates of GHG emissions isannual (or multi-annual), since GHG inventories are submitted every year to theUNFCCC. (Parties to the Convention are not required to calculate their emissionson a more frequent basis).The relevant geographical scales for providing top-down estimates of GHGemissions are:o country level,o EU-15 and EU-27 level.EEA contacts• Andreas Barkman (andreas.barkman@eea.europa.eu, +45 33 36 72 19)• François Dejean (francois.dejean@eea.europa.eu, +45 33 36 72 59)86


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportEDGAR and UNFCCC greenhouse gas datasets: comparisons asindicator of accuracyJos G.J. Olivier 1 and John A. van Aardenne 2[1] Netherland Environmental Assessment Agency, Bilthoven, The Netherlands,[2] European Commission, DG Joint Research Center, Ispra, Italy.Description of EDGAR.The Emission Database for Global Atmospheric Research (EDGAR) contains globalanthropogenic emissions inventories of various trace gases. Over the years severaldatasets have been released that have been used as a priori information by theinverse modeling community. The current version EDGARv32 provides emissions forthe years 1990, 1995 and 2000.In EDGAR, emissions are calculated using an emission factor approach. Informationon activity data, emission factor and other explanatory variables are organized bysource category, country, and region or as grid maps. In general emissions are firstcalculated on a country basis by multiplying activity levels by compound specificemission factors (see equation 1). In addition, thematic maps on a 1 o x 1 o grid areused by relating a specific grid map to each emission process defined using a spatialallocation function to convert total country emissions to grid emissions per processinvolved. Temporal resolution of emissions within a year is not included explicitly,although - based on European and North American studies - monthly, weekly anddiurnal scale factors are proposed in the EDGAR documentation.i: compoundj: countryk: sectorl: process by fuel/technologym: abatement technologyAC: activity dataEF: emission factor (no explicit abatement technology specified but application of technologyincluded in emission factor for each year)t: time (year)Currently in a collaboration of the DG Joint Research Centre and the DutchEnvironmental Assessment Agency, a new database structure is under developmentin which a technology based approach and gridded emissions to 0.1 x 0.1 degree aredefined. The release of this EDGARv4 is foreseen in 2007.87


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportUncertainty in emissions inventory calculations.In EDGARv32 most data on emission activities (fuel consumption, industrialproduction etc.) is taken from international statistics such IEA Energy balances andUN industrial commodity and statistics. In National Communications this informationis in general from national statistical offices. Although quality control is a standardpractice in these datasets, uncertainty in these datasets is larger for non-OECDcountries, especially when countries face a change in government structure over timewith the Former Soviet Union as an illustrative example.Emission factor data in greenhouse gas emission inventories are often combinationof data from IPCC emissions guidelines and scientific publications. In general,uncertainty in these emission factors exist due to for example applicability of defaultemission factors and lack of information on installed abatement technology. Althoughnot applied in National Communications, allocation of national emissions to grid asdone in EDGAR is another source of uncertainty. Issue of concern here are theapplicability of the selected grid map to distribute emissions of a specific emissionsactivity. For some world regions locations of large industrial facilities are known, whilein regions where this information is missing, population density is used as proxy togrid these emissions. Also the temporal variation of the emission inventories isfurther source of uncertainty. Annual emissions have to be allocated to monthly,weekly or sometimes daily variations in the atmospheric dispersion models. Oftendata to allocate these emissions to a time pattern is not available on the global scale(e.g. monthly electricity production).Assessment of uncertaintyAlthough several methodologies have been mentioned in the literature to assessuncertainty/accuracy of emission inventories, due to time and data limitations mostinventory activities limit themselves to expert judgment, and for some sectors tostatistical uncertainty assessments (e.g. Monte Carlo approaches).Here three methods to estimate uncertainty in national and global emissioninventories are presented: (1) expert estimation of uncertainty in emission factors,activity data and combined regional and global budgets, (2) changes in subsequentemission reports as indicator of uncertainty, (3) difference between independentemission inventories as illustration.(1) Based on expert estimation, uncertainty estimates have been provided for theEDGAR datasets. Table 1 present the results for CO 2 , CH 4 and N 2 O emissions. Theoverall uncertainty of global and regional emission budgets as calculated in EDGARis ~10% for CO 2 , ~50% for CH 4 , and ~100% for N 2 O.88


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportTable 1: Uncertainty estimates of EDGARv32 greenhouse gas calculations.Main source Sub-category Activity Emission factors Global and regional emissionsdata CO 2 CH 4 N 2 O CO 2 CH 4 N 2 OFossil fuel use Fossil fuel combustion S S M M S M MFossil fuel production S M M - M M -Biofuel Biofuel combustion L S M L L L LIndustry/ Iron & steel production S - S - S -solvent use Non-ferro production S - S - - S -Chemicals production S - S L - S MCement production S S - - S - -Solvent use M - - - - - -Miscellaneous V - - - - - -Landuse/ Agriculture S - L L - L Lwaste treatment Animals (excreta; ruminants) S - M L - M LBiomass burning L S M L L L LLandfills L - M - - L -Agricultural waste burning L - L L - L LUncontrolled waste burning L - - - - - -Natural sources Natural soils M - L L - L LGrasslands M - M L - M LNatural vegetation M - M - - M -Oceans/wetlands M - L L - L LLightning S - - - - - -CO 2 CH 4 N 2 O CO 2 CH 4 N 2 OAll sources - - - - S M LNotes: Expert judgement of uncertainty ranges, which were assigned with the following classification in terms of order ofmagnitude of the uncertainty in mind: S = small (10%); M = medium (50%); L = large (100%); V = very large (>100%)."-" Indicates that the compound is not applicable for this source or that emissions are negligible.(2) Another approach to be used as indicator for uncertainty in emission inventoriesis the changes in subsequent national emission reports to the UNFCCC. Asillustrated in Figure 2, the changes between following emission reports can be up to50%.UNFCCC CH4 total: changes in subsequent 2001 emissions60%50%40%30%LY05/04LY06/05LY06/0420%10%0%-10%Australia-20%-30%-40%AustriaBelgiumCanadaDenmarkEuropean CommunityFinlandFranceGermanyGreeceIrelandItalyJapanNetherlandsNew ZealandPortugalSpainSwedenSwitzerlandUnited KingdomUnited States of AmericaFigure 2: Change in CH 4 emissions are reported in subsequent years.89


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report(3) By comparing EDGAR CH 4 estimates with CH 4 calculations in NationalCommunications an indication of the total uncertainty and the uncertainty by sectorscan be analyzed. For example, Figure 3 presents the difference between EDGARyear 2000 emissions and national calculations. In the case of France and UnitedKingdom these differences are large. The difference for the United Kingdom seemsto be related to the waste sector in which EDGAR calculates about 400 Gg, whileaccording to UK calculations the total should amount up to 1500 Gg. Further analysisshows that probably this discrepancy is caused by the uncertainty in landfilled wastedata with as key parameters the municipal solid waste generated by capita, thefraction of waste put into landfills and the amount of landfill gas recovered.Gg CH4450040003500300025002000150010005000AustriaBelgium 1)EDGARUNFCCCFRADEUCH4 total (2000)DenmarkFinlandFranceGermanyGreeceIrelandItalyLuxembourgNetherlandsUKUSAPortugalSpainSwedenUnited KingdomEU-15 * 1/10USA * 1/10Figure 3: Difference between UNFCCC reported CH 4 emissions and EDGARv32 data for theyear 2000.As shown in the illustrations above and in more detail during the presentation of thisworkshop, a first indication of the uncertainty of emission inventories can be providedthrough comparison of independent emission inventory data such as the EDGARdata and National Communications. This type of assessment allows focusing thediscussion to specific sectors where the differences are large. This providesinformation on where the inventory improvement should be focused and it providesinverse modelers with information on likely causes of large differences between apriori and a posteriori emission calculations.Besides the uncertainty discussion the quality of emission inventories should improveto facilitate correct application in the inverse models. This means that besides theaccuracy of the emissions budget, the temporal and spatial patterns of inventoriesshould be accurate.90


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportAgriculture, Forestry and Other Land Uses (AFOLU): Realities andneeds for Kyoto reportingGünther SeufertEuropean Commission, DG Joint Research Center, Ispra, Italy.Selected JRC activities to improve AFOLU-reporting in the context of theEC Inventory systemThe European Community (EC), as a party to the United Nations FrameworkConvention on Climate Change (UNFCCC), reports annually on greenhouse gas(GHG) inventories within the area covered by its Member States. The legal basis ofthe compilation of the EC inventory is Council Decision No 280/2004/EC concerninga mechanism for monitoring Community greenhouse gas emissions and forimplementing the Kyoto Protocol.According to Implementing Provisions (2005/166/EC), the Joint Research Centre(JRC) assists in the improvement of methodologies for the Agriculture and LULUCFsectors. It does so by intercomparing methodologies used by the MS for estimating emissions andremovals, by leading projects for improving/harmonising the methodologies used forestimating GHG emissions and sinks, by providing EU-wide estimates with various models/methods for emissions andremovals with a focus on Agriculture and LULUCF (including inverse modellingusing ambient air measurement of GHGs).Figure 1: EC Inventory Compilation91


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 1 shows the compilation part of the inventory system of the EuropeanCommunity. The DG Environment of the European Commission is responsible forpreparing the inventory of the European Community (EC) while each Member Stateis responsible for the preparation of its own inventory which is the basic input for theinventory of the European Community. DG Environment is supported in theestablishment of the inventory by the following main institutions: the EuropeanEnvironment Agency (EEA) and its European Topic Centre on Air and ClimateChange (ETC/ACC) as well as the following other DGs of the European Commission:Eurostat, and the Joint Research Centre (JRC).In order to implement this task, JRC established a dedicated project (seehttp://ies.jrc.cec.eu.int/ghgdata.html) in FP6 (2002-2006, GHG DATA, Data QualitySystem for GreenHouse Gas Emissions and Sinks) and in FP7 (2007-2013, GHGAFOLU, GreenHouse Gases in Agriculture, Forestry and Other Land Uses).Selected activities of JRC Project GHG-Data/GHG AFOLU:Check of MS submissions to the annual EC inventory for sectors agriculture andLULUCF, and defending the EC-NIR during the annual NFCCC reviewEC experts sent to UNFCCC roster for reviewing the NIR of other partiesExpert input to writing and reviewing IPCC Guidelines 2006 (for the sectorAgriculture, Forestry and other Land Uses, AFOLU)Participation in research projects (e.g., Carbodata, CarboEurope-IP, NitroEurope-IP, CarboInvent, CAPRI-DynaSpat, Evergreen, Natair)AFOLU-DATA - web based information system for Policy-Research-Data (seehttp://afoludata.jrc.it/)In order to harmonise and to improve monitoring and reporting of GHG emissionsand sinks in the sectors agriculture and LULUCF, the project organised as series ofworkshops with experts from Member States and with the research community: Workshop on Carbon Sinks by Land Use Change and Forestry (LUCF), Ispra,Feb. 20-21, 2002 (Sink experts of MS, CarboEurope, DG ENV, EEA, FAO) Pilot Study on harmonising reporting on LUCF: Ispra, 2002-2003 (Participation of6 MS) Workshop on Emissions and Projections of GHG from Agriculture, Copenhagen,Feb. 2003 (with EEA) Inverse Modeling Workshop: Ispra, Oct.23-24 2003 (sink experts, DG ENV, EEA,research projects) Expert meeting on improving the quality of GHG emission inventories for Cat. 4D,Ispra, Oct. 21-22, 2004 Workshop on inventories and projections of GHG and NH3 emissions fromagriculture in Central and Eastern Europe, Ispra, 23 - 24.6.2005 Improving the Quality of Community GHG Inventories and Projections for theLUCF Sector, Ispra, 22.- 23.9.2005 Improving the Quality of Community GHG Inventories and Projections for theLUCF Sector, Ispra, 22.- 23.9.2005, with sink experts from 21 EU-Member States92


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportTechnical meeting on specific forestry issues related to reporting and accountingunder the Kyoto Protocol (Ispra, 27.- 29.11.2006, with sink experts from 26 KyotoParties)Workshop on “Atmospheric monitoring and inverse modelling for verification ofnational and EU bottom-up GHG inventories“ (Ispra, March 8-9, 2007)Perspectives of AFOLU reporting under the Kyoto ProtocolAnnex I Parties to the Protocol had to select, by end of 2006, any or all of thefollowing human-induced activities under Article 3.4: revegetation, forestmanagement, cropland management, grazing land management.In addition, by end of 2006, Parties had to adopt a definition of forest by selecting:- tree crown cover threshold 10 - 30 %;- land area threshold 0.05 - 1 ha;- tree height threshold 2 - 5 metres; and- minimum width as recommended by GPG LULUCFTable1: EU-15 Member State’s selections of threshold values for the forest definition forreporting under Article 3.3Table 2: New Member State’s selections of threshold values for the forest definition forreporting under Article 3.393


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportTable 3: EU-15 Member State’s elections of activities under Article 3.4Table 4: New Member State’s elections of activities under Article 3.4Perspectives of reporting under the Kyoto Protocol: the UNFCCC reports will be more and more strictly reviewed the Kyoto reports will only be strictly reviewed in 2010, the information content of the UNFCCC NIR of most countries still has got a lot ofgaps for the LULUCF sector any gap, i.e. lack of transparency, in the Kyoto supplementary information maytrigger the exclusions from flexible mechanism and the adjustment process Annual reporting is done with the help of CRF tables (Common ReportingFormat), requesting carbon pool changes in the 5 compartments abovegroundbiomass, belowground biomass, litter, dead wood, and soil. The summary tableshown in Fig. 2 has a total 19 tables behind, with data to be submitted by"geographical locations" and by afforestation/reforestation, deforestation, and, ifelected, forest management, cropland management, grazing land management,or revegetation.Figure 2: CRF tables (Common Reporting Format)94


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportSelected essentials of the terrestrial carbon cycle:Biological sinks are relevant, are visible in the atmosphere, and must beconsidered properly to safeguard the environmental integrity of the KPThe atmosphere does not see stocks but fluxes; this is most relevant whenecosytems are disturbed or not in equilibrium. “Hidden” fluxes like heterotrophicrespiration or trade offs with other GHGs may introduce bias due to non-reportingPresent sinks in temperate regions result from temporary changes in land use – afeature having serious implications for permanency and additionalitySink saturation is biome- and land use specific, the main reservoir is always in thesoil; therefore, reporting sinks from land use changes without data on soils isquestionableAny soil carbon stock change is relevant, but will only be visible with very gooddata, which are not available in almost all casesAny terrestrial sink may easily turn into a source, e.g., estimated 0.5 Gt of Creleased in Europe in heatwave 2003 compared to 2002ConclusionsEmissions and sinks of greenhosue gases in the sector AFOLU are different fromone-directional emissions in the other sectors. Biological sinks are part of the naturalcarbon cycle with short-term and long-term components. Monitoring emissions andsinks from land use change requires data on stock changes in all ecosystemcompartments, including belowground, which are simply not available.Considering that reporting principles like “as far as practicable” or “as far as data areavailabile” are prevailing in UNFCCC/IPCC reporting rulebooks, especially for thesector AFOLU, considering further that the definition of “direct human induced” ishighly voluntary, one may easily conclude that we will miss key drivers and keynumbers of the European terrestrial carbon budget.95


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report5 European and International GHG Monitoring Programs96


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe AGAGE network for ground based measurements of non-CO 2GHGs: Monitoring of atmospheric concentrations and emissionestimatesDerek CunnoldSchool of Earth and Atmospheric Sciences, Georgia Tech, USAThe Advanced Global Atmospheric Gases Experiment (AGAGE) program consists ofcontinuous ground-based measurements of mostly long-lived atmospheric gases at 5remote sites around the world at approximately hourly intervals. Each measurementis calibrated against on site standards which trace back to primary standards atScripps Institution of Oceanography. The measured gases are mostly halogenswhich play a role in stratospheric ozone destruction and/or those which contribute toradiative heating; methane and nitrous oxide are also being measured. The sites arelocated at Mace Head, Ireland (53 o N, 10 o W) (previously at Adrigole, Ireland, 52 o N,10 o W), Trinidad Head, California (41 o N, 124 o W) (previously at Cape Meares, Oregon,45 o N, 124 o W), Ragged Point, Barbados (13 o N, 59 o W), Cape Matatula, AmericanSamoa (14 o S, 171 o W), and at Cape Grim, Tasmania (41 o S, 145 o E). The reader isreferred to Prinn et al. (2000) and to the AGAGE web site(http://www.agage.eas.gatech.edu) for additional details.AGAGE was preceded by the Atmospheric Lifetime Experiment (ALE) program,which began in 1978, and the Global Atmospheric Gases Experiment (GAGE)program which began in about 1985. Measurements have been made with gaschromatographs mostly with electron capture detectors and more recently with massspectrometer detection systems. The early measurements consisted of CFCs, methylchloroform, carbon tetrachloride and nitrous oxide and then methane. More recentlyHCFCs, HFCs, and halons and some shorter lived halocarbons have beenmeasured. The measurements are available fromhttp://cdiac.esd.ornl.gov/ndps/alegage.html or through links from the AGAGE website. From the beginning of the ALE/GAGE/AGAGE program samples of baseline airfrom Cape Grim were taken several times per year and they have been archived inAustralia. Analyses of these samples have enabled continuation of the time seriesback to 1978 for gases that have been stable in the archive tanks. TheALE/GAGE/AGAGE time series have resulted in globally average estimated radiativeforcing rates (Figure 1).The emphasis of the AGAGE program has been on estimating the global emissionsof industrially produced gases as a function of time. For gases whose lifetimes arelonger than about a year the five globally distributed AGAGE measurement sites aretypically adequate, and annual emissions have been estimated by an inverseprocedure using a 12 box model (four boxes in the lower troposphere, four in theupper troposphere, and four in the stratosphere, see for example Cunnold et al.,[1997]). More recently emissions on roughly the continental scale have been madeusing the MATCH model (e.g. [Chen and Prinn, 2006]). Calculations with theMATCH model have included measurements from other measurement networks (egNOAA/ESRL and SOGE). It is to be noted that the SOGE program, as well as97


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportmeasurement at several sites in Asia, are affiliated with AGAGE with commonabsolute standards being used for all the measurements.Figure 2 shows a typical time series of AGAGE measurements (for HFC-134a, areplacement refrigerant) at Mace Head. The red dots indicate measurements thathave been impacted by the regional emissions from the UK and Europe. Black dotsindicate baseline measurements. The classification into black and red dots for all themeasured gases have been performed statistically using measurements of severalspecies simultaneously. Back trajectory calculations have shown no statisticallysignificant differences versus the statistical classification method. The measurementsof regional pollution effects have been used to estimate emissions from the UK andEurope both by inverse methods (e.g. [Manning et al., 2003]) and by correlations withsimultaneously measured gases (typically CO) for which the emission distributionsare relatively well known (e.g. [Reimann et al., 2005]). The baseline values aretypically used to provide the global and continental scale emission estimates.An important use of the long AGAGE time series of methyl chloroform measurementshas been to provide a time series of annual global mean estimates of OH values inthe troposphere (e.g. [Prinn et al., 2005]). This series indicates approximately 5%lower than average OH values in 1997-1999. The estimation procedure has relativelysmall uncertainties because only a few companies produced methyl chloroform andalmost all the emissions occurred within approximately 6 months of production.Moreover there are no significant natural sources of methyl chloroform.Recent examples of time series of annual (smoothed) global emissions determinedfrom AGAGE measurements (as well as from NOAA/ESRL measurements) areshown in Clerbaux and Cunnold (Chapter 1 of WMO 2006). Examples includeestimates for the CFCs and carbon tetrachloride (WMO Figure 1-20) and for theHCFCs (WMO Figure 1-21). The uncertainties in the estimates are typicallydominated by atmospheric lifetime uncertainties (particularly for carbon tetrachloride),but precisions of the measurements and modeling uncertainties make significantcontributions to the uncertainties in the individual non-smoothed annual emissionestimates. Difficulties in getting bottom up estimates of all these industrially producedgases to agree with the top down estimates are illustrated in Chapter 8 of the report.The difficulties arise both because of some uncertainties in the reported worldwideproduction of these gases but more especially because the varied uses of the gasesmake it difficult to quantify the rates at which the gases reach the atmospherefollowing their production.Atmospheric emissions on the continental scale, or more precisely a limited numberof independent pieces of information (e.g. ten) on the emissions, have beenproduced by inverse methods using the global Model for Atmospheric Transport andChemistry (MATCH) model [Rasch et al., 1997] in which the global chemistry ofKuhlmann et al. [2003] was included. Chen and Prinn (2005) showed that methanecalculations with the MATCH model using National Center for EnvironmentalPrediction (NCEP) winds produced good simulations of the observed transition inmethane mole fractions at the Samoa site from the El Nino period in 1997/1998 tothe La Nina period in 1998/1999. Equally good simulations of methane changesresulting from North Atlantic Oscillation (NAO) effects at Mace Head, Ireland wereproduced for 1996 (air coming from Europe) versus 2000 (air coming from the clean98


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportAtlantic Ocean sector). The spatial resolution of the model for these calculations wasapproximately 2 o latitude by 2 o longitude and the model produced good simulations ofthe amplitudes and frequencies of regional pollution events and the seasonal cyclesat many of the worldwide methane high frequency measurement sites (includingMace Head). Poorer simulations occurred at sites which were located close tostrongly emitting regions. These results suggest that several years of measurementsmay be needed to characterize emissions from a region and meteorologically realisticmodels should be used for inverting the measurements.MATCH model calculations are also being made for N 2 O (J. Huang, privatecommunication, 2007). Prinn et al. [1990] had previously performed calculations withthe 12 box model and some of the conclusions from that study are similar. Theynoted that as a result of the long atmospheric lifetime of N 2 O (approximately 135years) and the resulting small observed differences, approximately 0.7 ppb, betweenthe mole fractions in the two hemispheres, inferences about the spatial distribution ofthe emissions (equivalently the subdivision into various emission categories) weresensitive to the transport rate between the troposphere and the stratosphere. Thisexchange is typically not well simulated by models. Because of the small differencesin baseline values between the various sites, calibration differences between variousnetworks and/or observers need to be accurately known (e.g. to 0.1 ppb). FortunatelyAGAGE and NOAA/ESRL have both been making high frequency measurements ofmany gases at the Samoa site for many years and the difference between the twosets of measurements of N 2 O from 1999 to 2006 is 0.2 ppb, based on the NOAA2000 and the AGAGE SIO 1998 calibration scales.Recent results from inverse modeling calculations using AGAGE measurements ofN 2 O, methane [Chen and Prinn, 2006] and methyl chloride [Yoshida et al., 2006], allof which have large natural sources, all indicate increased tropical sources versusthe a priori estimates. However additional observation sites in the tropics are neededin order to better evaluate respectively the oceanic, biomass burning and tropicalwetland sources of these emissions. High frequency measurements are particularlyuseful for evaluating regional or smaller scale emissions. However, to fully utilize andinterpret such measurements, more high frequency observation sites in stronglyemitting regions are desirable. Moreover models with improved sub grid scalerepresentations of planetary boundary layer thicknesses and of vertical mixing areneeded to interpret the measurements, and measurements of vertical profiles of thegases would be useful for testing the model simulations.ReferencesChen, Y.-H., and R.G. Prinn, Estimation of atmospheric methane emissions between 1996and 2001 using a three-dimensional global chemical transport model, J. Geophys. Res.,111, D10307, doi:1029/2005JD006058, 2006.Chen, Y.-H., and R.G. Prinn, Atmospheric modeling of high- and low-frequency methaneobservations: Importance of interannually varying transport, J. Geophys. Res., 110,D10303, doi:1029/2004JD005542, 2005.Clerbaux, C., and D.M. Cunnold, Lead authors, Chapter 1: Long-lived Compounds, inScientific Assessment of Ozone Depletion: 2006, World Meteorological Organization,Global Ozone Research and Monitoring Project – report 50, Geneva, Switzerland, 2006.99


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportCunnold, D.M., R. Weiss, R.G. Prinn, D. Hartley, P.G. Simmonds, P.J. Fraser, B. Miller, F.N.Alyea, and L. Porter, GAGE/AGAGE measurements indicating reductions in globalemissions of CCl 3 F and CCl 2 F 2 in 1992-1994, J. Geophys. Res., 102, 1259-1269, 1997.Kuhlmann, R.V., M.G. Lawrence, and P.J. Crutzen, A model for studies of troposphericozone and nonmethane hydrocarbons: Model description and ozone results, J. Geophys.Res., 108 (D9), 4294, doi:1029/2002JD002893, 2003.Manning, A.J., D.B. Ryall, R.G. Derwent, P.G. Simmonds, and S. O’Doherty, EstimatingEuropean emissions of ozone-depleting and greenhouse gases using observations and amodeling back-attribution technique, J. Geophys. Res., 108 (D14), 4405, 2003.Prinn R. G., et al., Evidence for variability of atmospheric hydroxyl radicals over the pastquarter century, Geophys. Res. Lett., 32, L07809, doi:10.1029/2004GL022228, 2005.Prinn, R.G., et al., A history of chemically and radiatively important gases in air deduced fromALE/GAGE/AGAGE, J. Geophys. Res., 105, 17751-17792, 2000.Prinn, R., D. Cunnold, R. Rasmussen, P. Simmonds, F. Alyea, A. Crawford, P. Fraser, andR. Rosen, Atmospheric emissions and trends of nitrous oxide deduced from 10 years ofALE-GAGE data, J. Geophys. Res., 95, 18369-18471, 1990.Rasch, P.J., N.M. Mahowald, and B.E. Eaton, Representations of transport, convection, andthe hydrologic cycle in chemical transport models: Implications for the modeling of shortlivedand soluble species, J. Geophys. Res., 102 (D23), 28,127-28,138, 1997.Reimann, S., et al., Estimation of European methyl chloroform emissions by analysis of longtermmeasurements, Nature, 433, 506-509, 2005.Yoshida Y., Y. Wang, C. Shim, D. Cunnold, D. R. Blake, G. S. Dutton, Inverse modeling ofthe global methyl chloride sources, J. Geophys. Res., 111, D16307,doi:10.1029/2005JD006696, 2006.Figure 1: Radiative forcing of the atmosphere by gases measured by theALE/GAGE/AGAGE and by analyses of the archived air samples from Cape Grim, Australia.Methane values prior to GAGE measurements in 1986 are based on measurements byNOAA and others. CFC-114, CFC-115, CH 3 Cl, CH 2 Cl 2 , CHCl 3 and CCl 2 CCl 2 have only beenmeasured by AGAGE since 1998; consistent with Clerbaux and Cunnold [2006], constantvalues have been assumed over the entire 1979-2006 period for these gases in this figure.100


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 2: Time series of the individual measurements of HFC-134a (CH 2 FCF 3 ) by AGAGEGC-MS instruments at Mace Head, Ireland. The measured values have been separated intobaseline values (black dots) and values influenced by regional pollution (red dots) using theAGAGE statistically based algorithm.101


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe WMO GAW Global GHG ProgrammeLen BarrieAtmospheric Research and Environment Programme, World Meteorological Organization,Geneva, Switzerland1. IntroductionThe Global Atmosphere Watch (GAW) Programme of the World MeteorologicalOrganization (WMO) was established in 1989. It is focused upon the role ofatmospheric chemistry in global change [GAW Strategic Plan: 2008-2015, 2007].Consisting of a partnership of managers, scientists and technical expertise from 80countries, GAW is coordinated by the WMO Secretariat in Geneva and the OpenProgramme Area Group on Environmental Pollution and Atmospheric Chemistry(OPAG-EPAC) of the WMO Commission for Atmospheric Sciences (CAS). Theinternational greenhouse gas measurement community that gather every two yearsat meetings co-sponsored by WMO and IAEA are involved in nationally fundedmeasurement programmes that constitute the global long term greenhousemonitoring network supported by GAW. The first meeting of this group, held in 1975at Scripps Institute of Oceanography, was co-sponsored by WMO (Figure 1). It was amilestone in leadership of global greenhouse gas monitoring by US-NOAA.Comparison of this small group with the larger group that met thirty years later at the13 th meeting shows how much our community has grown. Note that two membersare in both pictures: Dr. David Lowe of New Zealand and Professor C.S. Wong ofCanada.Figure 1: The 1 st WMO sponsored CO 2 experts meeting at Scripps, La Jolla, California,1975. Back left to right: Dave Lowe (New Zealand), Ernie Hughes (NIST), Bob Bacastow(Scripps), Don Pack (1st dir. of NOAA/GMCC), Walter Bischof (Sweden), Arnold Bainbridge(Scripps), C.S. Wong (Canada), Ken Pettit (AES, Canada), Walter Komhyr (NOAA). Front leftto right Graeme Pearman (CSIRO, Australia), Michel Benarie (IRCHA, France), LesterMachta (NOAA), Charles (Dave) Keeling (Scripps) and G. Kronebach of WMO Secretariat,Geneva (photo supplied courtesy of P. Tans).102


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe WMO/GAW office and leaders of its Scientific Advisory Groups (SAGs) havebeen actively involved in supporting the United Nations Framework Convention onClimate Change (UNFCCC) through contributions to the Strategic ImplementationPlan of the Second Report on the Adequacy of the Global Observing Systems forClimate by the Global Climate Observing Strategy (GCOS). This plan is officiallyaccepted by the Parties to the Convention. Essential Climate Variables (ECVs) thatneed to be systematically measured globally in order to address major issues areofficially recognized. Greenhouse gases, ozone and aerosols are amongst thoseECVs and GAW is designated as the lead international programme in furthering theobservational requirements. In October 2005, the steering committee of the GlobalClimate Observing System (GCOS) which is co-sponsored by WMO approved theGCOS-GAW Agreement establishing the “WMO-GAW Global Atmospheric CO 2 &CH 4 Monitoring Network” as a comprehensive network of GCOS.The focus, goals and structure of GAW are outlined in detail in the StrategicImplementation Plan 2008-2011 [GAW Report 172, 2007]. Recognizing the need tobring scientific data and information to bear in the formulation of national andinternational policy, the GAW mission is to: Reduce environmental risks to society and meet the requirements ofenvironmental conventions, Strengthen capabilities to predict climate, weather and air quality, Contribute to scientific assessments in support of environmental policy,Through: Maintaining and applying global, long-term observations of the chemicalcomposition and selected physical characteristics of the atmosphere, Emphasising quality assurance and quality control, Delivering integrated products and services of relevance to users.This mission is conducted through the ongoing activities of the group of expertsrepresenting carbon cycle research and measurements that meet every two yearswith the last meeting the 13 th marking the thirtieth anniversary in global coordinationof carbon dioxide measurements [GAW Report 168, 2005]. Associated with this is themeeting of the GAW Scientific Advisory Group for Greenhouse Gases (SAG-GG)chaired by Dr. Ed Dlugokencky. The 14 th CO 2 Experts Meeting is hosted by theFinnish Meteorological Institute Helsinki, Finland September 2007 (see the GAWwebsite).2. GAW MonitoringGlobal GAW networks focus on six measurement groups: greenhouse gases, UVradiation, ozone, aerosols, major reactive gases (CO, VOCs, NO y and SO 2 ), andprecipitation chemistry. The GAW Station Information System (GAWSIS) wasdeveloped and is maintained by the Swiss GAW programme. It is the host of all GAWmetadata on observatory managers, location and measurement activities. Accordingto GAWSIS there are 24 Global, 640 Regional and 73 Contributing stations which areoperating or have submitted data to a GAW World Data Centre. GAW Scientific103


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportAdvisory Groups (SAGs) for each of the six measurement groups establishmeasurement standards and requirements while calibration and quality assurancefacilities ensure valid observations. Five GAW World Data Centres collect, documentand archive data and quality assurance information and make them freely availableto the scientific community for analysis and assessments. Note the linkages of GAWto contributing partner networks and to aircraft and satellite observations thatcontribute to Integrated Global Atmospheric Chemistry Observations (IGACO).In the past decade, the emphasis of the GAW community on standardization,calibration, quality assurance, data archiving/analysis and building the air chemistrymonitoring networks has resulted in major advances. Figure 2 shows thecomponents diagram of the “WMO-GAW Global Atmospheric CO 2 & CH 4 MonitoringNetwork”.SCIENTIFIC ADVISORYGROUP(SAG) for GHGs(Expert C MeasurementCommunity)WMO/GAWSecretariatAREPCAS/WG forEnvironmental PollutionAnd AtmosphericChemistryQA & CALIBRATION CENTRES:NOAA/CMDL, MeteoSwiss/EMPA,Japan Met. Agency(JMA)Calibration, TrainingSite Visits, ComparisonsTwinningWorkshopsCENTRAL CALIBRATION.LABORATORY (CCL)World Reference StandardUS- NOAABIPM/CCQMContributingNetworksGAW STATIONS & GAWSISGlobal RegionalSatellite & AircraftObservationsAnalysisGAW WORLD DATA CENTRE forGREENHOUSE GASES:(WDCGG)SynthesisIGACOFigure 2: Components of the “WMO-GAW Global Atmospheric CO 2 & CH 4 MonitoringNetwork”, a comprehensive network of GCOS.There are GAW Global, Regional and Contributing stations that support themonitoring of GAW target variables in each of the six groups. Global and Regionalstations are operated by a WMO Member and are defined by Technical Regulationsadopted by the WMO Executive Council in 1992 [EC XLIV, 1992] as well as the GAWStrategic Implementation Plan [GAW Strategic Plan: 2008-2015, 2007]. Contributingstations are those that conform to GAW measurement guidelines, quality assurancestandards and submit data to GAW data centres. They are mostly in partnernetworks that fill major gaps in the global monitoring network. The differencebetween a Global and a Regional GAW station lies in the facilities available for longterm measurements, the number of GAW target variables measured, the scientificactivity at the site and the commitment of the host country. The location of the 24GAW Global stations is shown in Figure 3a.To monitor global distributions and trends of particular variables with sufficientresolution to answer outstanding gaps in understanding of environmental issuesrelated to global warming due to greenhouse gases requires not only Global but alsoRegional and Contributing stations. The GAW global network for surface basedcarbon dioxide observations is shown in Figure 3b. This differs from the global mapof all stations at which carbon dioxide measurements and research are performed in104


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportthat it represented stations operating routinely that link their observations through theWMO reference scale maintained at NOAA GMD in Boulder and that submit theirdata to the GAW World Data Centre for Greenhouse Gases. In future, many morestations will hopefully be added to fill gaps in Asia, Africa and South America. Also,aircraft and satellite observations will be added as integrated global carbonatmospheric observation system as outlined in the IGACO [2004] report.WORLD METEOROLOGICAL ORGANIZATIONGLOBAL ATMOSPHERE WATCH GLOBAL NETWORKAlert80Point BarrowNy ÅlesundPallas-Sodankylä8040MaceHeadZugspitze-HohenpeissenbergJungfraujoch400Mauna LoaSamoaIzanaAssekrem -TamanrassetArembepeKenyaMt WaliguanBukit KotoTabangMinamitorishima0Danum Valley40Cape Point40Amsterdam Island Cape GrimUshuaiaLauder0 80 160160 80Neumayer StationSouth PoleMay 2006Figure 3a: Global observatories in theGAW network.Figure 3b: The WMO-GAW GlobalAtmospheric CO 2 & CH 4 MonitoringNetwork a comprehensive network ofthe Global Climate ObservingWhere do carbon research and systematic observation programmes fit amongst themany projects, programmes, strategies and systems involved in support globalcarbon observations? This is an often-asked question by many carbon cycle experts,managers and policy makers interested in the global carbon cycle and its impact onglobal change. One way of viewing the hierarchy of programmes and theirconnection to each other and to major users of the outcome of systematicobservations research is shown in Figure 4.A Hierarchy OfStrategies, System s, Program m es, Netw orks,Related To System atic Atm ospheric CO2 ObservationsUN-FR AM EW O RKCONVENTIO N O NC LIM ATE CH ANGEGEOSSW M OG C O S W CRPIG OSIGACO IG COSatellitesW M O -G A WSurfaceContinuousSurfaceFlaskCEOSIG ACO-G H GC om m ercialA ircraftLightA ircraftR outine O cean PCO2 M sm tsCLIM ATE R ESEARCHC OM M UNITYFigure 4: The hierarchy of international activities related to promoting, organizing andconducting systematic atmospheric observations of carbon dioxide and other greenhousegases. The foundation for this system are networks and facilities operated by leadingcountries in the field in cooperation with many other countries. The leaders includeUS/NOAA, Australia, Canada, China, France, Finland, Germany, Japan and Switzerland.105


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe bottom-up programme starts with national efforts with coordination through GAWand linkage to the UNFCC through GCOS, a WMO co-sponsored climate observationsystem. In turn, it links to the satellite community through CEOS and the informalIntegrated Global Observing Strategy that spawn the IGACO and IGCO strategyreports.3. Users and ProductsThe WMO-GAW Global Atmospheric CO 2 & CH 4 Monitoring Network is a globallycoordinated effort that relies upon the bottom-up activities of major national andregional research centres around the world. Mergeability of data is ensured throughlinking of all observations, no matter what type (in situ flask or continuous, flux tower,aircraft or satellite), through careful calibration to the WMO/GAW World ReferenceStandard Scale for CO 2 and CH 4 maintained by NOAA-ESRL, USA. Exchange ofdata takes place through the GAW World Data Centre for Greenhouse Gasesmaintained by the Japan Meteorological Agency (http://gaw.kishou.go.jp/wdcgg.html)and through secondary data products such as GLOBALVIEW(http://www.esrl.noaa.gov/gmd/ccgg/globalview/) maintained by NOAA-ESRL.While NOAA maintains ~70% of the stations shown in Figure 3b many countriescontribute to sample collection in that network and many others maintain themselves30% of the total surface-based network. Systematic commercial aircraftmeasurements are mainly performed by Japanese research institutes (NationalInstitute for Environmental Science, Meteorological Research Institute) in cooperationwith Japan Airlines (JAL). NOAA-ESRL also has initiated a network of verticalprofiling using flask sampling from aircraft flying up to 10 km. This community hastaken advantage of the WMO/GAW services in coordination to collaborate in issuingannual Greenhouse Gas Bulletins such as that shown in Figure 5.WMOGreenhouse Gas BulletinThe State of Greenhouse Gases in the AtmosphereUsing Global Observations up to December 2004Executive summaryThe latest analysis of data from the WMO-GAW Global Greenhouse GasMonitoring Network shows that the globally averaged atmosphericcarbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) have allreached new highs in 2004 with CO 2 at 377.1 ppm, CH 4 at 1783 ppb, andN 2 O at 318.6 ppb. These value are higher than those in pre-industrialtimes by 35%, 155%, and 18% respectively. Atmospheric growth rates ofthese gases are consistent with previous years, though CH 4 growth hasslowed during the past decade. The NOAA Annual Greenhouse GasIndex (AGGI) shows that from 1990 to 2004 the total atmosphericradiative forcing by all long-lived greenhouse gases has increased by20%.WMOGlobal Atmosphere WatchNo. 1Figure 5: The first WMO Greenhouse gas bulletin was issued in March 2006(http://www.wmo.ch/web/arep/gaw/ghg/ghgbull06.html). Future bulletins will be issuedannually before the international meeting of the Parties to the United Nations FrameworkConvention on Climate Change (UNFCC)106


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe CO 2 and CH 4 inversion modelling community utilizes observations to serve theneeds of countries. In particular this workshop deals with quantification of methaneemissions in Europe using observations and inversion modelling that is independentof the bottom-up estimates made by various means. This is an essential activity inconstraining the uncertainties of bottom-up estimates. The full power of inversionmodelling relies on the quality and quantity of observations as well as the accuracy ofmodels in representing transport, dispersion and convection. It is the goal of WMO-GAW to assist the community in providing the best observations for these purposesand to promote the use of the best meteorological drivers in these inversion models.ReferencesEC XLIV, 1992, Resolution 3, WMO Technical Regulations, 1, Chapter B.2, GlobalAtmosphere Watch, GAW, 1992.GAW Current activities of the Global Atmosphere Watch Programme (as presented at Cg-XIV, May 2003), Report 152, 2003.IGACO, The Integrated Global Atmospheric Chemistry Observations (IGACO), Report ofIGOS-WMO-ESA, GAW Report 159, 53 pp, 2004.13th WMO/IAEA Meeting of Experts on Carbon Dioxide Concentration and Related TracersMeasurement Techniques, Boulder, Colorado, USA, 19-22 September 2005, (GAW Rep.No. 168. WMO TD 1359), 2005.GAW Strategic Plan: 2008-2015, GAW Report 172, 2007.107


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportRAMCES - The French Network of Atmospheric Greenhouse GasMonitoringMartina Schmidt, Michel Ramonet, Victor Kazan, Cyril Messager, Marc Delmotte,Claire Valant, Alexis Crevier, P. Galdemard, Anne Royer, Adrien Royer, BenoitWastine, Olivier Cloué and Philippe CiaisLaboratoire des Sciences du Climat et de l’Environnement LSCE/IPSL, CEA/CNRS/UVSQ,Gif-sur-Yvette, France1. IntroductionThe RAMCES CO 2 and Radon-222 monitoring program was initiated in 1980 at theAmsterdam Island observatory [Gaudry et al., 1983; 1990; Monfray et al., 1996;Ramonet et al., 1996] and was extended at Mace Head, Ireland, in 1992 [Bousquetet al., 1996; Biraud et al., 2000; 2002] and at two further sites in France (Gif-sur-Yvette and Puy de Dome, 2001). In addition, a flask sampling program was initiatedat LSCE in 1996. Flasks are sampled at 12 fixed surface sites, three on-board smallaircrafts and ships in Indian Ocean. At LSCE the samples are analysed for CO 2isotopes ( 13 C and 18 O) and for CO 2 , CH 4 , N 2 O, SF 6 and CO mixing ratios. In thissummary we will describe our existing network and the plans of extension during thenext years.Figure 1: RAMCES flask sampling and in-situ measurement network. The different symbolsrepresent the instrumentation and the type of sampling.2. Continous CO 2 measurementsFigure 2 shows the daily average of the CO 2 mixing ratios at our four establishedmeasurement sites Amsterdam Island, Mace Head, Puy de Dome and Gif-sur-Yvette.The three western European sites reflect different environments from a marine siteoccasionally influenced by long range transport over Europe (Mace Head), to sites108


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportwhich are more influenced by rural (Puy de Dôme) and urban activities (Saclay).Continuous measurements of CO 2 and Radon-222 have been established in eachobservatory and CH 4 , N 2 O, SF 6 and CO is measured at Saclay.Figure 2: Continuously analysis of CO 2 mixing ratio at Amsterdam Island (red), Mace Head(blue), Puy de Dôme (green) and Gif-sur-Yvette (black).In 2005 we added two new stations Biscarosse (France) and Hanle (India) to ournetwork, which are both equipped with a new developed CO 2 analyser (CARIBOU).In 2006 we equipped a new site in the Orleans Forest, France (Trainou Tower) with aGC system and a CARIBOU in order to analyse in-situ CO 2 , CH 4 , N 2 O, SF 6 and COin 3 heights (50m, 110m and 180m). In future Orleans will be for RAMCES a“supersite” with multispecies measurements at a tower and vertical profiles fromairborne measurements up to 3000m. Our airborne program at the forest of Orleanswas initiated in 1996 with flask sampling between 100 and 3000m heights with afrequency of 2-3 flights per month. Within the European project CARBOEUROPE wewere installing an insitu CO 2 analyser (Condor) in the small aircraft and increasingthe frequency to 2 flights per week.109


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupWorkshop GHG inventories "Atmospheric " - report monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportTable 1: In-situ measurement sites of RAMCES.Table 1: In-situ measurement sites of RAMCES.Site ID Country Latitude Longitude Alt. (m asl) Mesures Instruments PériodeSite ID Country Latitude Longitude Alt. (m asl) Mesures Instruments PériodeIleAMS France 37°48'S 77°32'E 70 CO2URAS/Siemens 1980-…AmsterdamIleAMS France 37°48'S 77°32'E 70 CO2 CO2URAS/Siemens LOFLO 1980-… 2006-…AmsterdamCO2 Rn222 LOFLO CAFAR/Dérouleur 2006-… 1967-…Rn222 Météo CAFAR/Dérouleur 1967-…MétéoMace Head MHD Irlande 53°20'N 09°54'W 25 CO2Siemens1992-…Mace Head MHD Irlande 53°20'N 09°54'W 25 CO2Rn222SiemensDérouleur1992-…1996-…Rn222Dérouleur1996-…MétéoMétéoPuy de Dôme PUY France 45°46'N 02°57'E 1465 CO2 Licor 2001-…Puy de Dôme PUY France 45°46'N 02°57'E 1465 CO2 Licor 2001-…Gif-sur GIF France 48°43'N 02°09'E 20 CO2LOFLO2005-…Gif-surYvette GIF France 48°43'N 02°09'E 20 CO2LOFLO2005-…YvetteCO2CARIBOU2006-…CO2CARIBOU2006-…CO2, CH4, N2O, SF6 Multi-GC2001-…CO2, CH4, N2O, SF6 Multi-GC2001-…COCOGC-COGC-CO2004-…2004-…Rn222Rn222DérouleurDérouleur2001-…2001-…Météo MétéoBiscarosse France 44°22'N 01°'14'W 120 CO2 CARIBOU 2005-…BIS France 44°22'N 01°'14'W 120 CO2 CARIBOU 2005-…Hanle HLE India India 32°47'N 32°47'N 78°58'E 78°58'E 4517 4517 CO2 CO2 CARIBOU CARIBOU 2005-… 2005-…Trainou(OrleansTower)TRA France 47°58'N 47°58'N 2°07'W 2°07'W 131 131 CO2 CO2CARIBOU CARIBOU 2006-…CH4, N2O, CH4, SF6, N2O, CO SF6, Multi-GC CO Multi-GC 2006-…Rn222 Rn222 ANSTO ANSTOMétéo Météo2006-…2006-…3. Flask sampling network and and measurement facility facilityAs an extension of of the the RAMCES monitoring monitoring network, network, a flask a flask sampling sampling program program was wasinitiated at LSCE in in 1996. 1996. Flasks Flasks are are sampled sampled in fixed in fixed surface surface sites, sites, on-board on-board small smallaircrafts [Ramonet et et al., al., 2002], 2002], and and ships ships in Indian in Indian Ocean Ocean and North and North Atlantic Atlantic (Figure (Figure1 and Table 2). 2). At At LSCE LSCE the the samples samples are are analysed analysed for CO for 2 isotopes CO 2 isotopes ( 13 C and ( 13 C 18 and O) 18 O)and and for CO 2 , CH CH 4 , 4 , NN 2 O, 2 O, SF SF 6 and 6 and CO CO mixing mixing ratios. ratios.Table 2: Flask sampling sites of RAMCES.Table 2: Flask sampling sites of RAMCES.Site Code Latitude Longitude Alt. (m) Country Start Interval CollaboratorSite Code Latitude Longitude Alt. (m) Country Start Interval CollaboratorIle Amsterdam AMS 37°48'S 77°32’E 70 France 1996 4 / month IPEVIle Amsterdam AMS 37°48'S 77°32’E 70 France 1996 4 / month IPEVMace Head MHD 53°20'N 9°54’W 25 Ireland 1996 4 / month UGCMace Head MHD 53°20'N 9°54’W 25 Ireland 1996 4 / month UGCPuy de Dôme PUY 45°46'N 2°58'E 1465 France 2001 4 / month LaMPPuy de Dôme PUY 45°46'N 2°58'E 1465 France 2001 4 / month LaMPOrléans 1 ORL 47°50'N 2°30'E 100-3000 France 1996 2-3 / month Météo FranceOrléansTver 1 1 TVRORL56°27'N47°50'N32°55'E2°30'E100-3000100-3000RussiaFrance199819961 / month2-3 / monthBGC, IPEEMétéo FranceHegyatsal Tver 1 HUN TVR 46°57’N 56°27'N 16°39’E 32°55'E 100-3000 100-3000 HangaryRussia 2001 1998 2 / month 1 / month HMS BGC, IPEEHegyatsal Griffin 1 1 GRI HUN 56°33’N 46°57’N 2°59’W 16°39’E 100-3000 100-3000 Scottland Hangary 2001 2001 2 / month 2 / month IERM HMSIleGriffinGrande 1 LPOGRI48°48'N56°33’N3°35'W2°59’W20100-3000FranceScottland199820012 / month2 / monthLPOIERMIle Tromelin Grande TRM LPO 15°54' 48°48'N S 54°31'E 3°35'W 10 20 France France 1998 1998 4 / month 2 / month Météo France LPOCape Tromelin Grim CGO TRM 40°41’S 15°54' S 144°41’E 54°31'E 164 10 Austalia France 1998 1998 2 / month 4 / month CSIRO Météo FranceCape Begur Grim BGU CGO 41°58'N 40°41’S 3°14'E 144°41’E 13 164 Spain Austalia 2000 1998 4 / month 2 / month U. Barcelona CSIROFinokalia Begur FIK BGU 35°19'N 41°58'N 25°40'E 3°14'E 150 13 Greece Spain 2001 2000 2 / month 4 / month U. Heraklin U. BarcelonaFinokalia Hanle HLE FIK 32°47'N 35°19'N 78°58'E 25°40'E 4517 150 India Greece 2000 2001 3 / month 2 / month IIAP U. HeraklinPic Hanle du Midi PDM HLE 42°56’N 32°47'N 0°08’E 78°58'E 2877 4517 France India 2001 2000 4 / month 3 / month LA - OMP IIAPMarion Pic du Dufres Midi MDF PDM 42°56’N Indian Ocean 0°08’E 20 2877 France 1996 2001 2 / year 4 / month LBCM LA - OMPMarion Dufres 2 MDF Indian Ocean 20 1996 2 / year LBCM110110


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report4. Future plansIn 2007 and 2008 we will equip three new stations with insitu CO 2 analyzer (Ivittuut,Greenland and Bellegarde tower, near Toulouse France, and a station in WestAfrica). Puy de Dôme station will be upgraded with a new GC system in order toanalyse CH 4 , N 2 O, CO and SF 6 . In cooperation with the University of Bremen we willinstall a FTIR at Trainou tower to analyse the CO 2 column density (2008). A furtherfocus will be on the automation and on the real-time data transmission of our insitumeasurements.ReferencesBiraud, S., P. Ciais, M. Ramonet, P. Simmonds, V. Kazan, P. Monfray, S. O'Doherty, T.G.Spain, and S.J. Jennings. European greenhouse gas emissions estimated fromcontinuous atmospheric measurements and Radon-222 at Mace Head, Ireland. J.Geophys. Res., 105 (D1), 1351-1366, 2000.Biraud, S., P. Ciais, M. Ramonet, P. Simmonds, V. Kazan, P. Monfray, S. O'Doherty, T.G.Spain, and S.J. Jennings. Quantification of Carbon Dioxide, Methane, Nitrous Oxide, andChloroform emissions over Ireland from atmospheric observations at Mace Head. Tellus54 (1), 41-60, 2002.Bousquet, P., A. Gaudry, P. Ciais, V. Kazan, P. Monfray, P.G. Simmonds, S.G. Jennings, etT.C. O'Connor, Atmospheric CO 2 concentration variations recorded at Mace Head,Ireland, from 1992 to 1994., Phys. Chem. Earth., 21, 477-481, 1996.Gaudry A., Ascencio J.M., Lambert G. Preliminary study of CO 2 Variations at AmsterdamIsland (Territoires des Terres Australes et Antarctiques Francaises). J.Geophys. Res., 88(C2), 323-1329, 1983.Gaudry, A., P. Monfray, G. Polian, et G. Lambert, Radon-calibrated emissions of CO 2 fromSouth Africa, Tellus, 42B, 9-19, 1990.Monfray, P., M. Ramonet, et D. Beardsmore, Longitudinal and vertical gradient over thesubtropical/subantarctic oceanic CO 2 sink, Tellus, 48B, 445-456, 1996.Ramonet, M., et P. Monfray, CO 2 Baseline concept in 3-D atmospheric transport models,Tellus, 48B, 502-520, 1996.111


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportMeasurements of greenhouse gases at the Mediterranean island ofLampedusaAlcide G. di Sarra 1 , Salvatore Piacentino 2 , Paolo Chamard 1 , Florinda Artuso 1 ,Salvatore Chiavarini 1 , Francesco Monteleone 3 , Damiano Sferlazzo 2 , Fabrizio Anello 3 ,Carlo Bommarito 3 , Lorenzo De Silvestri 1 , Daniela Meloni 1[1] ENEA, ACS, Rome, Italy[2] ENEA, ACS, Lampedusa, Italy[3] ENEA, ACS, Palermo, ItalyThe Italian agency for new technologies, energy, and environment (ENEA) maintainsa measurement station (http://www.palermo.enea.it/lampedusa) dedicated to thestudy of climate on the island of Lampedusa (35.5°N, 12.6°E), in the southern sectorof the central Mediterranean. Lampedusa is small (22 km 2 surface area), rocky, poorof vegetation, and far from significant sources of anthropogenic pollutants. Itsmaximum elevation is 130 m. The measurement station is located on a plateau, closeto the North-Eastern coast of the island. A village of about 5000 inhabitants is locatedin the South-Eastern part of the island. During the tourist season (mostly July andAugust) the population increases significantly.Fig. 1: Map of the central Mediterranean. The position of Lampedusa is indicated by thearrow.First measurements at Lampedusa were started in 1992, with a weekly air samplingprogram dedicated to CO 2 [Chamard et al., 2003]. The set of measured quantitieswas progressively expanded; at present, the main aim of the station is the study ofchanges in atmospheric composition, and of the influence they produce on theradiative budget of the atmosphere, and on climate. Combined observations ofmeteorological parameters, greenhouse gases [Chamard et al., 2003; Artuso et al.,2007], aerosols [Pace et al., 2005, 2006; Meloni et al., 2006, 2007], radiative fluxes[di Sarra et al., 2002; Meloni et al., 2005], total [di Sarra et al., 2002] and surfaceozone, water vapour are routinely carried out.112


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportTable 1 shows the list of greenhouse gases measured at Lampedusa, the startingdate of each measurement, and the measurement techniques (NDIR: non dispersiveinfrared spectrometry, GC: gas chromatography, FID: flame ionization detection;ECD: electron capture detection; MS: mass spectrometry). Measurements ofadditional halogenated species will be added shortly. All measurements refer toWorld Meteorological Organization reference standards. Flask data are routinelyprovided to the World Data Center for Greenhouse Gases(http://gaw.kishou.go.jp/wdcgg.html), as a contribution to the regional network ofGlobal Atmosphere Watch. CO 2 and CH 4 data contribute also to the Carboeuropedatabase (http://www.carboeurope.org), to the Globalview-CO 2 and Globalview-CH 4programs (http://www.esrl.noaa.gov/gmd/ccgg/globalview/). Within a cooperation withthe Global Monitoring Division of the National Oceanic and AtmosphericAdministration, Carbon Cycle Greenhouse Gases (CCGG) group, additional weeklysamplings as part of the NOAA Cooperative air sampling network, were started inOctober 2006. These measurements include, beside CO 2 , CH 4 , N 2 O, and SF 6 ,weekly determinations of 13 C, 18 O, H 2 , CO.Table 1: List of measured greenhouse gases, and starting date of measurement.Chemical species flask continuous techniqueCO 2 1992 1998 NDIRCH 4 1995 2005 GC-FIDN 2 O 1998 2005 GC-ECDCFC-11 1996 2005 GC-ECDCFC-12 1997 2005 GC-ECDHFC-134a 2003 GC-MSHCFC-22 2003 GC-MSHCFC-141b 2004 GC-MSHCFC-142b 2004 GC-MSSF 6 2004 GC-MSFigure 2 shows the evolution of the monthly average mixing ratio (calculated fromweekly flask data) of the greenhouse gases measured at Lampedusa. At the bottomof the graph monthly means of total ozone and aerosol optical depth at 500 nm arealso displayed.CO 2 increases by about 1.7 ppm/year, with strong year-to-year variability. Peaks inthe CO 2 annual growth rate occur in 1998 and in 2001-2002. As discussed by Artusoet al. [2007], the methane growth rate over the investigation period is about 2ppb/year. Evidences for a reduction in CFC-12 and, to a lesser extent, CFC-11,appear in the dataset, as a consequence on the limitations on global emissionsfollowing the Montreal Protocol. HFCs, HCFCs, and SF 6 , whose emissions are alsocontrolled by the Montreal and the Kyoto Protocols, display a relatively fast increase.Because of the small local sources and limited vegetation, the CO 2 monthly averagedaily cycle is small throughout the year. The largest amplitudes (less than 2 ppm)occur in summer, when a maximum appears during the morning, possibly because of113


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric Workshop "Atmospheric monitoring and monitoring inverse modelling and inverse for modelling verification for of verification national and of EU national bottomupGHG inventories up GHG " - inventories report " -and EU bottom-reportthe daily variation the daily of variation the marine of the boundary marine layer boundary depth. layer The depth. methane The daily methane cycle daily is cycle isalso largest also in summer, largest in with summer, an amplitude with an of amplitude about 20 of ppb about in 20 July-August. ppb in July-August. The Themonthly mean monthly methane mean mixing methane ratio mixing has a ratio maximum has a during maximum nighttime during and nighttime decreases and decreasesduring the morning. during the The morning. methane The reaction methane with reaction OH probably with OH plays probably a significant plays a role significant in role inthe determination the determination of the daily of behaviour. the daily behaviour.Due to the Due distance to the from distance relevant from sources relevant of sources greenhouse of greenhouse gases, their gases, behaviour their at behaviour atLampedusa Lampedusa is largely determined is largely determined by long-range by long-range transport: CO transport: 2 and CHCO 4 weekly 2 and CH and 4 weekly andcontinuous continuous data show data a significant show a dependency significant dependency on the origin on of the the origin airmasses of the (see airmasses (seee.g. [di Sarra e.g. et [di al., Sarra 2005; et Artuso al., 2005; et al., Artuso 2007]). et al., The 2007]). short-term The short-term CO 2 evolution CO 2 is evolution isstrongly modulated strongly modulated by the influence by the of influence anthropogenic of anthropogenic sources (dominant sources (dominant winter) in winter)and vegetation and vegetation (dominant (dominant summer) in in summer) Europe, in and Europe, the small and the sources small and sources andvegetation in vegetation Africa. Enhanced in Africa. methane Enhanced amounts methane are amounts found, among are found, airmasses among from airmasses fromAfrica, for trajectories Africa, for trajectories coming from coming the Algerian from the sector, Algerian as a sector, possible as consequence a possible consequence of ofleakage in gas leakage and oil in gas drillings and and oil drillings pipelines. and pipelines.Figure 2: Evolution Figure 2: of Evolution the monthly of the average monthly mixing average ratio mixing of the greenhouse ratio of the greenhouse gases measured gases measuredat Lampedusa. at Lampedusa. Monthly means Monthly of total means ozone of and total aerosol ozone and optical aerosol depth optical at 500 depth nm are at 500 also nm are alsodisplayed. displayed.114114


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportAt the national level, activities at Lampedusa are included within the Italian networkfor the measurement of greenhouse gases [Apadula et al, 2005]. Intercomparisonexercises and an integrated assessment of the data quality are being carried outwithin the national network. As a complement to the observations at Lampedusa, andas an integration of the Italian national network, in April 2005 a weekly flask programwas started on the Madonie mountains (37°52’N 14°04’E, 1756 m altitude), in Sicily.Weekly measurements of CO 2 , CH 4 , N 2 O, CFC-11 and CFC-12 are routinely carriedout.ReferencesApadula, F., F. Artuso, P. Chamard, F. De Nile, A. di Sarra, L. Lauria, A. Longhetto, F.Monteleone, S. Piacentino, R. Santaguida, C. Vannini, The network for background CO 2measurement in Italy, 12th WMO/IAEA Meeting of Experts on Carbon DioxideConcentration and Related Tracer Measurement Techniques, World MeteorologicalOrganization Global Atmosphere Watch Report n. 161 (WMO TD no. 1275), 173-175,2005.Artuso, F., P. Chamard, S. Piacentino, A. di Sarra, D. Meloni, F. Monteleone, D. Sferlazzo,and F. Thiery, Atmospheric methane in the Mediterranean: analysis of measurements atthe island of Lampedusa during 1995-2005, Atmos. Environ., 41, 3877-3888, 2007.Chamard, P., F. Thiery, A. di Sarra, L. Ciattaglia, L. De Silvestri, P. Grigioni, F. Monteleone,and S. Piacentino, Interannual variability of atmospheric CO 2 in the Mediterranean:Measurements at the island of Lampedusa, Tellus, 55B, 83-93, 2003.di Sarra, A., P. Chamard, S. Piacentino, F. Monteleone, L. Ciattaglia, and F. Artuso,Influence of the CO 2 latitudinal gradient on the observations at the Mediterranean islandof Lampedusa, Seventh International Carbon Dioxide Conference, Extended Abstracts,ISBN 0-9772755-0-7, Published by Committee of Seventh International Carbon DioxideConference, National Oceanic and Atmospheric Administration, FF-254 225, 2005.Meloni, D., A. di Sarra, J. R. Herman, F. Monteleone, and S. Piacentino, Comparison ofground-based and TOMS erythemal UV doses at the island of Lampedusa in the period1998-2003: Role of tropospheric aerosols, J. Geophys. Res., 110, D01202, doi:10.1029/2004JD005283, 2005.Meloni, D., A. di Sarra, G. Pace, and F. Monteleone, Optical properties of aerosols over thecentral Mediterranean. 2. Determination of single scattering albedo at two wavelengthsfor different aerosol types, Atmos. Chem. Phys., 6, 715–727, 2006.Meloni, D., A. di Sarra, G. Biavati, J.J. DeLuisi, F. Monteleone, G. Pace, S. Piacentino, andD. Sferlazzo, Seasonal behavior of Saharan dust events at the Mediterranean island ofLampedusa in the period 1999-2005, Atmos. Environ., 41, 3041-3056, 2007.Pace, G., D. Meloni, and A. di Sarra, Forest fire aerosol over the Mediterranean basin duringsummer 2003, J. Geophys. Res., 110, D21202, doi:10.1029/2005JD005986, 2005.Pace, G., A. di Sarra, D. Meloni, S. Piacentino, and P. Chamard, Optical properties ofaerosols over the central Mediterranean. 1. Influence of transport and identification ofdifferent aerosol types, Atmos. Chem. Phys., 6, 697–713, 2006.115


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportLong-Term Monitoring of Greenhouse Gases at JungfraujochStefan Reimann, Martin K. Vollmer, Martin Steinbacher, Doris Folini, Matthias Hill,Brigitte BuchmannEMPA, Laboratory for Air Pollution/Environmental Technology, Dübendorf, SwitzerlandContinuous atmospheric measurements of trace gases in the atmosphere can notonly be used to detect global trends of these substances but also to estimate theirregional emissions. In fact, long-term continuous measurements have the potential tobe used as an independent tool for verification of anthropogenic emissions ofsubstances regulated under international treaties such as the Montreal and KyotoProtocol.Continuous in-situ measurements of halogenated greenhouse gases(chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs),hydrofluorocarbons (HFCs) and Halons) are performed at the high-Alpine site ofJungfraujoch since January 2000 by gas chromatography-mass spectrometry (GC-MS) [Reimann et al. 2004]. Jungfraujoch is the highest site worldwide to host thiskind of measurements. The connection of these measurements with inversemodelling can be used for the independent source allocation for these tracecompounds. Occurrence of these halocarbons in the atmosphere is due to theirwidespread usage with a large variety of applications such as foam blowing,refrigeration and fire extinction. The continuous measurements of halocarbons at theJungfraujoch are part of the SOGE network (System for Observation of HalogenatedGreenhouse Gases in Europe) with the aims to determine trends of halocarbons andto estimate the spatial distribution and strength of their European sources. WithinSOGE, fully intercalibrated in situ data have been measured since 2001 with analmost identical technique at four European background stations (i.e. Mace Head,Ireland; Ny-Ålesund, Spitsbergen; Jungfraujoch, Switzerland and Monte Cimone,Italy).In addition, continuous in-situ measurements of methane (CH 4 ), nitrous oxide (N 2 O)and sulfur hexafluoride (SF 6 ) are performed at Jungfraujoch since the beginning of2005 by gas chromatography – flame ionization/electron capture detection (GC-FID/ECD). Sources of CH 4 and N 2 O are both anthropogenic and natural and theirconcentrations have considerably increased after the beginning of industrialization.Jungfraujoch often is under the influence of clean air masses, which are notinfluenced by the regional boundary layer. However, boundary layer air isoccasionally transported to the height of Jungfraujoch, leading to an increase inconcentrations. The elevation of the concentrations of specific substances is therebyrepresentative for the emissions that the air masses have been exposed to duringtheir travel in the boundary layer. The difference between the background and theelevated peak values can therefore be used to estimate emissions from theEuropean continent.116


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFor the estimation of the European sources a tracer-ratio method is applied whichuses European emissions of carbon monoxide (CO) as a priori information [Vestreng2004]. Apart from this information the calculation of the emissions uses the yearlyaveraged elevations over the baseline for CO and the halocarbons within thefollowing formula:EmissionHALO EmissionCO ElevatedHALOElevatedCOUsing this method European sources of following groups of substances could beestimated from the measurements at Jungfraujoch: different HFCs [Reimann et al.2004; Vollmer et al. 2006; Stemmler et al. 2007], the foam blowing agent HCFC-141b[Derwent et al. 2007] and the ozone-depleting 1,1,1-trichloroethane [Reimann et al.2005].For Switzerland the formula has been adapted by using Swiss CO emissions as apriori information (BAFU/FOEN 2006) trajectories for checking that the influenceduring the last 2 days was exclusively from the Swiss boundary layer. Emissionestimates of halocarbons are regularly compared with those provided by the Swissauthorities to the UNFCCC, showing a satisfactorily consistency between the twoapproaches.For the localisation of potent European sources of halocarbons a trajectory modelwas used, based on the Swiss Alpine Model [Reimann et al. 2004]. The resultsshould be regarded as indicative, showing only potential source regions. Results ofthe temporal development of the emissions for HCFC 141b and HFC 365mfc, seenwith the trajectory statistics, are shown in Figure 1 [Stemmler et al. 2007]. Thereby,air from Italy used to be polluted with the now forbidden HCFC 141b – but emissionshave declined dramatically. On the other hand, emissions of the substitute HFC365mfc, which is predominately used in foam blowing, have increased substantially.Interestingly a new source in France is visible, which corresponds to the location of afactory producing the substance for the European market.This approach has the potential to be used validate yearly emissions of greenhousegases down to the country level, submitted to the UNFCCC within the Kyoto Protocol.A severe example of non-reporting is shown in Figure 2, where measurements fromJungfraujoch show clear indications of emissions of the foam-blowing agent HFC-152a from Italy together with other countries in Europe. However, these emissionsare not reported by Italy to the UNFCCC. Thus, emission estimations usingatmospheric measurements have the advantage to provide real-world checks for theinventories, which are solely based on activities and emission functions. This couldbe extremely important in view of compliance difficulties and verification of actualemissions, if countries have to prove comprehensively that their emissionscorrespond to the truth within the Kyoto Protocol and possible future internationaltreaties.117


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report2003 2004 2005HCFC141b2003 2004 2005HFC365mfcFigure 1: Source regions resulting from trajectory statistics of the HCFC 141b and the HFC365mfc from 2003-2005 seen at Jungfraujoch. Units indicate averaged excursions above thebaseline, linked to trajectories that passed over the respective grid cell [Stemmler et al.2007].2003 2004A) B)AustriaBelgiumNetherlandsGermanyFranceItaly2003520 t330 t4 t1880 t460 t- t2004529 t288 t5 t1333 t297 t- tFigure 2: A) Source regions resulting from trajectory statistics of the HFC 152a from 2003-2004 seen at Jungfraujoch. Units indicate averaged excursions above the baseline, linked totrajectories that passed over the respective grid cell [Greally et al. 2007]. B) Submissions ofthe National Communications to the UNFCCC from 2003-2004.118


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportReferencesBAFU/FOEN, Swiss Greenhouse Gas Inventory 2004, 2006.Derwent, R. G., P. G. Simmonds, B. R. Greally, S. O'Doherty, A. McCulloch, A. Manning, S.Reimann, D. Folini and M. K. Vollmer, The phase-in and phase-out of Europeanemissions of HCFC-141b and HCFC-142b under the Montreal Protocol: Evidence fromobservations at Mace Head, Ireland and Jungfraujoch, Switzerland from 1994 to 2004,Atmospheric Environmen,t 41 (4): 757-767, 2007.Greally, B. R., A. J. Manning, S. Reimann, A. McCulloch, J. Huang, B. L. Dunse, P. G.Simmonds, R. G. Prinn, P. J. Fraser, D. M. Cunnold, S. O'Doherty, L. W. Porter, K.Stemmler, M. K. Vollmer, C. R. Lunder, N. Schmidbauer , O. Hermansen, J. Arduini, P.K. Salameh, P. B. Krummel, R. H. J. Wang, D. Folini, R. F. Weiss, M. Maione, G.Nickless, F. Stordal and R. G. Derwent, Observations of 1,1-difluoroethane (HFC-152a)at AGAGE and SOGE monitoring stations in 1994–2004 and derived global andregional emission estimates, J. Geophys. Res., 112,: D06308,doi:10.1029/2006JD007527, 2007.Reimann, S., A. J. Manning, P. G. Simmonds, D. M. Cunnold, R. H. J. Wang, J. L. Li, A.McCulloch, R. G. Prinn, J. Huang, R. F. Weiss, P. J. Fraser, S. O'Doherty, B. R.Greally, K. Stemmler, M. Hill and D. Folini, Low European methyl chloroform emissionsinferred from long-term atmospheric measurements, Nature, 433 (7025): 506-508,2005.Reimann, S., D. Schaub, K. Stemmler, D. Folini, M. Hill, P. Hofer, B. Buchmann, P. G.Simmonds, B. R. Greally and S. O'Doherty, Halogenated greenhouse gases at theSwiss High Alpine Site of Jungfraujoch (3580 m asl): Continuous measurements andtheir use for regional European source allocation, J. Geophys. Res. 109(D5): art. no.-D05307, 2004.Stemmler, K., D. Folini, S. Ubl, M. K. Vollmer, S. Reimann, S. O'Doherty, B. Greally, P. G.Simmonds and A. Manning, European emissions of HFC-365mfc, a chlorine freesubstitute for the foam blowing agents HCFC-141b and CFC-11, Environ. Sci. Technol.41, 1145-1151, 2007.Vestreng, V., et. al., Inventory Review 2004, Emission Data reported to CLRTAP and underthe NEC Directive, EMEP/EEA Joint Review Report, EMEP/MSC-W Note 1/2004,2004.Vollmer, M. K., S. Reimann, D. Folini, L. W. Porter and L. P. Steele, First appearance andrapid growth of anthropogenic HFC-245fa (CHF 2 CH 2 CF 3 ) in the atmosphere, Geophys.Res. Lett., 33 (20), 2006.119


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportICOS - Integrated Carbon Observation SystemA new research infrastructure to decipher the greenhouse gasbalance of Europe and adjacent regionswww.icos-infrastructure.euCoordinator:Core Team:Contact:Philippe Ciais – Laboratoire de Laboratoire des Sciences du Climatet de l'Environnement, CEA-CNRS-UVSQ, FranceTimo Vesala, Finland, University of HelsinkiErnst-Detlef Schultz, Germany, Max-Planck-GesellschaftIngeborg Levin, Germany, University of HeidelbergRiccardo Valentini, Italy, University of TusciaHan Dolman, The Netherlands, Vrije University AmsterdamJohn Grace, United Kingdom, University of EdinburghCecilia Garrec – project office cecilia.garrec@cea.frMission statementTo provide the long-term observations required to understand the present stateand predict future behavior of the global carbon cycle and greenhouse gasemissionsTo monitor and assess the effectiveness of carbon sequestration and/orgreenhouse gases emission reduction activities on global atmosphericcomposition levels, including attribution of sources and sinks by region and sectorBrief descriptionICOS is a new European Research Infrastructure for quantifying and understandingthe greenhouse balance of the European continent and of adjacent regions.It was realized early that, high precision long-term carbon cycle observations formthe essential basis of carbon cycle understanding and that these observations mustbe secured beyond the lifetime of a research project. ICOS aims to build a network ofstandardized, long-term, high precision integrated monitoring of:atmospheric greenhouse gas concentrations of CO 2 , CH 4 , CO and radiocarbon-CO 2 to quantify the fossil fuel componentecosystem fluxes of CO 2 , H 2 O, and heat together with ecosystem variables.The ICOS infrastructure will integrate terrestrial and atmospheric observations atvarious sites into a single, coherent, highly precise dataset. These data will allow aunique regional top-down assessment of fluxes from atmospheric data, and a120


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportbottom-up assessment from ecosystem measurements and fossil fuel inventories.Target is a daily mapping of sources and sinks at scales down to about 10 km, as abasis for understanding the exchange processes between the atmosphere, theterrestrial surface and the ocean.Ecosystem observation sitesAtmospheric concentration sitesFigure 1: Existing European research network of ecosystem observation sites (left) andatmospheric concentration (right) among which the ICOS main sites and associated sites willbe selected and essential new sites implemented.The ICOS Research Infrastructure was selected by the European Strategy Forum forResearch Infrastructures (ESFRI) roadmap in October 2006 as one of the vital newEuropean Research Infrastructures for the next 20 years. ICOS was initiated bysuccessful developments of the research tools and capacity building at the Europeanlevel necessary to quantify and understand the sources and sinks of greenhousegases at regional and continental scales (see AEROCARB (terminated),CARBOEUROPE, NITROEUROPE, and CARBOOCEAN).ICOS contributes to the implementation of the Integrated Global Carbon ObservationSystem (IGCO). At the same time, ICOS fulfils the monitoring obligations of Europeunder the United Nations Framework Convention on Climate Change (UNFCCC).The list of variables covered in ICOS are central to GEOSS (Global EarthObservation System of Systems) as recommended to ‘support the development ofobservational capabilities for Essential Climate Variables (ECVs) Further, ICOScontributes to the GEOSS aims by implementing in Europe the IGOS-P (IntegratedGlobal Observing Strategy - Partnership) for Atmospheric Chemistry Observations(IGACO) and for Integrated Global Carbon Observations (IGCO).121


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHGWorkshopinventories"Atmospheric" - reportmonitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe ICOS building blocksThe ICOS building blocksFigure 2: The ICOS elements.Figure 2: The ICOS elements. A Central Co-ordination Office which co-ordinates all activities, and which is A Central Co-ordination Office which co-ordinates all activities, and which isresponsible for data management, data diffusion and outreach. Associated withresponsible for data management, data diffusion and outreach. Associated withthe co-ordination office will be the established a data centre, the Carbon Portal,the co-ordination office will be the established a data centre, the Carbon Portal,providing free access to the ICOS data,providing free access to the ICOS data, A Central Analytical Laboratory for calibration, quality control and atmospheric A Central Analytical Laboratory for calibration, quality control and atmosphericanalyses for the entire network,analyses for the entire network, An Atmospheric Thematic Center responsible for continuous and discontinuous An Atmospheric Thematic Center responsible for continuous and discontinuousair sampling, instrument development/servicing and data processing,air sampling, instrument development/servicing and data processing, An Ecosystem Thematic Center responsible for total ecosystem flux An Ecosystem Thematic Center responsible for total ecosystem fluxmeasurements and component fluxes and carbon pools, including datameasurements and component fluxes and carbon pools, including dataprocessing and instrument development,processing and instrument development, A network of Main Observation Sites which are connected in a distributed network A network of Main Observation Sites which are connected in a distributed networkof about 30 atmospheric and 30 ecosystem sites located across Europe, withof about 30 atmospheric and 30 ecosystem sites located across Europe, withsecured funding coverage for 20 years,secured funding coverage for 20 years, Associated networks of Regional Observation sites which will contribute to the Associated networks of Regional Observation sites which will contribute to theICOS objectives, and share data with the Infrastructure.ICOS objectives, and share data with the Infrastructure.Implementation strategyImplementation strategyThe implementation of ICOS will take place in two steps:The implementation of ICOS will take place in two steps:During the Preparatory Phase starting in 2008 until 2011, the funding commitmentsDuring the Preparatory Phase starting in 2008 until 2011, the funding commitmentswill have been endorsed by the governments and mother institutions, the building ofwill have been endorsed by the governments and mother institutions, the building ofthe central facilities will be initiated, and the project will be technically developed upthe central facilities will be initiated, and the project will be technically developed upto the level of a demonstration year of full operation, but with a reduced number ofto the level of a demonstration year of full operation, but with a reduced number ofobservational sites.observational sites.122122


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportDuring the follow-up Operational Phase from 2012 until 2031, after the full scaledeployment of the network, it will be run in an operational mode, and greenhouse gasconcentrations and fluxes will be determined on a routine basis.ActivitiesThe ICOS work plan of the preparatory phase is organised around eightcomplementary work packages:WP 1 corresponds to the consortium organization and management of theproject,WP 2 provides legal and governance models,WP 3 coordinates the financial/fund raising work,WP 4 considers the integration of essential external datasets into ICOS, andinvolves data providers, in particular for fossil fuel emission data and biomass andsoil carbon inventories,WP 5 corresponds the technical work to build the distributed network of field sites,including network design, equipment selection, testing and optimisation,WP 6 will carry out the preparation for building the atmospheric and ecosystemthematic centers, as well as the central analytical laboratory,WP 7 will apply the technical solutions retained in WP 5-6, to execute theDemonstration Experiment, a one-year test run where the infrastructure will beoperated with a small number of sites,WP 8 will organize the project-level outreach, the construction of the web basedCarbon Portal, as well as training and capacity building necessary for the futureoperational phase.Links to ICOSThe links between ICOS, other European projects, and international coordinationbodies and programs include:CARBOEUROPE (FP6, IP) will be a prime user of the ICOS data, and providesadvanced research tools to use the infrastructure observations.CARBOAFRICA (FP6, IP), (Western Africa) and CIRCE (FP6) (Mediterraneanregions), and research in third countries such as China, India, and Russia will beable to use the ICOS methodology for establishing new high precisionmeasurements.IMECC (FP6, I3) will provide key network design tools to the ICOS PreparatoryPhase, funding for ecosystem measurement sensors and standard preparationfacilities as well as pilot Near-Real-Time concentration data products.GEMS and GEOLAND (FP-6, IP) projects (part of the GMES program), withsuccessors in FP-7, will use the high quality atmospheric and ecosystemvalidation data provided by ICOS.GEOMON (FP-6, IP) will ensure the link to ICOS with forthcoming satelliteobservations of column integrated CO 2 (NASA/OCO, JAXA/GOSAT missions)and CH 4 (ESA/SCIAMACHY instrument on ENVISAT) and CO (NASA/MOPITT).123


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportGEOSS will use the European implementation of the Integrated Global CarbonObservation strategy (IGCO) for atmospheric and biospheric observations, and ofthe Integrated Global Atmospheric Composition Observation strategy (IGACO)provided by ICOS.IPCC panel members will have access to unique, high precision long term data tounderstand the carbon cycle and the current perturbation attributed toanthropogenic activities.PartnershipThe ICOS preparatory phase includes 14 research laboratories and SMEs from 11European countries.CEA Philippe Ciais Commissariat à l’Energie Atomique FranceMPG Ernst-Detlef Schulze Max-Planck-Gesellschaft. GermanyUNITUS Riccardo Valentini University of Tuscia ItalyUHEI-IUP Ingeborg Levin University of Heidelberg GermanyVUA Han Dolman Vrije University Amsterdam The NetherlandsUHEL Timo Vesala University of Helsinki FinlandUEDIN John Grace University of Edinburgh United KingdomCNRS-INSU Nicole Papineau Centre National de la RechercheFranceScientifique-Institut National des Sciencesde l'UniversULUND Anders Lindroth Lunds Universitet SwedenRISEO Kim Pilegaard Forskningscenter Risø, Danmarks Tekniske DenmarkUniversitetSJ BERWIN Ramón García-Gallardo SJ Berwin LLP BelgiumUA Reinhart Ceulemans Universiteit Antwerpen BelgiumCEAM Maria J. Sanz Fundación Centro de Estudios Ambientales Spaindel MediterraneoISBE Michal V. Marek Ústav systémové biologie a ekologie AVR, v.v.i.Czech Republic124


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe preparatory phase was initiated by 6 institutes which will form a network ofcontact points within each country (France, Finland, Germany, Italy, Netherlands,and United Kingdom). The preparatory proposal phase will be coordinated in France.Germany will develop the Central Analytic Laboratory (CAL), while Italy will organizethe Ecosystem Thematic Center (ETC) and France will establish the AtmosphericThematic Center (ATC). The United Kingdom and Finland will contribute to thedevelopment of sensors for flows on the ecosystems. The Netherlands willcoordinate the studies of optimization of the network and definition of the schedule ofconditions of the stations of reference.These principal partners, along with the French Ministry of Research are joined byrepresentatives from different institutes in five other countries (Sweden, Denmark,Belgium, Spain, and the Czech Republic). Additional countries have alreadyexpressed their interest (Norway, Israel) and processes will be in place to add newmembers who will have the support of their country during the preparatory phase.A certain number of international organizations have also expressed their interestand have provided letters of support for the preparatory phase and include:World Meteorological Organization (WMO)National Institute For Environmental Studies (NIES)National Oceanic and Atmospheric Administration (NOAA)Integrated Global Atmospheric Composition Strategy (IGACO)Integrated Global Carbon Observing Strategy IGCOGlobal Carbon Project (GCP) IGBP-WCRP-IHDPFLUXNETMeteo-FranceInstitut National de Recherche Agronomique (INRA)Umweltbundesamt (UBA)Finnish Meteorological Institute (FMI)Direction Générale de la Recherche et de l’Innovation (DGRI)Centre National de la Recherche Scientifique (CNRS)Commissariat à l’Energie Atomique (CEA)Ministerium für Wissenschaft, Forschung und Kunst Baden-WürttembergMinistero dell’Ambiente e della Tutela del Territorio e del mareMinistero dell’Universita e della RicercaNetherlands Organization for Scientific Research (NOW)Finnish Ministry of EducationDepartment for Environment Food and Rural Affairs (DEFRA)Swedish Research CouncilSpanish Ministry of Education and Science (MEC)Research Foundation Flanders (FWO)Czech Science FoundationDanish Agency for ScienceMinisterio de Medio AmbienteResearch Council of NorwayNational Agency for New Technologies, Energy and Environment (ENEA)SwitzerlandJapanUSASwitzerlandFrance, USAAustraliaUSAFranceFranceGermanyFinlandFranceFranceFranceGermanyItalyItalyNetherlandsFinlandUKSwedenSpainFlandersCzech RepublicDenmarkSpainNorwayItalyExpected impactThe synergy between the atmospheric concentration measurements on the one handand the knowledge of local ecosystem fluxes on the other hand, has shown effectivein reducing the uncertainties on carbon assessments. However, in Europe,125


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportobservatories are all managed differently for each country and data is nothomogenously processed.The value added impact of the infrastructure will allow an enhanced visibility anddissemination of European greenhouse gas data and products that are both longtermand carefully calibrated. ICOS seeks to meet the data needs of carbon cycleand climate researchers as well as those of politicians and the general public. ICOSwill serve as the backbone to users engaged in developing data assimilation modelsof greenhouse gas sources and sinks, namely reverse modelling, which allows thededuction of surface carbon flux pattern.A common data center, the Carbon Portal put into place by ICOS, will provide freeaccess to ICOS data services, as well as to links with inventory data, and outreachmaterial. This portal will allow the production web based tools for the survey ofsources and sinks in near real time. ICOS will deliver the information in near real timewith a quantification of the uncertainty associated with the results due to the use ofseveral different models using different methodologies.ICOS will enable Europe to be a key global player for in situ observations ofgreenhouse gases, data processing and user-friendly access to data products forvalidation of remote sensing products, scientific assessments, modeling and dataassimilation.126


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportGMES and the GMES Atmosphere ServiceVirginia Puzzolo 1 and Julian Wilson 2[1] European Commission GMES bureau, Bruxelles[2] European Commission DG Joint Research Centre, Institute for Environment andSustainability, Ispra, Italy1. GMES - overall introductionGlobal Monitoring for Environment and Security (GMES) is an EU-led initiative, inwhich ESA will implement the space component and the Commission will manageactions for identifying and developing services relying both on in-situ and remotesensing data [1].The objective of GMES is to provide, on a sustained basis, reliable and timelyservices related to environmental and security issues in support of public policymakers’ needs. In particular, the challenge for GMES is to gather together existingdata collected from space-borne, airborne and in-situ observation systems to provideinnovative, cost-effective, sustainable and user-friendly services, that enabledecision-makers to better anticipate or mitigate crisis situations and issues relating tothe management of the environment and security.GMES will be developed in steps starting with three Fast-Track services (land,marine, emergency) which were selected based on the criteria of existing capacitiesand structures, user uptake and conditions for long-term sustainability [2]. Using thesame criteria, new pilot services including the GMES Atmosphere Service are beingprogressively introduced to provide a broader range of services to support a widerange of needs2. GMES contribution to climate changeAs highlighted in the second adequacy report to the United Nations FrameworkConvention on Climate Change (UNFCCC), there are serious deficiencies to meetthe observational needs of the UNFCCC. Therefore, Parties to UNFCCC will lack thenecessary information to effectively plan for and manage their response to climatechange [3].In this framework GMES will contribute to improving monitoring capacity throughsupporting systematic and sustained observations of the pressures and driving forceson the environment and its state in the main Earth compartments (Land, Marine andAtmosphere). In particular, GMES will contribute to the Global Observing Systems forClimate and will also support the development of policies and appropriate adaptationstrategies as well as the tracking progress on Kyoto protocol commitments at bothEuropean and National level.127


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportPolicy users of climate change information require outputs that make use of a broadrange of GMES products (e.g. information on boundary conditions including, forexample, vegetation characteristics, soil properties and moisture, sea surfacetemperature). This means that climate change is an horizontal issue withdependencies on the GMES services on Land, Marine and Atmosphere. In particular,the services required to provide climate change relevant information include: The generation of observation datasets and reanalyses of past observational dataenabling adequate descriptions of the status and evolution of the Earth systemcompartments. This capability should exist through the implementation of theGMES Pilot Services, and especially those on marine, land and atmospheremonitoring, which include a global component by design and through their linkswith GEOSS. It should be noted that long-term availability of the infrastructure tocapture and process the necessary observations is crucial for the provision ofrelevant information. The elaboration of state of the art, long-term scenarios developed in response tocandidate policy actions, i.e. numerate answers, and estimates of their uncertainty,to realistic 'what if' questions. Due to the strong couplings and feedbacks betweenall the compartments of the Earth system, and the need to take into account theinfluence of human activities, this information capability should be based on Earthsystem models. Several relevant and world-class capacities for Earth systemmodelling exist at European level. The analysis of long-term scenario outputs obtained through Earth system models,and their interpretation in terms of possible measures for adaptation and mitigationof climate change impacts to be channelled to decision makers. This capabilityrequires close cooperation between Earth science and social sciencecommunities, and especially economists and sociologists.3. GMES Atmosphere ServiceThe GMES Atmospheric Service is the first pilot service launched after the 3 initialFast-Track services. This Service will provide coherent information on theatmospheric composition at local and regional, European, and global scale in supportof European policies and for the benefit of European citizens.The service will complement and build on existing efforts and proven mechanismsand be based on the results and experiences gained from atmosphere-related GMESprojects in accordance with the prioritising criteria of the GMES Action Plan 2004-2008. An important aspect of its implementation will be the consistent and systematicassimilation and exploitation of all available observations, including in-situ, remotesensing and space-based observations to provide tailored information to the publicand government authorities.The GMES Atmospheric Service will play an important role in the context of theGMES contribution to the activity of the Group on Earth Observations (GEO) and toits global 10-year implementation plan for a Global Earth Observing System ofSystems (GEOSS). It will be the major contribution, together with the Global128


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportComponent of the GMES Land Monitoring Core Service and with the GMES MarineCore Service, to the Global Climate Observing System (GCOS), providing part of theidentified Essential Climate Variables [3].3.1. GMES Atmosphere Service WorkshopGMES is a user-driven initiative and for this reason, a user workshop was organisedin Brussels on 6-7 December 2006 to discuss ‘Objectives and requirements’,‘Implementation issues’ and ‘Conditions for sustainability’ of the GMES AtmosphereService (GAS). The workshop was structured in three parallel sessions arounddifferent user streams: ´Air Quality’, ‘Climate Change/Forcing’ and‘O3/UV/Renewable Energies’. The main outcomes of the workshop report are brieflyreported below.Objectives and requirementsThe workshop identified the main user communities, however further discussion isnecessary to make clear the role of the GMES Atmosphere Service in the provisionof information directly to citizens. The GMES service architecture of core anddownstream services was broadly accepted. Nevertheless, a number of issuesrequiring further consideration were identified (e.g. consistency of assessment,interference of the GMES Atmosphere Service and existing market services,targeting and borderline of core/downstream services, etc.). The scope of the coreservice, presented in the workshop orientation paper, broadly matched the needsidentified during the workshop, and adjustments to all three user streams of theGMES Atmosphere Service were identified.In particular for climate change/forcing, the workshop supported the inclusion ofroutine data assimilation, and inverse modelling/synthesis inversions in the coreservice, to provide: 3D distributions including profiles of carbon dioxide, methane, ozone, aerosols(type-resolved; clear definition required); 3D distributions including profiles of gaseous precursors to methane, ozone andaerosols (e.g., CO, SO 2 , NOx and Volatile Organic Carbon Components); Estimates of surface fluxes of CO 2 , CH 4 , etc. 3D fields and long-term records of atmospheric dynamics and thermodynamicquantities including clouds; Consistent high-resolution datasets between the air/land/ocean services.The emphasis for the Climate Change/Forcing stream should be the GCOS essentialclimate variables as a minimum. High spatial and temporal resolution of the analyseswas seen as key. An ambition to include water vapour and CO 2 cycle was expressedas well as emissions sources not currently included in UNFCCC but relevant toEuropean policy development (e.g. volcanoes, shipping).129


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportImplementation issuesAcquisition and processing of observations (both space and in-situ), analysis andforecast, and product generation, dissemination and archiving were identified as keyfunctions of the GMES Atmosphere Service. The workshop did not give a clearanswer to the questions on architecture and governance of the service. As regard togaps and obstacles, a number of key issues, which should be addressed in theimplementation process, were highlighted such as transition from research activitiesto operational services, sustainability of satellite operational data after 2010,accessibility of data (especially in-situ) and the integration of non-EU data sources.Condition for sustainabilityThe discussion on sustainability addressed long term data availability for both in-situand space based data, funding issues and the role of R&D. As regards in-situobservations, the extent of coverage through legislative requirements could be apossible way to secure sustainability and long term data availability. The spacecomponent of the GMES Atmosphere Service seems to face too great dependencyon short term satellites and gaps in data availability after 2010. About financialissues, both community budget and national contributions are considered to be ofequal importance.Besides the funding optimization and coordination, guidance for seeking EU funding,long term funding for research networks and other issues were raised in theworkshop. The close link between the service and R&D activities was recognized asthe major condition for the GMES Atmosphere Service sustainability.3.2. GMES Atmosphere Service Implementation ProcessThe GMES Atmosphere Service workshop represents the starting point of itsimplementation process. The next step will consist of setting up a GMES AtmosphereService Implementation Group (IG) representing, as much as possible, the opinionsof the important user communities, experts and inter alia national representatives.The IG will address the most crucial issues of the GMES Atmosphere Serviceimplementation process, such us: scope, service functionality and architecture,requirements for observation infrastructure (both space and in situ), structure andgovernance, funding, and will provide and action plan for the implementation and theoperational validation of the GMES Atmosphere Service.References[1] COM(2005)565[2] COM(2004)65[3] Second Adequacy Report of the Global Observing Systems for Climate in support of theUNFCCC (2003). http://www.wmo.ch/web/gcos/Second_Adequacy_Report.pdf130


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report6 Poster Presentations131


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportGreenhouse Gas observations within the monitoring network of theGerman Federal Environmental Agency (Umweltbundesamt)Frank Meinhardt, Ludwig Ries and Karin UhseGerman Federal Environmental AgencyGerman Greenhouse gas (GHG) observations have been started in the beginning ofthe 1970s. The first continental observation station was established by the GermanScience Foundation at the Schauinsland observatory in the Black Forest. Todayseveral stations, with continuous GHG measurements are operated by the FederalEnvironmental Agency and add to national and international monitoring programmes.The main tasks of the Federal Environmental Agency network are the observation oflong term trends of air pollutants, the transboundary transport of air pollutants andthe monitoring of climate relevant components. These tasks have mainly beencarried out in the frame of the EMEP (European Monitoring and Evaluation Program)and GAW (Global Atmosphere Watch) programmes.The GAW Global station Zugspitze and the Regional station Schauinsland contributeto GAW programme with continuous measurements of CO 2 , CH 4 , N 2 O as well asSF 6 . The GAW regional station Neuglobsow contributes observations of CO 2 andCH 4 . Beyond that there are two additional, so called GAW contributing stations, thenorth-sea coastal station Westerland, which supplies CO 2 observations, and the lowrange mountain station Schmücke, where additional CH 4 measurements areperformed. The operation at the stations Brotjacklriegel and Deuselbach, where alsoGHG monitoring was performed, have been shut down in 2004. The continuousSchauinsland measurements supplemented by flask samples (University ofHeidelberg) and the Westerland CO 2 observations are currently integrated in the EUfundedProject CarboEurope-IP.The Schauinsland and Zugspitze stations are equipped with modified GC systemswhich were set up in 2000. These instruments base on a HP 6890 GC. Combinedwith a dedicated inlet system it is possible to obtain a quasi-continuous operation.These instruments are suited to measure the atmospheric mixing ratios of the fourmost important Greenhouse gases CO 2 , CH 4 , N 2 O and SF 6 with high precision. Table1 shows the typical reproducibility of the standard gas measurements for thesesystems, run at Schauinsland and Zugspitze.Table 1: Reproducibility of the measured GHGComponentCO 2CH 4N 2 OSF 6Reproducibility0.08 ppm1 ppb0.15 ppb0.1 ppt132


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe working standards of the GC systems refer to standards provided byNOAA/CMDL, thus the atmospheric measurements are related to the internationaladmitted NOAA scales. The Schauinsland station serves as the calibration laboratoryfor the GHG standards in the UBA network.Selected resultsCarbon dioxide (CO 2 )Figure 1 shows the CO 2 increase at different stations. CO 2 increases worldwide withthe same rate. The seasonal variations of the CO 2 mixing ratios observed at midnorthern latitudes are caused by photosynthesis and respiration of the continentalbiosphere. As expected, the unselected CO 2 record at the Schauinsland station haspronounced seasonal variations due to the biogenic and anthropogenic impact in thesurroundings of the station.400390380370CO2 [ppm]360350340330320310Mauna Loa, Hawaii Schauinsland Zugspitze300195819591961196319651967196919701972197419761978198019811983198519871989199119921994199619982000200120032005Figure 1: Long term trend of the unselected monthly means of carbon dioxide mixing ratio atSchauinsland station compared to measurements at the GAW global stations Mauna Loaand Zugspitze.Methane (CH 4 )Methane is besides CO 2 the most important anthropogenic greenhouse gas. CH 4mixing ratios have been measured at the Schauinsland station since 1991 and since1994 at the GAW regional station Neuglobsow. Figure 2 shows the comparison of themixing ratios between both stations.133


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report200019501900CH4[ppb]185018001750SchauinslandNeuglobsow170019921993199419951996199719981999200020012002200320042005Figure 2: Comparison of the unselected monthly mean methane mixing ratios at the GAWregional stations Neuglobsow and Schauinsland.The large seasonal variation of the CH 4 mixing ratio at Neuglobsow is probably dueto the stronger influence of local sources at this site compared to the mountainstation Schauinsland. Maximum concentrations at Neuglobsow are reached duringwinter months, the minimum is observed in summer. At the Schauinsland station nosystematic seasonal variation is detectable. Both records show a distinct increase ofthe CH 4 concentration.Sulfurhexafluoride (SF 6 )Since the beginning of the Sulfurhexafluorid (SF 6 ) observations at the Schauinslandstation in 2001, annual mean concentrations have increased from about 5 ppt up toalmost 6.5 ppt in 2006. This corresponds to an increase of 30% within 5 years. Incomparison with CO 2 , SF 6 has a 22000 times higher greenhouse potential. SF 6 hasan atmospheric lifetime of about 3000 years. Due to this long lifetime, any additionalSF 6 emission will cause an increase of the SF 6 mixing ratio in the atmosphere. Astabilisation of this greenhouse gas is only possible if the emissions are completelystopped. Due to its physical properties SF 6 is mainly used in electrical equipment andin high voltage applications..134


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report7.06.56.0SF6 [ppt]5.55.04.54.02000 2001 2002 2003 2004 2005Figure 3: Monthly mean sulfurhexafluoride (SF 6 ) concentrations at the Schauinsland station.135


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportSet-up of a continuous greenhouse gas monitoring station for CO 2 ,CH 4 , N 2 O, SF 6 , and CO in Northern ItalyBert Scheeren, Peter Bergamaschi, and Günther SeufertEuropean Commission DG Joint Research Centre, Institute for Environment andSustainability, I-21020 Ispra (VA), ItalyIntroductionThe Climate Change Unit of the Institute for Environment and Sustainability of theJoint Research Centre in Ispra is currently setting up a continuous long-termgreenhouse gas (GHG) monitoring station for CO 2 , CH 4 , N 2 O, SF 6 , and CO inNorthern Italy. The rational behind this project is the following: To contribute to the sparse continuous GHG monitoring network in SouthernEurope. To support inverse modelling “top-down” emission estimates (e.g. using TM54DVAR [Bergamaschi et al., 2007]). To follow and verify the development of GHG trends in Europe in relation toemission reduction measures under the Kyoto protocolFigure 1: Impression (Google earth) of the Campo dei Fiori mountain (1200 m asl) situatedon the border between the Po Valley and the Alps.Location of the monitoring siteA suitable location for the JRC GHG monitoring site is the top of Campo dei Fiori(1200 m asl) mountain located at about 10 km north of Ispra bordering the city ofVarese (Figure 1). The mountain top hosts a RAI TV tower and a regionalmeteorological station providing detailed meteorological observations for the site (aswell as historical meteorological data). Our planning is to start measuring from theCampo dei Fiori from 2008. To do so we intend to deploy a mobile laboratory to beplaced at the base of the Campo dei Fiori TV tower.136


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportOur main area of interest is the Po Valley (Figure 2) being one of the most pollutedregions in Europe. When approaching from the South the Campo dei Fiori is the firstsmall mountain bordering the south flank of the Alps. Hence, polluted air massescoming from the Po Valley with a south to southeasterly flow are able to reach thestation relatively unhindered. This makes the Campo dei Fiori an excellent potentiallocation to monitor pollution from northern Italy and the Po Valley.Figure 2: Our main region of interest is the Po Valley south of the Campo dei Fiori mountainsite (Google earth image).Measurement techniqueFigure 3: Schematic of the Gas Chromatograph set-up for measuring CO 2 , CH 4 , N 2 O andSF 6 .137


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportWe will use an automated Agilent 6890N gas chromatography system based on theconcept originally developed by Worthy et al. [1998] and similar to the GC systemsas currently applied in the CHIOTTO tall tower project [Vermeulen et al., 2007].Initially, the GC system will be set-up to measure CO 2 , CH 4 , N 2 O and SF 6 with theoption to eventually change from CO 2 to CO when a Licor based CO 2 monitoringsystem is in place. Primary standards provided by NOAA/GMD and workingstandards from Deuste Steininger (Germany) will constitute our primary andsecondary scale respectively. A schematic of our GC system is shown in Figure 3.Air mass origin and air quality in the Ispra areaWe investigated the air mass origin for the Ispra (EMEP site) area based on dailymean 22.5 degrees wind sector classification for the period 1997 to 2004 to analyzethe general air mass origin in the station area. The daily sector values are based on96 hours 2D backward trajectories with a horizontal resolution of 50x50 km 2calculated with the NWP (Numerical Weather Predicition) model PARLAM-PS(Norwegian Meteorological Institute) available from the EMEP website(http://www.emep.int/Traj_data/traj2D.html). The 4 day backward trajectories arecalculated by following an air parcel every 2 hour for 96 hours back in time, 4 termsper day at 0, 6, 12 and 18 hour GMT. If a trajectory starts from outside the EMEPdomain, then the coordinates giving the air parcel position are set to (0,0). The areaaround the arrival point extends from a radius of 150 km to a radius of 1500 km. Thecriteria for allocation of trajectories to one sector is that at least 50% of its givenpositions are found within that sector, otherwise sector 9 (not attributable) isallocated.Our analysis, presented in Figure 4, indicates that winds from a SE to SW directiondominate the area for about 22% of the days per year, whereas the NE to NW sectorrepresents 51% of the days. 27% of the days can not be attributed to a single sector.The same trajectory analysis is used to create a trajectory crossings map or area‘footprint’ for the Ispra EMEP site as shown for the year 2005 and 2006 in Figure 5(images available from http://www.emep.int/Traj_data/traj2D.html). To compose atrajectory crossings map each grid cell crossed by a trajectory on its way to the IspraEMEP site is accounted and the total number of times a grid cell is crossed by atrajectory is summed for the whole year.Figure 4: Air mass origin in the JRC-Ispra area based on 4-day back trajectory analysis byEMEP for 1997-2004.138


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportThe Ispra EMEP area footprint for the year 2005 and 2006 both show that thestrongest influence is expected from the northeastern part of Italy includingSwitzerland and the southeastern part of France which is in agreement with thelonger term analysis presented in Figure 5.2005 2006Figure 5: Footprint of the JRC-Ispra area based on 4 day back trajectory crossings by EMEPfor the year 2005 and 2006. The units are total number of times the trajectory has crossedthe JRC EMEP grid cell for the whole year.When we look at the origin of the air pollution in the form of elevated CO, O 3 and NOconcentrations that arrive at the Ispra EMEP site for the year 2000 (Figure 6), wefind, as can be expected, that the strongest pollution events are associated withsoutheasterly to southwesterly winds, whereas northwesterly winds are muchcleaner.Figure 6: Air pollution origin for the Ispra area (EMEP site data) for the year 2000. Plots bycourtesy of Jean-Philippe Putaud, JRC Ispra.In Figure 7 we show the daily mean CO and O 3 concentrations for the Ispra area(EMEP site data). The CO concentrations are lowest during spring and summer and139


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and and inverse modelling for for verification of national of national and and EU bottomupGHG inventories " -" report -EU bottom-by a factor of of 10 10 higher during winter mainly from from local local domestic wood wood burning burning for forresidential heating. The ozone concentrations are are highest during during summer summer when whenphotochemical production conditions are are optimal (high (high solar solar intensity, high high humidity).2000CO CO daily mean concentration at the at the EMEP-JRC for the for year the year 2002 20026060O 3 daily O 3 mean daily mean concentration concentration the EMEP-JRC at the EMEP-JRC for the year for the 2002 year 2002CO [ppbv]CO [ppbv]1750150012501000750 750500 500250 2500 00 0 50 50 100 100 150 150 200 200 250 250 300 300 350 350day day of year of yearO3 [ppbv]50 5040 4030 3020 2010 100 00 0 50 50 100 100 150 150 200 200 250 250300 300350350O3 [ppbv]day of year day of yearFigure 7: 7: Daily mean CO CO and and O 3 Oconcentrations 3 for for the the Ispra Ispra area area (EMEP (EMEP site site data). data).In general, the relative humidity (RH) (RH) in in the the Ispra Ispra area area is moderate is to high to high (60-80%) (60-80%)apart from Föhn episodes were RHs RHs can can drop drop to to 20%. 20%. These These Föhn Föhn episodes bring bringrelative clean northwesterly air air masses from from the the Alps Alps to the to the region region in the in the form form of ofstrong gusty winds. Precipitation in in the the area area is mainly is in the in the form form of short of short periods periods of ofintense rain by by synoptic disturbances during winter winter and and early early spring spring and andthunderstorms during the the summertime and and early early fall. fall.Summarizing, we we can conclude that that the the foreseen Campo dei dei Fiori Fiori mountain station stationhas good potential for for becoming an an important continuous monitoring station stationrepresentative for for northern Italy, notably the the Po Po Valley, which which will will allow allow us to us optimize to optimizeinverse modelling “top-down” greenhouse gas gas emission estimates for for these these regions regionswith the new TM5 4DVAR inverse modelling system (Bergamaschi et al., et al., 2007). 2007).OutlookIn 2007 the GC system will will be be set-up and and tested at at the the JRC JRC laboratory followed followed by bythe first atmospheric observations from from an an elevated location at the at the JRC. JRC. In the In themeanwhile we will prepare the the set-up of of the the long-term continuous Campo Campo dei dei Fiori Fiorimountain station so so that we we will will be be able able to to start start with with measurements in the in the first first half half of of2008.ReferencesBergamaschi, P., P., J.F. Meirink, M. M. Krol, Krol, and and G.M. G.M. Villani, Villani, New New TM5-4DVAR inverse inverse modelling modellingsystem to to estimate global and and European CH CH 4 sources, 4 this this report., report., 2007. 2007.Vermeulen, A., A., G. G. Pieterse, A. A. Manning, M. M. Schmidt, L. Haszpra, L. E. Popa, E. Popa, R. Thompson, R. J. J.Moncrieff, A. A. Lindroth, P. P. Stefani, J. J. Morguí, E. E. Moors, R. Neubert, R. M. Gloor, M. Gloor, CHIOTTO - -Continuous HIgh-precisiOn Tall Tall Tower Observations of greenhouse of gases, gases, this this report, report,2007.Worthy D.E.F., I. I. Levin, N.B.A. Trivett, A.J. A.J. Kuhlmann, J.F. J.F. Hopper, and and M.K. M.K. Ernst, Ernst, Seven Sevenyears of of continuos methane observations at at a remote a boreal boreal site site in Ontario, in Ontario, Canada, Canada, J. J.Geophys. Res., 103 (D13), 15995-16007, 1998. 1998.140


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportAcknowledgementsWe kindly acknowledge the constructive advice and input of Martina Schmidt andcolleagues (LSCE Paris), Ingeborg Levin and colleagues (University Heidelberg),Rolf Neubert and colleagues (CIO Groningen), and Alex Vermeulen (ECN Petten).For more information:Bert Scheeren, tel: 0039 0332 786701e-mail: hubertus.scheeren@jrc.it141


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report4D-VAR System for Inverse Modeling of Atmospheric CH 4 :Sensitivity Analyses using Synthetic ObservationsMaria Gabriella Villani 1 , Peter Bergamaschi 1 , Jan Fokke Meirink 2 , and Maarten Krol 3,4[1] European Commission DG Joint Research Centre, Institute for Environment andSustainability, Ispra, Italy[2] Institute for Marine and Atmospheric Research Utrecht, University of Utrecht, Utrecht, theNetherlands[3] Wageningen University and Research Centre, Wageningen, the Netherlands[4] Netherlands Institute for Space Research, Utrecht, the NetherlandsIntroductionThe new TM5-4DVAR system allows to optimize emissions of individual model gridcells, and from different source categories [Bergamaschi et al., 2007b; Meirink et al.,2007]. At the same time, very large observational data sets such as high frequency insitu measurements and global satellite data (e.g. from SCIAMACHY) can be used(e.g. see Bergamaschi et al. [2005, 2007a], Meirink et al. [2006, 2007]).This work presents preliminary results of a set of sensitivity experiments that usesynthetic observations to study the system performance in more detail. For thispurpose ground-based observations are generated by model forward runs, where theapplied CH 4 emissions inventories are assumed to represent the 'true' emissions.These measurements are then assimilated in a model run, which uses emissionsperturbed from the 'true' emissions. The comparisons between retrieved and trueemissions provide insights on the impact of the ground-based observations in the4DVAR system optimization.Experiments set-upModelThe model adopted in this study is the off-line chemistry-transport model TM5 [Krol etal., 2005]. The experiments are performed using a CH 4 single-tracer version whereCH 4 oxidation is based on a prescribed OH field. TM5 is run on a global domain of6 o x4 o , and the year 2003 is chosen for the assimilation period. The 4DVARassimilation system will be described in detail in Meirink et al. [2007].ObservationsSynthetic observations are created at 45 sites (NOAA sites, Figure 1). A constantmeasurement uncertainty of 3 ppb is assumed for the observations. In addition, themodel representativeness error is estimated based on the 3D gradient of simulatedCH 4 mixing ratios and included in the overall data uncertainty [Bergamaschi et al.,2005]. Three different datasets are generated by using different samplingfrequencies: continuous measurements sampled every 3 hours (CM); flask142


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportmeasurements weekly sampled (FM); and a mixed set of flask measurements (38sites) and continuous measurements (7 sites) (FM+CM).EmissionsThe bottom-up inventories applied as true emissions are as described inBergamaschi et al. [2007a]. They consist of 11 source categories. The spatialcorrelation length is assumed to be 500 km. In addition, temporal correlations arespecified for emissions of consecutive months. For most anthropogenic categorieswe assumed very strong temporal correlations, while for emissions with largeseasonal variations (wetlands, rice) temporal correlations are set to zero.The 4DVAR optimization is started using a priori emissions that are perturbed fromthe 'true' emissions. In the sensitivity experiments presented here, emissions fromrice cultivation were perturbed, decreasing them uniformly in space and time by 50%of the ‘true’ value. Emissions from all other source categories were not modified.ResultsPreliminary results are shown in Figures 2-5.The 4DVAR system can retrieve total emissions for each grid cell reasonably wellwhen using comprehensive networks of continuous ground-based measurements.This can be seen from Figure 2, which shows the comparison between retrieved CH 4total annual emissions and the true values, and the grid-cell error reduction resultingfrom the 4D-VAR optimization.The decrease of sampling frequency in observations leads to significant deteriorationof retrieved emissions. This is observed from the maps of the methane total annualemissions, and total annual mixing ratios at four sites, obtained by assimilating thethree sets of observations, CM, FM, and FM+CM (Figures 3 and 4).There are limitations to retrieve the correct partitioning among source categories.Figure 5 shows that rice cultivation emissions are not fully recovered. The missingcontribution is attributed to other source categories emitting at the same regions asrice cultivation. This occurs despite the fact that some of these categories arecharacterized by different time correlations.AcknowledgmentsThis work has been supported by the European Commission RTD project GEMS("Global and regional Earth-system (Atmosphere) Monitoring using Satellite and insitudata", contract number SIP4-CT-2004-516099, 6th Framework Programme).ReferencesBergamaschi, P., C. Frankenberg, J.F. Meirink, M. Krol, F. Dentener, T. Wagner, U. Platt,J.O. Kaplan, S. Körner, M. Heimann, E.J. Dlugokencky, and A. Goede, Satellitechartography of atmospheric methane from SCIAMACHY onboard ENVISAT: (II)143


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportEvaluation based on inverse model simulations, J. Geophys. Res., 112, D02304,doi:10.1029/2006JD007268, 2007a.Bergamaschi, P., J.F. Meirink, M. Krol, and G.M. Villani, New TM5-4DVAR inverse modellingsystem to estimate global and European CH 4 sources, this report, 2007b.Bergamaschi, P., M. Krol, F. Dentener, A. Vermeulen, F. Meinhardt, R. Graul, M. Ramonet,W. Peters, and E.J. Dlugokencky, Inverse modelling of national and European CH 4emissions using the atmospheric zoom model TM5, Atmos. Chem. Phys., 5, 2431-2460,2005.Krol, M.C., S. Houweling, B. Bregman, M. van den Broek, A. Segers, P. van Velthoven, W.Peters, F. Dentener, and P. Bergamaschi, The two-way nested global chemistry-transportzoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417-432, 2005.Meirink J.F., Bergamaschi P., and Krol M.: Four-dimensional variational data assimilation forinverse modelling of methane emissions, 2007, paper in preparationMeirink, J.F., H.J. Eskes, and A.P.H. Goede, Sensitivity analysis of methane emissionsderived from SCIAMACHY observations through inverse modelling, Atmos. Chem. Phys.,6, 1275-1292, 2006.Figure 1: Map of the sites chosen to obtain synthetic observations.144


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of of national and and EU EU bottomuupGHG inventories " " - - reportFigure 2: 2: Left: CH 4 4 total annual Emissions. CH 4 total annual emissions for for all all categories. TopToppanel: ‘true’ emissions (total 511.9 Tg CH 4 /yr). Bottom panel: a a posteriori emissions (tot.(tot.510.9 Tg Tg CH CH 4 4 /yr). Right top panel: Difference between true and a a posteriori emissions. RightRightbottom panel: Grid-cell error reduction resulting from the 4D-VAR optimization (calculated as:as:(a (a priori - a - a posteriori)/ a priori).Figure 3: 3: Maps show the differences between derived, and true CH CH 4 4 total annual emissionsfor for all all categories (total emissions 511.9 Tg CH 4 /yr). Synthetic observations at at differentsampling frequencies have been used. Top left panel: continuous measurements (total(totalemissions 510.9 Tg Tg CH 44 /yr). Top right panel: flask and continuous measurements (total(totalemissions 510.1 Tg Tg CH 4 4 /yr). Bottom panel: flask measurements (total emissions 509.4 509.4 TgTgCH CH 4 /yr). 4 /yr).145145


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFigure 4: CH 4 mixing ratios at atmospheric monitoring stations (flask and continuousmeasurements). Blue line: a priori mixing ratios; Red line: a posteriori mixing ratios. Blacksymbols: synthetic observations.Figure 5: Each ‘two-graphs panel’ shows the true emissions on the top part, and thedifference between a posteriori and true emission on the bottom part. Top-left panel: totalemissions (all categories) (true: 511.9 Tg CH 4 /yr; a posteriori: 510.9 Tg CH 4 /yr). Top-right:rice cultivation (true: 79.6 Tg CH 4 /yr; a posteriori: 59 Tg CH 4 /yr). Bottom-left: wetlands (true:149.7 Tg CH 4 /yr; a posteriori: 159.5 Tg CH 4 /yr). Bottom-right: ruminants (true: 88.6 Tg;CH 4 /yr a posteriori: 93.2 Tg CH 4 /yr).146


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report7 ANNEX1: Workshop AgendaThursday, 08 March 20079:00-9:10 Welcome/Introduction (F. Raes)Presentation of EU projects (chair: P. Bergamaschi)9:10-9:30 CarboEurope-IP (C. Roedenbeck)9:30-9:50 CHIOTTO project (A. Vermeulen)9:50-10:10 IMECC (P. Rayner)10:10-10:30 GEMS-IP (P. Rayner)10:30-10:50 coffee break10:50-11:10 GEOMON-IP (P. Rayner)11:10-11:20 NitroEurope-IP (P. Bergamaschi)11:20-11:40 HYMN (P. Bousquet)11:40-12:00 SOGE (S. Reimann)12:00-12:20 Geoland (J.C. Calvet)12:20-14:00 lunchInverse modelling studies (chair: A. Vermeulen)14:00-14:20 Top-down estimates of European GHG emissions (A. Manning)14:20-14:40 Methane and Nitrous Oxide flux estimates for Europe using tall towerobservations and the COMET inverse model (A. Vermeulen)14:40-15:00 New TM5-4DVAR inverse modelling system to estimate global andEuropean CH 4 sources (P. Bergamaschi)15:00-15:20 LSCE inverse modelling (P. Bousquet)147


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report15:20-15:40 coffee break15:40-16:00 Atmospheric methods in the presence of partial carbon accounting (P.Rayner)16:00-16:20 An estimate of net CO 2 exchange across the terrestrial biosphere ofNorth America for 2000-2005 (M. Krol and W. Peters)Discussion (chair: P. Bousquet)16:20 - 17:20 Discussiontopics: further requirements / research+development needs for atmospheric models andinversion techniques Dependence on bottom-up inventories Separation of anthropogenic and natural sources Requirements of monitoring network for inverse modelling Representativeness of stations, model representativeness errors, and regions ofinfluence (sensitivity) of monitoring stations.20:00 workshop dinner (Ristorante Conca Azzurra, Ranco)Friday, 09 March 2007EU-level reporting on sources and sinks to UNFCCC and bottom-up inventories(chair: F. Dentener)9:00-9:20 EU-level reporting on sources and sinks to UNFCCC: the mandate to DGENV and EEA (E. Kitou)9:20-9:40 European GHG emissions (F. Dejean)9:40-10:00 EDGAR (J. v. Aardenne / J. Olivier)10:00-10:20 Agriculture, Forestry and Other Land Uses (AFOLU): Realities andneeds for Kyoto reporting (G. Seufert)10:20-10:40 coffee break148


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportEuropean and international GHG monitoring programs (chair: M. Ramonet)10:40-11:00 The AGAGE network for ground based measurements of non-CO2GHGs: Monitoring of atmospheric concentrations and emission estimates (D.Cunnold)11:00-11:20 The WMO GAW Global GHG Programme (L. Barrie)11:20-11:40 RAMCES / LSCE and CarboEurope GHG monitoring network (M.Schmidt)11:40-12:00 GHG monitoring at Lampedusa, Italy (A. di Sarra)12:00-12:20 GHG monitoring at Jungfraujoch (S. Reimann)12:20-13:30 lunch13:30-14:00 ICOS (C. Roedenbeck)14:00-14:20 GMES-GAS (J. Wilson / V. Puzzolo)Discussion (chair L. Barrie)14:20-15:20 discussiontopics: Evaluation of existing European monitoring programs for verification of EuropeanGHG emissions Further steps and requirements towards an integrated operational Europeanmonitoring system149


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - report8 ANNEX2: Workshop ParticipantsHakam Al-Hanbali tel.: +46 8 698 1417Swedish Environmental Protection Agency fax: +46 8 20 29 25BlekholmsterassenS-10648 Stockholm hakam.al-hanbali@naturvardsverket.seFrancesco Apadula tel.: +39 02 3992 5235CESI RICERCA fax: +39 02 3992 5235Via RubattinoI-20134 Milano apadula@cesiricerca.itFlorinda Artuso tel.: +390630483232ENEA fax: +390630486678Via AnguillareseI-00123 S.Maria di Galeria-Rome florinda.artuso@casaccia.enea.itLeonard A. Barrie tel.: +41 22 730 8240WMO fax: 41 22 730 82497 bis Ave de la PaixCH-1211 Geneve 2Lbarrie@wmo.intPeter Bergamaschi tel.: +39 0332 789621European Commission, DG JRC fax: +39 0332 785704Joint Research CentreInstitute for Environment and Sustainability peter.bergamaschi@jrc.itI-21020 IspraPhilippe Bousquet tel.: +33 1 69 08 77 18IPSL/LSCECEA Saclay, Orme des Merisiers, bat701F - 91191 Gif sur Yvettepbousquet@cea.frJean-Christophe Calvet tel.: +33 561079341Météo-France42 Avenue G. CoriolisF - 31057 Toulouse Cedex 1calvet@meteo.frDerek Cunnold tel.: 404 894 3814Georgia Tech fax: 404 894 5638School of Earth & Atmospheric SciencesUSA - 30332-0340 Atlanta, GAcunnold@eas.gatech.eduSorin Deaconu tel.: +40-21-2071128National Environmental Protection AgencyGovernment of RomaniaAleea Campul cu Florisorin.deaconu@anpm.ro3A, Bloc M49A, Sc. C, Ap. 1RO - 062022 Bucharest150


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportFrancois Dejean tel.: +45 33 36 72 59European Environment AgencyKongens Nytorv 6DK-1050 CopenhagenFrancois.Dejean@eea.europa.euFrank Dententer tel.: +39 0332 786392European Commission, DG JRC fax: +39 0332 785704Joint Research CentreInstitute for Environment and Sustainability frank.dentener@jrc.itI-21020 IspraAlcide di Sarra tel.: +39 06 3048 4986ENEA, ACS-CLIM-OSS fax: +39 06 3048 6678Via Anguillarese 301I - 00123 S. Maria di Galeriadisarra@casaccia.enea.itAnke Herold tel.: +49 30 28048686Oeko-Institut fax: +49 30 28048688Novalisstr. 10D - 10115 Berlina.herold@oeko.deAnastasios Kentarchos tel.: +32 2 2986733European CommissionRue Du Champ de MarsB - 1050 Brusselsanastasios.kentarchos@ec.europa.euErasmia Kitou tel.: +32 2 29 58 219European CommissionDG-EnvironmentB-1049 Bruxelles Erasmia.KITOU@ec.europa.euMaarten Krol tel.: +31 30 2532291University Utrecht fax: +31 30 2543163PrincetonpleinNL - 3584CC UtrechtM.C.Krol@phys.uu.nlHelena Looström Urban tel.: +46 8 6988512Swedish EPA fax: +46 8 202925BlekholmsterassenS - 10648 Stockholmhelena.loostrom.urban@naturvardsverket.seAlistair Manning tel.: +44 1392 884243Met Office fax: +44 1392 885681FitzRoy RoadUK - EX1 3PB EXETERalistair.manning@metoffice.gov.ukFrank Meinhardt tel.: +49 7602 910014Umweltbundesamt fax: +49 7602 243Postfach 1229D - 79196 Kirchzartenfrank.meinhardt@uba.de151


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportSuvi Monni tel.: +39 0332 789794European Commission, DG JRC fax: +39 0332 785704Joint Research CentreInstitute for Environment and Sustainability suvi.monni@jrc.itI-21020 IspraLorenz Moosmann tel.: +43 1 31304 5854Umweltbundesamt GmbH fax: +43 1 31304 5800Spittelauer LändeA-1080 Wien lorenz.moosmann@umweltbundesamt.atTeemu Oinonen tel.: +358 9 17341Statistics Finland fax: +358 9 1734 3429P.O.Box 6A00022 Tilastokeskus teemu.oinonen@stat.fiFIN - 00022 HelsinkiJos G.J. Olivier tel.: +31 30 274 3035MNP fax: +31 30 274 4464P.O. Box 303NL-3720 AH Bilthovenjos.olivier@mnp.nlVirginia Puzzolo tel.: +32 2 2990115GMES Bureau fax: +32 2 2920767BREY1 9/230, Avenue d'AuderghemB - 1040 Bruxellesvirginia.puzzolo@ec.europa.euFrank RaesEuropean Commission, DG JRC tel.: +39 0332 789959Joint Research Centre fax: +39 0332 785704Institute for Environment and SustainabilityI-21020 Ispra frank.raes@jrc.itMichel Ramonet tel.: +33 1 69 08 40 14LSCE fax: +33 1 69 08 77 16LSCE CE Saclay - Orme des MerisiersF - 91191 Gif sur Yvettemichel.ramonet@cea.frPeter Rayner tel.: +33 1 69 08 88 11LSCE/IPSL fax: +33 1 69 08 77 16Laboratoire CEA-CNRS-UVSQBat. 701 LSCE - CEA de Saclaypeter.rayner@cea.frOrme des MerisiersF-91191 Gif sur YvetteStefan Reimann tel.: +41 44 823 4638EMPAUeberlandstr. 129CH - 8600 Duebendorfstefan.reimann@empa.chIrene Remy Xueref tel.: +33 1 69 08 98 01LSCE, CEA/CNRS/IPSL/UVSQ fax: +33 1 69 08 77 16Bat. 703 Pte 24, Orme des MerisiersF - 91191 GIF-SUR-YVETTE CEDEXirene.xueref@cea.fr152


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportWorkshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportChristian Rödenbeck tel.: +49 3641 57 6354Max Planck Institute for Biogeochemistry fax: +49 3641 57 70Hans-Knöll-Str. 10D-07745 Jena Christian.Roedenbeck@bgc-jena.mpg.deMichiel Roemer tel.: +31 55 5493789TNO fax: +31 55 5493252Laan van WestenenkNL - 7334 DT Apeldoornmichiel.roemer@tno.nlBert Scheeren tel.: +39 0332 786701European Commission, DG JRC fax: +39 0332 785022Joint Research CentreInstitute for Environment and Sustainability hubertus.scheeren@jrc.itI-21020 IspraMartina Schmidt tel.: +33 1 69 08 69 15Laboratoire des Sciences du Climat et de fax: +33 1 69 08 77 16l'Environnement (LSCE)Orme des Merisiers, Bat. 703, Pce 17C Martina.Schmidt@cea.frF - 91191 Gif-sur-Yvette CEDEXGuenther Seufert tel. :+39 0332 785784European Commission, DG JRC fax: +39 0332 785022Joint Research CentreInstitute for Environment and Sustainability guenther.seufert@jrc.itI-21020 IspraKlára Tarczay tel.: +36 1 3464805Hungarian Meteorological ServiceKitaibel PálutcaH - 1024 Budapesttarczay.k@met.huJohn van Aardenne tel.: +39 0332 785833European Commission, DG JRC fax: +39 0332 785704Joint Research CentreInstitute for Environment and Sustainability john.van-aardenne@jrc.itI-21020 IspraAlex Vermeulen tel.: +31 224564194ECN fax: +31 224568488Westerduinweg,3NL - 1755 ZG Pettena.vermeulen@ecn.nlMaria Gabriella Villani tel.: +39 0332 786620European Commission, DG JRC fax: +39 0332 785704Joint Research CentreInstitute for Environment and Sustainability maria-gabriella.villani@jrc.itI-21020 IspraJulian Wilson tel.: +39 0332 786620European Commission, DG JRC fax: +39 0332 785704Joint Research CentreInstitute for Environment and Sustainability julian.wilson@jrc.itI-21020 Ispra153


Workshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottomupGHG inventories " - reportEuropean CommissionEUR 22893 EN – Joint Research Centre – Institute for Environment and SustainabilityTitle: Atmospheric Monitoring and Inverse Modelling for Verification of National and EU Bottom-upGHG Inventories - report of the workshop "Atmospheric monitoring and inverse modelling forverification of national and EU bottom-up GHG inventories" under the mandate of Climate ChangeCommittee Working Group I, Casa Don Guanella, Ispra, Italy (08-09 March 2007)Editor: P. BergamaschiLuxembourg: Office for Official Publications of the European Communities2007 – 153 pp.EUR – Scientific and Technical Research series – ISSN 1018-5593ISBN 978-92-79-06621-4AbstractThe workshop "Atmospheric monitoring and inverse modelling for verification of national and EUbottom-up GHG inventories" was held on 08-09 March 2007 in Ispra, Italy, under the mandate ofEuropean Climate Change Committee Working Group 1, as follow-up of a first workshop on 23-24 October 2003.This report presents the summary and conclusions of the workshop and summaries of allworkshop presentations.155


Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - reportThe mission of the JRC is to provide customer-driven scientific and technical supportfor the conception, development, implementation and monitoring of EU policies. As aservice of the European Commission, the JRC functions as a reference centre ofscience and technology for the Union. Close to the policy-making process, it servesthe common interest of the Member States, while being independent of specialinterests, whether private or national.LB-NA-22893-EN-C

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