NATIONAL AND EU BOTTOM-UP GHG INVENTORIES

Workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” - report

ATMOSPHERIC

AND

NATIONAL AND EU

INVERSE MODELLING

report of the workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” under the mandate of Climate Change Committee Working Group I Casa Don Guanella, Ispra, Italy (08-09 March 2007)

Editor: P. Bergamaschi

FOR

VERIFICATION

GHG INVENTORIES

OF

MONITORING

BOTTOM-UP

EUR 22893 EN2007


ATMOSPHERIC

AND

NATIONAL AND EU

INVERSE MODELLING

report of the workshop “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories” under the mandate of Climate Change Committee Working Group I Casa Don Guanella, Ispra, Italy (08-09 March 2007)

Editor: P. Bergamaschi

FOR

VERIFICATION

GHG INVENTORIES

OF

MONITORING

BOTTOM-UP

EUR 22893 EN2007


The mission of the Institute for Environment and Sustainability is to provide scientific-technical support to the European Union’s Policies for the protection and sustainable development of the European and global environment.

European Commission Joint Research Centre Institute for Environment and Sustainability

Contact information

Peter Bergamaschi European Commission Joint Research Centre Institute for Environment and Sustainability Climate Change Unit TP 290 I-21020 Ispra (Va) Tel. +39 0332 789621 [email protected]

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EUR 22893 EN ISBN 978-92-79-06621-4 ISSN 1018-5593

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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 bottom-up GHG inventories " - report

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CONTENTS

1 SUMMARY AND CONCLUSIONS......................................................................................................... 3

2 EU PROJECTS......................................................................................................................................... 10

CarboEurope-IP - Assessment of the European Terrestrial Carbon Balance (C. Rödenbeck et al.) ......... 11CHIOTTO - Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gases (A. Vermeulen et al.) ........................................................................................................................................ 16IMECC - Infrastructure for Measurement of the European Carbon Cycle (P. Rayner)............................. 21GEMS-IP - Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in-situ data (P. Rayner) ......................................................................................................................................... 24GEOMON-IP - Global Earth Observation and Monitoring (P. Rayner) ................................................... 27NitroEurope-IP - The nitrogen cycle and its influence on the European greenhouse gas balance (P. Bergamaschi et al.)..................................................................................................................................... 30HYMN - HYdrogen, Methane and Nitrous oxide: Trend variability, budgets and interactions with the biosphere (P. van Velthoven and P. Bousquet)..................................................................................... 33SOGE - System for Observation of Halogenated Greenhouse Gases in Europe (S. Reimann) .................. 35Geoland (J.-C. Calvet) ............................................................................................................................... 38

3 INVERSE MODELLING STUDIES ...................................................................................................... 41

Baseline trends and top-down estimates of UK and NW European GHG emissions (A. Manning) ........... 42Methane flux estimates for Europe using tall tower observations and the COMET inverse model (A. Vermeulen and G. Pieterse)........................................................................................................................ 48New TM5-4DVAR inverse modelling system to estimate global and European CH4 sources (P. Bergamaschi et al.)..................................................................................................................................... 54Inverse modelling activities at LSCE: from global to regional scales (P . Bousquet)................................ 60Top-down Methods in the Presence of Partial Carbon Accounting (P. Rayner) ....................................... 67Data assimilation of atmospheric CO2: CarbonTracker (W. Peters) ......................................................... 69

4 EU-LEVEL REPORTING ON SOURES AND SINKS TO UNFCCC AND BOTTOM-UP INVENTORIES ........................................................................................................................................ 75

EU reporting on sources and sinks (E. Kitou)............................................................................................ 76European greenhouse gas emissions (F. Dejean) ...................................................................................... 81EDGAR and UNFCCC greenhouse gas datasets: comparisons as indicator of accuracy (J. Olivier and J. van Aardenne).................................................................................................................................. 87Agriculture, Forestry and Other Land Uses (AFOLU): Realities and needs for Kyoto reporting (G. Seufert) ....................................................................................................................................................... 91

5 EUROPEAN AND INTERNATIONAL GHG MONITORING PROGRAMS .................................. 96

The AGAGE network for ground based measurements of non-CO2 GHGs: Monitoring of atmospheric concentrations and emission estimates (D. Cunnold) ............................................................ 97The WMO GAW Global GHG Programme (L. Barrie) ............................................................................ 102RAMCES - The French Network of Atmospheric Greenhouse Gas Monitoring (M. Schmidt et al.) ........ 108Measurements of greenhouse gases at the Mediterranean island of Lampedusa (A. di Sarra et al.)....... 112Long-Term Monitoring of Greenhouse Gases at Jungfraujoch (S. Reimann) .......................................... 116ICOS - Integrated Carbon Observation System (P. Ciais et al.) .............................................................. 120GMES and the GMES Atmospheric Service (V. Puzzolo and J. Wilson).................................................. 127

6 POSTER PRESENTATIONS ............................................................................................................... 131

Greenhouse Gas observations within the monitoring network of the German Federal Environmental Agency (Umweltbundesamt) (F. Meinhardt et al.)........................................................... 132Set-up of a continuous greenhouse gas monitoring station for CO2, CH4, N2O, SF6, and CO in Northern Italy (B. Scheeren et al.)............................................................................................................ 1364D-VAR System for Inverse Modeling of Atmospheric CH4: Sensitivity Analyses using Synthetic Observations (M.G. Villani et al.) ............................................................................................................ 142

7 ANNEX1: WORKSHOP AGENDA ..................................................................................................... 147

8 ANNEX2: WORKSHOP PARTICIPANTS......................................................................................... 150

CONTENTS

1 SUMMARYANDCONCLUSIONS........................................................................................... 3.

2 EUPROJECTS........................................................................................................................ 10.. CarboEurope-IP - Assessment of the European Terrestrial Carbon Balance (C. Rödenbeck et al.)........... 11.. CHIOTTO - Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gases (A. Vermeulen et al.)................................................................................................................... 16. IMECC - Infrastructure for Measurement of the European Carbon Cycle (P. Rayner)............................ 21.. GEMS-IP - Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in-situ data

(P. Rayner)................................................................................................................................ 24. .GEOMON-IP - Global Earth Observation and Monitoring (P. Rayner)................................................... 27. �NitroEurope-IP�-�The�nitrogen�cycle�and�its�influence�on�the�European�greenhouse�gas�balance�

(P. Bergamaschi et al.)................................................................................................................ 30. HYMN - HYdrogen, Methane and Nitrous oxide: Trend variability, budgets and interactions with

the biosphere (P. van Velthoven and P. Bousquet)........................................................................... 33. SOGE - System for Observation of Halogenated Greenhouse Gases in Europe (S. Reimann)................. 35. Geoland (J.-C. Calvet)................................................................................................................. 38

3 INVERSEMODELLINGSTUDIES......................................................................................... 41 Baseline trends and top-down estimates of UK and NW European GHG emissions (A. Manning)............ 42. �Methane�flux�estimates�for�Europe�using�tall�tower�observations�and�the�COMET�inverse�model�

(A. Vermeulen and G. Pieterse).................................................................................................... 48. New TM5-4DVAR inverse modelling system to estimate global and European CH4 sources

(P. Bergamaschi et al.)................................................................................................................ 54. Inverse modelling activities at LSCE: from global to regional scales (P . Bousquet).............................. 60. Top-down Methods in the Presence of Partial Carbon Accounting (P. Rayner)...................................... 67. .Data assimilation of atmospheric CO2: CarbonTracker (W. Peters)..................................................... 69

4 EU-LEVELREPORTINGONSOURESANDSINKSTOUNFCCCANDBOTTOM-UPINVENTORIES....................................................................................................................... 75

. EU reporting on sources and sinks (E. Kitou).................................................................................. 76.

. European greenhouse gas emissions (F. Dejean)............................................................................. 81.

. .EDGAR and UNFCCC greenhouse gas datasets: comparisons as indicator of accuracy (J. Olivier and J. van Aardenne).................................................................................................... 87.

Agriculture, Forestry and Other Land Uses (AFOLU): Realities and needs for Kyoto reporting (G. Seufert)............................................................................................................................... 91.

5 EUROPEANANDINTERNATIONALGHGMONITORINGPROGRAMS............................... 96.. .The AGAGE network for ground based measurements of non-CO2 GHGs: Monitoring of atmospheric

concentrations and emission estimates (D. Cunnold)....................................................................... 97.. The WMO GAW Global GHG Programme (L. Barrie)..........................................................................102 RAMCES - The French Network of Atmospheric Greenhouse Gas Monitoring (M. Schmidt et al.)............108. Measurements of greenhouse gases at the Mediterranean island of Lampedusa (A. di Sarra et al.)........112. Long-Term Monitoring of Greenhouse Gases at Jungfraujoch (S. Reimann).........................................116 ICOS - Integrated Carbon Observation System (P. Ciais et al.).........................................................120 GMES and the GMES Atmospheric Service (V. Puzzolo and J. Wilson).................................................127

6 POSTERPRESENTATIONS....................................................................................................131 Greenhouse Gas observations within the monitoring network of the German Federal Environmental

Agency (Umweltbundesamt) (F. Meinhardt et al.)............................................................................132.. Set-up of a continuous greenhouse gas monitoring station for CO2, CH4, N2O, SF6, and CO in

Northern Italy (B. Scheeren et al.)................................................................................................136. 4D-VAR System for Inverse Modeling of Atmospheric CH4: Sensitivity Analyses using Synthetic

Observations (M.G. Villani et al.)...................................................................................................142

7 ANNEX1:WORKSHOPAGENDA...........................................................................................147

8 ANNEX2:WORKSHOPPARTICIPANTS...............................................................................150


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1 SUMMARY AND CONCLUSIONS Peter Bergamaschi1, Erasmia Kitou2, Francois Dejean3, and Frank Raes1

(reviewed by all workshop participants)

[1] European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy [2] European Commission, DG Environment, Brussels, Belgium [3] European Environment Agency, Climate change and energy, Copenhagen, Denmark

The workshop "Atmospheric monitoring and inverse modelling for verification of national 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 inverse modelling (IM), can trace back observed atmospheric concentrations of GHGs to their 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 relevant in the context of the reporting of national GHG emissions to UNFCCC (which are based on bottom-up inventories).

The objective of the workshop was to bring together scientists working on atmospheric monitoring and inverse modelling and EU national experts responsible for 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 verifying

bottom-up GHG inventories identify further research and infrastructure needs for the application of AM/IM in

the context of national GHG inventories

Major developments since first workshop

AM/IM studies have made substantial progress over the last years, due to the availability of new observational data and due to further development of inverse models.

A number of new inverse modelling studies have been presented at European scale 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 from individual model grid cells, hence better resolving the true footprints (regions of influence) of individual measurement sites.


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Substantial efforts have been made during the last years to setup further European monitoring stations, including 8 tall towers (within the CHIOTTO project), most of which are now operational. However, the European monitoring programs are still very heterogeneous. They are largely funded within different European research projects, complemented also by some national and international activities. This caused (and continues to cause) major data gaps.

An important step towards an operational European GHG monitoring program has now been made with the ICOS ("Integrated Carbon Observation System") proposal. This project aims to setup an integrated European GHG monitoring network.

Applicability of inverse modelling for different GHGs

CO2Bottom-up estimates of fossil CO2 emissions are assumed to be relatively accurate. They are usually used as input data in inverse modelling studies for CO2 aiming at quantifying the net CO2 fluxes from the terrestrial biosphere. Critical for these IM results, however, are also the assumed spatio-temporal variations of fossil fuel emissions (for which uncertainties are larger than for national totals, and which are based on scientific bottom-up inventories), as demonstrated in a recent CarboEurope-IP study. The reporting of LULUCF sources and sinks under UNFCCC is considered complete for those countries that don’t have significant areas of unmanaged forests. However with regard to the theoretical requirements, the actual reporting in inventories is not yet complete in practice. The reporting of LULUCF activities under the Kyoto Protocol only covers some carbon stock changes ("partial carbon accounting"). Thus, AM/IM can be used as check for UNFCCC GHG inventories, but not for the information on LULUCF activities under the Kyoto Protocol.IM/AM of CO2, however, is generally of great importance, since the carbon cycle is very sensitive to climate. A notable example is a significant CO2 flux anomaly over Western Europe related to the heatwave 2003, detected consistently by 2 independent inverse models (CarboEurope-IP).

CH4CH4 is an attractive application for inverse modelling, in particular because uncertainties of bottom-up inventories remain considerable (especially in certain sectors as e.g. landfills, agriculture, and energy related fugitive emissions). Uncertainties of top-down estimates of national total emissions from various studies are estimated in the order of 30-100%, but are likely to become more accurate in the future with improvements of inverse models and if the number of monitoring stations is increased. On the European scale, CH4 emissions are dominated by anthropogenic emissions (estimated natural emissions EU-15: ~5-20 %), but there are significant differences in the relative contribution of natural CH4 sources among different countries. Top-down estimates for European (EU-15) total CH4 emissions from various studies (including some updates presented at the workshop) show broad consistency with values reported to UNFCCC when taking into account the corrections for the natural sources (using scientific bottom-up estimates). Differences for emission estimates from individual countries (differences among top-down


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estimates, and between top-down and bottom-up) are still larger. Better quantification of the uncertainties of both bottom-up and top-down emission estimates is crucial for better comparisons.

N2ORelatively high uncertainties of N2O bottom-up inventories make N2O a potentially good candidate for inverse modelling. N2O top-down estimates based on a single station (Mace Head, Ireland) appear very promising and show broad consistency with UNFCCC estimates (within the relatively large uncertainties of both bottom-up and top-down estimates). However, relatively small spatio-temporal gradients of atmospheric concentrations require very careful inter-calibration of different monitoring stations. Very important for N2O is also better quantification of natural N2Osources and of anthropogenic, indirect N2O sources.

HFCs, PFCs, SF6Comparisons between top-down and bottom-up approaches for emissions of fluorinated gases are most promising, because emissions of these gases are almost entirely due to anthropogenic activities. Hence, top-down estimates can be directly compared with values reported to UNFCCC. Several studies (including updates presented at the workshop) clearly demonstrate the usefulness of AM/IM to indicate potential gaps in the reporting. For example, recent IM studies attribute significant HFC-152a emissions to two European countries, for which no emissions were reported to UNFCCC. Furthermore, atmospheric monitoring and inverse modelling is particularly suited to address some F-gases which are currently not covered by the official bottom-up inventories (e.g. HFC-365mfc). Finally, AM/IM may provide additional knowledge on the timing of larger releases of F-gases related to industrial processes.

European GHG monitoring programs The availability of spatially and temporally comprehensive atmospheric measurements is crucial for inverse modelling. Efforts have been made during the last years to setup further monitoring stations (including 8 tall towers in Europe within the CHIOTTO project) and to better intercalibrate the different measurements. However, the European monitoring programs are still very heterogeneous (largely funded within different European research projects, but complemented also by some national and international activities). This caused (and continues to cause) major data gaps in space and time. Unfortunately, some GHG monitoring stations were discontinued (e.g. German UBA stations Deusselbach and Zingst). In particular high-accuracy quasi-continuous surface in-situ measurements provide significant constraints on regional emissions and should be further extended.

An important step towards an operational European GHG monitoring program has now 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 an integrated European monitoring program appears central to ensure the availability of long-term high-accuracy atmospheric GHG measurements. With the envisaged ~30 operational 'backbone' European atmospheric monitoring sites the ICOS network would provide a solid basis for top-down estimates of European GHG emissions.


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During the preparatory phase of ICOS (2008-2011), the legal design and funding structure are to be decided. If successful, ICOS could start its operational phase in 2012. Even for the positive scenario that ICOS will be setup, the availability of atmospheric monitoring data until the operational phase of ICOS starts, i.e. for the period 2007-2012, relies on the support of existing and additional monitoring sites. In particular the monitoring of non-CO2 GHGs, which seem to be the gases with the largest potential for comparison between top-down and bottom-up inventories, is currently not satisfactorily covered by European monitoring programs.

In addition to an appropriate European network, it would be important to support further global monitoring stations – in particular in regions with large emissions or trends (e.g. China, India) – and satellite retrievals – in particular for CO2 and CH4, for which recent research demonstrated considerable progress and the potential to provide valuable complementary information in regions with scarce in situ monitoring (e.g. tropical land masses).

Inverse modelling systems Since the previous workshop, considerable progress has been made with the development of inverse modelling systems. New systems (e.g. "4DVAR", or "Ensemble Kalman Filter") now allow the estimate of emissions from individual model grid cells. Thus, they better resolve the true footprints (regions of influence) of individual measurement sites, minimizing the so-called aggregation error.

First inverse model intercomparisons have been performed for CO2 (CarboEurope-IP) demonstrating the robustness of the top-down estimates for larger European regions. Detailed model intercomparisons for European CH4 and N2O inversions are planned within NitroEurope-IP (2008-2010). These and further intercomparisons also for inversions of other GHGs are still needed in order to:

Better quantify model errors (in particular transport errors and representativeness errors). Ideally, top-down estimates should be generally based on several independent 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 the emissions sufficiently).

Further development of atmospheric transport models (e.g. simulation of vertical transport) and further increase of the resolution of the models would allow to better simulate atmospheric monitoring stations and to better resolve national boundaries. Furthermore, high-resolution mesoscale models would be useful for comparison with emissions on regional (sub-national) scales.

In addition, independent validation of atmospheric models remains important (e.g. using tracers as 222Rn and meteorological data, such as e.g. boundary layer height).


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Bottom-up inventories Many IM approaches use bottom-up inventories as a priori information. These bottom-up inventories have to be provided to the models at the resolution of the model grid cells. This information is not provided by the official UNFCCC inventories, but by scientific inventories such as EDGAR. Better spatial and temporal disaggregation of such scientific bottom-up inventories would help to further improve atmospheric simulations significantly.

Furthermore, also uncertainty estimates of bottom-up emissions are usually set as 'boundary conditions' for the inverse modelling systems. Better estimates of these uncertainties remain very important. While UNFCCC inventories should provide uncertainty estimates of national totals, scientific inventories should provide also spatially and temporally disaggregated uncertainties. In this context also the information about (spatial and temporal) correlation of uncertainties is very important.

Furthermore, scientific bottom-up inventories should provide better estimates of natural sources, in particular for CH4 and N2O. Natural sources should have high priority 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-up inventories are large, or for which UNFCCC data are incomplete, e.g. CH4, N2O,and fluorinated gases.

Several studies based on high-resolution inverse atmospheric transport models demonstrated that top-down emission estimates can be provided on various spatial scales relevant for comparison with UNFCCC inventories, e.g. national scale (for larger European countries) and European scale (EU-15, and potentially EU-25, and EU-27). However, the number of countries for which top-down estimates are currently available at national level remains limited, mainly due to limited observational data.

Some IM approaches avoid the use of bottom-up inventories, aiming at independent 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 between atmospheric concentrations and bottom-up inventories, to narrow down the uncertainties 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 anthropogenic emissions) requires reliable estimates of natural GHG emissions. For several GHGs, the natural emissions are relatively small or negligible (e.g. fluorinated gases).

AM/IM can assess reliability of emission estimates from countries for which official bottom-up inventory data are lacking or incomplete, but for which emissions and


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trends are important for the global GHG budgets (e.g. China, India), and which are, therefore, also of high political interest.

Apart from the direct use for comparison with reported anthropogenic GHG emissions, the global and regional AM/IM is very important to quantify total emissions into the atmosphere, including natural emissions of the major GHGs CO2, CH4 and N2O, which may considerably increase in the future as a consequence of climate change.

The accuracy of the top-down emission estimates increases with the number of available atmospheric measurements. Crucial is long-term continuity and high quality of measurements (including thorough intercalibration). A set of around 30 monitoring stations across Europe, as envisaged by the ICOS proposal for an integrated operational European network, should provide a sound basis for IM on the European scale.

Recommendations: Further steps towards enhanced use of atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories

In order to develop the potential of using inverse modelling results for comparisons with 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 realistic uncertainty estimates. This will be addressed to some extent e.g. for CH4 and N2Owithin NitroEurope-IP (2008-2010), but should be further investigated also for the other GHGs.

Further identify the gases and countries for which AM/IM could provide estimates with similar or lower uncertainty level than currently achieved in bottom-up inventories.

Provide more detailed comparisons between top-down estimates and bottom-up inventory data. Comparisons in the most promising areas should be emphasised, in particular for emissions of non-CO2 GHGs.

Further investigate the possible use of additional tracers or isotopes for sector-specific top-down estimates. E.g. measurements of atmospheric 14CO2 allow independent estimates of the fraction of fossil CO2 emissions and may become very useful to check the consistency of reported fossil CO2 emissions.

In order to better use inverse modelling for improving the reliability and comprehensiveness of bottom-up inventories submitted to the UNFCCC (independent verification and consistency checks), European Member States and the European Commission should:


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As far as possible, secure the long-term availability of spatially and temporally comprehensive atmospheric measurements to be used in inverse modelling systems, by: o Supporting research projects and projects under GMES-AS, in order to ensure

the continuation of the European atmospheric monitoring until the operational phase of ICOS could start, i.e. for the period 2007-2012.

o Supporting the ICOS project which is the central prerequisite for the long-term high-accuracy monitoring of GHGs on the European scale. ICOS should cover all relevant GHGs.

Support the further development of atmospheric monitoring and inverse modelling of GHGs, in particular non-CO2 GHGs, which seem to be the gases with the largest 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 compilers and AM/IM scientists to continuously assess the usefulness of AM/IM for improving greenhouse gas inventories.

List of Acronyms

CarboEurope-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 monitoring system" http://ftp.bgc-jena.mpg.de/pub/outgoing/afreib/ICOS/ICOS_Flyer1.pdf

NitroEurope-IP"The nitrogen cycle and its influence on the European greenhouse gas balance", EU research project (FP6)http://www.nitroeurope.eu/


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2 EU projects


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CarboEurope-IP - Assessment of the European Terrestrial Carbon Balance

Christian Rödenbeck1, Martin Heimann1, and Philipp Ciais2

[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, France

The aim of CarboEurope-IP is to estimate the European carbon balance in the recent past and present. It will provide a scientifically sound, independent verification of national and European CO2 sources and sinks over a five-years period (2004-2008) as a template for the First Commitment period and give scientific advice how to deal with sinks in the Second Commitment Period. The strength of CarboEurope-IP is primarily in the comprehensive experimental strategy that allows by data analysis the across-scale validation and verification through the multiple constraint approach. Dissemination of results via publications, demonstration and training activities and advise to policy makers and activities synthesise project results in order to support the implementation of observing and monitoring schemes related to the UNFCCC and the Kyoto Protocol. Training activities will increase the skills of young researchers for international, interdisciplinary research about the Carbon Cycle.

CarboEurope-IP is organised along four "Components":

Figure 1: Components and their interactions in CarboEurope-IP

Component 1: Ecosystems

The Ecosystem Component quantifies the carbon fluxes of the variety of land cover and uses of the European continent, thus providing input into the spatial scaling and


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bottom up modelling efforts. The flux data are verified and supported by ecosystem level data of carbon stock changes in biomass and soil, which also allow estimates of the permanence of sinks. The gathered data will also provide the basis for parameterisation of models for up-scaling of carbon fluxes to the regional and continental scale as well as data synthesis and modelling of effects of driving forces on the Carbon Cycle such as land management, disturbance by harvest, etc. which are not yet included in larger scale biogeochemical models.

Component 2: Atmosphere

In the Atmosphere Component, the atmospheric network spatial coverage is extended over Southern and Western Europe by adding new continuous monitoring stations, increasing the frequency of vertical profiles sampling through the Planetary Boundary Layer and aloft, and finally to optimise the atmospheric data selection, using in situ meteorological data and other tracers such as 222Rn, to extract from continuous CO2 time series representative measurements of regional sources and sinks activity

The measurements at continental scale provide the boundary conditions for both the regional experiment and the integration efforts. Novel in the strategy is the incorporation of CO2 concentration measurements at the flux tower sites to complement the atmospheric monitoring at free tropospheric sites, tall towers, and aircraft. This provides a strong link between the ecological and continental scale observations.

Component 3: Regional Experiment

The Regional Experiment Component provides a direct link between the ecology and continental scale measurements and models. Continental scale models provide the boundary conditions for the regional carbon balance. Upscaling of the flux towers is performed with forward meso-scale models and calibrated biogeochemical models for the long term (20 yrs). At the regional scale inverse model techniques are used similar to those developed at the continental scale, thus establishing a methodological link with the larger scale inverse modelling estimates. The regional experiment will test and provide aggregation algorithms that will be used in the upscaling efforts in the Continental Integration Component.

Component 4: Continental Integration

The Continental Integration Component relies on the data streams collected by the other Components of the IP, including syntheses of existing data. Conversely, it provides guidance on how to fill in gaps in the current Observing System and help design optimal observation strategies in the future. This integration is achieved by means of a numerical modelling framework that bridges across scales going from process-studies up to the continental budget. In this framework, diverse approaches of top-down, bottom-up, sectorial, process based and extrapolation techniques have


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to be employed, compared for consistency and ultimately merged in a most comprehensive 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 for estimating the spatially explicit continental carbon balance and its variability at a resolution of 10 to 50 km for at least the length of a Commitment Period by

Implementation of nested atmospheric model hierarchy over the European domain for the top-down determination of surface sources and sinks by means of inverse methods.

Implementation of bottom-up models for the extrapolation and upscaling of surface flux measurements and carbon inventories.

Acquisition of remote sensing products for upscaling Development and implementation of a carbon data assimilation system for the

European domain.

As a test case, the summer period of 2003 is used. During this period, extreme drought and heat wave conditions over wide parts of Europe lead to strong responses in the European carbon cycle, allowing to unravel mechanisms in the European carbon balance. As an example, Figs 3 and 4 show the responses of ecosystem models and top-down inverse flux estimates to the 2003 extreme event.


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Figure 3: Growing season NEP anomaly in 2003, simulated by 4 prognostic and 3 diagnostic terrestrial ecosystem models [Vetter et al., in prep.]. The response to the 2003 anomalous climate conditions are seen as reduced NEP in wide parts of Western Europe.


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Figure 4: Summer (JJA) anomalies of the CO2 flux in 5 parts of Europe as estimated by the top-down inversion of atmospheric data. The figure compares independent estimates by the CarboEurope-IP partners LSCE (Paris) and MPI-BGC (Jena). Strongly increased surface-atmosphere flux in `Western Europe' in 2003 reflects the carbon cycle response as detected by the atmospheric observations.

Workshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories " - report

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Figure 4: Summer (JJA) anomalies of the CO2 flux in 5 parts of Europe as estimated by the top-down inversion of atmospheric data. The figure compares independent estimates by the CarboEurope-IP partners LSCE (Paris) and MPI-BGC (Jena). Strongly increased surface-atmosphere flux in `Western Europe' in 2003 reflects the carbon cycle response as detected by the atmospheric observations.


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CHIOTTO - Continuous HIgh-precisiOn Tall Tower Observations of greenhouse gases

Alex T. Vermeulen1, Gerben Pieterse1, Andrew Manning2, Martina Schmidt3, Laszlo Haszpra4, Elena Popa4, Rona Thompson5, John Moncrieff6, Anders Lindroth7, Paolo Stefani8, Josep Morguí9, Eddy Moors10, Rolf Neubert11, Manuel Gloor12

[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, UK

1. Objectives of CHIOTTO

The CHIOTTO project is an EU FP5 funded collaborative project that ran from November 2002 to May 2006. The CHIOTTO project objective is to build an improved infrastructure for the continuous monitoring of the concentrations of greenhouse gases on the European continent above the surface layer using tall towers. The project is based on and extended existing research projects (AEROCARB, TCOS Siberia and TACOS). This project formed an important step towards a fully operational continuous observing system in the framework of the Kyoto Protocol for the sources and sinks of the most important greenhouse gases (CO2, CH4, N2O, CO, SF6) over Europe. An important aspect of the objective is the establishment of high quality calibrations for the existing and new atmospheric measurement stations, and the implementation of a near-online data-transmission system for tall tower measurements. An important target of the project is to make the measurements intercomparable between the institutes operating the air sampling networks. Quality controlled atmospheric concentration, CO2 flux and additional meteorological data are archived in 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 towers with the atmospheric stations networks in a synergetic approach enabling the tall towers to become atmospheric monitoring sites for use in transport models. In the course of the project we have implemented all new and existing measurement systems. We have worked on establishing the precision targets for the measurements and implemented a calibration and intercomparison protocol to achieve those targets for the individual towers and between towers. In Table 1 an overview is given of the tall towers in the CHIOTTO project, their positions, the parameters measured and the operators. In Figure 1 their positions can


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be viewed on the map of Europe. In Table 2 the Precision and accuracy targets for the CHIOTTO measurements as a function of measured species can be found.

Table 1: Tall tower data summary

Hght Position Concentration measurement (levels) Flux meas Name (m) Lon Lat CO2 CH4 N2O SF6 CO 222Rn Flasks CO2 CH4 Operator Cabauw NL 200 04°56’ 51°58’ 4 4 4 4 4 1 2 ECN Griffin UK 232 -2°59' 56°33’ 1 1 1 1 1 UEDIN Hegyhatsal H 117 16o39’ 46o57' 4 1 1 1 1 2 ELTE Orleans/Trainou F 131 2°07’ 46°58’ 3 3 3 3 3 1 LSCE Norunda S 102 17°28’ 60°05’ 4 2 2 2 LUPG Florence I 245 11°16’ 43°49’ 1 1 1 1 1 UNITUS Ochsenkopf D 163 11°49’ 50°03’ 3 3 3 3 MPIBGC Bialystok PL 300 22°45’ 52°15’ 5 5 5 5 5 MPIBGC

Table 2: Precision and accuracy targets for the CHIOTTO measurements as a function of measured species

2. Scientific progress made in CHIOTTO

In the 1st year of the CHIOTTO project we have defined the exact requirements for the equipment to be used and we have defined the measurement, calibration and data submission protocols. These are the foundation of the project. On the basis of this information the new equipment was purchased, customized and installed. In the 2nd year we continued to implement these choices of the 1st year, building together the instrumentation with all modifications required to meet our specific and high demands regarding quality and materials. In the third and last year and the following 6 month extension, most of the towers have been equipped and started either the initial or operational mode. The CHIOTTO concentration data is now being used widely, examples are the FP6 CarboEurope-IP and NitroEurope-IP to derive estimates for the strengths of CO2 and CH4, N2O sources and sinks of Europe, in combination with other measurements types and other (global and local) networks. CHIOTTO will continue as an integral part of the atmospheric component of the CarboEurope-IP, that has officially started in January 2004.


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be viewed on the map of Europe. In Table 2 the Precision and accuracy targets for the CHIOTTO measurements as a function of measured species can be found.

Table 1: Tall tower data summary

Hght Position Concentration measurement (levels) Flux meas Name (m) Lon Lat CO2 CH4 N2O SF6 CO 222Rn Flasks CO2 CH4 Operator Cabauw NL 200 04°56’ 51°58’ 4 4 4 4 4 1 2 ECN Griffin UK 232 -2°59' 56°33’ 1 1 1 1 1 UEDIN Hegyhatsal H 117 16o39’ 46o57' 4 1 1 1 1 2 ELTE Orleans/Trainou F 131 2°07’ 46°58’ 3 3 3 3 3 1 LSCE Norunda S 102 17°28’ 60°05’ 4 2 2 2 LUPG Florence I 245 11°16’ 43°49’ 1 1 1 1 1 UNITUS Ochsenkopf D 163 11°49’ 50°03’ 3 3 3 3 MPIBGC Bialystok PL 300 22°45’ 52°15’ 5 5 5 5 5 MPIBGC

Table 2: Precision and accuracy targets for the CHIOTTO measurements as a function of measured species

2. Scientific progress made in CHIOTTO

In the 1st year of the CHIOTTO project we have defined the exact requirements for the equipment to be used and we have defined the measurement, calibration and data submission protocols. These are the foundation of the project. On the basis of this information the new equipment was purchased, customized and installed. In the 2nd year we continued to implement these choices of the 1st year, building together the instrumentation with all modifications required to meet our specific and high demands regarding quality and materials. In the third and last year and the following 6 month extension, most of the towers have been equipped and started either the initial or operational mode. The CHIOTTO concentration data is now being used widely, examples are the FP6 CarboEurope-IP and NitroEurope-IP to derive estimates for the strengths of CO2 and CH4, N2O sources and sinks of Europe, in combination with other measurements types and other (global and local) networks. CHIOTTO will continue as an integral part of the atmospheric component of the CarboEurope-IP, that has officially started in January 2004.


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Figure 1: The influence function for the year 2002 of the 8 CHIOTTO tall towers derived by the COMET trajectory model

Figure 2: Example of one of the complex system designs for the high resolution measurements system, here for the Bialystok tower. All devices, tubings and fittings are mission critical and have to comply to high standards concerning minimisation of leakage, inertness and durability.


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Table 3: Implementation dates for the start of observations in the framework of the CHIOTTO project.

Concentration measurement start date

Name CO2 CH4 N2O SF6 CO 222Rn Flasks

Cabauw NL (1992) Nov 04

(1993)Nov 04

(2000)Nov 04

Nov 04 Nov 04 Nov 05 Nov 06

Griffin 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 07

Norunda S (1998) Jan 05

- 2007* 2007* 2007* - -

Florence I Aug 05 Aug 05 Aug 05 Aug 05 - - -

Ochsenkopf D (2000) Jan 06

Apr 06 Apr 06 Apr 06 Apr 06 - Jan 06

Bialystok PL Sep 05 Sep 05 Sep 05 Sep 05 Sep 05 - Sep 05

3. Scientific achievements

Main result of the project is that 7 out of 8 towers are now operational in the new or modified setup. In the reporting period the towers of Hegyhatsal and Ochsenkopf became operational (again). The only tower not operational yet is the Trainou/Orleans tower, but here the negotiations with the tower owner have finally been settled. All equipment for this tower has been ready for some time now. The equipment is not yet installed, only the inlet lines to the tower. The container with instrumentation will be shipped to the tower and installed at latest in September 2006.Partner MPI-BGC invested an enormous effort in providing all partners with the high precision calibrated Working Standards 2005. Circulation of the important intercalibration Traveling Standards has started at the end of 2005. The first intercomparison exercises started in the reporting periods and the first results show reasonable performance, while there is still room for improvement. Unfortunately the Florence tower will have to be relocated as the tower will be deconstructed due to airport security reasons. UNITUS has found a new suitable location in the Umbria region in Italy. The work in, and results of CHIOTTO thus far have been presented at several talks and posters during scientific meetings.

4. Conclusions

The CHIOTTO project succeeded in setting up the pre-operational tall tower greenhouse gas concentration observation network of European dimension, using high quality equipment, measurement procedures, calibration and intercomparison schemes. Delays due to logistical problems have however shortened the period in


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which real operational measurements have been acquired by the 8 Tall Towers. The 6-month extension granted allowed us to create a reasonable amount of inter-calibrated and high quality continous measurements series at almost all towers. Most measurements will continue after the end of the project in the framework of the CarboEurope IP as far as can be foreseen now.

The CHIOTTO Tower data will provide a wealth of information on GHG sources and sinks, where the tall towers allow to overcome some observational problems of the surface 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-component Current transport models to describe the fate of atmospheric tracers folllowing emission have been improved over the last years so we can start exploiting the information content of the tall tower surface observations.

References

Haszpra, L., Barcza, Z., Davis, K., Tarczay, K., Long term tall tower carbon dioxide flux monitoring 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 CO2 monitoring and research programs. In: 12th WMO/IAEA meeting of experts on carbon dioxide concentration and related tracer measurement techniques, Toronto, Canada, 15-18 September 2003 (eds.: Worthy, D., Huang, L.). WMO GAW Report No. 161., 154-158, 2005.

Haszpra L., Barcza, Z., CO2 monitoring and research programs in Hungary. In: 13th WMO/IAEA meeting of experts on carbon dioxide concentration and related tracer measurement 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 modeling of atmosphere-canopy exchange of acidifying and eutrophying components and carbon dioxide 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 technique and 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 in Europe: first results. In: 13th WMO/IAEA meeting of experts on carbon dioxide concentration and related tracer measurement techniques, Boulder, Colorado, U.S.A., 19-23 September 2005 (ed.: Miller, J.). WMO GAW, Vienna, Report nr 168, 2006.


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IMECC - Infrastructure for Measurement of the European Carbon Cycle

Peter Rayner

Laboratoire des Sciences du Climat et l'Environnement (LSCE), Gif sur Yvette, France

IMECC is an Integrated Infrastructure Initiative (I3) under the European Commission’s 6th Framework Programme. IMECC aims to build the infrastructure for a coordinated, calibrated, integrated and accessible dataset for characterizing the function of the European terrestrial biosphere.

Web-site: www.imecc.org

IMECC’s main strategies are:

Improving the comparability of atmospheric measurements of greenhouse gases and isotopic composition

Coordinating optimal development of infrastructure via comprehensive experimental 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 on atmospheric composition.

IMECC contains three classes of activities:

Networking Activities: Designed to improve cohesion, comparability and access to European carbon cycle measurements

Transnational Access Activities: Designed to broaden and improve access to European carbon cycle measurement facilities

Joint Research Activities: Designed to support new technologies for European carbon cycle measurements


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Networking Activities

Many inferences in carbon cycle monitoring are based on either temporal or spatial gradients of measurements. For these to be reliable measurements must be precisely comparable. Many of the networking activities in IMECC are aimed at improving comparability of measurements made at different locations or by different laboratories.

Network Design Tool: Provides a service in which an experimenter can determine the impact of a potential future measurement on knowledge of carbon fluxes and accounting

Network of Quality Control for Atmospheric Measurements: Establishes the degree of interoperability of European atmospheric measurement laboratories.

Isotope Standard Preparation: Establishes and circulates a new primary standard for the isotopic composition of CO2 in air.

Network of Algorithms and Software for Flux Measurements: Inter-comparison of flux calculation methodologies in use within Europe

Terrestrial Carbon Data Centre (TCDC): Establishes a data centre for all IMECC data and other data on the terrestrial carbon cycle in Europe.

Trasnational Access activities

The network of analysis laboratories and measurement locations throughout Europe represents a distributed measurement infrastructure. The aim of these activities is to improve access to this infrastructure. Note that the quality of this access is supported by the networking activities.

Access to measurement facilities: Gives external users access to a network of the highest quality atmospheric measurement laboratories

Access to CarboEurope-IP Atmospheric Network: Gives external users access to a network of atmospheric sampling stations.

Access to CarboEurope-IP Terrestrial Network: Gives users broader access to a network of ecosystem manipulation experiments in the Mediterranean region

Access to European Ecosystem Measurement Laboratories: Gives external users access to a network of ecosystem measurement facilities

Research activities

The distributed measurement infrastructure is constantly growing. For example assimilation systems such as those provided by GEMS-IP or direct measurement by satellite will soon provide large amounts of data. These new measurement systems


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must also be linked into the total measurement infrastructure and these links must take account of measurement and operational characteristics. The Joint Research Activities are designed to develop this infrastructure.

Real-Time in situ Atmospheric CO2 Data: Provides real-time data on atmospheric CO2 composition for use in operational data assimilation systems

Ground-based remote sensing of GHG: Develops and deploys a groundbased Fourier transform interferometer (FTIR) for validation of satellite CO2measurements

Real-Time Ecosystem and Flux Data: Produces real-time ecosystem fluxes and function for use in operational data assimilation and carbon accounting


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GEMS-IP - Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in-situ data

Peter Rayner


GEMS is an integrated project under the European Commission’s 6th Framework Programme. The GEMS project will create a new European operational system for operational global monitoring of atmospheric chemistry and dynamics and an operational system to produce improved medium-range & short-range air-chemistry forecasts, 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 for GEMS arises because many satellite measures contain signals of atmospheric composition. These signals are small so can only be extracted in contexts where other variables are well-constrained. Atmospheric data assimilation provides a framework for estimating disparate atmospheric quantities. The most efficient way to utilise these signals is to attach their retrieval to an existing state-of-art assimilation system such as that at ECMWF.

The objective of the GHG subproject is to develop an operational system to monitor the concentrations of greenhouse gases (CO2 and CH4), and their associated surface sources 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 processes

The main applications in the framework of GHG accounting are likely to be the estimate of net fluxes from a region (either geographic or political) and the calculation of lateral boundary conditions for more detailed regional inversions.

Current status GEMS is 2 years through its 4-year life. The project has constructed an assimilation system at ECMWF for CO2 and CH4 based on measurements from the Advanced Infra-Red Sounder (AIRS). Figure 1 shows validation of the assimilation at Molokai


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Island, Hawaii. The validation data is from an airborne profile (data kindly provided by NOAA/ESRL). The figure shows the observations, firstguess simulation and the assimmilated profile. AIRS is sensitive to CO2 in the mid and upper troposphere. We see then that, as we move up in the atmosphere, the assimilation is pulled towards the validation data away from the first-guess.

Figure 1: Assimilated and observed CO2 profile over Molokai Island, Hawaii for May 11 2003. The blue curve shows the case with no CO2 assimilation, the red curve the assimilated CO2 profile and the black curve the profile as measured by in-situ aircraft observations. We thank NOAA/ESRL for providing this profile.

AIRS is, of course, not the only instrument providing information on greenhouse gas concentration. The SCIAMACHY instrument on board ENVISAT provides data on the column-integrated concentration of various greenhouse gases (Figure 2 for atmospheric CH4). These measurements provide considerable information on spatial structures of concentration, but still require bias correction. The simultaneous assimilation of SCIAMACHY data and highly precise surface data into a flux inversion allows one to make such corrections. Along with the unknown fluxes, the inversion solves for a series of correction factors on the SCIAMACHY data. In this way the corrections are consistent with the physics of atmospheric transport.


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Figure 2: Top: SCIAMACHY retrievals of column-averaged CH4 mixing ratios [Frankenberg, et al., 2006], corrected with bias correction from TM5-4DVAR data assimilation system (bias correction 2nd order polynomial as function of latitude and month) including high-accuracy surface measurements from NOAA/ESRL. Bottom: Assimilated column-averaged CH4 mixing ratios based on TM5-4DVAR system [Bergamaschi et al., 2007]. We thank NOAA/ESRL for providing the CH4 surface data.

References

Bergamaschi, P., J.F. Meirink, M. Krol, and G.M. Villani, New TM5-4DVAR inverse modelling system to estimate global and European CH4 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 from SCIAMACHY on board ENVISAT: Analysis of the years 2003 and 2004, J. Geophys. Res., 111, D07303, doi:10.1029/2005JD006235, 2006


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GEOMON-IP - Global Earth Observation and Monitoring

Peter Rayner


GEOMON is an integrated project under the European Commission’s sixth Framework Programme. It’s main objective is to construct a prototype system for atmospheric composition monitoring for climate applications, by the combination of ground-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 to changes in surface emissions?

2. How to validate top-down satellite observation of the changing atmospheric composition, and integrate them with ground based stations and airborne observations into a coherent picture?

3. What are the global trends of atmospheric composition from ground-based and satellite observations assimilated in modelling studies, and what key measurements should be added for reducing uncertainties on surface emissions and atmospheric processes?

In this summary we focus on the application to the greenhouse gases although the scope of the project includes chemically active species and aerosols.

Strategy

1. 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 greenhouse gases, reactive gases, aerosols, and stratospheric ozone.

GEOMON activities

GEOMON is divided into four data gathering activities, three of which parallel the global activities of GEMS. A modelling activity integrates data from the four data gathering activities and the final activity is responsible for data archiving and outreach. The activities are briefly summarised below.


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Activity 1: Greenhouse gases

Improve the CO2 and CH4 monitoring in situ programs. Add new sites in Cyprus and 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 aircraft measurements).

Improve links between ground-based and satellite measurements using models.

Activity 3: Atmospheric aerosols

Enhance and consolidate ground-based monitoring of aerosols and their properties.

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 multidimensional characterisation of O3-relevant species. Development of associated observation operators. Validate satellite data records suitable for long-term studies.

Integration of ground-based with satellite data. Provision of data for model validation.

Evaluation of trends from data series, for O3, NO2, BrO, T. Support of Montreal and Kyoto Policies.


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Activity 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 composition parameters including near-real time data products, as a prototype for a European contribution to GEOSS.

Establish links to other GEOSS observing systems, national and international databases of past and ongoing scientific projects, and other relevant activities.

Establish targeted outreach actions towards international Earth Observation coordination bodies, programs, the scientific assessment community, and the general public.

Provide user friendly tools including graphical presentation of data and synthesis information for any interested party.


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NitroEurope-IP - The nitrogen cycle and its influence on the European greenhouse gas balance

Peter Bergamaschi1 Alex Vermeulen2, Martin Heimann3, Philippe Bousquet4, Philippe Ciais4, Alistair Manning5

[1] European Commission DG Joint Research Centre, Institute for Environment and Sustainability, 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, UK

NitroEurope-IP (NEU) is an integrated European research project on the nitrogen cycle. It includes the setup and operation of a European network to measure nitrogen fluxes and pools, and various modelling activities, ranging from plot-scale and landscape modelling to European wide up-scaling and European/global inverse modelling. An important issue for NEU is the coupling and interaction of the nitrogen and carbon cycles. NEU is funded under the EU's FP6, and will run for 5 years from February 2006 until 2011. Detailed information about the project can be found at http://www.nitroeurope.eu/.

Here we describe the NEU workpackage "Independent inverse modelling of European N2O and CH4 emissions"

The main objective of this work package is to derive European and national estimates of N2O and CH4 emissions based on atmospheric observations and inverse modelling. A central prerequisite to improve the confidence of top-down estimates obtainted by inverse modelling is the application of different, independent models (e.g. Gurney et al. [2002], Bergamaschi et al. [2004]). Therefore we will apply 5 independent inverse modelling systems, including Eulerian and Langrangian particle dispersion models (see Table 1).

Specific objectives are

To provide more realistic estimates of overall uncertainties of top-down emission estimates.

To trace back differences among top-down estimates, in particular related to model transport and inversion techniques.

To investigate the difference between approaches which use and those which do not use a priori information from bottom-up inventories

This inverse modelling workpackage will start February 2008 and run for 3 years until 2011.We plan to use continuous CH4 and N2O meausurement from CHIOTTO [Vermeulen, 2007a] and further European monitoring stations. This should provide a reasonable


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data set for year 2006 or 2007. Nevertheless, there exist some important regional gaps in Europe. Furthermore, good intercalibration between different monitoring stations remains an important issue, in particular for N2O. Directly linked to NEU, a proposal for the setup of a European non-CO2 GHG network had been submitted (MANOMETER "Methane And Nitrous Oxide Monitoring of the European Troposphere: European and Russian sources (and sinks)"). Despite positive evaluation, however, MANOMETER was not funded by the EU.

Table 1: Inverse models foreseen in NEU inverse modelling work package

partner model short description

JRC TM5-4DVAR model

New 4DVAR inverse modelling system [Bergamaschi et al., 2007], based on Eulerian two-way nested zoom model TM5 [Krol et al., 2005]

MPI TM3 or mesoscalemodel

TM3: Eulerian model [Heimann, and Körner, 2003]

LSCE LMDZ model Eulerian model with flexible grid size, high resolution over Europe [Bousqet, 2007]

ECN COMET FLEXPART

Lagrangian trajectory model [Vermeulen, 2006, 2007b]Lagrangian particle disp. model [Stohl, 1998]

UKM NAME model Lagrangian particle dispersion model [Manning et al., 2003, Manning, 2007]

References

Bergamaschi, P., J.F. Meirink, M. Krol, and G.M. Villani, New TM5-4DVAR inverse modelling system to estimate global and European CH4 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 CH4emissions 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 EU greenhouse gas emission inventories – report of the workshop “Inverse modelling for potential verification of national and EU bottom-up GHG inventories” under the mandate of the Monitoring Mechanism Committee WG-1 23–24 October 2003, JRC, Ispra, 146 pp., 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 regional estimates of CO2 sources and sinks using atmospheric transport models, Nature, 415, 626-630, 2002.


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Heimann, M. and Körner, S., The Global Atmospheric Tracer Model TM3. Model Description and 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-transport zoom 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, Estimating European emissions of ozone-depleting and greenhouse gases using observations and a modeling 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 GHG emissions, 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 model technique 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 tower observations and the COMET inverse model, this report, 2007b.

Figure 1: Comparison of various top-down estimates of CH4 emissions from some European countries (see also Bergamaschi et al. [2005]).


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HYMN - HYdrogen, Methane and Nitrous oxide: Trend variability, budgets and interactions with the biosphere

Peter van Velthoven1 and Philippe Bousquet2

[1] Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands. [2] Laboratoire des Sciences du Climat et l'Environnement (LSCE), Gif sur Yvette, France

The EU HYMN project (Hydrogen, Methane and Nitrous oxide: Trend variability, budgets and interactions with the biosphere) focuses on the trends and life cycles of methane (CH4), nitrous oxide (N2O), and molecular hydrogen (H2). Ever-increasing human activities on a global scale are the cause of the rising concentrations of various long-lived greenhouse gases in the atmosphere, including methane and nitrous oxide. The possible transition to a so-called ‘hydrogen economy’ in the coming decades is likely to cause a significant increase in future atmospheric molecular hydrogen levels. Associated with this are possible impacts on climate forcing, air quality and ozone depletion.

In the Kyoto Protocol, concrete ceilings have been set for greenhouse gas emissions of the participating countries in 2012. It addresses emissions of CO2, CH4, N2O, and the fully human-made HFCs, PFCs and SF6. For the EU this implies a targeted reduction of 8 % in for the Kyoto commitment period 2008-2012 relative to 1990 levels. A significant contribution to this target is expected to be made by reductions in CH4 (about 32 %) and N2O (about 12%) emissions. For the post-Kyoto era more severe emission reductions are being discussed. The need for substantial quantitative reductions in anthropogenic greenhouse gas emissions is recognised more and more internationally – less agreement exists on methods to achieve such reductions. For the climate gases with both natural and anthropogenic components (CO2, CH4 and N2O) a thorough assessment of their budgets could serve as a starting point for future regulations, since different abatement strategies will have different potential, efficiency and impact. Finding the optimal strategy for reductions in greenhouse gas emissions is complex, and different trade offs are possible. For example, the efficacy of methane emission regulation to mitigate climate forcing has been reported to be relatively high, amongst others because of its impact on tropospheric ozone. Furthermore, regulation of greenhouse gas emissions may have consequences for air quality and ozone depletion. Methane emissions as well as pollutant emissions of NOX, CO and VOCs contribute to tropospheric O3, which is a toxic gas, an important oxidant, as well as a potent greenhouse gas. Finally it can be noted that the global atmospheric cycles of methane, nitrous oxide and hydrogen, are coupled and include various interactions with the biosphere which need to be taken into account. Some of these are still badly understood. An example is the recent finding of large methane sources in the tropics.

HYMN aims to provide information to guide policy making with respect to future emission regulations taking into account these issues. It therefore focuses on gathering in-depth knowledge of the budgets and biogeochemical cycles of the gases, including their geographical distribution, the magnitude and variability of their sources, sinks and dispersion, and the feedbacks that connect land-biosphere


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processes with the life cycles of these gases through atmospheric chemistry and long-range transport. The objectives of HYMN are: 1. To improve the process modelling of the land-biosphere-atmosphere exchange of

the HYMN gases and to provide global and regional estimates of their natural sources and sinks

2. To contribute to global monitoring by provision of multi-year global satellite data sets of the CH4 and CO distribution and long-term time series for CH4 and N2O at a range of observing stations

3. To provide advice on the further optimisation of monitoring networks for these gases.

4. To quantify atmospheric loss of CH4 and H2 and the impact of changing anthropogenic and natural (climate-induced) emissions on regional OH trends and on current and future global CH4 and H2 levels.

5. To quantify how the possible future change to a hydrogen economy will affect the H2 distribution and the distribution of CH4 and O3 through changes in emissions of H2 and pollutants (NOX, CO, VOCs).

6. To evaluate simulations with a coupled atmospheric chemistry-biosphere model for CH4, N2O and H2 by comparison to ground based and satellite observations on a global and regional scale.

7. To make new estimates of the sources and sinks of CH4 and H2, including their temporal and spatial variability

Apart from classical surface observations that are part of the GAW and CMDL networks, HYMN will derive new detailed information on the regional scale about methane and nitrous oxide from recently become available satellite observations from SCIAMACHY and IASI, and from remote sensing observations by FTIR. These observational data sets will be homogenised and evaluated against each other in order to derive consistent long-term time series. The error statistics of the observations will be carefully determined. By subsequently applying advanced emission inversion and data assimilation techniques to the validated observations in atmospheric chemistry models, the sources and sinks of the HYMN gases will be quantified on regional scales (up to 1x1 degree). The coupling between their life cycles and OH will be investigated focussing on presently not well understood relations between their inter-annual variations and trends.

The atmospheric chemistry models will furthermore be applied to investigate the effects of a future transfer to a hydrogen economy and of the associated reduction in fossil fuel burning emissions (NOX, CO, VOCs) on the coupled cycles of H2, CH4, OH, and O3 taking into account interactions with the biosphere simulated with the LPJ land-biosphere model.

Three partners in HYMN will apply atmospheric chemistry models and perform data assimilation/emission inversions: KNMI (the TM5 model), Univ. Oslo (the Oslo CTM2), and CEA LPCE/CNRS (the LMDZ-INCA model). Univ. Bristol will further develop their land-biosphere model LPJ and couple its output to the atmospheric chemistry models. Univ. Heidelberg and CNRS will provide new satellite data from SCIAMACHY resp. IASI. Partners from the FTIR network (Univ. Bremen, BIRA-IASB, Univ. Karlsruhe, Univ. Liège, Chalmers Univ., FZ Karlsruhe) will provide homogenized time series of CH4 and N2O observations.


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SOGE - System for Observation of Halogenated Greenhouse Gases in Europe

Stefan Reimann1, Frode Stordal2, Peter Simmonds3, Simon O’Doherty3, Martin K. Vollmer1, Brian Greally3, Michela Maione4, Igor Arduini4, Chris Lunder5, Norbert Schmidbauer5, Doris Folini1, Alistair Manning6

[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) (Figure 1). Calibration has been of primary importance to SOGE and a rigorous and traceable calibration system for the GC-MS’s is successfully maintained and extended. Coupled to the extended calibration, comparison of data from Mace Head, Jungfraujoch, Ny-Ålesund and Monte Cimone demonstrates good agreement for expected baseline levels of the targeted gases. SOGE is linked to the world-wide networks of AGAGE (Advanced Global Atmospheric Gases Experiment) and NOAA (National Oceanic & Atmospheric Administration) in terms of common standards and quality assurance tools.

Figure 1: The four SOGE network stations.

The SOGE system contributes to global observing networks to determine trends of CFCs (chlorofluorocarbons), HCFCs (hydrofluorochlorocarbons), long-lived


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SOGE - System for Observation of Halogenated Greenhouse Gases in Europe

Stefan Reimann1, Frode Stordal2, Peter Simmonds3, Simon O’Doherty3, Martin K. Vollmer1, Brian Greally3, Michela Maione4, Igor Arduini4, Chris Lunder5, Norbert Schmidbauer5, Doris Folini1, Alistair Manning6

[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) (Figure 1). Calibration has been of primary importance to SOGE and a rigorous and traceable calibration system for the GC-MS’s is successfully maintained and extended. Coupled to the extended calibration, comparison of data from Mace Head, Jungfraujoch, Ny-Ålesund and Monte Cimone demonstrates good agreement for expected baseline levels of the targeted gases. SOGE is linked to the world-wide networks of AGAGE (Advanced Global Atmospheric Gases Experiment) and NOAA (National Oceanic & Atmospheric Administration) in terms of common standards and quality assurance tools.

Figure 1: The four SOGE network stations.

The SOGE system contributes to global observing networks to determine trends of CFCs (chlorofluorocarbons), HCFCs (hydrofluorochlorocarbons), long-lived


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HFC 134a

0

50

100

150

200

2000 2001 2002 2003 2004

ppt

Monte CimoneJungfraujochMace HeadNy-Alesund

chlorinated solvents (CCl4, CH3CCl3), brominated organic compounds (halons, CH3Br) and HFCs (hydrofluorocarbons). Due to its broad coverage of Europe it provides an important link between the global and regionally representative background concentrations.As an example the data series of HFC-134a is shown in Figure 2, where a common trend in the background concentrations is overlaid by higher peak values at Jungfraujoch and Monte Cimone, which are located nearer to important European source regions in comparison to the more remote site of Mace Head and the Arctic site of Ny-Alesund.

Figure 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 provide measurements which can be used are in support of the Kyoto and the Montreal protocols, in assessing the compliance of European regions with the protocol requirements. In particular the observation system has been set up to (i) detect trends in the concentrations of greenhouse active and ozone-destroying halocarbons, (ii) verify reported emissions and validate emission inventories for a series of halocarbons for Europe as a whole as well as for certain regions, (iii) develop observational capacity for all halocarbons included in the Kyoto protocol for which this was previously not existing, and (iv) develop a strategy for a cost-effective long-term observation system for halocarbons in Europe. The second objective has been to predict and assess impacts of the halocarbons on the climate and on the ozone layer. This implies extensive exploitation of existing data.The European emissions of halocarbons are regularly estimated within SOGE within several publications. For example, HCFC-141b emissions have been shown to have


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declined 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-152a emissions have been estimated over the last years [O'Doherty et al. 2004; Greally et al. 2007].In the last years SOGE has been extended to Asia. Within SOGE-A (Asia) a new instrument has been deployed at a station North of Beijing to perform continuous in-situ measurements of ozone-depleting substances (CFCs, HCFCs, halons and long-lived chlorinated solvents). Resulting emission estimates will be the first of its kind from China and will be used to verify compliance of China with the requirements of the Montreal protocol and its amendments.

References

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 European emissions of HCFC-141b and HCFC-142b under the Montreal Protocol: Evidence from observations 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 AGAGE and SOGE monitoring stations in 1994–2004 and derived global and regional emission estimates, 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 of hydrofluorocarbon 134a and hydrochlorofluorocarbons 141b, 142b, and 22 from Advanced 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 free substitute 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 and rapid growth of anthropogenic HFC-245fa (CHF2CH2CF3) in the atmosphere, Geophys. Res. Lett. 33 (20), 2006.


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Geoland

Jean-Christophe Calvet

Météo-France, Toulouse, France

The GEOLAND project

GEOLAND is carried out in the context of Global Monitoring of Environment and Security (GMES), a joint initiative of European Commission (EC) and European Space Agency (ESA), which aims to build up a European capacity for GMES. GEOLAND is designed to fundamentally support this initiative, focusing on the GMES priorities "Land Cover Change in Europe", "Environmental Stress in Europe", and "Global Vegetation Monitoring". In FP6, the GEOLAND integrated project started in 2004 and the technical work was completed in December 2006. The ambition of the GEOLAND consortium is to develop and demonstrate a range of reliable, affordable and cost efficient European geo-information services, supporting the implementation of European directives and their national implementation, as well as European and International policies. Thus, the GMES initiative is considered a unique opportunity to integrate existing technology with innovative and scientifically sound elements into sustainable services.

The Land Carbon component of GEOLAND

The objective of the land carbon component of GEOLAND is to develop a bottom-up (Fig. 1) multimodel carbon accounting system accounting for weather and climate variability, coupled with an Earth Observation (EO) data assimilation system. This new tool will support Kyoto (and post-Kyoto) reporting activities. The main achievement so far (Fig. 2) consisted in performing the 'greening' of the land surface operational platforms of meteorological services (ECMWF and Météo-France). Namely, a CO2 responsive capability was introduced in the land surface models 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 a spatial resolution of 25 km. The modelled carbon flux is fully consistent with the modelled water flux, soil moisture, vegetation biomass and leaf area index.A demonstration EO data assimilation system was implemented over southwestern France (Fig. 3), and a simplified version was successfully applied at a global scale.

Prospects

Future activities will focus on the representation of carbon storage and soil respiration in the modelling platforms of meteorological services, on the development of the operational use of EO data assimilation, on the improvement of the spatial resolution over Europe (1-10 km), and on linking the products with forest and soil carbon inventory activities in Europe.


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Figure 1: Complementarities of the GMES atmosphere and vegetation integrated projects within FP6.

Figure 2: A result of GEOLAND: greening of land surface models used in atmospheric models (Météo-France and ECMWF), and near-real time demonstration products derived from the ORCHIDEE model.


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Figure 3: Assimilation of soil moisture and Leaf Area Index observations over southwestern France: example over the SMOSREX experimental site (De Rosnay et al. 2006).


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3 Inverse Modelling Studies


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Baseline trends and top-down estimates of UK and NW European GHG emissions

Alistair Manning

Met Office, Exeter, UK

Introduction

High frequency observations on the remote west coast of Ireland have been used to estimate the Northern Hemisphere background trends of methane, nitrous oxide and a range of HFCs. Subtracting the background concentration from the observation yields a quantity that represents the impact of regional pollution on the atmospheric concentration. By understanding the recent history of the air arriving at the observation station and importantly how emissions dilute with time it is possible, through inverse modelling, to estimate the magnitude of emissions from different geographical regions. Emission estimates for the UK and other European regions over a 12 year period (1995-2006) are presented for methane, nitrous oxide and a range of important HFCs (134a, 152a, 125, 365mfc).

Northern Hemisphere baseline trends

The Mace Head observing station is situated on the west coast of Ireland and is part of the global AGAGE (Advanced Global Atmospheric Gases Experiment) network of sites. It is ideally situated to observe air from the Atlantic that has received no land emissions for thousands of kilometres. The majority of air therefore is a good representation of mid-latitude composition, referred to as baseline air, and can be used to assess annual and seasonal trends. The Mace Head station records a comprehensive set of greenhouse gases to state-of-the-art accuracy at high time resolution.

This study attempts to isolate those times from 1995 onwards that are representative of mid-latitude baseline air by using a sophisticated atmospheric dispersion model, NAME (Numerical Atmospheric dispersion Modelling Environment) and statistical post-processing. NAME is a Lagrangian model developed following the Chernobyl accident to understand the movement of material in the atmosphere and has since been used in a wide range of emergency and policy-based applications. In this work the 3D meteorology has been obtained from the UK Met Office numerical weather prediction model, the Unified Model, with a horizontal resolution of 55km falling to 40km, a vertical resolution of 12 levels in 1995 increasing to 33 levels by 2006 and an analysis output every 3 hours.

NAME has been run backwards in time for ten days for each 3 hour period from 1995 until the end of 2006 releasing thousands of model particles at Mace Head. For each 3 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 hour interval (figure 1). By selecting only those times when the history of the air mass indicates negligible impact from the European land mass, or transport from tropical


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latitudes or significant local contributions, it is possible to isolate those times classed as mid-latitude baseline. For a selection of greenhouse gases measured at Mace Head these baseline observations have been selected and then further refined by application of a statistical filter to remove outlying data points. Monthly averages of the resulting data are used to assess the annual and seasonal baseline trends in each 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 indicate greater surface contributions from that area to the air arriving at Mace Head during that 3 hour period.

Estimating European emissions using top-down inversion methodology

The aim is to generate regional emission distribution estimates from ‘polluted’ (above baseline) observations. The emissions are defined as the geographical vector e that has n elements indicating n geographical regions. The NAME model is used to predict the concentration time-series (t elements) at each observation point (Mace Head) from each potential source region. This information is captured in the transport and dilution matrix A which has t rows and n columns. The observation time-series representing regional pollution is determined by subtracting the estimated time-varying baseline concentration from each observation, this generates the observation vector m that has t elements.

For methane the observations from three stations have been used to better constrain the inversion. The stations are Mace Head in Ireland, Deuselbach in western Germany and Neuglobsow in eastern Germany. All three are Global Atmospheric Watch (GAW) stations. Concurrent data from all three stations only exist up until the end of 2004. In order to be able to use the data from all three stations it is necessary that all the observations are inter-comparable so that all the data can be represented on the same calibration scale. In this case the data were all converted onto the Japanese scale. As both of the German sites are within the ‘polluted’ regional domain it is difficult to estimate the baseline concentrations at these stations. As all three stations have similar latitudes it has been assumed here that the baseline concentration estimated at Mace Head would be applicable at both of these others stations. Also because methane has natural biogenic sources that can exist close to the observation sites and thus dominate the signal, all observations recorded during periods when ‘local’ effects would be significant, e.g. low wind speed and low


44

boundary layer periods, have been removed from the analysis. For all of the other gases investigated only Mace Head data was available and no data selection criteria were applied.

The results of the different trace gases are shown in figures 3-6, the country and regional totals have been determined by summing the emissions from the relevant grids 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 the range of uncertainty within the results and arise from repeating the inversion methodology multiple times with different noise perturbations applied to the observation time-series. The annual estimates have been calculated by averaging the relevant six month solutions weighted by their overlap period with the given year. The UNFCCC inventory estimates for the NW European region were obtained from the web site (www.unfccc.int), the UK estimates were provided by the collator of the UK 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 points show the monthly-averaged observations.


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Figure 3: Annual average Methane emission estimates for the UK and northwest Europe (Mt/yr). Black line = UNFCCC and Blue line (with uncertainty range) = NAME methodology

Figure 4: Annual average Nitrous Oxide emission estimates for the UK and northwest Europe (kt/yr). Black line = UNFCCC and Blue line (with uncertainty range) = NAME methodology

(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 methodology


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(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 methodology

Discussion

The mid-latitude baseline concentrations of methane increased between 1995 and 2000 but since that time have remained relatively stable, the actual annual trend is difficult 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 baseline concentrations have all increased during the measurement period.

The NAME estimated methane emissions for NW Europe follow a similar trend to that reported by through the UNFCCC (figure 3). The UK estimates follow a significantly different trend. The NAME estimates show a fairly static emission whereas the UNFCCC estimates have steady declined over the same period. It must be remembered that the NAME methodology makes no distinct between anthropogenic and biogenic emissions unlike the UNFCCC which is purely anthropogenic. It would therefore be expected that the NAME estimates should be similar to or larger than the 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 estimates are in good agreement (figure 4). The UNFCCC estimates for the UK lie entirely within the uncertainty range of the NAME estimates. The trend in the NW European estimates is very similar with the UNFCCC estimates being at the top end of the uncertainty range of the NAME estimates.

For HFC-134a (figure 5a) the patterns of UK emissions are similar in that both estimates show a strong increase in emissions over the 12 year period. However the UNFCCC estimates start increasing two years ahead of the NAME inversion estimates. In the last three years the NAME estimates have levelled off whereas the UNFCCC estimates have continued to grow.


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The overall emission estimates of HFC-152a for the UK are similar between the two methods (figure 5b) but profile is significantly different. The increase in emissions occurs two years later using the inversion method and shows a sharp decline in use in the last two years when compared to the UNFCCC estimates.

The inversion estimates for UK emissions of HFC-125 and HFC-365mfc (figures 6a and 6b) show strong increases over the measurement period.

The NAME inversion estimates have significant uncertainty. This uncertainty has been captured by solving the inversion multiple times with randomly applied perturbations to the observed time-series and using two different skill score (cost) functions. The noise signal applied to the observations represents the uncertainties in the inversion assumptions, in the transport and dispersion and to a minor degree in the observations themselves.

The use of multiple observation sites better constrains the inversion equations and therefore enables improved emission estimates to be calculated. Each observing station is impacted by the emissions from different geographical areas to different extents and therefore using multiple stations enables the inversion estimates to be solved on a higher resolution grid. It is assumed that the observations from the different sites can be inter-compared, i.e. they are reported on the same calibration scale or can be converted between scales.

The inversion methodology reported here requires high-frequency long-term high-quality measurements. It also assumes that a time-varying baseline concentration can be estimated for each measurement site. Isotopic measurements of a trace gas may enable the source categorisation of the emissions, as each source type emits the trace gas with a different isotopic signature.

Acknowledgements It is important to recognise and acknowledge the work of the scientists developing and maintaining the observation stations that have provided data for this study, namely Mace Head, Deuselbach and Neuglobsow, and also their funders. The key role of GAW is also vital for enabling access to this data. Defra, UK need to be recognised for providing the resources to enable the modelling.


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Methane flux estimates for Europe using tall tower observations and the COMET inverse model

Alex Vermeulen1, Gerben Pieterse1,2

[1] Netherlands Energy Research Foundation (ECN), Petten, Netherlands [2] Currently at: IMAU – Institute for Marine and Atmospheric Research, Utrecht, Netherlands

1. Model and data description

1.1 Model descriptionThe COMET (CO2 MEthane Transport) model is a Lagrangian model that can be used for both predictive and inverse modelling purposes. COMET uses backward trajectories to establish the source-receptor relationship, the so-called source-receptor matrix (SRM). The calculations described in this paper were performed using trajectory and mixing layer height data derived from three nested grids (resolution from 2 to 0.5 degrees) with 3-hourly resolution ECMWF analysed operational meteorological data. The vertical resolution used is L61. Using these meteorological data, the 3-D 144 h backward trajectories were calculated from the ECMWF wind fields using the Flextra model [Stohl and Thomson, 1999].To account for mixing of the source signal in the planetary boundary layer with the free troposphere, two vertical layers are distinguished, a mixing layer and a reservoir layer. The initial methane concentration at the start of each trajectory is taken in this case from the two-weekly averages of the calculated methane concentrations of the TM2 model [Heimann, 1996] for 1995 as calculated by Houweling et al. [1999]. The height of the mixed layer in contact with the surface varies as a function of atmospheric stability. All emissions are first accumulated in this mixed layer and when the mixed layer height changes, mass transfer takes place with the reservoir layer.The area that influences the concentrations in the column of air in the mixed layer is assumed to be circular and the diameter of this circle is assumed to change linearly with travel time; from large at the start of the backward trajectory to small at the destination. This cone-shaped trajectory path defines a highly simplified parametrised form of the real region of influence, determined by advection, convection and turbulent diffusion. Normal trajectory models only follow an infinitesimal narrow path, ignoring the effect of turbulent diffusion along this path. An alternative to the single trajectory approach is to use an ensemble of trajectories to get at least some information on the accuracy of the trajectory information and the influence of for example 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 emissions from 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 is flexible in this and in this paper we choose to select an aggregation level with a resolution of 0.2 degree. After evaluation of all arrival times the sum of the contributions per grid cell is evaluated.Usually the cells surrounding the receptor point(s) contain very high contributions and the cells further away shows rapidly decreasing values. Then an automated routine


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aggregates neighbouring grid cells in pairs to form areas with the same or higher average total contribution as that of the individual maximum for the whole grid. The joined cells with lower than the maximum contribution are then again joined pair by pair, etc. This leads to a highly linearised version of the Source Receptor Matrix, that is more suitable for the Singular Value Decomposition inversion routine (see Vermeulen et al., [1999]).

1.2 Cabauw observation Cabauw tower (51o58' N, 4o55' E) is located near the centre of the Netherlands, 20 km southwest of the city of Utrecht. The Royal Netherlands Meteorological Institute (KNMI) is using this tall tower for boundary layer studies. The direct surroundings of the Cabauw tower is just below sea level in a polder area. It consists of flat meadows and ditches, with some scattered villages. On this site a 213m high meteorological tower is situated. Since 1993 ECN performs high precision measurements of vertical gradients of greenhouse gas concentrations along this tower.

1.3 Emission data METDAT (METhane DATabase) is a high-resolution database for CH4 emissions that has been developed for the Netherlands and the Northwestern part of Europe. It has a spatial resolution of 500x500 m2 and 5x5 km2, 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 United Kingdom. For the remaining regions the values for the emissions are derived from the EDGAR database version 2.0 [Olivier et al., 1996].Both data sets are based on the inventories in the year 1995. The following source categories 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 water treatment, 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 source category, an estimate was made for the temporal variation in emissions. Emission variation between years and months as well as within weeks and days has been determined [Berdowski et al., 1998].

2. Results

Figure 1 shows a small part of the measured and modelled time series for methane at Cabauw tower. For periods of several weeks to months the model can explain up to 90% (R2=0.90) of the observed variability of the methane mixed layer bulk concentration. For the whole time series of 2002 the R2 equals 0.72. Figure 2 illustrates the RMSE of the methane modelled values when evaluated against the observations. From the figure a slight tendency of the model to underestimate the concentrations can be deduced. For the mixed layer bulk concentration the average error is around zero, while for the lower observation levels the model tends to produce too low values on average.


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Figure 1: Comparison of modelled and measured bulk concentration of methane in the mixed layer for March to May 2002 at Cabauw tall tower.

Figure 2: Density of the model error for methane at the different observation heights at Cabauw

Figure 3: Prior (black line) and posterior estimates (red bars, error bars indicate inversion uncertainty) of the emission of methane around Cabauw for the 25 aggregated source regions that could be determined through the inverse COMET calculation for observed methane concentration at Cabauw in 2002

Figure 3 shows the prior emissions (black line) of 25 aggregated regions that can be resolved from 1 year (2002) of methane observations at Cabauw. The region numbers (at the x-axis) grow with distance from Cabauw and with the size of the


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source region. The resulting emissions are shown on the map in Figure 4. The map also shows how the aggregation areas get bigger with distance from the receptor point Cabauw. The procedure can be repeated by inverting time-series for Cabauw of the respective years 2000-2006. The result of this exercise is shown in Table 1. As expected the emissions estimated from the inversion process are higher than the prior METDAT fluxes, as the latter do not contain the natural fluxes. Interannual variability of the fluxes is quite large. However, the overall uncertainty of the annual flux for The Netherlands is estimated by the inversion routine to be 20-30%, so the year to year differences found here can not be seen as highly significant.

Figure 4: Posterior emissions, determined by the COMET model and the SVD inversion when applied to the 2002 timeseries for methane concentrations at Cabauw

Table 2: Prior and posterior estimates of the annual mean emission of methane from the Netherlands, determined through the inverse COMET calculation for observed methane concentration at Cabauw for the years 2000-2006. In 2004 not enough observations were available.

Year Emission kTon CH4/yrPrior (METDAT, 1998) 1020

2000 1600 2001 2000 2002 1350 2003 1600 2005 1350 2006 1950


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The exercise using observations from one station can be repeated by using time series from other (tall tower) observation sites and/or by extending the analysis to multiple years. This will allow to reduce the inversion error drastically and to extend the spatial coverage of the area for which the fluxes can be inverted. Figure 5 illustrates that about 160 individual aggregated cells can be distinguished with high accuracy for a synthetic data experiment where forward calculated concentrations for 1 year for 8 receptor points (CHIOTTO tall towers [Vermeulen et al., 2007]) have been inverted using the source aggregation+SVD method.

Figure 5: Left: Prior (red) and Posterior (blue) methane emission estimates for aggregated areas in Europe using the observations of the 8 CHIOTTO tall towers in a synthetic data experiment. The gray area indicates the uncertainty of the emission estimate. Right: The resulting aggregated source areas for the synthetic inversion. The observation sites are indicated with red triangles.

Acknowledgements. The authors would like to thank KNMI for hosting the measurements at Cabauw tower. ECMWF and KNMI are acknowledged for the access to the ECMWF MARS meteorological archive. Financial support for this research has come from the Ministry of VROM, Novem/Senter and the European Commission (CarboEurope-IP and CHIOTTO projects).

References

Berdowski, 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 Checks for 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 gas fluxes from observed atmospheric mixing ratios, in: Approaches to scaling of trace gas fluxes in ecosystems, edited by: Bouwman, A. F., 277–295, Elsevier, Amsterdam, ISBN 0-444-82934-2, 1999.


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Houweling, S., Kaminski, T., Dentener, F., Lelieveld, J., and Heimann, M., Inverse modeling of 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 global emission inventories of greenhouse gases and ozone-depleting substances for all anthropogenic and most natural sources on a per country basis and on 1ox1o 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 of NW-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 technique and 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 in Europe: first results. In: 13th WMO/IAEA meeting of experts on carbon dioxide concentration 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.


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New TM5-4DVAR inverse modelling system to estimate global and European CH4 sources

Peter Bergamaschi1, Jan Fokke Meirink2, Maarten Krol2,3, and Maria Gabriella Villani1

[1] European Commission DG Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy [2] Institute for Marine and Atmospheric Research Utrecht, University of Utrecht, The Netherlands[3] Wageningen University and Research Centre, Wageningen, The Netherlands

Introduction

A new, 4-dimensional variational (4DVAR) inverse modelling system has been developed for inverse modelling of atmospheric methane. The main advantage of the new system is that it allows optimizing emissions of individual model grid cells. At the same time very large sets of observational data can be used (e.g. high-frequency in situ measurements or satellite data). In contrast, the previously widely used synthesis inversions were restricted to the optimization of emissions of larger, pre-defined regions (e.g. continents, or countries), but could not further optimize spatial emission distributions within these pre-defined regions. Therefore, these approaches were prone to the so-called aggregation error [Kaminski et al., 2001].

4DVAR technique

4DVAR techniques are widely used in numerical weather prediction in order to optimize the initial state of the atmosphere. For application to inverse modelling, we extend 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: n

iiiBB xHyxHyxxxxxJ

1iOBS,

TiOBS,2

1T21 1-

i1 RB

(with x : state vector; Bx : a priori estimate of x ; B background error covariance matrix; OBSy : observations; )(xH model simulations of observations; R observations error covariance matrix; i index of assimilation time window) is minimized iteratively, by evaluating its gradient:

n

iiB xHyxxxJ

1iOBS,

1-i

Ti

Ti

T1i-

T1

1 RHMMMB

(with M: atmospheric transport model; MT: adjoint of M; H observation operator) and applying the ECMWF conjugate gradient minimization algorithm [Fisher and Courtier, 1995]. We apply the atmospheric transport TM5 [Krol et al., 2005], and developed its adjoint model for this purpose. Figure 1 shows the iterative


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minimization for a 1-year global 4DVAR inversion, illustrating the very rapid decrease of the gradient norm.

Figure 1: Reduction of gradient norm by iterative 4DVAR minimization using the ECMWF conjugate gradient algorithm.

First global 4DVAR inversions and comparison with synthesis inversion

Figure 2 shows an example of a global 4DVAR inversion, using a priori emissions from 11 different source categories. We assumed constant uncertainties of monthly emissions per grid cell, ranging between 20 and 80% for the different source categories. Furthermore, a spatial decorrelation length of 500 km was assumed. As observational data we used the CH4 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 and tropical 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 data were used, and for which 7 big global regions were defined (this synthesis inversion is described in detail in [Bergamaschi et al., 2007]). Obviously, despite the limitations of the synthesis inversion, the agreement between both approaches is surprisingly good: This is attributed to the fact that the synthesis inversion with the 11 source categories already provides relatively high flexibility. Furthermore, the applied background observations do not provide strong constraints on emissions of individual model grid cells, but rather on larger scale emissions.


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Figure 2: Global 4DVAR inversion for year 2003 using surface background observations from 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].


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First European TM5-4DVAR inversions

Figure 4 shows a coupled global - European inversion, applying the 1ox1o zooming over the European domain. Observational data include a number of high-frequency observations over Europe (and are identical to those used in a country-based synthesis inversion [Bergamaschi et al., 2005]; however we use here different a priori emission inventories for the 4DVAR inversion). The first results for a series of 4DVAR scenarios yield a posteriori total emissions for the EU-15 countries close to those derived from the synthesis inversion (20-23 Tg CH4/yr derived in the synthesis inversion for scenarios S1-S9 [Bergamaschi et al., 2005]). The influence of the a priori emissions and further parameters of the 4DVAR system are currently further investigated.

Figure 4: Coupled global-European 4DVAR inversion for year 2001 using high-frequency observations from several European monitoring stations (complemented by European and global flask measurements). Upper panel: a priori emissions; lower panel: Inversion increment (i.e. a posteriori - a priori emissions).


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Figure 5 shows the achieved uncertainty reduction in the 4DVAR inversion (approximation based on leading eigenvectors). The figure illustrates the significant uncertainty 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 complex systems with very large numbers of parameters (emissions from individual model grid cells) and very large numbers of observations (both in the order of 104 - 106).

First 4DVAR results show high consistency with previous synthesis inversions (both on global and European scale). The much higher flexibility of the 4DVAR system becomes apparent when using continental high-frequency surface observations (or satellite data), minimizing the 'aggregation-error'.

The first preliminary results of the European 4DVAR inversion yield total CH4emissions from EU-15 countries close to previous synthesis inversions [Bergamaschi et al., 2005].

Acknowledgments

We are grateful to the NOAA, AGAGE, and GAW networks for the provision of their observational data. Furthermore, we thank A. Vermeulen, M. Ramonet, and F. Meinhardt for delivery of high-frequency measurements at various European monitoring stations.

References

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, Satellite chartography of atmospheric methane from SCIAMACHY onboard ENVISAT: (II)


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Evaluation 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 CH4 emissions 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 and distribution 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 error in 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 in atmospheric transport inversions. J. Geophys. Res., 106, 4703–4715, 2001.


Meirink J.F., Bergamaschi P., and Krol M.: Four-dimensional variational data assimilation for inverse modelling of methane emissions, paper in preparation, 2007,

Meirink, J.F., H.J. Eskes, and A.P.H. Goede, Sensitivity analysis of methane emissions derived from SCIAMACHY observations through inverse modelling, Atmos. Chem. Phys.,6, 1275-1292, 2006.


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Inverse modelling activities at LSCE: from global to regional scales

Philippe Bousquet

Laboratoire des Sciences du Climat et de l’Environnement (LSCE-IPSL), Gif sur Yvette, France

Acknowledgements : C. Aulagnier, F.M. Bréon, F. Chevallier, C. Carouge, S. Houweling, T. Lauvaux, P. Peylin, P. Rayner, L. Rivier and P. Ciais.

“Traditional” approach

Atmospheric inverse modelling is a technique to analyse atmospheric observations of a gas (concentrations) in terms of sources and sinks (fluxes), generally using a transport model to link fluxes and concentrations. LSCE has been involved in inversion 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 sources and sinks of CO2 over large regions against available observations assimilated as monthly 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 of emissions or sinks [Bousquet et al., 1999b]. Inverse formalism is based on [Tarantola, 1987] and explicitly builds and inverts the matrices involved in the inverse calculation, producing estimates of both fluxes and their uncertainties. Recently inversion of long-lived reactive gas emissions has been implemented for methyl-chloroform and methane [Bousquet et al., 2006; Bousquet et al., 2005]. Comparison with land-surface models calculations were also performed systematically [Peylin et al., 2005].

This so-called “traditional approach” provided a lot of scientific results from global to continental scales. For instance, it confirmed that a large terrestrial carbon sink exist over non-tropical northern hemisphere lands [Gurney et al., 2002]. It also provided evidences that the oceanic climatology of CO2 air-sea fluxes was overestimating southern ocean sinks by a factor of 2. After integrating winter measurements in the climatology, the inverse assessment was confirmed. Most of these results, consistently obtained by several research groups, were emphasized by the TRANSCOM inter-comparison project, which provided a very efficient and stimulating framework to the CO2 community since the mid 1990s [Baker et al., 2006; Denning et al., 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 the 2 main limitations of atmospheric inversions are the lack of observations and the errors in atmospheric transport modelling. The former limits the spatio-temporal resolution of atmospheric inversions and the latter creates large spreads in the estimations of CO2 sources and sinks between the different research groups. For instance, the partition of the northern hemispheric carbon sink between North America, Asia and Europe is still uncertain largely because errors in transport modelling [Peylin et al., 2002]. An important point about most CO2 inversions performed so far is that fossil fuel emissions were more or less prescribed annually according to inventories in order to retrieve land and ocean sinks. A recent


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comparison exercise within TRANSCOM showed that accounting for hourly to annual variability of fossil emissions in a pixel-based inversion produces a significant effect on retrieved sinks in Europe (Figure 1 : Peylin, Houweling, pers. com.).

Figure 1 : Monthly inverse fluxes (January and July) estimated with LMDZ transport model for a standard inversion using EDGAR annual fossil fuel emissions (lower panels) and for an inversion using the difference between IER hourly fossil emissions and EDGAR annual fossil fuel emissions (upper panels) [P. Peylin, S. Houweling, pers. com.]

Interannual variability

After 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 in transport modelling as it uses changes of concentrations to infer changes in the fluxes, thus cancelling (at least partly) systematic modelling errors. Adapting our methodology to analyse long time series of observations, we found that land CO2fluxes were 2-3 times as large the air-sea fluxes [Bousquet et al., 2000]. IAV of CO2land sinks was also successfully compared with vegetation models estimates, providing hints on the underlying processes that are not directly accessible with inverse models. Recently, we explained the small CH4 growth rate of the early 2000s by a compensatory effect between reduced wetland emissions and increasing anthropogenic emissions, especially in Asia [Bousquet et al., 2006]. The partition between natural and anthropogenic emissions is based on (1) different spatio-temporal patterns of the emissions, (2) the use of 13CH4 observations, and (3) the additional assumption that anthropogenic emissions vary more smoothly in time than natural ones. We also found that European CH4 emissions were decreasing on average since the late 1980s (Figure 2) mainly due to energy-related and ruminant


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animal emissions, in good agreement with EDGAR3.2 inventory [Olivier and Berdowski, 2001].

Figure 2: CH4 emissions for Europe (Atlantic to the Ural Montains, in Tg CH4/yr) for different anthropogenic sources as inferred by atmospheric inversions from Bousquet et al. [2006]. Solid black line represents the standard inversion. Shaded area represents the range of the 18 inversions performed. Seasonality has been removed. Black boxes (right panel) stands for prior estimates and their uncertainties. Blue bars represent posterior uncertainties. Red dots represents estimates from EDGAR inventory (a 20% error was assumed).

Recent developments

The traditional inverse approach has several caveats, nicely summarized by Bergamaschi and Houweling in the first Inverse Modelling Workshop report [Bergamaschi et al.,2004]. Among all issues, the use of large regions was pointed out to cause the so-called aggregation error: if the prescribed flux patterns within each region is wrong, an error is made and carried out in the inverse procedure. As initiated by [Rödenbeck et al., 2003], we handled this problem by solving fluxes at model resolution. Such inverse system are totally underdetermined (much more unknowns than observations) and require the use of additional constraints to link model pixels, such as distance-based correlation of errors. For observations, as the knowledge of continental sources and sinks of greenhouse gas became a priority in


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the mid 1990s, continuous monitoring sites largely developed, especially in Europe and North America, providing much more information at regional (space) and synoptic (time) scales. First satellite retrievals of greenhouse gas also recently became available, as SCIAMACHY for CH4 [Frankenberg et al., 2005], largely increasing the number of observations constraining inverse problems. With large observation and flux spaces (typically 106 x 106 matrices for inversion of satellite data at model resolution), traditional matricial approach cannot be applied anymore. New methods must be used that generally directly minimizes a cost function. At LSCE, we developed a variational approach based on the 4D-VAR assimilation system of ECMWF [Chevallier et al., 2005]. This method was first applied to study the potential of future OCO mission (Figure 3) to retrieve CO2 from space [Chevallier et al., 2006].Other groups also developed variational approaches or Ensemble Kalman filters, and applied them to CO2 or CH4 inverse problems [Bergamaschi et al., 2007b, Peters et al., 2005]. These methods can handle very large inverse systems and can give a direct estimate of flux uncertainties, but at a higher numerical cost than simple flux estimates.

Figure 3: Fractional error reduction of the monthly mean grid point CO2 surface fluxes [Chevallier et al., 2006]. Error reduction is defined as 1- a/ b, with a the posterior error standard deviation and b the prior error standard deviation.

The use of satellite data and continuous surface measurements may remove the limitation of the number of observations in the next years but the quality of atmospheric transport modelling is still a major issue. Global models proved to properly represent monthly concentrations at marine and remote sites. However, as land carbon budget is an important scientific and political target at regional-to-country scales, one needs models that are capable to reproduce concentrations of


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greenhouse gas close to the surface of the continental planetary boundary layer. The major limitations of global models are their coarse resolutions and the quality of their vertical transport (both turbulence and convection). Several strategies were developed 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 and Talagrand, 2006; Hourdin et al., 2002] which is zoomable over 1 region (with resolution of typically 40km x 40km). We are also using regional models to evaluate their ability to improve the modelling of monitoring sites located in a complex environment: urban or industrial areas, mountains, valleys, etc. For instance, within the CARBOEUROPE-IP regional experiment ([Dolman et al., 2006], see also Rödenbeck et al., this report), we will try to estimate the carbon budget of a 300km x 300km region in the south west of France using a mesoscale model at 2 km resolution coupled with a Lagrangian transport model together with intensive campaigns with high tower and aircraft CO2 measurements [Lauvaux et al., 2007].

Finally, another path to estimate greenhouse gas sources and sinks is to optimize some parameters of vegetation and ocean models instead of the exchange fluxes themselves. One advantage is that there are much less parameters to estimate as compared to flux estimate in atmospheric inversion. Such systems are called Carbon Data Assimilation Systems (CCDAS, [Rayner et al., 2005]). They are based on the coupling of one vegetation model and one atmospheric model and the use of different kind of observations: atmospheric concentrations, direct flux measurements, satellite retrievals of surface properties. LSCE is developing a CCDAS based on LMDZ and vegetation model ORCHIDEE [Krinner et al., 2005].

References

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Bergamaschi, P., Behrend, H., and Jol, A. (Eds.): Inverse modelling of national and EU greenhouse gas emission inventories – report of the workshop “Inverse modelling for potential verification of national and EU bottom-up GHG inventories” under the mandate of the Monitoring Mechanism Committee WG-1 23–24 October 2003, JRC, Ispra, 146 pp., 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, Satellite chartography 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 modelling system to estimate global and European CH4 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 of


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anthropogenic 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 annual atmospheric CO2 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 OH variability as inferred by an inversion of atmospheric transport and chemistry of methyl chloroform, Atmos Chem Phys, 5, 2635-2656, 2005.

Bousquet, P., P. Peylin, P. Ciais, C. Le Quere, P. Friedlingstein, and P. P. Tans, Regional changes 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 annual atmospheric CO2 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 carbon observatory to the estimation of CO2 sources and sinks: Theoritical study in a variational data 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 CO2 sources and sinks from satellite observations: Method and application 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 SF6 - 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 Regional Experiment Strategy in Les Landes, South West France, May-June 2005, Bulletin of the American Meteorological Society, 2006.

Enting, I. G., C. M. Trudinger, and R. J. Francey, A Synthesis Inversion of the Concentration and Delta-C-13 of Atmospheric CO2, Tellus B, 47, 35-52, 1995.

Frankenberg, C., J. F. Meirink, M. van Weele, U. Platt, and T. Wagner, Assessing methane emissions 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 regional estimates of CO2 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 CO2 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: Model mean 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, and E. A. Holland, Interactive chemistry in the Laboratoire de Meteorologie Dynamique


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Hourdin, F., and A. Armengaud, The use of finite-volume methods for atmospheric advection of 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: Adjoint derivation and parametrization of subgrid-scale transport, Quarterly Journal of the Royal Meteorological Society, 132, 567-583, 2006.

Hourdin, F. D., F. Couvreux, and L. Menut, Parameterization of the dry convective boundary layer 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 coupled atmosphere-biosphere system, Global. Biogeochem. Cycles, 19, GB1015, doi:1010.1029/2003GB002199, 2005.

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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 campaign with synthetic data , submitted to Atmos. Chem. Phys., 2007.

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Tarantola, A., Inverse problem theory, Amsterdam, The Netherlands, 1987.


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Top-down Methods in the Presence of Partial Carbon Accounting

Peter Rayner


In this brief summary I will try to describe some of the limitations of top-down methods in the presence of partial carbon accounting and uses of these methods. This is a completely personal view.

How top-down methods work

Top-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 described using regional patterns and these patterns may refer to different processes occurring at the same place and time. (There remains a debate about starting with such prior estimates but we can include the case with no prior information by treating the initial uncertainty as infinite.)

2. Insert these fluxes into an atmospheric transport model and compare with observations.

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 a point 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 the observations is well-known in many inverse problems. In the context of accounting it is only a problem if one process is inside and the other outside the accounting framework. In general equifinality arises because the atmospheric datasets are too sparse but there are cases where we could not hope, even in principle, to distinguish processes. 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-down methods but they can be falsified. This can be achieved by following the first two steps of the top-down algorithm listed earlier. The comparison of atmospheric concentrations (driven by the best estimate of fluxes) and observations provides one test of the accounts. It requires extra work since parts of the full carbon budget not included in the partial account must be calculated. Such a technique works best at a regional scale where mismatches with the atmosphere cannot be blamed on errors in


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budgets from other regions. Note the use of assimilated fields as boundary conditions for regional inversions in the GEMS summary. Another approach is to use the atmosphere to test the models that often underpin carbon accounting procedures. Such models (either empirical or mechanistic) describe quantities like the productivity of current and preexisting land cover. As shown in various studies combining terrestrial models and atmospheric observations in a data assimilation framework, the parameters in such models are often observable, using atmospheric concentrations.


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Data assimilation of atmospheric CO2: CarbonTracker

Wouter Peters1,2,3, Maarten Krol3, Andy Jacobson1,2, Ken Masarie1, Pieter Tans1,Arlyn Andrews1, Lori Bruhwiler1, Tom Conway1, Adam Hirsch1,2, John B. Miller1,2,Gabrielle Pétron1,2, Colm Sweeney1,2, Doug Worthy4, Jim Randerson5, and Guido van der Werf6

[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 Netherlands

CarbonTracker is the name of the new data assimilation system for CO2 developed at NOAA ESRL. Its primary purpose is to convert high precision mole fraction observations of CO2 into estimates of surface fluxes which are needed to improve our understanding of the carbon cycle. The system design is based on ensemble Kalman filtering techniques that are gaining traction in the operational meteorological forecast community due to its efficiency in solving large optimization problems efficiently. CarbonTracker is envisioned to handle continuous data from many sites in the near-future, including flux observation sites and possibly satellite observed radiances in CO2 absorption bands.

At the heart of CarbonTracker is a large set of calibrated CO2 mole fraction observa-tions from the Cooperative Air Sampling Network, as well as from a set of instru-mented towers that measure CO2 continuously. Specifically, daytime average CO2mole 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 KWKT tower in Texas, (4) the 40m level of the tower in Fraserdale, Canada operated by the Meteoro-logical Service Canada (MSC), and (5) the 23m level of the tower at Candle Lake, Canada operated by MSC. In total, more than 28,000 observations are used to estimate weekly global CO2 fluxes for the period 2000-2005.

Surface fluxes of CO2 are simulated using a set of flux ‘modules’, each designed to represent a specific process in the carbon cycle. We currently consider exchange be-tween the atmosphere and the biosphere and oceans, as well as emissions from bio-mass burning and fossil fuel use. The flux modules are designed to be simple yet carry the most important variability that is hard to constrain from CO2 observations alone. This includes for instance the effect of sunlight and temperature on photosynthesis and respiration, and the increasing ocean exchange as a function of wind speed. Ocean and biospheric exchange are both optimized to fit simulated CO2to the observed records.

The biosphere model currently used in CarbonTracker is the Carnegie-Ames Stanford Approach (CASA) biogeochemical model. This model calculates global carbon fluxes using input from weather models to drive biophysical processes, as


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well as satellite observed Normalized Difference Vegetation Index (NDVI) to track plant phenology. The version of CASA model output used so far was driven by year specific weather and satellite observations, and including the effects of fires on photosynthesis 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 Net Primary Production (NPP) and ecosystem respiration (RE). Higher frequency variations (diurnal, synoptic) are added to Gross Primary Production (GPP=2*NPP) and RE(=NEE-GPP) fluxes every 3 hours using a simple temperature Q10 relationship assuming a global Q10 value of 1.5 for respiration, and a linear scaling of photosynthesis with solar radiation. The procedure is very similar, but NOT identical to the procedure in Olsen and Randerson [2004] and based on ECMWF analyzed meteorology. Note that the introduction of 3-hourly variability conserves the monthly mean NEE from the CASA model. Instantaneous NEE for each 3-hour interval is thus created 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 summations are done over one month in time, per gridbox. The instantaneous fluxes yielded realistic diurnal cycles when used in the TransCom Continuous experiment.

Ocean exchange of CO2 in our system is computed using climatological air-sea differences in partial pressure of CO2 combined with 3-hourly wind speed and barometric pressure from the atmospheric transport model. Seawater pCO2 is provided by Takahashi et al. [2002]. Atmospheric pCO2 is, however, modulated by the surface barometric pressure in the atmospheric transport model following the formulation of Kettle and Merchant [2005]. The gas transfer coefficient (k) is parameterized following the quadratic wind speed formulation of Wanninkhof [1992] for instantaneous winds. Gas exchange is computed every 3 hours using the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast meteorology of the atmospheric transport model. The introduction of high resolution wind speed and pressure variability on the ocean fluxes follows the methods of Bates and 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 are taken into account. Air-sea transfer is inhibited by the presence of sea ice, and for this work fluxes are scaled by the daily sea ice fraction in each gridbox provided by the ECMWF forecast data.

Fossil fuel emissions are prescribed in the system and not optimized. Without detailed observations of the 14C content of CO2 in the atmosphere current atmospheric based estimates can not improve on bottom-up inventories. The current implementation 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 derived from the Blasing et al. [2005] inventory. Similarly, monthly fire emissions are


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prescribed to the atmosphere based on emission estimates from the Global Fire Emissions Database version 2 (GFED2).

During the assimilation, the difference between forecast and observed CO2 mole fractions drives changes to a set of linear scaling factors on the fluxes. The scaling factors are estimated for each week and assumed constant over this period. Each scaling factor is associated with a particular region of the global domain, and currently the geographical distribution of the regions is fixed. The choice of regions is a strong a priori constraint on the resulting fluxes and should be approached with care to avoid so-called "aggregation errors" [Kaminsky, 1999]. We chose an approach in which the ocean is divided up into 11 large basins encompassing large-scale ocean circulation features, as in the TransCom inversion study (e.g. Gurney et al., [2002]). The terrestrial biosphere is divided up according to ecosystem type as well as geographical location. Thereto, each of the 11 TransCom land regions contains a maximum of 19 ecosystem types derived from the Olson ecosystem database [Olson, 1992].

The final product from the assimilation has been assessed rigorously from a statistical point of view. Both parameter and observation residuals were found to be Gaussian and normally distributed relative to the prescribed errors.

Estimated fluxes for the period 2000-2005 indicate that the North American biosphere was a sink of carbon of close to 0.7 PgC/yr. This sink is geographically located in Central Canada and across the East coast of the United States. It is largest in areas dominated by forest ecosystems, although a substantial part of it occurs on grass and shrub lands that remain from agricultural practices. Strong uptake in croplands derived from the atmospheric data is somewhat misleading: the harvested goods are exported horizontally and consumed thereby releasing most of the 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 Canada that year. In contrast, 2004 was a relatively strong uptake year due to a relatively wet summer and longer growing season. Principal component analysis did not reveal a quantitative correlation between the derived fluxes and either temperature, rainfall, or drought indices. This lack of correlation between net-flux and climate variables was also seen in analyses of eddy-covariance observations of CO2 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 free troposphere with a large set of CO2 mole fractions from the NOAA ESRL Aircraft Sampling Program. The agreement with close to 14,000 independent samples reveals a small seasonal cycle in their difference which is generally much smaller (<0.5 ppm) than the monthly variability.

Current and future space based CO2 observations observe column abundances rather than surface or free tropospheric samples. Validation of for instance OCO therefore depends partly on upward looking spectrometers (FTS) such as placed at the WLEF site in Wisconsin. CarbonTracker compares favorably to the CO2 column


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observations from this location both on the seasonal cycle and synoptic variations (R2=0.93). A similar picture emerges when CarbonTracker is compared to partial column CO2 from 885 profiles across North America. Together, this suggests that CarbonTracker is a good extrapolation of sparse CO2 observations that can be used to check satellite retrieved CO2 across larger scales.

All CarbonTracker results, data, and extensive documentation is available at http://carbontracker.noaa.gov and can freely be used by other investigators. We plan to update CarbonTracker once per year to extend and expand the observations record and reflect the latest developments to our modeling framework.

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Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J., & Kasibhatla, P., Global estimation of burned area using MODIS active fire observations, Atmos. Chem. Phys., 6(4), 957-974, 2006.

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Reichstein, M., Papale, D., Valentini, R., Aubinet, M., Bernhofer, C., Knohl, A., et al., Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance 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 CO2 flux based on climatological surface ocean pCO2, and seasonal biological and temperature effects. Deep-Sea Research Part Ii-Topical Studies in Oceanography, 49 (9-10), 1601-1622, 2002.


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Van 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.

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Washenfelder, R. A., Toon, G. C., Blavier, J. F., Yang, Z., Allen, N. T., Wennberg, P. O., et al., 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 midlatitude drought 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 and hooked to the global 2-way nested TM5 transport model.

Figure 2: The five year average biological uptake pattern of CO2 derived with Carbon-Tracker.


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Figure 3: The distribution of observed-minus-modeled CO2 mole fraction for a large set of free tropospheric observations not used in the assimilation. The bars denote the average difference, while the whiskers reflect the standard deviation of the difference. The number above each bar shows the number of observations used in the average.

Figure 4: (left) Comparison of CarbonTracker against column CO2 observations from Park Falls, Wisconsin by Paul Wennberg and colleagues. (right) Similar comparison against 885 aircraft profiles reflecting partial CO2 columns up to ~8km.


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4 EU-level Reporting on Soures and Sinks to UNFCCC and Bottom-up Inventories


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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 estimates

Fuel 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%

EU reporting on sources and sinks

Erasmia Kitou

European Commission, DG Environment, Brussels, Belgium

Greenhouse gas (GHG) inventory

GHG inventory reports are prepared annually by all Member States (MS) and the European Community (EC). The EC inventory is compiled on the basis of the bottom-up inventories of the EC MS. The emissions of each source category are the sum of the emissions of the respective source and sink categories of the EC MS.

DG Environment is responsible for the inventory submission to the UNFCCC and the overall coordination. The EEA assisted by its Topic Center on Air Pollution and Climate Change, DG JRC and DG Eurostat are responsible for the compilation of the information at EC level. A comprehensive QA/QC process is in place to ensure that all parties involved review, assess and correct as necessary the inventory information.

GHG inventories capture primarily information on anthropogenic emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HfCs), perfluorocarbons (PFCs) and sulphur hexafluoride SF6 from 5 sectors: energy, industial processes, agriculture, forestry, waste for the year (X-2).

Key Category Analysis

The preparation of the inventory includes a key category analysis to determine for which categories it will be necessary to use higher Tier methods. A key source category is defined as an emission source that has a significant influence on a country’s GHG inventory in terms of the absolute level of emissions, the trend in emissions, or both. Depending on the tier, default, national or other emission factors based on modeling can be used.

Uncertainty Analysis

An uncertainty analysis is also required as part of the inventory preparation. The table below presents the uncertainties per level or trend at the EC level for the various sectors.


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Highest uncertainties both in terms of the level of emissions and the trends exist in the area of agriculture followed by waste.

Data Gaps

In terms of information provided by MS, we note that most gaps exist in relation to F-gases.

Fluorinated gases

Fluorinated gas emissions account for 1.6% of total EU-15 GHG emissions. HFCs from consumption of halocarbons showed large increases between 1990 and 2004. The main reason for this was the phase-out of ozone-depleting substances such as chlorofluorocarbons under the Montreal Protocol and the replacement of these substances with HFCs (mainly in refrigeration, air conditioning, foam production and as aerosol propellants). HFC emissions from production of halocarbons decreased substantially.

Energy sector

CRF Sector 1: ‘Energy’ contributes 80% to total GHG emissions and is the largest emitting sector in the EU-15. The most important energy-related gas is CO2 that makes up 78% of the total EU-15 GHG emissions while CH4 and N2O are each responsible for 1% of the total GHG emissions.

Reference Approach

The IPCC reference approach for CO2 from fossil fuels for the EU-15 is based on Eurostat energy data (NewCronos database). Energy statistics are submitted to Eurostat which is responsible for compiling the annual energy balances which are in turn used for the estimation of CO2 emissions from fossil fuels by MS and for the EU-15 as a whole. The Eurostat data for the EU-15 IPCC reference approach includes activity data, net calorific values and carbon emission factors as available in the Eurostat NewCronos database. For the calculation of CO2 emissions, the IPCC default carbon emission factors are used.


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Industrial Processes sector

CRF Sector 2 ‘Industrial processes’ is the third largest sector contributing 8% to total EU-15 GHG emissions. The most important GHGs from ‘Industrial processes’ are CO2 (5% of total GHG emissions), HFCs and N2O (1% each).

Uncertainties

The highest level uncertainty was estimated for CH4 from chemical industry and the lowest for CO2 from cement production. With regard to trend, SF6 from metal production shows the highest uncertainty estimates while CO2 from lime production the lowest. HFC emissions from the consumption of halocarbons have to be estimated based on leakage rates (obsolete equipment, use of equipment?). These assumptions are uncertain and real emissions may take place in different years than assumed.

Agriculture sector

CRF Sector 4 ‘Agriculture’ contributes 9% to total EU-15 GHG emissions, making it the second largest sector after ‘Energy’. The most important GHGs from ‘Agriculture’ are N2O and CH4 accounting for 5% and 4% of the total GHG emissions respectively.

Uncertainties

CH4 emissions from enteric fermentation is a key source category for cattle and sheep and are less uncertain (1%). Animal numbers are assumed to be correct with a maximum uncertainty of 10%, and also the emission factor known with a precision better than 20% for most countries, with 40% being the highest uncertainty estimate. N2O emissions from agricultural soils belong to the most uncertain source categories of national GHG inventories.

Table: Trend uncertainty for EU-15 emissions of N2O from agricultural soils by using different assumptions of correlation estimated using Monte Carlo simulation. “YES” denotes full correlation between years or Member States. Trend uncertainty is presented as percentage points.

For direct N2O 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 N2O emissions are estimated as up to 100% and 900% for the activity data and emission factor, respectively. Compared to these values, the


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sub-category of animal production is less uncertain, with a maximum uncertainty estimated (112%).

Waste sector

CRF Sector 6 ‘Waste’ is the fourth largest sector in the EU-15, contributing 2.7% to total GHG emissions. The highest level uncertainty was estimated for N2O from waste water handling.

Land-use, land-use change and forestry (LULUCF) sector

The 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, and

of latest information available.

It would be interesting to explore to what extent verification of greenhouse gas emissions provided by atmospheric observations could be combined with inverse modelling.

Non-CO2 emissions

Most non-CO2 emissions are due to: CH4 and NO2 deriving from wildfires - especially in the Mediterranean countries N2O from disturbances associated with land-use conversion to cropland

In most cases these emissions appear negligible in comparison to emissions / removals of CO2.

Uncertainties

The 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 functions

and 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).


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Difficult issues

Particularly problematic for the LULUCF sector is land identification and tracking land transitions over time for all activities such as afforestation and deforestation, forest- cropland-, grassland-management and revegetation. Also reporting of soils, especially organic soils, as well as reporting on forest fires for Mediterranean countries with regards to assessment of destroyed areas and the future of burned land.

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?


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European greenhouse gas emissions

François Dejean

European Environment Agency, Climate change and energy, Copenhagen, Denmark

1. The EEA and its role in GHG inventories

The EEA aims to support sustainable development and to help achieve significant and measurable improvement in Europe’s environment, through the provision of timely, targeted, relevant and reliable information to policy-making agents and the public. It is an EU body and has 32 member countries. The EEA coordinates the European environment information and observation network (Eionet), which includes more than 300 institutions, and delivers a wide range of information and assessments of: • The state of the environment and trends • Pressures on the environment and the driving forces behind them • Policies and their effectiveness • Outlooks/scenarios

In relation to air and climate change issues, the EEA delivers several reports, indicators, assessments and data, which are regularly updated and publicly available on the EEA website (www.eea.europa.eu). For example: EEA reports and technical reports:o Annual European Community greenhouse gas inventory 1990–2004 and

inventory 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 Indicators

The EEA and JRC assist the European Commission for the submission of the annual EC GHG inventory to the UNFCCC: • Preparation of initial checks of Member States‘ submissions in cooperation

with Eurostat and the JRC, and circulation of the results from initial checks (status reports, consistency and completeness reports)

• Consultation with Member States in order to clarify data and other information provided

• Preparation and circulation of the draft EC inventory and inventory reportbased on Member States' submissions (compilation work performed by ETC/ACC at UBA Vienna).


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• Preparation of the final EC inventory and inventory report (to be submitted by the Commission to the UNFCCC Secretariat)

• Assisting Member States in their reporting of GHG inventories by means of supplying software tools

• Maintenance of the inventory database and of inventory archives • Implementation of QA/QC procedures for the EC inventory as outlined in the EC

QA/QC programme.

2. EU bottom-up inventories

GHG emissions inventories for EU-27 and EU-15 are compiled annually, based on Member States bottom-up national inventories submitted to the UNFCCC. GHG inventories are established on an annual basis, by gas and by source and submitted according to a common reporting format (CRF).

The reported gases for each individual source are: CO2, CH4, N2O, SF6, HFCs, PFCs. The main source categories are:

Categories Sub-categories 1. Energy • Energy industries

• Manufacturing industries and construction

• Transport • Other sectors • Other • Fugitive emissions

2. Industrial processes • Mineral products • Chemical industry • Metal production • Production of halocarbons and SF6 • Consumption of halocarbons and SF6

3. Solvent and other product use

4. Agriculture • Enteric fermentation • Manure management • Rice cultivation • Agricultural soils

5. LULUCF • Forest land • Cropland • Grassland

6. Waste • Solid waste disposal on land • Wastewater handling • Waste incineration • Other

7. Other

Emissions from international bunker fuels (aviation and marine) are not included in these inventories.


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Guidance on reporting methodologies is provided in 1996 IPCC Guidelines for National Greenhouse Gas Inventories, which comprise: - the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, - Good Practice Guidance and Uncertainty Management in National Greenhouse

Gas Inventories, - Good Practice Guidance for Land Use, Land-Use Change and Forestry.

3. Sectoral trends in European greenhouse emissions as estimated in bottom-up inventories and submitted to the UNFCCC

At the EU-15 level, energy-related GHG emissions represent 80% of total anthropogenic emissions. Energy-related emissions include emissions from transport. 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.

- CO2 emissions from transport increased due to road transport growth. - N2O emissions from transport increased by more than 100 %, due to a

standardized use of catalytic converters, which produce N2O as a by-product.


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0

50

100

150

200

250

300

350

400

450

500

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

kt C

O2

eq

10 -̂1*CO2 emissions (without LULUCF) CH4 N2O HFCs 10*PFCs 10*SF6

- CO2 emissions from energy industries increased due to fossil fuel consumption in public electricity and heat plants.

In the EU-10, GHG emissions decreased between 1990 and 2004 in all the main sectors responsible for greenhouse gas emissions, except in the transport sector (+29%).

4. Trends by gas

Between 1990 and 2004, in the EU-15, the trends of GHG emissions by gas are as follows:- CO2 emissions increased by 4.4 %, - CH4 emissions decreased by 26 %, mainly due to the decline of coal mining

(fugitive emissions), reductions in solid waste disposal on land (waste sector) and falling cattle population (agriculture)

- N2O emissions decreased by 18%, mainly due to specific measures at adipic acid production plants (UK, Germany and France) (industrial processes) and the decline in fertilizer and manure use (agricultural soils),

- HFC, PFC and SF6 from industrial processes decreased overall, although this reflects large increases (expanding use of HFCs as a substitute for ozone depleting CFCs), offset by decreases of emissions from the production of halocarbons and SF6.

Emission trends by gas in the EU-15 between 1990 and 2004:


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For EU-10 countries, CO2 and CH4 emissions decreased significantly between 1990 and 2004 (-33% and -40%, respectively). N2O emissions were reduced by 15% while fluorinated gases emissions increased by as much as 147% (some countries still do not report F-gases).

5. Data quality and uncertainty issues

There are currently several review processes existing for GHG bottom-up inventories:

• In-country QA/QC reviews of inventories before these are actually submitted to the European Commission (DG Environment),

• Quality control of MS submissions (completeness, consistency, sector-specific checks) 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 formed

by reviewers of other countries.

In addition, Member States are performing uncertainty analyses on their own reported 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 on these estimates, the levels of uncertainty for the main GHG sources can be roughly represented as follows:

LULUCFCO2

Agriculture 41%-104%Higher uncertainty from N2OCH4 also uncertain

Waste 18%N2O and CH4

Fugitive emissions 11%CH4 and CO2

Industrial processes 8%F-gases mostly uncertain

Transport 3%Fuel combustion stationary 2%

2005 estim

atesU

nce

rtai

nty

lev

el

6. Relevancy of AM/IM studies in the context of bottom-up GHG inventories to the UNFCCC

Based on currently available results from AM/IM studies, it appears that top-down estimates 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:


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- for which anthropogenic and natural emissions can be easily be separated (in particular fluorinated gases, N2O and CH4),

- that correspond to emissions from CRF sectors where data uncertainty is currently estimated to be relatively high (LULUCF, agriculture, waste, F-gases from consumption of halocarbons).

The relevant time scale for providing top-down estimates of GHG emissions is annual (or multi-annual), since GHG inventories are submitted every year to the UNFCCC. (Parties to the Convention are not required to calculate their emissions on a more frequent basis).

The relevant geographical scales for providing top-down estimates of GHG emissions are: o country level, o EU-15 and EU-27 level.

EEA contacts • Andreas Barkman ([email protected], +45 33 36 72 19) • François Dejean ([email protected], +45 33 36 72 59)


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EDGAR and UNFCCC greenhouse gas datasets: comparisons as indicator of accuracy

Jos G.J. Olivier1 and John A. van Aardenne2

[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 global anthropogenic emissions inventories of various trace gases. Over the years several datasets have been released that have been used as a priori information by the inverse modeling community. The current version EDGARv32 provides emissions for the years 1990, 1995 and 2000. In EDGAR, emissions are calculated using an emission factor approach. Information on activity data, emission factor and other explanatory variables are organized by source category, country, and region or as grid maps. In general emissions are first calculated on a country basis by multiplying activity levels by compound specific emission factors (see equation 1). In addition, thematic maps on a 1o x 1o grid are used by relating a specific grid map to each emission process defined using a spatial allocation function to convert total country emissions to grid emissions per process involved. Temporal resolution of emissions within a year is not included explicitly, although - based on European and North American studies - monthly, weekly and diurnal scale factors are proposed in the EDGAR documentation.

i: compound j: country k: sector l: process by fuel/technology m: abatement technology AC: activity data EF: emission factor (no explicit abatement technology specified but application of technology included in emission factor for each year) t: time (year)

Currently in a collaboration of the DG Joint Research Centre and the Dutch Environmental Assessment Agency, a new database structure is under development in which a technology based approach and gridded emissions to 0.1 x 0.1 degree are defined. The release of this EDGARv4 is foreseen in 2007.


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Uncertainty in emissions inventory calculations.

In EDGARv32 most data on emission activities (fuel consumption, industrial production etc.) is taken from international statistics such IEA Energy balances and UN industrial commodity and statistics. In National Communications this information is in general from national statistical offices. Although quality control is a standard practice in these datasets, uncertainty in these datasets is larger for non-OECD countries, especially when countries face a change in government structure over time with the Former Soviet Union as an illustrative example. Emission factor data in greenhouse gas emission inventories are often combination of data from IPCC emissions guidelines and scientific publications. In general, uncertainty in these emission factors exist due to for example applicability of default emission factors and lack of information on installed abatement technology. Although not applied in National Communications, allocation of national emissions to grid as done in EDGAR is another source of uncertainty. Issue of concern here are the applicability of the selected grid map to distribute emissions of a specific emissions activity. For some world regions locations of large industrial facilities are known, while in regions where this information is missing, population density is used as proxy to grid these emissions. Also the temporal variation of the emission inventories is further source of uncertainty. Annual emissions have to be allocated to monthly, weekly or sometimes daily variations in the atmospheric dispersion models. Often data to allocate these emissions to a time pattern is not available on the global scale (e.g. monthly electricity production).

Assessment of uncertainty

Although several methodologies have been mentioned in the literature to assess uncertainty/accuracy of emission inventories, due to time and data limitations most inventory activities limit themselves to expert judgment, and for some sectors to statistical uncertainty assessments (e.g. Monte Carlo approaches).

Here three methods to estimate uncertainty in national and global emission inventories are presented: (1) expert estimation of uncertainty in emission factors, activity data and combined regional and global budgets, (2) changes in subsequent emission reports as indicator of uncertainty, (3) difference between independent emission inventories as illustration.

(1) Based on expert estimation, uncertainty estimates have been provided for the EDGAR datasets. Table 1 present the results for CO2, CH4 and N2O emissions. The overall uncertainty of global and regional emission budgets as calculated in EDGAR is ~10% for CO2, ~50% for CH4, and ~100% for N2O.


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Main source Sub-category Activity Emission factors Global and regional emissions data CO2 CH4 N2O CO2 CH4 N2O

Fossil 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 L

Biomass 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 - - - - - -

CO2 CH4 N2O CO2 CH4 N2OAll sources - - - - S M LNotes: Expert judgement of uncertainty ranges, which were assigned with the following classification in terms of order of magnitude 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.

UNFCCC CH4 total: changes in subsequent 2001 emissions

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

Australi

a

Austria

Belgium

Canad

a

Denmark

Europ

ean C

ommun

ity

Finlan

d

France

German

y

Greece

Irelan

dIta

lyJa

pan

Netherl

ands

New Zea

land

Portug

alSp

ain

Swed

en

Switz

erlan

d

United King

dom

United Sta

tes of

Ameri

ca

LY05/04

LY06/05

LY06/04

Table 1: Uncertainty estimates of EDGARv32 greenhouse gas calculations.

(2) Another approach to be used as indicator for uncertainty in emission inventories is the changes in subsequent national emission reports to the UNFCCC. As illustrated in Figure 2, the changes between following emission reports can be up to 50%.

Figure 2: Change in CH4 emissions are reported in subsequent years.


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CH4 total (2000)

0500

10001500200025003000350040004500

Austria

Belgium

1)

Denmark

Finlan

d

France

Germany

Greece

Irelan

dIta

ly

Luxem

bourg

Netherl

ands

Portu

gal

Spain

Swed

en

United

Kingdo

m

EU-15 *

1/10

USA *

1/10

Gg

CH

4

EDGARUNFCCC

USAUKDEUFRA

(3) By comparing EDGAR CH4 estimates with CH4 calculations in National Communications an indication of the total uncertainty and the uncertainty by sectors can be analyzed. For example, Figure 3 presents the difference between EDGAR year 2000 emissions and national calculations. In the case of France and United Kingdom these differences are large. The difference for the United Kingdom seems to be related to the waste sector in which EDGAR calculates about 400 Gg, while according to UK calculations the total should amount up to 1500 Gg. Further analysis shows that probably this discrepancy is caused by the uncertainty in landfilled waste data with as key parameters the municipal solid waste generated by capita, the fraction of waste put into landfills and the amount of landfill gas recovered.

Figure 3: Difference between UNFCCC reported CH4 emissions and EDGARv32 data for the year 2000.

As shown in the illustrations above and in more detail during the presentation of this workshop, a first indication of the uncertainty of emission inventories can be provided through comparison of independent emission inventory data such as the EDGAR data and National Communications. This type of assessment allows focusing the discussion to specific sectors where the differences are large. This provides information on where the inventory improvement should be focused and it provides inverse 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 improve to facilitate correct application in the inverse models. This means that besides the accuracy of the emissions budget, the temporal and spatial patterns of inventories should be accurate.


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Agriculture, Forestry and Other Land Uses (AFOLU): Realities and needs for Kyoto reporting

Günther Seufert

European Commission, DG Joint Research Center, Ispra, Italy.

Selected JRC activities to improve AFOLU-reporting in the context of the EC Inventory system

The European Community (EC), as a party to the United Nations Framework Convention on Climate Change (UNFCCC), reports annually on greenhouse gas (GHG) inventories within the area covered by its Member States. The legal basis of the compilation of the EC inventory is Council Decision No 280/2004/EC concerning a mechanism for monitoring Community greenhouse gas emissions and for implementing 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 LULUCF sectors. It does so by intercomparing methodologies used by the MS for estimating emissions and

removals, by leading projects for improving/harmonising the methodologies used for

estimating GHG emissions and sinks, by providing EU-wide estimates with various models/methods for emissions and

removals with a focus on Agriculture and LULUCF (including inverse modelling using ambient air measurement of GHGs).

Figure 1: EC Inventory Compilation


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Figure 1 shows the compilation part of the inventory system of the European Community. The DG Environment of the European Commission is responsible for preparing the inventory of the European Community (EC) while each Member State is responsible for the preparation of its own inventory which is the basic input for the inventory of the European Community. DG Environment is supported in the establishment of the inventory by the following main institutions: the European Environment Agency (EEA) and its European Topic Centre on Air and Climate Change (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 (see http://ies.jrc.cec.eu.int/ghgdata.html) in FP6 (2002-2006, GHG DATA, Data Quality System for GreenHouse Gas Emissions and Sinks) and in FP7 (2007-2013, GHG AFOLU, 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 and LULUCF, and defending the EC-NIR during the annual NFCCC review

EC experts sent to UNFCCC roster for reviewing the NIR of other parties Expert input to writing and reviewing IPCC Guidelines 2006 (for the sector

Agriculture, 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 (see

http://afoludata.jrc.it/)

In order to harmonise and to improve monitoring and reporting of GHG emissions and sinks in the sectors agriculture and LULUCF, the project organised as series of workshops 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 of 6 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 from agriculture in Central and Eastern Europe, Ispra, 23 - 24.6.2005

Improving the Quality of Community GHG Inventories and Projections for the LUCF Sector, Ispra, 22.- 23.9.2005

Improving the Quality of Community GHG Inventories and Projections for the LUCF Sector, Ispra, 22.- 23.9.2005, with sink experts from 21 EU-Member States


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Technical meeting on specific forestry issues related to reporting and accounting under the Kyoto Protocol (Ispra, 27.- 29.11.2006, with sink experts from 26 Kyoto Parties)

Workshop on “Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories“ (Ispra, March 8-9, 2007)

Perspectives of AFOLU reporting under the Kyoto Protocol

Annex I Parties to the Protocol had to select, by end of 2006, any or all of the following human-induced activities under Article 3.4: revegetation, forest management, 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 LULUCF

Table1: EU-15 Member State’s selections of threshold values for the forest definition for reporting under Article 3.3

Table 2: New Member State’s selections of threshold values for the forest definition for reporting under Article 3.3


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Perspectives 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 of

gaps for the LULUCF sector any gap, i.e. lack of transparency, in the Kyoto supplementary information may

trigger the exclusions from flexible mechanism and the adjustment process Annual reporting is done with the help of CRF tables (Common Reporting

Format), requesting carbon pool changes in the 5 compartments aboveground biomass, belowground biomass, litter, dead wood, and soil. The summary table shown in Fig. 2 has a total 19 tables behind, with data to be submitted by "geographical locations" and by afforestation/reforestation, deforestation, and, if elected, forest management, cropland management, grazing land management, or revegetation.

Figure 2: CRF tables (Common Reporting Format)

Table 3: EU-15 Member State’s elections of activities under Article 3.4

Table 4: New Member State’s elections of activities under Article 3.4


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Selected essentials of the terrestrial carbon cycle:

Biological sinks are relevant, are visible in the atmosphere, and must be considered properly to safeguard the environmental integrity of the KP

The atmosphere does not see stocks but fluxes; this is most relevant when ecosytems are disturbed or not in equilibrium. “Hidden” fluxes like heterotrophic respiration or trade offs with other GHGs may introduce bias due to non-reporting

Present sinks in temperate regions result from temporary changes in land use – a feature having serious implications for permanency and additionality

Sink saturation is biome- and land use specific, the main reservoir is always in the soil; therefore, reporting sinks from land use changes without data on soils is questionable

Any soil carbon stock change is relevant, but will only be visible with very good data, which are not available in almost all cases

Any terrestrial sink may easily turn into a source, e.g., estimated 0.5 Gt of C released in Europe in heatwave 2003 compared to 2002

Conclusions

Emissions and sinks of greenhosue gases in the sector AFOLU are different from one-directional emissions in the other sectors. Biological sinks are part of the natural carbon cycle with short-term and long-term components. Monitoring emissions and sinks from land use change requires data on stock changes in all ecosystem compartments, including belowground, which are simply not available. Considering that reporting principles like “as far as practicable” or “as far as data are availabile” are prevailing in UNFCCC/IPCC reporting rulebooks, especially for the sector AFOLU, considering further that the definition of “direct human induced” is highly voluntary, one may easily conclude that we will miss key drivers and key numbers of the European terrestrial carbon budget.


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5 European and International GHG Monitoring Programs


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The AGAGE network for ground based measurements of non-CO2GHGs: Monitoring of atmospheric concentrations and emission estimates

Derek Cunnold

School of Earth and Atmospheric Sciences, Georgia Tech, USA

The Advanced Global Atmospheric Gases Experiment (AGAGE) program consists of continuous ground-based measurements of mostly long-lived atmospheric gases at 5 remote sites around the world at approximately hourly intervals. Each measurement is calibrated against on site standards which trace back to primary standards at Scripps Institution of Oceanography. The measured gases are mostly halogens which play a role in stratospheric ozone destruction and/or those which contribute to radiative heating; methane and nitrous oxide are also being measured. The sites are located at Mace Head, Ireland (53oN, 10oW) (previously at Adrigole, Ireland, 52oN, 10oW), Trinidad Head, California (41oN, 124oW) (previously at Cape Meares, Oregon, 45oN, 124oW), Ragged Point, Barbados (13oN, 59oW), Cape Matatula, American Samoa (14oS, 171oW), and at Cape Grim, Tasmania (41oS, 145oE). The reader is referred 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 gas chromatographs mostly with electron capture detectors and more recently with mass spectrometer detection systems. The early measurements consisted of CFCs, methyl chloroform, carbon tetrachloride and nitrous oxide and then methane. More recently HCFCs, HFCs, and halons and some shorter lived halocarbons have been measured. The measurements are available from http://cdiac.esd.ornl.gov/ndps/alegage.html or through links from the AGAGE web site. From the beginning of the ALE/GAGE/AGAGE program samples of baseline air from Cape Grim were taken several times per year and they have been archived in Australia. Analyses of these samples have enabled continuation of the time series back to 1978 for gases that have been stable in the archive tanks. The ALE/GAGE/AGAGE time series have resulted in globally average estimated radiative forcing rates (Figure 1).

The emphasis of the AGAGE program has been on estimating the global emissions of industrially produced gases as a function of time. For gases whose lifetimes are longer than about a year the five globally distributed AGAGE measurement sites are typically adequate, and annual emissions have been estimated by an inverse procedure using a 12 box model (four boxes in the lower troposphere, four in the upper troposphere, and four in the stratosphere, see for example Cunnold et al.,[1997]). More recently emissions on roughly the continental scale have been made using the MATCH model (e.g. [Chen and Prinn, 2006]). Calculations with the MATCH model have included measurements from other measurement networks (eg NOAA/ESRL and SOGE). It is to be noted that the SOGE program, as well as


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measurement at several sites in Asia, are affiliated with AGAGE with common absolute standards being used for all the measurements.

Figure 2 shows a typical time series of AGAGE measurements (for HFC-134a, a replacement refrigerant) at Mace Head. The red dots indicate measurements that have been impacted by the regional emissions from the UK and Europe. Black dots indicate baseline measurements. The classification into black and red dots for all the measured gases have been performed statistically using measurements of several species simultaneously. Back trajectory calculations have shown no statistically significant differences versus the statistical classification method. The measurements of regional pollution effects have been used to estimate emissions from the UK and Europe both by inverse methods (e.g. [Manning et al., 2003]) and by correlations with simultaneously measured gases (typically CO) for which the emission distributions are relatively well known (e.g. [Reimann et al., 2005]). The baseline values are typically used to provide the global and continental scale emission estimates.

An important use of the long AGAGE time series of methyl chloroform measurements has been to provide a time series of annual global mean estimates of OH values in the troposphere (e.g. [Prinn et al., 2005]). This series indicates approximately 5% lower than average OH values in 1997-1999. The estimation procedure has relatively small uncertainties because only a few companies produced methyl chloroform and almost 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 determined from AGAGE measurements (as well as from NOAA/ESRL measurements) are shown in Clerbaux and Cunnold (Chapter 1 of WMO 2006). Examples include estimates for the CFCs and carbon tetrachloride (WMO Figure 1-20) and for the HCFCs (WMO Figure 1-21). The uncertainties in the estimates are typically dominated by atmospheric lifetime uncertainties (particularly for carbon tetrachloride), but precisions of the measurements and modeling uncertainties make significant contributions to the uncertainties in the individual non-smoothed annual emission estimates. Difficulties in getting bottom up estimates of all these industrially produced gases 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 worldwide production of these gases but more especially because the varied uses of the gases make it difficult to quantify the rates at which the gases reach the atmosphere following their production.

Atmospheric emissions on the continental scale, or more precisely a limited number of independent pieces of information (e.g. ten) on the emissions, have been produced by inverse methods using the global Model for Atmospheric Transport and Chemistry (MATCH) model [Rasch et al., 1997] in which the global chemistry of Kuhlmann et al. [2003] was included. Chen and Prinn (2005) showed that methane calculations with the MATCH model using National Center for Environmental Prediction (NCEP) winds produced good simulations of the observed transition in methane mole fractions at the Samoa site from the El Nino period in 1997/1998 to the La Nina period in 1998/1999. Equally good simulations of methane changes resulting from North Atlantic Oscillation (NAO) effects at Mace Head, Ireland were produced for 1996 (air coming from Europe) versus 2000 (air coming from the clean


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Atlantic Ocean sector). The spatial resolution of the model for these calculations was approximately 2o latitude by 2o longitude and the model produced good simulations of the amplitudes and frequencies of regional pollution events and the seasonal cycles at many of the worldwide methane high frequency measurement sites (including Mace Head). Poorer simulations occurred at sites which were located close to strongly emitting regions. These results suggest that several years of measurements may be needed to characterize emissions from a region and meteorologically realistic models should be used for inverting the measurements.

MATCH model calculations are also being made for N2O (J. Huang, private communication, 2007). Prinn et al. [1990] had previously performed calculations with the 12 box model and some of the conclusions from that study are similar. They noted that as a result of the long atmospheric lifetime of N2O (approximately 135 years) and the resulting small observed differences, approximately 0.7 ppb, between the mole fractions in the two hemispheres, inferences about the spatial distribution of the emissions (equivalently the subdivision into various emission categories) were sensitive to the transport rate between the troposphere and the stratosphere. This exchange is typically not well simulated by models. Because of the small differences in baseline values between the various sites, calibration differences between various networks and/or observers need to be accurately known (e.g. to 0.1 ppb). Fortunately AGAGE and NOAA/ESRL have both been making high frequency measurements of many gases at the Samoa site for many years and the difference between the two sets of measurements of N2O from 1999 to 2006 is 0.2 ppb, based on the NOAA 2000 and the AGAGE SIO 1998 calibration scales.

Recent results from inverse modeling calculations using AGAGE measurements of N2O, methane [Chen and Prinn, 2006] and methyl chloride [Yoshida et al., 2006], all of which have large natural sources, all indicate increased tropical sources versus the a priori estimates. However additional observation sites in the tropics are needed in order to better evaluate respectively the oceanic, biomass burning and tropical wetland sources of these emissions. High frequency measurements are particularly useful for evaluating regional or smaller scale emissions. However, to fully utilize and interpret such measurements, more high frequency observation sites in strongly emitting regions are desirable. Moreover models with improved sub grid scale representations of planetary boundary layer thicknesses and of vertical mixing are needed to interpret the measurements, and measurements of vertical profiles of the gases would be useful for testing the model simulations.

References

Chen, Y.-H., and R.G. Prinn, Estimation of atmospheric methane emissions between 1996 and 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 methane observations: 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, in Scientific Assessment of Ozone Depletion: 2006, World Meteorological Organization, Global Ozone Research and Monitoring Project – report 50, Geneva, Switzerland, 2006.


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Cunnold, 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 global emissions of CCl3F and CCl2F2 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 tropospheric ozone 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, Estimating European emissions of ozone-depleting and greenhouse gases using observations and a modeling back-attribution technique, J. Geophys. Res., 108 (D14), 4405, 2003.

Prinn R. G., et al., Evidence for variability of atmospheric hydroxyl radicals over the past quarter 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 from ALE/GAGE/AGAGE, J. Geophys. Res., 105, 17751-17792, 2000.

Prinn, R., D. Cunnold, R. Rasmussen, P. Simmonds, F. Alyea, A. Crawford, P. Fraser, and R. Rosen, Atmospheric emissions and trends of nitrous oxide deduced from 10 years of ALE-GAGE data, J. Geophys. Res., 95, 18369-18471, 1990.

Rasch, P.J., N.M. Mahowald, and B.E. Eaton, Representations of transport, convection, and the hydrologic cycle in chemical transport models: Implications for the modeling of short-lived and 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 long-term measurements, Nature, 433, 506-509, 2005.

Yoshida Y., Y. Wang, C. Shim, D. Cunnold, D. R. Blake, G. S. Dutton, Inverse modeling of the 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 the ALE/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 by NOAA and others. CFC-114, CFC-115, CH3Cl, CH2Cl2, CHCl3 and CCl2CCl2 have only been measured by AGAGE since 1998; consistent with Clerbaux and Cunnold [2006], constant values have been assumed over the entire 1979-2006 period for these gases in this figure.


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Figure 2: Time series of the individual measurements of HFC-134a (CH2FCF3) by AGAGE GC-MS instruments at Mace Head, Ireland. The measured values have been separated into baseline values (black dots) and values influenced by regional pollution (red dots) using the AGAGE statistically based algorithm.


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The WMO GAW Global GHG Programme

Len Barrie

Atmospheric Research and Environment Programme, World Meteorological Organization, Geneva, Switzerland

1. Introduction

The Global Atmosphere Watch (GAW) Programme of the World Meteorological Organization (WMO) was established in 1989. It is focused upon the role of atmospheric chemistry in global change [GAW Strategic Plan: 2008-2015, 2007].Consisting of a partnership of managers, scientists and technical expertise from 80 countries, GAW is coordinated by the WMO Secretariat in Geneva and the Open Programme Area Group on Environmental Pollution and Atmospheric Chemistry (OPAG-EPAC) of the WMO Commission for Atmospheric Sciences (CAS). The international greenhouse gas measurement community that gather every two years at meetings co-sponsored by WMO and IAEA are involved in nationally funded measurement programmes that constitute the global long term greenhouse monitoring network supported by GAW. The first meeting of this group, held in 1975 at Scripps Institute of Oceanography, was co-sponsored by WMO (Figure 1). It was a milestone 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 the 13th meeting shows how much our community has grown. Note that two members are in both pictures: Dr. David Lowe of New Zealand and Professor C.S. Wong of Canada.

Figure 1: The 1st WMO sponsored CO2 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 left to right Graeme Pearman (CSIRO, Australia), Michel Benarie (IRCHA, France), Lester Machta (NOAA), Charles (Dave) Keeling (Scripps) and G. Kronebach of WMO Secretariat, Geneva (photo supplied courtesy of P. Tans).


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The WMO/GAW office and leaders of its Scientific Advisory Groups (SAGs) have been actively involved in supporting the United Nations Framework Convention on Climate Change (UNFCCC) through contributions to the Strategic Implementation Plan of the Second Report on the Adequacy of the Global Observing Systems for Climate by the Global Climate Observing Strategy (GCOS). This plan is officially accepted by the Parties to the Convention. Essential Climate Variables (ECVs) that need to be systematically measured globally in order to address major issues are officially recognized. Greenhouse gases, ozone and aerosols are amongst those ECVs and GAW is designated as the lead international programme in furthering the observational requirements. In October 2005, the steering committee of the Global Climate Observing System (GCOS) which is co-sponsored by WMO approved the GCOS-GAW Agreement establishing the “WMO-GAW Global Atmospheric CO2 & CH4 Monitoring Network” as a comprehensive network of GCOS.

The focus, goals and structure of GAW are outlined in detail in the Strategic Implementation Plan 2008-2011 [GAW Report 172, 2007]. Recognizing the need to bring scientific data and information to bear in the formulation of national and international policy, the GAW mission is to:

Reduce environmental risks to society and meet the requirements of environmental 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 chemical composition 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 experts representing carbon cycle research and measurements that meet every two years with the last meeting the 13th marking the thirtieth anniversary in global coordination of carbon dioxide measurements [GAW Report 168, 2005]. Associated with this is the meeting of the GAW Scientific Advisory Group for Greenhouse Gases (SAG-GG) chaired by Dr. Ed Dlugokencky. The 14th CO2 Experts Meeting is hosted by the Finnish Meteorological Institute Helsinki, Finland September 2007 (see the GAW website).

2. GAW Monitoring

Global GAW networks focus on six measurement groups: greenhouse gases, UV radiation, ozone, aerosols, major reactive gases (CO, VOCs, NOy and SO2), and precipitation chemistry. The GAW Station Information System (GAWSIS) was developed and is maintained by the Swiss GAW programme. It is the host of all GAW metadata on observatory managers, location and measurement activities. According to GAWSIS there are 24 Global, 640 Regional and 73 Contributing stations which are operating or have submitted data to a GAW World Data Centre. GAW Scientific


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SCIENTIFIC ADVISORY GROUP(SAG) for GHGs

(Expert C Measurement Community)

QA & CALIBRATION CENTRES:NOAA/CMDL, MeteoSwiss/EMPA,

Japan Met. Agency(JMA)

CENTRAL CALIBRATION. LABORATORY (CCL)

World Reference Standard US- NOAA

GAW STATIONS & GAWSISGlobal Regional

GAW WORLD DATA CENTRE for GREENHOUSE GASES:(WDCGG)

Analysis

TwinningWorkshops

Calibration, Training Site Visits, Comparisons

SynthesisIGACO

ContributingNetworks

Satellite & AircraftObservations

CAS/WG forEnvironmental Pollution

And Atmospheric Chemistry

WMO/GAW Secretariat

AREP

BIPM/CCQM

Advisory Groups (SAGs) for each of the six measurement groups establish measurement standards and requirements while calibration and quality assurance facilities ensure valid observations. Five GAW World Data Centres collect, document and archive data and quality assurance information and make them freely available to the scientific community for analysis and assessments. Note the linkages of GAW to contributing partner networks and to aircraft and satellite observations that contribute 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 chemistry monitoring networks has resulted in major advances. Figure 2 shows the components diagram of the “WMO-GAW Global Atmospheric CO2 & CH4 Monitoring Network”.

Figure 2: Components of the “WMO-GAW Global Atmospheric CO2 & CH4 Monitoring Network”, a comprehensive network of GCOS.

There are GAW Global, Regional and Contributing stations that support the monitoring of GAW target variables in each of the six groups. Global and Regional stations are operated by a WMO Member and are defined by Technical Regulations adopted by the WMO Executive Council in 1992 [EC XLIV, 1992] as well as the GAW Strategic Implementation Plan [GAW Strategic Plan: 2008-2015, 2007]. Contributing stations are those that conform to GAW measurement guidelines, quality assurance standards and submit data to GAW data centres. They are mostly in partner networks that fill major gaps in the global monitoring network. The difference between a Global and a Regional GAW station lies in the facilities available for long term measurements, the number of GAW target variables measured, the scientific activity at the site and the commitment of the host country. The location of the 24 GAW Global stations is shown in Figure 3a.

To monitor global distributions and trends of particular variables with sufficient resolution to answer outstanding gaps in understanding of environmental issues related to global warming due to greenhouse gases requires not only Global but also Regional and Contributing stations. The GAW global network for surface based carbon dioxide observations is shown in Figure 3b. This differs from the global map of all stations at which carbon dioxide measurements and research are performed in


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Figure 3a: Global observatories in the GAW network.

Figure 3b: The WMO-GAW Global Atmospheric CO2 & CH4 Monitoring Network a comprehensive network of the Global Climate Observing

WORLD METEOROLOGICAL ORGANIZATIONGLOBAL ATMOSPHERE WATCH GLOBAL NETWORK

40

0

South Pole

Point Barrow

Mauna Loa

Alert

Pallas-Sodankylä

MinamitorishimaKenya

Assekrem -Tamanrasset

Arembepe

Ushuaia

Izana

Amsterdam IslandCape Grim

Cape Point

Samoa

Ny Ålesund

Lauder

Mace Head

40

80

40

0

40

80

160 80 0 80 160

May 2006

Zugspitze-Hohenpeissenberg

Mt Waliguan

Neumayer Station

Bukit Koto Tabang

Jungfraujoch

Danum Valley

A Hierarchy OfStrategies, System s, Program m es, Netw orks,

Related To System atic Atm ospheric CO2 Observations

GEOSS IGOS

IGCOIGACO

GCOS W CRPW MOUN-FRAM EW O RK

CONVENTION ON CLIM ATE CHANGE

CEOS

W MO-GAWLight

A ircraft

Com m ercial A ircraft

Satellites

Surface Flask

Surface Continuous

Routine Ocean PCO2 Msm ts

IGACO-GHG

CLIM ATE RESEARCH COM M UNITY

that it represented stations operating routinely that link their observations through the WMO reference scale maintained at NOAA GMD in Boulder and that submit their data to the GAW World Data Centre for Greenhouse Gases. In future, many more stations will hopefully be added to fill gaps in Asia, Africa and South America. Also, aircraft and satellite observations will be added as integrated global carbon atmospheric observation system as outlined in the IGACO [2004] report.

Where do carbon research and systematic observation programmes fit amongst the many projects, programmes, strategies and systems involved in support global carbon 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 on global change. One way of viewing the hierarchy of programmes and their connection to each other and to major users of the outcome of systematic observations research is shown in Figure 4.

Figure 4: The hierarchy of international activities related to promoting, organizing and conducting systematic atmospheric observations of carbon dioxide and other greenhouse gases. The foundation for this system are networks and facilities operated by leading countries in the field in cooperation with many other countries. The leaders include US/NOAA, Australia, Canada, China, France, Finland, Germany, Japan and Switzerland.


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WMOGreenhouse Gas Bulletin

The State of Greenhouse Gases in the AtmosphereUsing Global Observations up to December 2004

Executive summaryThe latest analysis of data from the WMO-GAW Global Greenhouse Gas Monitoring Network shows that the globally averaged atmospheric carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) have all reached new highs in 2004 with CO2 at 377.1 ppm, CH4 at 1783 ppb, and N2O at 318.6 ppb. These value are higher than those in pre-industrial times by 35%, 155%, and 18% respectively. Atmospheric growth rates of these gases are consistent with previous years, though CH4 growth has slowed during the past decade. The NOAA Annual Greenhouse Gas Index (AGGI) shows that from 1990 to 2004 the total atmosphericradiative forcing by all long-lived greenhouse gases has increased by 20%.

Global Atmosphere Watch

WMO

No. 1

The bottom-up programme starts with national efforts with coordination through GAW and linkage to the UNFCC through GCOS, a WMO co-sponsored climate observation system. In turn, it links to the satellite community through CEOS and the informal Integrated Global Observing Strategy that spawn the IGACO and IGCO strategy reports.

3. Users and Products

The WMO-GAW Global Atmospheric CO2 & CH4 Monitoring Network is a globally coordinated effort that relies upon the bottom-up activities of major national and regional research centres around the world. Mergeability of data is ensured through linking 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 Reference Standard Scale for CO2 and CH4 maintained by NOAA-ESRL, USA. Exchange of data takes place through the GAW World Data Centre for Greenhouse Gases maintained 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 countries contribute to sample collection in that network and many others maintain themselves 30% of the total surface-based network. Systematic commercial aircraft measurements are mainly performed by Japanese research institutes (National Institute for Environmental Science, Meteorological Research Institute) in cooperation with Japan Airlines (JAL). NOAA-ESRL also has initiated a network of vertical profiling using flask sampling from aircraft flying up to 10 km. This community has taken advantage of the WMO/GAW services in coordination to collaborate in issuing annual Greenhouse Gas Bulletins such as that shown in Figure 5.

Figure 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 issued annually before the international meeting of the Parties to the United Nations Framework Convention on Climate Change (UNFCC)


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WMOGreenhouse Gas Bulletin

The State of Greenhouse Gases in the AtmosphereUsing Global Observations up to December 2004

Executive summaryThe latest analysis of data from the WMO-GAW Global Greenhouse Gas Monitoring Network shows that the globally averaged atmospheric carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) have all reached new highs in 2004 with CO2 at 377.1 ppm, CH4 at 1783 ppb, and N2O at 318.6 ppb. These value are higher than those in pre-industrial times by 35%, 155%, and 18% respectively. Atmospheric growth rates of these gases are consistent with previous years, though CH4 growth has slowed during the past decade. The NOAA Annual Greenhouse Gas Index (AGGI) shows that from 1990 to 2004 the total atmosphericradiative forcing by all long-lived greenhouse gases has increased by 20%.

Global Atmosphere Watch

WMO

No. 1

The bottom-up programme starts with national efforts with coordination through GAW and linkage to the UNFCC through GCOS, a WMO co-sponsored climate observation system. In turn, it links to the satellite community through CEOS and the informal Integrated Global Observing Strategy that spawn the IGACO and IGCO strategy reports.

3. Users and Products

The WMO-GAW Global Atmospheric CO2 & CH4 Monitoring Network is a globally coordinated effort that relies upon the bottom-up activities of major national and regional research centres around the world. Mergeability of data is ensured through linking 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 Reference Standard Scale for CO2 and CH4 maintained by NOAA-ESRL, USA. Exchange of data takes place through the GAW World Data Centre for Greenhouse Gases maintained 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 countries contribute to sample collection in that network and many others maintain themselves 30% of the total surface-based network. Systematic commercial aircraft measurements are mainly performed by Japanese research institutes (National Institute for Environmental Science, Meteorological Research Institute) in cooperation with Japan Airlines (JAL). NOAA-ESRL also has initiated a network of vertical profiling using flask sampling from aircraft flying up to 10 km. This community has taken advantage of the WMO/GAW services in coordination to collaborate in issuing annual Greenhouse Gas Bulletins such as that shown in Figure 5.

Figure 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 issued annually before the international meeting of the Parties to the United Nations Framework Convention on Climate Change (UNFCC)


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The CO2 and CH4 inversion modelling community utilizes observations to serve the needs of countries. In particular this workshop deals with quantification of methane emissions in Europe using observations and inversion modelling that is independent of the bottom-up estimates made by various means. This is an essential activity in constraining the uncertainties of bottom-up estimates. The full power of inversion modelling relies on the quality and quantity of observations as well as the accuracy of models in representing transport, dispersion and convection. It is the goal of WMO-GAW to assist the community in providing the best observations for these purposes and to promote the use of the best meteorological drivers in these inversion models.

References

EC XLIV, 1992, Resolution 3, WMO Technical Regulations, 1, Chapter B.2, Global Atmosphere 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 of IGOS-WMO-ESA, GAW Report 159, 53 pp, 2004.

13th WMO/IAEA Meeting of Experts on Carbon Dioxide Concentration and Related Tracers Measurement 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.


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RAMCES - The French Network of Atmospheric Greenhouse Gas Monitoring

Martina Schmidt, Michel Ramonet, Victor Kazan, Cyril Messager, Marc Delmotte, Claire Valant, Alexis Crevier, P. Galdemard, Anne Royer, Adrien Royer, Benoit Wastine, Olivier Cloué and Philippe Ciais

Laboratoire des Sciences du Climat et de l’Environnement LSCE/IPSL, CEA/CNRS/UVSQ, Gif-sur-Yvette, France

1. Introduction

The RAMCES CO2 and Radon-222 monitoring program was initiated in 1980 at the Amsterdam Island observatory [Gaudry et al., 1983; 1990; Monfray et al., 1996; Ramonet et al., 1996] and was extended at Mace Head, Ireland, in 1992 [Bousquet et 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 initiated at LSCE in 1996. Flasks are sampled at 12 fixed surface sites, three on-board small aircrafts and ships in Indian Ocean. At LSCE the samples are analysed for CO2isotopes ( 13C and 18O) and for CO2, CH4, N2O, SF6 and CO mixing ratios. In this summary we will describe our existing network and the plans of extension during the next years.

Figure 1: RAMCES flask sampling and in-situ measurement network. The different symbols represent the instrumentation and the type of sampling.

2. Continous CO2 measurements

Figure 2 shows the daily average of the CO2 mixing ratios at our four established measurement sites Amsterdam Island, Mace Head, Puy de Dome and Gif-sur-Yvette. The three western European sites reflect different environments from a marine site occasionally influenced by long range transport over Europe (Mace Head), to sites


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which are more influenced by rural (Puy de Dôme) and urban activities (Saclay). Continuous measurements of CO2 and Radon-222 have been established in each observatory and CH4, N2O, SF6 and CO is measured at Saclay.

Figure 2: Continuously analysis of CO2 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 our network, which are both equipped with a new developed CO2 analyser (CARIBOU). In 2006 we equipped a new site in the Orleans Forest, France (Trainou Tower) with a GC system and a CARIBOU in order to analyse in-situ CO2, CH4, N2O, SF6 and CO in 3 heights (50m, 110m and 180m). In future Orleans will be for RAMCES a “supersite” with multispecies measurements at a tower and vertical profiles from airborne measurements up to 3000m. Our airborne program at the forest of Orleans was initiated in 1996 with flask sampling between 100 and 3000m heights with a frequency of 2-3 flights per month. Within the European project CARBOEUROPE we were installing an insitu CO2 analyser (Condor) in the small aircraft and increasing the frequency to 2 flights per week.


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Table 1: In-situ measurement sites of RAMCES.

Site ID Country Latitude Longitude Alt. (m asl) Mesures Instruments Période

IleAmsterdam

AMS France 37°48'S 77°32'E 70 CO2 CO2Rn222Météo

URAS/SiemensLOFLO CAFAR/Dérouleur

1980-…2006-…1967-…

Mace Head MHD Irlande 53°20'N 09°54'W 25 CO2 Rn222Météo

SiemensDérouleur

1992-…1996-…

Puy de Dôme PUY France 45°46'N 02°57'E 1465 CO2 Licor 2001-…

Gif-sur Yvette

GIF France 48°43'N 02°09'E 20 CO2 CO2CO2, CH4, N2O, SF6 CORn222Météo

LOFLO CARIBOUMulti-GC GC-CO Dérouleur

2005-…2006-…2001-…2004-…2001-…

Biscarosse BIS France 44°22'N 01°'14'W 120 CO2 CARIBOU 2005-…

Hanle HLE India 32°47'N 78°58'E 4517 CO2 CARIBOU 2005-…

Trainou (Orleans Tower)

TRA France 47°58'N 2°07'W 131 CO2 CH4, N2O, SF6, CO Rn222Météo

CARIBOUMulti-GC ANSTO

2006-…2006-…

3. Flask sampling network and measurement facility

As an extension of the RAMCES monitoring network, a flask sampling program was initiated at LSCE in 1996. Flasks are sampled in fixed surface sites, on-board small aircrafts [Ramonet et al., 2002], and ships in Indian Ocean and North Atlantic (Figure 1 and Table 2). At LSCE the samples are analysed for CO2 isotopes ( 13C and 18O) and for CO2, CH4, N2O, SF6 and CO mixing ratios.

Table 2: Flask sampling sites of RAMCES.

Site Code Latitude Longitude Alt. (m) Country Start Interval CollaboratorIle Amsterdam AMS 37°48'S 77°32’E 70 France 1996 4 / month IPEV

Mace 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 LaMP

Orléans 1 ORL 47°50'N 2°30'E 100-3000 France 1996 2-3 / month Météo FranceTver 1 TVR 56°27'N 32°55'E 100-3000 Russia 1998 1 / month BGC, IPEE

Hegyatsal 1 HUN 46°57’N 16°39’E 100-3000 Hangary 2001 2 / month HMS Griffin 1 GRI 56°33’N 2°59’W 100-3000 Scottland 2001 2 / month IERM

Ile Grande LPO 48°48'N 3°35'W 20 France 1998 2 / month LPO Tromelin TRM 15°54' S 54°31'E 10 France 1998 4 / month Météo France

Cape Grim CGO 40°41’S 144°41’E 164 Austalia 1998 2 / month CSIRO Begur BGU 41°58'N 3°14'E 13 Spain 2000 4 / month U. Barcelona

Finokalia FIK 35°19'N 25°40'E 150 Greece 2001 2 / month U. Heraklin Hanle HLE 32°47'N 78°58'E 4517 India 2000 3 / month IIAP

Pic du Midi PDM 42°56’N 0°08’E 2877 France 2001 4 / month LA - OMP Marion Dufres 2 MDF Indian Ocean 20 1996 2 / year LBCM


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Table 1: In-situ measurement sites of RAMCES.

Site ID Country Latitude Longitude Alt. (m asl) Mesures Instruments Période

IleAmsterdam

AMS France 37°48'S 77°32'E 70 CO2 CO2Rn222Météo

URAS/SiemensLOFLO CAFAR/Dérouleur

1980-…2006-…1967-…

Mace Head MHD Irlande 53°20'N 09°54'W 25 CO2 Rn222Météo

SiemensDérouleur

1992-…1996-…

Puy de Dôme PUY France 45°46'N 02°57'E 1465 CO2 Licor 2001-…

Gif-sur Yvette

GIF France 48°43'N 02°09'E 20 CO2 CO2CO2, CH4, N2O, SF6 CORn222Météo

LOFLO CARIBOUMulti-GC GC-CO Dérouleur

2005-…2006-…2001-…2004-…2001-…

Biscarosse BIS France 44°22'N 01°'14'W 120 CO2 CARIBOU 2005-…

Hanle HLE India 32°47'N 78°58'E 4517 CO2 CARIBOU 2005-…

Trainou (Orleans Tower)

TRA France 47°58'N 2°07'W 131 CO2 CH4, N2O, SF6, CO Rn222Météo

CARIBOUMulti-GC ANSTO

2006-…2006-…

3. Flask sampling network and measurement facility

As an extension of the RAMCES monitoring network, a flask sampling program was initiated at LSCE in 1996. Flasks are sampled in fixed surface sites, on-board small aircrafts [Ramonet et al., 2002], and ships in Indian Ocean and North Atlantic (Figure 1 and Table 2). At LSCE the samples are analysed for CO2 isotopes ( 13C and 18O) and for CO2, CH4, N2O, SF6 and CO mixing ratios.

Table 2: Flask sampling sites of RAMCES.

Site Code Latitude Longitude Alt. (m) Country Start Interval CollaboratorIle Amsterdam AMS 37°48'S 77°32’E 70 France 1996 4 / month IPEV

Mace 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 LaMP

Orléans 1 ORL 47°50'N 2°30'E 100-3000 France 1996 2-3 / month Météo FranceTver 1 TVR 56°27'N 32°55'E 100-3000 Russia 1998 1 / month BGC, IPEE

Hegyatsal 1 HUN 46°57’N 16°39’E 100-3000 Hangary 2001 2 / month HMS Griffin 1 GRI 56°33’N 2°59’W 100-3000 Scottland 2001 2 / month IERM

Ile Grande LPO 48°48'N 3°35'W 20 France 1998 2 / month LPO Tromelin TRM 15°54' S 54°31'E 10 France 1998 4 / month Météo France

Cape Grim CGO 40°41’S 144°41’E 164 Austalia 1998 2 / month CSIRO Begur BGU 41°58'N 3°14'E 13 Spain 2000 4 / month U. Barcelona

Finokalia FIK 35°19'N 25°40'E 150 Greece 2001 2 / month U. Heraklin Hanle HLE 32°47'N 78°58'E 4517 India 2000 3 / month IIAP

Pic du Midi PDM 42°56’N 0°08’E 2877 France 2001 4 / month LA - OMP Marion Dufres 2 MDF Indian Ocean 20 1996 2 / year LBCM


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4. Future plans

In 2007 and 2008 we will equip three new stations with insitu CO2 analyzer (Ivittuut, Greenland and Bellegarde tower, near Toulouse France, and a station in West Africa). Puy de Dôme station will be upgraded with a new GC system in order to analyse CH4, N2O, CO and SF6. In cooperation with the University of Bremen we will install a FTIR at Trainou tower to analyse the CO2 column density (2008). A further focus will be on the automation and on the real-time data transmission of our insitu measurements.

References

Biraud, 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 from continuous 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, and Chloroform 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, et T.C. O'Connor, Atmospheric CO2 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 CO2 Variations at Amsterdam Island (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 CO2 from South Africa, Tellus, 42B, 9-19, 1990.

Monfray, P., M. Ramonet, et D. Beardsmore, Longitudinal and vertical gradient over the subtropical/subantarctic oceanic CO2 sink, Tellus, 48B, 445-456, 1996.

Ramonet, M., et P. Monfray, CO2 Baseline concept in 3-D atmospheric transport models, Tellus, 48B, 502-520, 1996.


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Measurements of greenhouse gases at the Mediterranean island of Lampedusa

Alcide G. di Sarra1, Salvatore Piacentino2, Paolo Chamard1, Florinda Artuso1,Salvatore Chiavarini1, Francesco Monteleone3, Damiano Sferlazzo2, Fabrizio Anello3,Carlo Bommarito3, Lorenzo De Silvestri1, Daniela Meloni1

[1] ENEA, ACS, Rome, Italy [2] ENEA, ACS, Lampedusa, Italy [3] ENEA, ACS, Palermo, Italy

The Italian agency for new technologies, energy, and environment (ENEA) maintains a measurement station (http://www.palermo.enea.it/lampedusa) dedicated to the study of climate on the island of Lampedusa (35.5°N, 12.6°E), in the southern sector of the central Mediterranean. Lampedusa is small (22 km2 surface area), rocky, poor of vegetation, and far from significant sources of anthropogenic pollutants. Its maximum elevation is 130 m. The measurement station is located on a plateau, close to the North-Eastern coast of the island. A village of about 5000 inhabitants is located in the South-Eastern part of the island. During the tourist season (mostly July and August) the population increases significantly.

Fig. 1: Map of the central Mediterranean. The position of Lampedusa is indicated by the arrow.

First measurements at Lampedusa were started in 1992, with a weekly air sampling program dedicated to CO2 [Chamard et al., 2003]. The set of measured quantities was progressively expanded; at present, the main aim of the station is the study of changes in atmospheric composition, and of the influence they produce on the radiative budget of the atmosphere, and on climate. Combined observations of meteorological 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 surface ozone, water vapour are routinely carried out.


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Measurements of greenhouse gases at the Mediterranean island of Lampedusa

Alcide G. di Sarra1, Salvatore Piacentino2, Paolo Chamard1, Florinda Artuso1,Salvatore Chiavarini1, Francesco Monteleone3, Damiano Sferlazzo2, Fabrizio Anello3,Carlo Bommarito3, Lorenzo De Silvestri1, Daniela Meloni1

[1] ENEA, ACS, Rome, Italy [2] ENEA, ACS, Lampedusa, Italy [3] ENEA, ACS, Palermo, Italy

The Italian agency for new technologies, energy, and environment (ENEA) maintains a measurement station (http://www.palermo.enea.it/lampedusa) dedicated to the study of climate on the island of Lampedusa (35.5°N, 12.6°E), in the southern sector of the central Mediterranean. Lampedusa is small (22 km2 surface area), rocky, poor of vegetation, and far from significant sources of anthropogenic pollutants. Its maximum elevation is 130 m. The measurement station is located on a plateau, close to the North-Eastern coast of the island. A village of about 5000 inhabitants is located in the South-Eastern part of the island. During the tourist season (mostly July and August) the population increases significantly.

Fig. 1: Map of the central Mediterranean. The position of Lampedusa is indicated by the arrow.

First measurements at Lampedusa were started in 1992, with a weekly air sampling program dedicated to CO2 [Chamard et al., 2003]. The set of measured quantities was progressively expanded; at present, the main aim of the station is the study of changes in atmospheric composition, and of the influence they produce on the radiative budget of the atmosphere, and on climate. Combined observations of meteorological 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 surface ozone, water vapour are routinely carried out.


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Table 1 shows the list of greenhouse gases measured at Lampedusa, the starting date of each measurement, and the measurement techniques (NDIR: non dispersive infrared spectrometry, GC: gas chromatography, FID: flame ionization detection; ECD: electron capture detection; MS: mass spectrometry). Measurements of additional halogenated species will be added shortly. All measurements refer to World Meteorological Organization reference standards. Flask data are routinely provided to the World Data Center for Greenhouse Gases (http://gaw.kishou.go.jp/wdcgg.html), as a contribution to the regional network of Global Atmosphere Watch. CO2 and CH4 data contribute also to the Carboeurope database (http://www.carboeurope.org), to the Globalview-CO2 and Globalview-CH4programs (http://www.esrl.noaa.gov/gmd/ccgg/globalview/). Within a cooperation with the Global Monitoring Division of the National Oceanic and Atmospheric Administration, Carbon Cycle Greenhouse Gases (CCGG) group, additional weekly samplings as part of the NOAA Cooperative air sampling network, were started in October 2006. These measurements include, beside CO2, CH4, N2O, and SF6,weekly determinations of 13C, 18O, H2, CO.

Table 1: List of measured greenhouse gases, and starting date of measurement.

Chemical species flask continuous technique CO2 1992 1998 NDIR CH4 1995 2005 GC-FID N2O 1998 2005 GC-ECD CFC-11 1996 2005 GC-ECD CFC-12 1997 2005 GC-ECD HFC-134a 2003 GC-MS HCFC-22 2003 GC-MS HCFC-141b 2004 GC-MS HCFC-142b 2004 GC-MS SF6 2004 GC-MS

Figure 2 shows the evolution of the monthly average mixing ratio (calculated from weekly flask data) of the greenhouse gases measured at Lampedusa. At the bottom of the graph monthly means of total ozone and aerosol optical depth at 500 nm are also displayed.

CO2 increases by about 1.7 ppm/year, with strong year-to-year variability. Peaks in the CO2 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 2 ppb/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 emissions following the Montreal Protocol. HFCs, HCFCs, and SF6, whose emissions are also controlled by the Montreal and the Kyoto Protocols, display a relatively fast increase.

Because of the small local sources and limited vegetation, the CO2 monthly average daily 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 of


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the daily variation of the marine boundary layer depth. The methane daily cycle is also largest in summer, with an amplitude of about 20 ppb in July-August. The monthly mean methane mixing ratio has a maximum during nighttime and decreases during the morning. The methane reaction with OH probably plays a significant role in the determination of the daily behaviour.

Due to the distance from relevant sources of greenhouse gases, their behaviour at Lampedusa is largely determined by long-range transport: CO2 and CH4 weekly and continuous data show a significant dependency on the origin of the airmasses (see e.g. [di Sarra et al., 2005; Artuso et al., 2007]). The short-term CO2 evolution is strongly modulated by the influence of anthropogenic sources (dominant in winter) and vegetation (dominant in summer) in Europe, and the small sources and vegetation in Africa. Enhanced methane amounts are found, among airmasses from Africa, for trajectories coming from the Algerian sector, as a possible consequence of leakage in gas and oil drillings and pipelines.

Figure 2: Evolution of the monthly average mixing ratio of the greenhouse gases measured at Lampedusa. Monthly means of total ozone and aerosol optical depth at 500 nm are also displayed.


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the daily variation of the marine boundary layer depth. The methane daily cycle is also largest in summer, with an amplitude of about 20 ppb in July-August. The monthly mean methane mixing ratio has a maximum during nighttime and decreases during the morning. The methane reaction with OH probably plays a significant role in the determination of the daily behaviour.

Due to the distance from relevant sources of greenhouse gases, their behaviour at Lampedusa is largely determined by long-range transport: CO2 and CH4 weekly and continuous data show a significant dependency on the origin of the airmasses (see e.g. [di Sarra et al., 2005; Artuso et al., 2007]). The short-term CO2 evolution is strongly modulated by the influence of anthropogenic sources (dominant in winter) and vegetation (dominant in summer) in Europe, and the small sources and vegetation in Africa. Enhanced methane amounts are found, among airmasses from Africa, for trajectories coming from the Algerian sector, as a possible consequence of leakage in gas and oil drillings and pipelines.

Figure 2: Evolution of the monthly average mixing ratio of the greenhouse gases measured at Lampedusa. Monthly means of total ozone and aerosol optical depth at 500 nm are also displayed.


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At the national level, activities at Lampedusa are included within the Italian network for the measurement of greenhouse gases [Apadula et al, 2005]. Intercomparison exercises and an integrated assessment of the data quality are being carried out within the national network. As a complement to the observations at Lampedusa, and as an integration of the Italian national network, in April 2005 a weekly flask program was started on the Madonie mountains (37°52’N 14°04’E, 1756 m altitude), in Sicily. Weekly measurements of CO2, CH4, N2O, CFC-11 and CFC-12 are routinely carried out.

References

Apadula, 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 CO2measurement in Italy, 12th WMO/IAEA Meeting of Experts on Carbon Dioxide Concentration and Related Tracer Measurement Techniques, World Meteorological Organization 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 at the 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 CO2 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 CO2 latitudinal gradient on the observations at the Mediterranean island of Lampedusa, Seventh International Carbon Dioxide Conference, Extended Abstracts, ISBN 0-9772755-0-7, Published by Committee of Seventh International Carbon Dioxide Conference, National Oceanic and Atmospheric Administration, FF-254 225, 2005.

Meloni, D., A. di Sarra, J. R. Herman, F. Monteleone, and S. Piacentino, Comparison of ground-based and TOMS erythemal UV doses at the island of Lampedusa in the period 1998-2003: 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 the central Mediterranean. 2. Determination of single scattering albedo at two wavelengths for 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, and D. Sferlazzo, Seasonal behavior of Saharan dust events at the Mediterranean island of Lampedusa 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 during summer 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 of aerosols over the central Mediterranean. 1. Influence of transport and identification of different aerosol types, Atmos. Chem. Phys., 6, 697–713, 2006.


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Long-Term Monitoring of Greenhouse Gases at Jungfraujoch

Stefan Reimann, Martin K. Vollmer, Martin Steinbacher, Doris Folini, Matthias Hill, Brigitte Buchmann

EMPA, Laboratory for Air Pollution/Environmental Technology, Dübendorf, Switzerland

Continuous atmospheric measurements of trace gases in the atmosphere can not only be used to detect global trends of these substances but also to estimate their regional emissions. In fact, long-term continuous measurements have the potential to be used as an independent tool for verification of anthropogenic emissions of substances regulated under international treaties such as the Montreal and Kyoto Protocol.

Continuous in-situ measurements of halogenated greenhouse gases (chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and Halons) are performed at the high-Alpine site of Jungfraujoch since January 2000 by gas chromatography-mass spectrometry (GC-MS) [Reimann et al. 2004]. Jungfraujoch is the highest site worldwide to host this kind of measurements. The connection of these measurements with inverse modelling can be used for the independent source allocation for these trace compounds. Occurrence of these halocarbons in the atmosphere is due to their widespread usage with a large variety of applications such as foam blowing, refrigeration and fire extinction. The continuous measurements of halocarbons at the Jungfraujoch are part of the SOGE network (System for Observation of Halogenated Greenhouse Gases in Europe) with the aims to determine trends of halocarbons and to estimate the spatial distribution and strength of their European sources. Within SOGE, fully intercalibrated in situ data have been measured since 2001 with an almost 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 (CH4), nitrous oxide (N2O) and sulfur hexafluoride (SF6) are performed at Jungfraujoch since the beginning of 2005 by gas chromatography – flame ionization/electron capture detection (GC-FID/ECD). Sources of CH4 and N2O are both anthropogenic and natural and their concentrations have considerably increased after the beginning of industrialization.

Jungfraujoch often is under the influence of clean air masses, which are not influenced by the regional boundary layer. However, boundary layer air is occasionally transported to the height of Jungfraujoch, leading to an increase in concentrations. The elevation of the concentrations of specific substances is thereby representative for the emissions that the air masses have been exposed to during their travel in the boundary layer. The difference between the background and the elevated peak values can therefore be used to estimate emissions from the European continent.


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For the estimation of the European sources a tracer-ratio method is applied which uses European emissions of carbon monoxide (CO) as a priori information [Vestreng2004]. Apart from this information the calculation of the emissions uses the yearly averaged elevations over the baseline for CO and the halocarbons within the following formula:

ElevatedCOLOElevatedHAEmissionCOLOEmissionHA

Using this method European sources of following groups of substances could be estimated 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 influence during the last 2 days was exclusively from the Swiss boundary layer. Emission estimates of halocarbons are regularly compared with those provided by the Swiss authorities to the UNFCCC, showing a satisfactorily consistency between the two approaches.

For the localisation of potent European sources of halocarbons a trajectory model was used, based on the Swiss Alpine Model [Reimann et al. 2004]. The results should be regarded as indicative, showing only potential source regions. Results of the temporal development of the emissions for HCFC 141b and HFC 365mfc, seen with 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 emissions have declined dramatically. On the other hand, emissions of the substitute HFC 365mfc, which is predominately used in foam blowing, have increased substantially. Interestingly a new source in France is visible, which corresponds to the location of a factory producing the substance for the European market.

This approach has the potential to be used validate yearly emissions of greenhouse gases 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 from Jungfraujoch show clear indications of emissions of the foam-blowing agent HFC-152a from Italy together with other countries in Europe. However, these emissions are not reported by Italy to the UNFCCC. Thus, emission estimations using atmospheric measurements have the advantage to provide real-world checks for the inventories, which are solely based on activities and emission functions. This could be extremely important in view of compliance difficulties and verification of actual emissions, if countries have to prove comprehensively that their emissions correspond to the truth within the Kyoto Protocol and possible future international treaties.


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2003 2004 2005

HCFC141b

2003 2004 2005

HFC365mfc

Figure 1: Source regions resulting from trajectory statistics of the HCFC 141b and the HFC 365mfc from 2003-2005 seen at Jungfraujoch. Units indicate averaged excursions above the baseline, linked to trajectories that passed over the respective grid cell [Stemmler et al. 2007].

A) B) 2003 2004

Figure 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 to trajectories that passed over the respective grid cell [Greally et al. 2007]. B) Submissions of the National Communications to the UNFCCC from 2003-2004.

2003520 t 330 t

4 t 1880 t 460 t

- t

AustriaBelgium NetherlandsGermanyFranceItaly

2004529 t 288 t

5 t 1333 t 297 t

- t


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References

BAFU/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 European emissions of HCFC-141b and HCFC-142b under the Montreal Protocol: Evidence from observations 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 and regional 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 emissions inferred 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 the Swiss High Alpine Site of Jungfraujoch (3580 m asl): Continuous measurements and their 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 free substitute 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 under the 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 and rapid growth of anthropogenic HFC-245fa (CHF2CH2CF3) in the atmosphere, Geophys. Res. Lett., 33 (20), 2006.


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ICOS - Integrated Carbon Observation System

A new research infrastructure to decipher the greenhouse gas balance of Europe and adjacent regions

www.icos-infrastructure.eu

Coordinator: Philippe Ciais – Laboratoire de Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, France

Core Team: Timo Vesala, Finland, University of Helsinki Ernst-Detlef Schultz, Germany, Max-Planck-Gesellschaft Ingeborg Levin, Germany, University of Heidelberg Riccardo Valentini, Italy, University of Tuscia Han Dolman, The Netherlands, Vrije University Amsterdam John Grace, United Kingdom, University of Edinburgh

Contact: Cecilia Garrec – project office [email protected]

Mission statement

To provide the long-term observations required to understand the present state and predict future behavior of the global carbon cycle and greenhouse gas emissions

To monitor and assess the effectiveness of carbon sequestration and/or greenhouse gases emission reduction activities on global atmospheric composition levels, including attribution of sources and sinks by region and sector

Brief description

ICOS is a new European Research Infrastructure for quantifying and understanding the greenhouse balance of the European continent and of adjacent regions.

It was realized early that, high precision long-term carbon cycle observations form the essential basis of carbon cycle understanding and that these observations must be secured beyond the lifetime of a research project. ICOS aims to build a network of standardized, long-term, high precision integrated monitoring of:

atmospheric greenhouse gas concentrations of CO2, CH4, CO and radiocarbon-CO2 to quantify the fossil fuel component

ecosystem fluxes of CO2, H2O, and heat together with ecosystem variables.

The ICOS infrastructure will integrate terrestrial and atmospheric observations at various sites into a single, coherent, highly precise dataset. These data will allow a unique regional top-down assessment of fluxes from atmospheric data, and a


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bottom-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 a basis for understanding the exchange processes between the atmosphere, the terrestrial surface and the ocean.

Figure 1: Existing European research network of ecosystem observation sites (left) and atmospheric concentration (right) among which the ICOS main sites and associated sites will be selected and essential new sites implemented.

The ICOS Research Infrastructure was selected by the European Strategy Forum for Research Infrastructures (ESFRI) roadmap in October 2006 as one of the vital new European Research Infrastructures for the next 20 years. ICOS was initiated by successful developments of the research tools and capacity building at the European level necessary to quantify and understand the sources and sinks of greenhouse gases at regional and continental scales (see AEROCARB (terminated),CARBOEUROPE, NITROEUROPE, and CARBOOCEAN).

ICOS contributes to the implementation of the Integrated Global Carbon Observation System (IGCO). At the same time, ICOS fulfils the monitoring obligations of Europe under the United Nations Framework Convention on Climate Change (UNFCCC).

The list of variables covered in ICOS are central to GEOSS (Global Earth Observation System of Systems) as recommended to ‘support the development of observational capabilities for Essential Climate Variables (ECVs) Further, ICOS contributes to the GEOSS aims by implementing in Europe the IGOS-P (Integrated Global Observing Strategy - Partnership) for Atmospheric Chemistry Observations (IGACO) and for Integrated Global Carbon Observations (IGCO).

Ecosystem observation sites Atmospheric concentration sites


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The ICOS building blocks

Figure 2: The ICOS elements.

A Central Co-ordination Office which co-ordinates all activities, and which is responsible for data management, data diffusion and outreach. Associated with the co-ordination office will be the established a data centre, the Carbon Portal, providing free access to the ICOS data,

A Central Analytical Laboratory for calibration, quality control and atmospheric analyses for the entire network,

An Atmospheric Thematic Center responsible for continuous and discontinuous air sampling, instrument development/servicing and data processing,

An Ecosystem Thematic Center responsible for total ecosystem flux measurements and component fluxes and carbon pools, including data processing and instrument development,

A network of Main Observation Sites which are connected in a distributed network of about 30 atmospheric and 30 ecosystem sites located across Europe, with secured funding coverage for 20 years,

Associated networks of Regional Observation sites which will contribute to the ICOS objectives, and share data with the Infrastructure.

Implementation strategy

The implementation of ICOS will take place in two steps:

During the Preparatory Phase starting in 2008 until 2011, the funding commitments will have been endorsed by the governments and mother institutions, the building of the central facilities will be initiated, and the project will be technically developed up to the level of a demonstration year of full operation, but with a reduced number of observational sites.


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The ICOS building blocks

Figure 2: The ICOS elements.

A Central Co-ordination Office which co-ordinates all activities, and which is responsible for data management, data diffusion and outreach. Associated with the co-ordination office will be the established a data centre, the Carbon Portal, providing free access to the ICOS data,

A Central Analytical Laboratory for calibration, quality control and atmospheric analyses for the entire network,

An Atmospheric Thematic Center responsible for continuous and discontinuous air sampling, instrument development/servicing and data processing,

An Ecosystem Thematic Center responsible for total ecosystem flux measurements and component fluxes and carbon pools, including data processing and instrument development,

A network of Main Observation Sites which are connected in a distributed network of about 30 atmospheric and 30 ecosystem sites located across Europe, with secured funding coverage for 20 years,

Associated networks of Regional Observation sites which will contribute to the ICOS objectives, and share data with the Infrastructure.

Implementation strategy

The implementation of ICOS will take place in two steps:

During the Preparatory Phase starting in 2008 until 2011, the funding commitments will have been endorsed by the governments and mother institutions, the building of the central facilities will be initiated, and the project will be technically developed up to the level of a demonstration year of full operation, but with a reduced number of observational sites.


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During the follow-up Operational Phase from 2012 until 2031, after the full scale deployment of the network, it will be run in an operational mode, and greenhouse gas concentrations and fluxes will be determined on a routine basis.

Activities

The ICOS work plan of the preparatory phase is organised around eight complementary work packages:

WP 1 corresponds to the consortium organization and management of the project,

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, and

involves data providers, in particular for fossil fuel emission data and biomass and soil 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 ecosystem thematic centers, as well as the central analytical laboratory,

WP 7 will apply the technical solutions retained in WP 5-6, to execute the Demonstration Experiment, a one-year test run where the infrastructure will be operated with a small number of sites,

WP 8 will organize the project-level outreach, the construction of the web based Carbon Portal, as well as training and capacity building necessary for the future operational phase.

Links to ICOS

The links between ICOS, other European projects, and international coordination bodies and programs include:

CARBOEUROPE (FP6, IP) will be a prime user of the ICOS data, and provides advanced research tools to use the infrastructure observations.

CARBOAFRICA (FP6, IP), (Western Africa) and CIRCE (FP6) (Mediterranean regions), and research in third countries such as China, India, and Russia will be able to use the ICOS methodology for establishing new high precision measurements.

IMECC (FP6, I3) will provide key network design tools to the ICOS Preparatory Phase, funding for ecosystem measurement sensors and standard preparation facilities as well as pilot Near-Real-Time concentration data products.

GEMS and GEOLAND (FP-6, IP) projects (part of the GMES program), with successors in FP-7, will use the high quality atmospheric and ecosystem validation data provided by ICOS.

GEOMON (FP-6, IP) will ensure the link to ICOS with forthcoming satellite observations of column integrated CO2 (NASA/OCO, JAXA/GOSAT missions) and CH4 (ESA/SCIAMACHY instrument on ENVISAT) and CO (NASA/MOPITT).


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GEOSS will use the European implementation of the Integrated Global Carbon Observation strategy (IGCO) for atmospheric and biospheric observations, and of the Integrated Global Atmospheric Composition Observation strategy (IGACO) provided by ICOS.

IPCC panel members will have access to unique, high precision long term data to understand the carbon cycle and the current perturbation attributed to anthropogenic activities.

Partnership

The ICOS preparatory phase includes 14 research laboratories and SMEs from 11 European countries.

CEA Philippe Ciais Commissariat à l’Energie Atomique France MPG Ernst-Detlef Schulze Max-Planck-Gesellschaft. Germany UNITUS Riccardo Valentini University of Tuscia Italy UHEI-IUP Ingeborg Levin University of Heidelberg Germany VUA Han Dolman Vrije University Amsterdam The Netherlands UHEL Timo Vesala University of Helsinki Finland UEDIN John Grace University of Edinburgh United Kingdom CNRS-INSU Nicole Papineau Centre National de la Recherche

Scientifique-Institut National des Sciences de l'Univers

France

ULUND Anders Lindroth Lunds Universitet Sweden RISEO Kim Pilegaard Forskningscenter Risø, Danmarks Tekniske

Universitet Denmark

SJ BERWIN Ramón García-Gallardo SJ Berwin LLP Belgium UA Reinhart Ceulemans Universiteit Antwerpen Belgium CEAM Maria J. Sanz Fundación Centro de Estudios Ambientales

del Mediterraneo Spain

ISBE Michal V. Marek Ústav systémové biologie a ekologie AV R, v.v.i.

Czech Republic


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The preparatory phase was initiated by 6 institutes which will form a network of contact 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 organize the Ecosystem Thematic Center (ETC) and France will establish the Atmospheric Thematic Center (ATC). The United Kingdom and Finland will contribute to the development of sensors for flows on the ecosystems. The Netherlands will coordinate the studies of optimization of the network and definition of the schedule of conditions of the stations of reference.

These principal partners, along with the French Ministry of Research are joined by representatives from different institutes in five other countries (Sweden, Denmark, Belgium, Spain, and the Czech Republic). Additional countries have already expressed their interest (Norway, Israel) and processes will be in place to add new members who will have the support of their country during the preparatory phase.

A certain number of international organizations have also expressed their interest and have provided letters of support for the preparatory phase and include:

World Meteorological Organization (WMO) Switzerland National Institute For Environmental Studies (NIES) Japan National Oceanic and Atmospheric Administration (NOAA) USA Integrated Global Atmospheric Composition Strategy (IGACO) Switzerland Integrated Global Carbon Observing Strategy IGCO France, USA Global Carbon Project (GCP) IGBP-WCRP-IHDP Australia FLUXNET USA Meteo-France France Institut National de Recherche Agronomique (INRA) France Umweltbundesamt (UBA) Germany Finnish Meteorological Institute (FMI) Finland Direction Générale de la Recherche et de l’Innovation (DGRI) France Centre National de la Recherche Scientifique (CNRS) France Commissariat à l’Energie Atomique (CEA) France Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg Germany Ministero dell’Ambiente e della Tutela del Territorio e del mare Italy Ministero dell’Universita e della Ricerca Italy Netherlands Organization for Scientific Research (NOW) Netherlands Finnish Ministry of Education Finland Department for Environment Food and Rural Affairs (DEFRA) UK Swedish Research Council Sweden Spanish Ministry of Education and Science (MEC) Spain Research Foundation Flanders (FWO) Flanders Czech Science Foundation Czech Republic Danish Agency for Science Denmark Ministerio de Medio Ambiente Spain Research Council of Norway Norway National Agency for New Technologies, Energy and Environment (ENEA) Italy

Expected impact

The synergy between the atmospheric concentration measurements on the one hand and the knowledge of local ecosystem fluxes on the other hand, has shown effective in reducing the uncertainties on carbon assessments. However, in Europe,


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observatories are all managed differently for each country and data is not homogenously processed.

The value added impact of the infrastructure will allow an enhanced visibility and dissemination of European greenhouse gas data and products that are both long-term and carefully calibrated. ICOS seeks to meet the data needs of carbon cycle and climate researchers as well as those of politicians and the general public. ICOS will serve as the backbone to users engaged in developing data assimilation models of greenhouse gas sources and sinks, namely reverse modelling, which allows the deduction of surface carbon flux pattern.

A common data center, the Carbon Portal put into place by ICOS, will provide free access to ICOS data services, as well as to links with inventory data, and outreach material. This portal will allow the production web based tools for the survey of sources and sinks in near real time. ICOS will deliver the information in near real time with a quantification of the uncertainty associated with the results due to the use of several different models using different methodologies.

ICOS will enable Europe to be a key global player for in situ observations of greenhouse gases, data processing and user-friendly access to data products for validation of remote sensing products, scientific assessments, modeling and data assimilation.


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GMES and the GMES Atmosphere Service

Virginia Puzzolo1 and Julian Wilson2

[1] European Commission GMES bureau, Bruxelles [2] European Commission DG Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy

1. GMES - overall introduction

Global Monitoring for Environment and Security (GMES) is an EU-led initiative, in which ESA will implement the space component and the Commission will manage actions for identifying and developing services relying both on in-situ and remote sensing data [1].

The objective of GMES is to provide, on a sustained basis, reliable and timely services related to environmental and security issues in support of public policy makers’ needs. In particular, the challenge for GMES is to gather together existing data collected from space-borne, airborne and in-situ observation systems to provide innovative, cost-effective, sustainable and user-friendly services, that enable decision-makers to better anticipate or mitigate crisis situations and issues relating to the 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 capacities and structures, user uptake and conditions for long-term sustainability [2]. Using the same criteria, new pilot services including the GMES Atmosphere Service are being progressively introduced to provide a broader range of services to support a wide range of needs

2. GMES contribution to climate change

As highlighted in the second adequacy report to the United Nations Framework Convention on Climate Change (UNFCCC), there are serious deficiencies to meet the observational needs of the UNFCCC. Therefore, Parties to UNFCCC will lack the necessary information to effectively plan for and manage their response to climate change [3].

In this framework GMES will contribute to improving monitoring capacity through supporting systematic and sustained observations of the pressures and driving forces on the environment and its state in the main Earth compartments (Land, Marine and Atmosphere). In particular, GMES will contribute to the Global Observing Systems for Climate and will also support the development of policies and appropriate adaptation strategies as well as the tracking progress on Kyoto protocol commitments at both European and National level.


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Policy users of climate change information require outputs that make use of a broad range of GMES products (e.g. information on boundary conditions including, for example, vegetation characteristics, soil properties and moisture, sea surface temperature). This means that climate change is an horizontal issue with dependencies 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 data enabling adequate descriptions of the status and evolution of the Earth system compartments. This capability should exist through the implementation of the GMES Pilot Services, and especially those on marine, land and atmosphere monitoring, which include a global component by design and through their links with GEOSS. It should be noted that long-term availability of the infrastructure to capture and process the necessary observations is crucial for the provision of relevant information.

The elaboration of state of the art, long-term scenarios developed in response to candidate policy actions, i.e. numerate answers, and estimates of their uncertainty, to realistic 'what if' questions. Due to the strong couplings and feedbacks between all the compartments of the Earth system, and the need to take into account the influence of human activities, this information capability should be based on Earth system models. Several relevant and world-class capacities for Earth system modelling 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 mitigation of climate change impacts to be channelled to decision makers. This capability requires close cooperation between Earth science and social science communities, and especially economists and sociologists.

3. GMES Atmosphere Service

The GMES Atmospheric Service is the first pilot service launched after the 3 initial Fast-Track services. This Service will provide coherent information on the atmospheric composition at local and regional, European, and global scale in support of European policies and for the benefit of European citizens.

The service will complement and build on existing efforts and proven mechanisms and be based on the results and experiences gained from atmosphere-related GMES projects in accordance with the prioritising criteria of the GMES Action Plan 2004-2008. An important aspect of its implementation will be the consistent and systematic assimilation and exploitation of all available observations, including in-situ, remote sensing and space-based observations to provide tailored information to the public and government authorities.

The GMES Atmospheric Service will play an important role in the context of the GMES contribution to the activity of the Group on Earth Observations (GEO) and to its global 10-year implementation plan for a Global Earth Observing System of Systems (GEOSS). It will be the major contribution, together with the Global


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Component of the GMES Land Monitoring Core Service and with the GMES Marine Core Service, to the Global Climate Observing System (GCOS), providing part of the identified Essential Climate Variables [3].

3.1. GMES Atmosphere Service Workshop

GMES is a user-driven initiative and for this reason, a user workshop was organised in Brussels on 6-7 December 2006 to discuss ‘Objectives and requirements’, ‘Implementation issues’ and ‘Conditions for sustainability’ of the GMES Atmosphere Service (GAS). The workshop was structured in three parallel sessions around different user streams: ´Air Quality’, ‘Climate Change/Forcing’ and ‘O3/UV/Renewable Energies’. The main outcomes of the workshop report are briefly reported below.

Objectives and requirements

The workshop identified the main user communities, however further discussion is necessary to make clear the role of the GMES Atmosphere Service in the provision of information directly to citizens. The GMES service architecture of core and downstream services was broadly accepted. Nevertheless, a number of issues requiring 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 core service, presented in the workshop orientation paper, broadly matched the needs identified during the workshop, and adjustments to all three user streams of the GMES Atmosphere Service were identified.

In particular for climate change/forcing, the workshop supported the inclusion of routine data assimilation, and inverse modelling/synthesis inversions in the core service, 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 and

aerosols (e.g., CO, SO2, NOx and Volatile Organic Carbon Components); Estimates of surface fluxes of CO2, CH4, etc. 3D fields and long-term records of atmospheric dynamics and thermodynamic

quantities including clouds; Consistent high-resolution datasets between the air/land/ocean services.

The emphasis for the Climate Change/Forcing stream should be the GCOS essential climate variables as a minimum. High spatial and temporal resolution of the analyses was seen as key. An ambition to include water vapour and CO2 cycle was expressed as well as emissions sources not currently included in UNFCCC but relevant to European policy development (e.g. volcanoes, shipping).


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Implementation issues

Acquisition and processing of observations (both space and in-situ), analysis and forecast, and product generation, dissemination and archiving were identified as key functions of the GMES Atmosphere Service. The workshop did not give a clear answer to the questions on architecture and governance of the service. As regard to gaps and obstacles, a number of key issues, which should be addressed in the implementation process, were highlighted such as transition from research activities to 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 sustainability

The discussion on sustainability addressed long term data availability for both in-situ and space based data, funding issues and the role of R&D. As regards in-situ observations, the extent of coverage through legislative requirements could be a possible way to secure sustainability and long term data availability. The space component of the GMES Atmosphere Service seems to face too great dependency on short term satellites and gaps in data availability after 2010. About financial issues, both community budget and national contributions are considered to be of equal importance.

Besides the funding optimization and coordination, guidance for seeking EU funding, long term funding for research networks and other issues were raised in the workshop. The close link between the service and R&D activities was recognized as the major condition for the GMES Atmosphere Service sustainability.

3.2. GMES Atmosphere Service Implementation Process

The GMES Atmosphere Service workshop represents the starting point of its implementation process. The next step will consist of setting up a GMES Atmosphere Service Implementation Group (IG) representing, as much as possible, the opinions of the important user communities, experts and inter alia national representatives.

The IG will address the most crucial issues of the GMES Atmosphere Service implementation process, such us: scope, service functionality and architecture, requirements for observation infrastructure (both space and in situ), structure and governance, funding, and will provide and action plan for the implementation and the operational 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 the UNFCCC (2003). http://www.wmo.ch/web/gcos/Second_Adequacy_Report.pdf


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6 Poster Presentations


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Greenhouse Gas observations within the monitoring network of the German Federal Environmental Agency (Umweltbundesamt)

Frank Meinhardt, Ludwig Ries and Karin Uhse

German Federal Environmental Agency

German Greenhouse gas (GHG) observations have been started in the beginning of the 1970s. The first continental observation station was established by the German Science Foundation at the Schauinsland observatory in the Black Forest. Today several stations, with continuous GHG measurements are operated by the Federal Environmental Agency and add to national and international monitoring programmes. The main tasks of the Federal Environmental Agency network are the observation of long term trends of air pollutants, the transboundary transport of air pollutants and the monitoring of climate relevant components. These tasks have mainly been carried 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 contribute to GAW programme with continuous measurements of CO2, CH4, N2O as well as SF6. The GAW regional station Neuglobsow contributes observations of CO2 and CH4. Beyond that there are two additional, so called GAW contributing stations, the north-sea coastal station Westerland, which supplies CO2 observations, and the low range mountain station Schmücke, where additional CH4 measurements are performed. The operation at the stations Brotjacklriegel and Deuselbach, where also GHG monitoring was performed, have been shut down in 2004. The continuous Schauinsland measurements supplemented by flask samples (University of Heidelberg) and the Westerland CO2 observations are currently integrated in the EU-funded Project CarboEurope-IP.

The Schauinsland and Zugspitze stations are equipped with modified GC systems which were set up in 2000. These instruments base on a HP 6890 GC. Combined with 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 four most important Greenhouse gases CO2, CH4, N2O and SF6 with high precision. Table 1 shows the typical reproducibility of the standard gas measurements for these systems, run at Schauinsland and Zugspitze.

Table 1: Reproducibility of the measured GHG

Component Reproducibility CO2 0.08 ppm CH4 1 ppb N2O 0.15 ppb SF6 0.1 ppt


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The working standards of the GC systems refer to standards provided by NOAA/CMDL, thus the atmospheric measurements are related to the international admitted NOAA scales. The Schauinsland station serves as the calibration laboratory for the GHG standards in the UBA network.

Selected results

Carbon dioxide (CO2)

Figure 1 shows the CO2 increase at different stations. CO2 increases worldwide with the same rate. The seasonal variations of the CO2 mixing ratios observed at mid northern latitudes are caused by photosynthesis and respiration of the continental biosphere. As expected, the unselected CO2 record at the Schauinsland station has pronounced seasonal variations due to the biogenic and anthropogenic impact in the surroundings of the station.

Figure 1: Long term trend of the unselected monthly means of carbon dioxide mixing ratio at Schauinsland station compared to measurements at the GAW global stations Mauna Loa and Zugspitze.

Methane (CH4)

Methane is besides CO2 the most important anthropogenic greenhouse gas. CH4mixing ratios have been measured at the Schauinsland station since 1991 and since 1994 at the GAW regional station Neuglobsow. Figure 2 shows the comparison of the mixing ratios between both stations.


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Figure 2: Comparison of the unselected monthly mean methane mixing ratios at the GAW regional stations Neuglobsow and Schauinsland.

The large seasonal variation of the CH4 mixing ratio at Neuglobsow is probably due to the stronger influence of local sources at this site compared to the mountain station Schauinsland. Maximum concentrations at Neuglobsow are reached during winter months, the minimum is observed in summer. At the Schauinsland station no systematic seasonal variation is detectable. Both records show a distinct increase of the CH4 concentration.

Sulfurhexafluoride (SF6)

Since the beginning of the Sulfurhexafluorid (SF6) observations at the Schauinsland station in 2001, annual mean concentrations have increased from about 5 ppt up to almost 6.5 ppt in 2006. This corresponds to an increase of 30% within 5 years. In comparison with CO2, SF6 has a 22000 times higher greenhouse potential. SF6 has an atmospheric lifetime of about 3000 years. Due to this long lifetime, any additional SF6 emission will cause an increase of the SF6 mixing ratio in the atmosphere. A stabilisation of this greenhouse gas is only possible if the emissions are completely stopped. Due to its physical properties SF6 is mainly used in electrical equipment and in high voltage applications..


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Figure 3: Monthly mean sulfurhexafluoride (SF6) concentrations at the Schauinsland station.


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Set-up of a continuous greenhouse gas monitoring station for CO2,CH4, N2O, SF6, and CO in Northern Italy

Bert Scheeren, Peter Bergamaschi, and Günther Seufert

European Commission DG Joint Research Centre, Institute for Environment and Sustainability, I-21020 Ispra (VA), Italy

Introduction

The Climate Change Unit of the Institute for Environment and Sustainability of the Joint Research Centre in Ispra is currently setting up a continuous long-term greenhouse gas (GHG) monitoring station for CO2, CH4, N2O, SF6, and CO in Northern Italy. The rational behind this project is the following:

To contribute to the sparse continuous GHG monitoring network in Southern Europe.

To support inverse modelling “top-down” emission estimates (e.g. using TM5 4DVAR [Bergamaschi et al., 2007]).

To follow and verify the development of GHG trends in Europe in relation to emission reduction measures under the Kyoto protocol

Figure 1: Impression (Google earth) of the Campo dei Fiori mountain (1200 m asl) situated on the border between the Po Valley and the Alps.

Location of the monitoring site

A 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 of Varese (Figure 1). The mountain top hosts a RAI TV tower and a regional meteorological station providing detailed meteorological observations for the site (as well as historical meteorological data). Our planning is to start measuring from the Campo dei Fiori from 2008. To do so we intend to deploy a mobile laboratory to be placed at the base of the Campo dei Fiori TV tower.


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Our main area of interest is the Po Valley (Figure 2) being one of the most polluted regions in Europe. When approaching from the South the Campo dei Fiori is the first small mountain bordering the south flank of the Alps. Hence, polluted air masses coming from the Po Valley with a south to southeasterly flow are able to reach the station relatively unhindered. This makes the Campo dei Fiori an excellent potential location 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 mountain site (Google earth image).

Measurement technique

Figure 3: Schematic of the Gas Chromatograph set-up for measuring CO2, CH4, N2O and SF6.


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We will use an automated Agilent 6890N gas chromatography system based on the concept originally developed by Worthy et al. [1998] and similar to the GC systems as currently applied in the CHIOTTO tall tower project [Vermeulen et al., 2007].Initially, the GC system will be set-up to measure CO2, CH4, N2O and SF6 with the option to eventually change from CO2 to CO when a Licor based CO2 monitoring system is in place. Primary standards provided by NOAA/GMD and working standards from Deuste Steininger (Germany) will constitute our primary and secondary scale respectively. A schematic of our GC system is shown in Figure 3.

Air mass origin and air quality in the Ispra area

We investigated the air mass origin for the Ispra (EMEP site) area based on daily mean 22.5 degrees wind sector classification for the period 1997 to 2004 to analyze the general air mass origin in the station area. The daily sector values are based on 96 hours 2D backward trajectories with a horizontal resolution of 50x50 km2

calculated 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 are calculated by following an air parcel every 2 hour for 96 hours back in time, 4 terms per day at 0, 6, 12 and 18 hour GMT. If a trajectory starts from outside the EMEP domain, then the coordinates giving the air parcel position are set to (0,0). The area around the arrival point extends from a radius of 150 km to a radius of 1500 km. The criteria for allocation of trajectories to one sector is that at least 50% of its given positions are found within that sector, otherwise sector 9 (not attributable) is allocated.

Our analysis, presented in Figure 4, indicates that winds from a SE to SW direction dominate the area for about 22% of the days per year, whereas the NE to NW sector represents 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 a trajectory crossings map each grid cell crossed by a trajectory on its way to the Ispra EMEP site is accounted and the total number of times a grid cell is crossed by a trajectory is summed for the whole year.

Figure 4: Air mass origin in the JRC-Ispra area based on 4-day back trajectory analysis by EMEP for 1997-2004.


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We will use an automated Agilent 6890N gas chromatography system based on the concept originally developed by Worthy et al. [1998] and similar to the GC systems as currently applied in the CHIOTTO tall tower project [Vermeulen et al., 2007].Initially, the GC system will be set-up to measure CO2, CH4, N2O and SF6 with the option to eventually change from CO2 to CO when a Licor based CO2 monitoring system is in place. Primary standards provided by NOAA/GMD and working standards from Deuste Steininger (Germany) will constitute our primary and secondary scale respectively. A schematic of our GC system is shown in Figure 3.

Air mass origin and air quality in the Ispra area

We investigated the air mass origin for the Ispra (EMEP site) area based on daily mean 22.5 degrees wind sector classification for the period 1997 to 2004 to analyze the general air mass origin in the station area. The daily sector values are based on 96 hours 2D backward trajectories with a horizontal resolution of 50x50 km2

calculated 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 are calculated by following an air parcel every 2 hour for 96 hours back in time, 4 terms per day at 0, 6, 12 and 18 hour GMT. If a trajectory starts from outside the EMEP domain, then the coordinates giving the air parcel position are set to (0,0). The area around the arrival point extends from a radius of 150 km to a radius of 1500 km. The criteria for allocation of trajectories to one sector is that at least 50% of its given positions are found within that sector, otherwise sector 9 (not attributable) is allocated.

Our analysis, presented in Figure 4, indicates that winds from a SE to SW direction dominate the area for about 22% of the days per year, whereas the NE to NW sector represents 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 a trajectory crossings map each grid cell crossed by a trajectory on its way to the Ispra EMEP site is accounted and the total number of times a grid cell is crossed by a trajectory is summed for the whole year.

Figure 4: Air mass origin in the JRC-Ispra area based on 4-day back trajectory analysis by EMEP for 1997-2004.


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The Ispra EMEP area footprint for the year 2005 and 2006 both show that the strongest influence is expected from the northeastern part of Italy including Switzerland and the southeastern part of France which is in agreement with the longer term analysis presented in Figure 5.

2005 20062005 2006

Figure 5: Footprint of the JRC-Ispra area based on 4 day back trajectory crossings by EMEP for the year 2005 and 2006. The units are total number of times the trajectory has crossed the JRC EMEP grid cell for the whole year.

When we look at the origin of the air pollution in the form of elevated CO, O3 and NO concentrations that arrive at the Ispra EMEP site for the year 2000 (Figure 6), we find, as can be expected, that the strongest pollution events are associated with southeasterly to southwesterly winds, whereas northwesterly winds are much cleaner.

Figure 6: Air pollution origin for the Ispra area (EMEP site data) for the year 2000. Plots by courtesy of Jean-Philippe Putaud, JRC Ispra.

In Figure 7 we show the daily mean CO and O3 concentrations for the Ispra area (EMEP site data). The CO concentrations are lowest during spring and summer and


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CO daily mean concentration at the EMEP-JRC for the year 2002

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250

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750

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1250

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by a factor of 10 higher during winter mainly from local domestic wood burning for residential heating. The ozone concentrations are highest during summer when photochemical production conditions are optimal (high solar intensity, high humidity).

Figure 7: Daily mean CO and O3 concentrations for the Ispra area (EMEP site data).

In general, the relative humidity (RH) in the Ispra area is moderate to high (60-80%) apart from Föhn episodes were RHs can drop to 20%. These Föhn episodes bring relative clean northwesterly air masses from the Alps to the region in the form of strong gusty winds. Precipitation in the area is mainly in the form of short periods of intense rain by synoptic disturbances during winter and early spring and thunderstorms during the summertime and early fall.

Summarizing, we can conclude that the foreseen Campo dei Fiori mountain station has good potential for becoming an important continuous monitoring station representative for northern Italy, notably the Po Valley, which will allow us to optimize inverse modelling “top-down” greenhouse gas emission estimates for these regions with the new TM5 4DVAR inverse modelling system (Bergamaschi et al., 2007).

Outlook

In 2007 the GC system will be set-up and tested at the JRC laboratory followed by the first atmospheric observations from an elevated location at the JRC. In the meanwhile we will prepare the set-up of the long-term continuous Campo dei Fiori mountain station so that we will be able to start with measurements in the first half of 2008.

References

Bergamaschi, P., J.F. Meirink, M. Krol, and G.M. Villani, New TM5-4DVAR inverse modelling system to estimate global and European CH4 sources, this report., 2007.


Worthy D.E.F., I. Levin, N.B.A. Trivett, A.J. Kuhlmann, J.F. Hopper, and M.K. Ernst, Seven years of continuos methane observations at a remote boreal site in Ontario, Canada, J. Geophys. Res., 103 (D13), 15995-16007, 1998.


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CO daily mean concentration at the EMEP-JRC for the year 2002

0

250

500

750

1000

1250

1500

1750

2000

0 50 100 150 200 250 300 350

day of year

CO

[ppb

v]

O3 daily mean concentration at the EMEP-JRC for the year 2002

0

10

20

30

40

50

60

0 50 100 150 200 250 300 350

day of year

O3

[ppb

v]

by a factor of 10 higher during winter mainly from local domestic wood burning for residential heating. The ozone concentrations are highest during summer when photochemical production conditions are optimal (high solar intensity, high humidity).

Figure 7: Daily mean CO and O3 concentrations for the Ispra area (EMEP site data).

In general, the relative humidity (RH) in the Ispra area is moderate to high (60-80%) apart from Föhn episodes were RHs can drop to 20%. These Föhn episodes bring relative clean northwesterly air masses from the Alps to the region in the form of strong gusty winds. Precipitation in the area is mainly in the form of short periods of intense rain by synoptic disturbances during winter and early spring and thunderstorms during the summertime and early fall.

Summarizing, we can conclude that the foreseen Campo dei Fiori mountain station has good potential for becoming an important continuous monitoring station representative for northern Italy, notably the Po Valley, which will allow us to optimize inverse modelling “top-down” greenhouse gas emission estimates for these regions with the new TM5 4DVAR inverse modelling system (Bergamaschi et al., 2007).

Outlook

In 2007 the GC system will be set-up and tested at the JRC laboratory followed by the first atmospheric observations from an elevated location at the JRC. In the meanwhile we will prepare the set-up of the long-term continuous Campo dei Fiori mountain station so that we will be able to start with measurements in the first half of 2008.

References

Bergamaschi, P., J.F. Meirink, M. Krol, and G.M. Villani, New TM5-4DVAR inverse modelling system to estimate global and European CH4 sources, this report., 2007.


Worthy D.E.F., I. Levin, N.B.A. Trivett, A.J. Kuhlmann, J.F. Hopper, and M.K. Ernst, Seven years of continuos methane observations at a remote boreal site in Ontario, Canada, J. Geophys. Res., 103 (D13), 15995-16007, 1998.


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Acknowledgements

We kindly acknowledge the constructive advice and input of Martina Schmidt and colleagues (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 786701 e-mail: [email protected]


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4D-VAR System for Inverse Modeling of Atmospheric CH4:Sensitivity Analyses using Synthetic Observations

Maria Gabriella Villani1, Peter Bergamaschi1, Jan Fokke Meirink2, and Maarten Krol3,4

[1] European Commission DG Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy [2] Institute for Marine and Atmospheric Research Utrecht, University of Utrecht, Utrecht, the Netherlands[3] Wageningen University and Research Centre, Wageningen, the Netherlands [4] Netherlands Institute for Space Research, Utrecht, the Netherlands

Introduction

The new TM5-4DVAR system allows to optimize emissions of individual model grid cells, 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 in situ 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 use synthetic observations to study the system performance in more detail. For this purpose ground-based observations are generated by model forward runs, where the applied CH4 emissions inventories are assumed to represent the 'true' emissions. These measurements are then assimilated in a model run, which uses emissions perturbed from the 'true' emissions. The comparisons between retrieved and true emissions provide insights on the impact of the ground-based observations in the 4DVAR system optimization.

Experiments set-up

Model

The model adopted in this study is the off-line chemistry-transport model TM5 [Krol et al., 2005]. The experiments are performed using a CH4 single-tracer version where CH4 oxidation is based on a prescribed OH field. TM5 is run on a global domain of 6ox4o, and the year 2003 is chosen for the assimilation period. The 4DVAR assimilation system will be described in detail in Meirink et al. [2007].

Observations

Synthetic observations are created at 45 sites (NOAA sites, Figure 1). A constant measurement uncertainty of 3 ppb is assumed for the observations. In addition, the model representativeness error is estimated based on the 3D gradient of simulated CH4 mixing ratios and included in the overall data uncertainty [Bergamaschi et al., 2005]. Three different datasets are generated by using different sampling frequencies: continuous measurements sampled every 3 hours (CM); flask


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measurements weekly sampled (FM); and a mixed set of flask measurements (38 sites) and continuous measurements (7 sites) (FM+CM).

Emissions

The bottom-up inventories applied as true emissions are as described in Bergamaschi et al. [2007a]. They consist of 11 source categories. The spatial correlation length is assumed to be 500 km. In addition, temporal correlations are specified for emissions of consecutive months. For most anthropogenic categories we assumed very strong temporal correlations, while for emissions with large seasonal variations (wetlands, rice) temporal correlations are set to zero. The 4DVAR optimization is started using a priori emissions that are perturbed from the 'true' emissions. In the sensitivity experiments presented here, emissions from rice 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.

Results

Preliminary results are shown in Figures 2-5.

The 4DVAR system can retrieve total emissions for each grid cell reasonably well when using comprehensive networks of continuous ground-based measurements. This can be seen from Figure 2, which shows the comparison between retrieved CH4total annual emissions and the true values, and the grid-cell error reduction resulting from the 4D-VAR optimization.

The decrease of sampling frequency in observations leads to significant deterioration of retrieved emissions. This is observed from the maps of the methane total annual emissions, and total annual mixing ratios at four sites, obtained by assimilating the three 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 missing contribution is attributed to other source categories emitting at the same regions as rice cultivation. This occurs despite the fact that some of these categories are characterized by different time correlations.

Acknowledgments

This work has been supported by the European Commission RTD project GEMS ("Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in-situ data", contract number SIP4-CT-2004-516099, 6th Framework Programme).

References

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, Satellite chartography of atmospheric methane from SCIAMACHY onboard ENVISAT: (II)


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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 modelling system to estimate global and European CH4 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 CH4emissions using the atmospheric zoom model TM5, Atmos. Chem. Phys., 5, 2431-2460, 2005.


Meirink J.F., Bergamaschi P., and Krol M.: Four-dimensional variational data assimilation for inverse modelling of methane emissions, 2007, paper in preparation

Meirink, J.F., H.J. Eskes, and A.P.H. Goede, Sensitivity analysis of methane emissions derived from SCIAMACHY observations through inverse modelling, Atmos. Chem. Phys.,6, 1275-1292, 2006.

Figure 1: Map of the sites chosen to obtain synthetic observations.


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Figure 2: Left: CH4 total annual Emissions. CH4 total annual emissions for all categories. Top panel: ‘true’ emissions (total 511.9 Tg CH4/yr). Bottom panel: a posteriori emissions (tot. 510.9 Tg CH4/yr). Right top panel: Difference between true and a posteriori emissions. Right bottom panel: Grid-cell error reduction resulting from the 4D-VAR optimization (calculated as: (a priori - a posteriori)/ a priori).

Figure 3: Maps show the differences between derived, and true CH4 total annual emissions for all categories (total emissions 511.9 Tg CH4/yr). Synthetic observations at different sampling frequencies have been used. Top left panel: continuous measurements (total emissions 510.9 Tg CH4/yr). Top right panel: flask and continuous measurements (total emissions 510.1 Tg CH4/yr). Bottom panel: flask measurements (total emissions 509.4 Tg CH4/yr).


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145




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Figure 4: CH4 mixing ratios at atmospheric monitoring stations (flask and continuous measurements). Blue line: a priori mixing ratios; Red line: a posteriori mixing ratios. Black symbols: synthetic observations.

Figure 5: Each ‘two-graphs panel’ shows the true emissions on the top part, and the difference between a posteriori and true emission on the bottom part. Top-left panel: total emissions (all categories) (true: 511.9 Tg CH4/yr; a posteriori: 510.9 Tg CH4/yr). Top-right: rice cultivation (true: 79.6 Tg CH4/yr; a posteriori: 59 Tg CH4/yr). Bottom-left: wetlands (true: 149.7 Tg CH4/yr; a posteriori: 159.5 Tg CH4/yr). Bottom-right: ruminants (true: 88.6 Tg; CH4/yr a posteriori: 93.2 Tg CH4/yr).


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7 ANNEX1: Workshop Agenda

Thursday, 08 March 2007

9:00-9:10 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 break

10:50-11:10 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 lunch

Inverse 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 tower observations and the COMET inverse model (A. Vermeulen)

14:40-15:00 New TM5-4DVAR inverse modelling system to estimate global and European CH4 sources (P. Bergamaschi)

15:00-15:20 LSCE inverse modelling (P. Bousquet)


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15:40-16:00 Atmospheric methods in the presence of partial carbon accounting (P. Rayner)

16:00-16:20 An estimate of net CO2 exchange across the terrestrial biosphere of North America for 2000-2005 (M. Krol and W. Peters)

Discussion (chair: P. Bousquet)

16:20 - 17:20 Discussion

topics: further requirements / research+development needs for atmospheric models and

inversion 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 of

influence (sensitivity) of monitoring stations.

20:00 workshop dinner (Ristorante Conca Azzurra, Ranco)

Friday, 09 March 2007

EU-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 DG ENV 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 and needs for Kyoto reporting (G. Seufert)



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European and international GHG monitoring programs (chair: M. Ramonet)

10:40-11:00 The AGAGE network for ground based measurements of non-CO2 GHGs: 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 lunch

13:30-14:00 ICOS (C. Roedenbeck)

14:00-14:20 GMES-GAS (J. Wilson / V. Puzzolo)

Discussion (chair L. Barrie)

14:20-15:20 discussion topics: Evaluation of existing European monitoring programs for verification of European

GHG emissions Further steps and requirements towards an integrated operational European

monitoring system


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8 ANNEX2: Workshop Participants

Hakam Al-Hanbali tel.: +46 8 698 1417 Swedish Environmental Protection Agency fax: +46 8 20 29 25 Blekholmsterassen S-10648 Stockholm [email protected]

Francesco Apadula tel.: +39 02 3992 5235 CESI RICERCA fax: +39 02 3992 5235 Via Rubattino I-20134 Milano [email protected]

Florinda Artuso tel.: +390630483232 ENEA fax: +390630486678 Via Anguillarese I-00123 S.Maria di Galeria-Rome [email protected]

Leonard A. Barrie tel.: +41 22 730 8240 WMO fax: 41 22 730 8249 7 bis Ave de la Paix CH-1211 Geneve 2 [email protected]

Peter Bergamaschi tel.: +39 0332 789621 European Commission, DG JRC fax: +39 0332 785704 Joint Research Centre Institute for Environment and Sustainability [email protected] I-21020 Ispra

Philippe Bousquet tel.: +33 1 69 08 77 18 IPSL/LSCE CEA Saclay, Orme des Merisiers, bat701 F - 91191 Gif sur Yvette [email protected]

Jean-Christophe Calvet tel.: +33 561079341 Météo-France 42 Avenue G. Coriolis F - 31057 Toulouse Cedex 1 [email protected]

Derek Cunnold tel.: 404 894 3814 Georgia Tech fax: 404 894 5638 School of Earth & Atmospheric Sciences USA - 30332-0340 Atlanta, GA [email protected]

Sorin Deaconu tel.: +40-21-2071128 National Environmental Protection Agency Government of Romania Aleea Campul cu Flori [email protected] 3A, Bloc M49A, Sc. C, Ap. 1 RO - 062022 Bucharest


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Francois Dejean tel.: +45 33 36 72 59 European Environment Agency Kongens Nytorv 6 DK-1050 Copenhagen [email protected]

Frank Dententer tel.: +39 0332 786392 European Commission, DG JRC fax: +39 0332 785704 Joint Research Centre Institute for Environment and Sustainability [email protected] I-21020 Ispra

Alcide di Sarra tel.: +39 06 3048 4986 ENEA, ACS-CLIM-OSS fax: +39 06 3048 6678 Via Anguillarese 301 I - 00123 S. Maria di Galeria [email protected]

Anke Herold tel.: +49 30 28048686 Oeko-Institut fax: +49 30 28048688 Novalisstr. 10 D - 10115 Berlin [email protected]

Anastasios Kentarchos tel.: +32 2 2986733 European Commission Rue Du Champ de Mars B - 1050 Brussels [email protected]

Erasmia Kitou tel.: +32 2 29 58 219 European Commission DG-Environment B-1049 Bruxelles [email protected]

Maarten Krol tel.: +31 30 2532291 University Utrecht fax: +31 30 2543163 Princetonplein NL - 3584CC Utrecht [email protected]

Helena Looström Urban tel.: +46 8 6988512 Swedish EPA fax: +46 8 202925 Blekholmsterassen S - 10648 Stockholm [email protected]

Alistair Manning tel.: +44 1392 884243 Met Office fax: +44 1392 885681 FitzRoy Road UK - EX1 3PB EXETER [email protected]

Frank Meinhardt tel.: +49 7602 910014 Umweltbundesamt fax: +49 7602 243 Postfach 1229 D - 79196 Kirchzarten [email protected]


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Suvi Monni tel.: +39 0332 789794 European Commission, DG JRC fax: +39 0332 785704 Joint Research Centre Institute for Environment and Sustainability [email protected] I-21020 Ispra

Lorenz Moosmann tel.: +43 1 31304 5854 Umweltbundesamt GmbH fax: +43 1 31304 5800 Spittelauer Lände A-1080 Wien [email protected]

Teemu Oinonen tel.: +358 9 17341 Statistics Finland fax: +358 9 1734 3429 P.O.Box 6A 00022 Tilastokeskus [email protected] FIN - 00022 Helsinki

Jos G.J. Olivier tel.: +31 30 274 3035 MNP fax: +31 30 274 4464 P.O. Box 303 NL-3720 AH Bilthoven [email protected]

Virginia Puzzolo tel.: +32 2 2990115 GMES Bureau fax: +32 2 2920767 BREY1 9/230, Avenue d'Auderghem B - 1040 Bruxelles [email protected]

Frank Raes European Commission, DG JRC tel.: +39 0332 789959 Joint Research Centre fax: +39 0332 785704 Institute for Environment and Sustainability I-21020 Ispra [email protected]

Michel Ramonet tel.: +33 1 69 08 40 14 LSCE fax: +33 1 69 08 77 16 LSCE CE Saclay - Orme des Merisiers F - 91191 Gif sur Yvette [email protected]

Peter Rayner tel.: +33 1 69 08 88 11 LSCE/IPSL fax: +33 1 69 08 77 16 Laboratoire CEA-CNRS-UVSQ Bat. 701 LSCE - CEA de Saclay [email protected] des Merisiers F-91191 Gif sur Yvette

Stefan Reimann tel.: +41 44 823 4638 EMPA Ueberlandstr. 129 CH - 8600 Duebendorf [email protected]

Irene Remy Xueref tel.: +33 1 69 08 98 01 LSCE, CEA/CNRS/IPSL/UVSQ fax: +33 1 69 08 77 16 Bat. 703 Pte 24, Orme des Merisiers F - 91191 GIF-SUR-YVETTE CEDEX [email protected]


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Christian Rödenbeck tel.: +49 3641 57 6354 Max Planck Institute for Biogeochemistry fax: +49 3641 57 70 Hans-Knöll-Str. 10 D-07745 Jena [email protected]

Michiel Roemer tel.: +31 55 5493789 TNO fax: +31 55 5493252 Laan van Westenenk NL - 7334 DT Apeldoorn [email protected]

Bert Scheeren tel.: +39 0332 786701 European Commission, DG JRC fax: +39 0332 785022 Joint Research Centre Institute for Environment and Sustainability [email protected] I-21020 Ispra

Martina Schmidt tel.: +33 1 69 08 69 15 Laboratoire des Sciences du Climat et de fax: +33 1 69 08 77 16 l'Environnement (LSCE) Orme des Merisiers, Bat. 703, Pce 17C [email protected] F - 91191 Gif-sur-Yvette CEDEX

Guenther Seufert tel. :+39 0332 785784 European Commission, DG JRC fax: +39 0332 785022 Joint Research Centre Institute for Environment and Sustainability [email protected] I-21020 Ispra

Klára Tarczay tel.: +36 1 3464805 Hungarian Meteorological Service Kitaibel Pálutca H - 1024 Budapest [email protected]

John van Aardenne tel.: +39 0332 785833 European Commission, DG JRC fax: +39 0332 785704 Joint Research Centre Institute for Environment and Sustainability [email protected] I-21020 Ispra

Alex Vermeulen tel.: +31 224564194 ECN fax: +31 224568488 Westerduinweg,3 NL - 1755 ZG Petten [email protected]

Maria Gabriella Villani tel.: +39 0332 786620 European Commission, DG JRC fax: +39 0332 785704 Joint Research Centre Institute for Environment and Sustainability [email protected] I-21020 Ispra

Julian Wilson tel.: +39 0332 786620 European Commission, DG JRC fax: +39 0332 785704 Joint Research Centre Institute for Environment and Sustainability [email protected] I-21020 Ispra


155

European Commission

EUR 22893 EN – Joint Research Centre – Institute for Environment and Sustainability Title: Atmospheric Monitoring and Inverse Modelling for Verification of National and EU Bottom-up GHG Inventories - report of the workshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories" under the mandate of Climate Change Committee Working Group I, Casa Don Guanella, Ispra, Italy (08-09 March 2007) Editor: P. Bergamaschi Luxembourg: Office for Official Publications of the European Communities 2007 – 153 pp. EUR – Scientific and Technical Research series – ISSN 1018-5593 ISBN 978-92-79-06621-4

Abstract

The workshop "Atmospheric monitoring and inverse modelling for verification of national 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. This report presents the summary and conclusions of the workshop and summaries of all workshop presentations.


155

European Commission

EUR 22893 EN – Joint Research Centre – Institute for Environment and Sustainability Title: Atmospheric Monitoring and Inverse Modelling for Verification of National and EU Bottom-up GHG Inventories - report of the workshop "Atmospheric monitoring and inverse modelling for verification of national and EU bottom-up GHG inventories" under the mandate of Climate Change Committee Working Group I, Casa Don Guanella, Ispra, Italy (08-09 March 2007) Editor: P. Bergamaschi Luxembourg: Office for Official Publications of the European Communities 2007 – 153 pp. EUR – Scientific and Technical Research series – ISSN 1018-5593 ISBN 978-92-79-06621-4

Abstract

The workshop "Atmospheric monitoring and inverse modelling for verification of national 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. This report presents the summary and conclusions of the workshop and summaries of all workshop presentations.


LB-NA-22893-EN

-C

The mission of the JRC is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policy-making process, it serves the common interest of the Member States, while being independent of special interests, whether private or national.

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NATIONAL AND EU BOTTOM-UP GHG INVENTORIES

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