INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE

IPCC/OECD/IEA PROGRAMME ON NATIONAL
GREENHOUSE GAS INVENTORIES

Expert Group Meeting on
Methods for the Assessment of Inventory Quality(co-sponsored by CKO/CCB)

Bilthoven, The Netherlands
5-7 November 1997

Meeting Report

Supporting material prepared for consideration by the Intergovernmental Panel on Climate Change. This
supporting material has not been subject to formal IPCC review and approval process.

Acknowledgements
This report was prepared by Bo Lim and Pierre Boileau with contributions from Andre Van Amstel,
Leon Janssen, Jos Olivier, Carolien Kroeze, Tim Simmons, Susan Subak, Yamil Bonduki and Laurie
Michaelis. We are grateful to many scientists and inventory experts who provided comments on thedraft meeting report. We would also like to thank the speakers, co-chairs and rapporteurs at themeeting, including: Roberto Acosta, Dudley Sama Achu, Diogenes Alves, Gregg Marland, Wiley
Barbour, Oluf Bockman, Phillipe Ciais, Dick Derwent, Xu Deying, David Fowler, Kari Gronfors,
Oystein Hov, William Irving, Pojanie Khummunghol, John Taylor, D.C. Parashar, Bojan Rode,
Arthur Rypinski, Ron Sass, Youba Sokona, Leonor Tarrason, Ian Galbally, Arvin Mosier, Katarina
Mareckova, Gregg Marland, Thomas Martinsen, Frank Neitzert, Tinus Pulles and G.X. Xing. We
would also like to thank the members of the secretariat and organisers of the meeting from the Dutch
National Institute for Public Health and the Environment (RIVM) and the CKO/CCB Centre on
Climate Research Utrecht, and Wageningen Climate Change and Biosphere Programme. In
particular, Margot Van Blomestein (RIVM), Audrey Glynn (OECD), Karen Treanton (IEA) andDavid Griggs (IPCC), without whom the organisation of the meeting would have been very difficult.
Ann Johnston and Amy Emmert provided editing and word processing assistance.

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Table of Contents
SUMMARY ...........................................................................................................................................................1
1. BACKGROUND ...............................................................................................................................................3
2. OBJECTIVES ...................................................................................................................................................3
3. VERIFYING THE EFFECTIVENESS OF THE IPCC METHODOLOGY...............................................4
4. ASSESSING THE QUALITY OF NATIONAL INVENTORIES ................................................................5
4.1 APPROACH 1: INVENTORY QUALITY ASSURANCE ...................................................................................................6
4.1.1 Background ...............................................................................................................................................6
4.1.2 Assessment.................................................................................................................................................6
4.2 APPROACH 2: INVENTORY COMPARISONS .............................................................................................................7
4.2.1 Background ................................................................................................................ ...............................7
4.2.2 Assessment................................................................................................................ .................................7
4.2.3 Limitation of comparisons................................................................................................... ......................8
4.3 APPROACH 3: MODEL COMPARISONS ...................................................................................................................94.3.1 Background ................................................................................................................ ...............................94.3.2 Assessment................................................................................................................ .................................94.3.3 Limitation of comparisons................................................................................................... ....................11
4.4 APPROACH 4: DIRECT EMISSION MEASUREMENTS...............................................................................................13
4.4.1 Background .............................................................................................................................................13
4.4.2 Assessment...............................................................................................................................................13
5. EVALUATING THE UNCERTAINTIES IN NATIONAL GREENHOUSE GAS INVENTORIES ......14
5.1 CARBON DIOXIDE ..............................................................................................................................................15
5.2 METHANE ............................................................................................................................... ..........................17
5.3 NITROUS OXIDE ................................................................................................................................................19
6. EXPERT GROUP RECOMMENDATIONS ...............................................................................................21
7. NEXT STEPS ..................................................................................................................................................22
REFERENCES....................................................................................................................................................23
ANNEX 1: WORKING GROUP REPORTS....................................................................................................25
ANNEX 2: GLOSSARY .....................................................................................................................................36
ANNEX 3: LIST OF PARTICIPANTS.............................................................................................................40

Summary
In September 1996, the IPCC Plenary identified the quality of national greenhouse gas inventories asa priority. Governments noted that the Revised 1996 IPCC Guidelines for National Greenhouse Gas
Inventories (Guidelines) should result in emissions estimates that can be monitored and verified forcompliance. They requested that the IPCC/OECD/IEA Programme on National Greenhouse Gas
Inventories convene an expert meeting on the quality of national greenhouse gas (GHG) inventories.
At the invitation of the Dutch Government, an IPCC Expert Meeting on Methods for the Assessmentof Inventory Quality was held in Bilthoven (5-7 November 1997). The primary objectives of themeeting were:•= to examine whether the IPCC Methodology produces emissions estimates of good quality
which can be monitored and independently verified;•= to examine approaches for assessing and improving the quality of national GHG inventories
and to identify the potential of these approaches;•= to identify sources of uncertainty in national GHG inventories and to suggest ways for
reducing them;•= to recommend actions for improving the Revised 1996 IPCC Guidelines for National
Greenhouse Gas Inventories.
The meeting examined four approaches to assess the quality of GHG inventories. These were:
Inventory quality assurance - Evaluation of the underlying data and algorithms of inventories
according to a range of criteria.
Inventory comparisons - Comparisons among inventories, methodologies and input data to identify
major discrepancies.
Model comparisons - Comparisons of inventories with atmospheric budgets to assess the accuracy of
the inventories.
Direct emission measurements - Comparisons of inventories with atmospheric measurements toassess the accuracy of inventories.
Comparisons among the diverse approaches were difficult. The suitability of each approach depends
on the gas, and source/sink sector under consideration. Participants nevertheless endorsed all
approaches as appropriate techniques for assessing and improving inventory quality in different
ways.
Both inventory quality assurance and inventory comparisons were recognised as effective tools that
could be applied at a national level. The participants recommended further work in these areas.
Over the longer term, model comparisons were recognised as useful to test the scientific credibility of
GHG inventories. At the continental level, models could be used to estimate carbon fluxes fromland-use change and forestry. Model comparisons are valuable since this is an area with medium tohigh uncertainty in the reported global estimates. At the national level, model comparisons show
greater potential for assessing inventories of CH4 than for other GHGs. Participants recommended
continued co-operation between the inventories community and modellers.
Direct emission measurements lend themselves to the evaluation of national inventories of CH4 andN2O. This approach is applicable to both diffuse sources and localised ‘hotspots’ of trace gases.
Further progress is needed to ensure that national GHG inventories can be monitored and verified for
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1compliance under the Kyoto Protocol. Several steps were recommended to advance current
understanding of sources and removals of GHGs. The following steps are seen as critical toproviding better inventory data. Over the long term, their implementation may lead to further
revision of the Guidelines - the basis for legally binding commitments.
Firstly, participants suggested there is a need to develop a common terminology for inventory
quality. Key criteria are completeness, consistency, transparency, and comparability. Other terms
include: accuracy, precision, error, and uncertainty. Although their meanings are distinct, theseterms are often not used rigorously. Validation and verification were considered as useful concepts
to describe quality assessment. The Glossary of Terms prepared for the meeting could help develop
consensus on their definitions (see Annex 2).
=

Secondly, participants identified and quantified sources of uncertainty in national emissions
estimates, and recommended ways to minimise them. For biogenic and natural sources and sinks,
however, there are limitations imposed by the natural variability of the system. More data andimproved methods will help reduce uncertainties, but not eliminate them. For many gases andsectors, the barrier to better inventory quality is often the underlying data, not the IPCC
Methodology, which is scientifically robust. But some methodologies are still evolving; such as
land-use change and forestry, soil carbon, and the ‘new gases’ (e.g. HFCs, PFCs, and SF6). For the‘new gases’, the main issue is tracking changes in emission rates as a result of evolving technology.
Thirdly, participants developed likely ranges of uncertainty for GHG inventories by sector. They
identified a framework for estimating and reporting degrees of uncertainty for national estimates as ahigh priority. Such a framework would improve the current IPCC classification scheme of high,medium and low confidence levels. Both qualitative and quantitative approaches were suggested, allrequiring further technical development and discussion. Future meetings on uncertainties wererecommended to address this issue.
Participants recommended that the IPCC develop codes of good practice to improve the availabilityof underlying data for inventories. These codes will help countries prepare national inventories,
develop emission factors and conduct direct emissions measurements. They would be based oninternationally accepted procedures. Moreover, they could be applied to obtain data in regions where
none exist and to extend the existing global databases on activity data and emission factors. These
data could eventually be incorporated into the Guidelines. An example is conducting forest surveys
in order to establish growth rates, carbon densities, etc.
Participants felt that a clearer definition of the terms anthropogenic and natural was an important
requirement for reducing the uncertainty of biogenic sources and removals of GHG. Thisclarification would help differentiate between anthropogenic and natural emissions in national GHG
inventories - a principal objective of the Guidelines.
Finally, participants recommended the use of the IPCC Reference Approach from the Energy Sectorof the Guidelines as a verification procedure for CO2 emissions from the energy sector. Theysuggested that the IPCC develop reference approaches in other sectors, again for verification.

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21. BackgroundIn 1996, the IPCC Plenary in Mexico City identified inventory quality as an emerging issue. The
Plenary noted that the ‘Guidelines should produce estimates that can be monitored and independently
verified through consistency with independent sources of information and comparability between
countries.
One year later, Annex-I Parties agreed to legally binding commitments under the Protocol (Kyoto, 112 December 1997). Clearly, the quality of national GHG inventories has implications for theeffective monitoring and verification of commitments under the Protocol. The uncertainty of
estimates may also significantly influence the design of a trading system. Neither of these technicalissues has yet been resolved.
To address the IPCC request, an IPCC Expert Meeting on Methods for the Assessment of Inventory
Quality was held in Bilthoven (5-7 November 1997). The meeting was hosted by the Dutch National
Institute for Public Health and the Environment (RIVM) and co-sponsored by the CKO/CCB Centre
on Climate Research Utrecht, and Wageningen Climate Change and Biosphere Programme.
The meeting brought together over 60 participants from 20 countries. Among these were
atmospheric modellers, empirical scientists and inventory experts. The meeting examined a range ofapproaches for the validation and verification of national GHG inventories, as well as the estimation
and reporting of uncertainties in national estimates. The meeting also considered the direction of
future work. This report summarises discussions and presents recommendations from that meeting.

2. Objectives
The main objectives of the meeting were :•= to examine whether the IPCC Methodology produces national, regional and global emissions
estimates of good quality which can be monitored and independently verified;•= to examine approaches for assessing and improving the quality of national GHG inventories
and to identify the potential of these approaches;•= to identify sources of uncertainty in national GHG inventories and to suggest ways for
reducing them;•= to recommend actions for improving the Revised 1996 IPCC Guidelines for National
Greenhouse Gas Inventories.
In addressing these objectives, a clear direction for future work should be obtained. The technicaloptions were examined while keeping in mind their relevance to policy.

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33. Verifying the effectiveness of the IPCC methodology
Determining the effect of increased GHG emissions on the environment requires a methodology
which will accurately estimate emissions at a global scale. The Guidelines is a methodology appliedby most non-Annex I Parties. One of the meeting’s objectives was to verify the methodology.
National GHG inventories were aggregated on a global scale and compared with IPCC global
budgets (Table 3-1 to Table 3-3). For each gas, national communications submitted to the UNFCCC
and country studies were used to obtain global inventory estimates. These data were augmented withemissions estimates from published scientific databases (Van Amstel et al., 1997).
For CO2, the difference between the estimated global budget of fossil fuel and the sum of theavailable inventories is small (<10%). It must be noted, however, that the IPCC global budget (1996)
was also obtained from a global database of emissions estimates. Because of this, the estimatedglobal budget and the sum of inventory data are expected to be similar. This makes the comparison
less meaningful.
Table 3-1: Global totals for carbon dioxide
SourceFirst national communicationscCountry studiesdGlobal database
Total

a

Number ofcountries
34
31
124
189

Total emissions(Tg/year.)
13 6755 081
6 666
25 422Global budgetb(Tg/year.)e
28 233a = UNFCCC (1997), b = IPCC (1996), c = Braatz et. al. (1996), d = TNO-RIVM (1996)
e = Global anthropogenic budget from IPCC, 1996, Second Assessment Report (7.7 GtC x 44/12)

For CH4, there is a discrepancy between the global budget and the sum of the available inventories.
In this case, the difference is within the expected 30% level of uncertainty. The global budget was
obtained from observed atmospheric increases and is therefore independent of the inventory data.
The agreement between the two budgets gives confidence in the inventory methods.
Table 3-2: Global totals for methane
SourceFirst national communicationscCountry studiesdGlobal database
Total

a

Number ofcountries
33
31
125
189

Total emissions(Tg/year.)

Global budgetb(Tg/year.)
108
66
121
295

375

e

a = UNFCCC (1997), b = IPCC (1996), c = Braatz et. al. (1996), d = TNO-RIVM (1996))
e = Global anthropogenic budget from IPCC, 1996, Second Assessment Report

For N2O, the sum of available inventories falls within the range of the calculated global budget. The
global budget was obtained from observed atmospheric increases and is therefore independent of theinventories.

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Table 3-3: Global totals for nitrous oxide
SourceFirst national communicationscCountry studiesdGlobal database
Total

a

Number ofcountries
33
31
125
189

Global budgetb(Tg N2O/year.)

Total emissions(Tg N2O/year.)
2.05
0.12
2.8
4.9

5 to 13ea = UNFCCC (1997), b = IPCC (1996), c = Braatz et. al. (1996), d = TNO-RIVM (1996)
e = Global anthropogenic budget from IPCC, 1996, Second Assessment Report (3 to 8 Tg(N)/yr. multiplied by 44/28)

The values in Table 3-3 were obtained primarily by using the previous IPCC Methodology for N2O(IPCC, 1995). Using the current Guidelines (IPCC, 1997) the global emission was estimated at 11
Tg N2O/year. (Mosier et al., 1998). This is in the midrange of the global budget again giving
confidence in the current Guidelines methodology at the global level.

4. Assessing the quality of national inventories
The meeting considered several approaches to assessing the quality of GHG inventories:

Approach 1: inventory quality assurance
Evaluation of the underlying data and algorithms of inventories according to a range of criteria.
Approach 2: inventory comparisonsComparisons among national, regional and global inventories, including their estimation
methodologies and input data.
Approach 3: model comparisonsComparisons of national, regional and global inventories with atmospheric budgets derived fromatmospheric concentrations and chemical transport models.
Approach 4: direct emission measurements
Comparisons of national or regional inventories with atmospheric measurements upwind or
downwind from local, national or regional sources.
As a fifth approach, participants suggested the use of indicators of economic and other activities toverify emission trends in countries. One drawback of this method is that correlation between
emissions and an independent variable does not necessarily imply cause and effect. At best, the useof indicators would be limited to flagging anomalies, rather than for assessing inventory quality.
Examples are Schipper et al. (1997) for CO2 indicators and Bosseboeuf et al. (1997) for energy
indicators.
It should be noted that Approaches 1, 2 and 4 were examined in relation to each of the main GHG.
For Approach 1 the details are provided in Table 5-1 to Table 5-3. Approach 3, model comparisons,was examined separately in greater detail.

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4.1 Approach 1: inventory quality assurance
4.1.1 Background
This approach evaluates the sources of underlying data and the methods used to prepare inventories.
Evaluation may include: tracing the original sources of emission factors and testing the sample set for
appropriateness, reproducibility, statistical variance, etc.; assessing the robustness of the survey
techniques used for collecting activity data; identifying and evaluating the reliability of national andinternational sources of emission factors and activity data, and comparing independent sources ofthese data; assessing the algorithms used to prepare emissions estimates. This approach can allow
the uncertainties and systematic differences of data and emissions estimates to be quantified.
4.1.2 Assessment
Criteria for inventory quality commonly include: accuracy, precision, uncertainty, error, reliability,
completeness, comparability and transparency. Participants had difficulty in ranking these criteria
since their relative importance depends on the purpose of the inventory (scientific, policy, orotherwise). They recognised the need to clarify what is meant by accurate inventories, and suggested
that a common terminology be developed by the IPCC. The Glossary of Terms prepared for thismeeting was a useful starting point. Some participants suggested that the Glossary be expanded toinclude ‘top-down’ and ‘bottom-up’.
Most participants agreed that key criteria (unranked) were: completeness, consistency, transparency,
comparability, and accuracy. Others differentiated comparability from consistency. For consistency,
there must be internal agreement on methods or data within a country whereas comparability means
consistency of methods or data between countries.
The definitions of validation and verification were debated. Many participants thought thesedefinitions were clear. Validation is a procedure which provides, by reference to independentsources, evidence that an inquiry is free from bias or otherwise conforms to its declared purpose.
Verification is a procedure to test the internal agreement of data or procedures. Some argued thatvalidation and verification were used interchangeably. Further clarification is needed.
National representatives believed inventories had two main purposes: analysing trends, and assessing
the effects of policy and measures. The quality of GHG emissions estimates should be adequate forboth purposes. Participants felt a need to develop standard operating procedures in National
Communications and that certified ISO standards should be developed. Some participants
recognised that the uncertainty of inventories was an issue for emissions trading and for monitoring
compliance under the Protocol.
Atmospheric modellers felt that the quality of anthropogenic GHG inventories was adequate for their
purposes. Uncertainties of anthropogenic emission estimates based on inventories are typically lower
than, or comparable with, model uncertainties or those of natural fluxes.
Participants also recognised that data generation and management in inventories were problems.
Some participants felt strongly that the barrier to improving inventory data was internal co-ordinationin developing countries. Generating more data would not solve the problem. These countries should
improve their capacity to manage information systematically. Many participants said that moreinvestment should go to developing countries where energy and land-use change and forestry
statistics were poorly known. Some suggested that satellite measurements and remote sensing could
help resolve the data issue. Others suggested that this method of collecting data was too expensive tobe practical.
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4.2 Approach 2: inventory comparisons4.2.1 Background
Inventory comparisons are often a two-step process. The first step is a comparison of national,
regional or global emissions estimates at a sectoral level. This initial comparison identifies grossinconsistencies, but does not provide reasons for discrepancies. Only a second comparison ofemission factors, activity data, or algorithms, provides clues as to why the emissions estimates differ.
Providing there is sufficient transparency in the inventory documentation, these simple comparisons
can prove useful.
Several researchers have made inventory comparisons. Moran and Salt (1996) compared theinventories prepared by the IPCC Methodology with those by CORINAIR. Marland and Rotty(1984) established uncertainty ranges of emissions estimates. Marland and Boden (1993) and
Graedel et al. (1993) used comparisons of activity data and inventories for quality control. Intercountry comparisons of inventory data have also been performed in the development of the IPCC
Guidelines (IPCC 1992a and b, 1994).
4.2.2 Assessment
Carbon dioxide
As a first step, GHG inventories from different sources (e.g. UNFCCC, United States Countries
Studies Programme (USCSP) with the Emissions Database for Global Atmospheric Research(EDGAR)) were compared (Van Amstel et al., 1997 and Marland et al., 1997). Comparisons showedthat differences between the inventories are generally less than 10% for fossil fuel emissions;differences are greater in the land-use change and forestry, agriculture, and for biofuel combustion(Van Amstel et al., 1997).
Participants discussed ways to improve the IPCC Methodology. For energy and industrial processes,
no immediate action was considered necessary. This was not the case for land-use change andforestry. Here, problems originate from both the methods and data. Participants were more confident
with flux estimates from forestry than for data from land-use change. They noted that clarification of
the term anthropogenic was a high priority.
Of the approaches evaluated, participants identified inventory comparisons as the most useful forevaluating and improving CO2 inventory quality. National CO2 inventories in the energy sector
should be compared with estimates from the IPCC Reference Approach. The International Energy
Agency has already published these emissions estimates using the IEA energy balances as input data
(IEA, 1997). Results show that differences between national and IEA emissions estimates are often
within 5%. For several countries, these variations can be explained.
MethaneMethane inventories from National Communications and the EDGAR scientific database differ in all
sectors by more than 10%. Discrepancies are highest in the land-use change and forestry, andagriculture sectors (Van Amstel et al., 1997).
Participants noted that comparisons between EDGAR and IPCC results had been useful in identifying
where the gaps were in methane inventories. The general point was made that sometimes changes inannual flux were due to natural variability rather than changes in activities, e.g. variability intemperature can affect methane emissions from rice paddies. In this case, emission factors are notstatic over time.
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In The Netherlands, a comparison between the revised method and earlier estimates for methane
emissions from landfills confirmed that it was important to consider the time dynamic. First-orderdecay equations can be used for regions with no historical data for wastewater generation or
landfilling. Information on the range of landfill sizes by area extent and volume would be useful.
Also, estimating the proportion of total waste disposed in the size categories would improve theaccuracy of the methodology.
Some verification exercises have been completed for biomass burning. Researchers at King’s
College, London estimated CH4 emissions from biofuel for many countries. These results were foundto differ from national inventories by as much as a thousand fold. Better emission factors are neededfor developing countries for all categories of biomass burning.
For enteric fermentation, comparisons of emission factors would be more revealing than comparisonsof the emissions themselves.
Nitrous oxide
The Revised IPCC Methodology for agricultural N2O demonstrates that globally, emissions for this
sector were significantly underestimated by about a factor of three, based on the 1995 Guidelines.
Comparisons of inventories based on an outdated IPCC Methodology may therefore be of limited
value.
Participants thought that national GHG inventories for all gases could be compared with:•= other national inventories;•= emission factors from independent databases;•= inventories prepared with the Revised IPCC default Methodology;•= scientific databases, e.g. GEIA and EDGAR inventories.
If independent national data are used to prepare inventories and these are compared, this isconsidered to be validation. However, if similar emission factors are used to construct inventories,
the data are not independent. Comparison of these inventories is considered to be verification.
Furthermore, if the activity data originate from similar data sources, this is also consideredverification. Verification techniques can be used to check the completeness of inventories and to
identify outliers. Two conditions for proper verification are well-defined sectoral reporting formats
and sufficient detail in standard reporting tables.
Currently, the IPCC Overview and Summary Tables do not supply sufficient detail for comparisons.
For complete verification of national inventories, more detailed information than is provided in the
IPCC Guidelines is necessary. Detailed worksheets are required.
For more general conclusions about inventory and model comparisons, see Van Amstel et al. (1997).
4.2.3 Limitation of comparisonsBecause definitions of sources/sinks of inventories often differ, it is difficult to make directcomparisons without first harmonising the sectors. Therefore, to carry out such comparisons, thestructure of the emissions datasets must be aligned and be comparable.

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4.3 Approach 3: model comparisons4.3.1 Background
Comparisons of atmospheric modelling results with inventories are relatively novel. Such an
approach might potentially be used as an independent source to validate GHG inventories. In thisapproach, inventories are compared with emissions estimates (or budgets) derived from observed
atmospheric concentrations and chemical transport models, using techniques such as inverse
modelling. Likewise, estimated and observed atmospheric concentrations can also be compared. In
this case, models are applied in the forward mode to obtain estimated concentrations. Gridded
emissions estimates are typically used as input data. A key feature of model comparisons is that thesignificance of the results depends on both the difference and the confidence limits of the two
estimates.
4.3.2 Assessment
Carbon dioxide
At present, the uncertainty of the global CO2 budget calculated by models is estimated to be about
25%. This is considerably higher than the uncertainty of national inventories (about 10%)(Van
Amstel et al., 1997). The uncertainty of atmospheric models would need to be improved if theirresults were to serve as useful comparisons with GHG inventories.
Currently, these cannot be used to validate or verify CO2 inventories at the national level. The use ofmodels is more promising at the global scale (Table 4-1). Fluxes from the terrestrial biosphere are notas well known compared to fluxes from other reservoirs (e.g. fossil fuels, oceans). Consequently,
models could be used to constrain terrestrial CO2 fluxes. Currently, the spatial resolution of models is
limited to the zonal and continental level. To obtain regional and national detail, more atmosphericmeasurements and progress in model development would be needed. With the possible exception of
isotopic studies, few other options to validate terrestrial carbon fluxes exist.
Table 4-1: Uncertainty and potential for use of modelling for CO 2
Model scale

Model uncertainty at this scale

Potential for use of the model atthis scale

Global+/- 1 Gt C

Land use-change and forestry;*systematic bias can be
determined.

Zonal+/- 1 Gt C

Sources and sinks can bediscriminated roughly inlatitudinal bands.

Regional

Need for measurements overcontinental areas at stations andfrom aircraft.

In the coming years, regional
budgets of the biosphere arepossible.

National/Local

There is a need for feasibility
studies.

--* Based on the assumption that fossil fuel emissions and sea-air exchange are reasonably well known.

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Methane.
Models estimate CH4 budgets at the global and zonal scales with reasonable success. In Europe,
where the sampling network is well developed, regional budgets are feasible. The uncertainties ininventories and atmospheric budgets are of a similar magnitude (25-30%)(Van Amstel et al., 1997).
Such comparisons could be used to validate national inventories. Comparisons could potentially yield
the sectoral detail needed for the refinement of the Guidelines, particularly if supplementary isotopic
information is available.
Amongst the gases considered, CH4 shows the highest potential for validation using models. This isdue to two factors. First, the atmospheric distribution of CH 4 is relatively non-homogeneous; thisfacilitates the use of atmospheric transport models to map emissions estimates. Second, model
uncertainties are comparable (20-40%) with uncertainties of national inventories (+/-30%) (Table 42). Uncertainties, here, are derived by expert judgement. Both factors greatly enhance theeffectiveness of model comparisons. Moreover, participants noted that the potential of using models
exists at all spatial scales. Widespread application of this approach is currently limited by the small
number of monitoring sites and measurement data.
Table 4-2: Uncertainty and potential for use of modelling for CH 4
Model scale

Model uncertainty at this scale

Potential for use of the model atthis scale

Global+/- 20%Global estimates "with
improved accuracy".

Zonal> 20%, depending on latitude.

Zonal estimates,
Rough sector analysis.

Regional

20 - 40%
Need for more measurements.

Country budgets,
Rough sector analysis.

National/Local

20 - 40%
Need for more measurementsfrom stations, aircraft andsatellites.

Country scale GHG budget
validation. Rough sector
disaggregation.

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10Nitrous oxide
Nitrous oxide budgets can be estimated at global and zonal scales. Based on a preliminary
uncertainty analysis, the uncertainties in inventories (70-100%) are larger than global budgets (3050%) (Van Amstel et al., 1997).
However, for N2O, the potential for inventory validation was greater than anticipated. Modellers
identified their potential use with enthusiasm. At all spatial scales, model uncertainties (+/- 40%)
were high (derived by expert judgement). But these uncertainties were comparable to those ofnational N2O inventories (Table 5-3). The similarity of the uncertainty ranges between models andinventories allows for model comparisons to be used for validation. At Mace Head (Ireland),regional scale experiments have been successful in constructing emission budgets of anthropogenic
N2O from European sources (Derwent et al., 1998). Further modelling efforts should focus on
improving the understanding of the stratospheric sink.
Table 4-3: Uncertainty and potential for use of modelling for N 2O
Model scale

Model uncertainty at this scale

Potential for use of the model atthis scale

Global+/- 50%Estimate stratospheric sink
constrain anthropogenic source.

Zonal

4 zonal bands +/- 30%.

Estimate zonal budget.

Regional

Need measurements of GHGs
over continents andsatellite data.

Continental/country budget.
Country budget validation.

National/Local+/- 40% for some countries.

Country scale GHG budget
validation.

Note : Inverse modelling requires a new focus on the design of the monitoring networks from background measuring
stations to new stations allocated downflow from polluted areas (continental areas) (relevant for compliance)

4.3.3 Limitation of comparisons
Model comparisons are limited by several factors. The most important of these are the comparabilityof datasets and uncertainties surrounding inventories and models.
Comparability of the dataset
The structure of emissions estimates from atmospheric models and inventories varies. Inverse
models generally provide estimates of net (anthropogenic and natural) concentrations, or budgets andsource categories, of a gas at a particular location and time. By contrast, inventories prepared for the
UNFCCC include only anthropogenic emissions/removals, they are averaged over one year, and are
typically at the national scale and by sector. For comparisons to be meaningful, emissions estimatesfrom both the inventories and models must relate to the same group of GHG source/sink sectors, andover the same time period.

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11=Uncertainties in model comparisons
Both inventories and model comparisons are subject to sampling errors, systematic bias, andtheoretical uncertainties. In national inventories, uncertainties are present in field measurements,
upscaling of measurements, and the statistics of national (economic) activities. Uncertainties in
global budgets come about from the changes in reservoirs, the magnitude of the reservoirs
themselves, calibration of the models, interpolation of the field measurements, the inherent
limitations of the models and uncertainty in processes. If a modelling technique has a wider range oferror than an inventory methodology, it may not improve the accuracy or precision of an inventory.
Any difference between the inventory and modelling estimates must also be tested for significance.
Therefore, more extensive quantification of uncertainties in inventories and atmospheric models is
required.

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4.4 Approach 4: direct emission measurements
4.4.1 Background
With current observing techniques, it is possible to measure the flux of a gas upwind and downwindof major cities and even nations. These and similar techniques provide direct emission and uptake
measurements on scales approaching national inventories which have been applied on the east coastof the United States and in the United Kingdom. They may offer an alternative method of validating
national GHG emissions and removals.
4.4.2 Assessment
Carbon dioxide
It was not clear to participants at the meeting that direct flux measurements would lead to costeffective improvements for estimating CO2 from Energy and Industrial Processes. Forest tower flux
measurements might help to estimate the net transfer of carbon from forests to the atmosphere.
MethaneDirect Emissions Measurements lend themselves to large-scale experiments and surveys of oil andgas leaks, landfills, enteric fermentation, biomass burning and agricultural CH4. Atmosphericmeasurements need to be interpreted in conjunction with remote sensing and isotopic analysis.
Nitrous oxide
Typically, biogenic emissions of N2O show high spatial and temporal variability. Measurements andemissions should therefore be integrated over sufficiently long sampling periods to smooth out
signals and seasonal variations. Currently, about half of global fertiliser N is applied in Asia and thisis expected to increase. Yet research on emissions has mostly been carried out in Western Europe
and North America, where climates and agricultural practices differ.
Direct flux measurements of agricultural sources at representative sites can be used to improve or tovalidate emissions estimates. Presently, emissions estimates are based on a limited number ofmeasurements. These do not always cover the ‘full cycle’ of crop production, agricultural, animal
and waste management practices. The use of standard measurement techniques, developed inWestern Europe and North America, for obtaining estimates is recommended for areas whereemission factors are lacking.
In the case of biomass burning, measurements should be carried out over sufficiently long sampling
periods to capture post-burn effects following deforestation.

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5. Evaluating the uncertainties in national greenhouse gas
inventories
There are five independent sources of uncertainty in emission inventories:•= measurements of fluxes;•= development of emission factors;•= activity data;•= calculation method;•= errors in upscaling from the process to the country level.
Developing quantitative knowledge about each of these sources is an essential step in improving thequality of inventories. Measurement uncertainty can be quantified and reduced only through adetailed understanding of the measurement procedures and sampling techniques. Uncertainties in theinventory method varies by source/sink and can be understood through detailed study of individual
categories.
One step is to identify some of the main sources of uncertainty in the national communications andin the IPCC Methodology. Sources of uncertainty in inventories vary depending on the activity. For
fossil fuel combustion, the emission factors are fairly well known compared to the fuel use.
Conversely, for agricultural N 2O, fertiliser statistics are generally better than the emission factors.
The IPCC Guidelines contain a table on the sources of uncertainties in GHG estimates. Participantsat the meeting considered how this table could be improved, e.g. emission factors and activity data.
The table was highly contentious. Participants considered such a table was very useful but it shouldbe improved by:Firstly, developing the table with greater sectoral breakdown, and making it consistent with other
IPCC Reporting Tables. A rating system was proposed as a semi-quantitative approach to estimating
uncertainties. This procedure would standardise uncertainty estimation, and be an immediate
improvement over the current IPCC Methodology. Countries could assign an uncertainty rating to anemission coefficient, activity data or emissions estimate. To capture the uncertainty due to thealgorithm used, an additional column should be added to the table.
Secondly, countries should be encouraged to report their inventory data as a range rather than an
absolute number. This would illustrate the variability which can be expected, given the uncertaintyof the estimate. The Guidelines should also help differentiate between random error and systematicbias, and to identify the main sources of error.
Finally, some participants suggested developing a hierarchy of uncertainties, as this might establish
priorities to reduce them.

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Participants then considered the:•= uncertainty of national GHG inventories on a sectoral basis;•= reduction of uncertainty in the IPCC Guidelines;•= quantification of uncertainty in national GHG inventories;•= potential uses of the approaches for reducing uncertainties.
Participants emphasised that they each applied different methods to define, estimate and report
uncertainties.

5.1 Carbon dioxide
Three sectors were considered for CO2: Energy, Industrial Processes and Land-use Change andForestry. Land-use change was separated from forestry because of their differing uncertainties.
Participants estimated the uncertainty in GHG inventories and discussed ways of quantifying andreducing uncertainties.
Uncertainty in GHG inventories
Participants felt reasonably confident with the quality of energy data; energy emissions were
estimated with the lowest uncertainty (<10%). Emissions and removals from land-use change werehighest (>50%), forestry was slightly lower (>25%) (Table 5-1). Uncertainties for emissions fromindustrial processes were estimated at 15%. This uncertainty might be reduced if combustion andprocess data could be separated.
Quantifying uncertainty
A priority is to develop a framework for estimating and reporting uncertainties. The
IPCC/OECD/IEA Inventories Programme should study how countries estimate their uncertainties fornational inventories and use these ideas to scope future work.
Several semi-quantitative techniques for estimation of uncertainties should be evaluated according to
the attributes of the method and the quality of the underlying data. These ranking systems could bebased on one of four proposed schemes:•= classification as quantified by percentage ranges of uncertainty (a nominal scale could be ‘very
high, high, medium, low and very low’),•= uncertainties of sectoral emissions expressed as a percentage of its contribution to total
national emissions,•= upper and lower ranges for emissions estimates, and•= quality assurance ranking systems.
Reducing uncertainty
Participants felt that improved activity data are required for:•= energy data in some countries (e.g. former Soviet Union);•= land-use data for all countries;•= forest inventories, deforestation and reforestation rates;
They also felt that a clearer definition of anthropogenic and natural emissions in the land-use change
and forestry sector was required.
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Table 5-1: Uncertainty and proposed improvements for CO 2 emission estimates
EnergySector

Uncertainty<10%

Industrial Processes

15%

Land-use Change>50%

Forestry>25%

Overarching--s:\applic\inv\workshop\tdbu\postmeet\tdbusm11.doc

Inventory quality assurance
The quality is good for fuel combustion where
activity data exist.
Better focus is needed on the industrial process.
Distinguish between combustion emissions andprocess.
Better tropical deforestation data are needed.
Better reforestation data are needed.
Basic data on soil carbon processes.
Better data on land-use statistics.
Better forest inventories.
Anthropogenic definition.
The problem is not the methodology.
To reduce uncertainty we need better activity
data.
Need to develop a ‘docket’ approach for fieldtesting reports and quality assurance trail.

Inventory comparisonsComparisons should be made based on ‘IPCC
Reference Approach’.
--

The IPCC should collaborate with and support
Land-use Change and Forestry global
databases.
-Comparisons should be made between
national communications and international
databases.

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5.2 Methane
Three sectors were considered for CH4: Energy; Agriculture; and Waste. Further disaggregation ofthese sectors is shown in Table 5-2. The uncertainty in GHG inventories, quantifying uncertainty andreducing uncertainty were discussed.
Uncertainty in GHG inventories
Here uncertainties are provided for ‘best’ case scenarios. These values are thought to be achievablefor most national, annual inventories. For most sectors, uncertainties fell in the range of +/-30%. The
highest uncertainties are for biofuel combustion and biomass burning. Both sources are important fordeveloping countries.
Quantifying uncertainty
This could be greatly improved by specific reporting of the emission factors, algorithm and activity
data. Disaggregation would lead to better scientific understanding. Care should be taken to avoid ascientific preoccupation with detail.
Reducing uncertainty
In most sectors, the largest gains can be made by improving the activity data, rather than themethodology (Table 5-2).

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Table 5-2: Uncertainty and proposed improvements for CH 4 emission estimates
SectorEnergyAgricultureand Land-use
ChangeWaste

Category

Uncertainty

Coal Mining+/- 30%Oil and Gas
production/distribution/transmission
Oil Refining
BiofuelCombustion--

Inventory quality assurance
Complete disaggregation from the country scale to coalmining region, to individual mine, coal handling practices,above and below ground operations, open-cast vs. deepmining.
Quantify and refine country to country variations at
production sites, transmission and distribution pipeline
losses, leakage losses at consumer premises.

-+/- 35%

Quantify and improve databases on refinery losses
Improve statistics and emission factors for small sources,
domestic stoves and industrial biomass usage.

Enteric
Fermentation+/- 20%Manure+/- 40%Rice+/- 30%Complete disaggregation into animal species, housing, feed,
management, age and population statistics on countryspecific basis.
Need to separate intensive management lagoons, anddisposal on arable and non-arable land.
Scaling with respect to cultivar type, rice management, water
regimes, use of organic waste and fertiliser.

Large Scale
Biomass Burning+/- 50%Landfills+/- 30%s:\applic\inv\workshop\tdbu\postmeet\tdbusm11.doc

Attention should be given to area burnt, biomass density,
previous extent of burning, ecosystem burnt, statistics of fuelburning, actual emission factors and measurements fordifferent crop residues.
More attention to be given to historical waste disposal rates,
reporting landfill operations by extent and volume, landfill
gas extraction, measurement of landfill gas decay functions.

Inventory comparisons

Direct emission
measurements

--

--

--

Aircraft surveys of pipeline
leakage.

-Comparison between
countries and withincountries.
---Remote sensing and direct
measurements offer
significant potential.
Remote sensing and direct
measurements offer
significant potential.
---Comparisons are difficult
because most countries use
the same methodology.
----

Imagery for area under ricecultivation, isotopic analysis
with measurements.
Remote sensing of fires
Land-use data from satellites
Large scale experimental
campaigns.
--

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5.3 Nitrous oxide
Three sectors were considered for N2O: Energy; Industrial Processes and Agriculture. The issues of
uncertainty in GHG inventories, quantifying uncertainty and reducing uncertainty were discussed.
Uncertainty in GHG inventories
Participants expressed uncertainties as a percentage range. It was felt that biases are introduced into
inventories when an emission factor is measured in one world region, and then applied to another.
The ranges in Table 5-3 take this bias into account. Uncertainty of agricultural emissions result fromtwo sources, the range of default emission factors in the Guidelines, and the cumulative errors
propagated throughout the calculations. For other sectors, uncertainties are cited from the EDGAR
database. The highest uncertainties originate from agriculture, and the lowest from industrial
processes. Globally, there are only a few major sources of industrial N2O, which makes it easier toobtain site-specific emissions.
Quantifying uncertainty
The possibility of unidentified sources and sinks contributes to uncertainty. Potential candidates inthis area are soil sinks and an atmospheric source of N 2O through oxidation of NH3.
Reducing uncertainty
To reduce uncertainties, analytical methodologies to measure emissions from all sectors are a highpriority. Another priority is clarification of the term anthropogenic, particularly for biogenic sources,
like biomass burning and agriculture.

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Table 5-3: Uncertainty and proposed improvements for N 2O emission estimates
SectorEnergyCategory
Fossil Fuel

Uncertainty
30-70%

Industry-

30%

Agriculture andLand-use Change

Biomass Burning

70-80%

Agricultural soils•= Direct•= Animal Waste
Management•= Indirect
New Sources/Sinks

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70-80%
70-80%
80-100%?

Inventory quality assurance Inventory comparisons
IPCC should provide a
IPCC should provide a
recommended methodology. recommended
methodology.
IPCC should provide a
IPCC should provide a
recommended methodology. recommended
methodology.
IPCC should provide a
IPCC should provide a
recommended methodology. recommended
methodology.
Clarification of the term
Compare countries andanthropogenic.
regions with similar
IPCC should provide a
conditions.
recommended methodology.
Research on processmodelling.
Promote research toquantify/verify:
the unknown soil sink,industrial sources,
atmospheric source of N2O
through oxidation of NH3

Direct emission measurements
Promote research to quantify emissions
from: mobile sources (emissions from 3way catalysts), fluidised bed reactors.
Direct measurement of major sources byrecommended methods.
Remote sensing of the extent of forest
fires.

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6. Expert group recommendations
The expert meeting recommended several steps to advance current understanding of sources and removalsof GHG inventories. These steps were seen as critical to producing better emissions estimates, and forfurther revisions of the Guidelines.
Inventory quality assurance. In the immediate term, further research on inventory quality assurance is ahigh priority for verifying and for assessing the quality of national GHG inventories for all gases.
Inventory comparisons. Existing inventories (national and scientific) should be compared with default
and independently derived emission factors and international statistics, at a range of scales to find thelargest differences. These differences should be explained using the details of activity data and emission
factors. This applies in particular to sectors with high uncertainty such as agriculture.

Model comparisons. In the longer term, model comparisons show potential for validation and verificationof global, regional, and national GHG inventories. The use of model comparisons is recommended, using
both forward and inverse models. To improve the reliability and accuracy of GHG emission inventories,
model comparisons should be carried out at various levels. More GHG measurements would be neededover continental areas in order to use the full capacity of models.
Direct emission measurements. In the medium term, direct emissions measurements are a priority for
improving estimates of CH4 from several emissions sources. These include: oil and gas leaks; landfills;
biomass burning; and rice paddies.

Uncertainty. A high priority is a framework to define, estimate, and report degrees of uncertainty in GHG
inventories. First, the IPCC should study how countries estimate uncertainties for their national
inventories. Second, a framework on uncertainties should be developed through the IPCC process. This
framework should allow degrees of uncertainties to be reported at a higher level of sectoral detail than in
the current Guidelines. Clearer reporting of the sources of uncertainty could be achieved by explicit
reporting of uncertainties for emission factors, activity data and the algorithms used in calculating
estimates.
Codes of practice. To help reduce uncertainties in national GHG inventories, codes of good practice forthe preparation of national inventories and the measurement of emission factors and direct emissions ofCO2, CH4 and N2O should be developed through the IPCC process. These codes of good practice should
be based on accepted procedures currently employed in countries with mature research programmes.
They should be applied to obtain more comprehensive emissions data in regions where good quality data
are lacking. The number of direct measurements of industrial N2O from major point sources should be
increased.
Data generation. To reduce uncertainties in national inventories, a higher priority should be placed ondata generation.
Carbon dioxide. In all countries, improved activity data need to be collected for land-use, forest
inventories, rates of reforestation, deforestation and afforestation. Priority should be given to flux
measurement from forests and a better understanding of soil carbon exchange processes.
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Efforts should focus on reducing systematic rather than random error in energy statistics. For
verification, participants strongly recommended comparing emissions estimated using both national
methodologies and the IPCC Reference Approach.
Methane. Every effort should be made to improve the disaggregation and systematic collection ofbasic data. Priority should be given to documenting and reporting of historical waste disposal rates,
land area and volume extents, landfill gas extraction and landfill organic matter decay functions.
Other priorities include carrying out large-scale measurements to determine emission rates from coal,oil and natural gas production and distribution.
Nitrous oxide. For improving inventory quality, participants agreed that existing N2O inventories
should be compared at a range of scales and the largest differences identified, particularly forbiogenic (agricultural) emissions. Furthermore, there is a need to clarify which part of agriculturalemissions are anthropogenic and which are natural.
Anthropogenic. Broad consensus on this term should be developed to differentiate anthropogenic fromnatural sources and removals of GHG. This will help reduce uncertainty in land-use change and forestry
inventories and agricultural sources. Several interpretations of anthropogenic are possible. Theirappropriateness depends, in particular, on the requirements of the Convention.
Emission factors. Country-specific emission factors should be compared with data from the literature.
Glossary of terms. A glossary of common terminology relating to the quality of national GHG inventories
should be developed through the IPCC process.
The Glossary of Terms prepared for this meeting is a useful starting point and it could be incorporated into
the Guidelines.
Co-operation. There is a need for increased co-operation between the inventories and modelling
communities. This will result in a better assessment of anthropogenic influence and its overall effect on
the climate system.
Halocarbons and sulphur hexafluoride. A high priority should be placed on improving the IPCC
estimation methodologies for HFCs, PFCs, and SF 6.
These gases are included in the national targets under the Kyoto Protocol. The IPCC Reporting Tables
should be disaggregated so that emission estimates for the individual gases can be monitored against
atmospheric measurements.

7. Next steps
Following technical review and incorporation of comments, the final meeting report will be distributed togovernments for information. Opportunities to enhance the co-operation between the Inventories
Programme and Working Group I of IPCC will be explored.

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indicators: the on-going European effort towards a common methodology, Energy Policy, vol. 25, no. 7-9,
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Graedel, T.E., T.S. Bates, A.F. Bouwman, D. Cunnold, J. Dignon, I. Fung, D.J. Jacob, B.K. Lamb, J.A.
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Marland, G., A. Brenkert and J. Olivier: 1. Co2 from fossil fuel burning: a comparison of ORNL and
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Global Atmospheric N2O Budget: Nitrous oxide Emissions Through the Agricultural Nitrogen Cycle;
Nutrient Cycling in Agroecosystems; In Press.
Olivier J.G.J, Bouwman A.F., van der Maas C.W.M., Berdowski J.J.M., Veldt C., Bloos J.P.J.,
Visschedijk A.J.H., Zandveld P.Y.J. and Haverlag J.L., 1996: 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 1°x1° Grid; TNO-RIVM 1996; Bilthoven, theNetherlands.
Schipper, L. and R. Haas, 1997: The political relevance of energy and CO2 indicators - An introduction,s:\applic\inv\workshop\tdbu\postmeet\tdbusm11.doc

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Energy Policy, vol. 25, no. 7-9.
Van Amstel A.R., C. Kroeze, L.H.J.M. Janssen, J.G.J. Olivier and J.T. van der Wal, 1997: Greenhouse
Gas Emission Accounting, Preliminary Study as Input to a Joint International IPCC Expert
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Annex 1: Working Group Reports
Modelling: carbon dioxide, methane, nitrous oxide
6 November 1997
Chairs: Leon Janssen, Leonor Tarrason
Rapporteurs: Dick Derwent, Carolien Kroeze, John Taylor
The working group discussed Approach 3 described in the issues paper and three questions listed below.
QUESTION 1. What do we achieve by introducing model comparisons in inventory validation?
Opportunities for the comparison of atmospheric models with inventories include:
1. Testing inventories, e.g. forward methods (short term)•= consistent with atmospheric growth rate•= consistent with spatial distribution at monitoring sites•= consistent with seasonal variation at monitoring sites
2. Refinement of guidelines, e.g. emission factors from rice paddies and biomass burning (short term).
3. Data generation, e.g. inverse methods to estimate national inventory where no other data are available(long term).
4. Accuracy, e.g. compare inventory with inverse methods - validation? (long term).
Recommendation 1:
The expert meeting recommends the use of model/inventory comparisons, both forward and inverse
at a range of scales, in order to improve the reliability and accuracy of greenhouse gas emission
inventories.
QUESTION 2. How can model comparisons be used as validation tools for emission inventories? (Identify
methods and application areas).

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Table 1. Overview of modelling approaches for estimating GHG fluxes
BudgetConcentration measurement

Modelling approach

Global budget- global growth rate
- stratospheric gradient- ice core data

0-D box model

Zonal budgets
e.g. 10 degree latitude belts

Zonal gradient (pole to pole) at
surface- multi-box model- 2-D advective-convective
transport model

Regional budgets, e.g.
continental scale- satellite measurements- global networks atbackground locations- 3-D global tracer transportmodels- GCMsLocal budgets, e.g. country- local measurement networks(not background)- aircraft- 3-D regional models nested in
global models

The following tables (Tables 2-4) summarise our response to the present uncertainty in the budgets and thepotential for use of modelling to constrain the budgets.
Table 2. Present uncertainty and potential for use of modelling for CO 2
CO2

Present uncertainty

Potential for use

Global scale+/- 1 Gt C

Land use (deforestation) (*);
systematic bias can be
determined

Zonal scale+/- 1 Gt C

Sources and sinks can bediscriminated roughly inlatitudinal bands

Regional scale

Need for measurements overcontinental areas at stations andfrom aircraft

In the coming years, possible
regional budgets of thebiosphere

Country/local scale

There is a need for feasibility
studies

Note *: Recognising that fossil fuel emission and sea-air exchange are reasonably well known.

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Table 3. Present uncertainty and potential for use of modelling for CH 4.
CH4

Present uncertainty

Potential for use

Global scale+/- 20%Global estimates "with
improved accuracy"

Zonal scale> 20%, depending on latitude- zonal estimates- rough sector analysis

Regional scale

20 - 40%
Need for more measurements- country budgets- rough sector analysis

Country/local scale

20 - 40%
Need for more measurementsfrom stations, aircraft andsatellites- country scale GHG budget
validation- rough sector disaggregation

Table 4. Present uncertainty and potential for use of modelling for N 2O.
N2O

Present uncertainty

Potential for use

Global scale+/- 50%- estimate stratospheric sink
- constrain anthropogenic
sourceZonal scale

4 zonal bands +/- 30%

estimate zonal budget

Regional scale- need measurements of GHGs
over continents- satellite data

- continental/country budget- country budget validation

Country/local scale+/- 40% for some countries

country scale GHG budget
validation

Recommendation 2:
The expert meeting recognises that more greenhouse gas measurements over continental areas areneeded in order to use the full capacity of models to improve the confidence in greenhouse gas
emission inventories.
Note: Inverse modelling requires the focus on the design of the monitoring networks to shift frombackground measuring stations to new stations allocated downflow from polluted areas (continental areas)(relevant for compliance).
QUESTION 3. Are the uncertainties in the models and observations sufficiently quantified to allow for a
useful comparison? See above table.

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Methane: agriculture and waste
6 November 1997
Chairs: Ron Sass and D.C. Parashar
Rapporteur: Dr. Susan Subak
The meeting began with a discussion about the difference between validation and verification. Most
agreed that a narrow definition for validation was useful, related to access to the primary informationneeded to derive an emissions inventory. Most of the subsequent discussion was on the problems inachieving improved accuracy without reference to these terms, but was generally concerned with problemsof validation rather than verification.
Initially, however, it was pointed out that the main methane inventory comparison, between EDGAR and
IPCC results, was verification rather than validation and had been useful in identifying where the gaps
were.
•= The general point was made that sometimes changes in annual flux were due to natural variability
rather than changes in activities, e.g. variability in temperature can affect methane from ricepaddies, so it follows that emissions factors are not naturally static over time.
•= The group agreed that Table 2-1 would look quite different if countries tried to estimate theuncertainties for their own countries, for both emissions factors and activity data, and that theyshould be encouraged to do so.
•= Top-down air sampling approaches hold the potential for decreasing uncertainty from all methane
sources discussed.
Rice cultivation
Results from a process model were presented showing very good agreement between modelled results andmeasured results of annual aggregates in Texas and China. The model is related to cultivar, temperature,
water regime, soils and fertiliser type and application. The research reporting said that the greatest
variation in flux was due to cultivars rather than the other parameters that have been studied to a greater
extent in the past. This researcher said that in their experiments, in cases where the cultivar type was
known, the level of accuracy appeared to be quite high (within 10%). Another rice specialist present said
that he agreed that there should be more research on cultivars but that nutrient supply and soil texture are
related to a given cultivar and that it is helpful to know the organic content of soils in addition to soil
texture.
Given the scarcity of detailed data for the relevant flux parameters it was discussed whether there may be
any useful socio-economic proxies for flux. It was believed that yield was a good crude proxy because itcorrelated with higher fluxes on an area basis. GNP per capita has been proposed as a proxy for type of
fertiliser application, with higher GNPs denoting greater use of inorganic as opposed to organic fertilisers.
While detailed information is often not available about the type of fertilisers used, it was felt thatassuming certain fertiliser applications based on agricultural culture would be better than using the GNP
proxy.
The point was also made that often the poorest people in the world grow rice, that this group has used
traditional agricultural techniques and would continue to do so unless given strong incentives to change.
Therefore, rice management is fairly predictable in those regions that are least likely to have formal data.
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•= The results from process models are encouraging and should be used as default emissions factors incountries that do not have their own measurements.
•= Improvement in data is needed for all parameters, especially cultivar type.
•= It was proposed that surveys should be sent out to improve our understanding of rice management
practices.
•= While isotopic analysis does not clearly separate rice paddies from wetlands, isotopic constraints
may be useful in regions with few wetlands.
•= Inverse techniques based on air sampling at the regional and local, not just global, scale would be
helpful.
•= Better scaling up of site air measurements from heterogeneous sites is needed.
•= Summation of country totals based on 1996 default IPCC emissions factors totalled 33-49 Tg, ascompared with 60 Tg point estimate given for the global balance (within 20-100 Tg), (33-49 Tg is
2based on flux density of 20, 25 and 30 g/m as a world average).
•= Additional crop sources exist, such as cranberries and jute, but these were not judged to beimportant enough to warrant a methodology.
•= One participant suggested that it may be useful to invite countries to scale parameters from 0-1
instead of using absolute values, e.g. rice completely submerged during the growing season would
be scored 1.0 for the water regime parameter.
•= There was some agreement that it might help to set individual country default factors for allparameters and then ask them to explain how and why they changed them.
Livestock
The USA has broken down the Tier 2 categories in greater detail (age groups, management practices, feed
types, dairy production). They believe that the uncertainty is now below 20%. The Dutch representative
said that he believes that the uncertainty for the inventory from the Netherlands is also now lower than
25% for the emissions factor and 10% for the activities.
The existing inventory accounting structure does not help in integrated analysis. This is especially thecase for methane emissions from enteric fermentation that over time inventories may show a decline inmethane emissions due to improvements in animal productivity, but these reductions may come at theexpense of increased carbon dioxide emissions related to fossil fuels for better feed production. An
alternative accounting approach measures emissions per unit of product, e.g. carbon dioxide equivalent perpound of beef or milk. In the long run, it is hoped that process assessments could be compared withnational inventories.
Landfills
The USA bases much of its inventory on estimates from about one hundred of the largest landfills andthen extrapolates to include the thousands of small sites, although it acknowledges that this extrapolationis hard to make. It is difficult to include the time dynamic in that many landfills produce methane formore than three decades, and even the USA waste composition statistics only go back for about one
decade. In the Netherlands, a comparison between the revised method and earlier estimates confirmed that
it was important to consider the time dynamic. First-order decay functions can be used for regions that do
not have historical waste generation and landfilling statistics

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It would be useful if countries included information estimating the range of landfill sizes by area extent
and volume, and estimated the proportion of the total waste disposed of in the different size categories.
Biomass burning
There was insufficient time to discuss this category in any depth. It was recalled that some verification
exercises had been completed. Researchers at King’s College London had estimated methane from
biofuels used for energy production for many countries, and these results were found to differ fromnational inventories from this category by as much as one thousand fold. Better emissions factors wereneeded for most developing regions for all categories of biomass burning.

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Carbon dioxide: energy, industrial processes and land-use change
6 November 1997
Chairs: Youba Sokona and Gregg Marland
Rapporteur: Tim Simmons
The aim of the Group was to identify and suggest means of improving the quality of CO2 inventories sothat they represented unbiased estimates of actual emissions at the lowest practicable variance. New
mechanisms for emissions estimation were also sought.
Inventories were seen as serving three main functions:•= to permit control of compliance with targets established by the UNFCCC•= to provide data and rationale for emissions mitigation policies•= to assist in the development of global climate models.
It was recognised that the first of these functions requires only total emission figures but that greater
spatial and activity detail will be required for the others.
Quality improvements
It was recommended that national CO2 inventories be systematically compared with those of a similarcountry and with the IPCC reference approach in order to reveal any omissions and confirm correct
application of the methodologies. Inventories should also be accompanied by an account of which of the
IPCC standard source categories have been estimated in the inventory and the reasons for any omissions
or additions.
The methods for estimation of emissions in the energy and industrial processes sectors were now
considered reasonably complete and further refinement unnecessary at the moment.
The random variations in activity data for the larger countries were felt to be below the current estimatesof uncertainty given in the Guidelines. These uncertainty estimates include systematic bias in activity
figures. This led to a situation in which the trends in activity data and emissions showed greater stability
than individual data points. The group recommended that efforts be made to estimate the magnitude ofsystematic bias as well as random error and to reflect them in uncertainty ranges on the main aggregates in
the inventories. Despite the general acceptability of data quality in the major countries, it was thoughtthat many countries would wish to see improvements in some elements of their data and that, in particular,
the developing countries would benefit from investment in their data collection systems arrangements
which would permit them to assess their emissions and participate more actively in inventory formulation.
Methodologies for CO2 from land-use change were still under development and it was thought premature
to consider improvements to quality whilst fundamental choices on the HWP module remained to bemade.
It was not clear to the Group that direct flux measurements would lead to cost-effective improvements tothe estimation of CO2 from Energy and Industrial Processes. Anthropogenic sinks were not considered as
the Group had no expert in these matters.

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New methods
In reviewing the completeness of the existing methodologies for emissions from land use it was noted that
remote sensing by satellites could be added as a means of providing rapid estimates of activity data.

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Nitrous oxide: validation and verification
6 November 1997
Chairs: Arvin Mosier and G.X. Xing
Rapporteur: Jos Olivier

Introduction
Dominating sources of N2O on a global level are:•= agriculture•= industrial processes•= large-scale biomass burning•= road transport (in future).
Agriculture is by far the dominant source, in particular in developing countries, although in mostindustrialised countries too, this source contributes a large share to the national total.
Agricultural emissions vary widely in time and space. They depend on:•= different climates, agricultural practices•= seasonal variation, crop rotation effects•= soil variables: wetness, temperature, mineral N content, available C
Actual emissions from soils are usually much lower than the potential emissions due to limitations
imposed by the soil variables mentioned above. Emissions often occur as bursts over short periods due to(short) interruptions of these limiting conditions. These bursts may give rise to high contributions toannual emissions. measurements and emission compilations should therefore be done over a sufficiently
long period, and detailed enough to cover both such bursts of emissions and the seasonal variations (e.g.
integration over one or more years). Currently, about half of global of N use is in Asia, and the fractionneed there and in other developing countries will increase. However, the main research on emissions has
been done in Western Europe and North America, with very different climates and agricultural practices
from those of Asia and other continents.

Approach 2: inventory comparisonsNational inventories can be compared using three methods:
1. Intercountry comparison.
(e.g. emission densities or aggregated emission factors comparisons)
2. Comparison with a well-defined reference method (e.g. simple Tier 1 + default IPCC emission
factors). This could be identical or similar to the IPCC Standard Data Tables 1-6 in the original IPCC
GHG Inventory Guidelines.
This could also be a comparison with authoritative databases using the defined reference method,
analogous to, for example, the IEA database for CO 2 from energy combustion.
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33

energy combustion and GEIA/EDGAR data for other sources.
Validation, an independent check of national inventories, is only possible by the first method, because
data of different countries are in principle independently determined (except for cases where default IPCC
factors were used). The other methods are not independent, since the activity data basically comes fromthe same source; emission factors may be independently estimated, if the national factors are based on
country-specific information, e.g. local measurements.
Verification is possible with all three methods:•= methods 2 and 3 can be used for a quick screening of completeness and correctness of allocation tosource categories;•= all methods can be used as a screening test for identifying outliers in sectoral emissions.
Outliers flag that either country-specific circumstances should be substantiated (e.g. application of
specific reduction technologies), or that there are possible errors/weak assumptions in emission factors ineither of the inventories.
Each of these verification methods requires:•= well-defined sectoral reporting formats;•= sufficient detail in standard reporting tables;•= for easy comparison and first analysis of reasons for outliers.
The current IPCC Overview/Summary Tables do not supply sufficient detail, nor does the requirement for
submission of all underlying tables because of the lack of uniformity in these tables (except for countries
using the ‘Worksheets’). However, the original IPCC Standard Data Tables 1-6 or similar tables do ingeneral give the required minimum detail.

Approach 4: Direct emission measurements
National total emissions of N2O vary largely in time and space. Validation of national totals, essentiallythe sum of the dominating sources mentioned above, may be possible on a sectoral basis because theemission of the different source categories may show a different seasonal variation and are also distributed
differently in the national territory. This is also true for the natural sources, which may be discriminated inthis way. So, validation may be possible by sectoral assessments.

Agriculture
Direct flux measurements of agricultural sources at representative sites can be used for improving or
validating emission estimates, since present estimates are generally based on only a few measurements,
which do not cover the ‘full cycle’ of crop production, or specific agricultural and animal management
practices (including waste management).
Local flux measurements need to cover complete annual cycles, which includes the important stages of
total production cycles. Also they need to be done in the appropriate climate and management
circumstances. In addition, the location of possible other large sources nearby needs to be known, to
exclude interference by these other sources.
It is strongly recommended to base measurement practices on existing methods used in Western Europe
and North America.
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Industrial sources
Stack measurements will be sufficient to get accurate estimates for these sources.

Biomass burning
As in agriculture, sufficiently long measurements are required for covering the important post-burn effects
in the case of deforestation.

Inherent uncertainty
Agriculture
Upscaling, i.e. integration in time and space, will always be necessary and is an intrinsic source of
uncertainty.

Industrial sources
Small (for identified sources).

Biomass burning, more general land-use change
The difficulty in monitoring the spatially wide spread activity data causes an intrinsic uncertainty in theemission estimates. Also, uncertainty in future land-use after the biomass burning causes uncertainty inthe net emissions over a number of years.

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1Annex 2: Glossary
Absolute error
The absolute deviation of x (a variate) from its ‘true’ value.
Activity data

Data on the magnitude of human activity resulting in emissions or removals taking place during a given
period of time.
Accuracy
The exactness of an estimate. (Accuracy is to be contrasted with precision)
Anthropogenic
Man-made, resulting from human activities. (Anthropogenic emissions are to be distinguished fromnatural emissions)
Bottom-up
In the context of the discussions, emissions inventories derived from activity data, emission factors andalgorithms such as those provided in the IPCC Guidelines.
Census
The complete enumeration of a population or groups at a point in time with respect to well-defined
characteristics.
Confidence
Refer to confidence interval
Confidence Interval
Define two statistics t1 and t2 (functions of sample values only) such that, θ being a parameter under
estimate,

Pr(t1 ≤ θ ≤ t 2 ) = α
where α is some fixed probability, the interval between t 1 and t2 is called a confidence interval.
Consistency
Concerned with the internal agreement of data or procedures. (Consistency is to be contrasted withvalidity)
COP
The Conference of the Parties under the UN Framework Convention on Climate Change.
Distribution function
Function F(x) of a variate x is the total frequency of members with variate values less than or equal to x.
1

All definitions have been extracted from M.G. Kendall and W.R. Buckland (1975); A Dictionary of Statistical Terms;
International Statistics Institute; U.K., or the Revised IPCC Guidelines for National Greenhouse Gas Inventories;
Paris, 1997; except Accuracy, Precision and Uncertainty extracted from Mirriam-Webster 1997.

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Emission factor
A coefficient which relates the activity data to the amount of chemical compound emitted.
Error of estimation
In regression analysis where the regression equation is used to estimate the ‘dependent’ from given valuesof the ‘independent’ variates, the difference between the estimated and the observed value of thedependent variate.
Estimate
The particular value yielded by an Estimator in a given set of circumstances.
Estimator
A rule or method of estimating a constant of a parent population.
FAO
Food and Agriculture Organisation of the United Nations.
IEA
The International Energy Agency. An autonomous body attached to the OECD.
See also OECD.
IPCC
The Intergovernmental Panel on Climate Change. A special intergovernmental body established by UNEP
and the WMO to provide assessments of the results of climate change research to policy makers.
Mean
The value obtained by summing all variates in a distribution and dividing by the number of variates.
MedianThat value of the variate which divides the total frequency into two halves.
Mode
The variate which occurs most frequently in the distribution.
OECD
The Organisation for Economic Co-operation and Development. A regional organisation of free-market
democracies in North America, Europe and the Pacific.
Precision
Repeatability of an estimate. In general the precision of an estimator varies with the square root of thenumber of observations upon which it is based. (Precision is to be contrasted with accuracy)
Probability distribution
A distribution giving the probability of a value x as a function of x.
Qualitative data
Relates to data in the form of expressions or descriptive text. (Qualitative data is to be contrasted withquantitative data)
Quantitative datas:\applic\inv\workshop\tdbu\postmeet\tdbusm11.doc

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Relates to data in the form of numerical quantities such as measurements or counts. (Quantitative data is
to be contrasted with qualitative data)
Quality control
Tracing and eliminating systematic variations in quality, or reducing them to an acceptable level, leaving
the remaining variation to chance.
Quartile
In the particular case of a continuous distribution, the value at which 25% of the variates have values
above (upper quartile) or below (lower quartile) it.
Reliability
That part which is due to permanent systematic effects, and therefore persists from sample to sample, as
distinct from error effects.
Standard deviation
A measure of the dispersion of a frequency distribution; equal to the positive square root of the variance.
Stochastic approximation procedure
A non-parametric method of iterative estimation for a functional or regression relationship which
incorporates random elements.
Top-down
In the context of these discussions, emission inventories derived or correlated with atmosphericconcentrations using computer models.
Uncertainty
The range of error of an estimate. (Also loosely defined as the range between the upper and lower
quartiles)
UNECE
United Nations Economic Commission for Europe.
UNEP
United Nations Environment Programme.
UNFCCC
United Nations Framework Convention on Climate Change.
US EPA
United States Environmental Protection Agency.
Validation
A procedure which provides, by reference to independent sources, evidence that an inquiry is free frombias or otherwise conforms to its declared purpose. (Validity is to be contrasted with consistency)
Variance
The mean of the squares of variations from the arithmetic mean.

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38

′ 2

∞

ò (x − µ ) dF−∞

1

Verification
A procedure to test the internal agreement of data or procedures. (Verification is to be distinguished fromvalidation)
WMO
The World Meteorological Organisation of the United Nations.

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Annex 3: List of Participants
AUSTRALIA
Mr. Ian GALBALLYCSIRO-DAR
Private Bag No. 1
Aspendale
Victoria 3195
Australia
Tel: (61 3) 9239 4684
Fax: (61 3) 9239 4444
Email: ieg@dar.csiro.au
Mr. John TAYLOR
CRESAustralian National University
Canberra ACT 0200
Australia
Tel: (61 6) 249 2635
Fax: (61 6) 249 0757
Email: taylorj@cres.anu.edu.au
BOLIVIAMr. Freddy TEJADA
Subsecretaria de Medio Ambiente
710 Mendez Arcos
Esq. Plaza Espana
La-PazBolivia
Tel: 591 2 416301
Fax: 591 2 416301
Email: pncambio@clima.rds.org.bo
BRAZILMr. Diogenes ALVES
Instituto Nacional de Pesquisas Espaciais
Av. dos Astronautas, 1758 - CP 515
12.201-970 Sao Jose dos Campos
Sao Paulo
BrazilTel: (55 12) 3456492
Fax: (55 12) 345 6468
Email: dalves@dpi.inpe.br
CAMEROON
Mr. Dudley Sama ACHU
Senior Environmental Engineer
Ministry of Environment & Forestry
Minefide 17 Floor
Immeuble Ministeriel No 2
YaoundeCameroon
Tel: (237) 239461/229482
Fax: (237) 239 461/226685
Email:

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CANADA
Mr. Frank NEITZERTEngineer, Greenhouse Gas Division
Pollution Data Branch
Environment Canada
351, St. Joseph Blvd., 10th Floor
Hull, Quebec K1A 0H3
CanadaTel: (1 819) 994 3098
Fax: (1 819) 953 9542
Email: frank.neitzert@ec.gc.ca
Mr. Trevor SCHOLTZ
CGEIC
Canadian Global Emissions Interpretation Centre
Atmospheric Environment Service
4905 Dufferin St
Downsview
Ontaria
Canada M3H 5T4
Tel: 905 822 4111 Ext. 524
Fax: 905 823 1446
CHINA
Ms. Jing LI
Research AssistantInstitute of Atmospheric Physics, Chinese Academy of
Sciences
Beijing 100029, Peoples Republic of China
Tel: 0086-010-62369578-4
Fax: 0086-010-62028604
Email: scas@lasgsgi4.iap.ac.cn
CHINA
Mr. Guang Xi XING
Academica Sinica
Chinese Academy of Sciences, Institute of Soil Science
71 East Beijing Road
PO Box 821
210008 Nanjing
Tel: (86 25) 336 0874
Fax: (86 25) 335 3590
Email: Xinggx@issas.ac.cn
Mr. Xu DEYING
Chinese Academy of Forestry, Ecology andEnvironment Institute
Wan Shou Shan
Beijing 100091
Tel:
Fax: (86 1) 62881937
Email: deyingxu@fee.forestry.ac.cn
CHINA
Mr. Yi-Fan LI

40

CGEIC
Atmospheric Environment Service
4905 Dufferin St
Downsview, Ontario
Tel: (416) 739 4892
Fax: (416) 739 4288
Email: yi-fan.li@ec.gc.ca

INDIA
Mr. D.C PARASHAR
National Physical Laboratory
Dr. K.S Krishnan RoadNew Delhi 110012, India
Tel: (91 11) 578 9142
Fax: (91 11) 575 2678 / 576 4189
Email: parashar@csnpl.ren.nic.in

Mr. Liu XUEYI
Chief, Energy and Environment Division
Energy Research InstituteZhansi Menlu Shabe Changping
Beijing 102206
Tel: (010) 697 33106 (O)(010) 62353591 (H)Fax: (010) 69732059
Email: xuhqing@public3.bta.net.cn

JORDANMr. Faris AL-JUINAIDIThe General Corporation for the Environment Protection, P
O Box 1408
Amman 11941, Jordan
Tel: (962 6) 835 794
Fax: (962 6) 835 794
Email: climate@go.com.jo

CZECH REPUBLIC
Mr. Pavel FOTT
National Expert
Czech Hydrometeorological Institute
Ba Sabatce 17
143 06 Prague 4
Czech Republic
Tel: (420 2) 4403 2456
Fax: (420 2) 4403 2468
Email: fott@chmi.cz

NETHERLANDS
Mr. Frank DENTENER
Institute for Marine and Atmospheric Research (IMAU),
Utrech University
Princetonplein 5
NL 3548 CC Utrecht
Netherlands
Tel: (31 30) 2532291
Fax: (31 30) 2543163
Email: F.J.Dentener@fys.ruu.nl

FINLAND
Mr. Kari GRONFORS
Environment and Energy
PB 4V
Fin-00022 Statistics Finland
Tel: +358 9 1734 2728
Fax: +358 9 1734 2786
Email: kari.gronfors@stat.fi

Mr. Jan BERDOWSKI
Senior Scientist
TNO
P O Box 342
NL 7300 Apeldoorn
Netherlands
Tel: 31 55 549 3171
Fax: 31 55 549 3252
Email: j.m.m.berdowski@mep.tno.nl

FRANCE
Mr. Philippe CIAIS
CNRS-CEA
Centre de Fables Radioactivités
Avenue de la Terrasse
9119 Gif sur Yvette
Cedex France
Tel: 33 1 69 08 95 06
Fax: 33 1 69 08 77 16
Email: ciais@obelix.saclay.cea.fr

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Mr. Jeljer HOEKSTRA
Researcher, KEMAP O Box 9035
6800 ET Arnhem
Netherlands
Tel: (31 26) 356 2383
Fax: (31 26) 351 5022
Email: j.hoekstra@kema.nl

41

Mr. Jos G.J. OLIVIER
Senior Scientist/Coordinator, Global and Environmental
IssuesRIVM National Institute of Public Health and theEnvironment
Laboratory for Waste Materials and Emissions
PO Box 1
NL-3720 BA Bilthoven
Netherlands
Tel: (31 30) 274 3035/2759
Fax: (31 30) 274 4417
Email: jos.olivier@rivm.nl
Mr. Leen HORDIJK
Wageningen Agricultural University
P O Box 9101
6700 HB Wageningen
Netherlands
Tel: (31) 317 484919
Fax: (31) 317 484839
Email: leen.hordijk@wimek.cmkw.wau.nl
Ms. Carolien KROEZE
Wageningen Inst. for Env. and Clim. Research
Wageningen Agricultural University
PO Box 9101
6700 HB Wageningen
Netherlands
Tel: (31 317) 485 070 / 485 090
Fax: (31 317) 484 839
Email:
Carolien.Kroeze@wimek.cmkw.wau.nl
Mr. M.J.P. (Tinus) PULLESTNO Institute of Environmental Sciences,
Energy Research and Process Innovation
Laan van Westenenk 501
PO Box 342
7300 AH Apeldoorn, Netherlands
Tel: (31 55) 549 3762
Fax: (31 55) 549 3252
Email: M.P.J.Pulles@mep.tno.nl
Mr. André van AMSTEL
Senior Research Asociate
Wageningen Agricultural University
WIMEK/Wageningen Inst. for Env and Climate Research
P O Box 9101
NL-6700 HB Wageningen
Netherlands
Tel: 00 31 317 484812
Fax: 0031 317 484839
Email: avanamstel@tip.nl (home)
Andre.VanAmstel@algemeen.cmkw.wau.nl (office)s:\applic\inv\workshop\tdbu\postmeet\tdbusm11.doc

Mr. Alex VERMEULEN
Netherlands Energy Research Foundation ECN
Postbus 1 1755 ZG Petten
Westerduinweg 3, 1755 LE Petten
Netherlands
Tel: (31 224) 564949
Fax: (31 224) 564480
Email: a.vermeulen@ecn.nl
Mr. Leon JANSSEN
Laboratory of Air Research
RIVM
The Netherlands
Tel: (31) 302742771
Fax: (31) 302287531
Email: leon.janssen@rivm.nl
Mr. Paul RUYSSENAARS
Ministry of Housing, Spatial Planning and the Environment,
Air and Energy (640)P O Box 30945
2500 GX Den Haag, The Netherlands
Tel: (31) 70 3394351
Fax: (31) 70 3391310
Email: P.G.Ruyssenaars@minvrom.DGM.nl
Mr. John VAN AARDENNE
Wageningen Institute for Environment
Research
Wageningen Agriculture University
P O Box 9101
6700 HB Wageningen
Tal: 0317 (4) 85121
Fax: 0317 (4) 84839

andClimateNORWAYMr. Oluf BOCKMAN
Private Consultant
Research Centre Porsgrunn
Norsk Hydro ASA
P O Box 2560
N-3901 Porsgrunn
NorwayTel: 47 3556 3093/47 3556 4333
Fax: 47 35 56 2302
Email:Mr. Oystein HOV
Norwegian Institute for Air Research (NILU)P O Box 100
N-2007 Kjeller
NorwayTel: (47) 6389 8000
Fax: (47) 6389 8050
Email: oystein.hov@nilu.no42

Ms. Leonor TARRASONNorwegian Meteorological Institute
P O Box 43
Blindern
0313 Oslo 3
NorwayTel: (47) 22 963000/22963330
Fax: (47) 22 696 355
Email: leonor.tarrason@dnmi.no
Mr. Thomas Martinsen
Norwegian Pollution Control Authority
PO Box 8100 Dep.
0032 Oslo
Norwaytel: (47 22) 57 34 38
fax: (47 22) 67 67 06
Email: thomas.martinsen@sftospost.md.dep.telemax.no
POLAND
Mr. Edward RADWANSKI
Polish Foundation for Energy Efficiency
Ministry of Environmental Protection
ul. Brodzinskiego 21
01-557 Warsaw, Poland
Tel: (48 22) 39 49 45
Fax: (48 22) 39 49 45
Email: fewewar@ternet.pl
SENEGAL
Mr. Youba SOKONASOKONA
Head of Energy Programme
ENDA -TW
54, rue Carnot, BP 3370 Dakar
SenegalTel: (22 1) 8225 983/822 496
Fax: (22 1) 8217595/8235157
Email: ysokona@enda.sn / energy2@enda.sn
SLOVAK REPUBLIC
Ms. Katarina MARECKOVA
Dept. of Environmental Protection
Slovak Hydrometeorological InstituteJeseniova 17
SK - 833 15 Bratislava
Slovak Republic
Tel: (421 7) 378 5496
Fax: (421 7) 375 670
Email: katarina@vsdec.shmu.sk

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SLOVENIA
Mr. Bojan RODE
Adviser to the Director
Hydrometeorological Insitutute of Slovenia
Vojkova 1/b1000 Ljubljana
Slovenia
Tel: (386 61) 327 461
Fax: (386 61) 133 1396
Email: bojan.rode@rzs-hm.si
THAILAND
Ms. Pojanie KHUMMONGKOLSchool of Energy and Materials
Suksawad 48 Bongmod
Rasburana
Bangkok 10140
ThailandTel: (662) 470 8651
Fax: (662) 427 9062/470 8603
Email: ipojgkol@cc.kmitt.ac.th
UNITED KINGDOM
Mr. R. G. DERWENT
Atmospheric Processes Research Division
Meteorological Office, Room 156
London Road, Bracknell
Berkshire RG12 2SZ
Tel: (44 1344) 854 624
Fax: (44 1344) 854 493
Email: rgderwent@meto.govt.uk
Ms. Susan SUBAK
Centre for Social and Economic Research on the Global
Environment (CSERGE)
School of Environmental Sciences
University of East Anglia
Norwich
NR4 7TJ
United Kingdom
Tel: 44(0) 1603 592539
Fax: 44 (0) 1603 593739
Email: s.subak@uea.ac.uk
Mr. David FOWLER
Institute of Terrestrial Ecology
Edinburgh Research Station
Bush Estate, Penicuik
Midlothian
EH26 OQB
Tel: 44 (0) 131 445 4343
Fax: 44 (0) 131 445 3943
email: D.Fowler@ite.ac.uk

43

Mr. Tim SIMMONSAVONLOG Limited
Warley Cottage, 49 Headley Chase
Brentwood, Essex, CM14 5DH
United Kingdom
Tel: (44 1277) 222 125
Fax: (44 1277) 222 124
Email: tim.simmons@aol.com
Mr. Keith SMITH
Institute of Ecology and Resource Management (IERM)
University of EdinburghWest Mains Road
Edinburgh EH9 3JG
United Kingdom
Tel: (44 131) 535 4173
Fax: (44 131) 667 2601
Email: k.a.smith@ed.ac.uk
UNITED STATES
Mr. Wiley BARBOUR
Office of Policy, Planning & Evaluation
US Environmental Protection Agency
Climate Policy and Programs Division (2175)
401 M Street SW, Washington, DC 20460
Tel: (1 202) 260 6972
Fax: (1 202) 260 6405
Email: barbour.wiley@epamail.epa.gov
Mr. Gregg MARLAND
Environmental Sciences Division
Oak Ridge National Laboratory
PO Box 2008, Bldg. 1000
Oak Ridge, TN 37831-6335
Tel: (1 615) 574 0390/241 4850
Fax: (1 615) 574 2232
Email: gum@ornl.gov
Mr. Arvin MOSIER
USDA/ARSPO Box E
301 S. Howes, Rm 420
Fort Collins, Colorado 80522
United States
Tel: (1 970) 490 8250
Fax: (1 970) 490 8213 / 8310
Email: amosier@lamar.colostate.edu
Mr. Ronald L. SASS
Dept. of Ecology & Evolutionary Biology, MS 170
Rice University
6100 South Main
Houston, Texas 77005
United States
Tel: (1 713) 527 4066
Fax: (1 713) 285 5232
Email: sass@ruf.rice.edu

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Mr. Arthur RYPINSKI
Economist
Energy Information Administration
US Department of Energy
EI-81
1000 Independence Avenue, SW
Washington DC 20585
Tel (1 202) 586 8425
Fax: (1 202) 586 3045
Email: arthur.rypinski@eia.doe.gov
UNITED STATES
Mr. William IRVING
Program Analyst
US EPA APPD (6202J)
401 M Street, SW
Washington DC 20460
Tel: (202) 233 9065/ 546 9065
Fax: (202) 233 9569
Email: irving.bill@epamail.epa.gov
Ms. Carmen M BENKOVITZBNLAUI
Environmental Chemistry Division
Brookhaven National Laboratory
Department of Applied Science
Building 815E
Upton , New York 11973-5000
Tel: (516) 344 4135
Fax: (516) 344 2887
Email: cmb@bnl.gov
UNFCCC
Mr. Roberto ACOSTA MORENOUNFCCC, Climate Change Secretariat
P O Box 260 124, D-53153 Bonn
Germany
Tel: (49 228) 815 1419
Fax: (49 228) 815 1999
Email: r.acosta@unfccc.de

Mr. James GRABERT
Associate Programme Officer
Communication, Assessment & Review Programme
UNFCCC, Climate Change Secretariat
P O Box 260 124, D 53153 Bonn
Germany
Tel: 49 228 815 1413
Fax: 49 228 815 1999
Email: jgrabert@unfccc.de

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IPCC/OECD/IEA Secretariats
Sir John HOUGHTON
IPCC Working Group 1
Hadley Centre for Climate Prediction & Research
Metheorological OfficeLondon Road, Bracknell
Berkshire RG12 2SY, United Kingdom
Tel: (44 171) 276 2026/71/80
Fax: (44 1654) 767939
Email: jthoughton@ipccwg1.demon.co.uk
Mr. David GRIGGS
Meteorological Office, Hadley Climate Centre
IPCC WGI Technical Support Unit
London Road
Bracknell
Berkshire RG12 2SY, United Kingdom
Tel: (44 1344) 856 615
Fax: (44 1344) 856 912
Email: djgriggs@meto.gov.uk
Ms. Bo LIM
OECD, Environment Directorate
2 rue Andre Pascal
75775 Paris Cedex 16
FranceTel: (33 1) 45 24 78 94
Fax: (33 1) 45 24 76 78
Email: bo.lim@oecd.org
Mr. Pierre BOILEAU
OECD, Environment Directorate
9 rue de la Federation
75739 Paris Cedex 15
FranceTel: (33-1) 4524 1690
Fax: (33-1) 4524 7876
Email: pierre.boileau@oecd.org
Mr Yamil BONDUKI
OECD, Environment Directorate
2 rue Andre Pascal
75775 Paris Cedex 16
FranceTel: (33 1) 45 24 1914
Fax: (33 1) 45 24 76 78
Email: yamil.bonduki@oecd.org
Ms. Karen TREANTONInternational Energy Agency
9 rue de la Federation
75739 Paris
Cedex 15
FranceTel: (33) 1 40 57 66 33
Fax: (33) 1 40 57 66 49
Email: karen.treanton@iea.org

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