Expert Meeting on Uncertainty and Validation of
Emission Inventories

23-25 March 2010
Utrecht, the Netherlands

Task Force on National Greenhouse Gas Inventories

2

Expert Meeting on Uncertainty and Validation of
Emission Inventories

23-25 March 2010
Utrecht, the Netherlands

Task Force on National Greenhouse Gas Inventories

3

Supporting material prepared for consideration by the Intergovernmental Panel on Climate Change. This
supporting material has not been subject to formal IPCC review processes. Neither the papers presented at theexpert meeting nor this report of its proceedings has been subjected to IPCC review.

The IPCC would like to thank TNO and the Government of the Netherlands for hosting this meeting andproviding technical support.

Published by the Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the IPCC
© The Intergovernmental Panel on Climate Change (IPCC), 2010
Please cite as:
IPCC 2010, Expert Meeting on Uncertainty and Validation of Emission Inventories eds: Eggleston H.S.,
Baasansuren J., Tanabe K., Srivastava N., Meeting Report of the Expert Meeting on Uncertainty and Validationof Emission Inventories, Utrecht, the Netherlands, 23-25 March, 2010, Pub. IGES, Japan 2010
IPCC Task Force on National Greenhouse Gas Inventories (TFI)
Technical Support Unit
℅ Institute for Global Environmental Strategies
2108 -11, Kamiyamaguchi
Hayama, Kanagawa
JAPAN, 240-0115
Fax: (81 46) 855 3808
http://www.ipcc-nggip.iges.or.jp
Printed in Japan
ISBN 978-4-88788-065-8

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Table of Contents
FOREWORD ........................................................................................................................................................... 6
Executive Summary ................................................................................................................................................ 7
1. Introduction ..................................................................................................................................................... 8
2. Summary of Discussions ................................................................................................................................ 9
2.1
Additional information on estimating uncertainties in GHG inventories ................................................. 9
2.2
Use of measurements and/or modelling to validate emission inventories ............................................. 9Annex 1.
Annex 2.
Annex 3.
Annex 4.
Annex 5.

References ...................................................................................................................................... 14
Questions and Answers .................................................................................................................. 15
Agenda ............................................................................................................................................ 21
Participant list .................................................................................................................................. 22
Presentations .................................................................................................................................. 26

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FOREWORD
The IPCC’s Task Force on National Greenhouse Gas Inventories has, as part of its mandate, the objective of
encouraging users to adopt the IPCC methodological guidelines for estimating national inventories ofgreenhouse gases. This report is one of a series, developed through expert meetings, which aims to assist usersof the guidelines by addressing specific problem areas.
In this case the expert meeting focused on dealing with uncertainty and with emission inventory validation. Since
the 2006 IPCC Guidelines for National Greenhouse Gas Inventories was produced the science of remotesensing and ambient measurements has developed through the use of techniques such as inverse modeling toincrease the potential applicability of the techniques to inventory validation. In addition, it has become clear thatsome users of the guidelines on uncertainty management need additional help to apply the methods in theguidelines correctly. This meeting aimed at considering both issues.
We would like to thank all those involved in this meeting. In particular we would like to express our sincere
thanks and appreciation to TNO and the Government of the Netherlands for their support by hosting thismeeting.

Thelma Krug
Co-Chair Task Force Bureau

Taka Hiraishi
Co-Chair Task Force Bureau

6Executive Summary
The meeting reviewed both the use of the current IPCC guidelines on uncertainty management and recent
developments related to inventory validation.
On the current IPCC guidelines on uncertainty management, the meeting agreed that the current guidelines stillreflect the state of the art but need to be supplemented by easy-use advice on how to approach uncertainties for
those with less experience. A first set of Q&A was provided for the TFI web site.
There was consensus that, while remote sensing, ambient measurement and inverse modelling techniques havebeen successfully demonstrated they are currently not sufficiently developed to provide comprehensive
verification at the required accuracy, much is to be gained from working together, to improve verification
techniques as well as gain better understanding of inventory estimates, and of natural emissions and removals.
The meeting acknowledged that growing international interest in monitoring and verification is increasing theimportance of dealing properly with uncertainty and is suggesting new initiatives for emissions inventories that
can improve prospects for independent verification.
The participants looked forward to future collaboration and identified a number of areas for possible prioritization
over the next few years and hoped that the group would meet again in a similar forum in say two years’ time toreview progress.

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1.

Introduction

The expert meeting of Intergovernmental Panel on Climate Change (IPCC) reviewing the use of the IPCC
guidance on emission inventories was held in May 2008 in Helsinki, Finland1. The meeting identified uncertainty
as an area where inventory compilers were having difficulties in following the IPCC Guidelines and Good
Practice Guidance and suggested holding an expert meeting to produce some additional material to assist
inventory compilers.
Subsequently the IPCC’s Task Force on National Greenhouse Gas Inventories (TFI) Bureau decided to expand
the idea to include the linked concept of validation/verification of emission inventories and convene an expert
meeting.
The IPCC expert meeting on Uncertainty and Validation of Emission Inventories was held in Utrecht, theNetherlands from 23 to 25 March 2010 and hosted by TNO (Netherlands Organization for Applied Scientific
Research). A total of 50 participants including IPCC TFI Co-chairs, Task Force Bureau (TFB) members, invited
experts/researchers and members of Technical Support Unit (TSU) attended the meeting.
The meeting discussed a number of topics in two main areas: the use of the uncertainty guidance in the IPCC
Guidelines, and ambient measurements systems (e.g. satellite, aircraft, flux towers, ground based
measurements etc.) for the validation/verification of emission inventories.
This report summarizes discussions and conclusions of the meeting, and aims to provide additional information
to inventory compilers on how they should assess inventory uncertainty at all tiers, how this should be reported
and how inventories may be validated and verified.

1The

IPCC Expert Meeting on IPCC Guidance on estimating emissions and removals of greenhouse gases from land uses such as
agriculture and forestry8

2.

Summary of Discussions

The meeting was opened by Mr. Taka Hiraishi, IPCC TFI Co-chair. He welcomed all participants and outlined thebackground of the meeting.
This was followed by the welcome speech of Mrs. Aukje Hassoldt, TNO. She noted the importance of themeeting and highlighted the TNO activities on environmental issues including climate change.
Welcome remarks were followed by introductory and invited presentations. The presentations and discussions
dealt with a number of topics, specifically:

Uncertainty assessments of emission inventories
Treatment of uncertainties associated with remote sensing
Use of ambient measurements and inverse modelling
Current capabilities of ambient measurement systems - e.g. satellite, aircraft, flux towers, ground based
measurements etc.
 Anticipated improvements of these systems over time in respect of their capabilities to validate emission
inventories
 In the context of specific IPCC categories how can these systems be used to validate emission
estimates
In the afternoon session on second day two breakout groups (BOGs) dealing with additional information on
estimating uncertainties in GHG inventories (BOG1) and use of measurements and/or inverse modelling tovalidate emission inventories (BOG2) were convened to continue further deliberations. The BOGs presented theoutcome of their discussions to the plenary in the afternoon on the final day.
2.1 Additional information on estimating uncertainties in GHG inventories
After considering the existing guidance on uncertainties given in the IPCC Guidelines (IPCC 2000, 2003 and2006), the information presented and their experience in using the guidelines, the meeting participants came tothe following conclusions:


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The guidance on uncertainty assessment provided in the 2006 IPCC Guidelines is technically correct.
However, it presents a very technical description and therefore an easy-to-use guide is needed to assist
users new to the topic. This can be written as Q&A and presented as linked pages on the TFI web site.
A first version of the Q&A is presented in Annex 2.
The participants highlighted the increasing emphasis and attention on uncertainty analysis asmonitoring and verification of emissions are getting more important and financial mechanisms are put
into place.
The participants recognized that finer spatial and temporal resolution, plus inclusion of bothanthropogenic and natural sources and sinks, might be desirable in emissions inventories in order toreduce uncertainty in comparisons with monitored concentrations and to improve prospects forindependent monitoring and verification.
Uncertainty assessment is more than just a calculation of uncertainty range. Users should try tounderstand the reasons for the uncertainty. Scientific understanding of uncertainties and their reasons
will guide users towards improving emission inventory.

2.2 Use of measurements and/or modelling to validate emission inventories
The meeting participants considered how existing techniques using remote sensing and ambient data could be
used to validate or assist inventory compilers, either now or in the foreseeable future.
There was consensus that, while remote sensing, ambient measurement and inverse modelling techniques havebeen successfully demonstrated they are currently not sufficiently developed to provide a comprehensive routine
verification at the desired accuracy, much is to be gained from working together, to improve verification9

techniques as well as gain better understanding of inventory estimates, and of natural emissions and removals.
The participants felt use of forward transport simulations of inventory-based (bottom-up) and inverse model (topdown) flux estimations should be conducted and validated against independent observations, as and whenavailable.
The participants looked forward to future collaboration and identified a number of areas for possible prioritization
over the next few years and hoped that the group would meet again in a similar forum in say two years’ time toreview progress.
The meeting also concluded that:


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

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Although encouraged rather than required by existing IPCC guidance, verification of GHG inventories,(defined here as the comparison of inventory estimates with independent estimates) is potentially a key
element of ensuring inventory quality and increasing the confidence of results. This requires
development of better techniques for observation and interpretation and better understanding of therelationship between inventory estimates and natural fluxes.
Several techniques are available or planned that show promise for verifying the GHG inventories atdifferent scales or levels of aggregation (i.e. regional, national, local or source-specific).
These techniques include using measurements of atmospheric concentrations, often combined withinverse modelling techniques, and other observations that can be used to estimate the total fluxes of agiven GHG.
Currently, the direct applicability of these techniques to the GHG inventories is limited by the density ofmeasurements, accuracy of the models, and the ability to attribute fluxes to anthropogenic versus
natural sources and sinks. Bottom-up information on the fluxes not covered by the GHG inventories will
be necessary for effective comparisons using these verification techniques.
As a consequence, these techniques are at present usually limited to comparisons of the results of
GHG inventories for selected gases in certain well-sampled regions (e.g. Europe, North America andAustralia).
The research community anticipates that capabilities will improve over time as measurement density
improves (i.e., through new satellite sensors, expanded surface network and number of gases andisotopes sampled), as the atmospheric chemistry and transport and statistical models continue to betested and improved.
In order for techniques based on remote sensing (RS) or ambient measurements to be effective inverifying GHG inventories, improvement in our abilities to partition the fluxes into different components
is necessary. Partitioning requires improvements in other observations (e.g. of various ecosystem andocean parameters), integration with flux information and an overall synthesis.
It is anticipated that with additional coordination and investment countries could make significant
progress over the next decade toward a globally linked and integrated observational network. From theinventory compilers’ point of view, the key issues include continuity of critical data records, newobservational capabilities and data sharing.
The inventory compiler community and the research communities would both benefit from continued
discussion of the specific verification needs for GHG inventories and the evolving capabilities of thesetechniques. In particular, the communities would benefit from having a sustained interaction on specificand verification questions so that further research efforts can be policy-relevant.
Some RS techniques, particularly for area and area change mapping, are sufficiently well-developed
today that they could be used in combination with other data (e.g. in-situ measurements and otherauxiliary information) to improve the quality of GHG inventories directly by contributing activity data (e.g.,land conversion data). More work needs to be done in improving the availability, accessibility andprocessing of RS information (e.g. data, satellite images) and developing standards. Interpretation anddata analysis may be resource-intensive.
One use of the GHG emissions inventories is to attribute climate change as being anthropogenic per
Article 2. Much of the uncertainty in that attribution, particularly at the national level, is now based on
the inventories. A consistent treatment of uncertainty across both these inventories and inverse
methods for deriving emissions is necessary for comparison. Also, transparent, research-based
approach is needed to propagate uncertainties when combining sectoral and regional emissions.
10


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The inventory compiler also needs methods for comparing inventories to inversions. More research isneeded to determine how to conduct verification and also case studies demonstrating applications ofthese methods.
Some inventory verification challenges are presented below (Table 1) as an illustrative list of areas inwhich evolving techniques could be employed to verify and raise the quality of GHG inventories:

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Table 1 Some inventory challenges where these verification techniques could be employed
Area
Emissions from theuse of fluorinatedgases

Identified needs/gaps
ISSUE:
Unclear if timing and location of emission estimates are correct. There is very little
tracking of activity data for many end-use applications.
NEED:
Integrated totals at higher temporal resolution so that the inventory compilers cancheck the rates of emission and timing of release etc.;
Better spatial resolution

Fugitive and vented
CH4 emissions fromoil and gas systems,
coal mining andlandfills

ISSUE:
Generic EFs or equivalent fail to characterize emission rates over different spatial
and temporal scales
NEED:
Independent checks on emission factors and emissions estimates, particularly for
upstream oil and gas production emissions.
Observations that identify emissions but do not quantify them can also be helpful
for indicating sources that are currently being missed.

N2O emissions fromsoils (direct andindirect)

ISSUE:
N2O emission from soils very uncertain and unverified. So, try meso-scale toprovide better spatial and temporal integration than plot level data
NEED:
Independent quantification of indirect emissions from volatilization and redeposition, leaching and run-off. Assessment of emission rates from “other lands”
e.g., water bodies, coastal areas etc.
Process-based modeling has also been used successfully to reduce emissions.
These models may use remote sensing and weather data to provide a more
precise estimate of emissions. Combining these approaches with mesoscalemodel inversions could lead to more confidence in inventories of soil N2O
emissions

Forest fires, biomassburning in other land
uses and otherdisturbances

ISSUE:
Estimates of emissions from biomass burning in non-forest land uses and otherdisturbances are very uncertain and unverified.
NEED:
Identification of area burnt and mass of biomass fuel available for combustion, andcombustion emission factors;
Actual measurement of N2O and CH4 from fires. Measurement of CO, NOx etc.
could serve as a proxy.

Carbon Capture andStorage sites

ISSUE:
Leakage from geological storage sites are challenging to identify and even moredifficult to quantify. Leakage may occur over a wide area which needs to becompletely covered.
NEED:
Accurate time series of isotopically distinguishable background emission rates atappropriate scales (storage sites i.e. 10-100 km)
Ability to identify leaks
Quantification of diffuse emissions

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Overall inventory
totals by gas

ISSUE:
Independent check on emission inventories currently largely lacking.
NEED:
Independent estimates of national fluxes of GHG

Peatlands

ISSUE:
Diffuse emissions occurring over a wide area are poorly quantified and understood.
Generic EFs fail to characterize the relationship to management practices
NEED:
Focus on estimating total CH4 and N2O emissions

Methane frompermafrost melting
and clatharates

ISSUE:A poorly understood source with no current estimation method.
NEED:
Magnitude of, and changes in, emissions from permafrost melting and methane
clatharates (not necessarily an inventory issue)

Black carbon andaerosols

ISSUE:
Spatial and temporal resolution of emission important. Can be very transient,making comparison difficult. Sources not well understood.
NEED:
Measurements to validate emission estimates

Fluxes on coastal
oceansISSUE:
Not currently included in inventories and data required to calibrate estimates.
NEED:
Concentration data. Inventory guidance needed; separation of anthropogenic andnatural likely to be challenging

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Annex 1.

References

IPCC (1997). Houghton J.T., Meira Filho L.G., Lim B., Treanton K., Mamaty I., Bonduki Y., Griggs D.J., and Callander
B.A. (Eds). Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. IPCC/OECD/IEA, Paris, France.
IPCC (2000). Penman J., Kruger D., Galbally I., Hiraishi T., Nyenzi B., Emmanuel S., Buendia L., Hoppaus R., Martinsen
T., Meijer J., Miwa K., and Tanabe K. (Eds) Good Practice Guidance and Uncertainty Management in National
Greenhouse Gas Inventories. IPCC/OECD/IEA/IGES, Hayama, Japan.
IPCC (2003). Penman J., Gytarsky M., Hiraishi T., Krug T., Kruger D.,Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K.
and Wagner F. (Eds.). Good Practice Guidance for Land Use, Land-Use Change and Forestry, IPCC/IGES, Hayama,
Japan.
IPCC (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National
Greenhouse Gas Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds).
Published: IGES, Japan14

Annex 2.
1.

Questions and Answers

Key Definitions
1.1. How do we understand “uncertainty” in emission inventories? How to distinguish between variability anduncertainty?
Uncertainty: Lack of knowledge of the true value of a variable. Uncertainty comprises two parts:
Random variation around a mean value. Generally these can be quantified and the IPCC guidelines give methods
deal with the random errors.

Bias cannot be quantified and so the IPCC guidelines give approaches to minimise bias.

Uncertainty depends on the analyst’s state of knowledge, which in turn depends on the quality and quantity of applicabledata as well as knowledge of underlying processes and inference methods. Uncertainty may arise for an inability to quantify
the variable. For example, soil nitrous oxide emissions can vary from year to year due to weather conditions, and if emission
factors do not account for weather, then some of the uncertainty will be due to variability in emissions across time (andspace) that is not quantified.
Variability: How a variable differs from time to time, place to place or between members of a population. A number ofsource categories, mainly in the AFOLU sector may show temporal variability on long time scales, due to for instance wild
fires, rain fall, temperatures etc.
Uncertainty: Lack of knowledge of the true value of a variable that can be described as a probability density
function (PDF) characterising the range and likelihood of possible values. Uncertainty depends on the analyst’s
state of knowledge, which in turn depends on the quality and quantity of applicable data as well as knowledge ofunderlying processes and inference methods.
Variability: Heterogeneity of a variable over time, space or members of a population (...). Variability may arise, forexample, due to differences in design from one emitter to another (inter-plant or spatial variability) and in operating
conditions from one time to another at a given emitter (intra-plant variability). Variability is an inherent property ofthe system or of nature, and not of the analyst..
IPCC 2006, Vol. 1, Ch 3, Section 3.1.3 Key concepts and terminology

1.2. What is the difference between accuracy and precision? Does uncertainty assessment relate to both?
Essentially accuracy is a measure of the lack of bias of an estimate while precision is a measure of the repeatability of theresult. In general lack of precision is caused by random variation in input data (e.g. experimental error) while lack of
accuracy results from biases from incomplete understanding (or modelling) of the process. So, for example:


Estimates of CO2 emissions from fuel use can be based on measurements of the carbon content of the fuel.
Reductions in the random error in the measurements will lead to improvements in the precision of the result;however the accuracy may be limited by a bias caused by an incomplete knowledge of the oxidation rate of the
combustion process.



Emissions or removals from forests are modelled by changes in carbon stocks. The precision achieved will be theresult of the uncertainty derived from the experimental error of the measured values of the various parametersused. On the other hand, there may be inaccuracy caused by such things as the measured parameters not being
measured under the same climatic and ecological situation as the forest area being estimated; by an incomplete
knowledge of all the processes removing carbon from the forest; or by the model of carbon stocks being
incomplete. All these causes of inaccuracy lead to bias.

Emissions of F-gases for refrigeration are estimated from monitoring the imports, exports, production and use of
the chemicals. Data on these product flows often come from surveys, all of which are subject to random survey
errors, which lead to a lack of precision. While biases in the surveys (e.g. caused by missing some sectors) orincorrect understanding of leakage processes lead to inaccuracy and bias.

The process of uncertainty estimation aims to quantify precision. Before this uncertainty estimation is performed all known orsuspected biases should be removed or corrected.
Accuracy is improved by evaluating methods and assumptions as well as input data to ensure they are free from bias.
“Accuracy: Agreement between the true value and the average of repeated measured observations or estimatesof a variable. An accurate measurement or prediction lacks bias or, equivalently, systematic error.
Precision: Agreement among repeated measurements of the same variable. Better precision means less random
error. Precision is independent of accuracy”.

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IPCC 2006, Vol. 1, Ch 3, Section 3.1.3 Key concepts and terminology“The quantitative uncertainty analysis tends to deal primarily with random errors based on the inherent variabilityof a system and the finite sample size of available data, random components of measurement error, or inferences
regarding the random component of uncertainty obtained from expert judgement. In contrast, systematic errorsthat may arise because of imperfections in conceptualisation, models, measurement techniques, or other systems
for recording or making inferences from data, can be much more difficult to quantify. As mentioned in Section 3.5,
Reporting and Documentation, it is good practice for potential sources of uncertainty that have not been quantified
to be described, particularly with respect to conceptualisation, models, and data and to make every effort toquantify them in the future.
Good practice requires that bias in conceptualisations, models, and inputs to models be prevented wherever
possible, such as by using appropriate QA/QC procedures. Where biases cannot be prevented, it is good practice
to identify and correct them when developing a mean estimate of the inventory. In particular, the point estimatethat is used for reporting the inventory should be free of biases as much as it is practical and possible. Once
biases are corrected to the extent possible, the uncertainty analysis can then focus on quantification of the random
errors with respect to the mean estimate”.
IPCC 2006, Vol. 1, Ch 3, Section 3.1.4 Basis for uncertainty analysis

1.3. How would uncertainty in activity data and/or emission factors be reflected in the emission estimate?
Where uncertainty in emission factors and activity data arise from random factors (e.g. experimental error, survey errors)then this will lead to a quantifiable uncertainty in the result. However if this uncertainty is systematic (e.g. where emission
factors are estimated from a sub-population and so are not directly applicable to the entire population; or where a survey
does not randomly sample the entire population, e.g. it misses small plant) it might lead to bias. Random errors are
expressed as an uncertainty range. Bias should be minimised by thorough evaluation of the data and assumptions used,good QA/QC and review. In some cases, it is possible to correct for biases such as errors in measurements or analyticalmethods where the error has been quantified and can be used to adjust the estimates.
2.

Why do we need uncertainty assessment of emission inventories?
2.1. Why uncertainty assessment is considered important in GHG inventory preparation? How should the result of
uncertainty assessment be used?
Uncertainty assessment is needed to help guide inventory improvements.
“An uncertainty analysis should be seen, first and foremost, as a means to help prioritise national efforts to reduce
the uncertainty of inventories in the future, and guide decisions on methodological choice. For this reason, themethods used to attribute uncertainty values must be practical, scientifically defensible, robust enough to be
applicable to a range of categories of emissions by source and removals by sinks, methods and national
circumstances, and presented in ways comprehensible to inventory users.”
IPCC 2006, Vol.1, Ch 3, Section 3.1.1 Overview of uncertainty analysis“… Uncertainty information is not intended to dispute the validity of the inventory estimates, but to help prioritiseefforts to improve the accuracy of inventories in the future and guide decisions on methodological choice, …”
IPCC 2000, Ch 6, Section 6.1 Overview

Uncertainty information is used principally to identify planned improvements in emission inventories.Uncertainty assessment
is also important in comparisons with atmospheric measurements and modelling. Without uncertainty estimates, it is difficult
to determine the extent to which atmospheric measurements (such as atmospheric inversions of CO2 concentrations) can becompared to emission estimates. Uncertainty of estimates or measurements may also assist in informing decisions about
efforts to mitigate emissions.
2.2. Can we reduce uncertainties by using higher tier methods to estimate GHG emissions/removals? Sometimes
higher tier methods result in wider confidence interval. How should this be interpreted?
Moving to a higher tier method should result in a better estimate with reduced uncertainty. However note that:
Sometimes the higher tier method better estimates uncertainty revealing the Tier 1 result underestimated the true
uncertainty.

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Using higher tiers aims to reduce bias (e.g. it uses more disaggregated data to move to a more detailed stratification) but
this can sometimes decrease precision, giving a higher uncertainly value.
“Choice of methodological tier for emissions and removals estimation can affect the uncertainty analysis in two
different ways. Firstly, moving to higher tier inventory methods should typically reduce uncertainties, provided thehigher tier methods are well implemented, because they should reduce bias and better represent the complexity ofthe system. Secondly, moving to higher tier methods may result in increased estimates of uncertainty in somecircumstances. Often, this increase in the estimated uncertainty does not actually represent a decrease inknowledge; rather, it typically reveals a more realistic acknowledgment of the limitations of existing knowledge.
This may occur where there was an incomplete accounting of the greenhouse gas emissions in the lower tiermethod, or where application of higher tier methods reveals additional complexity and uncertainties that were notfully apparent in the lower tier method. This really means that the uncertainty was underestimated previously andmoving to the higher tier method in reality produces a more accurate estimate of uncertainty. In some cases, anincrease in uncertainty may occur for one inventory development method versus another because each method
has different data requirements.”
IPCC 2006, Vol. 1, Ch 3, Section 3.1.7, Implications of methodological choice

2.3. Can we compare uncertainty estimates between counties?
Yes – the numbers should be comparable if both countries have followed the guidelines correctly. While Approach 2 should
give a quantitatively better estimate of the confidence limits Approach 2 and Approach 1 give comparable results especiallywith respect to ranking the contributions to overall inventory uncertainty. Large differences in inventory uncertainties shouldreflect real differences in the uncertainties of the estimates. However, in practice, there are wide differences between the
results that are not easily explained.
An inventory with a high uncertainty may result from a preponderance of sources with intrinsically high uncertainties (e.g.
land use sectors) or from a lack of knowledge of the input data more generally. High uncertainty at the national, annual level
is often the result of large variability (see Q&A 1.2).
2.4. How can we ensure consistency in uncertainty assessment among different sectors?
There is a need to ensure correct application of guidelines on uncertainty across sectors and source categories. All theinventory compilers for different sectors in the team should have close consultation and discussion so that they arrive atcommon understanding of “uncertainty”. The uncertainty estimation should be explicitly included within the inventory
planning process, based on a common understanding of uncertainty, and using similar definitions and ways to quantify
uncertainty across sectors and source categories.
3.

How do we you perform an uncertainty analysis as part of emission inventories?
3.1. How do you start an uncertainty assessment?
a.

Decide on approach to use (look at expertise and resources available)b.

Collect uncertainty information for all data inputs. Where Approach 2 is used full probability density function (PDF)
will be needed for activity data, emission factors and any other parameters used. If a full PDF is not available anormal or lognormal PDF can be based on the mean and uncertainty. Knowledge of the causes of uncertainty will
inform this step.

c.

Decide how to aggregate sources and sinks to minimise impact of correlations (or for Monte-Carlo develop method
to incorporate correlations in activity data and emission factors)d.

Combine uncertainty information to estimate overall inventory uncertainty either using error propagation or MonteCarlo methods as decided in step ”a”

e.

Report result and document methodf.

Use uncertainly information in developing improvement plan for inventory

3.2. How should the data and information on uncertainty be collected?
Uncertainty information should be collected together with activity and emission factor data. Generally the experts providing
both types of data have the best information and knowledge on the weaknesses and uncertainties in their data.
Communication on understanding of uncertainties and the objectives of the uncertainty assessment with the data providers
is crucial. In some cases data providers might be reluctant to provide uncertainty data. In such cases the inventory compiler

17could use a formal or informal “expert elicitation” process to help the experts to quantify their understanding of the quality ofthe data in terms of uncertainty ranges and/or PDFs.
As a fall back option when no data have been included by the data providers, the default uncertainty ranges given in the2006 IPCC Guidelines could be used for a first uncertainty assessment. If these source categories appear to have major
contributions to the overall inventory uncertainty, further work on these uncertainties could be planned for next inventory
cycles.
“…Ideally, the inventory compiler should collect activity data, emission factors, and uncertainty information at thesame time because this is the most efficient strategy.”
IPCC 2006, Vol. 1, Ch 1 p 1.11, Box 1.1.“… Activity data are often collected and published regularly by national statistical agencies, which may havealready assessed the uncertainties associated with their data as part of their data collection procedures. Thesepreviously developed uncertainty estimates can be used to construct PDFs. This information will not necessarily
have been published, so it is recommended to contact the statistical agencies directly. Since economic activity
data are not usually collected for the purpose of estimating greenhouse gas emissions and removals, it is goodpractice to assess the applicability of the uncertainty estimates before using them.”
IPCC 2006, Vol. 1, Ch 3, p 3.18

Anytime you have a survey or a census you must have a point estimate and an uncertainty interval. Surveys need a valid
statistical design so that variances and co-variances can be empirically derived. Surveys may use stratification to sample
more efficiently. Samples should be randomized within the strata. Weights may also be needed depending on the
representativeness of each sample. An example of a statistically-based survey is the US National Resources Inventory. In
order to start collecting such uncertainty data:
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Consult with your statistical agencies to collect this data
Always ask for point estimates and uncertainty data, if necessary ask for uncertainty information in a second step
If you don't get it: use your own expert judgment as a start; you can always improve in the next inventory cycle!
As a start you can use information in the 2006 Guidelines and EFDB
Make comparisons with other datasets and other countries. An example of collection of uncertainty information
can be a comparison of two databases e.g. If in a country fuel consumption is recorded in energy balance and inparallel of CLRTAP emissions database
Be aware that experts might be optimistic/subjective with respect to the quality of their data

3.3. How should we choose the approach to uncertainty assessment, Approach 1 or Approach 2? Can we use both
approaches in the same GHG inventory?
You will always need to consider the resources and expertise available. Approach 1 requires fewer resources and limited
expertise compared with Approach 2.
Approach 1 will usually be good enough if you only use uncertainty for inventory improvement. Approach 2 will be more
appropriate for scientific applications where there are high uncertainty ranges and correlations.
Approach 2 can be nested in an Approach 1 for the overall inventory uncertainty assessment. Approach 2 can for instancebe used for specific complex calculations in restricted source or sink categories. The resulting uncertainty range for thissource or sink then can be used as input into Approach 1 to combine these results with the entire inventory.
Where Approach 2 is used the guidelines also encourage the use of Approach 1 as a QA/QC check.
“Where the conditions for applicability are met (relatively low uncertainty, no correlation between sources except
those dealt with explicitly by Approach 1), Approach 1 and Approach 2 will give identical results. However, andperhaps paradoxically, these conditions are most likely to be satisfied where Tier 2 and Tier 3 methods are widelyused and properly applied in the inventory, because these methods should give the most accurate and perhaps
also the most precise results. There is therefore no direct theoretical connection between choice of Approach andchoice of Tier. In practice, when Tier 1 methods are applied, Approach 1 will usually be used while the ability toapply Approach 2 is more likely where Tier 2 and 3 methods are being used, moreover for quantifying theuncertainty of emissions/removal estimates of complex systems such as in the AFOLU Sector. When Approach 2
is selected, as part of QA/QC activities inventory agencies also are encouraged to apply Approach 1 because of
the insights it provides and because it will not require a significant amount of additional work. Where Approach 2 isused, its estimates of overall uncertainty are to be preferred when reporting uncertainties.”

18

IPCC 2006, Vol. 1, Ch 3, Subsection 3.2.3.5 Guidance on choice of Approach“In some cases, most of the category uncertainties in an inventory might be estimated using Approach 2, withrelatively few estimated using Approach 1. It is possible to incorporate Approach 1 estimates of uncertainty forsome categories into an Approach 2 methodology for combining uncertainties for the total inventory. This is doneby using the uncertainty half-range obtained from Approach 1 to specify an appropriate PDF model to represent
uncertainty for each category as part of the Monte Carlo simulation.”
IPCC 2006, Vol. 1, Ch 3, Subsection 3.2.3.3 Hybrid combinations of Approaches 1 and 2

3.4. What are the key differences between Approach 1 and Approach 2
While Approach 1 technically is limited to specific circumstances the 2006 IPCC Guidelines give ways to deal with thesesituations and so it gives useful results for all emission inventories. Approach 2 does not have these limitations.
Approach 1 is only strictly correct when uncertainties are less than 0.3, distributions are normal and there are nocorrelations. However, the 2006 IPCC Guidelines give an empirical equation for dealing with large uncertainties and theimpact of correlations can be reduced by grouping data.
Approach 2 can deal with all these situations. Approach 2 requires significantly more data and resources. It needs not justthe uncertainty but also the probability density function, which can be non-symmetrical. Approach 2 will be required if arealistic quantitative uncertainty range is to be calculated in cases where large uncertainties in activity data and / oremission factors occur.
Where the standard deviation for any parameter is greater than about 0.3, normal probability functions will give nonnegligible probabilities for negative values. In real life such negative values can obviously not occur. Another option would
be to use a log-normal distribution in this case.
3.5. How to interpret and use the output data of the uncertainty assessment
A Tier 2 KCA uses the uncertainty data together with the magnitude of each source or sink category to highlight those that
contribute to the overall inventory uncertainty. Thus improvements can be targeted to reduce overall inventory uncertainty.
Both approaches give estimates of the confidence intervals of the inventory that can be used in verification (comparison withother independent estimates).
4.

Pitfalls
4.1. In the AFOLU sector, Approach 1 uncertainty assessment sometimes using Equation 3.2 sometimes results in
extremely large uncertainty values because the denominator becomes close to zero when emissions and removals
are nearly equal. Is this appropriate? Can we somehow avoid it?
This is because uncertainty is presented as a relative uncertainty (a fraction uncertainty divided by the point estimate). As
sources and sinks balance the point estimate goes to zero but the absolute value of the uncertainties might still besignificant. The calculated value of the relative uncertainty in such cases becomes very large and is not meaningful. It may
be better to present absolute uncertainty values when emissions and removals nearly balance. Just show the absolutevalues of 95% confidence interval. These absolute values can be combined with absolute values of the rest of the inventory
to give overall inventory uncertainty.
4.2. How should we treat correlation between factors in uncertainty assessment?
Assume there is no correlation unless there is evidence of correlation. If there is evidence, correlation should be taken into
account. See IPCC 2006, Vol.1, Ch 3, p 3.25-3.26 “Dependence and correlations among inputs”.
An example where correlation is likely to be important would be land use data. As the area of one land use is increasing, theareas of other land uses will by necessity be decreasing (assuming the land base is constant across the time series, which
is a good practice). This relationship creates correlations in the data which can be derived if the land survey has astatistically-based design. In this case, the compiler would need to derive the covariances across all combination of land use
and time in order to fully address the uncertainty.

19

20

Annex 3.

AgendaTuesday 23 March
09:30 –10:00
10:00 –10:30

10:30 –13:00

13:00 –14:00

14:00 –17:00

18:00

REGISTRATION
Welcome
Co-Chairs (TFI)
Mrs. Aukje Hassoldt (TNO) Introduction (Simon Eggleston, TSU)
 Uncertainty analysis and inventory verification in IPCC 2006 Guidelines (Simon
Eggleston, TSU)
 Uncertainty about Inventory Estimates: A Canadian perspective (Frank Neitzert,
Canada) Assessment of uncertainties in activity data and emission factors for Indian emission
inventories (Amit Garg, India)
 Experiences in Mexico with uncertainty analysis in AFOLU (Bernardus de Jong, Mexico)
LUNCH
 Experiences in quantifying the uncertainty of the estimates of Canada's National
Forest Carbon Monitoring, Accounting, and Reporting System, NFCMARS (Juha
Metsaranta, Canada) Uncertainty in remote sensing data and their analysis for forest inventory (Yasumasa
Hirata, Japan)
 Direct measurement of GHG emissions: The case of fugitive methane from landfills(Antoine Babilotte, France)
 Inverse modeling of GHG and aerosol precursor emissions using satellite
measurements: Status and future perspectives (Sander Houweling and Martijn Schaap,
The Netherlands)
 Integrating observations & inventories to improve emission estimates: a frameworkfor synthesis (Riley Duren, USA)RECEPTION

Wednesday 24 March

10:00 –13:00







Integrating observations from remote sensing, inventories and flux data with models
for estimating terrestrial carbon sources and sinks (Beverly Law, USA)Top down atmospheric and bottom up estimates of the European GHG balance(Philippe Ciais, France)
The successes, limitations and uncertainties of estimating emissions
from measurements (Alistair Manning, UK)
Use of inverse modeling to evaluate emissions inventories (Stephen Ogle, USA)
Validation/verification capabilities of ambient mixing ratio measurements and inverse
modeling (Lingxi Zhou, China)
Inverse modeling of European GHG emissions and verification of bottom-up
inventories (Peter Bergamaschi, Italy)

13:00 –14:00

LUNCH

14:00 –17:00

Breakout Group 1: Additional information on estimating uncertainties in GHG inventories
Breakout Group 2: Use of measurements and/or inverse modeling to validate emission inventories

18:00

RECEPTION

Thursday 25 March
10:00 –13:00
13:00 –14:00
14:00 –17:00

Breakout Groups continue and agree text for meeting report
LUNCH
Plenary: Agree Draft Meeting Report21Annex 4.

Participant list

Argentina
Leonidas Osvaldo Girardin
Fundacion Bariloche
Piedras 482 2h, Buenos Aires,
1070 Argentina
Tel: +54 11 4331 2021
Fax: +54 11 4331 2021
logirardin@fundacionbariloche.org.ar
Dario Gomez
Atomic Energy Commission of Argentina
Avenida General Paz 1499, San Martin,
B1650KNA Argentina
Tel: +54 11 67727130
Fax: +54 11 67727130
dgomez@cnea.gov.ar
dario_r_gomez@yahoo.com.ar
Benin
G. H. Sabin Guendehou
Benin Centre for Scientific andTechnical Research
03 P.O. Box 2048 or 1665 Cotonou
Republic of Benin
Tel: +229 95 05 93 91
Fax: +229 21 32 36 71
sguendehou@yahoo.com
guensab@yahoo.fr
Bolivia
Emilio Garcia-Apaza
National Program on Climate Change
Mercado Street 1328, Mariscal Ballivian
Building, La Paz, 14482 Bolivia
Tel: +591 2 2200206
Fax: +591 2 2204037
e.garcia@ua.es
garciaa_emilio@yahoo.com
BrazilThelma Krug (TFI Co-chair)
NPE - Instituto Nacional dePesquisas Espaciais
Av. Dos Astronautas, 1758 - Jardim da Granja,
São José dos Campos - SP,
12227-010 Brazil
Tel: +55 12 39456861
Fax: +55 12 39412077
thelmakrug@dir.inpe.br
Thelma@dir.iai.int
Newton Paciornik
Ministry of Science and Technology of Brazil
Praia do Flamengo 200, 7º, Rio de Janeiro,
22210-901 Brazil
Tel: +55 21 25550308
Fax: +55 21 25550306
npaciornik@mct.gov.br

CambodiaThy Sum
Department of Climate Change, Ministryof Environment# 48, Samdech Preahsihanouk Blvd,
Chankarmon, Phnom Penh, 855
Cambodia
Tel: +855 23 218370
Fax: +855 23 218370
cceap@online.com.kh
sumthy@yahoo.com
CanadaJuha, Mikael Metsaranta
Natural Resources Canada, Canadian
Forest Service
Northern Forestry Centre,
5320-122 St Edmonton, AB
CanadaTel: +1 780 435 7210
Fax: +1 780 435 7359
jmetsara@nrcan.gc.ca
Frank Neitzert
Environment Canada
200 Sacré-Coeur Blvd. Gatineau, Quebec,
K1A 0H3 Canada
Tel: +1 819 934 3764
Fax: +1 819 953 3006
frank.neitzert@ec.gc.ca
China
Baozhang Chen
Institute of Geographic Sciences and Natural
Resources Research, Chinese Academy Sciences (CAS)11A Da Tun Road, Anwai, Chaoyang District,
Beijing, 100101 P.R. China
Tel: +86 10 64889574
Fax: +86 10 64889574
Baozhang.chen@igsnrr.ac.cn
baozhang.chen0808@gmail.com
Lingxi Zhou
Chinese Academy of Meteorological Sciences (CAMS)
China Meteorological Administration (CMA)
CAMS, 46 Zhongguancun Nandajie,
Beijing 100081 P.R. China
Tel: +86 10 58995279
Fax: +86 10 62176414
zhoulx@cams.cma.gov.cn
zhoulx2007@gmail.com
Czech Republic
Miloš Tichý
ConsultantTřístoličná 5, Prague 5, 15000
Tel: +420 606707332
Fax: +420 226514812
milos.tichy@sujb.cz

22France
Antoine Babilotte
Veolia Environment R&D
Zone portuaire de Limay, 78520 - LIMAYFranceTel: +33 624 531 769
Fax: +33 130 985 499
Antoine.babilotte@veolia.com
Frédéric Chevallier
LSCE
Bat 701, Point courrier 129,
L´Orme des Merisiers,
91191 Gif-sur-Yvette Cedex
FranceTel: +33 1 6908 7729
Fax: +33 1 6908 7716
frederic.chevallier@lsce.ipsl.fr
Philippe Ciais
Laboratory for Climate Sciences and the Environment,
CEA/LSCE
Orme des merisiers, 91191 Gif sur
Yvette Cedex France
Tel: +33 1 69 08 95 06
Fax: +33 1 69 08 77 16
Philippe.ciais@lsce.ipsl.fr
India
Amit Garg
Indian Institute of Management Ahmedabad
W16B, IIMA Campus, Vastrapur, Ahmedabad,
380015 India
Tel: +91 79 6632 4952
Fax: +91 79 6632 6896
amitgarg@iimahd.ernet.in
Prabir Kumar Patra
Research Institute for Global Change,
JAMSTEC
3173-25 Showa-machi, Kanazawa-ku,
Yokohama-shi, 236 0001 Japan
Tel: +81 45 778 5727
Fax: +81 45 778 5496
prabir@jamstec.go.jp
Italy
Peter Bergamaschi
European Commission Joint Research Centre
Institute for Environment and Sustainability(IES)
TP 290, I-21027 Ispra (Va) Italy
Tel: +39 0332 789621
Fax: +39 0332 785704
peter.bergamaschi@jrc.ec.europa.eu

Japan
Yasumasa Hirata
Forestry and Forest Products Research Institute(FFPRI)
1 Matsunosato, Tsukuba, Ibaraki, 305-8687
Japan
Tel: +81 29 829 8314
Fax: +81 29 873 3799
hirat09@affrc.go.jp
Mitsuo Matsumoto
Forestry and Forest Products Research Institute(FFPRI)
1 Matsunosato, Tsukuba, Ibaraki, 305-8687
Japan
Tel: +81 29 829 8330
Fax: +81 29 874 3720
machan@ffpri.affrc.go.jp
Taka Hiraishi (TFI Co-chair)c/o IGES
2108-11 Kamiyamaguchi Hayama, Kanagawa, 240-0115
Japan
Tel: +81 46 855 3758
Fax: +81 46 855 3808
hiraishi@iges.or.jp
JordanJabur Ali Mustafa Daradkah
Ministry of Environment - Jordan
Ministry of Environment - Faisal Str.
Um othaina - Amman
P.O. Box 1408, Amman, 11941
JordanTel: +962 6 5560113
Fax: +962 6 5524693
j_daradkah @ yahoo.com
Kazakhstan
Valentina Idrissova
Kazakh Research Institute for Ecology
and Climate
Almaty, Seifullin, 597, 050022
Kazakhstan
Tel: +7 727 2675269
v.idrissova@gmail.com
MexicoBernardus De JongEl Colegio de la Frontera Sur
Carr a Reforma km 15.5 s/n,Ranchería Guineo 2da Sección,
Villahermosa, Tabasco, 86280
MexicoTel: +52 993 3136110 ext.3401
bjong@ecosur.mx

23New ZealandMike Rollo
Ministry of Agriculture and Forestry/AgResearch
Private Bag 3123, Hamilton, 3240
New ZealandTel: +64 7 838 5100
Fax: +64 7 838 5155
mike.rollo@agresearch.co.nz
Syria
Sadeddin Kherfan
Arab University of Science & Technology
Homs-Ghota, Najm St, P.O. Box 5051, 031
Syria
Tel: +963 944 458 468
Fax: +963 33 458903
skherfan@yahoo.com
Tanzania
Hubert Eliapenda Meena
CEEST Foundation
17 Kaunda Drive, Oysterbay, P.O. BOX 5511,
Dar es Salaam Tanzania
Tel: +255 22 2667569
Fax: +255 22 2667569
hemeena@yahoo.com
ceest@ceest.co.tz
Thailand
Savitri Garivait
The Joint Graduate School of Energy andEnvironment (JGSEE)King Mongkut’s University of Technology
Thonburi (KMUTT)
126 Pracha-Uthit Road, Bangmod, Tungkru, Bangkok
10140, Thailand
Tel: +66 2 4708309, ext. 4134
Fax: +66 2 8729805
savitri_g@jgsee.kmutt.ac.thsavitrigarivait@yahoo.com
The Netherlands
Sander Houweling
SRON Netherlands Institute forSpace ResearchInstitute for Marine and Atmospheric Research
Utrecht (IMAU)
Sorbonnelaan 2, Utrecht, 3584 CA
The Netherlands
Tel: +31 302 532 291
Fax: +31 302 543 163
s.houweling@sron.nl
Tinus Pulles
TNO Built Environment and Geosciences
P.O. Box 80015, Utrecht 3508 TA
The Netherlands
Tel: +31 888 662067
Fax: +31 888 662044
Tinus.Pulles@tno.nl

Martjin Schaap
TNO Built Environment and Geosciences
P.O. Box 80015, Utrecht 3508 TA
The Netherlands
Tel: +31 88 8662074
Fax: +31 88 8662044
Martijn.schaap@tno.nl
Jos Olivier
Netherlands Environmental Assessment Agency (PBL)
P.O. Box 303, Bilthoven
NL-3720 AH, The Netherlands
Tel: +31-30-2743035
Fax: +31-30-2744479
jos.olivier@pbl.nl
United Kingdom
Joanna MacCarthy
AEA
The Gemini Building, Fermi Avenue, Harwell, Oxfordshire
OX11 0QR
United Kingdom
Tel: +44 870 190 3857
Fax: +44 870 190 6318
Joanna.MacCarthy@aeat.co.uk
Alistair James Manning
Met Office
FitzRoy Road, Exeter, Devon EX1 3PB
United Kingdom
Tel: +44 1392 884243
alistair.manning@metoffice.gov.uk
Jim Penman
UK Dept of Energy and Climate Change
3 Whitehall Place, London SW1A 2HD
United Kingdom
Tel: +44 300 068 5646
Jim.Penman@decc.gsi.gov.uk
USAPhilip DeCola
Office of Science and Technology
Policy/White House
1924 Park Rd, NW, Washington, DC
20010 USA
Tel: +1 202 641 7178
Tel: +1 202 588 0312
Fax: +1 202 456 6027
pdecola@ostp.eop.gov
pldecola@comcast.net
Riley Duren
NASA Jet Propulsion Laboratory
m/s 180-404, 4800 Oak Grove Dr, Pasadena,
CA 91109 USA
Tel: +1 818 354 5753
Riley.M.Duren@jpl.nasa.gov

24

William Irving
USEPA
1200 Pennsylvania Avenue, NW,
Washington, DC 20460, 6207J
USA
Tel: +1 202 343 9065
Fax: +1 202 343 2359
Irving.bill@epa.gov

UNFCCC
Javier Hanna
UNFCCC secretariat
Martin Luther King strasse 8,
Bonn D-53175 Germany
Tel: +49 228 815 1368
Fax: +49 228 815 1999
jhanna@unfccc.int

Beverly Law
Oregon State University
328 Richardson Hall, Corvallis, OR 97331
USA
Tel: +1 541 737 6111
Fax: +1 541 737 1393
Bev.law@oregonstate.edu

IPCC TSU℅IGES
2108-11 Kamiyamaguchi, Hayama,
Kanagawa, 240-0115 Japan

Gregg Marland
Oak Ridge National Laboratory
Bethel Valley Road, Oak Ridge, Tennessee
37831-6335 USA
Tel: +1 865 241 4850
Fax: +1 865 574 2232
marlandgh@ornl.gov
Stephen Ogle
Colorado State University
Natural Resource Ecology Laboratory
Department 1499
Colorado State University
Fort Collins, Colorado 80523 USA
Tel: +1 970 491 7662
Fax: +1 970 491 1965
ogle@nrel.colostate.edu
Michael John Prather
Department of Earth System Science,
UC Irvine
3329 Croul Hall, Earth System Science Dept., Irvine CA
92697-3100 USA
Tel: +1 949 824 5838
Fax: +1 949 824 3874
mprather@uci.edu
VietnamNguyen Mong Cuong
Research Center for Climate Change andSustainable Development
No. 10/42 Yen Hoa Street, Cau Giay District,Hanoi Vietnam
Tel: +844 3 8341208
Fax: +844 3 7759382
cuongccsd@yahoo.com
cuongnguyencc@gmail.com

Simon Eggleston
Tel: +81 46 855 3751
Fax: +81 46 855 3808
eggleston@iges.or.jp
Kiyoto Tanabe
Tel: +81 46 855 3752
Fax: +81 46 855 3808
tanabe@iges.or.jp
Nalin Srivastava
Tel: +81 46 855 3754
Fax: +81 46 855 3808
srivastava@iges.or.jp
Jamsranjav Baasansuren
Tel: +81 46 855 3757
Fax: +81 46 855 3808
baasansuren@iges.or.jp
Eriko Nakamura
Tel: +81 46 855 3750
Fax: +81 46 855 3808
nakamura@iges.or.jp
Jacques Kouazounde
Tel: +81 46 855 3759
Fax: +81 46 855 3808
kouazounde@iges.or.jp
Li LiuTel: +81 46 855 3756
Fax: +81 46 855 3808
l-liu@iges.or.jp

25

Annex 5.

Presentations

All presentations can be downloaded from: http://www.ipcc-nggip.iges.or.jp/meeting/meeting.html• Introduction (Simon Eggleston, TSU)
• Uncertainty analysis and inventory verification in IPCC 2006 Guidelines (Simon Eggleston, TSU)
• Uncertainty about Inventory Estimates: A Canadian perspective (Frank Neitzert, Canada)• Assessment of uncertainties in activity data and emission factors for Indian emission inventories (Amit Garg, India)• Experiences in Mexico with uncertainty analysis in AFOLU (Bernardus de Jong, Mexico)• Experiences in quantifying the uncertainty of the estimates of Canada's National Forest Carbon Monitoring, Accounting,and Reporting System, NFCMARS (Juha Metsaranta, Canada)• Uncertainty in remote sensing data and their analysis for forest inventory (Yasumasa Hirata, Japan)• Direct measurement of GHG emissions: The case of fugitive methane from landfills (Antoine Babilotte, France)• Inverse modeling of GHG and aerosol precursor emissions using satellite measurements: Status and future perspectives(Sander Houweling and Martijn Schaap, The Netherlands)• Integrating observations & inventories to improve emission estimates: a framework for synthesis (Riley Duren, USA)• Integrating observations from remote sensing, inventories and flux data with models for estimating terrestrial carbon
sources and sinks (Beverly Law, USA)
• Top down atmospheric and bottom up estimates of the European GHG balance (Philippe Ciais, France)• The successes, limitations and uncertainties of estimating emissions from measurements (Alistair Manning, UK)• Use of inverse modeling to evaluate emissions inventories (Stephen Ogle, USA)
• Validation/verification capabilities of ambient mixing ratio measurements and inverse modeling (Lingxi Zhou, China)• Inverse modeling of European GHG emissions and verification of bottom-up inventories (Peter Bergamaschi, Italy)

26