Ucertainty estimates as part of the inventory process

Kristin Rypdal, CICERO

Issues

• How are uncertainties addressed throughout the inventory cycle?

• How can users apply the reported uncertainty information?

• How can inventory compilers gain from addressing and estimating uncertainties?

Prioritization methodological choice

(key category Analysis,

reduce uncertainties)

Key category Analysis

(uncertainty input)

Reporting (inventory,

uncertainties and documentation)

Data collection (QA/QC &

Uncertainty assessment)

Estimation(QA/QC &

Uncertainty estimation)

Inventory compilation (QA/QC,

time-series consistency,

uncertainty compilation)

Good practice inventoriescontain neither under- nor overestimates and uncertainties are

reduced as far as is practicable

Application of inventory information

• Users would often like to see uncertainty estimates as a measure of inventory quality – How good is this indicator?

• Pollutants and source composition– Countries having a large fraction of their inventory as CO2

from fossil fuels will always have lower inventory uncertainties than those with larger fractions of CH4 and N2O (and LULUCF)

– CO2 less than ±5%, CH4 20-50 %, N2O factor 2 or more

Application of inventory information (cont.)

• Source uncertainties are at present assessed differently by countries expected to have rather similar circumstances– Subjectivity in expert judgments– (overconfident or too

uncertain)• E.g. N2O uncertainties in MS range from 6 to 200 %

• Trend estimate more robust

– Differences can partly be explained by lack of guidance on what types of errors which should be assessed and included in uncertainty estimates

Types of errors

• Random component measurement error • Measurement error systematic component (bias)• Lack of representativeness of data• Misreporting or misclassification• Lack of completeness• Bias and random errors from modeling

– All errors should be addressed when assessing uncertainties and, and the basis for the assessment should be documented

– Note the large overlap between uncertainty assessment and QA/QC!

How can inventory compilers gain from an uncertainty analysis?

• Prioritisation (key category assessment)

• Data collection and QA/QC

• Uncertainty estimations should be integrated in the inventory cycle not done in the end!

Prioritisation – key category asessment

• ”A category which is prioritised within the national inventory system because its estimate has significant influence on a country’s inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions or both”

• Aiming at reducing uncertainties

• Qualitatively

• Quantitatively (approach 2)– Size of emissions times

uncertainty

• Rank sources according to their contribution to uncertainties

Example of Approach 2 Level Assessment for a GHG inventory for 2003.

A B C D E F G

Ex,t Ex,t IPCC Category number

IPCC Category Greenhouse gas (Gg CO2

eq) (Gg CO2 eq)

Level assessment with uncertainty

Cumulative total of column F

3A1a Forest Land remaining Forest Land: carbon stock change in living biomass CO2 -21354 21354 0.23 0.23

3C1 Direct N2O Emissions from managed soils: agricultural soils N2O 2608 2608 0.18 0.41

3A3a Grassland Remaining Grassland: net carbon stock change in mineral soils CO2 2907 2907 0.09 0.50

3C2 Indirect N2O Emissions from managed soils N2O 592 592 0.06 0.56

1A3b Road Transportation: Cars with Catalytic Converters N2O 410 410 0.05 0.61

2B2 Nitric Acid Production N2O 1396 1396 0.04 0.66

3A2a Cropland Remaining Cropland: net carbon stock change in organic soils CO2 1324 1324 0.04 0.70

3A4ai2 Peatlands converted for peat extraction CO2 547 547 0.04 0.73

3A2a Cropland Remaining Cropland: net carbon stock change in mineral soils CO2 -1113 1113 0.03 0.77

4A Solid Waste Treatment and Disposal CH4 2497 2497 0.03 0.80

Example of Approach 1 Level Assessment for a GHG inventory 2003.

A B C D E F G

Ex,t Ex,t IPCC Category number

IPCC Category Greenhouse Gas

(Gg CO2 eq)

(Gg CO2 eq)

Lx,t Cumulative total of column F

3A1a Forest Land remaining Forest Land CO2 -21354 21354 0.193 0.193

1A1 Energy Industries: Solid fuels CO2 17311 17311 0.157 0.350

1A3b Road Transportation CO2 11447 11447 0.104 0.454

1A1 Energy Industries: Other fuels CO2 9047 9047 0.082 0.536

1A1 Energy Industries: Gaseous fuels CO2 6580 6580 0.060 0.595

1A4 Other Sectors: Liquid fuels CO2 5651 5651 0.051 0.646

1A2 Manufacturing Industries and Construction: Solid fuels CO2 5416 5416 0.049 0.695

1A2 Manufacturing Industries and Construction: Liquid fuels CO2 4736 4736 0.043 0.738

1A1 Energy Industries: Liquid fuels CO2 3110 3110 0.028 0.767

3A3a Grassland Remaining Grassland CO2 2974 2974 0.027 0.793

3C1 Direct N2O Emissions from managed soils N2O 2619 2619 0.024 0.817

4A Solid Waste Treatment and Disposal CH4 2497 2497 0.023 0.840

1A2 Manufacturing Industries and Construction: Gaseous fuels CO2 2174 2174 0.020 0.859

Prioritisation – reducing uncertainties

• Inventory development– Methodologies

• higher tiers

– Data collection • new surveys• literature review• unpublished national information

– Research

• Costs: how much can uncertainties be reduced and what are the costs?

Data collection and QA/QC

• Assessing uncertainties jointly with category-specific QA/QC is very efficient– Especially when contacting data providers– Can also contribute to reducing uncertainties as some types of

uncertainties can be corrected when identified

• Need to use systematic methods

• Make inquiries about the different types of errors

• Use of ”default” uncertainties a last

resort/for verification

Combining uncertainties

• Level and trend

• Tier 1 (error propagation)

• Tier 2 (Monte Carlo or similar techniques)

• Tier 1 well suited to estimate level uncertainty

• Tier 2 needed to more accurately estimate the uncertainty in the trend

UNCERTAINTY INPUT IS MUCH MORE IMPORTANT

Uncertainties derived from independent sources

• Emissions can be derived from atmospheric concentrations of gases through modeling

• Most suited for fluorinated gases without natural sources and sinks– Less suited for CO2, N2O and CH4

• Most suited for larger areas

• Increased number of measurement stations and better inventories of natural emissions are needed to reduce uncertainties

Conclusions and future prospects

• Uncertainties are not a good measure of inventory quality

• Uncertainties in emissions of CH4 and N2O (and LULUCF) will due to their inherent variability never be reduced to the level of CO2

– But the gap can be reduced

• The subjectivity component in uncertainty estimates will probably be reduced through use of the 2006 IPCC Guidelines and better competence of inventory compilers

• Inventory quality needs to be measured using also other indicators (transparency and review reports)

Conclusions and future prospects

• Uncertainties can be reduced and uncertainty estimates improved by addressing category-specific QA/QC and uncertainties at the data collection step

• Need to develop systematic methods for expert judgments addressing all errors

• This workshop is an important contribution!