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Ucertainty estimates as part of the inventory process

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?.

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Ucertainty estimates as part of the inventory process

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  1. Ucertainty estimates as part of the inventory process Kristin Rypdal, CICERO

  2. 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?

  3. Good practice inventories contain neither under- nor over estimates and uncertainties are reduced as far as is practicable

  4. 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

  5. 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

  6. 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!

  7. 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!

  8. ”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 Prioritisation – key category asessment

  9. 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?

  10. 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

  11. 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

  12. 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

  13. 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)

  14. 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!

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