Use of Models and Facility-Level Data in Greenhouse Gas Inventories

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Use of Models and Facility-Level Data in Greenhouse Gas Inventories …It is important to realise the difference between [the previous section and this section] involves testing model output with field data that were used as a basis for calibration (i.e., parameterization). In contrast, evaluation with independent data is done with a completely independent set of data from model calibration, providing a more rigorous assessment of model components and results. Optimally, independent evaluation should be based on measurements from a monitoring network or from research sites that were not used to calibrate model parameters. The network would be similar in principle to a series of sites that are used for a measurement-based inventory. However, the sampling does not need to be as dense because the network is not forming the basis for estimating carbon stock changes or non-CO 2 greenhouse gas fluxes, as in a purely measurement-based inventory, but is used to check model results… …In some cases, independent evaluation may demonstrate that the model-based estimation system is inappropriate due to large and unpredictable differences between model results and the measured trends from the monitoring network. Problems may stem from one of three possibilities: errors in the implementation step, poor input data, or an inappropriate model. Implementation problems typically arise from computer programming errors, while model inputs may generate erroneous results if these data are not representative of management activity or environmental conditions… Chapter 2 “Generic Methodologies Applicable to Multiple Land-Use Categories”, page 2.53, Volume 4, 2006 IPCC Guidelines The results of this evaluation should be documented and reported. The meeting participants emphasized the importance of following the good practice approach as outlined above by the 2006 IPCC Guidelines. In particular they agreed that these steps should cover the following points: • Testing should cover different conditions, circumstances and spatial scales. • Partial or component tests for the measurable parts should be performed. • An analysis of fit of external data with model estimates is needed and should be reported. • Evaluation of model output through model inter-comparison, if possible. This will show which models best represent local conditions. • Evaluation of model through comparison with Tier 1/2 results. Differences between a complex model and lower tier approaches may be due to the model better representing the real world, being driven by factors that are not included in the lower tier. Therefore it is important to explain significant differences in terms of the physical processes represented in the model. It is also important to show that the evaluation of a model considers the whole range of values (outputs) encountered. In addition it may be possible to produce some indicators that show the model is performing correctly. Reporting such indicators and showing they are correctly conserved will demonstrate model robustness. Examples include: • AFOLU sector models should conserve mass and land area. • Energy sector models should be consistent with the energy balance. • In some industrial sectors a mass balance is possible (e.g. carbon in refineries and iron and steel plant). In addition, some intermediate outputs of the model at an adequately disaggregated level may greatly help users of reported information to assess the final outputs of the models. 3.2.3 Uncertainty and sensitivity analysis Uncertainty and sensitivity analysis should also be performed as part of model evaluation and is important so that a rigorous measure of model confidence, based on model inputs and structure, can be reported. The error distribution of key parameters; results of either error propagation or Monte-Carlo analysis; the results of an evaluation of uncertainties with regard to uncertainties in input data and model structure and assumptions as well as the results of a sensitivity analysis or identification of key parameters/inputs to which the model outputs are more sensitive should all be reported. IPCC Expert Meeting Report 16 TFI
Use of Models and Facility-Level Data in Greenhouse Gas Inventories 3.3 Interpretation of model results In order to assist all stakeholders in the correct interpretation of the model results, experience suggests that it would be useful to also supply, as part of the model and inventory documentation: • A comparison of implied emission factors with either country-specific factors or, if not available, IPCC default values. This comparison should also provide an explanation for any significant differences. • An explanation of any unusual input values and results (i.e. outliers with respect to some reference data). • The distribution of input and output values. IPCC Expert Meeting Report 17 TFI
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Use of Models and Facility-Level Data in Greenhouse Gas Inventories

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