The primary goals for this session are to familiarize participants with work of the IPCC National Greenhouse Gas (GHG) Inventory Programme, identify potential data contributions for estimating GHG emission factors associated with the effects of land-use change and promote the use of these data by LTER scientists and inventory compilers. We aim to accomplish these goals by bringing together ecosystem scientists focused on GHG flux estimation from changing land-use at long-term research sites and inventory specialists for a brain-storming and information exchange session. In this session, participants will be familiarized with the IPCC National Greenhouse Gas Inventory Programme and the Emission Factor Database (EFDB). The session will proceed with discussion on data needs, data types and data sources for the EFDB. The IPCC EFDB is an important tool available for estimation of GHG fluxes, and an updated companion for the IPCC Guidelines for National Greenhouse Gas Inventories that is seen as a worldwide resource for greenhouse gas inventory compilers and developers. It was formally launched at UNFCCC-COP8 in October 2002. It is available on the TFI website (http://www.ipcc-nggip.iges.or.jp/EFDB/) and CD-ROM.
Change in GHG fluxes are estimated with carbon mass balance approaches and CO2, N2O and CH4 emission data. A challenge posed for developing inventory emission factor datasets includes scaling to annualized values. TFI TSU participants will elaborate on this and other challenges for open discussion and explore means of overcoming these challenges with working group participants. Data gaps will also be presented. To develop the background for this work, TFI participants will provide a brief overview of the Agriculture, Forestry and Other Land Use (AFOLU) sector of the 2006 IPCC inventory guidelines. Of particular interest are new emission factor data sources for developing countries.
1. Introduction, 2. Present EFDB and background to the IPCC inventory guidelines and the AFOLU sector, 3. Information exchange on data needs, data types and data sources, 4. Brainstorm about data (including data sources) that could be compiled and synthesized or resurrected and scaled to produce annualized values, 5. Further Discussion