Uncertainties of a Regional Terrestrial Biota Full Carbon Account: A Systems Analysis



We discuss the background and methods for estimating uncertainty in a holistic manner in a regional terrestrial biota Full Carbon Account (FCA) using our experience in generating such an account for vast regions in northern Eurasia (at national and macroregional levels). For such an analysis, it is important to (1) provide a full account; (2) consider the relevance of a verified account, bearing in mind further transition to a certified account; (3) understand that any FCA is a fuzzy system; and (4) understand that a comprehensive assessment of uncertainties requires multiple harmonizing and combining of system constraints from results obtained by different methods. An important result of this analysis is the conclusion that only a relevant integration of inventory, process-based models, and measurements in situ generate sufficient prerequisites for a verified FCA. We show that the use of integrated methodology, at the current level of knowledge, and the system combination of available information, allow a verified FCA for large regions of the northern hemisphere to be made for current periods and for the recent past.


terrestrial biota regional full greenhouse account uncertainty verification certification Northern Eurasia 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alexeyev, V. A., & Birdsey, R. A. (Eds.) (1994). Carbon in Ecosystems of Forests and Wetlands of Russia, Sukachev Institute of Forestry, Krasnoyarsk [in Russian].Google Scholar
  2. Bare, B. B., & Mendoza, G. A. (1991). Timber harvest scheduling in a fuzzy decision environment. Canadian Journal of Forest Research, 22, 423–428.CrossRefGoogle Scholar
  3. Beer, C., Lucht, W., Schmullius, C., & Shvidenko, A. (2006). Small net carbon dioxide uptake by Russian forests. Geophysical Research Letters, 33 L15403, doi:10.1029/2006GL0026919.CrossRefGoogle Scholar
  4. Berger, J. (1985). Statistical decision theory & bayesian analysis, second edition. New York, USA: Wiley.Google Scholar
  5. Cess, R. D., Zhang, M.-H., Potter, G. L., Barker, H. W., Colman, R. A., Dazlich, D. A., et al. (1993). Uncertainties in carbon dioxide radiative forcing in atmospheric general circulation models. Science, 262, 1252–1255.CrossRefGoogle Scholar
  6. Chen, Q., & Mynett, A. E. (2003). Integration of data mining techniques and heuristic knowledge in fuzzy logic modeling of eutrophication in Taihu Lake. Ecological Modelling, 162,(1–2), 55–67.CrossRefGoogle Scholar
  7. Cogan, B. (2001). Certainty and uncertainty in science. Scientific Computing World, pp. 28–30. (December)Google Scholar
  8. Collins, W. D., Ramaswamy, V., Schwarzkopf, M. D. Y., Sun, R., Portmann, W., Fu, Q., et al. (2005) Radiative forcing by well mixed greenhouse gases: Estimates from climate models in the IPCCAR 4. Journal of Geophysical Research. Available at Scholar
  9. EEA (2005). Annual European community greenhouse gas inventory 1990–2003 and inventory report 2005. Submission of the UNFCCC Secretariat, revised final version, 27 May, Technical Report 4/2005 of the European Environment Agency.Google Scholar
  10. FAO (2002–2005). Proceedings, expertmeetings on harmonizing forest-related definitions for use by different stakeholders: First meeting, 23–25 January 2002, Rome; second meeting, 11–13 September 2002, Rome; third meeting, 17–20 January 2005, Rome.Google Scholar
  11. Gillenwater, M., Sussman, F., & Cohen, J. (2007). Practical policy applications of uncertainty analysis for national greenhouse gas inventories. Water, Air and Soil Pollution: Focus (in press) doi:10.1007/s11267-006-9118-2.Google Scholar
  12. GCP (2003). Global Carbon Project 2003 Science framework and implementation, Earth System Science Partnership IGBP, IHDP, WCRP, DIVERSITAS, Global Carbon Project Report No.1, Canberra, Australia.Google Scholar
  13. Haimes, Y. Y., Barry, T., & Lambert, J. H. (1994). Proceedings of the workshop, ‘Where and how can you specify a probability distribution when you don’t know much?’ Risk Analysis, 14(5), 661–706.CrossRefGoogle Scholar
  14. Hattis, D., & Burmaster, D. E. (1994). Assessment of variability and uncertainty distributions for practical risk analysis. Risk Analysis 14, 713–730.CrossRefGoogle Scholar
  15. Heath, L. S., & Smith, J. E. (2000). An assessment of uncertainty in forest carbon budget projections. Environmental Science and Policy, 3, 73–82.CrossRefGoogle Scholar
  16. Hofman, F. O., & Hammonds, J. S. (1994). Propagation of uncertainty in risk assessments: The need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. Risk Analysis, 14, 707–712.CrossRefGoogle Scholar
  17. IPCC (1997). Revised 1996 IPCC guidelines for national greenhouse gas inventories. volume 1: Greenhouse gas inventory reporting instructions, volume 2: Greenhouse gas inventory workbook, volume 3: Greenhouse gas inventory reference manual. IPCC/OECD/IEA. Intergovernmental panel on climate change IPCC working group i wG i technical support unit, Bracknell, United Kingdom. Available at: Scholar
  18. IPCC (1998). Managing uncertainty in national greenhouse gas inventories. Report of the meeting of the IPCC/OECD/IEA programme on national greenhouse gas inventories, held 13–15 October in Paris, France.Google Scholar
  19. IPCC (2000). Good practice guidance and uncertainty management in national greenhouse gas inventories. In J. Penman, D. Kruger, I. Galbally, T. Hiraishi, B. Nyenzi, S. Emmanuel, L. Buendia, R. Hoppaus, T. Martinsen, J. Meijer, K. Miwa, & K. Tanabe (eds.), Intergovernmental panel on climate change IPCC national gas inventories program, technical support unit. Institute for Global Environmental Strategies, Hayama, Kanagawa, Japan.Google Scholar
  20. IPCC (2004a). Documents in support of the writing process for the IPCC working group II fourth assessment report. Volume produced for the first Lead Authors’ Meeting, held 20–23 September in Vienna, Austria.Google Scholar
  21. IPCC (2004b). Describing scientific uncertainties in climate change to support analysis of risk and of options. In M. Manning, M. Petit, D. Easterling, J. Murphy, A. Patwardhan, H.-H. Rogner, R. Swart, & G. Yohe (Eds.), Report on IPCC Workshop, held 11–13 May in Maynooth, Co. Kildare, Ireland. Available at Scholar
  22. Isaev, A. S., & Korovin, G. N. (1998). Carbon in forests of northern Eurasia. In G. A. Zavarzin (Ed.), Carbon turnover in territories of Russia (pp. 63–95). Moscow: Ministry of Science and Technology of the Russian Federation, [in Russian].Google Scholar
  23. Isaev, A. S., Korovin, G. N., Utkin, A. I., Pryashnikov, A. A., & Zamolodchikov D. G. (1995). Carbon stock and deposition in phytomass of the Russian forests. Water, Air and Soil Pollution, 70, 247–256.CrossRefGoogle Scholar
  24. Jonas, M., & Nilsson, S. (2007). Prior to economic treatment of emissions and their uncertainties under the Kyoto Protocol: Scientific uncertainties that must be kept in mind. Water, Air and Soil Pollution: Focus (in press.) doi:10.1007/s11267-006-9113-7.Google Scholar
  25. Jonas, M., Nilsson, S., Shvidenko, A., Stolbovoi, V., Gluck, M., Obersteiner, M., et al. (1999). Full Carbon Accounting and the Kyoto Protocol: A Systems-Analytical View, Interim Report IR-99-025, International Institute for Applied Systems Analysis, Laxenburg, Austria. Available at: Scholar
  26. Kosko, B. (1994). Fuzzy thinking. London, UK: Flamingo.Google Scholar
  27. Lapenis, A., Shvidenko, A., Sheschenko, A. D., Nilsson S., & Aiyyer A. (2005). Acclimation of Russian forests to recent changes in climate. Global Change Biology, 11, 1–13.CrossRefGoogle Scholar
  28. MacFarlane, D. W., Green, E. J., & Valentine, H. T. (2000). Incorporating uncertainty into the parameters of a forest process model. Ecological Modelling, (1): 27–40.CrossRefGoogle Scholar
  29. Mendoza, G. A., & Sprouse, W. L. (1989). Forest planning and decision making under fuzzy environments: An overview and illustration. Forest Science, 32, 481–502.Google Scholar
  30. Monni, S., Syri, S., & Savolainen, I. (2004). Uncertainties in the finnish greenhouse gas emission inventory. Environmental Science and Policy, 7, 87–98.CrossRefGoogle Scholar
  31. Morgan, M. G., & Henrion, M. (1990). Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. New York, USA: Cambridge University Press.Google Scholar
  32. Moss, R. H., & Schneider, S. H. (2000). Uncertainties in the IPCC TAR: Recommendations to lead authors for more consistent assessment and reporting. In R. Pachauri, T. Taniguchi, & K. Tanaka (Eds.), Guidance papers on the cross cutting issues of the third assessment report of the IPCCC intergovernmental panel on climate change (33–51). Geneva, Switzerland.Google Scholar
  33. Myneni, R. B., Dong, J., Tucker, C. J., Kaufmann, R. K., Kauppi, P. E., Liski, J., et al. (2001). A large carbon sink in the woody biomass of northern forests. Proceedings of the National Academy of Sciences, 9826, 14784–14789, Washington, D.C., USA: National Academy of Sciences.Google Scholar
  34. Nahorski, Z., & Jeda, W. (2007). Processing national CO2 inventory emissions data and their total uncertainty estimates. Water, Air and Soil Pollution: Focus (in press) doi:10.1007/s11267-006-9114-6.Google Scholar
  35. National Assessment Synthesis Team (2001). Climate change impacts on the United States: The potential consequences of climate variability and change. Report for the US Global Change Research Program, Cambridge, UK: Cambridge University Press.Google Scholar
  36. Nilsson, S., Jonas, M., & Obersteiner, M. (2000b). The forgotten obligations in the Kyoto negotiations, Document made available on the Internet by the International Institute for Applied Systems Analysis, Laxenburg, Austria, Scholar
  37. Nilsson, S., Jonas, M., Obersteiner, M., & Victor, D. (2001). Verification: The gorilla in the struggle to slow global warming. Forestry Chronicle, 77, 475–478.Google Scholar
  38. Nilsson, S., Jonas, M., Stolbovoi, V., Shvidenko, A., Obersteiner, M., & McCallum I. (2003b). The missing sink. Forestry Chronicle, 79(6), 1071–1074.Google Scholar
  39. Nilsson, S., Shvidenko, A., Stolbovoi, V., Gluck, M., Jonas, M., & Obersteiner, M. (2000a). Full carbon account for Russia. Interim Report IR-00-021, International Institute for Applied Systems Analysis, Laxenburg, Austria. Available at:, Study also featured in: New Scientist, 2253, 18–19, 26 August.Google Scholar
  40. Nilsson, S., Vaganov, E. A., Rozhkov, V. A., Shvidenko, A. Z., Stolbovoi, V. S., McCallum, I., et al. (2003a). Greenhouse gas balance and mitigation strategies for Russia. Paper given at the World Climate Conference, held in Moscow, Russia, 29 September–3 October, (Abstracts, 242–243).Google Scholar
  41. Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1–2), 43–64.CrossRefGoogle Scholar
  42. Parysow, P., Gertner, G., & Westervelt, J. (2000). Efficient approximation for building error budgets for process models. Ecological Modelling, 135(2–3), 111–125.CrossRefGoogle Scholar
  43. Rowe, W. D. (1994). Understanding uncertainty. Risk Analysis, 14(5), 743–750.CrossRefGoogle Scholar
  44. Rypdal, K. L., & Winiwarter, W. (2001). Uncertainties in greenhouse gas inventories — evaluation, comparability and implications. Environmental Science Policy, 4, 107–116.CrossRefGoogle Scholar
  45. Schulze, E.-D., Valentini, R., & Sanz, M.-J. (2002). The long way from Kyoto to Marrakesh: Implications of the Kyoto Protocol negotiations for global ecology. Global Change Ecology, 8, 505–518.CrossRefGoogle Scholar
  46. Shvidenko, A., & Nilsson, S. (2002). Dynamics of Russian forests and the carbon budget in 1961–1998: An assessment based on long-term forest inventory data. Climatic Change, 55, 5–37.CrossRefGoogle Scholar
  47. Shvidenko, A., & Nilsson, S. (2003). A synthesis of the impact of Russian forests on the global carbon budget for 1961–1968. Tellus, 55B, 391–415.Google Scholar
  48. Shvidenko, A., Nilsson, S., Rojkov, V., & Strakhov, V. (1996). Carbon budget of the Russian boreal forests: A system analysis approach to uncertainty. In M. J. Apps & D. T. Price (Eds.), Forest ecosystems, forest management and the global carbon cycle, (145–162)NATO ASI, Series 1, Vol. 40.Google Scholar
  49. Shvidenko, A., Shepashenko, D., & Nilsson, S. (2002). Aggregated models of phytomass of major forest forming species of Russia. Forest Inventory and Management, 1, 50–57, Krasnoyarsk [in Russian].Google Scholar
  50. Shvidenko, A., Shepashenko, D., Nilsson, S., & Bouloui, Yu. (2004). The system of models of biological productivity of Russian forests. Forestry and Forest Management, 2, 40–44 [in Russian].Google Scholar
  51. Shvidenko, A., Shepaschenko, D., Nilsson, S., & Vaganov, E. (2007). Dynamics of phytomass and net primary production of Russian forests: New estimates. Doklady of the Russian Academy of Sciences (in press).Google Scholar
  52. Steffen, W., Noble, I., Canadell, J., Apps, M., Schulze, E.-D., Jarvis, P. G., et al. (1998). The terrestrial carbon cycle: Implications for the Kyoto Protocol. Science, 280, 1393–1394.CrossRefGoogle Scholar
  53. Vasiliev, S. V., Titlyanova, A. A., & Velichko, A. A. (eds). (2001). West Siberian peatlands and carbon cycle: Past and present, Proceedings of the International Symposium, held 18–22 August at Noyabrsk, Russia.Google Scholar
  54. Wang, Y. P., & Barret, D. J. (2003). Estimating regional terrestrial carbon fluxes for the Australian continent using a multiple-constraint approach: 1. Using remotely sensed data and ecological observation of net primary production. Tellus, 55B, 270–279.Google Scholar
  55. Wan-Xiong, W., Yi-Min, L., Zi-Zhen, L., & Fengxiang, Y. (2003). A fuzzy description of some ecological concepts. Ecological Modelling, 169(2–3), 361–366.CrossRefGoogle Scholar
  56. Zadeh, L. (1965). Fuzzy sets. Information and Control, 8, 338–353.CrossRefGoogle Scholar
  57. Zaehle, S., Sitch, S., Smith, B., & Hatterman, F. (2005). Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Global Biogeochemical Cycles, 19, GB3020.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media B.V. 2007

Authors and Affiliations

  1. 1.International Institute for Applied Systems AnalysisLaxenburgAustria
  2. 2.Center For Ecology and HydrologyMonks WoodUK
  3. 3.Department of Geography, Climate and Land Surface Systems Interaction Centre (CLASSIC)University of LeicesterLeicesterUK

Personalised recommendations