Abstract
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.
Similar content being viewed by others
Notes
We note that Jonas and Nilsson (2007) go one terminological step further than we do here and strictly distinguish between “validation” and “verification” by applying science-theoretical principles. However, although we use the term “verification” somewhat indifferently, our ultimate understanding of verification, especially in the context of our integrated (multimeasurement/modeling) approach presented here, is in line with the bottom-up/top-down accounting/verification approach discussed by Jonas and Nilsson.
We distinguish between a full carbon budget (FCB) as a natural system and a full carbon account (FCA) as an artificial accounting system.
References
Alexeyev, V. A., & Birdsey, R. A. (Eds.) (1994). Carbon in Ecosystems of Forests and Wetlands of Russia, Sukachev Institute of Forestry, Krasnoyarsk [in Russian].
Bare, B. B., & Mendoza, G. A. (1991). Timber harvest scheduling in a fuzzy decision environment. Canadian Journal of Forest Research, 22, 423–428.
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.
Berger, J. (1985). Statistical decision theory & bayesian analysis, second edition. New York, USA: Wiley.
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.
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.
Cogan, B. (2001). Certainty and uncertainty in science. Scientific Computing World, pp. 28–30. (December)
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 IPCC AR 4. Journal of Geophysical Research. Available at http://www.cgd.ucar.edu/cms/wcollins/papers/.
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.
FAO (2002–2005). Proceedings, expert meetings 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.
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.
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.
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.
Hattis, D., & Burmaster, D. E. (1994). Assessment of variability and uncertainty distributions for practical risk analysis. Risk Analysis 14, 713–730.
Heath, L. S., & Smith, J. E. (2000). An assessment of uncertainty in forest carbon budget projections. Environmental Science and Policy, 3, 73–82.
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.
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: http://www.ipcc-nggip.iges.or.jp/public/gl/invs1.htm.
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.
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.
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.
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 http://ipcc-wg1.ucar.edu/meeting/URW/.
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].
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.
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.
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: http://www.iiasa.ac.at/Publications/Documents/IR-99-025.pdf.
Kosko, B. (1994). Fuzzy thinking. London, UK: Flamingo.
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.
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.
Mendoza, G. A., & Sprouse, W. L. (1989). Forest planning and decision making under fuzzy environments: An overview and illustration. Forest Science, 32, 481–502.
Monni, S., Syri, S., & Savolainen, I. (2004). Uncertainties in the finnish greenhouse gas emission inventory. Environmental Science and Policy, 7, 87–98.
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.
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.
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.
Nahorski, Z., & Jęda, 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.
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.
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, http://www.iiasa.ac.at/Research/FOR/carb_kyoto.html?sb=10.
Nilsson, S., Jonas, M., Obersteiner, M., & Victor, D. (2001). Verification: The gorilla in the struggle to slow global warming. Forestry Chronicle, 77, 475–478.
Nilsson, S., Jonas, M., Stolbovoi, V., Shvidenko, A., Obersteiner, M., & McCallum I. (2003b). The missing sink. Forestry Chronicle, 79(6), 1071–1074.
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: http://www.iiasa.ac.at/Publications/Documents/IR-00-021.pdf, Study also featured in: New Scientist, 2253, 18–19, 26 August.
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).
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.
Parysow, P., Gertner, G., & Westervelt, J. (2000). Efficient approximation for building error budgets for process models. Ecological Modelling, 135(2–3), 111–125.
Rowe, W. D. (1994). Understanding uncertainty. Risk Analysis, 14(5), 743–750.
Rypdal, K. L., & Winiwarter, W. (2001). Uncertainties in greenhouse gas inventories – evaluation, comparability and implications. Environmental Science Policy, 4, 107–116.
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.
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.
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.
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.
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].
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].
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).
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.
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.
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.
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.
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8, 338–353.
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.
Acknowledgment
The SIBERIA-II project (EVG-2001-00008), 2003–2005, was funded by the European Commission (Generic Activity 7.2: Development of Generic Earth Observation Technologies).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nilsson, S., Shvidenko, A., Jonas, M. et al. Uncertainties of a Regional Terrestrial Biota Full Carbon Account: A Systems Analysis. Water Air Soil Pollut: Focus 7, 425–441 (2007). https://doi.org/10.1007/s11267-006-9119-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11267-006-9119-1