Abstract
Our study focuses on uncertainty in greenhouse gas (GHG) emissions from anthropogenic sources, including land use and land-use change activities. We aim to understand the relevance of diagnostic (retrospective) and prognostic (prospective) uncertainty in an emissions-temperature setting that seeks to constrain global warming and to link uncertainty consistently across temporal scales. We discuss diagnostic and prognostic uncertainty in a systems setting that allows any country to understand its national and near-term mitigation and adaptation efforts in a globally consistent and long-term context. Cumulative emissions are not only constrained and globally binding but exhibit quantitative uncertainty; and whether or not compliance with an agreed temperature target will be achieved is also uncertain. To facilitate discussions, we focus on two countries, the USA and China. While our study addresses whether or not future increase in global temperature can be kept below 2, 3, or 4 °C targets, its primary aim is to use those targets to demonstrate the relevance of both diagnostic and prognostic uncertainty. We show how to combine diagnostic and prognostic uncertainty to take more educated (precautionary) decisions for reducing emissions toward an agreed temperature target; and how to perceive combined diagnostic and prognostic uncertainty-related risk. Diagnostic uncertainty is the uncertainty contained in inventoried emission estimates and relates to the risk that true GHG emissions are greater than inventoried emission estimates reported in a specified year; prognostic uncertainty refers to cumulative emissions between a start year and a future target year, and relates to the risk that an agreed temperature target is exceeded.
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Notes
The four parameters in WBGU’s “historical responsibility” approach are (i) 1990, (ii) 2050, (iii) 25 %, and (iv) 1990; and (i) 2010, (ii) 2050, (iii) 33 %, and (iv) 2010 in its “future responsibility” approach. In both approaches, the probability of exceeding the 2 ºC temperature target refers to cumulative emission constraints for 2000–2049.
IIASA’s World Population Program reports 7.8 and 9.9 for the 10th and 90th percentiles.
Note that applying the 2 °C Check Tool as described in Section 2.3 but to a cumulative emissions constraint for 2000–2050 of 1800, instead of 1500 Pg CO2-eq, does not encounter any limitations, which is why the risk interval is minimal for maximal uncertainty in p/c emissions and consists of a single value only.
Ito (2011) provides a historical meta-analysis of global NPP (1860s–2000s) which allows Haberl and Erb’s HANPP concept with reference to 2000 to be put into a long-term temporal perspective.
See http://unfccc.int/parties_and_observers/parties/annex_i/items/2774.php for Annex I countries to the UNFCCC.
Respectively, carbon dioxide, methane, nitrous oxide, hydrofluorocarbon, sulfur hexafluoride, perfluorocarbon
We employ a total uncertainty in relative terms of 7.5 % (representing the median of the relative uncertainty class 5–10 %) for reporting the emissions of the six Kyoto GHGs excluding emissions from land use and land-use change in both reference and target year; and 0.75 for the correlation in these uncertainties (Jonas et al. 2010b).
Respectively, Global Trade and Environment Model; Integrated Model to Assess the Greenhouse Effect; Prospective Outlook on Long-term Energy Systems (model)
References
Allen MR et al (2009) Warming caused by cumulative carbon emissions toward the trillionth tonne. Nature 458(7242):1163–1166
Canadell JG et al (2007) Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. PNAS 104(47):18866–18870
EC (2011) 2011 literature review archives – policy/mitigation. Environment Canada, Gatineau, http://www.ec.gc.ca/sc-cs/default.asp?lang=En&n=ACF3648C-1 (accessed 25 January 2012)
Erb K-H et al (2009a) Analyzing the global human appropriation of net primary production – processes, trajectories, implications. An introduction. Ecol Econ 69(2):250–259
Erb K-H et al (2009b) Embodied HANPP: mapping the spatial disconnect between global biomass production and consumption. Ecol Econ 69(2):328–334
Fisher BS, Nakicenovic N (lead authors) (2007) Issues related to mitigation in the long-term context. In: Climate Change 2007: Mitigation of Climate Change. Metz B et al. (eds). Cambridge University Press, United Kingdom, 169–250
GCI (2012) Contraction and convergence: climate justice without vengeance. Global Commons Institute (GCI), United Kingdom, http://www.gci.org.uk (accessed 14 March 2012)
Haberl H et al (2007) Quantifying and mapping the human apprpriation of net primary production in earth’s terrestrial ecosystems. PNAS 104(31):12942–12947
Haberl H et al (2009) Using embodied HANPP to analyze teleconnections in the global land system: conceptual considerations. Geogr Tidsskr-Dan J Geogr 109(2):119–130, http://rdgs.dk/djg/pdfs/109/2/Pp_119-130_109_2.pdf
Houghton RA (2008) Carbon flux to the atmosphere from land-use changes: 1850–2005. In TRENDS: a compendium of data on global change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, United States of America. http://cdiac.ornl.gov/trends/landuse/houghton/houghton.html (accessed 09 January 2012)
IIASA (2007) Uncertainty in greenhouse gas inventories. IIASA Policy Brief, #01, International Institute for Applied Systems Analysis, Austria. http://www.iiasa.ac.at/Publications/policy-briefs/pb01-web.pdf
Ito A (2011) A historical meta-analysis of global terrestrial net primary productivity: are estimates converging? Glob Change Biol 17(10):3161–3175
Jonas M et al (2010a) Benefits of dealing with uncertainty in greenhouse gas inventories: introduction. Clim Change 103(1–2):3–18
Jonas M et al (2010b) Comparison of preparatory signal analysis techniques for consideration in the (post-) Kyoto policy process. Clim Change 103(1–2):175–213
Jonas M et al (2010c) Dealing with uncertainty in GHG inventories: how to go about it? In: Marti K, Ermoliev Y, Makowski M (eds) Coping with uncertainty: robust solutions. Springer, Berlin, pp 229–245
Jonas M et al (2011) Lessons to be learned from uncertainty treatment: conclusions regarding greenhouse gas inventories. In: White T et al (eds) Greenhouse gas inventories: dealing with uncertainty. Springer, Netherlands, pp 339–343
Lieberman D et al. (eds.) (2007) Accounting for climate change. Uncertainty in greenhouse gas inventories—verification, compliance, and trading. Springer, Netherlands
Matthews HD et al (2009) The proportionality of global warming to cumulative carbon emissions. Nature 459(7248):829–833
Meinshausen M (2005) Emission & concentration implications of long-term climate targets. Dissertation, DISS. ETH NO. 15946, Swiss Federal Institute of Technology Zurich, Switzerland. http://www.up.ethz.ch/publications/documents/Meinshausen_2005_dissertation.pdf (accessed 30 September 2011)
Meinshausen M et al (2009) Greenhouse-gas emission targets for limiting global warming to 2 °C. Nature 458(7242):1158–1162
Pan Y et al (2011) A large and persistent carbon sink in the world’s forests. Science 333(6045):988–993
Peters GP et al (2011a) Rapid growth in CO2 emissions after the 2008–2009 global financial crisis. Nat Clim Change 2:2–4
Peters GP et al (2011b) Growth in emission transfers via international trade from 1990–2008. PNAS 108(21):8903–8908
Raupach MR et al (2011) The relationship between peak warming and cumulative CO2 emissions, and its use to quantify vulnerabilities in the carbon-climate-human system. Tellus 63B(2):145–164
Rogelj J et al (2011) Emission pathways consistent with a 2 °C global temperature limit. Nat Clim Change 1:413–418
Salk C, Jonas M, Marland G (2013) Strict accounting with flexible implementation: the first order of business in the next climate treaty. Carbon Manag 4(3):253–256
Victor DG (2009) Global warming: why the 2 °C goal is a polticial delusion. Nature 459(7249):909
WBGU (1995) Scenario for the derivation of global CO2 reduction targets and implementation strategies. Statement at UNFCCC COP1 in Berlin. Special Report, German Advisory Council on Global Change. http://www.wbgu.de/fileadmin/templates/dateien/veroeffentlichungen/sondergutachten/sn1995/wbgu_sn1995_engl.pdf (accessed 17 November 2011)
WBGU (2009) Solving the climate dilemma: the budget approach. Special Report, German Advisory Council on Global Change. http://www.wbgu.de/fileadmin/templates/dateien/veroeffentlichungen/sondergutachten/sn2009/wbgu_sn2009_en.pdf (accessed 28 September 2011)
White T et al (eds) (2011) Greenhouse gas inventories: dealing with uncertainty. Springer, Netherlands
WRI (2011) CAIT: Greenhouse gas sources & methods. Document accompanying the Climate Analysis Indicators Tool (CAIT), v.9.0. World Resources Institute, Washington DC. http://cait.wri.org/downloads/cait_ghgs.pdf (accessed 17 January 2012)
Zickfeld K et al (2009) Setting cumulative emissions targets to reduce the risk of dangerous climate change. PNAS 106(38):16129–16134
Acknowledgement
This study was financially supported by the Austrian Climate Research Programme (B068706).
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This article is part of a Special Issue on “Third International Workshop on Uncertainty in Greenhouse Gas Inventories” edited by Jean Ometto and Rostyslav Bun.
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Jonas, M., Marland, G., Krey, V. et al. Uncertainty in an emissions-constrained world. Climatic Change 124, 459–476 (2014). https://doi.org/10.1007/s10584-014-1103-6
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DOI: https://doi.org/10.1007/s10584-014-1103-6