Probabilistic Forecast for Twenty-First-Century Climate Based on Uncertainties in Emissions (Without Policy) and Climate Parameters

A. P. Sokolov Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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P. H. Stone Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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C. E. Forest Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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R. Prinn Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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M. C. Sarofim Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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M. Webster Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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S. Paltsev Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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C. A. Schlosser Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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D. Kicklighter The Ecosystems Center, Marine Biological Laboratory, Woods Hole, Massachusetts

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S. Dutkiewicz Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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C. Wang Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts

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B. Felzer The Ecosystems Center, Marine Biological Laboratory, Woods Hole, Massachusetts

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J. M. Melillo The Ecosystems Center, Marine Biological Laboratory, Woods Hole, Massachusetts

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Abstract

The Massachusetts Institute of Technology (MIT) Integrated Global System Model is used to make probabilistic projections of climate change from 1861 to 2100. Since the model’s first projections were published in 2003, substantial improvements have been made to the model, and improved estimates of the probability distributions of uncertain input parameters have become available. The new projections are considerably warmer than the 2003 projections; for example, the median surface warming in 2091–2100 is 5.1°C compared to 2.4°C in the earlier study. Many changes contribute to the stronger warming; among the more important ones are taking into account the cooling in the second half of the twentieth century due to volcanic eruptions for input parameter estimation and a more sophisticated method for projecting gross domestic product (GDP) growth, which eliminated many low-emission scenarios.

However, if recently published data, suggesting stronger twentieth-century ocean warming, are used to determine the input climate parameters, the median projected warming at the end of the twenty-first century is only 4.1°C. Nevertheless, all ensembles of the simulations discussed here produce a much smaller probability of warming less than 2.4°C than implied by the lower bound of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) projected likely range for the A1FI scenario, which has forcing very similar to the median projection in this study. The probability distribution for the surface warming produced by this analysis is more symmetric than the distribution assumed by the IPCC because of a different feedback between the climate and the carbon cycle, resulting from the inclusion in this model of the carbon–nitrogen interaction in the terrestrial ecosystem.

* Current affiliation: Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania.

+ AAAS Science and Technology Policy Fellow, Washington, D.C.

# Current affiliation: Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts.

@ Current affiliation: Department of Earth and Environmental Sciences, Lehigh University, Bethlehem, Pennsylvania.

Corresponding author address: Andrei Sokolov, Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave., E40-431, Cambridge, MA 02139. Email: sokolov@mit.edu

Abstract

The Massachusetts Institute of Technology (MIT) Integrated Global System Model is used to make probabilistic projections of climate change from 1861 to 2100. Since the model’s first projections were published in 2003, substantial improvements have been made to the model, and improved estimates of the probability distributions of uncertain input parameters have become available. The new projections are considerably warmer than the 2003 projections; for example, the median surface warming in 2091–2100 is 5.1°C compared to 2.4°C in the earlier study. Many changes contribute to the stronger warming; among the more important ones are taking into account the cooling in the second half of the twentieth century due to volcanic eruptions for input parameter estimation and a more sophisticated method for projecting gross domestic product (GDP) growth, which eliminated many low-emission scenarios.

However, if recently published data, suggesting stronger twentieth-century ocean warming, are used to determine the input climate parameters, the median projected warming at the end of the twenty-first century is only 4.1°C. Nevertheless, all ensembles of the simulations discussed here produce a much smaller probability of warming less than 2.4°C than implied by the lower bound of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) projected likely range for the A1FI scenario, which has forcing very similar to the median projection in this study. The probability distribution for the surface warming produced by this analysis is more symmetric than the distribution assumed by the IPCC because of a different feedback between the climate and the carbon cycle, resulting from the inclusion in this model of the carbon–nitrogen interaction in the terrestrial ecosystem.

* Current affiliation: Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania.

+ AAAS Science and Technology Policy Fellow, Washington, D.C.

# Current affiliation: Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts.

@ Current affiliation: Department of Earth and Environmental Sciences, Lehigh University, Bethlehem, Pennsylvania.

Corresponding author address: Andrei Sokolov, Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave., E40-431, Cambridge, MA 02139. Email: sokolov@mit.edu

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  • Babiker, M. H., J. M. Reilly, M. Mayer, R. S. Eckaus, I. S. Wing, and R. C. Hyman, 2001: The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Revisions, sensitivities, and comparisons of results. MIT Joint Program on the Science and Policy of Global Change, Rep. 71, 90 pp. [Available online at http://web.mit.edu/globalchange/www/MITJPSPGC_Rpt71.pdf].

    • Search Google Scholar
    • Export Citation
  • Babiker, M. H., G. E. Metcalf, and J. Reilly, 2003: Tax distortions and global climate policy. J. Environ. Econ. Manage., 46 , 269287.

    • Search Google Scholar
    • Export Citation
  • Bonan, G. B., K. W. Oleson, M. Vertenstein, S. Lewis, X. Zeng, Y. Dai, R. E. Dickinson, and Z-L. Yang, 2002: The land surface climatology of the Community Land Model coupled to the NCAR Community Climate Model. J. Climate, 15 , 31233149.

    • Search Google Scholar
    • Export Citation
  • Calbó, J., W. Pan, M. Webster, R. G. Prinn, and G. J. McRae, 1998: Parameterization of urban subgrid-scale processes in global atmospheric chemistry models. J. Geophys. Res., 103 , 34373451.

    • Search Google Scholar
    • Export Citation
  • Clarke, L. E., J. A. Edmonds, H. D. Jacoby, H. M. Pitcher, J. M. Reilly, and R. G. Richels, 2007: CCSP Synthesis and Assessment Product 2.1, Part A: Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations, U.S. Climate Change Science Program, Department of Energy, Washington, DC, 154 pp.

  • Curtis, P. S., and X. Wang, 1998: A meta-analysis of elevated CO2 effects on woody plant mass, form, and physiology. Oecologia, 113 , 299313.

    • Search Google Scholar
    • Export Citation
  • Dalan, F., P. H. Stone, I. Kamenkovich, and J. Scott, 2005a: Sensitivity of the ocean’s climate to diapycnal diffusivity in EMIC. Part I: Equilibrium state. J. Climate, 18 , 24602481.

    • Search Google Scholar
    • Export Citation
  • Dalan, F., P. H. Stone, and A. P. Sokolov, 2005b: Sensitivity of the ocean’s climate to diapycnal diffusivity in EMIC. Part II: Global warming scenario. J. Climate, 18 , 24822496.

    • Search Google Scholar
    • Export Citation
  • Dimaranan, B., and R. McDougall, cited. 2002: Global trade, assistance, and production: The GTAP 5 data base. Center for Global Trade Analysis, Purdue University, West Lafayette, IN. [Available online at https://www.gtap.agecon.purdue.edu/databases/v5/v5_doco.asp].

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., X. Gao, and T. Oki, 2002: The Second Global Soil Wetness Project (GSWP2). International GEWEX Project Office Publication 37, 75 pp.

    • Search Google Scholar
    • Export Citation
  • Domingues, C. M., J. A. Church, N. J. White, P. J. Gleckler, S. E. Wijffels, P. M. Barker, and J. R. Dunn, 2008: Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Nature, 453 , 10901094.

    • Search Google Scholar
    • Export Citation
  • Edmonds, J. A., and J. M. Reilly, 1985: Future global energy and carbon dioxide emissions. Atmospheric carbon dioxide and the global carbon cycle, DOE/ER-0239, U.S. Department of Energy, Office of Energy Research, Washington, DC, 215–246.

    • Search Google Scholar
    • Export Citation
  • Felzer, B., D. W. Kicklighter, J. M. Melillo, C. Wang, Q. Zhuang, and R. Prinn, 2004: Effects of ozone on net primary production and carbon sequestration in the conterminous United States using a biogeochemistry model. Tellus, 56B , 230248.

    • Search Google Scholar
    • Export Citation
  • Follows, M. J., T. Ito, and S. Dutkiewicz, 2006: A compact and accurate carbonate chemistry solver for ocean biogeochemistry models. Ocean Modell., 12 , 290301.

    • Search Google Scholar
    • Export Citation
  • Forest, C. E., P. H. Stone, A. P. Sokolov, M. R. Allen, and M. Webster, 2002: Quantifying uncertainties in climate system properties with the use of recent climate observations. Science, 295 , 113117.

    • Search Google Scholar
    • Export Citation
  • Forest, C. E., P. H. Stone, and A. P. Sokolov, 2006: Estimated PDFs of climate system properties including natural and anthropogenic forcings. Geophys. Res. Lett., 33 , L01705. doi:10.1029/2005GL023977.

    • Search Google Scholar
    • Export Citation
  • Forest, C. E., P. H. Stone, and A. P. Sokolov, 2008: Constraining climate model parameters from observed 20th century changes. Tellus, 60A , 911920.

    • Search Google Scholar
    • Export Citation
  • Friedlingstein, P., and Coauthors, 2006: Climate–carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Climate, 19 , 33373353.

    • Search Google Scholar
    • Export Citation
  • Gouretski, V., and K. P. Koltermann, 2007: How much is the ocean really warming? Geophys. Res. Lett., 34 , L01610. doi:10.1029/2006GL027834.

    • Search Google Scholar
    • Export Citation
  • Gunderson, C. A., and S. D. Wullschleger, 1994: Photosynthetic acclimation in trees to rising atmospheric CO2: A broader perspective. Photosynth. Res., 39 , 369388.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., G. Russell, D. Rind, P. Stone, A. Lacis, S. Lebedeff, R. Ruedy, and L. Travis, 1983: Efficient three-dimensional global models for climate studies: Models I and II. Mon. Wea. Rev., 111 , 609662.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., A. Lacis, D. Rind, G. Russell, P. Stone, I. Fung, R. Ruedy, and J. Lerner, 1984: Climate sensitivity: Analysis of feedback mechanisms. Climate Processes and Climate Sensitivity, Geophys. Monogr., Vol. 29, Amer. Geophys. Union, 130–163.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., I. Fung, A. Lacis, D. Rind, S. Lebedeff, R. Ruedy, and G. Russell, 1988: Global climate change as forecast by Goddard Institute for Space Studies three-dimensional model. J. Geophys. Res., 93 , 93419364.

    • Search Google Scholar
    • Export Citation
  • Hansen, J., and Coauthors, 2002: Climate forcings in Goddard Institute for Space Studies SI2000 simulations. J. Geophys. Res., 107 , 4347. doi:10.1029/2001JD001143.

    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., and Coauthors, 2007: Understanding and attributing climate change. Climate Change 2007: The Physical Science Basis, S. Solomon et al. Eds., Cambridge University Press, 663–745.

    • Search Google Scholar
    • Export Citation
  • Holian, G. L., A. P. Sokolov, and R. G. Prinn, 2001: Uncertainty in atmospheric CO2 predictions from a global ocean carbon cycle model. MIT Joint Program on the Science and Policy of Global Change Report 80, Rep. 80, 25 pp. [Available online at http://web.mit.edu/globalchange/www/MITJPSPGC_Rpt80.pdf].

    • Search Google Scholar
    • Export Citation
  • Houghton, J. T., Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson, 2001: Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp.

    • Search Google Scholar
    • Export Citation
  • Iman, R. L., and W. J. Conover, 1982: A distribution-free approach to inducing rank correlation among input variables. Commun. Stat. Simul. Comput., 11 , 311334.

    • Search Google Scholar
    • Export Citation
  • Iman, R. L., and J. C. Helton, 1988: An investigation of uncertainty and sensitivity analysis techniques for computer models. Risk Anal., 8 , 7190.

    • Search Google Scholar
    • Export Citation
  • Jacoby, H., R. Eckaus, A. D. Ellermann, R. Prinn, D. Reiner, and Z. Yang, 1997: CO2 emissions limits: Economic adjustments and the distribution of burdens. Energy J., 18 , 3158.

    • Search Google Scholar
    • Export Citation
  • Kamenkovich, I. V., A. Sokolov, and P. H. Stone, 2002: An efficient climate model with a 3D ocean and statistical-dynamical atmosphere. Climate Dyn., 19 , 585598.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., T. F. Stoker, F. Joos, and G-K. Plattner, 2003: Probabilistic climate change projections using neural network. Climate Dyn., 21 , 257272.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., and Coauthors, 2008: A review of uncertainties in global temperature projections over the twenty-first century. J. Climate, 21 , 26512663.

    • Search Google Scholar
    • Export Citation
  • Lean, J., 2000: Evolution of the sun’s spectral irradiance since the Maunder Minimum. Geophys. Res. Lett., 27 , 24212424.

  • Levitus, S., J. Antonov, and T. P. Boyer, 2005: Warming of the World Ocean, 1955–2003. Geophys. Res. Lett., 32 , L02604. doi:10.1029/2004GL021592.

    • Search Google Scholar
    • Export Citation
  • Li, C., S. Frolking, and T. A. Frolking, 1992: A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. J. Geophys. Res., 97 , 97599776.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., 1996: Modeling the emissions of nitrous oxide (N2O) and methane (CH4) from the terrestrial biosphere to the atmosphere. Ph.D. thesis, MIT Joint Program on the Science and Policy of Global Change, Rep. 10, 219 pp. [Available online at http://globalchange.mit.edu/files/document/MITJPSPGC_Report10.pdf].

  • Mayer, M., C. Wang, M. Webster, and R. G. Prinn, 2000: Linking local air pollution to global chemistry and climate. J. Geophys. Res., 105 , 2286922896.

    • Search Google Scholar
    • Export Citation
  • McGuire, A. D., J. M. Melillo, L. A. Joyce, D. W. Kicklighter, A. L. Grace, B. Moore III, and C. J. Vorosmarty, 1992: Interactions between carbon and nitrogen dynamics in estimating net primary productivity for potential vegetation in North America. Global Biogeochem. Cycles, 6 , 101124.

    • Search Google Scholar
    • Export Citation
  • McGuire, A. D., L. A. Joyce, D. W. Kicklighter, J. M. Melillo, G. Esser, and C. J. Vorosmarty, 1993: Productivity response of climax temperate forests to elevated temperature and carbon dioxide: A North American comparison between two global models. Climatic Change, 24 , 287310.

    • Search Google Scholar
    • Export Citation
  • McGuire, A. D., and Coauthors, 1997: Equilibrium responses of global net primary production and carbon storage to doubled atmospheric carbon dioxide: Sensitivity to changes in vegetation nitrogen concentration. Global Biogeochem. Cycles, 11 , 173189.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2007a: Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 746–845.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007b: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88 , 13831394.

    • Search Google Scholar
    • Export Citation
  • Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley, 2008: Emulating IPCC AR4 atmosphere-ocean and carbon cycle models for projecting global-mean, hemispheric and land/ocean temperatures: MAGICC 6.0. Atmos. Chem. Phys. Discuss., 8 , 61536272.

    • Search Google Scholar
    • Export Citation
  • Melillo, J. M., A. D. McGuire, D. W. Kicklighter, B. Moore III, C. J. Vorosmarty, and A. L. Schloss, 1993: Global climate change and terrestrial net primary production. Nature, 363 , 234240.

    • Search Google Scholar
    • Export Citation
  • Morgan, M. G., and D. Keith, 1995: Subjective judgments by climate experts. Environ. Sci. Technol., 29 , 468476.

  • Moss, R. H., and S. H. Schneider, 2000: Towards consistent assessment and reporting of uncertainties in the IPCC TAR. Cross-Cutting Issues in the IPCC Third Assessment Report, R. Pachauri and T. Taniguchi, Eds., Cambridge University Press, 35–51.

    • Search Google Scholar
    • Export Citation
  • Nakicenovic, N., and Coauthors, 2000: Intergovernmental Panel on Climate Change Special Report on Emission Scenarios. Cambridge University Press, 570 pp.

    • Search Google Scholar
    • Export Citation
  • Norby, R. J., and Coauthors, 2005: Forest response to elevated CO2 is conserved across a broad range of productivity. Proc. Natl. Acad. Sci. USA, 102 , 1805218056.

    • Search Google Scholar
    • Export Citation
  • Nordhaus, W. D., and G. W. Yohe, 1983: Future paths of energy and carbon dioxide emissions. Changing Climate, Report of the Carbon Dioxide Assessment Committee of the National Academy of Science, National Academies Press, 87–152.

    • Search Google Scholar
    • Export Citation
  • Olivier, J. G. J., and J. J. M. Berdowski, 2001: Global emission sources and sinks. The Climate System, J. Berdowski, R. Guicherit, and B. J. Heij, Eds., Swets and Zeitlinger, 33–77.

    • Search Google Scholar
    • Export Citation
  • Paltsev, S., J. M. Reilly, H. D. Jacoby, R. S. Eckaus, J. McFarland, M. Sarofim, M. Asadoorian, and M. Babiker, 2005: The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4. MIT Joint Program for the Science and Policy of Global Change, Rep. 125, 72 pp. [Available online at http://web.mit.edu/globalchange/www/MITJPSPGC_Rpt125.pdf].

    • Search Google Scholar
    • Export Citation
  • Paltsev, S., J. M. Reilly, H. D. Jacoby, A. Gurgel, G. Metcalf, A. Sokolov, and J. Holak, 2008: Assessment of U.S. GHG cap-and-trade proposals. Climate Policy, 8.4 , 395420.

    • Search Google Scholar
    • Export Citation
  • Pan, Y., and Coauthors, 1998: Modeled responses of terrestrial ecosystems to elevated atmospheric CO2: A comparison of simulations by the biogeochemistry models of the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP). Oecologia, 114 , 389404.

    • Search Google Scholar
    • Export Citation
  • Peixoto, J. P., and A. H. Oort, 1992: Physics of Climate. AIP, 520 pp.

  • Plattner, G-K., and Coauthors, 2008: Long-term climate commitments projected with climate–carbon cycle models. J. Climate, 21 , 27212751.

    • Search Google Scholar
    • Export Citation
  • Prinn, R., and Coauthors, 1999: Integrated global system model for climate policy assessment: Feedbacks and sensitivity studies. Climatic Change, 41 , 469546.

    • Search Google Scholar
    • Export Citation
  • Prinn, R., J. Reilly, M. Sarofim, C. Wang, and B. Felzer, 2007: Effects of air pollution control on climate: results from an integrated assessment model. Human-Induced Climate Change: An Interdisciplinary Assessment, M. E. Schlesinger et al., Eds., Cambridge University Press, 93–102.

    • Search Google Scholar
    • Export Citation
  • Prinn, R., S. Paltsev, A. Sokolov, M. Sarofim, J. Reilly, and H. Jacoby, 2008: The influence on climate change of differing scenarios for future development analyzed using the MIT Integrated Global System Model. MIT Joint Program for the Science and Policy of Global Change, Rep. 163, 28 pp. [Available online at http://web.mit.edu/globalchange/www/MITJPSPGC_Rpt163.pdf].

    • Search Google Scholar
    • Export Citation
  • Raich, J. W., and Coauthors, 1991: Potential net primary productivity in South America: Application of a global model. Ecol. Appl., 1 , 399429.

    • Search Google Scholar
    • Export Citation
  • Reilly, J., and S. Paltsev, 2006: European greenhouse gas emissions trading: A system in transition. Economic Modeling of Climate Change and Energy Policies, M. De Miguel et al., Eds., Edward Elgar Publishing, 45–64.

    • Search Google Scholar
    • Export Citation
  • Reilly, J., J. Edmonds, R. Gardner, and A. Brenkert, 1987: Monte Carlo analysis of the IEA/ORAU energy/carbon emissions model. Energy J., 8 (3) 129.

    • Search Google Scholar
    • Export Citation
  • Reilly, J., and Coauthors, 1999: Multi-gas assessment of the Kyoto Protocol. Nature, 401 , 549555.

  • Russell, G. L., J. R. Miller, and L-C. Tsang, 1985: Seasonal ocean heat transport computed from an atmospheric model. Dyn. Atmos. Oceans, 9 , 253271.

    • Search Google Scholar
    • Export Citation
  • Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol optical depths. J. Geophys. Res., 98 , 2298722994.

    • Search Google Scholar
    • Export Citation
  • Schlosser, C. A., D. Kicklighter, and A. Sokolov, 2007: A global land system framework for integrated climate-change assessments. MIT Joint Program for the Science and Policy of Global Change, Rep. 147, 82 pp. [Available on line at http://web.mit.edu/globalchange/www/MITJPSPGC_Rpt147.pdf].

    • Search Google Scholar
    • Export Citation
  • Smith, S. J., R. Andres, E. Conception, and J. Lurz, 2004: Historical sulfur dioxide emissions 1850–2000: Methods and results. PNNL Research Rep. 14537, Pacific Northwest National Laboratory, 16 pp.

    • Search Google Scholar
    • Export Citation
  • Sokolov, A. P., 2006: Does model sensitivity to changes in CO2 provide a measure of sensitivity to other forcings? J. Climate, 19 , 32943306.

    • Search Google Scholar
    • Export Citation
  • Sokolov, A. P., and P. H. Stone, 1998: A flexible climate model for use in integrated assessments. Climate Dyn., 14 , 291303.

  • Sokolov, A. P., C. Wang, G. Holian, P. H. Stone, and R. Prinn, 1998: Uncertainty in the oceanic heat and carbon uptake and their impact on climate projections. Geophys. Res. Lett., 25 , 36033606.

    • Search Google Scholar
    • Export Citation
  • Sokolov, A. P., and Coauthors, 2005: The MIT Integrated Global System Model (IGSM) Version 2: Model description and baseline evaluation. MIT Joint Program for the Science and Policy of Global Change, Rep. 124, 40 pp. [Available online at http://web.mit.edu/globalchange/www/MITJPSPGC_Rpt124.pdf].

    • Search Google Scholar
    • Export Citation
  • Sokolov, A. P., S. Dutkiewicz, P. H. Stone, and J. R. Scott, 2007: Evaluating the use of ocean models of different complexity in climate change studies. MIT Joint Program for the Science and Policy of Global Change, Rep. 128, 23 pp. [Available online at http://web.mit.edu/globalchange/www/MITJPSPGC_Rpt128.pdf].

    • Search Google Scholar
    • Export Citation
  • Sokolov, A. P., D. W. Kicklighter, J. M. Melillo, B. Felzer, C. A. Schlosser, and T. W. Cronin, 2008: Consequences of considering carbon–nitrogen interactions on the feedbacks between climate and the terrestrial carbon cycle. J. Climate, 21 , 37763796.

    • Search Google Scholar
    • Export Citation
  • Sokolov, A. P., C. E. Forest, and P. H. Stone, 2009: Sensitivity of climate change projections to uncertainties in the estimates of observed changes in deep-ocean heat content. Climate Dyn., in press.

    • Search Google Scholar
    • Export Citation
  • Solomon, S., D. Qin, M. Manning, M. Marquis, K. Averyt, M. M. B. Tignor, H. L. Miller Jr., and Z. Chen, 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

    • Search Google Scholar
    • Export Citation
  • Stern, D. I., 2005: Beyond the environmental Kuznets curve: Diffusion of sulfur-emissions-abating technology. J. Environ. Dev., 14 , 101124.

    • Search Google Scholar
    • Export Citation
  • Stern, D. I., 2006: Reversal of the trend in global anthropogenic sulfur emissions. Global Environ. Change, 16 , 207220.

  • Stocker, T. F., W. S. Broecker, and D. G. Wright, 1994: Carbon uptake experiments with a zonally-averaged global ocean circulation model. Tellus, 46B , 103122.

    • Search Google Scholar
    • Export Citation
  • Stone, P. H., and M-S. Yao, 1987: Development of a two-dimensional zonally averaged statistical-dynamical model. Part II: The role of eddy momentum fluxes in the general circulation and their parameterization. J. Atmos. Sci., 44 , 37693786.

    • Search Google Scholar
    • Export Citation
  • Stone, P. H., and M-S. Yao, 1990: Development of a two-dimensional zonally averaged statistical-dynamical model. Part III: The parameterization of the eddy fluxes of heat and moisture. J. Climate, 3 , 726740.

    • Search Google Scholar
    • Export Citation
  • Tatang, M. A., W. Pan, R. G. Prinn, and G. J. McRae, 1997: An efficient method for parametric uncertainty analysis of numerical geophysical models. J. Geophys. Res., 102 , 2192521932.

    • Search Google Scholar
    • Export Citation
  • Wang, C., 2004: A modeling study on the climate impacts of black carbon aerosols. J. Geophys. Res., 109 , D03106. doi:10.1029/2003JD004084.

    • Search Google Scholar
    • Export Citation
  • Wang, C., R. G. Prinn, and A. Sokolov, 1998: A global interactive chemistry and climate model: Formulation and testing. J. Geophys. Res., 103 , 33993418.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., and D. Jacob, 1998: Anthropogenic forcing on tropospheric ozone and OH since preindustrial times. J. Geophys. Res., 103 , 3112331135.

    • Search Google Scholar
    • Export Citation
  • Webster, M. D., and A. P. Sokolov, 2000: A methodology for quantifying uncertainty in climate projections. Climatic Change, 46 , 417446.

    • Search Google Scholar
    • Export Citation
  • Webster, M. D., M. Babiker, M. Mayer, J. M. Reilly, J. Harnisch, R. Hyman, M. C. Sarofim, and C. Wang, 2002: Uncertainty in emissions projections for climate models. Atmos. Environ., 36 , 36593670.

    • Search Google Scholar
    • Export Citation
  • Webster, M. D., and Coauthors, 2003: Uncertainty analysis of climate change and policy response. Climatic Change, 62 , 295320.

  • Webster, M. D., S. Paltsev, J. Parsons, J. Reilly, and H. Jacoby, 2008: Uncertainty in greenhouse emissions and costs of atmospheric stabilization. MIT Joint Program for the Science and Policy of Global Change, Rep. 165, 28 pp. [Available online at http://globalchange.mit.edu/files/document/MITJPSPGC_Rpt165.pdf].

    • Search Google Scholar
    • Export Citation
  • Weyant, J. P., 2004: Introduction and overview. Energy Econ., 26 , 501515.

  • Weyant, J. P., and J. N. Hill, 1999: Introduction and overview. The Energy Journal Special Issue: The Costs of the Kyoto Protocol: A Multi-Model Evaluation, J. P. Weyant, Ed., IAEE, vii–xliv.

    • Search Google Scholar
    • Export Citation
  • Weyant, J. P., F. de la Chesnaye, and G. Blanford, 2006: Overview of EMF-21: Multigas mitigation and climate policy. Energy J., 3 , 132.

    • Search Google Scholar
    • Export Citation
  • Wigley, T. M. L., and S. C. B. Raper, 2001: Interpretation of high projections for global-mean warming. Science, 293 , 451454.

  • Yao, M-S., and P. H. Stone, 1987: Development of a two-dimensional zonally averaged statistical-dynamical model. Part I: The parameterization of moist convection and its role in the general circulation. J. Atmos. Sci., 44 , 6582.

    • Search Google Scholar
    • Export Citation
  • Zhuang, Q., and Coauthors, 2006: CO2 and CH4 exchanges between land ecosystems and the atmosphere in northern high latitudes over the 21st century. Geophys. Res. Lett., 33 , L17403. doi:10.1029/2006GL026972.

    • Search Google Scholar
    • Export Citation
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