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Long-term Greenhouse Gas Stabilization and the Risks of Dangerous Impacts

Long-term Greenhouse Gas Stabilization and the Risks of Dangerous Impacts. M. Webster , C.E. Forest, H. Jacoby, S. Paltsev, J. Parsons, R. Prinn, J. Reilly, M. Sarofim, A. Schlosser, A. Sokolov, P. Stone, C. Wang Engineering Systems Division

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Long-term Greenhouse Gas Stabilization and the Risks of Dangerous Impacts

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  1. Long-term Greenhouse Gas Stabilization and the Risks of Dangerous Impacts M. Webster, C.E. Forest, H. Jacoby, S. Paltsev, J. Parsons, R. Prinn, J. Reilly, M. Sarofim, A. Schlosser, A. Sokolov, P. Stone, C. Wang Engineering Systems Division MIT Joint Program on the Science and Policy of Global Change Massachusetts Institute of Technology Society for Risk Analysis New England Chapter April 28, 2009

  2. Calvin’s View on Risky Decisions

  3. Outline • Motivation • MIT IGSM Model Framework • Parametric Uncertainty • Resulting Uncertainty in Projections • Exploring Risk-Risk Tradeoffs

  4. Climate Change Policy: Choosing a Long-Term Target • UN Framework Convention on Climate Change • “…stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system.” • How do we choose this stabilization level?

  5. CCSP Product 2.1a • Study of GHG Stabilization Scenarios • Three Models: • MERGE (EPRI/Stanford) • MiniCAM (PNNL/UMD) • IGSM (MIT) Source: Clarke et al., 2007

  6. Stabilization Scenarios(Source: U.S. CCSP Product 2.1a)

  7. Global Mean Temperature Change(Deterministic)

  8. Question for this Study • How can we use information about risks of exceeding thresholds to guide our choice among long-run stabilization targets? • Use the uncertainty in the CCSP results from one model (MIT IGSM)? • Objective: Frame the choice of long-term stabilization target as a risk management decision • Consider risks of both climate impacts and abatement costs

  9. MIT Integrated Global Systems Model

  10. Uncertainty in Economics • 110 Uncertain Parameters, including: • Productivity growth rates (historical data) • Energy efficiency growth rate (historical data) • Ease of substituting inputs (historical data) • Costs of new technologies (expert judgment) • Main Uncertain Outputs: • Emissions (GHGs, urban pollutants) • Costs (consumption loss, carbon prices)

  11. GDP Growth Uncertainty

  12. Methodology • Latin Hypercube Monte Carlo • 400 random samples of all parameters • Impose correlation where justified by empirical data and/or theory • Impose each CCSP scenario as an emissions cap over time • Not a fixed radiative forcing target • No banking/borrowing • DO allow GHG trading using GWPs • DO allow trading between nations each period

  13. Uncertainty in CO2 Emissions(No Policy)

  14. Carbon Prices in 2020

  15. Global Welfare Loss (%) in 2020

  16. Uncertaintyin TotalPrimary Energy Sources2050

  17. Relative Contribution to Variance • Energy Supply • Energy Demand • Scale of Economy • Other Uncertainties • Predict which most affect cum. CO2, carbon prices.

  18. Top Ten Drivers of Uncertainty in Abatement Cost

  19. Uncertainty in Climate Parameters • Emissions Uncertainty from EPPA • Climate Sensitivity • Heat & Carbon Uptake by Deep Ocean • Radiative Forcing Strength of Aerosols • CO2 Fertilization Effect on Ecosystem • Trends in Precipitation Frequency

  20. Results: Temperature ChangeImpacts of Stabilization Paths Level 4 Level 3 Level 2 Level 1 No Policy Global Mean Surface Temperature Increase (oC) (1981-2000) to (2091-2100)

  21. Results: Sea Level Rise(Excluding Greenland and WAIS)

  22. Communicating the Odds of Temperature Change

  23. Communicating the Impact of Policy Stringent Policy (~550 ppm) No Policy

  24. USING THE IGSM, WHAT IS THE PROBABILITY OF GLOBAL WARMING for 1980-2100, WITHOUT & WITH A 450,550, 650 or 750 ppm CO2-equivalent STABILIZATION POLICY? (400 random samples for economics & climate assumptions)

  25. USING THE IGSM, WHAT IS THE PROBABILITY OF GLOBAL SEA LEVEL RISE for 2000-2100, WITHOUT & WITH A 450,550, 650 or 750 ppm CO2-equivalent STABILIZATION POLICY? (400 random samples for economics & climate assumptions)

  26. USING THE EPPA, WHAT IS THE PROBABILITY FOR WELFARE LOSS (% change in 2020), WITHOUT & WITH A 450,550, 650 or 750 ppm CO2-equivalent STABILIZATION POLICY? (400 random samples for economics assumptions)

  27. Marginal Reduction in Probability of Exceeding 5oC Global Temperature Change

  28. Tradeoffs in Choosing Stabilization Targets: Expected Values

  29. Risk-Risk Tradeoffs in Choosing Stabilization Targets

  30. Risk-Risk Tradeoffs in Choosing Stabilization Targets

  31. Key Insights • Economics • GDP growth important, not biggest driver • Energy demand parameters critical • High returns on reducing uncertainties in AEEI, elasticities of substitution, etc. • Climate Science • Uncertainty still wide • Mean and upper tails indicate likelihood of significant impacts without some GHG reductions

  32. Key Insights (II) • Decision-Making • Problem is one of risk management • Risk-risk tradeoffs give different insights than focusing on mean/reference values • Suggestive that for a 450ppm, cost risk may outweigh the reduction in temperature risk

  33. USING THE EPPA, WHAT IS THE PROBABILITY FOR WELFARE LOSS (% change in 2050), WITHOUT & WITH A 450,550, 650 or 750 ppm CO2-equivalent STABILIZATION POLICY? (400 forecasts with equally probable economics assumptions)

  34. Uncertainty in CO2 Emissions(No Policy)

  35. Why are the probabilities shifted to higher temperatures than in our previous calculations (Webster et al, 2003)? • Radiative Forcing Increases? • Emissions (higher lower bound) • Reduced Ocean Carbon Uptake • Additional forcing such as Black Carbon & Tropospheric Ozone (additional forcing included but still calibrated by net aerosols in 1990s) • Climate Model Response? • Climate Model Parameters show higher response • Learning? • Distributions better defined • Distributions shifted higher

  36. IPCC AR4 Temp Chg Uncertainty Relevant Comparison To IGSM No Policy

  37. Typical Production Function in EPPA

  38. Uncertainty in SO2 Emissions(No Policy)

  39. Uncertainty in SO2 Emissions(No Policy vs. CCSP-550)

  40. Uncertainty in Methane Emissions

  41. Uncertainty in NOx Emissions

  42. Uncertainty in BC Emissions

  43. Zonal Temperature Change2000-2100 (Median)

  44. Zonal Temperature Change2000-2100 (95th Percentile)

  45. PDFs of Global Mean Temp. Chg.

  46. PDFs of Sea Level Rise(Excluding Greenland and WAIS)

  47. Global Electricity Consumption by Technology and Fuel

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