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

Calvin’s View on Risky Decisions

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

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?

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

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

Global Mean Temperature Change (Deterministic)

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

MIT Integrated Global Systems Model

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)

GDP Growth Uncertainty

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

Uncertainty in CO 2 Emissions (No Policy)

Carbon Prices in 2020

Global Welfare Loss (%) in 2020

Uncertainty in Total Primary Energy Sources 2050

Relative Contribution to Variance Energy Supply Energy Demand Scale of Economy Other Uncertainties Predict which most affect cum. CO 2, carbon prices.

Top Ten Drivers of Uncertainty in Abatement Cost

Uncertainty in Climate Parameters Emissions Uncertainty from EPPA Climate Sensitivity Heat & Carbon Uptake by Deep Ocean Radiative Forcing Strength of Aerosols CO 2 Fertilization Effect on Ecosystem Trends in Precipitation Frequency

Results: Temperature Change Impacts of Stabilization Paths Global Mean Surface Temperature Increase ( o C) ( ) to ( ) No Policy Level 1 Level 3 Level 4 Level 2

Results: Sea Level Rise (Excluding Greenland and WAIS)

Communicating the Odds of Temperature Change

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

Δ T > 2 o C Δ T > 4 o C Δ T > 6 o C No Policy 400 in in 201 in 4 Stabilize at in 4001 in 41 in 400 Stabilize at in 1007 in 100<1 in 400 Stabilize at in 101 in 400<1 in 400 Stabilize at in 4<1 in 400 USING THE IGSM, WHAT IS THE PROBABILITY OF GLOBAL WARMING for , WITHOUT & WITH A 450, 550, 650 or 750 ppm CO 2 -equivalent STABILIZATION POLICY? (400 random samples for economics & climate assumptions)

Sea Level Rise > 0.2m Sea Level Rise > 0.4m Sea Level Rise > 0.6m No Policy 400 in in 209 in 100 Stabilize at in 4001 in 5< 1 in 400 Stabilize at in 1001 in 10< 1 in 400 Stabilize at in 101 in 50< 1 in 400 Stabilize at in 10<1 in 400 USING THE IGSM, WHAT IS THE PROBABILITY OF GLOBAL SEA LEVEL RISE for , WITHOUT & WITH A 450, 550, 650 or 750 ppm CO 2 -equivalent STABILIZATION POLICY? (400 random samples for economics & climate assumptions)

Δ WL>1% Δ WL>2% Δ WL>3% No Policy--- Stabilize at in 1001 in 400<1 in 400 Stabilize at in 1001 in 200<1 in 400 Stabilize at in 41 in 501 in 200 Stabilize at in 103 in 101 in 10 USING THE EPPA, WHAT IS THE PROBABILITY FOR WELFARE LOSS (% change in 2020), WITHOUT & WITH A 450, 550, 650 or 750 ppm CO 2 -equivalent STABILIZATION POLICY? (400 random samples for economics assumptions)

Marginal Reduction in Probability of Exceeding 5 o C Global Temperature Change Probability of exceeding target Reduction in Probability (percentage points) Cum. CO 2 Emissions (GtC) Reduction in Cumulative CO 2  Prob/  Cum No Policy 54.0% Stabilize at %51.5% % Stabilize at %2.3% % Stabilize at %0.3% % Stabilize at % %

Tradeoffs in Choosing Stabilization Targets: Expected Values

Risk-Risk Tradeoffs in Choosing Stabilization Targets

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

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

Δ WL>1% Δ WL>2% Δ WL>3% No Policy--- Stabilize at in 123 in 2003 in 400 Stabilize at in 31 in 203 in 400 Stabilize at in 103 in 51 in 4 Stabilize at in in in 100 USING THE EPPA, WHAT IS THE PROBABILITY FOR WELFARE LOSS (% change in 2050), WITHOUT & WITH A 450, 550, 650 or 750 ppm CO 2 -equivalent STABILIZATION POLICY? (400 forecasts with equally probable economics assumptions)

Uncertainty in CO 2 Emissions (No Policy)

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

IPCC AR4 Temp Chg Uncertainty Relevant Comparison To IGSM No Policy

Typical Production Function in EPPA

Uncertainty in SO 2 Emissions (No Policy)

Uncertainty in SO 2 Emissions (No Policy vs. CCSP-550)

Uncertainty in Methane Emissions

Uncertainty in NO x Emissions

Uncertainty in BC Emissions

Zonal Temperature Change (Median)

Zonal Temperature Change (95 th Percentile)

PDFs of Global Mean Temp. Chg.

PDFs of Sea Level Rise (Excluding Greenland and WAIS)

Global Electricity Consumption by Technology and Fuel

Historical (%) Projected Annual Average Growth Rate (%) RegionMeanStd Dev USA2.22.3% CAN2.32.3% MEX2.25.2% JPN4.93.5% ANZ2.01.8% EUR2.81.6% EET1.13.9% FSU1.15.3% ASI4.34.7% CHN4.33.7% IND2.32.7% IDZ2.75.0% AFR1.01.8% MES2.33.3% LAM1.72.0% ROW2.23.5% GLOBAL

Carbon Price Under Level 1 (450ppm)