86025 Energy Systems AnalysisArnulf Grubler Climate Change: Addressing Uncertainty, Inertia, and Equity

86025 Energy Systems AnalysisArnulf Grubler The Greenhouse Effect E. Boeker, Environmental Physics # updated for 2005 ann.average CO2 conc. CDIAC Gasconc. ppm GWP factor G. Warming (ºK) H 2 O CO # O N 2 O CH HFC-134 ~ Total 32.4

86025 Energy Systems AnalysisArnulf Grubler Source: IPCC AR4 WG1 2007

86025 Energy Systems AnalysisArnulf Grubler Temperature Change (over mean): Observations and Modeled by Including all Positive and Negative Forcings. Source: IPCC AR4 WG1 2007

EU Regional Climate Variability: Observations (b) modeled for present (c) and future (d) conditions. Note 2003 heat wave (a) being far outside both observational and model range. IPCC uncertainty terminology (adopted from Schneider and Moss) : <1% probability =“exceptionally unlikely” (but 2003 happened)

Height of Pasterze glacier in 1960 Current CC Impacts: 80 meters thinning of Pasterze glacier, Austria But… uncovering 5000 yr old vegetation

86025 Energy Systems AnalysisArnulf Grubler Global Carbon & Warming Budgets

86025 Energy Systems AnalysisArnulf Grubler Climate Change: Major Uncertainties Demographic (growth & composition) Economic (growth, structure, disparities) Social (values, lifestyles, policies) Technologic (rates & direction of change) Environmental (limits, adaptability) Valuation (discounting, non-market damages and benefits) Addressing uncertainties: Scenarios Research Social choice

86025 Energy Systems AnalysisArnulf Grubler A Taxonomy of SRES Scenarios (“baselines”, no climate policies)

Emissions vs. Energy Use & Technology in IPCC SRES Scenarios Uncertainty 1: Population and GDP growth, prices, policies Uncertainty 2: Resource availability, technology

INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE (IPCC)

Example Climate Change: Projected Global Mean Temperature Change and Sources of Uncertainty IPCC WG1 TAR: “By 2100, the range in the surface temperature response across the group of climate models run with a given scenario is comparable to the range obtained from a single model run with the different SRES scenarios”

Example Climate Change: Projected Global Mean Temperature Change and Sources of Uncertainty Baseline Uncertainty: 50 % climate (sensitivity) modeling 25% emissions (POP+GDP influence) 25% emissions (TECH influence) Uncertainty on extent and success of climate policies Minimum committed warming 1.5 °C (certainty)

SRES scenarios cumulative emissions , GtC < ppm CO °C global mean temperature change MAJOR CLIMATE CHANGE UNCERTAINTIES Socioeconomic (Future Cumulative Emissions SRES Scenarios) Carbon Cycle (Resulting CO 2 Concentration) and Climate Sensitivity (ºC for 2  CO 2 ) Vulnerability: lowhigh

Uncertainties (F=carbon cycle; Δ t 2x =climate sensitivity) in Stabilizing Climate Change at +2.5 ºC by 2100 EmissionsShadow Prices

86025 Energy Systems AnalysisArnulf Grubler North -- South Responsibility: Mostly in Annex-I Vulnerability: Mostly in “South” Adaptation capacity: Mostly in Annex-I Future emission growth: Mostly in “South” Near-term mitigation potential: highest in Annex-I Near-term mitigation costs: lowest in “South”

86025 Energy Systems AnalysisArnulf Grubler 1990 Per Capita CO2 by Source vs. Population

Distribution of Industrial CO2 Emissions since 1800

86025 Energy Systems AnalysisArnulf Grubler The Greenhouse “Barometer”

86025 Energy Systems AnalysisArnulf Grubler Agricultural Impacts for Alternative Climate Change Scenarios. Source: IIASA LUCC, 2000.

86025 Energy Systems AnalysisArnulf Grubler Environmental Change: Development vs. Climate More ecosystems will be destroyed by economic development than by the climate change this development induces Far more human lives are threatened by a lack of development than by any climate change resulting from a closure of the development gap Baselines: “business-as-usual” + climate control vs sustainability paradigm

86025 Energy Systems AnalysisArnulf Grubler Vulnerability to Economic Development vs. Seal Level Rise. Source: IPCC AR4 WG2, 2007

86025 Energy Systems AnalysisArnulf Grubler Reducing CC Vulnerabilities Economic & Social Development un-targeted and asymmetrical poverty vulnerability: - affluence vulnerability: + Adaptation targeted to CC Emissions reduction (mitigation) lowering CC but not eliminating it

86025 Energy Systems AnalysisArnulf Grubler Vulnerability to CC by 2050 (IPCC AR4 WG2 2007) A2 current adaptive capacityImproved adaptive capacity Mitigation only (550 stab)Mitigation + improved adaptation

86025 Energy Systems AnalysisArnulf Grubler Mitigation Options Demographic change Economic development Social behavior Efficiency Improvements Low carbon intensity Zero carbon (solar, nuclear) Carbon removal Non CO 2 gases (agriculture&industry) End deforestation Sink enhancements “geo-engineering”

86025 Energy Systems AnalysisArnulf Grubler Emissions & Reduction Measures Multiple sectors and stabilization levels Source: Riahi et al., TFSC 2007

Costs of Different Baselines and Stabilization Scenarios Source: IIASA, Deployment rate of efficiency and low-emission technologies Σ: The lower baseline emissions (efficiency, clean supply) the easier to achieve (currently unknown) climate targets

Stabilization Costs (% GDP loss, top,; and carbon price, $/tCO 2, bottom) by 2100 as a Function of Baseline, Model, and Stabilization Level Differences IPCC AR4 WG3 2007

Technology as Source and Remedy of Climate Change: IPCC Baselines and 550 ppmv Stabilization Scenarios (in GtC), Source: IIASA, BASELINE WITH FROZEN EFFICIENCY AND TECHNOLOGY Σ: With “frozen” efficiency and technology improvements emissions grow “through the roof”. Even with continued improvements, additional emission reduction is needed for climate stabilization

Baseline Emissions vs. Reductions in Illustrative 550 ppm Stabilization Scenarios (Source: Riahi, 2002) Σ: The higher the baseline emissions, the more reliance on “silver bullet” backstop technologies like CCS

Emission Reduction Measures Riahi et al. TFSC 2007 Emissions reductions due to climate policies Improvements incorporated in baselines Σ: Technological change in Baseline best hedging against target uncertainty

Emission Reduction Measures Riahi et al TFSC 2007 (0.9 incl. baseline) RF = Robustness factor of options across scenario uncertainty is highest for: F-gases and N2O reduction, energy conservation & efficiency, and biomass+CCS “wildcard” (if feasible)

Mitigation Technology Portfolio Analysis Paramount importance of Baseline Costs matter Diffusion time constants matter Differences in where technology is developed and where it is deployed Technological interdependence and systemic aspects important in “transition” analysis Non-energy, non-CO2 can help, but cannot solve problem Σ: Popular “wedge” analysis fails on all above accounts!

86025 Energy Systems AnalysisArnulf Grubler Energy Carbon and Climate: How far to go? Energy:  20 (5% exergy efficiency) Carbon: Zero (H2-economy) Damages: committed warming (>1.5 C?) Non-linear (catastrophic) change: ??? “Collateral damages” -- Geoengineering, e.g. aerosol cooling (white sky) -- sequestration (leakage, marine ecology) -- biomass (soil carbon, biodiversity, agriculture) -- solar (albedo changes)

86025 Energy Systems AnalysisArnulf Grubler Policy Conundrums Equitable quantitative targets at odds with economics or infeasible Cost optimal emission reduction: Start with inefficiencies in DCs but requires new instruments (CDM+) Separation of equity and efficiency (e.g. via tradable permit allocation) might be politically infeasible (unprecedented N-S resource transfers) Uncertainties cannot be ignored (soil C, avoided deforestation) Mitigation technology innovation “recharge” chain broken (declining R&D)

86025 Energy Systems AnalysisArnulf Grubler Allocating Emissions Reductions or Access to Global Commons