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Probabilistic assessment of optimal policies under climate uncertainty The 2◦ C constraint V. Bosetti, L. Drouet, M. Tavoni Euro-Mediterranean Center on Climate Change, CIP Division, Italy Fondazione Eni Enrico Mattei, Italy IEW 2013 Paris, 21 June 2013 Uncertainty in Integrated Assessment Incomplete understanding of climate change in many aspects (Heal and Millner, 2013): • scientific uncertainty • Models’ parameters • Various representations • socio-economic uncertainty • future emissions (GDP, productivity, time preference, technology change. . . ) • economic impacts Uncertainty is partially represented in current IAMs Decision makers need support tools with • representations and analyses of uncertainty, • assessment of robust policies Uncertainties propagation in IAM Socio-economic scenarios E Climate system T° Global economic impacts Uncertainties Socio-economic drivers Geophysical parameters Impact estimates Probabilistic approach Quantify the uncertainty using distribution rather than a fix values. • Assign weights to future scenarios • Measure the likelihood of model’s parameters • Combine distributions through the whole IAM Previous studies • Probabilistic Integrated Assessment of “Dangerous” Climate Change (Mastrandea and Schneider, 2004, Science) • Probabilistic cost estimates for climate change mitigations (Rogelj et al., 2013, Nature) WITCH • Hybrid integrated assessment model (Bosetti et al., 2009) • Economy: Ramsey-type optimal growth (inter-temporal) • Energy: Energy sector detail (technology portfolio) • Climate: Damage feedback (global variable) • Global, with the world divided in 13 regions • Optimization model: maximization of utility function In this work • The Climate module is deactivated • The damages are not computed • All countries act cooperatively to address the climate change issue Socio-economic challenges for mitigation Shared Socio-economic Pathways SSP5: SSP3: (Mitigation Challenges Dominate) (High Challenges) Conventional Development Fragmentation SSP2: (Intermediate Challenges Middle of the Road) SSP1: SSP4: (Low Challenges) (Adaptation Challenges Dominate) Sustainability Inequality Socio-economic challenges for adaptation from (O’Neill et al, 2012) SSPs implementation: BAU scenarios World Population World GDP (via TFP) SSP3 SSP5 12 12 SSP4 10 SSP2 World GDP (2005=1) World Population [Billions] 14 SSP1 8 SSP2 SSP4 4 8 SSP3 SSP5 SSP1 2000 2025 2050 2075 2100 SSPs’ likelihood Equal likelihood (no studies yet) 2000 2025 2050 2075 2100 Mitigation scenarios WITCH computes optimal emissions trajectories to comply with given carbon budgets. → Covering the space of mitigation. Kyoto GHG budgets over the 21st century Range [GtCO2 -eq] SSP1 SSP2 SSP3 SSP4 SSP5 1115–3805 1115–3717 1115–3447 1115–3624 1115–3971 GHG emissions trajectories CO2 emissions [GtC] 20 ssp SSP1 SSP2 SSP3 SSP4 10 SSP5 0 2020 2040 2060 2080 2100 Climate model SNEASY SNEASY (Urban and Keller, 2010) is a simple Earth System Model composed of • a climate module based on DOECLIM, • a carbon-cycle model, including feedbacks from CO2 concentration and temperature, • a Atlantic Meridional Overtuning Circulation (AMOC) Boxmodel • Timestep: 1 year • Inputs (provided by WITCH): • CO2 emissions, • non-CO2 radiative forcing components. Parameters’ estimation Climate: Carbon-cycle: • Climate sensitivity • Thermocline transfer rate • Ocean vertical diffusivity • Respiration sensitivity • Aerosol forcing scaling • Carbon fertilization Observation errors: CO2 concentrations, temperature and Ocean heat. Parameters are estimated from past observations using a Bayesian inversion technique based on a Monte-Carlo Markov Chain algorithm. The chain is used to compute probabilistic temperature projections Hindcast calibration period Atmospheric CO2 concentration [ppm] Surface temperature anomaly [K] 1.0 375 350 0.5 325 0.0 300 95% chain 1900 1950 2000 Ocean heat anomaly [10^21J] 1850 30 1 20 0 10 −1 0 −2 −10 −3 −20 −4 1900 1950 2000 Atm−ocean carbon flux [GtC] mean chain Observations 1950 1960 1970 1980 1990 2000 1986 1990 1994 Posterior marginal distributions Climate Sensitivity [C/Wm2] Ocean vertical diffusivity [cm2/s] Probability densities 2 4 6 Respiration Sensitivity 1 2 3 4 5 Initial temperature [C] −0.2 −0.1 0.0 0.1 Aerosol forcing scale 2 4 6 8 Carbon fertilization factor 0.0 0.5 1.0 1.5 Thermocline transfer rate [m/y] 0.25 0.50 0.75 1.00 Initial ocean heat [10ZJ] 0 10 20 30 40 50 Initial CO2 concentration [ppm] −60 −40 −20 0 280 282 284 286 288 Corridors of cost-efficient policies SSP1 cost-efficient corridors 20 CO2 emissions [GtC] Prob < 2C < 0.05 15 0.05 − 0.1 0.1 − 0.33 0.33 − 0.5 10 0.5 − 0.66 0.66 − 0.9 0.9 − 0.95 0.95 − 0.99 5 0 2025 2050 2075 2100 Scenario classification with a 2°C threshold Scenarios' likelihood to stay below 2°C in the 21st century Unlikely [<33%] Medium Likelihood [33−66%] Very Likely [>66%] CO2 emissions [Gt] 20 ssp SSP1 SSP2 SSP3 SSP4 10 SSP5 0 2025 2050 2075 2100 2025 2050 2075 2100 2025 2050 2075 2100 Mitigation cost distributions Mitigation cost distributions per likelihood to stay below 2C < 0.33 0.33−0.66 > 0.66 0 −20 % GDP Proba < 2C < 0.33 −40 0.33−0.66 > 0.66 −60 −80 2050 2100 2050 2100 2050 2100 Economic impacts Tol (2009) reviews estimates of economic impact of climate change from 14 studies • Mean estimates: Nordhaus(1994a), Frankhauser(1995), Tol(1995), Nordhaus and Young(1996), Mendelshon et al. (2000), Nordhaus and Boyer (2000), Maddison(2003), Rehdanz and Maddison(2005) • Skewed distribution: Nordhaus(1994b), Plambeck and Hope(1996), Hope(2006) • Normal distribution: Tol (2002), Nordhaus (2006) Analysis: • Various methods for estimation: aggregation, statistical evaluation • Positive impact for low warming • Uncertainty increases rapidly with warming Impact function fitting We perform Monte-Carlo regressions of a quadratic function: I = β1 T + β2 T 2 , where I is the the economic impact in % GDP and T the temperature increase in °C Marginal distributions of regression coefficients beta 1 beta 2 Probability density 0.4 0.75 0.3 0.50 0.2 0.25 0.1 0.0 0.00 0.0 2.5 5.0 7.5 −4 −3 −2 −1 0 Economic impacts as a function of temperature 5 ● ● ● ● ● ● 0 ● ● ● ● ● ● ● % GDP 95% CI ● −5 50% CI −10 −15 0 1 2 3 Temperature increase since preindustrial level [°C] 4 ● mean estimates ● Tol (2009) fit Economic impacts distributions Impact distributions from the expected temperature increase 5 0 + + + + + + 2070 2080 2090 2100 % GDP −5 −10 −15 −20 −25 2050 2060 Economic impacts distributions Impact distributions per likelihood to stay below 2C <0.33 0.33−0.66 >0.66 5 0 ● ● ● ● ● 2050 2100 2050 2100 ● −5 % GDP −10 −15 −20 −30 2050 2100 Conclusion Summary The probabilistic approach in IA allows to report and analyse the uncertainty. Further works • Risk management • Use the probabilistic information in the optimization frameworks • cost-effective analysis • cost-benefit analysis Remark Probabilistic approach is not always applicable (Kunreuther, 2013) Thanks! [email protected] The research leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2013 under grant agreement n°282846 (LIMITS) We thank K. Keller from the Pennsylvania State University for supplying the SNEASY code.