USAEE Conference 2016, October 26, Tulsa, Oklahoma, USA Socio-economic implications of climate stabilization scenarios from MIROC Earth System Model Diego SILVA HERRAN, Kaoru TACHIIRI Japan Agency for Marine-Earth Science and Technology (JAMSTEC) Project Team for Risk Information on Climate Change (SOUSEI) This research was supported by the Program for Risk Information on Climate Change (Theme B) of the Ministry of Education, Culture, Sports, Science and Technology of Japan.

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Contents Introduction Method: Integrated assessment model (IAM) Earth system model (ESM) Scenarios Results Energy, land use, emissions, costs Summary D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 SOUSEI Theme B: Climate change projection contributing to stabilization target setting Theme (Task) (i): Long-term global change projection based on diverse scenarios (i) a: Development of an earth system model dealing with variations of greenhouse gasses, land use change etc. Theme (Task) (ii): Obtaining scientific perceptions on large-scale variations and modifications of climate (i) b: Information gathering and examination on socio-economic scenarios toward stabilization target setting D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

Introduction: Setting targets for climate change mitigation and stabilization Climate uncertainties Climate models spread Source: IPCC AR5 Summary for Policy Makers, Fig SPM.10 D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

Representative concentration pathways (RCPs) Introduction: Mitigation target Emissions scenario Socio-economic impact Mitigation costs Energy Land use Climate system uncertainties Socio-economic uncertainties GHG concentration Representative concentration pathways (RCPs) Compatible fossil fuel emissions simulated by the CMIP5 models for the four RCP scenarios. Time series of annual emission (PgC yr–1). (Figure TS.19 in IPCC AR5, 2014) D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

GHG concentration (RCPs) Introduction: Mitigation target Emissions scenario Socio-economic impact Mitigation costs Energy Land use Climate system uncertainties Socio-economic uncertainties Climate model (MIROC-ESM) IAM (GCAM-SOUSEI) GHG concentration (RCPs) Assess the implications on global energy, land use, and mitigation costs of climate stabilization scenarios derived from the earth system model (ESM) Model for Interdisciplinary Research on Climate ESM (MIROC-ESM). These emission scenarios are consistent with stabilization of global temperatures modeled with an ESM coupling climate and carbon cycle processes in the atmosphere, land, and ocean. D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Method: Outline ESM (MIROC-ESM) IAM (GCAM-SOUSEI) Energy Land use Cost CO2 emissions Mitigation target (RCP2.6/4.5) CO2 concentration D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Method: MIROC-ESM Model for Interdisciplinary Research on Climate ESM (MIROC-ESM). Atmosphere, land and ocean components. includes biogeochemical processes in the land and the ocean. Applied to develop projections of climate change with high spatial and temporal resolutions Contributed to Coupled Model Intercomparison Project (CMIP) analyses. High climate sensitivity and strong feedback between climate and the carbon cycle. Source: Watanabe et al., 2011, Geosci Mod Dev, 4. D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

Method: GCAM-SOUSEI Direct descendant of GCAM (Global Change Assessment Model) Integrated assessment model (IAM), partial equilibrium, energy, agriculture and land use sectors Detailed description of technologies and demand/supply of energy/agriculture goods. 32 world regions, scenario analysis up to 2100, 5 year time step. GHG mitigation by means of carbon price. GCAM model Energy Agriculture and land use Climate Emissions Simplified image of GCAM model Socio-economic scenario Population GDP Policy scenarios Mitigation technologies Tax/Cap on emissions Mitigation target (temp.) Markets (prices) Input database Energy resources/ technologies Land use shares Base year demand/ supply (energy, agric.commodities) Demand = Supply Economy Inputs Outputs Land use Mitigation cost Concentration Radiative forcing Temperature (MAGICCv5.3) D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Method: Scenarios Scenario Description Reference No mitigation. RCP4.5-MIROC CO2 emissions from MIROC-ESM compatible with RCP4.5. RCP4.5-Std CO2 emissions from RCP database compatible with RCP4.5. RCP2.6-MIROC CO2 emissions from MIROC-ESM compatible with RCP2.6. RCP2.6-Std CO2 emissions from RCP database compatible with RCP2.6. Mitigation target: radiative forcing by 2100 (4.5 / 2.6 W/m2). Carbon price on CO2 emissions from fossil fuel and industry (FFI) in all regions from year 2020. Baseline socio-economic scenario (population, income) SSP2: Shared Socio-economic Pathway (SSP), intermediate challenges for mitigation and adaptation (O’Neill et al., 2014, Climatic Change). D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Method: Scenarios Source: Hajima et al., 2012, J Meteor Soc Japan, 90 (3). D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Results: Energy Energy intensity smaller, and electricity more important than liquids and solid fuels. Dependence on fossil fuels fell to half or less of total primary energy supply (TPES). Increased penetration of CCS technologies allowed fossil fuels to remain in energy supply. D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Results: Energy mix Primary energy Electricity supply D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Results: Land use RCP4.5 RCP2.6 MIROC Std Land use changes converged to the same levels in both scenarios, but occurred earlier in the stringent scenario (RCP2.6). Bioenergy crops expanded considerably over unmanaged land and natural forests, without compromising cropland for food. D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Results: Land use D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

Results: Mitigation costs The need for early emissions reductions in the stringent scenario (RCP2.6) increased mitigation costs sharply. Compared to standard scenarios, carbon prices were in average 60% and 30% larger in the intermediate (RCP4.5) and stringent (RCP2.6) stabilization scenarios, respectively. D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Summary: Stabilization scenarios indicated that society can still rely on fossil fuels to a large extent, provided that CCS and low-carbon technologies are largely deployed. Energy supply: lower energy intensity and increased energy efficiency (from higher penetration of electricity), decreasing share of fossil fuels. Land use changes: expansion of land for bioenergy over unmanaged lands and natural forests. Ambitious stabilization target required larger costs and sharper changes in energy and land use in the first half of the century. Compared to the standard RCPs, MIROC-ESM scenarios showed changes in energy and land systems more drastic, and higher mitigation costs. These differences were clear in the second half of the century for the intermediate stabilization target, and occurred earlier for the ambitious stabilization target. D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

Thank you very much!

Appendix: Structure of GCAM model Source: slide from Calvin, GCAM Community Meeting, 2012. D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Appendix Sequential approach Parallel process Source: Moss et al., 2010, Nature 463 (11). D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26

D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26 Appendix: SSPs Source: O’Neill et al., 2016, Global Env Change, In press. D. SILVA and K. TACIHIRI – USAEE 2016 – 2016.10.26