{"confidence_intervals":[{"benchmark_p50":2.7,"benchmark_source":"IPCC AR6 WG1","output":"Global mean temperature 2050 (\u00b0C)","p25":2.31,"p5":1.92,"p50":2.87,"p75":3.41,"p95":4.23,"scenario":"SSP2-4.5","units":"\u00b0C above pre-industrial","within_benchmark_range":true},{"benchmark_p50":-2.9,"benchmark_source":"DICE-2023","output":"GDP impact 2050 (% of GDP)","p25":-4.6,"p5":-6.4,"p50":-3.2,"p75":-1.8,"p95":-0.3,"scenario":"SSP2-4.5","units":"% GDP","within_benchmark_range":true},{"benchmark_p50":4500,"benchmark_source":"Swiss Re Institute, 2023","output":"Physical damage cost 2050 (USD bn)","p25":3240,"p5":1820,"p50":4820,"p75":6590,"p95":8910,"scenario":"SSP2-4.5","units":"USD billion (2020 prices)","within_benchmark_range":true},{"benchmark_p50":250,"benchmark_source":"IPCC AR6 WG3 Table SPM.2","output":"Remaining carbon budget (GtCO\u2082)","p25":178,"p5":102,"p50":245,"p75":318,"p95":412,"scenario":"1.5\u00b0C pathway","units":"GtCO\u2082 from 2023","within_benchmark_range":true},{"benchmark_p50":2.5,"benchmark_source":"NGFS Phase IV / IEA NZE","output":"Mitigation cost (% GDP)","p25":1.4,"p5":0.8,"p50":2.2,"p75":3.1,"p95":4.8,"scenario":"1.5\u00b0C pathway","units":"% GDP annual","within_benchmark_range":true}],"decomposition":[{"aleatory_pct":18,"dominant_source":"ECS range","epistemic_pct":62,"model":"ClimateModelService","model_id":"MDL-001","reducibility":"Partially reducible with better observational constraints","structural_pct":20},{"aleatory_pct":12,"dominant_source":"Discount rate choice","epistemic_pct":55,"model":"EconomicModelService","model_id":"MDL-002","reducibility":"Structural uncertainty irreducible (normative choice)","structural_pct":33},{"aleatory_pct":15,"dominant_source":"Damage function form","epistemic_pct":40,"model":"DamageModelService","model_id":"MDL-003","reducibility":"Structural uncertainty dominant; limited by data scarcity at high warming","structural_pct":45},{"aleatory_pct":25,"dominant_source":"Input parameter distributions","epistemic_pct":70,"model":"MonteCarloEngine","model_id":"MDL-005","reducibility":"Highly reducible with tighter parameter constraints","structural_pct":5},{"aleatory_pct":10,"dominant_source":"Double-counting methodology","epistemic_pct":65,"model":"GapAccountingEngine","model_id":"MDL-009","reducibility":"Partially reducible with improved NDC tracking data","structural_pct":25},{"aleatory_pct":20,"dominant_source":"Revenue-expenditure coupling assumptions","epistemic_pct":50,"model":"FiscalModelService","model_id":"MDL-004","reducibility":"Reducible with country-specific fiscal data integration","structural_pct":30}],"fan_chart":{"scenarios":{"SSP1-1.9":{"p25":[1.2,1.26,1.31,1.34,1.36,1.38,1.4,1.39,1.38,1.37,1.36],"p5":[1.15,1.18,1.2,1.21,1.22,1.23,1.24,1.23,1.22,1.21,1.2],"p50":[1.25,1.34,1.42,1.47,1.5,1.53,1.57,1.55,1.53,1.51,1.5],"p75":[1.32,1.44,1.54,1.62,1.67,1.71,1.76,1.74,1.72,1.7,1.68],"p95":[1.41,1.57,1.7,1.81,1.88,1.94,2.01,1.98,1.95,1.92,1.9]},"SSP2-4.5":{"p25":[1.22,1.31,1.44,1.61,1.79,1.96,2.31,2.63,2.91,3.14,3.32],"p5":[1.16,1.21,1.29,1.4,1.52,1.62,1.85,2.05,2.22,2.36,2.47],"p50":[1.26,1.38,1.55,1.75,1.97,2.17,2.6,2.99,3.33,3.61,3.84],"p75":[1.31,1.46,1.67,1.92,2.18,2.43,2.96,3.45,3.87,4.22,4.5],"p95":[1.39,1.58,1.84,2.14,2.47,2.77,3.46,4.09,4.65,5.12,5.5]},"SSP5-8.5":{"p25":[1.23,1.36,1.57,1.85,2.19,2.56,3.42,4.34,5.25,6.09,6.85],"p5":[1.17,1.25,1.4,1.61,1.86,2.14,2.76,3.44,4.14,4.79,5.37],"p50":[1.27,1.43,1.67,1.99,2.37,2.8,3.8,4.88,5.94,6.93,7.82],"p75":[1.32,1.51,1.79,2.15,2.59,3.09,4.26,5.53,6.79,7.96,9.02],"p95":[1.4,1.63,1.96,2.38,2.9,3.5,4.92,6.47,8.03,9.5,10.8]}},"years":[2025,2030,2035,2040,2045,2050,2060,2070,2080,2090,2100]},"last_mc_run":"2026-02-10","limitations":["Parameter correlations not modeled (treated as independent draws)","Structural uncertainty quantified by expert elicitation only","Tipping point cascades not included in standard MC runs","Fan charts reflect parameter uncertainty only, not model structural uncertainty"],"mc_summary":{"actual_convergence_pct":1.4,"convergence_achieved":true,"convergence_at_n":8200,"convergence_threshold_pct":2.0,"distribution_fits":{"carbon_budget_remaining_gt":{"distribution":"normal","mean":245,"p5":102,"p95":412,"std":88},"damage_cost_bn":{"distribution":"lognormal","mean":4820,"p5":1820,"p95":8910,"std":1940},"gdp_impact_pct":{"distribution":"normal","mean":-3.2,"p5":-6.4,"p95":-0.3,"std":1.8},"temperature_2050":{"distribution":"lognormal","mean":2.87,"p5":1.92,"p95":4.23,"std":0.61}},"engine":"MonteCarloEngine","last_run":"2026-02-10","n_simulations":10000,"outputs_tracked":["temperature_2050","gdp_impact_pct","damage_cost_bn","carbon_budget_remaining_gt"],"parameters_varied":12,"random_seed":42,"run_time_seconds":84},"methodology":"Monte Carlo sampling with N=10,000 draws per parameter. Parameters sampled independently from specified distributions (lognormal/normal/uniform). Sensitivity rankings derived from Sobol total-effect indices. Uncertainty decomposition estimated via variance-based global sensitivity analysis. Fan charts generated from ensemble output percentiles.","platform_version":"3.7.0","sensitivity_rankings":[{"distribution":"lognormal","impact_pct_high":28.4,"impact_pct_low":-18.2,"model":"ClimateModelService","notes":"Single largest uncertainty in temperature projection. IPCC AR6 likely range 2.5\u20134.0\u00b0C.","parameter":"Equilibrium Climate Sensitivity (ECS)","parameter_id":"P-CLM-001","rank":1,"uncertainty_type":"epistemic"},{"distribution":"uniform","impact_pct_high":31.7,"impact_pct_low":-24.1,"model":"DamageModelService","notes":"Highly contested. DICE uses \u03b1=2; empirical estimates range 1.5\u20134.","parameter":"Damage function exponent (\u03b1)","parameter_id":"P-DAM-001","rank":2,"uncertainty_type":"structural"},{"distribution":"uniform","impact_pct_high":29.5,"impact_pct_low":-32.0,"model":"EconomicModelService","notes":"Ethically contested. 1.4% (Stern) vs 5%+ (Nordhaus) implies 10\u00d7 NPV difference.","parameter":"Social discount rate (\u03c1)","parameter_id":"P-ECO-002","rank":3,"uncertainty_type":"epistemic"},{"distribution":"convergence","impact_pct_high":2.3,"impact_pct_low":-2.1,"model":"MonteCarloEngine","notes":"N=10,000 achieves <2% convergence error on GDP impact distributions.","parameter":"Monte Carlo sample size (N)","parameter_id":"P-UNC-001","rank":4,"uncertainty_type":"aleatory"},{"distribution":"normal","impact_pct_high":12.1,"impact_pct_low":-8.4,"model":"ClimateModelService","notes":"Land carbon uptake fraction. CMIP6 model spread: 0.28\u20130.52.","parameter":"Carbon cycle feedback \u03b2","parameter_id":"P-CLM-002","rank":5,"uncertainty_type":"epistemic"},{"distribution":"normal","impact_pct_high":9.8,"impact_pct_low":-11.3,"model":"EconomicModelService","notes":"Baseline SSP scenario dependent. Range 1.5\u20133.5% annual for advanced economies.","parameter":"GDP growth baseline (g\u2080)","parameter_id":"P-ECO-001","rank":6,"uncertainty_type":"epistemic"},{"distribution":"uniform","impact_pct_high":7.9,"impact_pct_low":-6.2,"model":"GapAccountingEngine","notes":"Expert judgment. NDC overlap estimation poorly constrained.","parameter":"Double-counting deduction factor (\u03b4)","parameter_id":"P-UNC-002","rank":7,"uncertainty_type":"epistemic"},{"distribution":"beta","impact_pct_high":6.4,"impact_pct_low":-5.8,"model":"DamageModelService","notes":"Fraction of damage that persists after adaptation. Expert range 0.4\u20130.85.","parameter":"Adaptation residual factor (\u03c6)","parameter_id":"P-DAM-002","rank":8,"uncertainty_type":"epistemic"},{"distribution":"lognormal","impact_pct_high":9.2,"impact_pct_low":-3.1,"model":"ClimateModelService","notes":"Highly asymmetric. Tipping points could amplify by 3\u00d7. Currently underweighted.","parameter":"Permafrost CO\u2082 feedback \u03b3","parameter_id":"P-CLM-003","rank":9,"uncertainty_type":"epistemic"},{"distribution":"normal","impact_pct_high":5.7,"impact_pct_low":-4.2,"model":"FinancialStressService","notes":"Limited empirical basis. Based on GFANZ consultation, 2024.","parameter":"Climate WACC premium (\u0394r)","parameter_id":"P-FIN-001","rank":10,"uncertainty_type":"epistemic"},{"distribution":"normal","impact_pct_high":4.6,"impact_pct_low":-3.8,"model":"AdaptationService","notes":"% GDP for infrastructure adaptation. UNEP range 0.1\u20132.0% GDP.","parameter":"Adaptation cost factor (\u03ba_adapt)","parameter_id":"P-ADP-001","rank":11,"uncertainty_type":"epistemic"},{"distribution":"normal","impact_pct_high":3.1,"impact_pct_low":-2.9,"model":"EconomicModelService","notes":"Standard DSGE value 1.5. Climate models use 1.0\u20132.0.","parameter":"Intertemporal elasticity \u03c8","parameter_id":"P-ECO-003","rank":12,"uncertainty_type":"epistemic"}],"tornado_data":[{"high":29.5,"label":"Discount rate \u03c1","low":-32.0,"param_id":"P-ECO-002"},{"high":31.7,"label":"Damage exponent \u03b1","low":-24.1,"param_id":"P-DAM-001"},{"high":28.4,"label":"ECS","low":-18.2,"param_id":"P-CLM-001"},{"high":9.8,"label":"GDP baseline g\u2080","low":-11.3,"param_id":"P-ECO-001"},{"high":12.1,"label":"Carbon feedback \u03b2","low":-8.4,"param_id":"P-CLM-002"},{"high":7.9,"label":"Double-count \u03b4","low":-6.2,"param_id":"P-UNC-002"},{"high":6.4,"label":"Adaptation residual \u03c6","low":-5.8,"param_id":"P-DAM-002"},{"high":5.7,"label":"WACC premium \u0394r","low":-4.2,"param_id":"P-FIN-001"},{"high":4.6,"label":"Adapt cost \u03ba","low":-3.8,"param_id":"P-ADP-001"},{"high":9.2,"label":"Permafrost \u03b3","low":-3.1,"param_id":"P-CLM-003"}]}