Model Comparison › Monte Carlo Uncertainty

Probabilistic Uncertainty Quantification (Monte Carlo)

Full stochastic N=2,000-sample simulation over 8 key model parameters. Produces calibrated probability distributions (p10/p25/p50/p75/p90) for cumulative emissions gap, warming probability, and carbon budget exhaustion. Tornado chart quantifies parameter-level sensitivity.

SEED: SAMPLES:

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P(stay ≤ 1.5°C)

Fraction of MC runs within budget

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Peak Warming (p50)

°C above pre-industrial by 2050

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Warming Range p10–p90

Likely range °C

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Cumulative Emissions (p50)

GtCO2e 2025–2050

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Budget Gap (p50)

GtCO2 vs 1.5°C (67%) budget

Emissions Fan Chart — Probability Bands (2025–2050)

Peak Warming Distribution

Cumulative Emissions Distribution

Tornado Chart — Parameter Sensitivity Ranking

Run simulation to see tornado chart.

Parameter Distribution Summary

Parameter	Distribution	Source	Sensitivity Rank

Sources: IPCC AR6 WG1 (carbon budgets, TCRE) · IPCC AR6 WG3 (technology abatement, CDR ranges) · UNEP EGR 2024 (baseline emissions) · IEA NZE 2023 (policy effectiveness) · NGFS Phase IV (scenario weighting) · FAO SOFA 2023 (land-use emissions) · IEA Methane Tracker 2023 (CH₄ abatement potential). Monte Carlo methodology: stratified random sampling, N=2,000 per run, 8 parameter dimensions.