Addressing climate change uncertainty with a monte carloversion of TIMES

The document summarizes research incorporating Monte Carlo analysis into the TIMES integrated assessment model to better represent uncertainty. Researchers applied 1,000 simulations each of baseline, 2°C, and 1.5°C scenarios while varying 18 uncertain input parameters. This provided probabilistic outputs rather than single deterministic results. Key findings included statistical distributions of temperature change, primary energy supply, and emissions across the scenarios accounting for uncertain inputs like climate sensitivity and discount rates. The approach allows evaluation of policy resilience under uncertainty.