Accounting for Aerosols in Climate Mitigation Pathways

To meet Paris-aligned climate goals and minimize temperature overshoot and its impacts, rapid and deep reductions in greenhouse-gas emissions from fossil-fuel combustion are required. Climate risk projections are strongly affected by uncertainty in anthropogenic aerosol effective radiative forcing (ERF) and by the co-evolution of air-pollutant emissions under decarbonization pathways. Because running large Earth System Model (ESM) ensembles remains computationally expensive for uncertainty quantification and broad policy-scenario exploration, reduced-complexity climate emulators are needed for efficient, transparent, and observation-connected assessments.

Here we develop an aerosol extension to the simple climate model (SCM) FaIR that emulates aerosol ERF from global anomalies in aerosol optical depth (ΔAOD) relative to a pre-industrial baseline for different species. Aerosol ERF is computed using a constrained parameterization that separates aerosol–radiation and aerosol–cloud interactions, with key parameters represented probabilistically and constrained by observational and model-based lines of evidence.

To emulate ΔAOD from emissions pathways, we implement an interpretable mapping calibrated to CMIP6 ESM output. An effective linear relationship between emission and burden anomalies is fitted using a single parameter that aggregates yield and lifetime effects. In a second step, we fit an effective optical parameter linking burden perturbations to ΔAOD. This produces model-dependent parameter distributions that enable propagation of both parametric uncertainty and between-model spread. In addition, we implement an integrated-assessment-model-based relationship linking air-pollutant emissions to CO₂ emissions under different air-quality policy stringencies, interpolated into a continuous air-quality parameter suitable for exploring uncertainty and its interaction with decarbonization trajectories.

We perform Monte Carlo ensembles sampling aerosol-ERF parameters, CMIP6-calibrated aerosol–AOD mappings, air-quality policy stringency, and net-zero timing, and evaluate impact-relevant climate risk metrics including peak warming, probability of remaining below 1.5 °C, threshold crossing year, overshoot duration, and warming rates computed over multiple near-term and decadal windows. Preliminary results show strong dependence of peak temperature outcomes on net-zero timing, while threshold-based metrics and warming rates exhibit pronounced sensitivity to air-quality assumptions, consistent with a partial loss of aerosol cooling under stricter pollution controls. Overall, the results indicate non-linear interactions between decarbonization timing, air-quality stringency, and warming-rate responses. The emulator provides a scalable basis for robust climate risk screening and for coupling SCM trajectories to impact assessments.

Keywords: Climate Change, Mitigation, Aerosols, Effective Radiative Forcing, Climate Emulators, Climate Modeling, CMIP6 Calibration, Air-quality Policy, Overshoot