[SLS] Emmie Le Roy (EAPS)

Note special time this week!

“A surrogate modeling framework for inferring internal variability and forced change in surface ozone”

Initial-condition ensembles of chemistry-climate models are useful tools for separating anthropogenic signals in atmospheric composition from the noise of internally generated climate variability. The noise generated by these ensembles can also be leveraged for risk assessment by quantifying the likelihood of extreme pollution outcomes under a fixed emissions scenario. However, the high computational cost of chemistry-climate models, especially when they include fully interactive chemistry schemes, limits their use for large-ensemble experiments. Here, we present a simple surrogate modeling framework for generating synthetic realizations (ensemble members) of surface ozone projections at a fraction of the cost of fully interactive chemistry-climate simulations. We first train a regression-based surrogate on a single interactive-chemistry realization to map meteorological variables to monthly ozone, and show that, when driven by meteorology from other realizations, the surrogate can reproduce the statistics of the initial-condition chemistry-climate model ensemble (CESM2-WACCM6). We then demonstrate its transferability to a non-interactive climate model configuration (CESM2), enabling the offline generation of large synthetic ozone ensembles for uncertainty quantification and risk assessment applications.