Predicting ice supersaturation for contrail avoidance: ensemble forecasting using ICON with two-moment ice microphysics

Abstract. Persistent contrails and contrail-induced cirrus clouds are considered the most significant non-CO2 contributors to aviation's climate impact. These clouds primarily form in ice-supersaturated regions (ISSRs), defined by relative humidity over ice (RHice) exceeding 100 %. Reliable prediction of RHice in the upper troposphere and lower stratosphere allows mitigating their formation by re-routing flights. We implemented a two-moment cloud ice microphysics parameterization within a ten-member Ensemble Prediction System (EPS) using the global ICON (ICOsahedral Nonhydrostatic) model. RHice predictions were evaluated against radiosonde and aircraft observations from the Northern Hemisphere during 2024–2025. Treating ISSR prediction (RHice > 100 %) as a binary classification problem, we find that the probability of detection (POD) of ISSRs increases to 0.6 for the two-moment scheme (ICON 2-Mom), compared to 0.4 for the operational ICON with a one-moment ice microphysics scheme, while maintaining a low false positive rate (FPR < 0.1). Further evaluation of the ICON 2-Mom EPS using Receiver Operating Characteristic (ROC) analysis shows a POD of 0.8 for a decision model that requires at least three ensemble members to predict ISSR, with an FPR of 0.13. Additionally, we incorporate ensemble spread information to develop a meta-model that further reduces the FPR. Since June 2024, more than 100 flights have been rerouted based on ICON 2-Mom EPS predictions in a contrail avoidance trial, demonstrating the practical value of improved ISSR forecasts for climate-conscious aviation. This study highlights the significant potential of ensemble-based modeling for predicting ISSRs and RHice, supporting environmentally optimized flight planning and advancing applications in weather and climate science.