While carbon dioxide emissions from aviation often dominate climate change discussions, non-CO2 effects such as contrails and contrail cirrus must also be considered. Despite varying estimates of their radiative forcing, avoiding contrails is a reasonable strategy for
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While carbon dioxide emissions from aviation often dominate climate change discussions, non-CO2 effects such as contrails and contrail cirrus must also be considered. Despite varying estimates of their radiative forcing, avoiding contrails is a reasonable strategy for reducing aviation’s climate effects. This study examines temperature and humidity, key atmospheric parameters for contrail formation, across different ECHAM/MESSy (European Centre Hamburg General Circulation Model/Modular Earth Submodel System) Atmospheric Chemistry (EMAC) model setups. EMAC, a general circulation model, is evaluated with various vertical resolutions and two nudging methods across seven specified dynamics setups. A higher vertical resolution aims to capture steep water vapour gradients near the tropopause, crucial for accurate contrail prediction. Comparisons with reanalysis data (March–April 2014) indicate a systematic cold bias (approximately 3–5 K in mid-latitudes), particularly in setups without mean temperature nudging. In the upper troposphere and lower stratosphere, all simulations exhibit a wet bias, while lower altitudes display a dry bias, both affecting contrail formation estimates. Point-by-point comparisons along aircraft trajectories confirm similar biases. Sensitivity experiments with varying thresholds of relative humidity over ice illustrate trade-offs between achieving high hit rates and minimising false alarms in contrail detection. A single-day case study integrating aircraft and satellite observations demonstrates that EMAC’s predicted contrail coverage aligns well with the observed formation. These results suggest that, despite existing temperature and humidity biases, EMAC generally captures regions favourable for contrail formation across diverse atmospheric conditions. Addressing model biases by refining temperature and humidity representation could significantly improve contrail prediction accuracy, strengthening contrail-avoidance strategies and supporting climate-optimised flight routing to mitigate aviation’s overall climate effect.