Correction of ERA5 temperature and relative humidity biases by bivariate quantile mapping for contrail formation analysis

Abstract. Aviation contributes to global emissions of carbon dioxide, aerosol particles, water vapor (WV), and other compounds. WV promotes the formation of condensation trails (contrails), which are known for their net warming effect on the climate. Contrail formation is often estimated using the Schmidt–Appleman criterion (SAc) together with meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric reanalysis model. We compare ERA5 output of temperature and relative humidity in the upper troposphere and lower stratosphere with 5 years of In-service Aircraft for a Global Observing System (IAGOS) observations over the North Atlantic. Good agreement was found for the temperature fields, with a maximum bias of −0.4 K (200 hPa level), while larger biases were found for relative humidity of up to −5.5 % (250 hPa level). Using original ERA5 data, conditions prone to contrail formation occurred 50.3 % and 7.9 % of the time for non-persistent and persistent contrails, respectively, while 44.0 % and 12.1 % were flagged in the IAGOS data. We propose a multivariate quantile mapping (QM) correction to remove systematic biases by post-processing ERA5 temperature and relative humidity fields with respect to contrail formation. The QM correction was applied to post-process ERA5 data, reducing the temperature bias to less than 0.1 K and the relative humidity bias to less than −1.5 %, resulting in 44 % and 10.9 % of the data points now being flagged for non-persistent and persistent contrail formation, respectively. Our bias correction generalizes well compared to the IAGOS observations. How it generalizes outside the IAGOS regions remains to be investigated.