Meteorological conditions for strongly warming contrails and the statistics of contrail's instantaneous radiative forcing

Abstract

Persistent contrails and contrail cirrus are estimated to have a larger climate impact than all CO2 emissions from global aviation since the introduction of jet engines. However, the measure for this impact, the radiative forcing (RF) or effective radiative forcing (ERF) comes with much larger uncertainties than those for CO2. This study investigates one of the major causes for uncertainty, the natural variability. Specifically, the weather-induced variability is examined from a large dataset of instantaneous radiative forcing (iRF) values, produced from ten years of MOZAIC flights and ERA-5 reanalysis data. Cdfs and pdfs of the iRF dataset show strong annual and interannual variations and a seasonal pattern. 80% of the contrails have a small positive iRF of up to 20 Wm-2, 10% of all cases have an iRF ≥ 19 Wm-2, but these have a disproportionally large climate impact, and the remaining 10% have negative iRF. The distribution of iRF values declines exponentially at positive values and is heavily skewed. Monte Carlo experiments reveal the difficulty of determining a precise long-term mean from measurement campaign data. Depending on the chosen sample size, calculated means scatter considerably, which is caused exclusively by weather variability. This variability is the lower limit for uncertainty, which suggests, that there is a fundamental limit to the precision with which the RF and ERF of contrail cirrus can be determined. The accurate local prediction of persistent contrails is still not possible because of errors in the humidity field in most weather prediction models. When the meteorological and dynamical conditions of persistent contrails and Big Hits (the strongest warming contrails) are known, they could be used as an addition to the SAC quantities to improve prediction possibilities. The data showed, that Big Hits favor small negative vertical velocities, small positive divergence, anticyclonic flow, low potential vorticities up to 4 PVU, large geopotential heights, and large lapse rates up to 10 K km-1. The last four variables showed the strongest separation of the pdf´s and are best suited for improving the prediction of persistent contrails. This was tested with a logistic regression model and with model output statistics for conditional probabilities. The results showed, that, 1) predicting Big Hits is quite reliable when it is already known that contrails will be persistent and 2) high probabilities for the persistence of contrails can be determined by introducing thresholds for dynamical variables and combining them with the SAC quantities. When such thresholds could be included in a weather prediction model like ECMWF´s integrated forecast model (IFS), probabilities for contrail persistence could be produced before the flight planning period. Avoidance of persistent contrails or Big Hits would then become more reliable.