The effect of uncertainty in humidity and model parameters on the prediction of contrail energy forcing

Previous work has shown that while the net effect of aircraft condensation trails (contrails) on the
climate is warming, the exact magnitude of the energy forcing per meter of contrail remains uncertain.
In this paper, we explore the skill of a Lagrangian contrail model (CoCiP) in identifying flight
segments with high contrail energy forcing. We find that skill is greater than climatological
predictions alone, even accounting for uncertainty in weather fields and model parameters.

We estimate the uncertainty in weather by using the ensemble ERA5 weather reanalysis from the European
Centre for Medium-Range Weather Forecasts (ECMWF) as Monte Carlo inputs to CoCiP. We unbias and correct
under-dispersion on the ERA5 humidity data by forcing a match to the distribution of in situ humidity
measurements taken at cruising altitude. We set aside CoCiP energy forcing estimates calculated using
one of the ensemble members as a proxy for ground truth, and report the skill of CoCiP in identifying
segments with large positive proxy energy forcing. We further estimate the uncertainty in the model
parameters in CoCiP by performing Monte Carlo simulations with CoCiP model parameters drawn from
uncertainty distributions consistent with the literature.

When CoCiP outputs are averaged over seasons to form climatological predictions, the skill in
predicting the proxy is 44%, while the skill of per-flight CoCiP outputs is 84%. If these results carry
over to the true (unknown) contrail EF, they indicate that per-flight energy forcing predictions can
reduce the number of potential contrail avoidance route adjustments by 2x, hence reducing both the cost
and fuel impact of contrail avoidance.