Weather Variability Induced Uncertainty of Contrail Radiative Forcing

Abstract

Persistent contrails and contrail cirrus are estimated to have a larger impact on climate than all CO2 emissions from global aviation since the introduction of jet engines. However, the measure for this impact, the effective radiative forcing (ERF) or radiative forcing (RF), suffers from uncertainties that are much larger than those for CO2. Despite ongoing research, the so called level of scientific understanding has not improved since the 1999 IPCC Special Report on Aviation and the Global Atmosphere. In this paper, the role of weather variability as a major component of the uncertainty range of contrail cirrus RF is examined. Using 10 years of MOZAIC flights and ERA-5 reanalysis data, we show that natural weather variability causes large variations in the instantaneous radiative forcing (iRF) of persistent contrails, which is a major source for uncertainty. Most contrails (about 80%) have a small positive iRF of up to 20 W m−2. IRF exceeds 20 W m−2 in about 10% of all cases but these have a disproportionally large climate impact, the remaining 10% have a negative iRF. The distribution of iRF values is heavily skewed towards large positive values that show an exponential decay. Monte Carlo experiments reveal the difficulty of determining a precise long-term mean from measurement or campaign data alone. Depending on the chosen sample size, calculated means scatter considerably, which is caused exclusively by weather variability. Considering that many additional natural sources of variation have been deliberately neglected in the present examination, the results suggest that there is a fundamental limit to the precision with which the RF and ERF of contrail cirrus can be determined. In our opinion, this does not imply a low level of scientific understanding; rather the scientific understanding of contrails and contrail cirrus has grown considerably over recent decades. Only the determination of global and annual mean RF and ERF values is still difficult and will probably be so for the coming decades, if not forever. The little precise knowledge of the RF and ERF values is, therefore, no argument to postpone actions to mitigate contrail’s warming impact.

Aviation contrail climate effects in the North Atlantic from 2016 to 2021

“…Between 2016 and 2019, we found the following: (i) the growth in total fuel consumption and CO 2 emissions (+3.13 % yr −1 ) outpaced the total flight distance (+3.05 % yr −1 ) due to the use of larger aircraft types (mean aircraft mass, +1.3 % yr −1 ) (Fig. S6); (ii) total NO x emissions grew by +4.5 % yr −1 , which is likely attributable to the higher combustion pressure and temperature in more fuel-efficient engines (Kyprianidis and Dahlquist, 2017; Freeman et al, 2018); (iii) the mean nvPM EI n increased by +0.5 % yr −1 ; and (iv) the contrail cirrus net RF showed significant inter-annual variability (up to ±19 % relative to the mean, ranging between 204 and 280 mW m −2 ), consistent with Wilhelm et al (2021), indicating a stronger dependence on meteorology than the total flight distance (Fig. S12).…”

Feasibility of contrail avoidance in a commercial flight planning system: an operational analysis

“…When the model is evaluated using forecast meteorology, the output can be interpreted as a forecast of expected contrail climate forcing for a specific aircraft flying nominal cruise conditions through a grid cell. While output accuracy is currently limited by known challenges in forecasting upper tropospheric humidity [25,26,28], this output effectively emulates the size and distribution of contrail forecast data for the purposes of quantifying cost and avoidance feasibility.…”

“…While further studies are required to assess the accuracy and efficacy of contrail forecast models [26][27][28], this study seeks to understand the costs and feasibility of contrail avoidance from an operations perspective, assuming accurate avoidance regions are available. This paper utilizes a prototypical contrail forecast model based on the CoCiP model publicly available through the Contrails API [29,30].…”

Comparing Two Contrail Models Under Certain and Uncertain Inputs

“…Computational models have collated legacy and new understanding of contrails, allowing the simulation of their lifecycle. Error bars given in IPCC charts are comprised of natural variability, model inadequacy, and scenario uncertainty [5]. Considering weather variability alone (a subset of natural variability), Monte Carlo runs conducted on a simple contrail model in [5] show large scatter of the results.…”

“…Error bars given in IPCC charts are comprised of natural variability, model inadequacy, and scenario uncertainty [5]. Considering weather variability alone (a subset of natural variability), Monte Carlo runs conducted on a simple contrail model in [5] show large scatter of the results. It is concluded that uncertainty levels are unlikely to ever reach values considered acceptable [5].…”