Properties of individual contrails: a compilation of observations and some comparisons

Abstract

Mean properties of individual contrails are characterized for a wide range of jet aircraft as a function of age during their life cycle from seconds to 11.5 h (7.4-18.7 km altitude, -88 to -31 degrees C ambient temperature), based on a compilation of about 230 previous in situ and remote sensing measurements. The airborne, satellite, and ground-based observations encompass exhaust contrails from jet aircraft from 1972 onwards, as well as a few older data for propeller aircraft. The contrails are characterized by mean ice particle sizes and concentrations, extinction, ice water content, optical depth, geometrical depth, and contrail width. Integral contrail properties include the cross-section area and total number of ice particles, total ice water content, and total extinction (area integral of extinction) per contrail length. When known, the contrail-causing aircraft and ambient conditions are characterized. The individual datasets are briefly described, including a few new analyses performed for this study, and compiled together to form a "contrail library" (COLI). The data are compared with results of the Contrail Cirrus Prediction (CoCiP) model. The observations confirm that the number of ice particles in contrails is controlled by the engine exhaust and the formation process in the jet phase, with some particle losses in the wake vortex phase, followed later by weak decreases with time. Contrail cross sections grow more quickly than expected from exhaust dilution. The cross-section-integrated extinction follows an algebraic approximation. The ratio of volume to effective mean radius decreases with time. The ice water content increases with increasing temperature, similar to non-contrail cirrus, while the equivalent relative humidity over ice saturation of the contrail ice mass increases at lower temperatures in the data. Several contrails were observed in warm air above the Schmidt-Appleman threshold temperature. The "emission index" of ice particles, i.e., the number of ice particles formed in the young contrail per burnt fuel mass, is estimated from the measured concentrations for estimated dilution; maximum values exceed 10(15) kg(-1). The dependence of the data on the observation methods is discussed. We find no obvious indication for significant contributions from spurious particles resulting from shattering of ice crystals on the microphysical probes

Statistical analysis of contrail to cirrus evolution during the Contrail and Cirrus Experiment (CONCERT)

“…The clustering k-mean method (Seber, 1984;Spath, 1985) is applied to the reference dataset (flights 19b and 16b) to partition the observations into k clusters to minimize the variance within each cluster (i.e. to minimize the distance between each data point and the centre of the cluster it belongs to).…”

High Depolarization Ratios of Naturally Occurring Cirrus Clouds Near Air Traffic Regions Over Europe

“…Radiative forcing (RF) results from direct engine emissions in the upper troposphere (Lund et al, 2017) and from contrails that form under suitable conditions (Burkhardt et al, 2010). In the last years, a lot of research effort has been dedicated to measuring and understanding contrails (Heymsfield et al, 2010;Iwabuchi et al, 2012;Kärcher et al, 2015;Schumann et al, 2017;Voigt et al, 2011) and to determining the climatic effect that arises from their increasing occurrence (Burkhardt & Kärcher, 2011).…”

The challenge of simulating the sensitivity of the Amazonian clouds microstructure to cloud condensation nuclei number concentrations

“…Like the CCP-CDP, the Cloud and Aerosol Spectrometer with Depolarization (CAS-DPOL) measures cloud particle size distributions at 1 Hz frequency (Baumgardner et al, 2011;Voigt et al, 2017 ner et al, 2014;Järvinen et al, 2016), but this capability was not used for our analysis. Our calculation of the cloud particle effective radius (Schumann et al, 2017) was again limited to particles between 3 -50 µm, which corresponds to 10 Mieambiguity corrected size bins, to account for consistency with the CDP. Further details on CAS-DPOL data evaluation are given in Kleine et al (2018).…”