Comparison of contrail predictions with observations

• Main Authors: Schumann, Ulrich, Graf, Kaspar, Bugliaro, Luca, Wirth, Martin, Ziereis, Helmut, Giez, Andreas, Jurkat, Tina, Kaufmann, Stefan, Schlage, Romy, Zahn, Andreas, Dörnbrack, Andreas, Krämer, Martina, Minikin, Andreas, Voigt, Christiane
• Format: Conference Object
• Language: unknown
• Published: 2015
• Subjects: Fernerkundung der Atmosphäre; Flugversuchstechnik OP; North Atlantic
• Online Access: https://elib.dlr.de/97373/; http://www.pa.op.dlr.de/tac/2015/restricted/index.html

This paper presents examples of comparison of modelled contrail cirrus properties with observed cirrus and contrail properties as obtained during the Mid-Latitude Cirrus Experiment (ML-CIRRUS) in March/April 2014. ML-CIRRUS was performed by more than 14 partner institutions (universities and research institutes), mainly from Germany, under coordination by DLR (see Voigt et al., EGU, 2015). During the ML-CIRRUS mission, 13 scientific mission flights have been performed with the High Altitude and Long Range research aircraft HALO at altitudes up to 13.8 km between 26 March and 15 April 2014 over Europe and the eastern part of the North Atlantic, often in high density air traffic regions. HALO was instrumented with about 3 tons of in-situ instruments for meteorology, gases, aerosols and cloud properties, with a high spectral resolution lidar and with other remote sensing instruments. Most of the measurement data are now available in the HALO data bank, though some still preliminary. A subset of the results has been presented at the EGU in Vienna in April 2015 (e.g., presentation by Christiane Voigt and the whole ML-CIRRUS team). The model results are obtained from simulations with the Contrail Cirrus Prediction tool (CoCiP) which has been developed to simulate and predict the properties of a large ensemble of contrails as a function of given air traffic and meteorology (Schumann, GMD, 2012). This paper will show that contrails cirrus is, to some degree, predictable on time scales of several days. A metric to measure the accuracy of the predictions is difficult to specify and will depend on the information that is needed from such predictions. Hence, the degree of agreement or disagreement between models and observations is hard to specify quantitatively. The main prerequisites for accurate predictions are accurate meteorological forecast fields (relative humidity, horizontal and vertical wind, temperature, cirrus ice water content, and top-of-the-atmosphere shortwave and longwave radiances), and accurate and ...