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Andreas Muhlbauer, Peter Spichtinger, and Ulrike Lohmann

Abstract

In this study, robust parametric regression methods are applied to temperature and precipitation time series in Switzerland and the trend results are compared with trends from classical least squares (LS) regression and nonparametric approaches. It is found that in individual time series statistically outlying observations are present that influence the LS trend estimate severely. In some cases, these outlying observations lead to an over-/underestimation of the trends or even to a trend masking. In comparison with the classical LS method and standard nonparametric techniques, the use of robust methods yields more reliable trend estimations and outlier detection.

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Manfred Wendisch, Paola Formenti, Tad Anderson, Alexander Kokhanovsky, Bernhard Mayer, Peter Pilewskie, Steve Platnick Jens Redemann, John Remedios, Peter Spichtinger, Didier Tanré, and Filip Vanhellemont
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Christiane Voigt, Ulrich Schumann, Andreas Minikin, Ahmed Abdelmonem, Armin Afchine, Stephan Borrmann, Maxi Boettcher, Bernhard Buchholz, Luca Bugliaro, Anja Costa, Joachim Curtius, Maximilian Dollner, Andreas Dörnbrack, Volker Dreiling, Volker Ebert, Andre Ehrlich, Andreas Fix, Linda Forster, Fabian Frank, Daniel Fütterer, Andreas Giez, Kaspar Graf, Jens-Uwe Grooß, Silke Groß, Katharina Heimerl, Bernd Heinold, Tilman Hüneke, Emma Järvinen, Tina Jurkat, Stefan Kaufmann, Mareike Kenntner, Marcus Klingebiel, Thomas Klimach, Rebecca Kohl, Martina Krämer, Trismono Candra Krisna, Anna Luebke, Bernhard Mayer, Stephan Mertes, Sergej Molleker, Andreas Petzold, Klaus Pfeilsticker, Max Port, Markus Rapp, Philipp Reutter, Christian Rolf, Diana Rose, Daniel Sauer, Andreas Schäfler, Romy Schlage, Martin Schnaiter, Johannes Schneider, Nicole Spelten, Peter Spichtinger, Paul Stock, Adrian Walser, Ralf Weigel, Bernadett Weinzierl, Manfred Wendisch, Frank Werner, Heini Wernli, Martin Wirth, Andreas Zahn, Helmut Ziereis, and Martin Zöger

Abstract

The Midlatitude Cirrus experiment (ML-CIRRUS) deployed the High Altitude and Long Range Research Aircraft (HALO) to obtain new insights into nucleation, life cycle, and climate impact of natural cirrus and aircraft-induced contrail cirrus. Direct observations of cirrus properties and their variability are still incomplete, currently limiting our understanding of the clouds’ impact on climate. Also, dynamical effects on clouds and feedbacks are not adequately represented in today’s weather prediction models.

Here, we present the rationale, objectives, and selected scientific highlights of ML-CIRRUS using the G-550 aircraft of the German atmospheric science community. The first combined in situ–remote sensing cloud mission with HALO united state-of-the-art cloud probes, a lidar and novel ice residual, aerosol, trace gas, and radiation instrumentation. The aircraft observations were accompanied by remote sensing from satellite and ground and by numerical simulations.

In spring 2014, HALO performed 16 flights above Europe with a focus on anthropogenic contrail cirrus and midlatitude cirrus induced by frontal systems including warm conveyor belts and other dynamical regimes (jet streams, mountain waves, and convection). Highlights from ML-CIRRUS include 1) new observations of microphysical and radiative cirrus properties and their variability in meteorological regimes typical for midlatitudes, 2) insights into occurrence of in situ–formed and lifted liquid-origin cirrus, 3) validation of cloud forecasts and satellite products, 4) assessment of contrail predictability, and 5) direct observations of contrail cirrus and their distinction from natural cirrus. Hence, ML-CIRRUS provides a comprehensive dataset on cirrus in the densely populated European midlatitudes with the scope to enhance our understanding of cirrus clouds and their role for climate and weather.

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