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search.filters.applied.f.access: openAccess × Has files: Yes × Publisher: Katlenburg-Lindau : EGU × Subject: Europe × Subject: aerosol composition × Subject: aerosol ×

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    Regional effects of atmospheric aerosols on temperature: An evaluation of an ensemble of online coupled models
    (Katlenburg-Lindau : EGU, 2017) Baró, Rocío; Palacios-Peña, Laura; Baklanov, Alexander; Balzarini, Alessandra; Brunner, Dominik; Forkel, Renate; Hirtl, Marcus; Honzak, Luka; Pérez, Juan Luis; Pirovano, Guido; San José, Roberto; Schröder, Wolfram; Werhahn, Johannes; Wolke, Ralf; Žabkar, Rahela; Jiménez-Guerrero, Pedro
    The climate effect of atmospheric aerosols is associated with their influence on the radiative budget of the Earth due to the direct aerosol-radiation interactions (ARIs) and indirect effects, resulting from aerosol-cloud-radiation interactions (ACIs). Online coupled meteorology-chemistry models permit the description of these effects on the basis of simulated atmospheric aerosol concentrations, although there is still some uncertainty associated with the use of these models. Thus, the objective of this work is to assess whether the inclusion of atmospheric aerosol radiative feedbacks of an ensemble of online coupled models improves the simulation results for maximum, mean and minimum temperature at 2m over Europe. The evaluated models outputs originate from EuMetChem COST Action ES1004 simulations for Europe, differing in the inclusion (or omission) of ARI and ACI in the various models. The cases studies cover two important atmospheric aerosol episodes over Europe in the year 2010: (i) a heat wave event and a forest fire episode (July-August 2010) and (ii) a more humid episode including a Saharan desert dust outbreak in October 2010. The simulation results are evaluated against observational data from the E-OBS gridded database. The results indicate that, although there is only a slight improvement in the bias of the simulation results when including the radiative feedbacks, the spatiotemporal variability and correlation coefficients are improved for the cases under study when atmospheric aerosol radiative effects are included.
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    An automatic observation-based aerosol typing method for EARLINET
    (Katlenburg-Lindau : EGU, 2018) Papagiannopoulos, Nikolaos; Mona, Lucia; Amodeo, Aldo; D'Amico, Giuseppe; Gumà Claramunt, Pilar; Pappalardo, Gelsomina; Alados-Arboledas, Lucas; Guerrero-Rascado, Juan Luís; Amiridis, Vassilis; Kokkalis, Panagiotis; Apituley, Arnoud; Baars, Holger; Schwarz, Anja; Wandinger, Ulla; Binietoglou, Ioannis; Nicolae, Doina; Bortoli, Daniele; Comerón, Adolfo; Rodríguez-Gómez, Alejandro; Sicard, Michaël; Papayannis, Alex; Wiegner, Matthias
    We present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with literature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59% (minimum) and 90% (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80%. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.