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- ItemCharacterization of organic aerosol across the global remote troposphere: A comparison of ATom measurements and global chemistry models(Katlenburg-Lindau : EGU, 2020) Hodzic, Alma; Campuzano-Jost, Pedro; Bian, Huisheng; Chin, Mian; Colarco, Peter R.; Day, Douglas A.; Froyd, Karl D.; Heinold, Bernd; Katich, Joseph M.; Jo, Duseong S.; Kodros, John K.; Nault, Benjamin A.; Pierce, Jeffrey R.; Ray, Eric; Schacht, Jacob; Schill, Gregory P.; Schroder, Jason C.; Schwarz, Joshua P.; Sueper, Donna T.; Tegen, Ina; Tilmes, Simone; Tsigaridis, Kostas; Yu, Pengfei; Jimenez, Jose L.The spatial distribution and properties of submicron organic aerosol (OA) are among the key sources of uncertainty in our understanding of aerosol effects on climate. Uncertainties are particularly large over remote regions of the free troposphere and Southern Ocean, where very few data have been available and where OA predictions from AeroCom Phase II global models span 2 to 3 orders of magnitude, greatly exceeding the model spread over source regions. The (nearly) pole-to-pole vertical distribution of nonrefractory aerosols was measured with an aerosol mass spectrometer onboard the NASA DC-8 aircraft as part of the Atmospheric Tomography (ATom) mission during the Northern Hemisphere summer (August 2016) and winter (February 2017). This study presents the first extensive characterization of OA mass concentrations and their level of oxidation in the remote atmosphere. OA and sulfate are the major contributors by mass to submicron aerosols in the remote troposphere, together with sea salt in the marine boundary layer. Sulfate was dominant in the lower stratosphere. OA concentrations have a strong seasonal and zonal variability, with the highest levels measured in the lower troposphere in the summer and over the regions influenced by biomass burning from Africa (up to 10 μgsm-3). Lower concentrations (~ 0:1 0.3 μgsm-3) are observed in the northern middle and high latitudes and very low concentrations (< 0:1 μgsm-3) in the southern middle and high latitudes. The ATom dataset is used to evaluate predictions of eight current global chemistry models that implement a variety of commonly used representations of OA sources and chemistry, as well as of the AeroCom-II ensemble. The current model ensemble captures the average vertical and spatial distribution of measured OA concentrations, and the spread of the individual models remains within a factor of 5. These results are significantly improved over the AeroCom-II model ensemble, which shows large overestimations over these regions. However, some of the improved agreement with observations occurs for the wrong reasons, as models have the tendency to greatly overestimate the primary OA fraction and underestimate the sec-ondary fraction. Measured OA in the remote free troposphere is highly oxygenated, with organic aerosol to organic carbon (OA= OC) ratios of ~ 2.2 2.8, and is 30 % 60% more oxygenated than in current models, which can lead to significant errors in OA concentrations. The model measurement comparisons presented here support the concept of a more dynamic OA system as proposed by Hodzic et al. (2016), with enhanced removal of primary OA and a stronger production of secondary OA in global models needed to provide better agreement with observations. © 2020 IEEE Computer Society. All rights reserved.
- ItemAn EARLINET early warning system for atmospheric aerosol aviation hazards(Katlenburg-Lindau : EGU, 2020) Papagiannopoulos, Nikolaos; D’Amico, Giuseppe; Gialitaki, Anna; Ajtai, Nicolae; Alados-Arboledas, Lucas; Amodeo, Aldo; Amiridis, Vassilis; Baars, Holger; Balis, Dimitris; Binietoglou, Ioannis; Comerón, Adolfo; Dionisi, Davide; Falconieri, Alfredo; Fréville, Patrick; Kampouri, Anna; Mattis, Ina; Mijić, Zoran; Molero, Francisco; Papayannis, Alex; Pappalardo, Gelsomina; Rodríguez-Gómez, Alejandro; Solomos, Stavros; Mona, LuciaA stand-alone lidar-based method for detecting airborne hazards for aviation in near real time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Research Lidar Network (EARLINET) delivers high-resolution preprocessed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally high resolution and thus provide the basis of the NRT early warning system (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna, Italy, in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network's preparedness to offer insight into natural hazards that affect the aviation sector. © 2020 Author(s).
- ItemRole of the dew water on the ground surface in HONO distribution: A case measurement in Melpitz(Katlenburg-Lindau : EGU, 2020) Ren, Yangang; Stieger, Bastian; Spindler, Gerald; Grosselin, Benoit; Mellouki, Abdelwahid; Tuch, Thomas; Wiedensohler, Alfred; Herrmann, HartmutTo characterize the role of dew water for the ground surface HONO distribution, nitrous acid (HONO) measurements with a Monitor for AeRosols and Gases in ambient Air (MARGA) and a LOng Path Absorption Photometer (LOPAP) instrument were performed at the Leibniz Institute for Tropospheric Research (TROPOS) research site in Melpitz, Germany, from 19 to 29 April 2018. The dew water was also collected and analyzed from 8 to 14 May 2019 using a glass sampler. The high time resolution of HONO measurements showed characteristic diurnal variations that revealed that (i) vehicle emissions are a minor source of HONO at Melpitz station; (ii) the heterogeneous conversion of NO2 to HONO on the ground surface dominates HONO production at night; (iii) there is significant nighttime loss of HONO with a sink strength of 0.16±0.12ppbv h-1; and (iv) dew water with mean NO-2 of 7.91±2.14 μgm-2 could serve as a temporary HONO source in the morning when the dew droplets evaporate. The nocturnal observations of HONO and NO2 allowed the direct evaluation of the ground uptake coefficients for these species at night: γNO2→HONO = 2.4±10-7 to 3.5±10-6, γHONO;ground = 1.7×10-5 to 2.8×10-4. A chemical model demonstrated that HONO deposition to the ground surface at night was 90 %-100% of the calculated unknown HONO source in the morning. These results suggest that dew water on the ground surface was controlling the temporal HONO distribution rather than straightforward NO2-HONO conversion. This can strongly enhance the OH reactivity throughout the morning time or in other planted areas that provide a large amount of ground surface based on the OH production rate calculation. © 2020 Copernicus GmbH. All rights reserved.
- ItemAn 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, MatthiasWe 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.