Browsing by Author "Richter, Andreas"

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    Intercomparison of NO2, O4, O3 and HCHO slant column measurements by MAX-DOAS and zenith-sky UV–visible spectrometers during CINDI-2
    Kreher, Karin; Van Roozendael, Michel; Hendrick, Francois; Apituley, Arnoud; Dimitropoulou, Ermioni; Friess, Udo; Richter, Andreas; Wagner, Thomas; Lampel, Johannes; Abuhassan, Nader; Ang, Li; Anguas, Monica; Bais, Alkis; Benavent, Nuria; Boesch, Tim; Bognar, Kristof; Borovski, Alexander; Bruchkouski, Ilya; Cede, Alexander; Chan, Ka Lok; Donner, Sebastian; Drosoglou, Theano; Fayt, Caroline; Finkenzeller, Henning; Garcia-Nieto, David; Gielen, Clio; Gomez-Martin, Laura; Hao, Nan; Henzing, Bas; Herman, Jay R.; Hermans, Christian; Hoque, Syedul; Irie, Hitoshi; Jin, Junli; Johnston, Paul; Butt, Junaid Khayyam; Khokhar, Fahim; Koenig, Theodore K.; Kuhn, Jonas; Kumar, Vinod; Liu, Cheng; Ma, Jianzhong; Merlaud, Alexis; Mishra, Abhishek K.; Mueller, Moritz; Navarro-Comas, Monica; Ostendorf, Mareike; Pazmino, Andrea; Peters, Enno; Pinardi, Gaia; Pinharanda, Manuel; Piters, Ankie; Platt, Ulrich; Postylyakov, Oleg; Prados-Roman, Cristina; Puentedura, Olga; Querel, Richard; Saiz-Lopez, Alfonso; Schoenhardt, Anja; Schreier, Stefan F.; Seyler, Andre; Sinha, Vinayak; Spinei, Elena; Strong, Kimberly; Tack, Frederik; Tian, Xin; Tiefengraber, Martin; Tirpitz, Jan-Lukas; van Gent, Jeron; Volkamer, Rainer; Vrekoussis, Mihalis; Wang, Shanshan; Wang, Zhuoru; Wenig, Mark; Wittrock, Folkard; Xie, Pinhua H.; Xu, Jin; Yela, Margarita; Zhang, Chengxin; Zhao, Xiaoyi (2020-05-06)
    In September 2016, 36 spectrometers from 24 institutes measured a number of key atmospheric pollutants for a period of 17 d during the Second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) that took place at Cabauw, the Netherlands (51.97 degrees N, 4.93 degrees E). We report on the outcome of the formal semi-blind intercomparison exercise, which was held under the umbrella of the Network for the Detection of Atmospheric Composition Change (NDACC) and the European Space Agency (ESA). The three major goals of CINDI-2 were (1) to characterise and better understand the differences between a large number of multi-axis differential optical absorption spectroscopy (MAX-DOAS) and zenith-sky DOAS instruments and analysis methods, (2) to define a robust methodology for performance assessment of all participating instruments, and (3) to contribute to a harmonisation of the measurement settings and retrieval methods. This, in turn, creates the capability to produce consistent high-quality ground-based data sets, which are an essential requirement to generate reliable long-term measurement time series suitable for trend analysis and satellite data validation. The data products investigated during the semi-blind intercomparison are slant columns of nitrogen dioxide (NO2), the oxygen collision complex (O-4) and ozone (O-3) measured in the UV and visible wavelength region, formaldehyde (HCHO) in the UV spectral region, and NO2 in an additional (smaller) wavelength range in the visible region. The campaign design and implementation processes are discussed in detail including the measurement protocol, calibration procedures and slant column retrieval settings. Strong emphasis was put on the careful alignment and synchronisation of the measurement systems, resulting in a unique set of measurements made under highly comparable air mass conditions. The CINDI-2 data sets were investigated using a regression analysis of the slant columns measured by each instrument and for each of the target data products. The slope and intercept of the regression analysis respectively quantify the mean systematic bias and offset of the individual data sets against the selected reference (which is obtained from the median of either all data sets or a subset), and the rms error provides an estimate of the measurement noise or dispersion. These three criteria are examined and for each of the parameters and each of the data products, performance thresholds are set and applied to all the measurements. The approach presented here has been developed based on heritage from previous intercomparison exercises. It introduces a quantitative assessment of the consistency between all the participating instruments for the MAX-DOAS and zenith-sky DOAS techniques.
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    Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?
    Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Bemans, J. M.; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C.; Glanville, Helen C.; Jones, Davey L.; Angel, Foey; Salminen, Janne; Newton, Ryan J.; Buergmann, Helmut; Ingram, Lachlan J.; Hamer, Ute; Siljanen, Henri M. P.; Peltoniemi, Krista; Potthast, Karin; Baneras, Lluis; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C.; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C.; Lopes, Ana R.; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S.; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindstrom, Eva S.; Basiliko, Nathan; Nemergut, Diana R. (Frontiers, 2016-02-24)
    Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.