2 results
Search Results
Now showing 1 - 2 of 2
- ItemLong-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories(Katlenburg-Lindau : EGU, 2018) Schmale, Julia; Henning, Silvia; Decesari, Stefano; Henzing, Bas; Keskinen, Helmi; Sellegri, Karine; Ovadnevaite, Jurgita; Pöhlker, Mira L.; Brito, Joel; Bougiatioti, Aikaterini; Kristensson, Adam; Kalivitis, Nikos; Stavroulas, Iasonas; Carbone, Samara; Jefferson, Anne; Park, Minsu; Schlag, Patrick; Iwamoto, Yoko; Aalto, Pasi; Äijälä, Mikko; Bukowiecki, Nicolas; Ehn, Mikael; Frank, Göran; Fröhlich, Roman; Frumau, Arnoud; Herrmann, Erik; Herrmann, Hartmut; Holzinger, Rupert; Kos, Gerard; Kulmala, Markku; Mihalopoulos, Nikolaos; Nenes, Athanasios; O'Dowd, Colin; Petäjä, Tuukka; Picard, David; Pöhlker, Christopher; Pöschl, Ulrich; Poulain, Laurent; Prévôt, André Stephan Henry; Swietlicki, Erik; Andreae, Meinrat O.; Artaxo, Paulo; Wiedensohler, Alfred; Ogren, John; Matsuki, Atsushi; Yum, Seong Soo; Stratmann, Frank; Baltensperger, Urs; Gysel, MartinAerosol-cloud interactions (ACI) constitute the single largest uncertainty in anthropogenic radiative forcing. To reduce the uncertainties and gain more confidence in the simulation of ACI, models need to be evaluated against observations, in particular against measurements of cloud condensation nuclei (CCN). Here we present a data set - ready to be used for model validation - of long-term observations of CCN number concentrations, particle number size distributions and chemical composition from 12 sites on 3 continents. Studied environments include coastal background, rural background, alpine sites, remote forests and an urban surrounding. Expectedly, CCN characteristics are highly variable across site categories. However, they also vary within them, most strongly in the coastal background group, where CCN number concentrations can vary by up to a factor of 30 within one season. In terms of particle activation behaviour, most continental stations exhibit very similar activation ratios (relative to particles 20nm) across the range of 0.1 to 1.0% supersaturation. At the coastal sites the transition from particles being CCN inactive to becoming CCN active occurs over a wider range of the supersaturation spectrum. Several stations show strong seasonal cycles of CCN number concentrations and particle number size distributions, e.g. at Barrow (Arctic haze in spring), at the alpine stations (stronger influence of polluted boundary layer air masses in summer), the rain forest (wet and dry season) or Finokalia (wildfire influence in autumn). The rural background and urban sites exhibit relatively little variability throughout the year, while short-term variability can be high especially at the urban site. The average hygroscopicity parameter, calculated from the chemical composition of submicron particles was highest at the coastal site of Mace Head (0.6) and lowest at the rain forest station ATTO (0.2-0.3). We performed closure studies based on -Köhler theory to predict CCN number concentrations. The ratio of predicted to measured CCN concentrations is between 0.87 and 1.4 for five different types of . The temporal variability is also well captured, with Pearson correlation coefficients exceeding 0.87. Information on CCN number concentrations at many locations is important to better characterise ACI and their radiative forcing. But long-term comprehensive aerosol particle characterisations are labour intensive and costly. Hence, we recommend operating migrating-CCNCs to conduct collocated CCN number concentration and particle number size distribution measurements at individual locations throughout one year at least to derive a seasonally resolved hygroscopicity parameter. This way, CCN number concentrations can only be calculated based on continued particle number size distribution information and greater spatial coverage of long-term measurements can be achieved.
- ItemNorth African mineral dust sources: New insights from a combined analysis based on 3D dust aerosol distributions, surface winds and ancillary soil parameters(Katlenburg-Lindau : EGU, 2020) Vandenbussche, Sophie; Callewaert, Sieglinde; Schepanski, Kerstin; De Mazière, MartineMineral dust aerosol is a key player in the climate system. Determining dust sources and the spatio-temporal variability of dust emission fluxes is essential for estimating the impact of dust on the atmospheric radiation budget, cloud and precipitation formation processes, the bio-productivity and, ultimately, the carbon cycle. Although much effort has been put into determining dust sources from satellite observations, geo-locating active dust sources is still challenging and uncertainties in space and time are evident. One major source of uncertainty is the lack of clear differentiation between near-source dust aerosol and transported dust aerosol. In order to reduce this uncertainty, we use 3D information on the distribution of dust aerosol suspended in the atmosphere calculated from spectral measurements obtained by the Infrared Atmospheric Sounding Interferometer (IASI) by using the Mineral Aerosols Profiling from Infrared Radiance (MAPIR) algorithm. In addition to standard dust products from satellite observations, which provide 2D information on the horizontal distribution of dust, MAPIR allows for the retrieval of additional information on the vertical distribution of dust plumes. This ultimately enables us to separate between near-source and transported dust plumes. Combined with information on near-surface wind speed and surface properties, low-altitude dust plumes can be assigned to dust emission events and low-altitude transport regimes can be excluded. Consequently, this technique will reduce the uncertainty in automatically geo-locating active dust sources. The findings of our study illustrate the spatio-temporal distribution of North African dust sources based on 9 years of data, allowing for the observation of a full seasonal cycle of dust emissions, differentiating morning and afternoon/evening emissions and providing a first glance at long-term changes. In addition, we compare the results of this new method to the results from Schepanski et al. (2012), who manually identified dust sources from Spinning Enhanced Visible and InfraRed Imager (SEVIRI) red-green-blue (RGB) images. The comparison illustrates that each method has its strengths and weaknesses that must be taken into account when using the results. This study is of particular importance for understanding future environmental changes due to a changing climate. © Author(s) 2020