Abstract. The long-range transport (LRT) of trace gases and aerosol particles plays an important role for the composition of the Amazonian rain forest atmosphere. Sulfate aerosols originate to a substantial extent from LRT sources and play an important role in the Amazonian atmosphere as strongly light-scattering particles and effective cloud condensation nuclei. The transatlantic transport of volcanic sulfur emissions from Africa has been considered as a source of particulate sulfate in the Amazon; however, direct observations have been lacking so far. This study provides observational evidence for the influence of emissions from the Nyamuragira–Nyiragongo volcanoes in Africa on Amazonian aerosol properties and atmospheric composition during September 2014. Comprehensive ground-based and airborne aerosol measurements together with satellite observations are used to investigate the volcanic event. Under the volcanic influence, hourly mean sulfate mass concentrations in the submicron size range reached up to 3.6 µg m−3 at the Amazon Tall Tower Observatory, the highest value ever reported in the Amazon region. The substantial sulfate injection increased the aerosol hygroscopicity with κ values up to 0.36, thus altering aerosol–cloud interactions over the rain forest. Airborne measurements and satellite data indicate that the transatlantic transport of volcanogenic aerosols occurred in two major volcanic plumes with a sulfate-enhanced layer between 4 and 5 km of altitude. This study demonstrates how African aerosol sources, such as volcanic sulfur emissions, can substantially affect the aerosol cycling and atmospheric processes in Amazonia.

Abstract. Size-resolved measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted over a full seasonal cycle at the remote Amazon Tall Tower Observatory (ATTO, March 2014–February 2015). In a preceding companion paper, we presented annually and seasonally averaged data and parametrizations (Part 1; Pöhlker et al., 2016a). In the present study (Part 2), we analyze key features and implications of aerosol and CCN properties for the following characteristic atmospheric conditions: Empirically pristine rain forest (PR) conditions, where no influence of pollution was detectable, as observed during parts of the wet season from March to May. The PR episodes are characterized by a bimodal aerosol size distribution (strong Aitken mode with DAit ≈ 70 nm and NAit ≈ 160 cm−3, weak accumulation mode with Dacc ≈ 160 nm and Nacc≈ 90 cm−3), a chemical composition dominated by organic compounds, and relatively low particle hygroscopicity (κAit≈ 0.12, κacc ≈ 0.18). Long-range-transport (LRT) events, which frequently bring Saharan dust, African biomass smoke, and sea spray aerosols into the Amazon Basin, mostly during February to April. The LRT episodes are characterized by a dominant accumulation mode (DAit ≈ 80 nm, NAit ≈ 120 cm−3 vs. Dacc ≈ 180 nm, Nacc ≈ 310 cm−3), an increased abundance of dust and salt, and relatively high hygroscopicity (κAit≈ 0.18, κacc ≈ 0.35). The coarse mode is also significantly enhanced during these events. Biomass burning (BB) conditions characteristic for the Amazonian dry season from August to November. The BB episodes show a very strong accumulation mode (DAit ≈ 70 nm, NAit ≈ 140 cm−3 vs. Dacc ≈ 170 nm, Nacc ≈ 3400 cm−3), very high organic mass fractions (∼ 90 %), and correspondingly low hygroscopicity (κAit≈ 0.14, κacc ≈ 0.17). Mixed-pollution (MPOL) conditions with a superposition of African and Amazonian aerosol emissions during the dry season. During the MPOL episode presented here as a case study, we observed African aerosols with a broad monomodal distribution (D ≈ 130 nm, NCN,10 ≈ 1300 cm−3), with high sulfate mass fractions (∼ 20 %) from volcanic sources and correspondingly high hygroscopicity (κ< 100 nm ≈ 0.14, κ>100nm≈ 0.22), which were periodically mixed with fresh smoke from nearby fires (D ≈ 110 nm, NCN,10 ≈ 2800 cm−3) with an organic-dominated composition and sharply decreased hygroscopicity (κ<150nm≈ 0.10, κ>150nm≈ 0.20). Insights into the aerosol mixing state are provided by particle hygroscopicity (κ) distribution plots, which indicate largely internal mixing for the PR aerosols (narrow κ distribution) and more external mixing for the BB, LRT, and MPOL aerosols (broad κ distributions). The CCN spectra (CCN concentration plotted against water vapor supersaturation) obtained for the different case studies indicate distinctly different regimes of cloud formation and microphysics depending on aerosol properties and meteorological conditions. The measurement results suggest that CCN activation and droplet formation in convective clouds are mostly aerosol-limited under PR and LRT conditions and updraft-limited under BB and MPOL conditions. Normalized CCN efficiency spectra (CCN divided by aerosol number concentration plotted against water vapor supersaturation) and corresponding parameterizations (Gaussian error function fits) provide a basis for further analysis and model studies of aerosol–cloud interactions in the Amazon.

Abstract. In the Amazonian atmosphere, the aerosol coarse mode comprises a complex, diverse, and variable mixture of bioaerosols emitted from the rain forest ecosystem, long-range transported Saharan dust (we use Sahara as shorthand for the dust source regions in Africa north of the Equator), marine aerosols from the Atlantic Ocean, and coarse smoke particles from deforestation fires. For the rain forest, the coarse mode particles are of significance with respect to biogeochemical and hydrological cycling, as well as ecology and biogeography. However, knowledge on the physicochemical and biological properties as well as the ecological role of the Amazonian coarse mode is still sparse. This study presents results from multi-year coarse mode measurements at the remote Amazon Tall Tower Observatory (ATTO) site. It combines online aerosol observations, selected remote sensing and modeling results, as well as dedicated coarse mode sampling and analysis. The focal points of this study are a systematic characterization of aerosol coarse mode abundance and properties in the Amazonian atmosphere as well as a detailed analysis of the frequent, pulse-wise intrusion of African long-range transport (LRT) aerosols (comprising Saharan dust and African biomass burning smoke) into the Amazon Basin.We find that, on a multi-year time scale, the Amazonian coarse mode maintains remarkably constant concentration levels (with 0.4 cm−3 and 4.0 µg m−3 in the wet vs. 1.2 cm−3 and 6.5 µg m−3 in the dry season) with rather weak seasonality (in terms of abundance and size spectrum), which is in stark contrast to the pronounced biomass burning-driven seasonality of the submicron aerosol population and related parameters. For most of the time, bioaerosol particles from the forest biome account for a major fraction of the coarse mode background population. However, from December to April there are episodic intrusions of African LRT aerosols, comprising Saharan dust, sea salt particles from the transatlantic passage, and African biomass burning smoke. Remarkably, during the core period of this LRT season (i.e., February–March), the presence of LRT influence, occurring as a sequence of pulse-like plumes, appears to be the norm rather than an exception. The LRT pulses increase the coarse mode concentrations drastically (up to 100 µg m−3) and alter the coarse mode composition as well as its size spectrum. Efficient transport of the LRT plumes into the Amazon Basin takes place in response to specific mesoscale circulation patterns in combination with the episodic absence of rain-related aerosol scavenging en route. Based on a modeling study, we estimated a dust deposition flux of 5–10 kg ha−1 a−1 in the region of the ATTO site. Furthermore, a chemical analysis quantified the substantial increase of crustal and sea salt elements under LRT conditions in comparison to the background coarse mode composition. With these results, we estimated the deposition fluxes of various elements that are considered as nutrients for the rain forest ecosystem. These estimates range from few g ha−1 a−1 up to several hundreds of g ha−1 a−1 in the ATTO region.The long-term data presented here provide a statistically solid basis for future studies of the manifold aspects of the dynamic coarse mode aerosol cycling in the Amazon. Thus, it may help to understand its biogeochemical relevance in this ecosystem as well as to evaluate to what extent anthropogenic influences have altered the coarse mode cycling already.

Most of today’s scientific research relies on computers and software not only for administrational tasks, but also for processing scientific information. Examples of such computer-aided research are the analysis of experimental data or the simulation of phenomena based on theoretical models. With the rapid increase of computational power, scientific software has integrated more and more complex scientific knowledge in a black-box fashion. As a consequence, its users do not know, and do not even have a chance of finding out, which assumptions and approximations their computations are based on. The black-box nature of scientific software has thereby become a major cause of mistakes. The present work starts with an analysis of this situation from the point of view of human-computer interaction in scientific research. It identifies the key role of digital scientific notations at the human-computer interface, reviews the most popular ones in use today, and describes a proof-of-concept implementation of Leibniz, a language explicitly designed as a digital scientific notation for models formulated as mathematical equations.

The main function of brains is to generate adaptive behavior. Far from being the stereotypical, robot-like insect, the fruit fly Drosophila exhibits astounding flexibility and chooses different courses of actions even under identical external circumstances. Due to the power of genetics, we now are beginning to understand the neuronal mechanisms underlying this behavioral flexibility. Interestingly, the evidence from studies of disparate behaviors converges on common organizational principles common to many if not all behaviors, such as modified sensory processing, involvement of biogenic amines in network remodeling, ongoing activity and modulation by feedback. Seemingly foreseeing these recent insights, already the first research fields in Drosophila behavioral neurogenetics reflected this constant negotiation between internal and external demands on the animal as the common mechanism underlying adaptive behavioral choice in Drosophila.

The main function of brains is to generate adaptive behavior. Far from being the stereotypical, robot-like insect, the fruit fly Drosophila exhibits astounding flexibility and chooses different courses of actions even under identical external circumstances. Due to the power of genetics, we now are beginning to understand the neuronal mechanisms underlying this behavioral flexibility. Interestingly, the evidence from studies of disparate behaviors converges on common organizational principles common to many if not all behaviors, such as modified sensory processing, involvement of biogenic amines in network remodeling, ongoing activity and modulation by feedback. Seemingly foreseeing these recent insights, already the first research fields in Drosophila behavioral neurogenetics reflected this constant negotiation between internal and external demands on the animal as the common mechanism underlying adaptive behavioral choice in Drosophila.

Tethering a fly for stationary flight allows for exquisite control of its sensory input, such as visual or olfactory stimuli or a punishing infrared laser beam. A torque meter measures the the turning attempts of the tethered fly around its vertical body axis. By punishing, say, left turning attempts (in a homogeneous environment), one can train a fly to restrict its behaviour to right turning attempts. It was recently discovered that this form of operant conditioning (called operant self-learning), may constitute a form of motor learning in Drosophila. Previous work had shown that Protein Kinase C (PKC) and the transcription factor dFoxP were specifically involved in self-learning, but not in other forms of learning. These molecules are specifically involved in various forms of motor learning in other animals, such as compulsive biting in Aplysia, song-learning in birds, procedural learning in mice or language acquisition in humans. Here we describe our efforts to decipher which PKC gene is involved in self-learning in Drosophila. We also provide evidence that motorneurons may be one part of the neuronal network modified during self-learning experiments. The collected evidence is reminiscent of one of the simplest, clinically relevant forms of motor learning in humans, operant reflex conditioning, which also relies on motorneuron plasticity

Tethering a fly for stationary flight allows for exquisite control of its sensory input, such as visual or olfactory stimuli or a punishing infrared laser beam. A torque meter measures the the turning attempts of the tethered fly around its vertical body axis. By punishing, say, left turning attempts (in a homogeneous environment), one can train a fly to restrict its behaviour to right turning attempts. It was recently discovered that this form of operant conditioning (called operant self-learning), may constitute a form of motor learning in Drosophila. Previous work had shown that Protein Kinase C (PKC) and the transcription factor dFoxP were specifically involved in self-learning, but not in other forms of learning. These molecules are specifically involved in various forms of motor learning in other animals, such as compulsive biting in Aplysia, song-learning in birds, procedural learning in mice or language acquisition in humans. Here we describe our efforts to decipher which PKC gene is involved in self-learning in Drosophila. We also provide evidence that motorneurons may be one part of the neuronal network modified during self-learning experiments. The collected evidence is reminiscent of one of the simplest, clinically relevant forms of motor learning in humans, operant reflex conditioning, which also relies on motorneuron plasticity