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Die Ergebnisse der Beobachtungen der meteorologischen Station zu Osnabrück in den Jahren 1899 und 1900 sind in Form von Tabellen und Diagrammen zusammengefasst. Bei den Ergebnissen handelt es sich um die Regenhöhe, die Temperatur, die relative Feuchtigkeit, der Dunstdruck, die Windrichtung und der Druck.
Die Ergebnisse der Beobachtungen der meteorologischen Station zu Osnabrück in den Jahren 1891 und 1892 sind in Form von Tabellen und Diagrammen zusammengefasst. Bei den Ergebnissen handelt es sich um die Regenhöhe, die Temperatur, die relative Feuchtigkeit, der Dunstdruck, die Windrichtung und der Druck.
New particle formation in the upper free troposphere is a major global source of cloud condensation nuclei (CCN)1,2,3,4. However, the precursor vapours that drive the process are not well understood. With experiments performed under upper tropospheric conditions in the CERN CLOUD chamber, we show that nitric acid, sulfuric acid and ammonia form particles synergistically, at rates that are orders of magnitude faster than those from any two of the three components. The importance of this mechanism depends on the availability of ammonia, which was previously thought to be efficiently scavenged by cloud droplets during convection. However, surprisingly high concentrations of ammonia and ammonium nitrate have recently been observed in the upper troposphere over the Asian monsoon region5,6. Once particles have formed, co-condensation of ammonia and abundant nitric acid alone is sufficient to drive rapid growth to CCN sizes with only trace sulfate. Moreover, our measurements show that these CCN are also highly efficient ice nucleating particles—comparable to desert dust. Our model simulations confirm that ammonia is efficiently convected aloft during the Asian monsoon, driving rapid, multi-acid HNO3–H2SO4–NH3 nucleation in the upper troposphere and producing ice nucleating particles that spread across the mid-latitude Northern Hemisphere.
A list of authors and their affiliations appears at the end of the paper New-particle formation is a major contributor to urban smog, but how it occurs in cities is often puzzling. If the growth rates of urban particles are similar to those found in cleaner environments (1–10 nanometres per hour), then existing understanding suggests that new urban particles should be rapidly scavenged by the high concentration of pre-existing particles. Here we show, through experiments performed under atmospheric conditions in the CLOUD chamber at CERN, that below about +5 degrees Celsius, nitric acid and ammonia vapours can condense onto freshly nucleated particles as small as a few nanometres in diameter. Moreover, when it is cold enough (below −15 degrees Celsius), nitric acid and ammonia can nucleate directly through an acid–base stabilization mechanism to form ammonium nitrate particles. Given that these vapours are often one thousand times more abundant than sulfuric acid, the resulting particle growth rates can be extremely high, reaching well above 100 nanometres per hour. However, these high growth rates require the gas-particle ammonium nitrate system to be out of equilibrium in order to sustain gas-phase supersaturations. In view of the strong temperature dependence that we measure for the gas-phase supersaturations, we expect such transient conditions to occur in inhomogeneous urban settings, especially in wintertime, driven by vertical mixing and by strong local sources such as traffic. Even though rapid growth from nitric acid and ammonia condensation may last for only a few minutes, it is nonetheless fast enough to shepherd freshly nucleated particles through the smallest size range where they are most vulnerable to scavenging loss, thus greatly increasing their survival probability. We also expect nitric acid and ammonia nucleation and rapid growth to be important in the relatively clean and cold upper free troposphere, where ammonia can be convected from the continental boundary layer and nitric acid is abundant from electrical storms.
Measurement of iodine species and sulfuric acid using bromide chemical ionization mass spectrometers
(2021)
Iodine species are important in the marine atmosphere for oxidation and new-particle formation. Understanding iodine chemistry and iodine new-particle formation requires high time resolution, high sensitivity, and simultaneous measurements of many iodine species. Here, we describe the application of a bromide chemical ionization mass spectrometer (Br-CIMS) to this task. During the iodine oxidation experiments in the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber, we have measured gas-phase iodine species and sulfuric acid using two Br-CIMS, one coupled to a Multi-scheme chemical IONization inlet (Br-MION-CIMS) and the other to a Filter Inlet for Gasses and AEROsols inlet (Br-FIGAERO-CIMS). From offline calibrations and intercomparisons with other instruments, we have quantified the sensitivities of the Br-MION-CIMS to HOI, I2, and H2SO4 and obtained detection limits of 5.8 × 106, 3.8 × 105, and 2.0 × 105 molec. cm−3, respectively, for a 2 min integration time. From binding energy calculations, we estimate the detection limit for HIO3 to be 1.2 × 105 molec. cm−3, based on an assumption of maximum sensitivity. Detection limits in the Br-FIGAERO-CIMS are around 1 order of magnitude higher than those in the Br-MION-CIMS; for example, the detection limits for HOI and HIO3 are 3.3 × 107 and 5.1 × 106 molec. cm−3, respectively. Our comparisons of the performance of the MION inlet and the FIGAERO inlet show that bromide chemical ionization mass spectrometers using either atmospheric pressure or reduced pressure interfaces are well-matched to measuring iodine species and sulfuric acid in marine environments.
Analyzing the impact of streamflow drought on hydroelectricity production: a global-scale study
(2021)
Electricity production by hydropower is negatively affected by drought. To understand and quantify risks of less than normal streamflow for hydroelectricity production (HP) at the global scale, we developed an HP model that simulates time series of monthly HP worldwide and thus enables analyzing the impact of drought on HP. The HP model is based on a new global hydropower database (GHD), containing 8,716 geo-localized plant records, and on monthly streamflow values computed by the global hydrological model WaterGAP with a spatial resolution of 0.5°. The GHD includes 44 attributes and covers 91.8% of the globally installed capacity. The HP model can reproduce HP trends, seasonality, and interannual variability that was caused by both (de)commissioning of hydropower plants and hydrological variability. It can also simulate streamflow drought and its impact on HP reasonably well. Global risk maps of HP reduction were generated for both 0.5° grid cells and countries, revealing that 67 out of the 134 countries with hydropower suffer, in 1 out of 10 years, from a reduction of more than 20% of mean annual HP and 18 countries from a reduction of more than 40%. The developed HP model enables advanced assessments of drought impacts on hydroelectricity at national to international levels.
Titanite is a potentially powerful U–Pb petrochronometer that may record metamorphism, metasomatism, and deformation. Titanite may also incorporate significant inherited Pb, which may lead to inaccurate and geologically ambiguous U–Pb dates if a proper correction is not or cannot be applied. Here, we present laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS)-derived titanite U–Pb dates and trace element concentrations for two banded calcsilicate gneisses from south-central Maine, USA (SSP18-1A and SSP18-1B). Single spot common Pb-corrected dates range from 400 to 280 Ma with ±12–20 Ma propagated 2SE. Titanite grains in sample SSP18-1B exhibit regular core-to-rim variations in texture, composition, and date. We identify four titanite populations: (1) 397 ± 5 Ma (95% CL) low Y + HREE cores and mottled grains, (2) 370 ± 7 Ma high Y + REE mantles and cores, (3) 342 ± 6 Ma cores with high Y + REE and no Eu anomaly, and (4) 295 ± 6 Ma LREE-depleted rims. We interpret the increase in titanite Y + HREE between ca. 397 and ca. 370 Ma to constrain the timing of diopside fracturing and recrystallization and amphibole breakdown. Apparent Zr-in-titanite temperatures (803 ± 36°C at 0.5 ± 0.2 GPa) and increased XDi suggest a thermal maximum at ca. 370 Ma. Population 3 domains dated to ca. 342 Ma exhibit no Eu anomaly and are observed only in compositional bands dominated by diopside (>80 vol%), suggesting limited equilibrium between titanite and plagioclase. Finally, low LREE and high U/Th in Population 4 titanite dates the formation of hydrous phases, such as allanite, during high XH2O fluid infiltration at ca. 295 Ma. In contrast to the well-defined date–composition–texture relationships observed for titanite from SSP18-1B, titanite grains from sample SSP18-1A exhibit complex zoning patterns and little correlation between texture, composition, and date. We hypothesize that the incorporation of variable amounts of radiogenic Pb from dissolved titanite into recrystallized domains resulted in mixed dates spanning 380–330 Ma. Although titanite may reliably record multiple phases of metamorphism, these data highlight the importance of considering U–Pb data along with chemical and textural data to screen for inherited radiogenic Pb.
The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986). In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Nino = Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference. It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CMFSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Attribution and detection of anthropogenic climate change using a backpropagation neural network
(2002)
The climate system can be regarded as a dynamic nonlinear system. Thus traditional linear statistical methods are not suited to describe the nonlinearities of this system which renders it necessary to find alternative statistical techniques to model those nonlinear properties. In addition to an earlier paper on this subject (WALTER et al., 1998), the problem of attribution and detection of the observed climate change is addressed here using a nonlinear Backpropagation Neural Network (BPN). In addition to potential anthropogenic influences on climate (CO2-equivalent concentrations, called greenhouse gases, GHG and SO2 emissions) natural influences on surface air temperature (variations of solar activity, volcanism and the El Niño/Southern Oscillation phenomenon) are integrated into the simulations as well. It is shown that the adaptive BPN algorithm captures the dynamics of the climate system, i.e. global and area weighted mean temperature anomalies, to a great extent. However, free parameters of this network architecture have to be optimized in a time consuming trial-and-error process. The simulation quality obtained by the BPN exceeds the results of those from a linear model by far; the simulation quality on the global scale amounts to 84% explained variance. Additionally the results of the nonlinear algorithm are plausible in a physical sense, i.e. amplitude and time structure. Nevertheless they cover a broad range, e.g. the GHG-signal on the global scale ranges from 0.37 K to 1.65 K warming for the time period 1856-1998. However the simulated amplitudes are situated within the discussed range (HOUGHTON et al., 2001). Additionally the combined anthropogenic effect corresponds to the observed increase in temperature for the examined time period. In addition to that, the BPN succeeds with the detection of anthropogenic induced climate change on a high significance level. Therefore the concept of neural networks can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Simulation of global temperature variations and signal detection studies using neural networks
(1998)
The concept of neural network models (NNM) is a statistical strategy which can be used if a superposition of any forcing mechanisms leads to any effects and if a sufficient related observational data base is available. In comparison to multiple regression analysis (MRA), the main advantages are that NNM is an appropriate tool also in the case of non-linear cause-effect relations and that interactions of the forcing mechanisms are allowed. In comparison to more sophisticated methods like general circulation models (GCM), the main advantage is that details of the physical background like feedbacks can be unknown. Neural networks learn from observations which reflect feedbacks implicitly. The disadvantage, of course, is that the physical background is neglected. In addition, the results prove to be sensitively dependent from the network architecture like the number of hidden neurons or the initialisation of learning parameters. We used a supervised backpropagation network (BPN) with three neuron layers, an unsupervised Kohonen network (KHN) and a combination of both called counterpropagation network (CPN). These concepts are tested in respect to their ability to simulate the observed global as well as hemispheric mean surface air temperature annual variations 1874 - 1993 if parameter time series of the following forcing mechanisms are incorporated : equivalent CO2 concentrations, tropospheric sulfate aerosol concentrations (both anthropogenic), volcanism, solar activity, and ENSO (all natural). It arises that in this way up to 83% of the observed temperature variance can be explained, significantly more than by MRA. The implication of the North Atlantic Oscillation does not improve these results. On a global average, the greenhouse gas (GHG) signal so far is assessed to be 0.9 - 1.3 K (warming), the sulfate signal 0.2 - 0.4 K (cooling), results which are in close similarity to the GCM findings published in the recent IPCC Report. The related signals of the natural forcing mechanisms considered cover amplitudes of 0.1 - 0.3 K. Our best NNM estimate of the GHG doubling signal amounts to 2.1K, equilibrium, or 1.7 K, transient, respectively.
Increasing atmospheric CO2 stimulates photosynthesis which can increase net primary production (NPP), but at longer timescales may not necessarily increase plant biomass. Here we analyse the four decade-long CO2-enrichment experiments in woody ecosystems that measured total NPP and biomass. CO2 enrichment increased biomass increment by 1.05 ± 0.26 kg C m−2 over a full decade, a 29.1 ± 11.7% stimulation of biomass gain in these early-secondary-succession temperate ecosystems. This response is predictable by combining the CO2 response of NPP (0.16 ± 0.03 kg C m−2 y−1) and the CO2-independent, linear slope between biomass increment and cumulative NPP (0.55 ± 0.17). An ensemble of terrestrial ecosystem models fail to predict both terms correctly. Allocation to wood was a driver of across-site, and across-model, response variability and together with CO2-independence of biomass retention highlights the value of understanding drivers of wood allocation under ambient conditions to correctly interpret and predict CO2 responses.
Invasive plant species are increasingly altering species composition and the functioning of ecosystems from a local to a global scale. The grass species Pennisetum setaceum has recently raised concerns as an invader on different archipelagos worldwide. Among these affected archipelagos are the Canary Islands, which are a hotspot of endemism. Consequently, conservation managers and stakeholders are interested in the potential spreading of this species in the archipelago. We identify the current extent of the suitable habitat for P. setaceum on the island of La Palma to assess how it affects island ecosystems, protected areas (PAs), and endemic plant species richness. We recorded in situ occurrences of P. setaceum from 2010 to 2018 and compiled additional ones from databases at a 500 m × 500 m resolution. To assess the current suitable habitat and possible distribution patterns of P. setaceum on the island, we built an ensemble model. We projected habitat suitability for island ecosystems and PAs and identified risks for total as well as endemic plant species richness. The suitable habitat for P. setaceum is calculated to cover 34.7% of the surface of La Palma. In open ecosystems at low to mid elevations, where native ecosystems are already under pressure by land use and human activities, the spread of the invader will likely lead to additional threats to endemic plant species. Forest ecosystems (e.g., broadleaved evergreen and coniferous forests) are not likely to be affected by the spread of P. setaceum because of its heliophilous nature. Our projection of suitable habitat of P. setaceum within ecosystems and PAs on La Palma supports conservationists and policymakers in prioritizing management and control measures and acts as an example for the potential threat of this graminoid invader on other islands.
Following votes in the Coniacian Working Group, the Cretaceous Subcommission and the International Commission on Stratigraphy, on May 1st, 2021, the International Union of Geological Sciences voted unanimously to ratify the Global Stratotype Section and Point (GSSP) proposal for the base of the Coniacian Stage of the Upper Cretaceous Series and Cretaceous System. The lower boundary of the Coniacian Stage is placed at the base of Bed 46 of the Salzgitter-Salder section in northern Germany. The boundary is defined by the first appearance of the inoceramid bivalve species Cremnoceramus deformis erectus (Meek) and complemented by the Navigation carbon isotope event. Additional data include the bivalve genus Didymotis, foraminifera, ammonite, nannofossil and organic-walled dinoflagellate cyst events. Three auxiliary sections (Słupia Nadbrzeżna, central Poland; Střeleč, Czech Republic; El Rosario, NE Mexico) supplement the details of the boundary record in various facies, and in differing geographic and biogeographic contexts.
The formation of secondary particles in the atmosphere accounts for more than half of global cloud condensation nuclei. Experiments at the CERN CLOUD (Cosmics Leaving OUtdoor Droplets) chamber have underlined the importance of ions for new particle formation, but quantifying their effect in the atmosphere remains challenging. By using a novel instrument setup consisting of two nano-particle counters, one of them equipped with an ion filter, we were able to further investigate the ion-related mechanisms of new particle formation. In autumn 2015, we carried out experiments at CLOUD on four systems of different chemical compositions involving monoterpenes, sulfuric acid, nitrogen oxides, and ammonia. We measured the influence of ions on the nucleation rates under precisely controlled and atmospherically relevant conditions. Our results indicate that ions enhance the nucleation process when the charge is necessary to stabilize newly formed clusters, i.e. in conditions where neutral clusters are unstable. For charged clusters that were formed by ion-induced nucleation, we were able to measure, for the first time, their progressive neutralization due to recombination with oppositely charged ions. A large fraction of the clusters carried a charge at 1.2 nm diameter. However, depending on particle growth rates and ion concentrations, charged clusters were largely neutralized by ion–ion recombination before they grew to 2.2 nm. At this size, more than 90 % of particles were neutral. In other words, particles may originate from ion-induced nucleation, although they are neutral upon detection at diameters larger than 2.2 nm. Observations at Hyytiälä, Finland, showed lower ion concentrations and a lower contribution of ion-induced nucleation than measured at CLOUD under similar conditions. Although this can be partly explained by the observation that ion-induced fractions decrease towards lower ion concentrations, further investigations are needed to resolve the origin of the discrepancy.
A new method for size-resolved chemical analysis of nucleation mode aerosol particles (size range from ∼10 to ∼30 nm) is presented. The Thermal Desorption Differential Mobility Analyzer (TD-DMA) uses an online, discontinuous principle. The particles are charged, a specific size is selected by differential mobility analysis and they are collected on a filament by electrostatic precipitation. Subsequently, the sampled mass is evaporated in a clean carrier gas and analyzed by a chemical ionization mass spectrometer. Gas-phase measurements are performed with the same mass spectrometer during the sampling of particles. The characterization shows reproducible results, with a particle size resolution of 1.19 and the transmission efficiency for 15 nm particles being slightly above 50 %. The signal from the evaporation of a test substance can be detected starting from 0.01 ng and shows a linear response in the mass spectrometer. Instrument operation in the range of pg m−3 is demonstrated by an example measurement of 15 nm particles produced by nucleation from dimethylamine, sulfuric acid and water.
A new method for size resolved chemical analysis of nucleation mode aerosol particles (size range from ~10 to ~30 nm) is presented. The Thermal Desorption Differential Mobility Analyzer (TD-DMA) uses an online, discontinuous principle. The particles are charged, a specific size is selected by differential mobility analysis and they are collected on a filament by electrostatic precipitation. Subsequently, the sampled mass is evaporated in a clean carrier gas and analyzed by a chemical ionization mass spectrometer. Gas phase measurements are performed with the same mass spectrometer during the sampling of particles. The characterization shows reproducible results, with a particle size resolution of 1.19 and the transmission efficiency for 15 nm particles being slightly above 50 %. The signal from the evaporation of a test substance can be detected starting from 0.01 ng and shows a linear response in the mass spectrometer. Instrument operation in the range of pg/m3 is demonstrated by an example measurement of 15 nm particles produced by nucleation from dimethylamine, sulfuric acid and water.