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Atmospheric nanoaerosols have extensive effects on the Earth’s climate and human health. This cumulative work focuses on the development and characterization of instrumentation for measuring various parameters of atmospheric nanoaerosols, and its use to understand new particle formation from organic precursors. The principal research question is, how the chemical composition of nanoaerosol particles can be measured and how atmospheric chemistry influences aerosol processes, especially new particle formation and growth. Therefore, nanoaerosols are investigated under various aspects. More specifically, an instrument is developed to analyze nanoparticles, and field as well as chamber studies are conducted.
The main project is the instrument development of the Thermal Desorption Differential Mobility Analyzer (TD-DMA, project 1, Wagner et al. (2018)). This instrument analyzes the chemical composition of small aerosol particles. By characterization and testing in chamber experiments, it is proven to be suitable for the analysis of freshly nucleated particles.
The second project (Wagner et al. (2017)) applies a broad spectrum of aerosol measurement instruments for the characterization of aerosol particles produced by a skyscraper blasting. A comprehensive picture of the particle population emitted by the demolition is obtained.
Project 3 (K¨urten et al. (2016)) is also an ambient aerosol measurement, focusing of new particle formation in a rural area in central Germany, and the ability of a negative nitrate CI-APi-TOF to detect various substances in atmosphere. Project 4 (Heinritzi et al. (2016)) is a characterization of the negative nitrate CI-APi-TOF used in projects 1, 3, 5, 6, 7 and 8. The following projects focus on understanding new particle formation from atmospherically abundant organic precursors. Key instruments comprise the negative nitrate CI-APiTOF for gas-phase measurements of the nucleating species, and various sizing and counting instruments for quantifying the particle formation and growth. Project 5 (Kirkby et al. (2016)) shows that biogenic organic compounds formed from alpha-pinene can nucleate on their own without the influence of e.g. sulfuric acid. Project 6 (Tr¨ostl et al. (2016)) describe the subsequent growth of these particles. Project 7 (Stolzenburg et al. (2018)) covers the temperature dependence of this growth and in project 8 (Heinritzi et al. (2018)), the suppressing influence of isoprene on the new particle formation is assessed.
We present a study characterizing aerosol particles resulting from a skyscraper blasting. High mass concentrations with a maximum of 844.9 μg m-3 were present for a short time period of approximately 15 minutes. They result in a day mean of 32.6 μg m-3 compared to a 27.6 μg m-3 background not exceeding the 50 μg m-3 EU maximum permissive value. The increase in particle number concentration was less pronounced with a maximum concentration of 6.9 ⋅ 104 cm-3 compared to the local background value of 1.8 ⋅ 104 cm-3. The size-resolved number concentration shows a single mode of ultrafine particles at approximately 93 nm. The spatial distribution of deposited dust was investigated with Bergerhoff glass collection vessels, showing a decrease with distance. In the deposited dust samples the concentrations of twelve metals was determined, non of them exceeded the regional background concentrations significantly. The chemical composition of individual particles emitted by the demolition was studied by Scanning Electron Microscopy. They were mainly concrete and steel particles, with 60% calcium carbonates, 19% calcium sulfates, 19% silicates and 2% steel. In energy-dispersive X-Ray Spectroscopy, no fibers like asbestos were observed. Using a broad spectrum of instruments and methods, we obtain comprehensive characterization of the particles emitted by the demolition.
The overarching goal of the thesis was to create a holistic predictive framework, a vegetation model, by improving the representations of and interactions between the biosphere, hydrosphere, atmosphere and pedosphere. Vegetation models rep- resent a crucial component of Earth system model since the properties of the land surface, via interactions with the atmosphere, can have extremely large climatic effect. Yet, there remains great uncertainty associated with the dynamics of the vegetated land surface. Various vegetation models have been critiqued for numerous reasons including overly simplistic representations of vegetation, prescribed vegetation, poor representations of diversity, inaccurate representations of competition, non-transparent model calibration, and poor responses to drought. The purpose of the creation of this "next generation" model was to address deficiencies common to current vegetation modelling paradigms.
The representation of the biosphere within this framework was improved via two separate development axes. First, ecological realism was improved by integrating concepts from community assembly theory, co-existence theory, and evolutionary theory. Explicitly, rather than defining teleonomic rules to define plant behaviour the process of natural selection is modelled. By modelling the pro- cess of natural selection and its affect on relative fitness, myriad "rules" which continually adapt to biotic and abiotic conditions "come out" as a consequence of the modelled dynamics rather than being "put in". In aDGVM2 (adaptive Dynamic Global Vegetation model 2) communities of plants and their trait values evolve through time, this evolution is constrained by trade-offs between traits. Poorly performing individuals are more likely to die and produce fewer copies of themselves, this results in a filtering of trait values. Further, the community and species’ trait values can evolve through successive generations via reproduction, mutation and crossover which we approximate by using a genetic optimisation algorithm. Thus, a plant community consisting of individuals and species with potentially novel and diverse trait values is assembled iteratively through time.
We tested the assertion that improved integration of concepts from community assembly, evolutionary, and co-existence theory could address limitations of DGVMs in Chapter 2. We demonstrated that such an approach does indeed allow diverse communities of plants to emerge from the modelling framework. We showed that the position of the emergent communities in trait space differed along abiotic gradients and that, in simulations where reproductive isolation was simulated, communities emerged which were composed of multiple co-existing clusters in trait-space. Simulated trait values of co-existing strategies emerging from aDGVM2 were often multimodal, indicative of the emergence of multiple life- history strategies.
Second, to successfully model how natural selection forms a community requires accurate representation of how resource availability affects fitness. In the majority of dynamic global vegetation models (DGVMs) there is no real representation of plant hydraulics with plant water availability being calculated as a simple function of relative soil moisture content and root fractions across a number of soil layers. Worryingly, a number vegetation models appear to under represent the magnitude of these observed responses to drought. This was deficiency was ad- dressed in Chapter 3 by designing a simplified version of the cohesion tension theory of sapwood ascent where elements determining plant conductances are considered in series and implementing a set of trait trade-offs which influence a plant’s hydraulic strategy whereby hydraulic safety trades-off against xylem and leaf conductivity.
Interactions between the biosphere, pedosphere, and hydrosphere can also potentially mediate water resource availability and thus fitness. In the majority of DGVMs the volume of soil explored and explorable by plant roots in fixed glob- ally and usually constrained to a depth not greater than 3m. However, we know that soils can have a strong effect on vegetation distributions, that soil depth is not constant globally, and that plants root to variable depths.
In Chapter 4 I explored interactions between soil depth, plant rooting and the emergent properties of communities and highlighted the importance of considering interactions between the biosphere, hydrosphere, pedosphere, and fire. Here I demonstrated that, in addition to fire and precipitation, edaphic constraints on the volume of soil explorable by plant roots (e.g. by shallow soils, lateritic layers, anoxic conditions due to water logging, toxicity resulting from heavy metal concentrations) can affect the process of plant community assembly, alter the mean values of multiple traits in communities, and the trait diversity of communities.
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A twentieth century-long coupled atmosphere-ocean regional climate simulation with COSMO-CLM (Consortium for Small-Scale Modeling, Climate Limited-area Model) and NEMO (Nucleus for European Modelling of the Ocean) is studied here to evaluate the added value of coupled marginal seas over continental regions. The interactive coupling of the marginal seas, namely the Mediterranean, the North and the Baltic Seas, to the atmosphere in the European region gives a comprehensive modelling system. It is expected to be able to describe the climatological features of this geographically complex area even more precisely than an atmosphere-only climate model. The investigated variables are precipitation and 2 m temperature. Sensitivity studies are used to assess the impact of SST (sea surface temperature) changes over land areas. The different SST values affect the continental precipitation more than the 2 m temperature. The simulated variables are compared to the CRU (Climatic Research Unit) observational data, and also to the HOAPS/GPCC (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data, Global Precipitation Climatology Centre) data. In the coupled simulation, added skill is found primarily during winter over the eastern part of Europe. Our analysis shows that, over this region, the coupled system is dryer than the uncoupled system, both in terms of precipitation and soil moisture, which means a decrease in the bias of the system. Thus, the coupling improves the simulation of precipitation over the eastern part of Europe, due to cooler SST values and in consequence, drier soil.
Historically, the expansion of soy plantations has been a major driver of land-use/cover change (LUCC) in Brazil. While a series of recent public actions and supply-chain commitments reportedly curbed the replacement of forests by soy, the expansion of the agricultural commodity still poses a considerable threat to the Amazonian and Cerrado biomes. Identification of areas under high risk of soy expansion is thus paramount to assist conservation efforts in the region. We mapped the areas suitable for undergoing transition to soy plantations in the Legal Amazon with a machine-learning approach adopted from the ecological modeling literature. Simulated soy expansion for the year 2014 exhibited favorable validation scores compared to other LUCC models. We then used our model to simulate how potential future infrastructure improvements would affect the 2014 probabilities of soy occurrence in the region. In addition to the 2.3 Mha of planted soy in the Legal Amazon in 2014, our model identified another 14.7 Mha with high probability of soy conversion in the region given the infrastructure conditions at that time. Out of those, pastures and forests represented 9.8 and 0.4 Mha, respectively. Under the new infrastructure scenarios simulated, the Legal Amazonian area under high risk of soy conversion increased by up to 2.1 Mha (14.6%). These changes led to up to 11.4 and 51.4% increases in the high-risk of conversion areas of pastures and forests, respectively. If conversion occurs in the identified high-risk areas, at least 4.8 Pg of CO2 could be released into the atmosphere, a value that represents 10 times the total CO2 emissions of Brazil in 2014. Our results highlight the importance of targeting conservation policies and enforcement actions, including the Soy Moratorium, to mitigate future forest cover loss associated with infrastructure improvements in the region.
We evaluate the near-surface representation of thermally driven winds in the Swiss Alps in a numerical weather prediction model at km-scale resolution. In addition, the influence of grid resolution (2.2 km and 1.1 km), topography filtering, and land surface datasets on the accuracy of the simulated valley winds is investigated. The simulations are evaluated against a comprehensive set of surface observations for an 18-day fair-weather summer period in July 2006. The episode is characterized by strong diurnal wind systems and the formation of shallow convection over the mountains, which transitions to precipitating convection in some areas. The near-surface winds (10 m above ground level) follow a typical diurnal pattern with strong daytime up-valley flow and weaker nighttime down-valley flow. At a 2.2 km resolution the valley winds are poorly simulated for most stations, while at a 1.1 km resolution the diurnal cycle of the valley winds is well represented in most large (e.g., Rhein valley at Chur and Rhone valley at Visp) and medium-sized valleys (e.g., Linth valley at Glarus). In the smaller valleys (e.g., Maggia valley at Cevio), the amplitude of the valley wind is still significantly underestimated, even at a 1.1 km resolution. Detailed sensitivity experiments show that the use of high-resolution land surface datasets, for both the soil characteristics as well as for the land cover, and reduced filtering of the topography are essential to achieve good performance at a 1.1 km resolution
The synthesis of polynitrogen compounds is of fundamental importance due to their potential as environmentally-friendly high energy density materials. Attesting to the intrinsic difficulties related to their formation, only three polynitrogen ions, bulk stabilized as salts, are known. Here, magnesium and molecular nitrogen are compressed to about 50 GPa and laser-heated, producing two chemically simple salts of polynitrogen anions, MgN4 and Mg2N4. Single-crystal X-ray diffraction reveals infinite anionic polythiazyl-like 1D N-N chains in the crystal structure of MgN4 and cis-tetranitrogen N44− units in the two isosymmetric polymorphs of Mg2N4. The cis-tetranitrogen units are found to be recoverable at atmospheric pressure. Our results respond to the quest for polynitrogen entities stable at ambient conditions, reveal the potential of employing high pressures in their synthesis and enrich the nitrogen chemistry through the discovery of other nitrogen species, which provides further possibilities to design improved polynitrogen arrangements.
State-of-the-art climate models contain, to a significant degree, empirical components. In particular, subgrid-scale (SGS) parameterizations are usually highly tuned against observations or high-resolution model data. While this enables the models to minimize the error during hindcasts, it is not guaranteed that it yields a benefit for climate projections because of climate change. In this thesis the Fluctuation-Dissipation theorem (FDT) is used to update the statistics of the system in the presence of an external forcing. If the empirical parameters are tuned objectively to the data (i.e., they depend on the statistics of the data), then they might be updated with the FDT. This ansatz is tested within a framework of a semi-empirical model (SEM) based on the leading variance patterns of a quasigeostrophic three-layer model (QG3LM) and supplemented by a purely data-driven parameterization. We show that the FDT is able to successfully update the tuning parameters of the data-driven SGS closure, resulting in a systematic improvement in model performance in comparison to an untreated SEM. Ideally, SGS parameterizations should contain little to no tuning parameters. Thus, complementary to the FDT approach we investigate a stochastic SGS closure constrained by first principles that is calculated using the stochastic mode reduction (SMR). The SMR allows for an analytic derivation of the SGS closure from the model equations while requiring only minimal tuning. We successfully apply the SMR to the QG3LM and construct the reduced stochastic model (RSM). Furthermore, we show that the RSM is more robust against an external forcing than the SEM. Additionally, we find that, under appropriate conditions, the FDT is able to update the empirical parts of the RSM. Yet, only for the response in mean streamfunction the RSM provides useful results, while the response in covariance of the streamfunction is incorrect for most cases. Nevertheless, we obtain a remarkably accurate response in both moments for the RSM in an idealized setting. In combination with the results of the FDT study this indicates that the considered RSM is too low dimensional and encourages us to investigate the response of larger RSMs in the future.
Abiotic formation of n-alkane hydrocarbons has been postulated to occur within Earth's crust. Apparent evidence was primarily based on uncommon carbon and hydrogen isotope distribution patterns that set methane and its higher chain homologues apart from biotic isotopic compositions associated with microbial production and closed system thermal degradation of organic matter. Here, we present the first global investigation of the carbon and hydrogen isotopic compositions of n-alkanes in volcanic-hydrothermal fluids hosted by basaltic, andesitic, trachytic and rhyolitic rocks. We show that the bulk isotopic compositions of these gases follow trends that are characteristic of high temperature, open system degradation of organic matter. In sediment-free systems, organic matter is supplied by surface waters (seawater, meteoric water) circulating through the reservoir rocks. Our data set strongly implies that thermal degradation of organic matter is able to satisfy isotopic criteria previously classified as being indicative of abiogenesis. Further considering the ubiquitous presence of surface waters in Earth’s crust, abiotic hydrocarbon occurrences might have been significantly overestimated.
To quantify water flows between groundwater (GW) and surface water (SW) as well as the impact of Abstract. To quantify water flows between groundwater (GW) and surface water (SW) as well as the impact of capillary rise on evapotranspiration by global hydrological models (GHMs), it is necessary to replace the bucket-like linear GW reservoir model typical for hydrological models with a fully integrated gradient-based GW flow model. Linear reservoir models can only simulate GW discharge to SW bodies, provide no information on the location of the GW table and assume that there is no GW flow among grid cells. A gradient-based GW model simulates not only GW storage but also hydraulic head, which together with information on SW table elevation enables the quantification of water flows from GW to SW and vice versa. In addition, hydraulic heads are the basis for calculating lateral GW flow among grid cells and capillary rise.
G³M is a new global gradient-based GW model with a spatial resolution of 5' that will replace the current linear GW reservoir in the 0.5° WaterGAP Global Hydrology Model (WGHM). The newly developed model framework enables inmemory coupling to WGHM while keeping overall runtime relatively low, allowing sensitivity analyses and data assimilation. This paper presents the G³M concept and specific model design decisions together with results under steady-state naturalized conditions, i.e. neglecting GW abstractions. Cell-specific conductances of river beds, which govern GW-SW interaction, were determined based on the 30'' steady-state water table computed by Fan et al. (2013). Together with an appropriate choice for the effective elevation of the SW table within each grid cell, this enables a reasonable simulation of drainage from GW to SW such that, in contrast to the GW model of de Graaf et al. (2015, 2017), no additional drainage based on externally provided values for GW storage above the floodplain is required in G³M. Comparison of simulated hydraulic heads to observations around the world shows better agreement than de Graaf et al. (2015). In addition, G³M output is compared to the output of two established macro-scale models for the Central Valley, California, and the continental United States, respectively. As expected, depth to GW table is highest in mountainous and lowest in flat regions. A first analysis of losing and gaining rivers and lakes/wetlands indicates that GW discharge to rivers is by far the dominant flow, draining diffuse GW recharge, such that lateral flows only become a large fraction of total diffuse and focused recharge in case of losing rivers and some areas with very low GW recharge. G³M does not represent losing rivers in some dry regions. This study presents the first steps towards replacing the linear GW reservoir model in a GHM while improving on recent efforts, demonstrating the feasibility of the approach and the robustness of the newly developed framework.