@phdthesis{Smirnov2023,
  author    = {Smirnov, Artem},
  title     = {Understanding the dynamics of the near-earth space environment utilizing long-term satellite observations},
  doi       = {10.25932/publishup-61371},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-613711},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxxvi, 286},
  year      = {2023},
  abstract  = {The near-Earth space environment is a highly complex system comprised of several regions and particle populations hazardous to satellite operations. The trapped particles in the radiation belts and ring current can cause significant damage to satellites during space weather events, due to deep dielectric and surface charging. Closer to Earth is another important region, the ionosphere, which delays the propagation of radio signals and can adversely affect navigation and positioning. In response to fluctuations in solar and geomagnetic activity, both the inner-magnetospheric and ionospheric populations can undergo drastic and sudden changes within minutes to hours, which creates a challenge for predicting their behavior. Given the increasing reliance of our society on satellite technology, improving our understanding and modeling of these populations is a matter of paramount importance. In recent years, numerous spacecraft have been launched to study the dynamics of particle populations in the near-Earth space, transforming it into a data-rich environment. To extract valuable insights from the abundance of available observations, it is crucial to employ advanced modeling techniques, and machine learning methods are among the most powerful approaches available. This dissertation employs long-term satellite observations to analyze the processes that drive particle dynamics, and builds interdisciplinary links between space physics and machine learning by developing new state-of-the-art models of the inner-magnetospheric and ionospheric particle dynamics. The first aim of this thesis is to investigate the behavior of electrons in Earth's radiation belts and ring current. Using ~18 years of electron flux observations from the Global Positioning System (GPS), we developed the first machine learning model of hundreds-of-keV electron flux at Medium Earth Orbit (MEO) that is driven solely by solar wind and geomagnetic indices and does not require auxiliary flux measurements as inputs. We then proceeded to analyze the directional distributions of electrons, and for the first time, used Fourier sine series to fit electron pitch angle distributions (PADs) in Earth's inner magnetosphere. We performed a superposed epoch analysis of 129 geomagnetic storms during the Van Allen Probes era and demonstrated that electron PADs have a strong energy-dependent response to geomagnetic activity. Additionally, we showed that the solar wind dynamic pressure could be used as a good predictor of the PAD dynamics. Using the observed dependencies, we created the first PAD model with a continuous dependence on L, magnetic local time (MLT) and activity, and developed two techniques to reconstruct near-equatorial electron flux observations from low-PA data using this model. The second objective of this thesis is to develop a novel model of the topside ionosphere. To achieve this goal, we collected observations from five of the most widely used ionospheric missions and intercalibrated these data sets. This allowed us to use these data jointly for model development, validation, and comparison with other existing empirical models. We demonstrated, for the first time, that ion density observations by Swarm Langmuir Probes exhibit overestimation (up to ~40-50\%) at low and mid-latitudes on the night side, and suggested that the influence of light ions could be a potential cause of this overestimation. To develop the topside model, we used 19 years of radio occultation (RO) electron density profiles, which were fitted with a Chapman function with a linear dependence of scale height on altitude. This approximation yields 4 parameters, namely the peak density and height of the F2-layer and the slope and intercept of the linear scale height trend, which were modeled using feedforward neural networks (NNs). The model was extensively validated against both RO and in-situ observations and was found to outperform the International Reference Ionosphere (IRI) model by up to an order of magnitude. Our analysis showed that the most substantial deviations of the IRI model from the data occur at altitudes of 100-200 km above the F2-layer peak. The developed NN-based ionospheric model reproduces the effects of various physical mechanisms observed in the topside ionosphere and provides highly accurate electron density predictions. This dissertation provides an extensive study of geospace dynamics, and the main results of this work contribute to the improvement of models of plasma populations in the near-Earth space environment.},
  language  = {en}
}
@phdthesis{Maiti2023,
  author    = {Maiti, Snehanshu},
  title     = {Magnetohydrodynamic turbulence and cosmic ray transport},
  doi       = {10.25932/publishup-58903},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-589030},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 81},
  year      = {2023},
  abstract  = {The first part of the thesis studies the properties of fast mode in magneto hydro-dynamic (MHD) turbulence. 1D and 3D numerical simulations are carried out to generate decaying fast mode MHD turbulence. The injection of waves are carried out in a collinear and isotropic fashion to generate fast mode turbulence. The properties of fast mode turbulence are analyzed by studying their energy spectral density, 2D structure functions and energy decay/cascade time. The injection wave vector is varied to study the dependence of the above properties on the injection wave vectors. The 1D energy spectrum obtained for the velocity and magnetic fields has 𝐸 (𝑘) ∝ 𝑘-2. The 2D energy spectrum and 2D structure functions in parallel and perpendicular directions shows that fast mode turbulence generated is isotropic in nature. The cascade/decay rate of fast mode MHD turbulence is proportional to 𝑘-0.5 for different kinds of wave vector injection. Simulations are also carried out in 1D and 3D to compare balanced and imbalanced turbulence. The results obtained shows that while 1D imbalanced turbulence decays faster than 1D balanced turbulence, there is no difference in the decay of 3D balanced and imbalanced turbulence for the current resolution of 512 grid points. "The second part of the thesis studies cosmic ray (CR) transport in driven MHD turbulence and is strongly dependent on it's properties. Test particle simulations are carried out to study CR interaction with both total MHD turbulence and decomposed MHD modes. The spatial diffusion coefficients and the pitch angle scattering diffusion coefficients are calculated from the test particle trajectories in turbulence. The results confirms that the fast modes dominate the CR propagation, whereas Alfv{\´e}n, slow modes are much less efficient with similar pitch angle scattering rates. The cross field transport on large and small scales are investigated next. On large/global scales, normal diffusion is observed and the diffusion coefficient is suppressed by 𝑀𝜁𝐴 compared to the parallel diffusion coefficients, with 𝜁 closer to 4 in Alfv{\´e}n modes than that in total turbulence as theoretically expected. For the CR transport on scales smaller than the turbulence injection scale 𝐿, both the local and global magnetic reference frames are adopted. Super diffusion is observed on such small scales in all the cases. Particularly, CR transport in Alfv{\´e}n modes show clear Richardson diffusion in the local reference frame. The diffusion transition smoothly from the Richardson's one with index 1.5 to normal diffusion as particle's mean free path decreases from 𝜆∥ ≫ 𝐿 to 𝜆∥ ≪ 𝐿. These results have broad applications to CRs in various astrophysical environments".},
  language  = {en}
}
@phdthesis{Aseev2020,
  author    = {Aseev, Nikita},
  title     = {Modeling and understanding dynamics of charged particles in the Earth's inner magnetosphere},
  doi       = {10.25932/publishup-47921},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-479211},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxii, 154},
  year      = {2020},
  abstract  = {The Earth's inner magnetosphere is a very dynamic system, mostly driven by the external solar wind forcing exerted upon the magnetic field of our planet. Disturbances in the solar wind, such as coronal mass ejections and co-rotating interaction regions, cause geomagnetic storms, which lead to prominent changes in charged particle populations of the inner magnetosphere - the plasmasphere, ring current, and radiation belts. Satellites operating in the regions of elevated energetic and relativistic electron fluxes can be damaged by deep dielectric or surface charging during severe space weather events. Predicting the dynamics of the charged particles and mitigating their effects on the infrastructure is of particular importance, due to our increasing reliance on space technologies. The dynamics of particles in the plasmasphere, ring current, and radiation belts are strongly coupled by means of collisions and collisionless interactions with electromagnetic fields induced by the motion of charged particles. Multidimensional numerical models simplify the treatment of transport, acceleration, and loss processes of these particles, and allow us to predict how the near-Earth space environment responds to solar storms. The models inevitably rely on a number of simplifications and assumptions that affect model accuracy and complicate the interpretation of the results. In this dissertation, we quantify the processes that control electron dynamics in the inner magnetosphere, paying particular attention to the uncertainties of the employed numerical codes and tools. We use a set of convenient analytical solutions for advection and diffusion equations to test the accuracy and stability of the four-dimensional Versatile Electron Radiation Belt (VERB-4D) code. We show that numerical schemes implemented in the code converge to the analytical solutions and that the VERB-4D code demonstrates stable behavior independent of the assumed time step. The order of the numerical scheme for the convection equation is demonstrated to affect results of ring current and radiation belt simulations, and it is crucially important to use high-order numerical schemes to decrease numerical errors in the model. Using the thoroughly tested VERB-4D code, we model the dynamics of the ring current electrons during the 17 March 2013 storm. The discrepancies between the model and observations above 4.5 Earth's radii can be explained by uncertainties in the outer boundary conditions. Simulation results indicate that the electrons were transported from the geostationary orbit towards the Earth by the global-scale electric and magnetic fields. We investigate how simulation results depend on the input models and parameters. The model is shown to be particularly sensitive to the global electric field and electron lifetimes below 4.5 Earth's radii. The effects of radial diffusion and subauroral polarization streams are also quantified. We developed a data-assimilative code that blends together a convection model of energetic electron transport and loss and Van Allen Probes satellite data by means of the Kalman filter. We show that the Kalman filter can correct model uncertainties in the convection electric field, electron lifetimes, and boundary conditions. It is also demonstrated how the innovation vector - the difference between observations and model prediction - can be used to identify physical processes missing in the model of energetic electron dynamics. We computed radial profiles of phase space density of ultrarelativistic electrons, using Van Allen Probes measurements. We analyze the shape of the profiles during geomagnetically quiet and disturbed times and show that the formation of new local minimums in the radial profiles coincides with the ground observations of electromagnetic ion-cyclotron (EMIC) waves. This correlation indicates that EMIC waves are responsible for the loss of ultrarelativistic electrons from the heart of the outer radiation belt into the Earth's atmosphere.},
  language  = {en}
}
@phdthesis{Matuschek2015,
  author    = {Matuschek, Hannes},
  title     = {Applications of reproducing Kernel Hilbert spaces and their approximations},
  school      = {Universit{\"a}t Potsdam},
  pages     = {83},
  year      = {2015},
  language  = {en}
}
@phdthesis{RodriguezZuluaga2020,
  author    = {Rodriguez Zuluaga, Juan},
  title     = {Electric and magnetic characteristics of equatorial plasma depletions},
  doi       = {10.25932/publishup-44587},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-445873},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xvi, 87},
  year      = {2020},
  abstract  = {Near-Earth space represents a significant scientific and technological challenge. Particularly at magnetic low-latitudes, the horizontal magnetic field geometry at the dip equator and its closed field-lines support the existence of a distinct electric current system, abrupt electric field variations and the development of plasma irregularities. Of particular interest are small-scale irregularities associated with equatorial plasma depletions (EPDs). They are responsible for the disruption of trans-ionospheric radio waves used for navigation, communication, and Earth observation. The fast increase of satellite missions makes it imperative to study the near-Earth space, especially the phenomena known to harm space technology or disrupt their signals. EPDs correspond to the large-scale structure (i.e., tens to hundreds of kilometers) of topside F region irregularities commonly known as Spread F. They are observed as depleted-plasma density channels aligned with the ambient magnetic field in the post-sunset low-latitude ionosphere. Although the climatological variability of their occurrence in terms of season, longitude, local time and solar flux is well-known, their day to day variability is not. The sparse observations from ground-based instruments like radars and the few simultaneous measurements of ionospheric parameters by space-based instruments have left gaps in the knowledge of EPDs essential to comprehend their variability. In this dissertation, I profited from the unique observations of the ESA's Swarm constellation mission launched in November 2013 to tackle three issues that revealed novel and significant results on the current knowledge of EPDs. I used Swarm's measurements of the electron density, magnetic, and electric fields to answer, (1.) what is the direction of propagation of the electromagnetic energy associated with EPDs?, (2.) what are the spatial and temporal characteristics of the electric currents (field-aligned and diamagnetic currents) related to EPDs, i.e., seasonal/geographical, and local time dependencies?, and (3.) under what conditions does the balance between magnetic and plasma pressure across EPDs occur? The results indicate that: (1.) The electromagnetic energy associated with EPDs presents a preference for interhemispheric flows; that is, the related Poynting flux directs from one magnetic hemisphere to the other and varies with longitude and season. (2.) The field-aligned currents at the edges of EPDs are interhemispheric. They generally close in the hemisphere with the highest Pedersen conductance. Such hemispherical preference presents a seasonal/longitudinal dependence. The diamagnetic currents increase or decrease the magnetic pressure inside EPDs. These two effects rely on variations of the plasma temperature inside the EPDs that depend on longitude and local time. (3.) EPDs present lower or higher plasma pressure than the ambient. For low-pressure EPDs the plasma pressure gradients are mostly dominated by variations of the plasma density so that variations of the temperature are negligible. High-pressure EPDs suggest significant temperature variations with magnitudes of approximately twice the ambient. Since their occurrence is more frequent in the vicinity of the South Atlantic magnetic anomaly, such high temperatures are suggested to be due to particle precipitation. In a broader context, this dissertation shows how dedicated satellite missions with high-resolution capabilities improve the specification of the low-latitude ionospheric electrodynamics and expand knowledge on EPDs which is valuable for current and future communication, navigation, and Earth-observing missions. The contributions of this investigation represent several 'firsts' in the study of EPDs: (1.) The first observational evidence of interhemispheric electromagnetic energy flux and field-aligned currents. (2.) The first spatial and temporal characterization of EPDs based on their associated field-aligned and diamagnetic currents. (3.) The first evidence of high plasma pressure in regions of depleted plasma density in the ionosphere. These findings provide new insights that promise to advance our current knowledge of not only EPDs but the low-latitude post-sunset ionosphere environment.},
  language  = {en}
}
@phdthesis{Schlemm2022,
  author    = {Schlemm, Tanja},
  title     = {The marine ice cliff instability of the Antarctic ice sheet},
  doi       = {10.25932/publishup-58633},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-586333},
  school      = {Universit{\"a}t Potsdam},
  pages     = {107},
  year      = {2022},
  abstract  = {The Antarctic ice sheet is the largest freshwater reservoir worldwide. If it were to melt completely, global sea levels would rise by about 58 m. Calculation of projections of the Antarctic contribution to sea level rise under global warming conditions is an ongoing effort which yields large ranges in predictions. Among the reasons for this are uncertainties related to the physics of ice sheet modeling. These uncertainties include two processes that could lead to runaway ice retreat: the Marine Ice Sheet Instability (MISI), which causes rapid grounding line retreat on retrograde bedrock, and the Marine Ice Cliff Instability (MICI), in which tall ice cliffs become unstable and calve off, exposing even taller ice cliffs. In my thesis, I investigated both marine instabilities (MISI and MICI) using the Parallel Ice Sheet Model (PISM), with a focus on MICI.},
  language  = {en}
}
@phdthesis{Kuhla2022,
  author    = {Kuhla, Kilian},
  title     = {Impact, distribution, and adaptation},
  doi       = {10.25932/publishup-55266},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-552668},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 309},
  year      = {2022},
  abstract  = {Weather extremes pose a persistent threat to society on multiple layers. Besides an average of ~37,000 deaths per year, climate-related disasters cause destroyed properties and impaired economic activities, eroding people's livelihoods and prosperity. While global temperature rises - caused by anthropogenic greenhouse gas emissions - the direct impacts of climatic extreme events increase and will further intensify without proper adaptation measures. Additionally, weather extremes do not only have local direct effects. Resulting economic repercussions can propagate either upstream or downstream along trade chains causing indirect effects. One approach to analyze these indirect effects within the complex global supply network is the agent-based model Acclimate. Using and extending this loss-propagation model, I focus in this thesis on three aspects of the relation between weather extremes and economic repercussions. First, extreme weather events cause direct impacts on local economic performance. I compute daily local direct output loss time series of heat stress, river floods, tropical cyclones, and their consecutive occurrence using (near-future) climate projection ensembles. These regional impacts are estimated based on physical drivers and local productivity distribution. Direct effects of the aforementioned disaster categories are widely heterogeneous concerning regional and temporal distribution. As well, their intensity changes differently under future warming. Focusing on the hurricane-impacted capital, I find that long-term growth losses increase with higher heterogeneity of a shock ensemble. Second, repercussions are sectorally and regionally distributed via economic ripples within the trading network, causing higher-order effects. I use Acclimate to identify three phases of those economic ripples. Furthermore, I compute indirect impacts and analyze overall regional and global production and consumption changes. Regarding heat stress, global consumer losses double while direct output losses increase by a factor 1.5 between 2000 - 2039. In my research I identify the effect of economic ripple resonance and introduce it to climate impact research. This effect occurs if economic ripples of consecutive disasters overlap, which increases economic responses such as an enhancement of consumption losses. These loss enhancements can even be more amplified with increasing direct output losses, e.g. caused by climate crises. Transport disruptions can cause economic repercussions as well. For this, I extend the model Acclimate with a geographical transportation route and expand the decision horizon of economic agents. Using this, I show that policy-induced sudden trade restrictions (e.g. a no-deal Brexit) can significantly reduce the longer-term economic prosperity of affected regions. Analyses of transportation disruptions in typhoon seasons indicate that severely affected regions must reduce production as demand falls during a storm. Substituting suppliers may compensate for fluctuations at the beginning of the storm, which fails for prolonged disruptions. Third, possible coping mechanisms and adaptation strategies arise from direct and indirect economic responses to weather extremes. Analyzing annual trade changes due to typhoon-induced transport disruptions depict that overall exports rise. This trade resilience increases with higher network node diversification. Further, my research shows that a basic insurance scheme may diminish hurricane-induced long-term growth losses due to faster reconstruction in disasters aftermaths. I find that insurance coverage could be an economically reasonable coping scheme towards higher losses caused by the climate crisis. Indirect effects within the global economic network from weather extremes indicate further adaptation possibilities. For one, diversifying linkages reduce the hazard of sharp price increases. Next to this, close economic interconnections with regions that do not share the same extreme weather season can be economically beneficial in the medium run. Furthermore, economic ripple resonance effects should be considered while computing costs. Overall, an increase in local adaptation measures reduces economic ripples within the trade network and possible losses elsewhere. In conclusion, adaptation measures are necessary and potential present, but it seems rather not possible to avoid all direct or indirect losses. As I show in this thesis, dynamical modeling gives valuable insights into how direct and indirect economic impacts arise from different categories of weather extremes. Further, it highlights the importance of resolving individual extremes and reflecting amplifying effects caused by incomplete recovery or consecutive disasters.},
  language  = {en}
}
@phdthesis{Bastian2023,
  author    = {Bastian, Martin},
  title     = {An emergent machine learning approach for seasonal cyclone activity forecasts},
  school      = {Universit{\"a}t Potsdam},
  pages     = {135},
  year      = {2023},
  abstract  = {Seasonal forecasts are of great interest in many areas. Knowing the amount of precipitation for the upcoming season in regions of water scarcity would facilitate a better water management. If farmers knew the weather conditions of the upcoming summer at sowing time, they could select those cereal species that are best adapted to these conditions. This would allow farmers to improve the harvest and potentially even reduce the amount of pesticides used. However, the undoubted advantages of seasonal forecasts are often opposed by their high degree of uncertainty. The great challenge of generating seasonal forecasts with lead times of several months mainly originates from the chaotic nature of the earth system. In a chaotic system, even tiny differences in the initial conditions can lead to strong deviations in the system's state in the long run. In this dissertation we propose an emergent machine learning approach for seasonal forecasting, called the AnlgModel. The AnlgModel combines the analogue method with myopic feature selection and bootstrapping. To benchmark the abilities of the AnlgModel we apply it to seasonal cyclone activity forecasts in the North Atlantic and Northwest Pacific. The AnlgModel demonstrates competitive hindcast skills with two operational forecasts and even outperforms these for long lead times. In the second chapter we comprehend the forecasting strategy of the Anlg-Model. We thereby analyse the analogue selection process for the 2017 North Atlantic and the 2018 Northwest Pacific seasonal cyclone activity. The analysis shows that those climate indices which are known to influence the seasonal cyclone activity, such as the Ni{\~n}o 3.4 SST, are correctly represented among the selected analogues. Furthermore the selected analogues reflect large-scale climate patterns that were identified by expert reports as being determinative for these particular seasons. In the third chapter we analyse the features that are used by the AnlgModel for its predictions. We therefore inspect the feature relevance (FR). The FR patterns learned by the AnlgModel show a high congruence with the predictor regions used by the operational forecasts. However, the AnlgModel also discovered new features, such as the SST anomaly in the Gulf of Guinea during November. This SST pattern exhibits a remarkably high predictive potential for the upcoming Atlantic hurricane activity. In the final chapter we investigate potential mechanisms, that link two of these regions with high feature relevance to the Atlantic hurricane activity. We mainly focus on ocean surface transport. The ocean surface flow paths are calculated using Lagrangian particle analysis. We demonstrate that the FR patterns in the region of the Canary islands do not correspond with ocean surface transport. It is instead likely that these FR patterns fingerprint a wind transport of latent heat. The second region to be studied is situated in the Gulf of Guinea. Our analysis shows that the FR patterns seen there do fingerprint ocean surface transport. However, our simulations also show that at least one other mechanism is involved in linking the Gulf of Guinea SST anomaly in November to the hurricane activity of the upcoming season. In this work the AnlgModel does not only demonstrate its outstanding forecast skills but also shows its capabilities as research tool for detecting oceanic and atmospheric mechanisms.},
  language  = {en}
}
@phdthesis{Braun2023,
  author    = {Braun, Tobias},
  title     = {Recurrences in past climates},
  doi       = {10.25932/publishup-58690},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-586900},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxviii, 251},
  year      = {2023},
  abstract  = {Our ability to predict the state of a system relies on its tendency to recur to states it has visited before. Recurrence also pervades common intuitions about the systems we are most familiar with: daily routines, social rituals and the return of the seasons are just a few relatable examples. To this end, recurrence plots (RP) provide a systematic framework to quantify the recurrence of states. Despite their conceptual simplicity, they are a versatile tool in the study of observational data. The global climate is a complex system for which an understanding based on observational data is not only of academical relevance, but vital for the predurance of human societies within the planetary boundaries. Contextualizing current global climate change, however, requires observational data far beyond the instrumental period. The palaeoclimate record offers a valuable archive of proxy data but demands methodological approaches that adequately address its complexities. In this regard, the following dissertation aims at devising novel and further developing existing methods in the framework of recurrence analysis (RA). The proposed research questions focus on using RA to capture scale-dependent properties in nonlinear time series and tailoring recurrence quantification analysis (RQA) to characterize seasonal variability in palaeoclimate records ('Palaeoseasonality'). In the first part of this thesis, we focus on the methodological development of novel approaches in RA. The predictability of nonlinear (palaeo)climate time series is limited by abrupt transitions between regimes that exhibit entirely different dynamical complexity (e.g. crossing of 'tipping points'). These possibly depend on characteristic time scales. RPs are well-established for detecting transitions and capture scale-dependencies, yet few approaches have combined both aspects. We apply existing concepts from the study of self-similar textures to RPs to detect abrupt transitions, considering the most relevant time scales. This combination of methods further results in the definition of a novel recurrence based nonlinear dependence measure. Quantifying lagged interactions between multiple variables is a common problem, especially in the characterization of high-dimensional complex systems. The proposed 'recurrence flow' measure of nonlinear dependence offers an elegant way to characterize such couplings. For spatially extended complex systems, the coupled dynamics of local variables result in the emergence of spatial patterns. These patterns tend to recur in time. Based on this observation, we propose a novel method that entails dynamically distinct regimes of atmospheric circulation based on their recurrent spatial patterns. Bridging the two parts of this dissertation, we next turn to methodological advances of RA for the study of Palaeoseasonality. Observational series of palaeoclimate 'proxy' records involve inherent limitations, such as irregular temporal sampling. We reveal biases in the RQA of time series with a non-stationary sampling rate and propose a correction scheme. In the second part of this thesis, we proceed with applications in Palaeoseasonality. A review of common and promising time series analysis methods shows that numerous valuable tools exist, but their sound application requires adaptions to archive-specific limitations and consolidating transdisciplinary knowledge. Next, we study stalagmite proxy records from the Central Pacific as sensitive recorders of mid-Holocene El Ni{\~n}o-Southern Oscillation (ENSO) dynamics. The records' remarkably high temporal resolution allows to draw links between ENSO and seasonal dynamics, quantified by RA. The final study presented here examines how seasonal predictability could play a role for the stability of agricultural societies. The Classic Maya underwent a period of sociopolitical disintegration that has been linked to drought events. Based on seasonally resolved stable isotope records from Yok Balum cave in Belize, we propose a measure of seasonal predictability. It unveils the potential role declining seasonal predictability could have played in destabilizing agricultural and sociopolitical systems of Classic Maya populations. The methodological approaches and applications presented in this work reveal multiple exciting future research avenues, both for RA and the study of Palaeoseasonality.},
  language  = {en}
}
@phdthesis{Schemenz2022,
  author    = {Schemenz, Victoria},
  title     = {Correlations between osteocyte lacuno-canalicular network and material characteristics in bone adaptation and regeneration},
  doi       = {10.25932/publishup-55959},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-559593},
  school      = {Universit{\"a}t Potsdam},
  pages     = {3, xii, 146},
  year      = {2022},
  abstract  = {The complex hierarchical structure of bone undergoes a lifelong remodeling process, where it adapts to mechanical needs. Hereby, bone resorption by osteoclasts and bone formation by osteoblasts have to be balanced to sustain a healthy and stable organ. Osteocytes orchestrate this interplay by sensing mechanical strains and translating them into biochemical signals. The osteocytes are located in lacunae and are connected to one another and other bone cells via cell processes through small channels, the canaliculi. Lacunae and canaliculi form a network (LCN) of extracellular spaces that is able to transport ions and enables cell-to-cell communication. Osteocytes might also contribute to mineral homeostasis by direct interactions with the surrounding matrix. If the LCN is acting as a transport system, this should be reflected in the mineralization pattern. The central hypothesis of this thesis is that osteocytes are actively changing their material environment. Characterization methods of material science are used to achieve the aim of detecting traces of this interaction between osteocytes and the extracellular matrix. First, healthy murine bones were characterized. The properties analyzed were then compared with three murine model systems: 1) a loading model, where a bone of the mouse was loaded during its life time; 2) a healing model, where a bone of the mouse was cut to induce a healing response; and 3) a disease model, where the Fbn1 gene is dysfunctional causing defects in the formation of the extracellular tissue. The measurement strategy included routines that make it possible to analyze the organization of the LCN and the material components (i.e., the organic collagen matrix and the mineral particles) in the same bone volumes and compare the spatial distribution of different data sets. The three-dimensional network architecture of the LCN is visualized by confocal laser scanning microscopy (CLSM) after rhodamine staining and is then subsequently quantified. The calcium content is determined via quantitative backscattered electron imaging (qBEI), while small- and wide-angle X-ray scattering (SAXS and WAXS) are employed to determine the thickness and length of local mineral particles. First, tibiae cortices of healthy mice were characterized to investigate how changes in LCN architecture can be attributed to interactions of osteocytes with the surrounding bone matrix. The tibial mid-shaft cross-sections showed two main regions, consisting of a band with unordered LCN surrounded by a region with ordered LCN. The unordered region is a remnant of early bone formation and exhibited short and thin mineral particles. The surrounding, more aligned bone showed ordered and dense LCN as well as thicker and longer mineral particles. The calcium content was unchanged between the two regions. In the mouse loading model, the left tibia underwent two weeks of mechanical stimulation, which results in increased bone formation and decreased resorption in skeletally mature mice. Here the specific research question addressed was how do bone material characteristics change at (re)modeling sites? The new bone formed in response to mechanical stimulation showed similar properties in terms of the mineral particles, like the ordered calcium region but lower calcium content compared to the right, non-loaded control bone of the same mice. There was a clear, recognizable border between mature and newly formed bone. Nevertheless, some canaliculi went through this border connecting the LCN of mature and newly formed bone. Additionally, the question should be answered whether the LCN topology and the bone matrix material properties adapt to loading. Although, mechanically stimulated bones did not show differences in calcium content compared to controls, different correlations were found between the local LCN density and the local Ca content depending on whether the bone was loaded or not. These results suggest that the LCN may serve as a mineral reservoir. For the healing model, the femurs of mice underwent an osteotomy, stabilized with an external fixator and were allowed to heal for 21 days. Thus, the spatial variations in the LCN topology with mineral properties within different tissue types and their interfaces, namely calcified cartilage, bony callus and cortex, could be simultaneously visualized and compared in this model. All tissue types showed structural differences across multiple length scales. Calcium content increased and became more homogeneous from calcified cartilage to bony callus to lamellar cortical bone. The degree of LCN organization increased as well, while the lacunae became smaller, as did the lacunar density between these different tissue types that make up the callus. In the calcified cartilage, the mineral particles were short and thin. The newly formed callus exhibited thicker mineral particles, which still had a low degree of orientation. While most of the callus had a woven-like structure, it also served as a scaffold for more lamellar tissue at the edges. The lamelar bone callus showed thinner mineral particles, but a higher degree of alignment in both, mineral particles and the LCN. The cortex showed the highest values for mineral length, thickness and degree of orientation. At the same time, the lacunae number density was 34\% lower and the lacunar volume 40\% smaller compared to bony callus. The transition zone between cortical and callus regions showed a continuous convergence of bone mineral properties and lacunae shape. Although only a few canaliculi connected callus and the cortical region, this indicates that communication between osteocytes of both tissues should be possible. The presented correlations between LCN architecture and mineral properties across tissue types may suggest that osteocytes have an active role in mineralization processes of healing. A mouse model for the disease marfan syndrome, which includes a genetic defect in the fibrillin-1 gene, was investigated. In humans, Marfan syndrome is characterized by a range of clinical symptoms such as long bone overgrowth, loose joints, reduced bone mineral density, compromised bone microarchitecture, and increased fracture rates. Thus, fibrillin-1 seems to play a role in the skeletal homeostasis. Therefore, the present work studied how marfan syndrome alters LCN architecture and the surrounding bone matrix. The mice with marfan syndrome showed longer tibiae than their healthy littermates from an age of seven weeks onwards. In contrast, the cortical development appeared retarded, which was observed across all measured characteristics, i. e. lower endocortical bone formation, looser and less organized lacuno-canalicular network, less collagen orientation, thinner and shorter mineral particles. In each of the three model systems, this study found that changes in the LCN architecture spatially correlated with bone matrix material parameters. While not knowing the exact mechanism, these results provide indications that osteocytes can actively manipulate a mineral reservoir located around the canaliculi to make a quickly accessible contribution to mineral homeostasis. However, this interaction is most likely not one-sided, but could be understood as an interplay between osteocytes and extra-cellular matrix, since the bone matrix contains biochemical signaling molecules (e.g. non-collagenous proteins) that can change osteocyte behavior. Bone (re)modeling can therefore not only be understood as a method for removing defects or adapting to external mechanical stimuli, but also for increasing the efficiency of possible osteocyte-mineral interactions during bone homeostasis. With these findings, it seems reasonable to consider osteocytes as a target for drug development related to bone diseases that cause changes in bone composition and mechanical properties. It will most likely require the combined effort of materials scientists, cell biologists, and molecular biologists to gain a deeper understanding of how bone cells respond to their material environment.},
  language  = {en}
}