Refine
Has Fulltext
- yes (153) (remove)
Year of publication
Document Type
- Doctoral Thesis (153) (remove)
Keywords
- Arktis (6)
- Arctic (5)
- Fernerkundung (4)
- Klimawandel (4)
- Nanopartikel (4)
- climate change (4)
- Anden (3)
- Andes (3)
- Atmosphäre (3)
- Holozän (3)
Institute
- Extern (153) (remove)
Genome-scale metabolic models are mathematical representations of all known reactions occurring in a cell. Combined with constraints based on physiological measurements, these models have been used to accurately predict metabolic fluxes and effects of perturbations (e.g. knock-outs) and to inform metabolic engineering strategies. Recently, protein-constrained models have been shown to increase predictive potential (especially in overflow metabolism), while alleviating the need for measurement of nutrient uptake rates. The resulting modelling frameworks quantify the upkeep cost of a certain metabolic flux as the minimum amount of enzyme required for catalysis. These improvements are based on the use of in vitro turnover numbers or in vivo apparent catalytic rates of enzymes for model parameterization. In this thesis several tools for the estimation and refinement of these parameters based on in vivo proteomics data of Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii have been developed and applied. The difference between in vitro and in vivo catalytic rate measures for the three microorganisms was systematically analyzed. The results for the facultatively heterotrophic microalga C. reinhardtii considerably expanded the apparent catalytic rate estimates for photosynthetic organisms. Our general finding pointed at a global reduction of enzyme efficiency in heterotrophy compared to other growth scenarios. Independent of the modelled organism, in vivo estimates were shown to improve accuracy of predictions of protein abundances compared to in vitro values for turnover numbers. To further improve the protein abundance predictions, machine learning models were trained that integrate features derived from protein-constrained modelling and codon usage. Combining the two types of features outperformed single feature models and yielded good prediction results without relying on experimental transcriptomic data. The presented work reports valuable advances in the prediction of enzyme allocation in unseen scenarios using protein constrained metabolic models. It marks the first successful application of this modelling framework in the biotechnological important taxon of green microalgae, substantially increasing our knowledge of the enzyme catalytic landscape of phototrophic microorganisms.
Floods continue to be the leading cause of economic damages and fatalities among natural disasters worldwide. As future climate and exposure changes are projected to intensify these damages, the need for more accurate and scalable flood risk models is rising. Over the past decade, macro-scale flood risk models have evolved from initial proof-of-concepts to indispensable tools for decision-making at global-, nationaland, increasingly, the local-level. This progress has been propelled by the advent of high-performance computing and the availability of global, space-based datasets. However, despite such advancements, these models are rarely validated and consistently fall short of the accuracy achieved by high-resolution local models. While capabilities have improved, significant gaps persist in understanding the behaviours of such macro-scale models, particularly their tendency to overestimate risk. This dissertation aims to address such gaps by examining the scale transfers inherent in the construction and application of coarse macroscale models. To achieve this, four studies are presented that, collectively, address exposure, hazard, and vulnerability components of risk affected by upscaling or downscaling.
The first study focuses on a type of downscaling where coarse flood hazard inundation grids are enhanced to a finer resolution. While such inundation downscaling has been employed in numerous global model chains, ours is the first study to focus specifically on this component, providing an evaluation of the state of the art and a novel algorithm. Findings demonstrate that our novel algorithm is eight times faster than existing methods, offers a slight improvement in accuracy, and generates more physically coherent flood maps in hydraulically challenging regions. When applied to a case study, the algorithm generated a 4m resolution inundation map from 30m hydrodynamic model outputs in 33 s, a 60-fold improvement in runtime with a 25% increase in RMSE compared with direct hydrodynamic modelling. All evaluated downscaling algorithms yielded better accuracy than the coarse hydrodynamic model when compared to observations, demonstrating similar limits of coarse hydrodynamic models reported by others. The substitution of downscaling into flood risk model chains, in place of high-resolution modelling, can drastically improve the lead time of impactbased forecasts and the efficiency of hazard map production. With downscaling, local regions could obtain high resolution local inundation maps by post-processing a global model without the need for expensive modelling or expertise.
The second study focuses on hazard aggregation and its implications for exposure, investigating implicit aggregations commonly used to intersect hazard grids with coarse exposure models. This research introduces a novel spatial classification framework to understand the effects of rescaling flood hazard grids to a coarser resolution. The study derives closed-form analytical solutions for the location and direction of bias from flood grid aggregation, showing that bias will always be present in regions near the edge of inundation. For example, inundation area will be positively biased when water depth grids are aggregated, while volume will be negatively biased when water elevation grids are aggregated. Extending the analysis to effects of hazard aggregation on building exposure, this study shows that exposure in regions at the edge of inundation are an order of magnitude more sensitive to aggregation errors than hazard alone. Among the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. The study provides the first mathematical proof and generalizeable treatment of flood hazard grid aggregation, demonstrating important mechanisms to help flood risk modellers understand and control model behaviour.
The final two studies focus on the aggregation of vulnerability models or flood damage functions, investigating the practice of applying per-asset functions to aggregate exposure models. Both studies extend Jensen’s inequality, a well-known 1906 mathematical proof, to demonstrate how the aggregation of flood damage functions leads to bias. Applying Jensen’s proof in this new context, results show that typically concave flood damage functions will introduce a positive bias (overestimation) when aggregated. This behaviour was further investigated with a simulation experiment including 2 million buildings in Germany, four global flood hazard simulations and three aggregation scenarios. The results show that positive aggregation bias is not distributed evenly in space, meaning some regions identified as “hot spots of risk” in assessments may in fact just be hot spots of aggregation bias. This study provides the first application of Jensen’s inequality to explain the overestimates reported elsewhere and advice for modellers to minimize such artifacts.
In total, this dissertation investigates the complex ways aggregation and disaggregation influence the behaviour of risk models, focusing on the scale-transfers underpinning macro-scale flood risk assessments. Extending a key finding of the flood hazard literature to the broader context of flood risk, this dissertation concludes that all else equal, coarse models overestimate risk. This dissertation goes beyond previous studies by providing mathematical proofs for how and where such bias emerges in aggregation routines, offering a mechanistic explanation for coarse model overestimates. It shows that this bias is spatially heterogeneous, necessitating a deep understanding of how rescaling may bias models to effectively reduce or communicate uncertainties. Further, the dissertation offers specific recommendations to help modellers minimize scale transfers in problematic regions. In conclusion, I argue that such aggregation errors are epistemic, stemming from choices in model structure, and therefore hold greater potential and impetus for study and mitigation. This deeper understanding of uncertainties is essential for improving macro-scale flood risk models and their effectiveness in equitable, holistic, and sustainable flood management.
During the last decades, therapeutical proteins have risen to great significance in the pharmaceutical industry. As non-human proteins that are introduced into the human body cause a distinct immune system reaction that triggers their rapid clearance, most newly approved protein pharmaceuticals are shielded by modification with synthetic polymers to significantly improve their blood circulation time. All such clinically approved protein-polymer conjugates contain polyethylene glycol (PEG) and its conjugation is denoted as PEGylation. However, many patients develop anti-PEG antibodies which cause a rapid clearance of PEGylated molecules upon repeated administration. Therefore, the search for alternative polymers that can replace PEG in therapeutic applications has become important. In addition, although the blood circulation time is significantly prolonged, the therapeutic activity of some conjugates is decreased compared to the unmodified protein. The reason is that these conjugates are formed by the traditional conjugation method that addresses the protein's lysine side chains. As proteins have many solvent exposed lysines, this results in a somewhat uncontrolled attachment of polymer chains, leading to a mixture of regioisomers, with some of them eventually affecting the therapeutic performance.
This thesis investigates a novel method for ligating macromolecules in a site-specific manner, using enzymatic catalysis. Sortase A is used as the enzyme: It is a well-studied transpeptidase which is able to catalyze the intermolecular ligation of two peptides. This process is commonly referred to as sortase-mediated ligation (SML). SML constitutes an equilibrium reaction, which limits product yield. Two previously reported methods to overcome this major limitation were tested with polymers without using an excessive amount of one reactant.
Specific C- or N-terminal peptide sequences (recognition sequence and nucleophile) as part of the protein are required for SML. The complementary peptide was located at the polymer chain end. Grafting-to was used to avoid damaging the protein during polymerization. To be able to investigate all possible combinations (protein-recognition sequence and nucleophile-protein as well as polymer-recognition sequence and nucleophile-polymer) all necessary building blocks were synthesized. Polymerization via reversible deactivation radical polymerization (RDRP) was used to achieve a narrow molecular weight distribution of the polymers, which is required for therapeutic use.
The synthesis of the polymeric building blocks was started by synthesizing the peptide via automated solid-phase peptide synthesis (SPPS) to avoid post-polymerization attachment and to enable easy adaptation of changes in the peptide sequence. To account for the different functionalities (free N- or C-terminus) required for SML, different linker molecules between resin and peptide were used.
To facilitate purification, the chain transfer agent (CTA) for reversible addition-fragmentation chain-transfer (RAFT) polymerization was coupled to the resin-immobilized recognition sequence peptide. The acrylamide and acrylate-based monomers used in this thesis were chosen for their potential to replace PEG.
Following that, surface-initiated (SI) ATRP and RAFT polymerization were attempted, but failed. As a result, the newly developed method of xanthate-supported photo-iniferter (XPI) RAFT polymerization in solution was used successfully to obtain a library of various peptide-polymer conjugates with different chain lengths and narrow molar mass distributions.
After peptide side chain deprotection, these constructs were used first to ligate two polymers via SML, which was successful but revealed a limit in polymer chain length (max. 100 repeat units). When utilizing equimolar amounts of reactants, the use of Ni2+ ions in combination with a histidine after the recognition sequence to remove the cleaved peptide from the equilibrium maximized product formation with conversions of up to 70 %.
Finally, a model protein and a nanobody with promising properties for therapeutical use were biotechnologically modified to contain the peptide sequences required for SML. Using the model protein for C- or N-terminal SML with various polymers did not result in protein-polymer conjugates. The reason is most likely the lack of accessibility of the protein termini to the enzyme. Using the nanobody for C-terminal SML, on the other hand, was successful. However, a similar polymer chain length limit was observed as in polymer-polymer SML. Furthermore, in case of the synthesis of protein-polymer conjugates, it was more effective to shift the SML equilibrium by using an excess of polymer than by employing the Ni2+ ion strategy.
Overall, the experimental data from this work provides a good foundation for future research in this promising field; however, more research is required to fully understand the potential and limitations of using SML for protein-polymer synthesis. In future, the method explored in this dissertation could prove to be a very versatile pathway to obtain therapeutic protein-polymer conjugates that exhibit high activities and long blood circulation times.
Arctic climate change is marked by intensified warming compared to global trends and a significant reduction in Arctic sea ice which can intricately influence mid-latitude atmospheric circulation through tropo- and stratospheric pathways. Achieving accurate simulations of current and future climate demands a realistic representation of Arctic climate processes in numerical climate models, which remains challenging.
Model deficiencies in replicating observed Arctic climate processes often arise due to inadequacies in representing turbulent boundary layer interactions that determine the interactions between the atmosphere, sea ice, and ocean. Many current climate models rely on parameterizations developed for mid-latitude conditions to handle Arctic turbulent boundary layer processes.
This thesis focuses on modified representation of the Arctic atmospheric processes and understanding their resulting impact on large-scale mid-latitude atmospheric circulation within climate models. The improved turbulence parameterizations, recently developed based on Arctic measurements, were implemented in the global atmospheric circulation model ECHAM6. This involved modifying the stability functions over sea ice and ocean for stable stratification and changing the roughness length over sea ice for all stratification conditions. Comprehensive analyses are conducted to assess the impacts of these modifications on ECHAM6's simulations of the Arctic boundary layer, overall atmospheric circulation, and the dynamical pathways between the Arctic and mid-latitudes.
Through a step-wise implementation of the mentioned parameterizations into ECHAM6, a series of sensitivity experiments revealed that the combined impacts of the reduced roughness length and the modified stability functions are non-linear. Nevertheless, it is evident that both modifications consistently lead to a general decrease in the heat transfer coefficient, being in close agreement with the observations.
Additionally, compared to the reference observations, the ECHAM6 model falls short in accurately representing unstable and strongly stable conditions.
The less frequent occurrence of strong stability restricts the influence of the modified stability functions by reducing the affected sample size. However, when focusing solely on the specific instances of a strongly stable atmosphere, the sensible heat flux approaches near-zero values, which is in line with the observations. Models employing commonly used surface turbulence parameterizations were shown to have difficulties replicating the near-zero sensible heat flux in strongly stable stratification.
I also found that these limited changes in surface layer turbulence parameterizations have a statistically significant impact on the temperature and wind patterns across multiple pressure levels, including the stratosphere, in both the Arctic and mid-latitudes. These significant signals vary in strength, extent, and direction depending on the specific month or year, indicating a strong reliance on the background state.
Furthermore, this research investigates how the modified surface turbulence parameterizations may influence the response of both stratospheric and tropospheric circulation to Arctic sea ice loss.
The most suitable parameterizations for accurately representing Arctic boundary layer turbulence were identified from the sensitivity experiments. Subsequently, the model's response to sea ice loss is evaluated through extended ECHAM6 simulations with different prescribed sea ice conditions.
The simulation with adjusted surface turbulence parameterizations better reproduced the observed Arctic tropospheric warming in vertical extent, demonstrating improved alignment with the reanalysis data. Additionally, unlike the control experiments, this simulation successfully reproduced specific circulation patterns linked to the stratospheric pathway for Arctic-mid-latitude linkages. Specifically, an increased occurrence of the Scandinavian-Ural blocking regime (negative phase of the North Atlantic Oscillation) in early (late) winter is observed. Overall, it can be inferred that improving turbulence parameterizations at the surface layer can improve the ECHAM6's response to sea ice loss.
Improving permafrost dynamics in land surface models: insights from dual sensitivity experiments
(2024)
The thawing of permafrost and the subsequent release of greenhouse gases constitute one of the most significant and uncertain positive feedback loops in the context of climate change, making predictions regarding changes in permafrost coverage of paramount importance. To address these critical questions, climate scientists have developed Land Surface Models (LSMs) that encompass a multitude of physical soil processes. This thesis is committed to advancing our understanding and refining precise representations of permafrost dynamics within LSMs, with a specific focus on the accurate modeling of heat fluxes, an essential component for simulating permafrost physics.
The first research question overviews fundamental model prerequisites for the representation of permafrost soils within land surface modeling. It includes a first-of-its-kind comparison between LSMs in CMIP6 to reveal their differences and shortcomings in key permafrost physics parameters. Overall, each of these LSMs represents a unique approach to simulating soil processes and their interactions with the climate system. Choosing the most appropriate model for a particular application depends on factors such as the spatial and temporal scale of the simulation, the specific research question, and available computational resources.
The second research question evaluates the performance of the state-of-the-art Community Land Model (CLM5) in simulating Arctic permafrost regions. Our approach overcomes traditional evaluation limitations by individually addressing depth, seasonality, and regional variations, providing a comprehensive assessment of permafrost and soil temperature dynamics. I compare CLM5's results with three extensive datasets: (1) soil temperatures from 295 borehole stations, (2) active layer thickness (ALT) data from the Circumpolar Active Layer Monitoring Network (CALM), and (3) soil temperatures, ALT, and permafrost extent from the ESA Climate Change Initiative (ESA-CCI). The results show that CLM5 aligns well with ESA-CCI and CALM for permafrost extent and ALT but reveals a significant global cold temperature bias, notably over Siberia. These results echo a persistent challenge identified in numerous studies: the existence of a systematic 'cold bias' in soil temperature over permafrost regions. To address this challenge, the following research questions propose dual sensitivity experiments.
The third research question represents the first study to apply a Plant Functional Type (PFT)-based approach to derive soil texture and soil organic matter (SOM), departing from the conventional use of coarse-resolution global data in LSMs. This novel method results in a more uniform distribution of soil organic matter density (OMD) across the domain, characterized by reduced OMD values in most regions. However, changes in soil texture exhibit a more intricate spatial pattern. Comparing the results to observations reveals a significant reduction in the cold bias observed in the control run. This method shows noticeable improvements in permafrost extent, but at the cost of an overestimation in ALT. These findings emphasize the model's high sensitivity to variations in soil texture and SOM content, highlighting the crucial role of soil composition in governing heat transfer processes and shaping the seasonal variation of soil temperatures in permafrost regions.
Expanding upon a site experiment conducted in Trail Valley Creek by \citet{dutch_impact_2022}, the fourth research question extends the application of the snow scheme proposed by \citet{sturm_thermal_1997} to cover the entire Arctic domain. By employing a snow scheme better suited to the snow density profile observed over permafrost regions, this thesis seeks to assess its influence on simulated soil temperatures. Comparing this method to observational datasets reveals a significant reduction in the cold bias that was present in the control run. In most regions, the Sturm run exhibits a substantial decrease in the cold bias. However, there is a distinctive overshoot with a warm bias observed in mountainous areas. The Sturm experiment effectively addressed the overestimation of permafrost extent in the control run, albeit resulting in a substantial reduction in permafrost extent over mountainous areas. ALT results remain relatively consistent compared to the control run. These outcomes align with our initial hypothesis, which anticipated that the reduced snow insulation in the Sturm run would lead to higher winter soil temperatures and a more accurate representation of permafrost physics.
In summary, this thesis demonstrates significant advancements in understanding permafrost dynamics and its integration into LSMs. It has meticulously unraveled the intricacies involved in the interplay between heat transfer, soil properties, and snow dynamics in permafrost regions. These insights offer novel perspectives on model representation and performance.
The Arctic is the hot spot of the ongoing, global climate change. Over the last decades, near-surface temperatures in the Arctic have been rising almost four times faster than on global average. This amplified warming of the Arctic and the associated rapid changes of its environment are largely influenced by interactions between individual components of the Arctic climate system. On daily to weekly time scales, storms can have major impacts on the Arctic sea-ice cover and are thus an important part of these interactions within the Arctic climate. The sea-ice impacts of storms are related to high wind speeds, which enhance the drift and deformation of sea ice, as well as to changes in the surface energy budget in association with air mass advection, which impact the seasonal sea-ice growth and melt.
The occurrence of storms in the Arctic is typically associated with the passage of transient cyclones. Even though the above described mechanisms how storms/cyclones impact the Arctic sea ice are in principal known, there is a lack of statistical quantification of these effects. In accordance with that, the overarching objective of this thesis is to statistically quantify cyclone impacts on sea-ice concentration (SIC) in the Atlantic Arctic Ocean over the last four decades. In order to further advance the understanding of the related mechanisms, an additional objective is to separate dynamic and thermodynamic cyclone impacts on sea ice and assess their relative importance. Finally, this thesis aims to quantify recent changes in cyclone impacts on SIC. These research objectives are tackled utilizing various data sets, including atmospheric and oceanic reanalysis data as well as a coupled model simulation and a cyclone tracking algorithm.
Results from this thesis demonstrate that cyclones are significantly impacting SIC in the Atlantic Arctic Ocean from autumn to spring, while there are mostly no significant impacts in summer. The strength and the sign (SIC decreasing or SIC increasing) of the cyclone impacts strongly depends on the considered daily time scale and the region of the Atlantic Arctic Ocean. Specifically, an initial decrease in SIC (day -3 to day 0 relative to the cyclone) is found in the Greenland, Barents and Kara Seas, while SIC increases following cyclones (day 0 to day 5 relative to the cyclone) are mostly limited to the Barents and Kara Seas.
For the cold season, this results in a pronounced regional difference between overall (day -3 to day 5 relative to the cyclone) SIC-decreasing cyclone impacts in the Greenland Sea and overall SIC-increasing cyclone impacts in the Barents and Kara Seas. A cyclone case study based on a coupled model simulation indicates that both dynamic and thermodynamic mechanisms contribute to cyclone impacts on sea ice in winter. A typical pattern consisting of an initial dominance of dynamic sea-ice changes followed by enhanced thermodynamic ice growth after the cyclone passage was found. This enhanced ice growth after the cyclone passage most likely also explains the (statistical) overall SIC-increasing effects of cyclones in the Barents and Kara Seas in the cold season.
Significant changes in cyclone impacts on SIC over the last four decades have emerged throughout the year. These recent changes are strongly varying from region to region and month to month. The strongest trends in cyclone impacts on SIC are found in autumn in the Barents and Kara Seas. Here, the magnitude of destructive cyclone impacts on SIC has approximately doubled over the last four decades. The SIC-increasing effects following the cyclone passage have particularly weakened in the Barents Sea in autumn. As a consequence, previously existing overall SIC-increasing cyclone impacts in this region in autumn have recently disappeared. Generally, results from this thesis show that changes in the state of the sea-ice cover (decrease in mean sea-ice concentration and thickness) and near-surface air temperature are most important for changed cyclone impacts on SIC, while changes in cyclone properties (i.e. intensity) do not play a significant role.
Moss-microbe associations are often characterised by syntrophic interactions between the microorganisms and their hosts, but the structure of the microbial consortia and their role in peatland development remain unknown.
In order to study microbial communities of dominant peatland mosses, Sphagnum and brown mosses, and the respective environmental drivers, four study sites representing different successional stages of natural northern peatlands were chosen on a large geographical scale: two brown moss-dominated, circumneutral peatlands from the Arctic and two Sphagnum-dominated, acidic peat bogs from subarctic and temperate zones.
The family Acetobacteraceae represented the dominant bacterial taxon of Sphagnum mosses from various geographical origins and displayed an integral part of the moss core community. This core community was shared among all investigated bryophytes and consisted of few but highly abundant prokaryotes, of which many appear as endophytes of Sphagnum mosses. Moreover, brown mosses and Sphagnum mosses represent habitats for archaea which were not studied in association with peatland mosses so far. Euryarchaeota that are capable of methane production (methanogens) displayed the majority of the moss-associated archaeal communities. Moss-associated methanogenesis was detected for the first time, but it was mostly negligible under laboratory conditions. Contrarily, substantial moss-associated methane oxidation was measured on both, brown mosses and Sphagnum mosses, supporting that methanotrophic bacteria as part of the moss microbiome may contribute to the reduction of methane emissions from pristine and rewetted peatlands of the northern hemisphere.
Among the investigated abiotic and biotic environmental parameters, the peatland type and the host moss taxon were identified to have a major impact on the structure of moss-associated bacterial communities, contrarily to archaeal communities whose structures were similar among the investigated bryophytes. For the first time it was shown that different bog development stages harbour distinct bacterial communities, while at the same time a small core community is shared among all investigated bryophytes independent of geography and peatland type.
The present thesis displays the first large-scale, systematic assessment of bacterial and archaeal communities associated both with brown mosses and Sphagnum mosses. It suggests that some host-specific moss taxa have the potential to play a key role in host moss establishment and peatland development.
The increasing number of known exoplanets raises questions about their demographics and the mechanisms that shape planets into how we observe them today. Young planets in close-in orbits are exposed to harsh environments due to the host star being magnetically highly active, which results in high X-ray and extreme UV fluxes impinging on the planet. Prolonged exposure to this intense photoionizing radiation can cause planetary atmospheres to heat up, expand and escape into space via a hydrodynamic escape process known as photoevaporation. For super-Earth and sub-Neptune-type planets, this can even lead to the complete erosion of their primordial gaseous atmospheres. A factor of interest for this particular mass-loss process is the activity evolution of the host star. Stellar rotation, which drives the dynamo and with it the magnetic activity of a star, changes significantly over the stellar lifetime. This strongly affects the amount of high-energy radiation received by a planet as stars age. At a young age, planets still host warm and extended envelopes, making them particularly susceptible to atmospheric evaporation. Especially in the first gigayear, when X-ray and UV levels can be 100 - 10,000 times higher than for the present-day sun, the characteristics of the host star and the detailed evolution of its high-energy emission are of importance.
In this thesis, I study the impact of stellar activity evolution on the high-energy-induced atmospheric mass loss of young exoplanets. The PLATYPOS code was developed as part of this thesis to calculate photoevaporative mass-loss rates over time. The code, which couples parameterized planetary mass-radius relations with an analytical hydrodynamic escape model, was used, together with Chandra and eROSITA X-ray observations, to investigate the future mass loss of the two young multiplanet systems V1298 Tau and K2-198. Further, in a numerical ensemble study, the effect of a realistic spread of activity tracks on the small-planet radius gap was investigated for the first time. The works in this thesis show that for individual systems, in particular if planetary masses are unconstrained, the difference between a young host star following a low-activity track vs. a high-activity one can have major implications: the exact shape of the activity evolution can determine whether a planet can hold on to some of its atmosphere, or completely loses its envelope, leaving only the bare rocky core behind. For an ensemble of simulated planets, an observationally-motivated distribution of activity tracks does not substantially change the final radius distribution at ages of several gigayears. My simulations indicate that the overall shape and slope of the resulting small-planet radius gap is not significantly affected by the spread in stellar activity tracks. However, it can account for a certain scattering or fuzziness observed in and around the radius gap of the observed exoplanet population.
The icosahedral non-hydrostatic large eddy model (ICON-LEM) was applied around the drift track of the Multidisciplinary Observatory Study of the Arctic (MOSAiC) in 2019 and 2020. The model was set up with horizontal grid-scales between 100m and 800m on areas with radii of 17.5km and 140 km. At its lateral boundaries, the model was driven by analysis data from the German Weather Service (DWD), downscaled by ICON in limited area mode (ICON-LAM) with horizontal grid-scale of 3 km.
The aim of this thesis was the investigation of the atmospheric boundary layer near the surface in the central Arctic during polar winter with a high-resolution mesoscale model. The default settings in ICON-LEM prevent the model from representing the exchange processes in the Arctic boundary layer in accordance to the MOSAiC observations. The implemented sea-ice scheme in ICON does not include a snow layer on sea-ice, which causes a too slow response of the sea-ice surface temperature to atmospheric changes. To allow the sea-ice surface to respond faster to changes in the atmosphere, the implemented sea-ice parameterization in ICON was extended with an adapted heat capacity term.
The adapted sea-ice parameterization resulted in better agreement with the MOSAiC observations. However, the sea-ice surface temperature in the model is generally lower than observed due to biases in the downwelling long-wave radiation and the lack of complex surface structures, like leads. The large eddy resolving turbulence closure yielded a better representation of the lower boundary layer under strongly stable stratification than the non-eddy-resolving turbulence closure. Furthermore, the integration of leads into the sea-ice surface reduced the overestimation of the sensible heat flux for different weather conditions.
The results of this work help to better understand boundary layer processes in the central Arctic during the polar night. High-resolving mesoscale simulations are able to represent temporally and spatially small interactions and help to further develop parameterizations also for the application in regional and global models.
With Arctic ground as a huge and temperature-sensitive carbon reservoir, maintaining low ground temperatures and frozen conditions to prevent further carbon emissions that contrib-ute to global climate warming is a key element in humankind’s fight to maintain habitable con-ditions on earth. Former studies showed that during the late Pleistocene, Arctic ground condi-tions were generally colder and more stable as the result of an ecosystem dominated by large herbivorous mammals and vast extents of graminoid vegetation – the mammoth steppe. Characterised by high plant productivity (grassland) and low ground insulation due to animal-caused compression and removal of snow, this ecosystem enabled deep permafrost aggrad-ation. Now, with tundra and shrub vegetation common in the terrestrial Arctic, these effects are not in place anymore. However, it appears to be possible to recreate this ecosystem local-ly by artificially increasing animal numbers, and hence keep Arctic ground cold to reduce or-ganic matter decomposition and carbon release into the atmosphere.
By measuring thaw depth, total organic carbon and total nitrogen content, stable carbon iso-tope ratio, radiocarbon age, n-alkane and alcohol characteristics and assessing dominant vegetation types along grazing intensity transects in two contrasting Arctic areas, it was found that recreating conditions locally, similar to the mammoth steppe, seems to be possible. For permafrost-affected soil, it was shown that intensive grazing in direct comparison to non-grazed areas reduces active layer depth and leads to higher TOC contents in the active layer soil. For soil only frozen on top in winter, an increase of TOC with grazing intensity could not be found, most likely because of confounding factors such as vertical water and carbon movement, which is not possible with an impermeable layer in permafrost. In both areas, high animal activity led to a vegetation transformation towards species-poor graminoid-dominated landscapes with less shrubs. Lipid biomarker analysis revealed that, even though the available organic material is different between the study areas, in both permafrost-affected and sea-sonally frozen soils the organic material in sites affected by high animal activity was less de-composed than under less intensive grazing pressure. In conclusion, high animal activity af-fects decomposition processes in Arctic soils and the ground thermal regime, visible from reduced active layer depth in permafrost areas. Therefore, grazing management might be utilised to locally stabilise permafrost and reduce Arctic carbon emissions in the future, but is likely not scalable to the entire permafrost region.