530 Physik
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- Institut für Physik und Astronomie (31) (remove)
The high-latitudinal thermospheric processes driven by the solar wind and Interplanetary Magnetic Field (IMF) interaction with the Earth magnetosphere are highly variable parts of the complex dynamic plasma environment, which represent the coupled Magnetosphere – Ionosphere – Thermosphere (MIT) system. The solar wind and IMF interactions transfer energy to the MIT system via reconnection processes at the magnetopause. The Field Aligned Currents (FACs) constitute the energetic links between the magnetosphere and the Earth ionosphere. The MIT system depends on the highly variable solar wind conditions, in particular on changes of the strength and orientation of the IMF.
In my thesis, I perform an investigation on the physical background of the complex MIT system using the global physical - numerical, three-dimensional, time-dependent and self-consistent Upper Atmosphere Model (UAM). This model describes the thermosphere, ionosphere, plasmasphere and inner magnetosphere as well as the electrodynamics of the coupled MIT system for the altitudinal range from 80 (60) km up to the 15 Earth radii.
In the present study, I developed and investigated several variants of the high-latitudinal electrodynamic coupling by including the IMF dependence of FACs into the UAM model. For testing, the various variants were applied to simulations of the coupled MIT system for different seasons, geomagnetic activities, various solar wind and IMF conditions. Additionally, these variants of the theoretical model with the IMF dependence were compared with global empirical models. The modelling results for the most important thermospheric parameters like neutral wind and mass density were compared with satellite measurements. The variants of the UAM model with IMF dependence show a good agreement with the satellite observations. In comparison with the empirical models, the improved variants of the UAM model reproduce a more realistic meso-scale structures and dynamics of the coupled MIT system than the empirical models, in particular at high latitudes. The new configurations of the UAM model with IMF dependence contribute to the improvement of space weather prediction.
Recent experiments show that transcription factors (TFs) indeed use the facilitated diffusion mechanism to locate their target sequences on DNA in living bacteria cells: TFs alternate between sliding motion along DNA and relocation events through the cytoplasm. From simulations and theoretical analysis we study the TF-sliding motion for a large section of the DNA-sequence of a common E. coli strain, based on the two-state TF-model with a fast-sliding search state and a recognition state enabling target detection. For the probability to detect the target before dissociating from DNA the TF-search times self-consistently depend heavily on whether or not an auxiliary operator (an accessible sequence similar to the main operator) is present in the genome section. Importantly, within our model the extent to which the interconversion rates between search and recognition states depend on the underlying nucleotide sequence is varied. A moderate dependence maximises the capability to distinguish between the main operator and similar sequences. Moreover, these auxiliary operators serve as starting points for DNA looping with the main operator, yielding a spectrum of target detection times spanning several orders of magnitude. Auxiliary operators are shown to act as funnels facilitating target detection by TFs.
Recent experiments show that transcription factors (TFs) indeed use the facilitated diffusion mechanism to locate their target sequences on DNA in living bacteria cells: TFs alternate between sliding motion along DNA and relocation events through the cytoplasm. From simulations and theoretical analysis we study the TF-sliding motion for a large section of the DNA-sequence of a common E. coli strain, based on the two-state TF-model with a fast-sliding search state and a recognition state enabling target detection. For the probability to detect the target before dissociating from DNA the TF-search times self-consistently depend heavily on whether or not an auxiliary operator (an accessible sequence similar to the main operator) is present in the genome section. Importantly, within our model the extent to which the interconversion rates between search and recognition states depend on the underlying nucleotide sequence is varied. A moderate dependence maximises the capability to distinguish between the main operator and similar sequences. Moreover, these auxiliary operators serve as starting points for DNA looping with the main operator, yielding a spectrum of target detection times spanning several orders of magnitude. Auxiliary operators are shown to act as funnels facilitating target detection by TFs.
Semi-empirical sea-level models (SEMs) exploit physically motivated empirical relationships between global sea level and certain drivers, in the following global mean temperature. This model class evolved as a supplement to process-based models (Rahmstorf (2007)) which were unable to fully represent all relevant processes. They thus failed to capture past sea-level change (Rahmstorf et al. (2012)) and were thought likely to underestimate future sea-level rise. Semi-empirical models were found to be a fast and useful tool for exploring the uncertainties in future sea-level rise, consistently giving significantly higher projections than process-based models.
In the following different aspects of semi-empirical sea-level modelling have been studied. Models were first validated using various data sets of global sea level and temperature. SEMs were then used on the glacier contribution to sea level, and to infer past global temperature from sea-level data via inverse modelling. Periods studied encompass the instrumental period, covered by tide gauges (starting 1700 CE (Common Era) in Amsterdam) and satellites (first launched in 1992 CE), the era from 1000 BCE (before CE) to present, and the full length of the Holocene (using proxy data). Accordingly different data, model formulations and implementations have been used. It could be shown in Bittermann et al. (2013) that SEMs correctly predict 20th century sea-level when calibrated with data until 1900 CE. SEMs also turned out to give better predictions than the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report (AR4, IPCC (2007)) models, for the period from 1961–2003 CE.
With the first multi-proxy reconstruction of global sea-level as input, estimate of the human-induced component of modern sea-level change and projections of future sea-level rise were calculated (Kopp et al. (2016)). It turned out with 90% confidence that more than 40 % of the observed 20th century sea-level rise is indeed anthropogenic. With the new semi-empirical and IPCC (2013) 5th assessment report (AR5) projections the gap between SEM and process-based model projections closes, giving higher credibility to both. Combining all scenarios, from strong mitigation to business as usual, a global sea-level rise of 28–131 cm relative to 2000 CE, is projected with 90% confidence. The decision for a low carbon pathway could halve the expected global sea-level rise by 2100 CE.
Present day temperature and thus sea level are driven by the globally acting greenhouse-gas forcing. Unlike that, the Milankovich forcing, acting on Holocene timescales, results mainly in a northern-hemisphere temperature change. Therefore a semi-empirical model can be driven with northernhemisphere temperatures, which makes it possible to model the main subcomponent of sea-level change over this period. It showed that an additional positive constant rate of the order of the estimated Antarctic sea-level contribution is then required to explain the sea-level evolution over the Holocene. Thus the global sea level, following the climatic optimum, can be interpreted as the sum of a temperature induced sea-level drop and a positive long-term contribution, likely an ongoing response to deglaciation coming from Antarctica.
Function by structure
(2015)
The main goal of this cumulative thesis is the derivation of surface emissivity data in the infrared from radiance measurements of Venus. Since these data are diagnostic of the chemical composition and grain size of the surface material, they can help to improve knowledge of the planet’s geology. Spectrally resolved images of nightside emissions in the range 1.0-5.1 μm were recently acquired by the InfraRed Mapping channel of the Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS-M-IR) aboard ESA’s Venus EXpress (VEX). Surface and deep atmospheric thermal emissions in this spectral range are strongly obscured by the extremely opaque atmosphere, but three narrow spectral windows at 1.02, 1.10, and 1.18 μm allow the sounding of the surface. Additional windows between 1.3 and 2.6 μm provide information on atmospheric parameters that is required to interpret the surface signals. Quantitative data on surface and atmosphere can be retrieved from the measured spectra by comparing them to simulated spectra. A numerical radiative transfer model is used in this work to simulate the observable radiation as a function of atmospheric, surface, and instrumental parameters. It is a line-by-line model taking into account thermal emissions by surface and atmosphere as well as absorption and multiple scattering by gases and clouds. The VIRTIS-M-IR measurements are first preprocessed to obtain an optimal data basis for the subsequent steps. In this process, a detailed detector responsivity analysis enables the optimization of the data consistency. The measurement data have a relatively low spectral information content, and different parameter vectors can describe the same measured spectrum equally well. A usual method to regularize the retrieval of the wanted parameters from a measured spectrum is to take into account a priori mean values and standard deviations of the parameters to be retrieved. This decreases the probability to obtain unreasonable parameter values. The multi-spectrum retrieval algorithm MSR is developed to additionally consider physically realistic spatial and temporal a priori correlations between retrieval parameters describing different measurements. Neglecting geologic activity, MSR also allows the retrieval of an emissivity map as a parameter vector that is common to several spectrally resolved images that cover the same surface target. Even applying MSR, it is difficult to obtain reliable emissivity maps in absolute values. A detailed retrieval error analysis based on synthetic spectra reveals that this is mainly due to interferences from parameters that cannot be derived from the spectra themselves, but that have to be set to assumed values to enable the radiative transfer simulations. The MSR retrieval of emissivity maps relative to a fixed emissivity is shown to effectively avoid most emissivity retrieval errors. Relative emissivity maps at 1.02, 1.10, and 1.18 μm are finally derived from many VIRTIS-M-IR measurements that cover a surface target at Themis Regio. They are interpreted as spatial variations relative to an assumed emissivity mean of the target. It is verified that the maps are largely independent of the choice of many interfering parameters as well as the utilized measurement data set. These are the first Venus IR emissivity data maps based on a consistent application of a full radiative transfer simulation and a retrieval algorithm that respects a priori information. The maps are sufficiently reliable for future geologic interpretations.