The evolution of the radiation belts in L-shell (L), energy (E), and equatorial pitch angle (alpha(0)) is analyzed during the calm 11-day interval (4-15 March) following the 1 March 2013 storm. Magnetic Electron and Ion Spectrometer (MagEIS) observations from Van Allen Probes are interpreted alongside 1D and 3D Fokker-Planck simulations combined with consistent event-driven scattering modeling from whistler mode hiss waves. Three (L, E, alpha(0)) regions persist through 11 days of hiss wave scattering; the pitch angle-dependent inner belt core (L similar to <2.2 and E < 700 keV), pitch angle homogeneous outer belt low-energy core (L > similar to 5 and E similar to < 100 keV), and a distinct pocket of electrons (L similar to [4.5, 5.5] and E similar to [0.7, 2] MeV). The pitch angle homogeneous outer belt is explained by the diffusion coefficients that are roughly constant for alpha(0) similar to <60 degrees, E > 100 keV, 3.5 < L < L-pp similar to 6. Thus, observed unidirectional flux decays can be used to estimate local pitch angle diffusion rates in that region. Top-hat distributions are computed and observed at L similar to 3-3.5 and E = 100-300 keV.

Terrestrial gravimetry is increasingly used to monitor mass transport processes in geophysics boosted by the ongoing technological development of instruments. Resolving a particular phenomenon of interest, however, requires a set of gravity corrections of which the uncertainties have not been addressed up to now. In this study, we quantify the time domain uncertainty of tide, global atmospheric, large-scale hydrological, and nontidal ocean loading corrections. The uncertainty is assessed by comparing the majority of available global models for a suite of sites worldwide. The average uncertainty expressed as root-mean-square error equals 5.1nm/s(2), discounting local hydrology or air pressure. The correction-induced uncertainty of gravity changes over various time periods of interest ranges from 0.6nm/s(2) for hours up to a maximum of 6.7nm/s(2) for 6months. The corrections are shown to be significant and should be applied for most geophysical applications of terrestrial gravimetry. From a statistical point of view, however, resolving subtle gravity effects in the order of few nanometers per square second is challenged by the uncertainty of the corrections. Plain Language Summary Many scientists are exploring ways to benefit from gravity measurements in fields of high societal relevance such as monitoring of volcanoes or measuring the amount of water in underground. Any application of such new methods, however, requires careful preparation of the gravity measurements. The intention of the preparation process is to ensure that the measurements do not contain information about processes that are not of interest. For that reason, the influence of atmosphere, ocean, tides, and hydrology needs to be reduced from the gravity. In this study, we investigate how this reduction process influences the quality of the measurement. We found that the precision degrades especially owing to the hydrology. The ocean plays an important role at sites close to the coast and the atmosphere at sites located in mountains. The overall errors of the reductions may complicate a reliable use of gravity measurements in certain studies focusing on very small signals. Nevertheless, the precision of gravity reductions alone does not obstruct a meaningful use of gravity measurements in most research fields. Details specifying the reduction precision are provided in this study allowing scientist dealing with gravity measurements to decide if their signal of interest can be reliably resolved.

Numerous preflight investigations were necessary prior to the exposure experiment BIOMEX on the International Space Station to test the basic potential of selected microorganisms to resist or even to be active under Mars-like conditions. In this study, methanogenic archaea, which are anaerobic chemolithotrophic microorganisms whose lifestyle would allow metabolism under the conditions on early and recent Mars, were analyzed. Some strains from Siberian permafrost environments have shown a particular resistance. In this investigation, we analyzed the response of three permafrost strains (Methanosarcina soligelidi SMA-21, Candidatus Methanosarcina SMA-17, Candidatus Methanobacterium SMA-27) and two related strains from non-permafrost environments (Methanosarcina mazei, Methanosarcina barkeri) to desiccation conditions (-80 degrees C for 315 days, martian regolith analog simulants S-MRS and P-MRS, a 128-day period of simulated Mars-like atmosphere). Exposure of the different methanogenic strains to increasing concentrations of magnesium perchlorate allowed for the study of their metabolic shutdown in a Mars-relevant perchlorate environment. Survival and metabolic recovery were analyzed by quantitative PCR, gas chromatography, and a new DNA-extraction method from viable cells embedded in S-MRS and P-MRS. All strains survived the two Mars-like desiccating scenarios and recovered to different extents. The permafrost strain SMA-27 showed an increased methanogenic activity by at least 10-fold after deep-freezing conditions. The methanogenic rates of all strains did not decrease significantly after 128 days S-MRS exposure, except for SMA-27, which decreased 10-fold. The activity of strains SMA-17 and SMA-27 decreased after 16 and 60 days P-MRS exposure. Non-permafrost strains showed constant survival and methane production when exposed to both desiccating scenarios. All strains showed unaltered methane production when exposed to the perchlorate concentration reported at the Phoenix landing site (2.4 mM) or even higher concentrations. We conclude that methanogens from (non-)permafrost environments are suitable candidates for potential life in the martian subsurface and therefore are worthy of study after space exposure experiments that approach Mars-like surface conditions.

A comprehensive description of electromagnetic processes related to equatorial plasma depletions (EPDs) is essential for understanding their evolution and day-to-day variability. Recently, field-aligned currents (FACs) flowing at both western and eastern edges of EPDs were observed to be interhemispheric rather than anti-parallel about the dip equator, as suggested by previous theoretical studies. In this paper, we investigate the spatial and temporal variability of the FACs orientation using simultaneous measurements of electron density and magnetic field gathered by ESA’s Swarm constellation mission. By using empirical models, we assess the role of the Pedersen conductance in the preference of the FACs to close either in the northern or southern magnetic hemisphere. Here we show that the closure of the FACs agrees with an electrostatic regime determined by a hemispherical asymmetry of the Pedersen conductance. That is, the EPD-related FACs close at lower altitudes in the hemisphere with the highest conductivity. The evidence of this conclusion stands on the general agreement between the longitudinal and seasonal variability of both the conductivity and the FACs orientation.

The Arctic environments constitute rich and dynamic ecosystems, dominated by microorganisms extremely well adapted to survive and function under severe conditions. A range of physiological adaptations allow the microbiota in these habitats to withstand low temperatures, low water and nutrient availability, high levels of UV radiation, etc. In addition, other adaptations of clear competitive nature are directed at not only surviving but thriving in these environments, by disrupting the metabolism of neighboring cells and affecting intermicrobial communication. Since Arctic microbes are bioindicators which amplify climate alterations in the environment, the Arctic region presents the opportunity to study local microbiota and carry out research about interesting, potentially virulent phenotypes that could be dispersed into other habitats around the globe as a consequence of accelerating climate change. In this context, exploration of Arctic habitats as well as descriptions of the microbes inhabiting them are abundant but microbial competitive strategies commonly associated with virulence and pathogens are rarely reported. In this project, environmental samples from the Arctic region were collected and microorganisms (bacteria and fungi) were isolated. The clinical relevance of these microorganisms was assessed by observing the following virulence markers: ability to grow at a range of temperatures, expression of antimicrobial resistance and production of hemolysins. The aim of this project is to determine the frequency and relevance of these characteristics in an effort to understand microbial adaptations in habitats threatened by climate change. The isolates obtained and described here were able to grow at a range of temperatures, in some cases more than 30 °C higher than their original isolation temperature. A considerable number of them consistently expressed compounds capable of lysing sheep and bovine erythrocytes on blood agar at different incubation temperatures. Ethanolic extracts of these bacteria were able to cause rapid and complete lysis of erythrocyte suspensions and might even be hemolytic when assayed on human blood. In silico analyses showed a variety of resistance elements, some of them novel, against natural and synthetic antimicrobial compounds. In vitro experiments against a number of antimicrobial compounds showed resistance phenotypes belonging to wild-type populations and some non-wild type which clearly denote human influence in the acquisition of antimicrobial resistance. The results of this project demonstrate the presence of virulence-associated factors expressed by microorganisms of natural, non-clinical environments. This study contains some of the first reports, to the best of our knowledge, of hemolytic microbes isolated from the Arctic region. In addition, it provides additional information about the presence and expression of intrinsic and acquired antimicrobial resistance in environmental isolates, contributing to the understanding of the evolution of relevant pathogenic species and opportunistic pathogens. Finally, this study highlights some of the potential risks associated with changes in the polar regions (habitat melting and destruction, ecosystem transition and re-colonization) as important indirect consequences of global warming and altered climatic conditions around the planet.

Interannual variations in ecosystem primary productivity are dominated by water availability. Until recently, characterizing the photosynthetic response of different ecosystems to soil moisture anomalies was hampered by observational limitations. Here, we use a number of satellite-based proxies for productivity, including spectral indices, sun-induced chlorophyll fluorescence, and data-driven estimates of gross primary production, to reevaluate the relationship between terrestrial photosynthesis and water. In contrast to nonwoody vegetation, we find a resilience of forested ecosystems to reduced soil moisture. Sun-induced chlorophyll fluorescence and data-driven gross primary production indicate an increase in photosynthesis as a result of the accompanying higher amounts of light and temperature despite lowered light-use-efficiency. Conversely, remote sensing indicators of greenness reach their detection limit and largely remain stable. Our study thus highlights the differential responses of ecosystems along a tree cover gradient and illustrates the importance of differentiating photosynthesis indicators from those of greenness for the monitoring and understanding of ecosystems. Plain Language Summary The capacity of vegetation to thrive and to sequester carbon depends on how much water they can have access to. In this work, we evaluate how different types of satellite observations can describe the response of vegetation to changes in soil moisture over the entire planet. The first source of observation measures only the greenness of the land surface, the second measures light that is emitted by pigments in plants which are photosynthetically active (chlorophyll fluorescence), and the third are simulations of gross carbon uptake derived from machine learning techniques. For periods of water shortage all three indicate a reduction of growth in ecosystems with few trees. However, in cold boreal forests, when soil moisture is particularly low, we still detect an increase in photosynthesis due to higher light and temperature conditions, but this is not reflected in the greenness indicator. This work illustrates how lack of water is not necessarily harmful for catching carbon through photosynthesis, but to monitor this effect, we need remote sensing indicators that measure more than just how green the plants are, and fluorescence is likely a good candidate.

Changes in the steepness of river profiles or abrupt vertical steps (i.e. waterfalls) are thought to be indicative of changes in erosion rates, lithology or other factors that affect landscape evolution. These changes are referred to as knickpoints or knickzones and are pervasive in bedrock river systems. Such features are thought to reveal information about landscape evolution and patterns of erosion, and therefore their locations are often reported in the geomorphic literature. It is imperative that studies reporting knickpoints and knickzones use a reproducible method of quantifying their locations, as their number and spatial distribution play an important role in interpreting tectonically active landscapes. In this contribution we introduce a reproducible knickpoint and knickzone extraction algorithm that uses river profiles transformed by integrating drainage area along channel length (the so-called integral or chi method). The profile is then statistically segmented and the differing slopes and step changes in the elevations of these segments are used to identify knickpoints, knickzones and their relative magnitudes. The output locations of identified knickpoints and knickzones compare favourably with human mapping: we test the method on Santa Cruz Island, CA, using previously reported knickzones and also test the method against a new dataset from the Quadrilatero Ferrifero in Brazil. The algorithm allows for the extraction of varying knickpoint morphologies, including stepped, positive slope-break (concave upward) and negative slope-break knickpoints. We identify parameters that most affect the resulting knickpoint and knickzone locations and provide guidance for both usage and outputs of the method to produce reproducible knickpoint datasets.

Climatic observables are often correlated across long spatial distances, and extreme events, such as heatwaves or floods, are typically assumed to be related to such teleconnections(1,2). Revealing atmospheric teleconnection patterns and understanding their underlying mechanisms is of great importance for weather forecasting in general and extreme-event prediction in particular(3,4), especially considering that the characteristics of extreme events have been suggested to change under ongoing anthropogenic climate change(5-8). Here we reveal the global coupling pattern of extreme-rainfall events by applying complex-network methodology to high-resolution satellite data and introducing a technique that corrects for multiple-comparison bias in functional networks. We find that the distance distribution of significant connections (P < 0.005) around the globe decays according to a power law up to distances of about 2,500 kilometres. For longer distances, the probability of significant connections is much higher than expected from the scaling of the power law. We attribute the shorter, power-law-distributed connections to regional weather systems. The longer, super-power-law-distributed connections form a global rainfall teleconnection pattern that is probably controlled by upper-level Rossby waves. We show that extreme-rainfall events in the monsoon systems of south-central Asia, east Asia and Africa are significantly synchronized. Moreover, we uncover concise links between south-central Asia and the European and North American extratropics, as well as the Southern Hemisphere extratropics. Analysis of the atmospheric conditions that lead to these teleconnections confirms Rossby waves as the physical mechanism underlying these global teleconnection patterns and emphasizes their crucial role in dynamical tropical-extratropical couplings. Our results provide insights into the function of Rossby waves in creating stable, global-scale dependencies of extreme-rainfall events, and into the potential predictability of associated natural hazards.

Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections.

This work develops hybrid methods of imaging spectroscopy for open pit mining and examines their feasibility compared with state-of-the-art. The material distribution within a mine face differs in the small scale and within daily assigned extraction segments. These changes can be relevant to subsequent processing steps but are not always visually identifiable prior to the extraction. Misclassifications that cause false allocations of extracted material need to be minimized in order to reduce energy-intensive material re-handling. The use of imaging spectroscopy aspires to the allocation of relevant deposit-specific materials before extraction, and allows for efficient material handling after extraction. The aim of this work is the parameterization of imaging spectroscopy for pit mining applications and the development and evaluation of a workflow for a mine face, ground- based, spectral characterization. In this work, an application-based sensor adaptation is proposed. The sensor complexity is reduced by down-sampling the spectral resolution of the system based on the samples’ spectral characteristics. This was achieved by the evaluation of existing hyperspectral outcrop analysis approaches based on laboratory sample scans from the iron quadrangle in Minas Gerais, Brazil and by the development of a spectral mine face monitoring workflow which was tested for both an operating and an inactive open pit copper mine in the Republic of Cyprus. The workflow presented here is applied to three regional data sets: 1) Iron ore samples from Brazil, (laboratory); 2) Samples and hyperspectral mine face imagery from the copper-gold-pyrite mine Apliki, Republic of Cyprus (laboratory and mine face data); and 3) Samples and hyperspectral mine face imagery from the copper-gold-pyrite deposit Three Hills, Republic of Cyprus (laboratory and mine face data). The hyperspectral laboratory dataset of fifteen Brazilian iron ore samples was used to evaluate different analysis methods and different sensor models. Nineteen commonly used methods to analyze and map hyperspectral data were compared regarding the methods’ resulting data products and the accuracy of the mapping and the analysis computation time. Four of the evaluated methods were determined for subsequent analyses to determine the best-performing algorithms: The spectral angle mapper (SAM), a support vector machine algorithm (SVM), the binary feature fitting algorithm (BFF) and the EnMap geological mapper (EnGeoMap). Next, commercially available imaging spectroscopy sensors were evaluated for their usability in open pit mining conditions. Step-wise downsampling of the data - the reduction of the number of bands with an increase of each band’s bandwidth - was performed to investigate the possible simplification and ruggedization of a sensor without a quality fall-off of the mapping results. The impact of the atmosphere visible in the spectrum between 1300–2010nm was reduced by excluding the spectral range from the data for mapping. This tested the feasibility of the method under realistic open pit data conditions. Thirteen datasets based on the different, downsampled sensors were analyzed with the four predetermined methods. The optimum sensor for spectral mine face material distinction was determined as a VNIR-SWIR sensor with 40nm bandwidths in the VNIR and 15nm bandwidths in the SWIR spectral range and excluding the atmospherically impacted bands. The Apliki mine sample dataset was used for the application of the found optimal analyses and sensors. Thirty-six samples were analyzed geochemically and mineralogically. The sample spectra were compiled to two spectral libraries, both distinguishing between seven different geochemical-spectral clusters. The reflectance dataset was downsampled to five different sensors. The five different datasets were mapped with the SAM, BFF and SVM method achieving mapping accuracies of 85-72%, 85-76% and 57-46% respectively. One mine face scan of Apliki was used for the application of the developed workflow. The mapping results were validated against the geochemistry and mineralogy of thirty-six documented field sampling points and a zonation map of the mine face which is based on sixty-six samples and field mapping. The mine face was analyzed with SAM and BFF. The analysis maps were visualized on top of a Structure-from-Motion derived 3D model of the open pit. The mapped geological units and zones correlate well with the expected zonation of the mine face. The third set of hyperspectral imagery from Three Hills was available for applying the fully-developed workflow. Geochemical sample analyses and laboratory spectral data of fifteen different samples from the Three Hills mine, Republic of Cyprus, were used to analyse a downsampled mine face scan of the open pit. Here, areas of low, medium and high ore content were identified. The developed workflow is successfully applied to the open pit mines Apliki and Three Hills and the spectral maps reflect the prevailing geological conditions. This work leads through the acquisition, preparation and processing of imaging spectroscopy data, the optimum choice of analysis methodology, and the utilization of simplified, robust sensors that meet the requirements of open pit mining conditions. It accentuates the importance of a site-specific and deposit-specific spectral library for the mine face analysis and underlines the need for geological and spectral analysis experts to successfully implement imaging spectroscopy in the field of open pit mining.