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Water scarcity, adaption on climate change, and risk assessment of droughts and floods are critical topics for science and society these days. Monitoring and modeling of the hydrological cycle are a prerequisite to understand and predict the consequences for weather and agriculture. As soil water storage plays a key role for partitioning of water fluxes between the atmosphere, biosphere, and lithosphere, measurement techniques are required to estimate soil moisture states from small to large scales.
The method of cosmic-ray neutron sensing (CRNS) promises to close the gap between point-scale and remote-sensing observations, as its footprint was reported to be 30 ha. However, the methodology is rather young and requires highly interdisciplinary research to understand and interpret the response of neutrons to soil moisture. In this work, the signal of nine detectors has been systematically compared, and correction approaches have been revised to account for meteorological and geomagnetic variations. Neutron transport simulations have been consulted to precisely characterize the sensitive footprint area, which turned out to be 6--18 ha, highly local, and temporally dynamic. These results have been experimentally confirmed by the significant influence of water bodies and dry roads. Furthermore, mobile measurements on agricultural fields and across different land use types were able to accurately capture the various soil moisture states. It has been further demonstrated that the corresponding spatial and temporal neutron data can be beneficial for mesoscale hydrological modeling. Finally, first tests with a gyrocopter have proven the concept of airborne neutron sensing, where increased footprints are able to overcome local effects.
This dissertation not only bridges the gap between scales of soil moisture measurements. It also establishes a close connection between the two worlds of observers and modelers, and further aims to combine the disciplines of particle physics, geophysics, and soil hydrology to thoroughly explore the potential and limits of the CRNS method.
The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all.
Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation.
In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes.
Participants of the 2017 European Space Weather Week in Ostend, Belgium, discussed the stakeholder requirements for space weather-related models. It was emphasized that stakeholders show an increased interest in space weather-related models. Participants of the meeting discussed particular prediction indicators that can provide first-order estimates of the impact of space weather on engineering systems.
The Atlantic Meridional Overturning Circulation (AMOC) is likely the most well-known system of ocean currents on Earth, redistributing heat, nutrients and carbon over a large part of the Earth’s surface and affecting global climate as a result. Due to enhanced freshwater fluxes into the subpolar North Atlantic as a response to global warming, the AMOC is expected, and may have already started, to weaken and these changes will likely have global impacts. It is therefore of considerable relevance to improve our understanding of past and future AMOC changes. My thesis tries to answer some of the open questions in this field by giving strong evidence that the AMOC has already weakened over the last century, by narrowing future projections of this slowdown and
by studying the impacts on global surface warming.
While there have been various studies trying to reconstruct the strength of the overturning circulation in the past, often based on model simulations in combination with observations (Jackson et al., 2016, Kanzow et al., 2010) or proxies (Frajka-Williams, 2015, Latif et al., 2006), the results so far, due to lack of direct measurements, have been inconclusive. In the first paper I build on previous work that links the anomalously low sea surface temperatures (SSTs) in the North Atlantic with the reduced meridional heat transport due to a weaker AMOC. Using the output of a high-resolution global climate model, I derive a characteristic spatial and seasonal SST fingerprint of an AMOC slowdown and an improved SST-based AMOC index. The same fingerprint is seen in
the observational SSTs since the late 19th Century, giving strong evidence that since then the AMOC has slowed down. In addition, the reconstruction of the historical overturning strength with the new AMOC index agrees well with and extends the results of earlier studies as well as the direct measurements from the RAPID project and shows a strong decline of the AMOC by about 15% (3±1 Sv) since the mid-20th Century (Caesar et al., 2018).
The reconstruction of the historical overturning strength with the AMOC index enables us to weight future AMOC projections based on their skill in modeling the historical AMOC as described in the second paper of this thesis (Olson et al., 2018). Using Bayesian model averaging we considerably narrow the projections of the CMIP5 ensemble to a decrease of -4.0 Sv and -6.8 Sv between the years 1960-1999 and 2060-2099 for the RCP4.5 and RCP8.5 emission scenarios, respectively. These values fit to, yet are at the lower end of, previously published estimates.
In the third paper I examine how the AMOC slowdown affects the global mean surface temperature (GMST) with a focus on how it will change the ocean heat uptake (OHC). Accounting for the effect of changes in the radiative forcing on the GMST, I test how AMOC variations correlate with the residual part of surface temperature changes in the past. I find that the correlation is positive which fits the understanding that the deep-water formation that is important in driving the AMOC cools the deep ocean and therefore warms the surface (Caesar et al., 2019). The future weakening of the overturning circulation could therefore delay global surface warming.
Due to nonlinear behavior and scale specific changes it can be difficult to study the dominant processes and modes that drive climate variability. In the fourth paper we develop and test a new technique based on the wavelet multiscale correlation (WMC) similarity measure to study climate variability on different temporal and spatial scales (Agarwal et al., 2018). In a fifth contribution to my thesis this method is applied to the observed sea surface temperatures. The results reconfirm well-known relations between SST anomalies such as the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) on inter-annual and decadal timescales, respectively. They
furthermore give new insights into the characteristics and origins of long-range teleconnections, for example, that the teleconnection between ENSO and Indian Ocean dipole exist mainly between the northern part of the ENSO tongue and the equatorial Indian Ocean, and provides therefore valuable knowledge about the regions that are necessary to include when modeling regional climate variability at a certain scale (Agarwal et al., 2019).
In summary, my PhD thesis investigates past and future AMOC variability and its effects on global mean surface temperature by utilizing a combination of observational sea surface data and the output of historical and future climate model simulations from both the high-resolution CM2.6 model as well as the CMIP5 ensemble. It further includes the development and validation of a new method to study climate variability, that, applied to the observed sea surface temperatures, gives new insight about teleconnections in the Earth System. My findings provide evidence that the AMOC has already slowed down, will continue to do so in the future, and will impact the global mean temperature. Further impacts of an AMOC slowdown may include increased sea-level rise at the U.S. east coast (Ezer, 2015), heat extremes in Europe (Duchez et al., 2016) and increased storm activity in the North Atlantic region (Jackson et al., 2015), all of which have significant socio-economic implications.
In this study, we detect high percentile rainfall events in the eastern central Andes, based on Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25 × 0.25°, a temporal resolution of 3 h, and for the duration from 2001 to 2018. We identify three areas with high mean accumulated rainfall and analyze their atmospheric behaviour and rainfall characteristics with specific focus on extreme events. Extreme events are defined by events above the 95th percentile of their daily mean accumulated rainfall. Austral summer (DJF) is the period of the year presenting the most frequent extreme events over these three regions. Daily statistics show that the spatial maxima, as well as their associated extreme events, are produced during the night. For the considered period, ERA-Interim reanalysis data, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) with 0.75° x0.75° spatial and 6-hourly temporal resolutions, were used for the analysis of the meso- and synoptic-scale atmospheric patterns. Night- and day-time differences indicate a nocturnal overload of northerly and northeasterly low-level humidity flows arriving from tropical South America. Under these conditions, cooling descending air from the mountains may find unstable air at the surface, giving place to the development of strong local convection. Another possible mechanism is presented here: a forced ascent of the low-level flow due to the mountains, disrupting the atmospheric stratification and generating vertical displacement of air trajectories. A Principal Component Analysis (PCA) in T-mode is applied to day- and night-time data during the maximum and extreme events. The results show strong correlation areas over each subregion under study during night-time, whereas during day-time no defined patterns are found. This confirms the observed nocturnal behavior of rainfall within these three hotspots.
We measure valence-to-core x-ray emission spectra of compressed crystalline GeO₂ up to 56 GPa and of amorphous GeO₂ up to 100 GPa. In a novel approach, we extract the Ge coordination number and mean Ge-O distances from the emission energy and the intensity of the Kβ'' emission line. The spectra of high-pressure polymorphs are calculated using the Bethe-Salpeter equation. Trends observed in the experimental and calculated spectra are found to match only when utilizing an octahedral model. The results reveal persistent octahedral Ge coordination with increasing distortion, similar to the compaction mechanism in the sequence of octahedrally coordinated crystalline GeO₂ high-pressure polymorphs.
We measure valence-to-core x-ray emission spectra of compressed crystalline GeO₂ up to 56 GPa and of amorphous GeO₂ up to 100 GPa. In a novel approach, we extract the Ge coordination number and mean Ge-O distances from the emission energy and the intensity of the Kβ'' emission line. The spectra of high-pressure polymorphs are calculated using the Bethe-Salpeter equation. Trends observed in the experimental and calculated spectra are found to match only when utilizing an octahedral model. The results reveal persistent octahedral Ge coordination with increasing distortion, similar to the compaction mechanism in the sequence of octahedrally coordinated crystalline GeO₂ high-pressure polymorphs.
Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003)
Solar wind observations show that geomagnetic storms are mainly driven by interplanetary coronal mass ejections (ICMEs) and corotating or stream interaction regions (C/SIRs). We present a binary classifier that assigns one of these drivers to 7,546 storms between 1930 and 2015 using ground‐based geomagnetic field observations only. The input data consists of the long‐term stable Hourly Magnetospheric Currents index alongside the corresponding midlatitude geomagnetic observatory time series. This data set provides comprehensive information on the global storm time magnetic disturbance field, particularly its spatial variability, over eight solar cycles. For the first time, we use this information statistically with regard to an automated storm driver identification. Our supervised classification model significantly outperforms unskilled baseline models (78% accuracy with 26[19]% misidentified interplanetary coronal mass ejections [corotating or stream interaction regions]) and delivers plausible driver occurrences with regard to storm intensity and solar cycle phase. Our results can readily be used to advance related studies fundamental to space weather research, for example, studies connecting galactic cosmic ray modulation and geomagnetic disturbances. They are fully reproducible by means of the underlying open‐source software (Pick, 2019, http://doi.org/10.5880/GFZ.2.3.2019.003)
In this paper we report a rare and fortunate event of fast magnetosonic (MS, also called equatorial noise) waves modulated by compressional ultralow frequency (ULF) waves measured by Van Allen Probes. The characteristics of MS waves, ULF waves, proton distribution, and their potential correlations are analyzed. The results show that ULF waves can modulate the energetic ring proton distribution and in turn modulate the MS generation. Furthermore, the variation of MS intensities is attributed to not only ULF wave activities but also the variation of background parameters, for example, number density. The results confirm the opinion that MS waves are generated by proton ring distribution and propose a new modulation phenomenon.