530 Physik
Refine
Document Type
- Article (5)
- Doctoral Thesis (4)
- Postprint (3)
- Habilitation Thesis (1)
- Other (1)
Is part of the Bibliography
- yes (14)
Keywords
- CU (2)
- MO (2)
- SIO₂ (2)
- complexes (2)
- crystalline (2)
- geomagnetic observatory data (2)
- geomagnetic storm drivers (2)
- glass (2)
- historical geomagnetic storms (2)
- oxygen (2)
Institute
- Institut für Geowissenschaften (14) (remove)
The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.
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.
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.
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.
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)
This cumulative thesis is concerned with the evolution of geomagnetic activity since the beginning of the 20th century, that is, the time-dependent response of the geomagnetic field to solar forcing. The focus lies on the description of the magnetospheric response field at ground level, which is particularly sensitive to the ring current system, and an interpretation of its variability in terms of the solar wind driving. Thereby, this work contributes to a comprehensive understanding of long-term solar-terrestrial interactions.
The common basis of the presented publications is formed by a reanalysis of vector magnetic field measurements from geomagnetic observatories located at low and middle geomagnetic latitudes. In the first two studies, new ring current targeting geomagnetic activity indices are derived, the Annual and Hourly Magnetospheric Currents indices (A/HMC). Compared to existing indices (e.g., the Dst index), they do not only extend the covered period by at least three solar cycles but also constitute a qualitative improvement concerning the absolute index level and the ~11-year solar cycle variability. The analysis of A/HMC shows that (a) the annual geomagnetic activity experiences an interval-dependent trend with an overall linear decline during 1900–2010 of ~5 % (b) the average trend-free activity level amounts to ~28 nT (c) the solar cycle related variability shows amplitudes of ~15–45 nT (d) the activity level for geomagnetically quiet conditions (Kp<2) lies slightly below 20 nT. The plausibility of the last three points is ensured by comparison to independent estimations either based on magnetic field measurements from LEO satellite missions (since the 1990s) or the modeling of geomagnetic activity from solar wind input (since the 1960s). An independent validation of the longterm trend is problematic mainly because the sensitivity of the locally measured geomagnetic activity depends on geomagnetic latitude. Consequently, A/HMC is neither directly comparable to global geomagnetic activity indices (e.g., the aa index) nor to the partly reconstructed open solar magnetic flux, which requires a homogeneous response of the ground-based measurements to the interplanetary magnetic field and the solar wind speed.
The last study combines a consistent, HMC-based identification of geomagnetic storms from 1930–2015 with an analysis of the corresponding spatial (magnetic local time-dependent) disturbance patterns. Amongst others, the disturbances at dawn and dusk, particularly their evolution during the storm recovery phases, are shown to be indicative of the solar wind driving structure (Interplanetary Coronal Mass Ejections vs. Stream or Co-rotating Interaction Regions), which enables a backward-prediction of the storm driver classes. The results indicate that ICME-driven geomagnetic storms have decreased since 1930 which is consistent with the concurrent decrease of HMC. Out of the collection of compiled follow-up studies the inclusion of measurements from high-latitude geomagnetic observatories into the third study’s framework seems most promising at this point.
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.
The habilitation deals with the numerical analysis of the recurrence properties of geological and climatic processes. The recurrence of states of dynamical processes can be analysed with recurrence plots and various recurrence quantification options. In the present work, the meaning of the structures and information contained in recurrence plots are examined and described. New developments have led to extensions that can be used to describe the recurring patterns in both space and time. Other important developments include recurrence plot-based approaches to identify abrupt changes in the system's dynamics, to detect and investigate external influences on the dynamics of a system, the couplings between different systems, as well as a combination of recurrence plots with the methodology of complex networks. Typical problems in geoscientific data analysis, such as irregular sampling and uncertainties, are tackled by specific modifications and additions. The development of a significance test allows the statistical evaluation of quantitative recurrence analysis, especially for the identification of dynamical transitions. Finally, an overview of typical pitfalls that can occur when applying recurrence-based methods is given and guidelines on how to avoid such pitfalls are discussed. In addition to the methodological aspects, the application potential especially for geoscientific research questions is discussed, such as the identification and analysis of transitions in past climates, the study of the influence of external factors to ecological or climatic systems, or the analysis of landuse dynamics based on remote sensing data.
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.