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- Institut für Geowissenschaften (179) (remove)
Subsea permafrost is perennially cryotic earth material that lies offshore. Most submarine permafrost is relict terrestrial permafrost beneath the Arctic shelf seas, was inundated after the last glaciation, and has been warming and thawing ever since. It is a reservoir and confining layer for gas hydrates and has the potential to release greenhouse gases and affect global climate change. Furthermore, subsea permafrost thaw destabilizes coastal infrastructure. While numerous studies focus on its distribution and rate of thaw over glacial timescales, these studies have not been brought together and examined in their entirety to assess rates of thaw beneath the Arctic Ocean. In addition, there is still a large gap in our understanding of sub-aquatic permafrost processes on finer spatial and temporal scales. The degradation rate of subsea permafrost is influenced by the initial conditions upon submergence. Terrestrial permafrost that has already undergone warming, partial thawing or loss of ground ice may react differently to inundation by seawater compared to previously undisturbed ice-rich permafrost. Heat conduction models are sufficient to model the thaw of thick subsea permafrost from the bottom, but few studies have included salt diffusion for top-down chemical degradation in shallow waters characterized by mean annual cryotic conditions on the seabed. Simulating salt transport is critical for assessing degradation rates for recently inundated permafrost, which may accelerate in response to warming shelf waters, a lengthening open water season, and faster coastal erosion rates. In the nearshore zone, degradation rates are also controlled by seasonal processes like bedfast ice, brine injection, seasonal freezing under floating ice conditions and warm freshwater discharge from large rivers. The interplay of all these variables is complex and needs further research. To fill this knowledge gap, this thesis investigates sub-aquatic permafrost along the southern coast of the Bykovsky Peninsula in eastern Siberia. Sediment cores and ground temperature profiles were collected at a freshwater thermokarst lake and two thermokarst lagoons in 2017. At this site, the coastline is retreating, and seawater is inundating various types of permafrost: sections of ice-rich Pleistocene permafrost (Yedoma) cliffs at the coastline alternate with lagoons and lower elevation previously thawed and refrozen permafrost basins (Alases). Electrical resistivity surveys with floating electrodes were carried out to map ice-bearing permafrost and taliks (unfrozen zones in the permafrost, usually formed beneath lakes) along the diverse coastline and in the lagoons. Combined with the borehole data, the electrical resistivity results permit estimation of contemporary ice-bearing permafrost characteristics, distribution, and occasionally, thickness. To conceptualize possible geomorphological and marine evolutionary pathways to the formation of the observed layering, numerical models were applied. The developed model incorporates salt diffusion and seasonal dynamics at the seabed, including bedfast ice. Even along coastlines with mean annual non-cryotic boundary conditions like the Bykovsky Peninsula, the modelling results show that salt diffusion minimizes seasonal freezing of the seabed, leading to faster degradation rates compared to models without salt diffusion. Seasonal processes are also important for thermokarst lake to lagoon transitions because lagoons can generate cold hypersaline conditions underneath the ice cover. My research suggests that ice-bearing permafrost can form in a coastal lagoon environment, even under floating ice. Alas basins, however, may degrade more than twice as fast as Yedoma permafrost in the first several decades of inundation. In addition to a lower ice content compared to Yedoma permafrost, Alas basins may be pre-conditioned with salt from adjacent lagoons. Considering the widespread distribution of thermokarst in the Arctic, its integration into geophysical models and offshore surveys is important to quantify and understand subsea permafrost degradation and aggradation. Through numerical modelling, fieldwork, and a circum-Arctic review of subsea permafrost literature, this thesis provides new insights into sub-aquatic permafrost evolution in saline coastal environments.
The humid tropics are the region with the highest rate of land-cover change worldwide. Especially prevalent is the deforestation of old-growth tropical forests to create space for cattle pastures and soybean fields.
The regional water cycle is influenced by vegetation cover in various ways. Especially evapotranspiration considerably contributes to water vapor content in the lower atmosphere. Besides active transpiration by plants, evaporation from wetted plant surfaces further known as interception loss is an important supply of water vapor. Changes in interception loss due to change in land cover and the related consequences on the regional water cycle in the humid tropics of Latin America are the research focus of my thesis. (1) In an experimental setup I assess differences in interception loss between an old-growth tropical forest and a soybean plantation. (2) In a modeling study, I examine interception losses of these two vegetation types compared to a younger secondary forest with the use of the Gash interception model, including an uncertainty analysis for the estimation of the necessary model parameters. (3) Studying the water balance of a 192-km² catchment I disentangle the influences of changes in land cover and climatic factors on interception loss.
The three different research sites in my thesis represent a currently typical spectrum for land-cover changes in Latin America. In the first example I study the consequences of deforestation of transitional forest, which forms the transition from the Brazilian tree savanna (cerrado) to tropical rain forest, for the establishment of soybean fields in the southern Amazon basin. The second study site is a young secondary forest within the “Agua Salud” project area in Panama as an example of reforestation of former pastures. The third study site is the Cirí Grande river catchment which comprises a mixture of young and old forests as well as pastures, which is typical for the southern sub-catchments of the Panama Canal.
The experimental approach consists of the indirect estimation of interception loss by measuring throughfall and stem flow. For the first experimental study I measured throughfall as well as stem flow manually. Measurements of the leaf area index of the two land covers do not show distinct differences; hence it could not serve as an explanation for the differences in the measured interception loss. The considerably higher interception loss at the soybean field is attributed to a possible underestimation of stemflow but also to the stronger ventilation within the well-structured plant rows causing higher evaporation rates. This situation is valid only for two months of the rainy season, when soybean plants are fully developed. In the annual balance evapotranspiration at the soybean site is clearly less than at the forest site, accelerating the development of fast runoff components and consequently discharge. In the medium term, a reduction of water availability in the study area can be expected.
For the modeling study, throughfall in a young secondary forest is sampled automatically. The resulting temporally high-resolution dataset allows the distinction between different precipitation and interception events. The core of this study is the sensitivity and uncertainty analysis of the Gash interception model parameters and the consequences for its results. Canopy storage capacity plays a key role for the model and parameter uncertainty. With increasing storage capacity uncertainty in parameter delineation also increases. Evaporation rate as the driving component of the interception process incorporates in this context the largest parameter uncertainty. Depending on the selected method for parameter estimation, parameter values may vary tremendously.
In the third study, I analyze the influence of interception loss on the water balance of the Cirí Grande catchment, incorporating the interlinked effects of temperature, precipitation and changes of the land use mosaic using the SWAT (soil water assessment tool) model. Constructing several land-cover scenarios I assess their influence on the catchment’s discharge. The results show that land-cover change exerts only a small influence on annual discharge in the Cirí Grande catchment whereas an increase in temperature markedly influences evapotranspiration. The temperature-induced larger transpiration and interception loss balances the simultaneous increase in annual precipitation, such that the resulting changes in annual discharge are negligible.
The results of the three studies show the considerable effect of land cover on interception. However, the magnitude of this effect can be masked by changes in local conditions, especially by an increase in temperature. Hence, the results cannot be transferred easily between the different study sites. For modeling purposes, this means that measurements of vegetation characteristics as well as interception loss at the respective sites are indispensable.
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.
Explaning change in flood hazard in the Mekong river : the hypothesis of nonstationary variance
(2013)
Seismology, like many scientific fields, e.g., music information retrieval and speech signal pro- cessing, is experiencing exponential growth in the amount of data acquired by modern seismo- logical networks. In this thesis, I take advantage of the opportunities offered by "big data" and by the methods developed in the areas of music information retrieval and machine learning to predict better the ground motion generated by earthquakes and to study the properties of the surface layers of the Earth. In order to better predict seismic ground motions, I propose two approaches based on unsupervised deep learning methods, an autoencoder network and Generative Adversarial Networks. The autoencoder technique explores a massive amount of ground motion data, evaluates the required parameters, and generates synthetic ground motion data in the Fourier amplitude spectra (FAS) domain. This method is tested on two synthetic datasets and one real dataset. The application on the real dataset shows that the substantial information contained within the FAS data can be encoded to a four to the five-dimensional manifold. Consequently, only a few independent parameters are required for efficient ground motion prediction. I also propose a method based on Conditional Generative Adversarial Networks (CGAN) for simulating ground motion records in the time-frequency and time domains. CGAN generates the time-frequency domains based on the parameters: magnitude, distance, and shear wave velocities to 30 m depth (VS30). After generating the amplitude of the time-frequency domains using the CGAN model, instead of classical conventional methods that assume the amplitude spectra with a random phase spectrum, the phase of the time-frequency domains is recovered by minimizing the observed and reconstructed spectrograms. In the second part of this dissertation, I propose two methods for the monitoring and characterization of near-surface materials and site effect analyses. I implement an autocorrelation function and an interferometry method to monitor the velocity changes of near-surface materials resulting from the Kumamoto earthquake sequence (Japan, 2016). The observed seismic velocity changes during the strong shaking are due to the non-linear response of the near-surface materials. The results show that the velocity changes lasted for about two months after the Kumamoto mainshock. Furthermore, I used the velocity changes to evaluate the in-situ strain-stress relationship. I also propose a method for assessing the site proxy "VS30" using non-invasive analysis. In the proposed method, a dispersion curve of surface waves is inverted to estimate the shear wave velocity of the subsurface. This method is based on the Dix-like linear operators, which relate the shear wave velocity to the phase velocity. The proposed method is fast, efficient, and stable. All of the methods presented in this work can be used for processing "big data" in seismology and for the analysis of weak and strong ground motion data, to predict ground shaking, and to analyze site responses by considering potential time dependencies and nonlinearities.