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Institute
- Geowissenschaften (864) (remove)
Paläoklimarekonstruktionen, die es sich zum Ziel gesetzt haben, Klima-Mensch Interaktionen auf lange Zeitreihen betrachtet zu erforschen, nehmen begünstigt durch die aktuell intensiv geführte Klimadebatte, einen immer größer werdenden Stellenwert in der öffentlichen und wissenschaftlichen Wahrnehmung ein. Denn trotz aller wissenschaftlicher Fortschritte, die in den vergangenen Jahrzehnten im Bereich der modernen Klimaforschung gemacht wurden, bleibt die zuverlässige Vorhersage und Modellierung von zukünftigen Klimaveränderungen noch immer eine der größten Herausforderungen unser heutigen Zeit. Betrachtet man die Karibik exemplarisch in diesem Rahmen, dann prognostizieren viele Modellrechnungen, infolge steigender Ozeantemperaturen, ein deutlich häufigeres Auftreten von tropischen Stürmen und Hurrikanen sowie eine Verschiebung hin zu höheren Sturmstärken. Dieser Trend stellt für die Karibik und viele daran angrenzende Staaten eine der größten Gefahren des modernen Klimawandels dar, den es wissenschaftlich über einen langen Zeitrahmen zu erforschen gilt.
Klimaprognosen stützen sich meist vollständig auf hoch-aufgelöste instrumentelle Datensätze. Diese sind aber alle durch einen wesentlichen Aspekt limitiert. Aufgrund ihrer eingeschränkten Verfügbarkeit (~150 Jahre) fehlt ihnen die erforderliche Tiefe, um die auf langen Zeitskalen operierenden Prozesse der globalen Klimadynamik adäquat abbilden zu können. Betrachtet man das Holozän in seiner Gesamtheit, so wurde die globale Klimadynamik über die vergangenen ~11,700 Jahre von periodisch auftretenden Prozessen und Abläufen gesteuert. Diese wirken grundsätzlich über Zeiträume von mehreren Jahrzehnten, teilweise Jahrhunderten und in einigen Fällen sogar Jahrtausenden. Viele dieser natürlichen Prozesse, können in der kurzen Instrumentellen Ära nicht gänzlich identifiziert und angemessen in Klimamodellen berücksichtig werden. Die alleinige Berücksichtigung der Instrumentellen Ära bietet daher nur eine eingeschränkte Perspektive, um die Ursachen und Abläufe von vergangenen sowie mögliche Folgen von zukünftigen Klimaveränderungen zu verstehen. Um diese Einschränkung zu überwinden, ist es somit erforderlich, dass die geowissenschaftliche Forschung mit Proxymethoden ein zusammenfassendes und mechanistisches Verständnis über alle Holozänen Klimaveränderungen erlangt.
Wenn man sich diese Limitierung, die ansteigenden Ozeantemperaturen und das in der Karibik in den vergangen 20 Jahren vermehrte Auftreten von starken tropischen Zyklonen ins Gedächtnis ruft, ist es nachvollziehbar, dass im Rahmen dieser Doktorarbeit ein zwei Jahrtausende langer und jährlich aufgelöster Klimadatensatz erarbeitet werden soll, der spät Holozäne Variationen von Ozeanoberflächenwasser-temperaturen (SST) und daraus resultierende lang-zeitliche Veränderungen in der Häufigkeit tropischer Zyklone widerspiegelt. In Zentralamerika wird das Ende der Maya Hochkultur (900-1100 n.Chr.) mit drastischen Umweltveränderungen (z.B. Dürren) assoziiert, die während der Mittelalterlichen Warmzeit (MWP; 900-1400 n.Chr.) durch eine globale Klimaveränderung hervorgerufen wurde. Die aus einem „Blue Hole“ abgeleiteten Informationen über Klimavariationen der Vergangenheit können als Referenz für die gegenwärtige Klimakriese verwendet werden.
Als „Blue Hole“ wird eine Karsthöhle bezeichnet, die sich subaerisch während vergangener Meeresspiegeltiefstände im karbonatischen Gerüst eines Riffsystems gebildet hat und in Folge eines Meeresspiegelanstiegs vollständig überflutet wurde. In einigen wenigen marinen „Blue Holes“ treten anoxische Bodenwasserbedingungen auf. Die in diesen anoxischen Karsthöhlen abgelagerten Abfolgen mariner Sedimente können als einzigartiges Klimaarchiv verwendet werden, da sie aufgrund des Fehlens von Bioturbation eine jährliche Schichtung (Warvierung) aufweisen.
In dieser kumulativen Dissertation über das „Great Blue Hole“ werden die Ergebnisse eines 3-jährigen Forschungsprojekts vorgestellt, dass das Ziel verfolgte einen wissenschaftlich herausragenden spät Holozänen Klimadatensatz für die süd-westliche Karibik zu erzeugen. Beim „Great Blue Hole“ handelt es sich um ein weltweit einzigartiges marines Sedimentarchiv für diverse spät Holozäne Klima-veränderungen, das im Zuge dieser Dissertation sowohl nach paläoklimatischen als auch nach sedimentologischen Fragestellungen untersucht wurde. Die vorliegende Doktorarbeit befasst sich im Einzelnen mit (1) der Ausarbeitung eines jährlich aufgelösten Archives für tropische Zyklone, (2) der Entwicklung eines jährlich aufgelösten SST Datensatzes und (3) einer kompositionellen Quantifizierung der sedimentären Abfolgen sowie einer faziell-stratigraphischen Charakterisierung von Schönwetter-Sedimenten und Sturmlagen. Zu jedem dieser drei Aspekte, wurde jeweils ein Fachartikel bei einer anerkannten wissenschaftlichen Fachzeitschrift mit „peer-review“ Verfahren veröffentlicht.
Der insgesamt 8.55 m lange Sedimentbohrkern („BH6“), der für diese Dissertation untersucht wurde, stammt vom Boden des 125 m tiefen und 320 m breiten „Great Blue Holes“, das sich in der flachen östlichen Lagune des 80 km vor der Küste von Belize (Zentralamerika) gelegenen „Lighthouse Reef“ Atolls befindet. Durch seine besondere Geomorphologie wirkt das, innerhalb des atlantischen „Hurrikan Gürtels“ positionierte, „Great Blue Hole“ wie eine gigantische Sedimentfalle. Die unter Schönwetter-Bedingungen kontinuierlich abgelagerten Abfolgen feinkörniger karbonatischer Sedimente, werden von groben Sturmlagen unterbrochen, die auf „over-wash“ Prozesse von tropischen Zyklonen zurückzuführen sind.
...
[Nachruf] Arno Semmel
(2010)
In November 2016, magnetotelluric (MT) data were collected at the Ceboruco Volcano in cooperation with the Centro de Sismología y Volcanología de Occidente (SisVoc, Universidad de Guadalajara, Mexico). The Ceboruco is a 2280 m high stratovolcano, located in Nayarit State, Mexico. It is placed in the central part of the Tepic-Zacoalco Rift (TZR), which constitutes the north-western end of the Trans-Mexican Volcanic Belt. Together with Chapala and Colima (in the Jalisco Block), they form the triple rift system developed as a consequence of the ongoing subduction of the Rivera and Cocos oceanic plates beneath the North American continental crust. Although its last eruption occurred in 1870, it is the most active volcano in the area, showing volcanic-earthquake activity together with ongoing vapor emissions. The survey was part of a geothermal project (CeMIEGeo-P24) and focused on the determination of electrical conductivity properties to characterize the deep structure and the geothermal potential of the Volcano. Frequency dependent magnetotelluric response functions were calculated from 25 broadband MT stations, which covered an area of 10 x 10 km2 including its crater, calderas and foreland. The results were interpreted using anisotropic 3-D forward modelling and isotropic 3-D inversion approaches, considering strong topographical effects. The final resistivity model implies a highly conductive layer, reaching from near-surface to approximately 2 km depth, which might be related to a hydrothermal system. Here, mineralized fluids and clay minerals can cause high conductivities around 1 S/m. For longer periods, the principal axes of the MT response tensors (phase tensor, apparent resistivity tensor) are in good agreement with the strike direction of the underlying rift system. However, they are not rendered by the isotropic inversion. Thus the data suggest an anisotropic electrical conductivity at greater depth with its principal axis determined by the response tensors.
The most frequently used boundary-layer turbulence parameterization in numerical weather prediction (NWP) models are turbulence kinetic energy (TKE) based-based schemes. However, these parameterizations suffer from a potential weakness, namely the strong dependence on an ad-hoc quantity, the so-called turbulence length scale. The physical interpretation of the turbulence length scale is difficult and hence it cannot be directly related to measurements or large eddy simulation (LES) data. Consequently, formulations for the turbulence length scale in basically all TKE schemes are based on simplified assumptions and are model-dependent. A good reference for the independent evaluation of the turbulence length scale expression for NWP modeling is missing. Here we propose a new turbulence length scale diagnostic which can be used in the gray zone of turbulence without modifying the underlying TKE turbulence scheme. The new diagnostic is based on the TKE budget: The core idea is to encapsulate the sum of the molecular dissipation and the cross-scale TKE transfer into an effective dissipation, and associate it with the new turbulence length scale. This effective dissipation can then be calculated as a residuum in the TKE budget equation (for horizontal sub-domains of different sizes) using LES data. Estimation of the scale dependence of the diagnosed turbulence length scale using this novel method is presented for several idealized cases.
Aim: Predicting future changes in species richness in response to climate change is one of the key challenges in biogeography and conservation ecology. Stacked species distribution models (S‐SDMs) are a commonly used tool to predict current and future species richness. Macroecological models (MEMs), regression models with species richness as response variable, are a less computationally intensive alternative to S‐SDMs. Here, we aim to compare the results of two model types (S‐SDMS and MEMs), for the first time for more than 14,000 species across multiple taxa globally, and to trace the uncertainty in future predictions back to the input data and modelling approach used.
Location: Global land, excluding Antarctica.
Taxon: Amphibians, birds and mammals.
Methods: We fitted S‐SDMs and MEMs using a consistent set of bioclimatic variables and model algorithms and conducted species richness predictions under current and future conditions. For the latter, we used four general circulation models (GCMs) under two representative concentration pathways (RCP2.6 and RCP6.0). Predicted species richness was compared between S‐SDMs and MEMs and for current conditions also to extent‐of‐occurrence (EOO) species richness patterns. For future predictions, we quantified the variance in predicted species richness patterns explained by the choice of model type, model algorithm and GCM using hierarchical cluster analysis and variance partitioning.
Results: Under current conditions, species richness predictions from MEMs and S‐SDMs were strongly correlated with EOO‐based species richness. However, both model types over‐predicted areas with low and under‐predicted areas with high species richness. Outputs from MEMs and S‐SDMs were also highly correlated among each other under current and future conditions. The variance between future predictions was mostly explained by model type.
Main conclusions: Both model types were able to reproduce EOO‐based patterns in global terrestrial vertebrate richness, but produce less collinear predictions of future species richness. Model type by far contributes to most of the variation in the different future species richness predictions, indicating that the two model types should not be used interchangeably. Nevertheless, both model types have their justification, as MEMs can also include species with a restricted range, whereas S‐SDMs are useful for looking at potential species‐specific responses.
Immersion freezing is the most relevant heterogeneous ice nucleation mechanism through which ice crystals are formed in mixed-phase clouds. In recent years, an increasing number of laboratory experiments utilizing a variety of instruments have examined immersion freezing activity of atmospherically relevant ice-nucleating particles. However, an intercomparison of these laboratory results is a difficult task because investigators have used different ice nucleation (IN) measurement methods to produce these results. A remaining challenge is to explore the sensitivity and accuracy of these techniques and to understand how the IN results are potentially influenced or biased by experimental parameters associated with these techniques.
Within the framework of INUIT (Ice Nuclei Research Unit), we distributed an illite-rich sample (illite NX) as a representative surrogate for atmospheric mineral dust particles to investigators to perform immersion freezing experiments using different IN measurement methods and to obtain IN data as a function of particle concentration, temperature (T), cooling rate and nucleation time. A total of 17 measurement methods were involved in the data intercomparison. Experiments with seven instruments started with the test sample pre-suspended in water before cooling, while 10 other instruments employed water vapor condensation onto dry-dispersed particles followed by immersion freezing. The resulting comprehensive immersion freezing data set was evaluated using the ice nucleation active surface-site density, ns, to develop a representative ns(T) spectrum that spans a wide temperature range (−37 °C < T < −11 °C) and covers 9 orders of magnitude in ns.
In general, the 17 immersion freezing measurement techniques deviate, within a range of about 8 °C in terms of temperature, by 3 orders of magnitude with respect to ns. In addition, we show evidence that the immersion freezing efficiency expressed in ns of illite NX particles is relatively independent of droplet size, particle mass in suspension, particle size and cooling rate during freezing. A strong temperature dependence and weak time and size dependence of the immersion freezing efficiency of illite-rich clay mineral particles enabled the ns parameterization solely as a function of temperature. We also characterized the ns(T) spectra and identified a section with a steep slope between −20 and −27 °C, where a large fraction of active sites of our test dust may trigger immersion freezing. This slope was followed by a region with a gentler slope at temperatures below −27 °C. While the agreement between different instruments was reasonable below ~ −27 °C, there seemed to be a different trend in the temperature-dependent ice nucleation activity from the suspension and dry-dispersed particle measurements for this mineral dust, in particular at higher temperatures. For instance, the ice nucleation activity expressed in ns was smaller for the average of the wet suspended samples and higher for the average of the dry-dispersed aerosol samples between about −27 and −18 °C. Only instruments making measurements with wet suspended samples were able to measure ice nucleation above −18 °C. A possible explanation for the deviation between −27 and −18 °C is discussed. Multiple exponential distribution fits in both linear and log space for both specific surface area-based ns(T) and geometric surface area-based ns(T) are provided. These new fits, constrained by using identical reference samples, will help to compare IN measurement methods that are not included in the present study and IN data from future IN instruments.
Immersion freezing is the most relevant heterogeneous ice nucleation mechanism through which ice crystals are formed in mixed-phase clouds. In recent years, an increasing number of laboratory experiments utilizing a variety of instruments have examined immersion freezing activity of atmospherically relevant ice nucleating particles (INPs). However, an inter-comparison of these laboratory results is a difficult task because investigators have used different ice nucleation (IN) measurement methods to produce these results. A remaining challenge is to explore the sensitivity and accuracy of these techniques and to understand how the IN results are potentially influenced or biased by experimental parameters associated with these techniques.
Within the framework of INUIT (Ice Nucleation research UnIT), we distributed an illite rich sample (illite NX) as a representative surrogate for atmospheric mineral dust particles to investigators to perform immersion freezing experiments using different IN measurement methods and to obtain IN data as a function of particle concentration, temperature (T), cooling rate and nucleation time. Seventeen measurement methods were involved in the data inter-comparison. Experiments with seven instruments started with the test sample pre-suspended in water before cooling, while ten other instruments employed water vapor condensation onto dry-dispersed particles followed by immersion freezing. The resulting comprehensive immersion freezing dataset was evaluated using the ice nucleation active surface-site density (ns) to develop a representative ns(T) spectrum that spans a wide temperature range (−37 °C < T < −11 °C) and covers nine orders of magnitude in ns.
Our inter-comparison results revealed a discrepancy between suspension and dry-dispersed particle measurements for this mineral dust. While the agreement was good below ~ −26 °C, the ice nucleation activity, expressed in ns, was smaller for the wet suspended samples and higher for the dry-dispersed aerosol samples between about −26 and −18 °C. Only instruments making measurement techniques with wet suspended samples were able to measure ice nucleation above −18 °C. A possible explanation for the deviation between −26 and −18 °C is discussed. In general, the seventeen immersion freezing measurement techniques deviate, within the range of about 7 °C in terms of temperature, by three orders of magnitude with respect to ns. In addition, we show evidence that the immersion freezing efficiency (i.e., ns) of illite NX particles is relatively independent on droplet size, particle mass in suspension, particle size and cooling rate during freezing. A strong temperature-dependence and weak time- and size-dependence of immersion freezing efficiency of illite-rich clay mineral particles enabled the ns parameterization solely as a function of temperature. We also characterized the ns (T) spectra, and identified a section with a steep slope between −20 and −27 °C, where a large fraction of active sites of our test dust may trigger immersion freezing. This slope was followed by a region with a gentler slope at temperatures below −27 °C. A multiple exponential distribution fit is expressed as ns(T) = exp(23.82 × exp(−exp(0.16 × (T + 17.49))) + 1.39) based on the specific surface area and ns(T) = exp(25.75 × exp(−exp(0.13 × (T + 17.17))) + 3.34) based on the geometric area (ns and T in m−2 and °C, respectively). These new fits, constrained by using an identical reference samples, will help to compare IN measurement methods that are not included in the present study and, thereby, IN data from future IN instruments.