Refine
Has Fulltext
- no (131)
Year of publication
- 2022 (131) (remove)
Document Type
- Article (131) (remove)
Language
- English (131) (remove)
Is part of the Bibliography
- yes (131)
Keywords
- climate change (7)
- permafrost (7)
- radiation belts (3)
- Andes (2)
- Central Andes (2)
- Climate change (2)
- Magnetotellurics (2)
- Precipitation (2)
- Siberia (2)
- Tibetan Plateau (2)
Institute
- Institut für Geowissenschaften (131) (remove)
Modern pollen-vegetation-climate relationships underpin palaeovegetation and palaeoclimate reconstructions from fossil pollen records. East Siberia is an ideal area for investigating the relationships between modern pollen assemblages and near natural vegetation under cold continental climate conditions. Reliable pollen-based quantitative vegetation and climate reconstructions are still scarce due to the limited number of modern pollen datasets. Furthermore, differences in pollen representation of samples from lake sediments and soils are not well understood. Here, we present a new pollen dataset of 48 moss/soil and 24 lake surface-sediment samples collected in Chukotka and central Yakutia in East Siberia. The pollen-vegetation-climate relationships were investigated by ordination analyses. Generally, tundra and taiga vegetation types can be well distinguished in the surface pollen assemblages. Moss/soil and lake samples contain generally similar pollen assemblages as revealed by a Procrustes comparison with some exceptions. Overall, modern pollen assemblages reflect the temperature and precipitation gradients in the study areas as revealed by constrained ordination analysis. We estimate the relative pollen productivity (RPP) of major taxa and the relevant source area of pollen (RSAP) for moss/soil samples from Chukotka and central Yakutia using Extended R-Value (ERV) analysis. The RSAP of the tundra-forest transition area in Chukotka and taiga area in central Yakutia are ca. 1300 and 360 m, respectively. For Chukotka, RPPs relative to both Poaceae and Ericaceae were estimated while RPPs for central Yakutia were relative only to Ericaceae. Relative to Ericaceae (reference taxon, RPP = 1), Larix, Betula, Picea, and Pinus are overrepresented while Alnus, Cyperaceae, Poaceae, and Salix are underrepresented in the pollen spectra. Our estimates are in general agreement with previously published values and provide the basis for reliable quantitative reconstructions of East Siberian vegetation.
As the recent permafrost thawing of northern Asia proceeds due to anthropogenic climate change, precise and detailed palaeoecological records from past warm periods are essential to anticipate the extent of future permafrost variations. Here, based on the modern relationship between permafrost and vegetation (represented by pollen assemblages), we trained a Random Forest model using pollen and permafrost data and verified its reliability to reconstruct the history of permafrost in northern Asia during the Holocene. An early Holocene (12-8 cal ka BP) strong thawing trend, a middle-to-late Holocene (8-2 cal ka BP) relatively slow thawing trend, and a late Holocene freezing trend of permafrost in northern Asia are consistent with climatic proxies such as summer solar radiation and Northern Hemisphere temperature. The extensive distribution of permafrost in northern Asia inhibited the spread of evergreen coniferous trees during the early Holocene warming and might have decelerated the enhancement of the East Asian summer monsoon (EASM) by altering hydrological processes and albedo. Based on these findings, we suggest that studies of the EASM should consider more the state of permafrost and vegetation in northern Asia, which are often overlooked and may have a profound impact on climate change in this region.
Although phytoliths are recognized as an important proxy for paleoenvironmental reconstruction, the quantitative relationship between phytoliths and climate is still debated. In order to provide an improved basis for phytolith-based paleoclimate reconstructions, a representative modern phytolith dataset is essential. Here, we synthesize a modern topsoil phytolith dataset for Northeast China, analyze its climatic significance, and apply it to a fossil phytolith series from the Hani peat core in Northeast China. The dataset comprises 660 topsoil phytolith assemblages from 289 sample sites. We compiled modern meteorological data to assess the quantitative relationship between the phytolith assemblages and climatic variables. Detrended correspondence analysis (DCA) and Redundancy analysis (RDA) were used to determine the dominant climatic variable influencing the phytolith distributions. The results showed that mean annual temperature (MAT) is the dominant variable controlling the spatial distribution of phytoliths, accounting for 8.91% of the total variance. Transfer function based on inverse deshrinking locally-weighted weighted averaging (LWWA_Inv) was developed for MAT (R-_boot(2) = 0.86, RMSEP = 1.02 degrees C). Applying the LWWA_Inv transfer function to fossil phytolith records from the Hani peat core enables quantitative inferences to be made about Holocene climate changes in Northeast China. Overall, combined with the LWWA_Inv method, the topsoil phytolith dataset of Northeast China can be used for reliable quantitative MAT reconstruction.
We investigate whether the distribution of maximum seasonal streamflow is significantly affected by catchment or climate state of the season/month ahead. We fit the Generalized Extreme Value (GEV) distribution to extreme seasonal streamflow for around 600 stations across Europe by conditioning the GEV location and scale parameters on 14 indices, which represent the season-ahead climate or catchment state. The comparison of these climate-informed models with the classical GEV distribution, with time-constant parameters, suggests that there is a substantial potential for seasonal forecasting of flood probabilities. The potential varies between seasons and regions. Overall, the season-ahead catchment wetness shows the highest potential, although climate indices based on large-scale atmospheric circulation, sea surface temperature or sea ice concentration also show some skill for certain regions and seasons. Spatially coherent patterns and a substantial fraction of climate-informed models are promising signs towards early alerts to increase flood preparedness already a season ahead.
Thousands of glacier lakes have been forming behind natural dams in high mountains following glacier retreat since the early 20th century. Some of these lakes abruptly released pulses of water and sediment with disastrous downstream consequences. Yet it remains unclear whether the reported rise of these glacier lake outburst floods (GLOFs) has been fueled by a warming atmosphere and enhanced meltwater production, or simply a growing research effort. Here we estimate trends and biases in GLOF reporting based on the largest global catalog of 1,997 dated glacier-related floods in six major mountain ranges from 1901 to 2017. We find that the positive trend in the number of reported GLOFs has decayed distinctly after a break in the 1970s, coinciding with independently detected trend changes in annual air temperatures and in the annual number of field-based glacier surveys (a proxy of scientific reporting). We observe that GLOF reports and glacier surveys decelerated, while temperature rise accelerated in the past five decades. Enhanced warming alone can thus hardly explain the annual number of reported GLOFs, suggesting that temperature-driven glacier lake formation, growth, and failure are weakly coupled, or that outbursts have been overlooked. Indeed, our analysis emphasizes a distinct geographic and temporal bias in GLOF reporting, and we project that between two to four out of five GLOFs on average might have gone unnoticed in the early to mid-20th century. We recommend that such biases should be considered, or better corrected for, when attributing the frequency of reported GLOFs to atmospheric warming.
Lithium and boron are trace components of magmas, released during exsolution of a gas phase during volcanic activity.
In this study, we determine the diffusivity and isotopic fractionation of Li and B in hydrous silicate melts.
Two glasses were synthesized with the same rhyolitic composition (4.2 wt% water), having different Li and B contents; these were studied in diffusion-couple experiments that were performed using an internally heated pressure vessel, operated at 300 MPa in the temperature range 700-1250 degrees C for durations from 0 s to 24 h. From this we determined activation energies for Li and B diffusion of 57 +/- 4 kJ/mol and 152 +/- 15 kJ/mol with pre-exponential factors of 1.53 x 10(-7) m(2)/s and 3.80 x 10(-8) m(2)/s, respectively.
Lithium isotopic fractionation during diffusion gave beta values between 0.15 and 0.20, whereas B showed no clear isotopic fractionation.
Our Li diffusivities and isotopic fractionation results differ somewhat from earlier published values, but overall confirm that Li diffusivity increases with water content. Our results on B diffusion show that similarly to Li, B mobility increases in the presence of water.
By applying the Eyring relation, we confirm that B diffusivity is limited by viscous flow in silicate melts.
Our results on Li and B diffusion present a new tool for understanding degassing-related processes, offering a potential geospeedometer to measure volcanic ascent rates.
Ground-motion models (GMMs) are often used to predict the random distribution of Spectral accelerations (SAs) at a site due to a nearby earthquake. In probabilistic seismic hazard and risk assessment, large earthquakes occurring close to a site are considered as critical scenarios. GMMs are expected to predict realistic SAs with low within-model uncertainty (sigma(mu)) for such rare scenarios. However, the datasets used to regress GMMs are usually deficient of data from critical scenarios. The (Kotha et al., A Regionally Adaptable Ground-Motion Model for Shallow Crustal Earthquakes in Europe Bulletin of Earthquake Engineering 18:4091-4125, 2020) GMM developed from the Engineering strong motion (ESM) dataset was found to predict decreasing short-period SAs with increasing M-W >= M-h = 6.2, and with large sigma(mu) at near-source distances <= 30km. In this study, we updated the parametrisation of the GMM based on analyses of ESM and the Near source strong motion (NESS) datasets. With M-h = 5.7, we could rectify the M-W scaling issue, while also reducing sigma(mu). at M-W >= M-h. We then evaluated the GMM against NESS data, and found that the SAs from a few large, thrust-faulting events in California, New Zealand, Japan, and Mexico are significantly higher than GMM median predictions. However, recordings from these events were mostly made on soft-soil geology, and contain anisotropic pulse-like effects. A more thorough non-ergodic treatment of NESS was not possible because most sites sampled unique events in very diverse tectonic environments. We provide an updated set of GMM coefficients,sigma(mu), and heteroscedastic variance models; while also cautioning against its application for M-W <= 4 in low-moderate seismicity regions without evaluating the homogeneity of M-W estimates between pan-European ESM and regional datasets.
Statistical distributions of flood peak discharge often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior.
This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage.
Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented.
In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice.
Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment, and the river system.
We then discuss to which extent the current knowledge supports or contradicts these hypotheses.
We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms.
We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research toward testing the hypotheses and improving the prediction of heavy tails.
Effect of temperature on the densification of silicate melts to lower earth's mantle conditions
(2022)
Physical properties of silicate melts play a key role for global planetary dynamics, controlling for example volcanic eruption styles, mantle convection and elemental cycling in the deep Earth. They are significantly modified by structural changes at the atomic scale due to external parameters such as pressure and temperature or due to chemistry. Structural rearrangements such as 4- to 6-fold coordination change of Si with increasing depth may profoundly influence melt properties, but have so far mostly been studied at ambient temperature due to experimental difficulties. In order to investigate the structural properties of silicate melts and their densification mechanisms at conditions relevant to the deep Earth's interior, we studied haplo basaltic glasses and melts (albite-diopside composition) at high pressure and temperature conditions in resistively and laser-heated diamond anvil cells using X-ray absorption near edge structure spectroscopy. Samples were doped with 10 wt% of Ge, which is accessible with this experimental technique and which commonly serves as a structural analogue for the network forming cation Si. We acquired spectra on the Ge K edge up to 48 GPa and 5000 K and derived the average Ge-O coordination number NGe-O, and bond distance RGe-O as functions of pressure. Our results demonstrate a continuous transformation from tetrahedral to octahedral coordination between ca. 5 and 30 GPa at ambient temperature. Above 1600 K the data reveal a reduction of the pressure needed to complete conversion to octahedral coordination by ca. 30 %. The results allow us to determine the influence of temperature on the Si coordination number changes in natural melts in the Earth's interior. We propose that the complete transition to octahedral coordination in basaltic melts is reached at about 40 GPa, corresponding to a depth of ca. 1200 km in the uppermost lower mantle. At the core-mantle boundary (2900 km, 130 GPa, 3000 K) the existence of non-buoyant melts has been proposed to explain observed low seismic wave velocity features. Our results highlight that the melt composition can affect the melt density at such extreme conditions and may strongly influence the structural response.