Tephra from andesitic Shiveluch volcano, Kamchatka, NW Pacific: chronology of explosive eruptions and geochemical fingerprinting of volcanic glass

The ~16-ka-long record of explosive eruptions from Shiveluch volcano (Kamchatka, NW Pacific) is refined using geochemical fingerprinting of tephra and radiocarbon ages. Volcanic glass from 77 prominent Holocene tephras and four Late Glacial tephra packages was analyzed by electron microprobe. Eruption ages were estimated using 113 radiocarbon dates for proximal tephra sequence. These radiocarbon dates were combined with 76 dates for regional Kamchatka marker tephra layers into a single Bayesian framework taking into account the stratigraphic ordering within and between the sites. As a result, we report ~1,700 high-quality glass analyses from Late Glacial–Holocene Shiveluch eruptions of known ages. These define the magmatic evolution of the volcano and provide a reference for correlations with distal fall deposits. Shiveluch tephras represent two major types of magmas, which have been feeding the volcano during the Late Glacial–Holocene time: Baidarny basaltic andesites and Young Shiveluch andesites. Baidarny tephras erupted mostly during the Late Glacial time (~16–12.8 ka BP) but persisted into the Holocene as subordinate admixture to the prevailing Young Shiveluch andesitic tephras (~12.7 ka BP–present). Baidarny basaltic andesite tephras have trachyandesite and trachydacite (SiO₂ < 71.5 wt%) glasses. The Young Shiveluch andesite tephras have rhyolitic glasses (SiO₂ > 71.5 wt%). Strongly calc-alkaline medium-K characteristics of Shiveluch volcanic glasses along with moderate Cl, CaO and low P₂O₅ contents permit reliable discrimination of Shiveluch tephras from the majority of other large Holocene tephras of Kamchatka. The Young Shiveluch glasses exhibit wave-like variations in SiO₂ contents through time that may reflect alternating periods of high and low frequency/volume of magma supply to deep magma reservoirs beneath the volcano. The compositional variability of Shiveluch glass allows geochemical fingerprinting of individual Shiveluch tephra layers which along with age estimates facilitates their use as a dating tool in paleovolcanological, paleoseismological, paleoenvironmental and archeological studies. Electronic tables accompanying this work offer a tool for statistical correlation of unknown tephras with proximal Shiveluch units taking into account sectors of actual tephra dispersal, eruption size and expected age. Several examples illustrate the effectiveness of the new database. The data are used to assign a few previously enigmatic wide-spread tephras to particular Shiveluch eruptions. Our finding of Shiveluch tephras in sediment cores in the Bering Sea at a distance of ~600 km from the source permits re-assessment of the maximum dispersal distances for Shiveluch tephras and provides links between terrestrial and marine paleoenvironmental records.

Intersectional Discrimination in EU Law - a critical reflection

This chapter introduces the concept of intersectionality in its relevance for anti-discrimination law. It illustrates the use (or non-use) of this concept by the Court of Justice, and provides examples of case law ignoring intersectional inequalities. Finally, it proposes to re-frame and re-focus EU anti-discrimination law around nodes of inequalities as a way to better address intersectional inequalities.

Validation of non‑stationary precipitation series for site‑specific impact assessment: comparison of two statistical downscaling techniques

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

A new statistical framework for the quantification of covariate associations with species distributions

Identifying processes that shape species geographical ranges is a prerequisite for understanding environmental change. Currently, species distribution modelling methods do not offer credible statistical tests of the relative influence of climate factors and typically ignore other processes (e.g. biotic interactions and dispersal limitation). We use a hierarchical model fitted with Markov Chain Monte Carlo to combine ecologically plausible niche structures using regression splines to describe unimodal but potentially skewed response terms. We apply spatially explicit error terms that account for (and may help identify) missing variables. Using three example distributions of European bird species, we map model results to show sensitivity to change in each covariate. We show that the overall strength of climatic association differs between species and that each species has considerable spatial variation in both the strength of the climatic association and the sensitivity to climate change. Our methods are widely applicable to many species distribution modelling problems and enable accurate assessment of the statistical importance of biotic and abiotic influences on distributions.