The Arctic Sea ice in the CMIP3 climate model ensemble – variability and anthropogenic change

The strongest manifestation of global warming is observed in the Arctic. The warming in the Arctic during the recent decades is about twice as strong as in the global average and has been accompanied by a summer sea ice decline that is very likely unprecedented during the last millennium. Here, Arctic sea ice variability is analyzed in the ensemble of CMIP3 models. Complementary to several previous studies, we focus on regional aspects, in particular on the Barents Sea. We also investigate the changes in the seasonal cycle and interannual variability. In all regions, the models predict a reduction in sea ice area and sea ice volume during 1900–2100. Toward the end of the 21st century, the models simulate higher sea ice area variability in September than in March, whereas the variability in the preindustrial control runs is higher in March. Furthermore, the amplitude and phase of the sea ice seasonal cycle change in response to enhanced greenhouse warming. The amplitude of the sea ice area seasonal cycle increases due to the very strong sea ice area decline in September. The seasonal cycle amplitude of the sea ice volume decreases due to the stronger reduction of sea ice volume in March. Multi-model mean estimates for the late 20th century are comparable with observational data only for the entire Arctic and the Central Arctic. In the Barents Sea, differences between the multi-model mean and the observational data are more pronounced. Regional sea ice sensitivity to Northern Hemisphere average surface warming has been investigated

Arctic sea ice area in CMIP3 and CMIP5 climate model ensembles – variability and change

The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the model results exhibit considerable spread. The last generation of climate models from World Climate Research Programme Coupled Model Intercomparison Project Phase 5 (CMIP5), when compared to the previous CMIP3 model ensemble and considering the whole Arctic, were found to be more consistent with the observed changes in sea ice extent during the recent decades. Some CMIP5 models project strongly accelerated (non-linear) sea ice loss during the first half of the 21st century. Here, complementary to previous studies, we compare results from CMIP3 and CMIP5 with respect to regional Arctic sea ice change. We focus on September and March sea ice. Sea ice area (SIA) variability, sea ice concentration (SIC) variability, and characteristics of the SIA seasonal cycle and interannual variability have been analysed for the whole Arctic, termed Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA changes to changes in Northern Hemisphere (NH) averaged temperature is investigated and several important dynamical links between SIA and natural climate variability involving the Atlantic Meridional Overturning Circulation (AMOC), North Atlantic Oscillation (NAO) and sea level pressure gradient (SLPG) in the western Barents Sea opening serving as an index of oceanic inflow to the Barents Sea are studied. The CMIP3 and CMIP5 models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle and in the aforementioned dynamical links. The spatial patterns of SIC variability improve in the CMIP5 ensemble, particularly in summer. Both CMIP ensembles depict a significant link between the SIA and NH temperature changes. Our analysis suggests that, on average, the sensitivity of SIA to external forcing is enhanced in the CMIP5 models. The Arctic SIA variability response to anthropogenic forcing is different in CMIP3 and CMIP5. While the CMIP3 models simulate increased variability in March and September, the CMIP5 ensemble shows the opposite tendency. A noticeable improvement in the simulation of summer SIA by the CMIP5 models is often accompanied by worse results for winter SIA characteristics. The relation between SIA and mean AMOC changes is opposite in September and March, with March SIA changes being positively correlated with AMOC slowing. Finally, both CMIP ensembles demonstrate an ability to capture, at least qualitatively, important dynamical links of SIA to decadal variability of the AMOC, NAO and SLPG. SIA in the Barents Sea is strongly overestimated by the majority of the CMIP3 and CMIP5 models, and projected SIA changes are characterized by a large spread giving rise to high uncertainty

Orbital electron capture by the nucleus

The theory of nuclear electron capture is reviewed in the light of current understanding of weak interactions. Experimental methods and results regarding capture probabilities, capture ratios, and EC/Beta(+) ratios are summarized. Radiative electron capture is discussed, including both theory and experiment. Atomic wave function overlap and electron exchange effects are covered, as are atomic transitions that accompany nuclear electron capture. Tables are provided to assist the reader in determining quantities of interest for specific cases

Arctic sea ice area changes in CMIP3 and CMIP5 climate models’ ensembles

The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the results exhibit considerable spread. Here, we compare results from the two last generations of climate models, CMIP3 and CMIP5, with respect to total and regional Arctic sea ice change. Different characteristics of sea ice area (SIA) in March and September have been analysed for the Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA to changes in Northern Hemisphere (NH) temperature is investigated and dynamical links between SIA and some atmospheric variability modes are assessed. CMIP3 (SRES A1B) and CMIP5 (RCP8.5) models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle. The spatial patterns of SIC variability improve in CMIP5 ensemble, most noticeably in summer when compared to HadISST1 data. A better simulation of summer SIA in the Entire Arctic by CMIP5 models is accompanied by a slightly increased bias for winter season in comparison to CMIP3 ensemble. SIA in the Barents Sea is strongly overestimated by the majority of CMIP3 and CMIP5 models, and projected SIA changes are characterized by a high uncertainty. Both CMIP ensembles depict a significant link between the SIA and NH temperature changes indicating that a part of inter-ensemble SIA spread comes from different temperature sensitivity to anthropogenic forcing. The results suggest that, in general, a sensitivity of SIA to external forcing is enhanced in CMIP5 models. Arctic SIA interannual variability in the end of the 20th century is on average well simulated by both ensembles. To the end of the 21st century, September variability is strongly reduced in CMIP5 models under RCP8.5 scenario, whereas variability changes in CMIP3 and in both ensembles in March are relatively small. The majority of models in both CMIP ensembles demonstrate an ability to capture a negative correlation of interannual SIA variations in the Barents Sea with North Atlantic Oscillation and sea level pressure gradient in the western Barents Sea opening serving as an index of oceanic inflow to the Sea

Variation in the upstream region of P-Selectin (SELP) is a risk factor for SLE

Systemic lupus erythematosus (SLE) is a complex autoimmune disease. Genome-wide linkage studies implicated a region containing the adhesion molecule P-Selectin. This family-based study revealed two regions of association within P-Selectin. The strongest signal, from a 21.4-kb risk haplotype, stretched from the promoter into the first two consensus repeat (CR) regions (P=8 × 10−4), with a second association from a 14.6-kb protective haplotype covering CR 2–9 (P=0.0198). The risk haplotype is tagged by the rare C allele of rs3753306, which disrupts the binding site of the trans-activating transcription factor HNF-1. One other variant (rs3917687) on the risk haplotype was significant after permutation (P10000<1 × 10−5), replicated in independent pseudo case-control analysis and was significant by meta-analysis (P=4.37 × 10−6). A third associated variant on the risk haplotype (rs3917657) replicated in 306 US SLE families and was significant in a joint UK-SLE data set after permutation. The protective haplotype is tagged by rs6133 (a non-synonymous variant in CR8 (P=9.00 × 10−4), which also shows association in the pseudo case-control analysis (P=1.09 × 10−3) and may contribute to another signal in P-Selectin. We propose that polymorphism in the upstream region may reduce expression of P-Selectin, the mechanism by which this promotes autoimmunity is unknown, although it may reduce the production of regulatory T cells

Rhythm in the speech of a person with right hemisphere damage: Applying the pairwise variability index

Although several aspects of prosody have been studied in speakers with right hemisphere damage (RHD), rhythm remains largely uninvestigated. This study compares the rhythm of an Australian English speaker with right hemisphere damage (due to a stroke, but with no concomitant dysarthria) to that of a neurologically unimpaired individual. The speakers' rhythm is compared using the pairwise variability index (PVI) which allows for an acoustic characterization of rhythm by comparing the duration of successive vocalic and intervocalic intervals. A sample of speech from a structured interview between a speech and language therapist and each participant was analysed. Previous research has shown that speakers with RHD may have difficulties with intonation production, and therefore it was hypothesized that there may also be rhythmic disturbance. Results show that the neurologically normal control uses a similar rhythm to that reported for British English (there are no previous studies available for Australian English), whilst the speaker with RHD produces speech with a less strongly stress-timed rhythm. This finding was statistically significant for the intervocalic intervals measured (t(8) = 4.7, p < .01), and suggests that some aspects of prosody may be right lateralized for this speaker. The findings are discussed in relation to previous findings of dysprosody in RHD populations, and in relation to syllable-timed speech of people with other neurological conditions

The ZEUS Forward Plug Calorimeter with Lead-Scintillator Plates and WLS Fiber Readout

A Forward Plug Calorimeter (FPC) for the ZEUS detector at HERA has been built as a shashlik lead-scintillator calorimeter with wave length shifter fiber readout. Before installation it was tested and calibrated using the X5 test beam facility of the SPS accelerator at CERN. Electron, muon and pion beams in the momentum range of 10 to 100 GeV/c were used. Results of these measurements are presented as well as a calibration monitoring system based on a $^{60}$Co source.Comment: 38 pages (Latex); 26 figures (ps

First Dark Matter Results from the XENON100 Experiment

The XENON100 experiment, in operation at the Laboratori Nazionali del Gran Sasso in Italy, is designed to search for dark matter WIMPs scattering off 62 kg of liquid xenon in an ultra-low background dual-phase time projection chamber. In this letter, we present first dark matter results from the analysis of 11.17 live days of non-blind data, acquired in October and November 2009. In the selected fiducial target of 40 kg, and within the pre-defined signal region, we observe no events and hence exclude spin-independent WIMP-nucleon elastic scattering cross-sections above 3.4 x 10^-44 cm^2 for 55 GeV/c^2 WIMPs at 90% confidence level. Below 20 GeV/c^2, this result constrains the interpretation of the CoGeNT and DAMA signals as being due to spin-independent, elastic, light mass WIMP interactions.Comment: 5 pages, 5 figures. Matches published versio

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests