Poorest countries experience earlier anthropogenic emergence of daily temperature extremes

Understanding how the emergence of the anthropogenic warming signal from the noise of internal variability translates to changes in extreme event occurrence is of crucial societal importance. By utilising simulations of cumulative carbon dioxide (CO2) emissions and temperature changes from eleven earth system models, we demonstrate that the inherently lower internal variability found at tropical latitudes results in large increases in the frequency of extreme daily temperatures (exceedances of the 99.9th percentile derived from pre-industrial climate simulations) occurring much earlier than for mid-to-high latitude regions. Most of the world's poorest people live at low latitudes, when considering 2010 GDP-PPP per capita; conversely the wealthiest population quintile disproportionately inhabit more variable mid-latitude climates. Consequently, the fraction of the global population in the lowest socio-economic quintile is exposed to substantially more frequent daily temperature extremes after much lower increases in both mean global warming and cumulative CO2 emissions

The inner nuclear membrane protein Src1 associates with subtelomeric genes and alters their regulated gene expression

Inner nuclear membrane proteins containing a LEM (LAP2, emerin, and MAN1) domain participate in different processes, including chromatin organization, gene expression, and nuclear envelope biogenesis. In this study, we identify a robust genetic interaction between transcription export (TREX) factors and yeast Src1, an integral inner nuclear membrane protein that is homologous to vertebrate LEM2. DNA macroarray analysis revealed that the expression of the phosphate-regulated genes PHO11, PHO12, and PHO84 is up-regulated in src1Δ cells. Notably, these PHO genes are located in subtelomeric regions of chromatin and exhibit a perinuclear location in vivo. Src1 spans the nuclear membrane twice and exposes its N and C domains with putative DNA-binding motifs to the nucleoplasm. Genome-wide chromatin immunoprecipitation–on-chip analyses indicated that Src1 is highly enriched at telomeres and subtelomeric regions of the yeast chromosomes. Our data show that the inner nuclear membrane protein Src1 functions at the interface between subtelomeric gene expression and TREX-dependent messenger RNA export through the nuclear pore complexes

Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6

Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple single-model initial-condition large ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Coupled Model Intercomparison Project CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method bias < 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method bias (up to 50 %), and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections

Simple strong glass forming models: mean-field solution with activation

We introduce simple models, inspired by previous models for froths and covalent glasses, with trivial equilibrium properties but dynamical behaviour characteristic of strong glass forming systems. These models are also a generalization of backgammon or urn models to a non--constant number of particles, where entropic barriers are replaced by energy barriers, allowing for the existence of activated processes. We formulate a mean--field version of the models, which keeps most of the features of the finite dimensional ones, and solve analytically the out--of--equilibrium dynamics in the low temperature regime where activation plays an essential role.Comment: 18 pages, 9 figure

The timing of anthropogenic emergence in simulated climate extremes

Determining the time of emergence of climates altered from their natural state by anthropogenic influences can help inform the development of adaptation and mitigation strategies to climate change. Previous studies have examined the time of emergence of climate averages. However, at the global scale, the emergence of changes in extreme events, which have the greatest societal impacts, has not been investigated before. Based on state-of-the-art climate models, we show that temperature extremes generally emerge slightly later from their quasi-natural climate state than seasonal means, due to greater variability in extremes. Nevertheless, according to model evidence, both hot and cold extremes have already emerged across many areas. Remarkably, even precipitation extremes that have very large variability are projected to emerge in the coming decades in Northern Hemisphere winters associated with a wettening trend. Based on our findings we expect local temperature and precipitation extremes to already differ significantly from their previous quasi-natural state at many locations or to do so in the near future. Our findings have implications for climate impacts and detection and attribution studies assessing observed changes in regional climate extremes by showing whether they will likely find a fingerprint of anthropogenic climate change

New mobilities across the lifecourse: A framework for analysing demographically-linked drivers of migration

Date of acceptance: 17/02/2015Taking the life course as the central concern, the authors set out a conceptual framework and define some key research questions for a programme of research that explores how the linked lives of mobile people are situated in time–space within the economic, social, and cultural structures of contemporary society. Drawing on methodologically innovative techniques, these perspectives can offer new insights into the changing nature and meanings of migration across the life course.Publisher PDFPeer reviewe

The WFC3 Galactic Bulge Treasury Program: A First Look at Resolved Stellar Population Tools

[Abridged] When WFC3 is installed on HST, the community will have powerful new tools for investigating resolved stellar populations. The WFC3 Galactic Bulge Treasury program will obtain deep imaging on 4 low-extinction fields. These non-proprietary data will enable a variety of science investigations not possible with previous data sets. To aid in planning for the use of these data and for future proposals, we provide an introduction to the program, its photometric system, and the associated calibration effort. The observing strategy is based upon a new 5-band photometric system spanning the UV, optical, and near-infrared. With these broad bands, one can construct reddening-free indices of Teff and [Fe/H]. Besides the 4 bulge fields, the program will target 6 fields in well-studied star clusters, spanning a wide range of [Fe/H]. The cluster data serve to calibrate the indices, provide population templates, and correct the transformation of isochrones into the WFC3 photometric system. The bulge data will shed light on the bulge formation history, and will also serve as population templates for other studies. One of the fields includes 12 candidate hosts of extrasolar planets. CMDs are the most popular tool for analyzing resolved stellar populations. However, due to degeneracies among Teff, [Fe/H], and reddening in traditional CMDs, it can be difficult to draw robust conclusions from the data. The 5-band system used for the bulge Treasury observations will provide indices that are roughly orthogonal in Teff and [Fe/H], and we argue that model fitting in an index-index diagram will make better use of the information than fitting separate CMDs. We provide simulations to show the expected data quality and the potential for differentiating between different star-formation histories.Comment: Accepted for publication in The Astronomical Journal. 9 pages, 8 figures, latex, AJ forma

Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions

Nitrous oxide (N_2O) is a potent long‐lived greenhouse gas (GHG) and the strongest current emissions of global anthropogenic stratospheric ozone depletion weighted by its ozone depletion potential. In California, N_2O is the third largest contributor to the state's anthropogenic GHG emission inventory, though no study has quantified its statewide annual emissions through top‐down inverse modeling. Here we present the first annual (2013–2014) statewide top‐down estimates of anthropogenic N_2O emissions. Utilizing continuous N_2O observations from six sites across California in a hierarchical Bayesian inversion, we estimate that annual anthropogenic emissions are 1.5–2.5 times (at 95% confidence) the state inventory (41 Gg N_2O in 2014). Without mitigation, this estimate represents 4–7% of total GHG emissions assuming that other reported GHG emissions are reasonably correct. This suggests that control of N_2O could be an important component in meeting California's emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO_2 equivalent) by 2030 and 2050, respectively. Our seasonality analysis suggests that emissions are similar across seasons within posterior uncertainties. Future work is needed to provide source attribution for subregions and further characterization of seasonal variability

Thermophile 90S Pre-ribosome Structures Reveal the Reverse Order of Co-transcriptional 18S rRNA Subdomain Integration

The ‘birth’ of the eukaryotic ribosome is preceded by RNA folding and processing reactions that depend on assembly factors and snoRNAs. The 90S (SSU-processome) is the earliest pre-ribosome structurally analyzed, which was suggested to assemble stepwise along the growing pre-rRNA from 5’>3’, but this directionality may not be accurate. Here, by analyzing the structure of series of novel 90S assembly intermediates isolated from Chaetomium thermophilum, we discover a reverse order of 18S rRNA subdomain incorporation. This revealed that large parts of the 18S rRNA 3’ and central domains assemble first into the 90S, before the 5’ domain is stably integrated. This final incorporation depends on a physical contact between a heterotrimer Enp2-Bfr2-Lcp1 recruited to the flexible 5’ domain and Kre33, which reconstitutes the Kre33-Enp-Brf2-Lcp5 module on the compacted 90S pre-ribosome. Keeping the 5’ domain temporarily segregated from the 90S scaffold could provide an extra time to complete the multifaceted 5’ domain folding, which depends on a distinct set of snoRNAs and processing factors

Influences of increasing temperature on Indian wheat: quantifying limits to predictability

As climate changes, temperatures will play an increasing role in determining crop yield. Both climate model error and lack of constrained physiological thresholds limit the predictability of yield. We used a perturbed-parameter climate model ensemble with two methods of bias-correction as input to a regional-scale wheat simulation model over India to examine future yields. This model configuration accounted for uncertainty in climate, planting date, optimization, temperature-induced changes in development rate and reproduction. It also accounts for lethal temperatures, which have been somewhat neglected to date. Using uncertainty decomposition, we found that fractional uncertainty due to temperature-driven processes in the crop model was on average larger than climate model uncertainty (0.56 versus 0.44), and that the crop model uncertainty is dominated by crop development. Simulations with the raw compared to the bias-corrected climate data did not agree on the impact on future wheat yield, nor its geographical distribution. However the method of bias-correction was not an important source of uncertainty. We conclude that bias-correction of climate model data and improved constraints on especially crop development are critical for robust impact predictions