An investigation into linearity with cumulative emissions of the climate and carbon cycle response in HadCM3LC

We investigate the extent to which global mean temperature, precipitation, and the carbon cycle are constrained by cumulative carbon emissions throughout four experiments with a fully coupled climate-carbon cycle model. The two paired experiments adopt contrasting, idealised approaches to climate change mitigation at different action points this century, with total emissions exceeding two trillion tonnes of carbon in the later pair. Their initially diverging cumulative emissions trajectories cross after several decades, before diverging again. We find that their global mean temperatures are, to first order, linear with cumulative emissions, though regional differences in temperature of up to 1.5K exist when cumulative emissions of each pair coincide. Interestingly, although the oceanic precipitation response scales with cumulative emissions, the global precipitation response does not, due to a decrease in precipitation over land above cumulative emissions of around one trillion tonnes of carbon (TtC). Most carbon fluxes and stores are less well constrained by cumulative emissions as they reach two trillion tonnes. The opposing mitigation approaches have different consequences for the Amazon rainforest, which affects the linearity with which the carbon cycle responds to cumulative emissions. Averaged over the two fixed-emissions experiments, the transient response to cumulative carbon emissions (TCRE) is 1.95 K TtC-1, at the upper end of the IPCC’s range of 0.8-2.5 K TtC-1

Use of simulation-based medical training in Swiss pediatric hospitals: a national survey.

Simulation-based medical training (SBMT) is a powerful tool for continuing medical education. In contrast to the Anglo-Saxon medical education community, up until recently, SBMT was scarce in continental Europe's pediatric health care education: In 2009, only 3 Swiss pediatric health care institutions used SBMT. The Swiss catalogue of objectives in Pediatrics does not acknowledge SBMT. The aim of this survey is to describe and analyze the current state of SBMT in Swiss pediatric hospitals and health care departments. A survey was carried out with medical education representatives of every institution. SBMT was defined as any kind of training with a mannequin excluding national and/or international standardized courses. The survey reference day was May 31st 2015. Thirty Swiss pediatric hospitals and health care departments answered our survey (response rate 96.8%) with 66.6% (20 out of 30) offering SBMT. Four of the 20 hospitals offering SMBT had two independently operating training simulation units, resulting in 24 educational units as the basis for our SBMT analysis. More than 90% of the educational units offering SBMT (22 out of 24 units) were conducting in-situ training and 62.5% (15 out of 24) were using high-technology mannequins. Technical skills, communication and leadership ranked among the top training priorities. All institutions catered to inter-professional participants. The vast majority conducted training that was neither embedded within a larger educational curriculum (19 out of 24: 79.2%) nor evaluated (16 out of 24: 66.6%) by its participants. Only 5 institutions (20.8%) extended their training to at least two thirds of their hospital staff. Two thirds of the Swiss pediatric hospitals and health care departments are offering SBMT. Swiss pediatric SBMT is inter-professional, mainly in-situ based, covering technical as well as non-technical skills, and often employing high-technology mannequins. The absence of a systematic approach and reaching only a small number of healthcare employees were identified as shortcomings that need to be addressed

Особливості та умови формування властивостей техногенних ґрунтів

This work utilises general numerical magnetic resonance imaging MRI simulations to predict the spatial specificity of the blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signal. A Monte Carlo simulation approach was utilized on a microvascular structure consisting of randomly oriented cylinders representing blood vessels. This framework was employed to numerically investigate the spatial specificity, defined as ratio of pial vessel to microvascular signal, of the spin echo BOLD fMRI signal as a function of field strength, echo time and tissue types [grey matter (GM) and cerebrospinal fluid (CSF), respectively]. Spatial specificity of spin echo BOLD fMRI signal was determined to increase with field strength up to 16 T and with maximal specificity for echo time shorter than tissue T(2). In addition, it was found that, for large pial vessels, the extravascular signal decay could not be described using the oversimplified but nevertheless commonly employed mono-exponential signal decay approximation (MEA). Consequently, a recently proposed model relying on the MEA deviates substantially from our results on the spatial specificity. A refinement of this model is proposed based on an available, more detailed signal description. Finally, the effect of CSF on the spatial specificity was investigated. While a large spatial specificity of the spin echo BOLD fMRI signal is observed if a pial vessel is surrounded by grey matter, this is greatly reduced for a pial vessel situated on a GM/CSF interface, rendering the suppression of pial vessels on the cortex surface unlikely

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding -1.2Wm-2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5K is assumed for climate sensitivit

Global warming will affect the maximum potential abundance of boreal plant species

Forecasting the impact of future global warming on biodiversity requires understanding how temperature limits the distribution of species. Here we rely on Liebig's Law of Minimum to estimate the effect of temperature on the maximum potential abundance that a species can attain at a certain location. We develop 95%‐quantile regressions to model the influence of effective temperature sum on the maximum potential abundance of 25 common understory plant species of Finland, along 868 nationwide plots sampled in 1985. Fifteen of these species showed a significant response to temperature sum that was consistent in temperature‐only models and in all‐predictors models, which also included cumulative precipitation, soil texture, soil fertility, tree species and stand maturity as predictors. For species with significant and consistent responses to temperature, we forecasted potential shifts in abundance for the period 2041–2070 under the IPCC A1B emission scenario using temperature‐only models. We predict major potential changes in abundance and average northward distribution shifts of 6–8 km yr−1. Our results emphasize inter‐specific differences in the impact of global warming on the understory layer of boreal forests. Species in all functional groups from dwarf shrubs, herbs and grasses to bryophytes and lichens showed significant responses to temperature, while temperature did not limit the abundance of 10 species. We discuss the interest of modelling the ‘maximum potential abundance’ to deal with the uncertainty in the predictions of realized abundances associated to the effect of environmental factors not accounted for and to dispersal limitations of species, among others. We believe this concept has a promising and unexplored potential to forecast the impact of specific drivers of global change under future scenarios.202

Reação de genótipos de batata à requeima (Phytophthora infestans).

bitstream/item/30474/1/boletim-83.pd

Nuclear Breathing Mode in the Relativistic Mean Field Theory

The breathing-mode giant monopole resonance is studied within the framework of the relativistic mean-field (RMF) theory. Using a broad range of parameter sets, an analysis of constrained incompressibility and excitation energy of isoscalar monopole states in finite nuclei is performed. It is shown that the non-linear scalar self-interaction and the resulting surface properties influence the breathing-mode considerably. It is observed that dynamical surface properties respond differently in the RMF theory than in the Skyrme approach. A comparison is made with the incompressibility derived from the semi-infinite nuclear matter and with constrained nonrelativistic Skyrme Hartree-Fock calculaions.Comment: Latex (12 pages) and 3 figures (available upon request) J. Phys. G (in press

Size-Extensive Molecular Machine Learning with Global Representations

Machine learning (ML) models are increasingly used in combination with electronic structure calculations to predict molecular properties at a much lower computational cost in high-throughput settings. Such ML models require representations that encode the molecular structure, which are generally designed to respect the symmetries and invariances of the target property. However, size-extensivity is usually not guaranteed for so-called global representations. In this contribution, we show how extensivity can be built into global ML models using, e. g., the Many-Body Tensor Representation. Properties of extensive and non-extensive models for the atomization energy are systematically explored by training on small molecules and testing on small, medium and large molecules. Our results show that non-extensive models are only useful in the size-range of their training set, whereas extensive models provide reasonable predictions across large size differences. Remaining sources of error for extensive models are discussed