Moving Beyond Concentrations: The Challenge of Limiting Temperature Change

The UN Framework Convention on Climate Change shifted the attention of the policy community from stabilizing greenhouse gas emissions to stabilizing atmospheric greenhouse gas concentrations. While this represents a step forward, it does not go far enough. We find that, given the uncertainty in the climate system, focusing on atmospheric concentrations is likely to convey a false sense of precision. The causal chain between human activity and impacts is laden with uncertainty. From a benefit-cost perspective, it would be desirable to minimize the sum of mitigation costs and damages. Unfortunately, our ability to quantify and value impacts is limited. For the time being, we must rely on a surrogate. Focusing on temperature rather than on concentrations provides much more information on what constitutes an ample margin of safety. Concentrations mask too many uncertainties that are crucial for policy making.

A Bivariate Time Series Approach to Anthropogenic Trend Detection in Hemispheric Mean Temperatures

A bivariate time series regression approach is used to model observed variations in hemispheric mean temperature over the period 1900-96. The regression equations include deterministic predictor variables and lagged values of the two predictands, and two different forms of this basic structure are employed. The deterministic predictors considered are simple linear trends, various climate model-generated time series based on different combinations of greenhouse gas, sulfate aerosol, and solar forcing, and the Southern Oscillation index (SOI). With linear trends as the only predictors, the best model is a fourth-order bivariate autoregressive model including lagged Southern Hemisphere (SH) to Northern Hemisphere (NH) dependence, as in previous work by Kaufmann and Stern. The estimated NH and SH trends are both + 0.67°C century-1, and both are highly statistically significant. If SOI is included as an additional predictor, however, a first-order time series model, with no SH to NH dependence, is an adequate fit to the data. This shows that SOI may be an important covariate in this kind of analysis. Further analysis uses climate model-generated forcing terms representing greenhouses gases, sulfate aerosols, and solar effects, as well as SOI. The statistical analysis makes extensive use of Bayes factors as a device for discriminating among a wide spectrum of possible models. The best fits to the data are obtained when all three forcing terms are included. Total sulfate aerosol forcing of 1.1 W m-2(with a corresponding climate sensitivity of ΔT2+ = 4.2cC) is preferred to -0.7 W m-2(with sensitivity of 2.3°C), but the Bayes factor discrimination between these cases is weak

Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0

Sea level rise (SLR) is one of the major impacts of global warming; it will threaten coastal populations, infrastructure, and ecosystems around the globe in coming centuries. Well-constrained sea level projections are needed to estimate future losses from SLR and benefits of climate protection and adaptation. Process-based models that are designed to resolve the underlying physics of individual sea level drivers form the basis for state-of-the-art sea level projections. However, associated computational costs allow for only a small number of simulations based on selected scenarios that often vary for different sea level components. This approach does not sufficiently support sea level impact science and climate policy analysis, which require a sea level projection methodology that is flexible with regard to the climate scenario yet comprehensive and bound by the physical constraints provided by process-based models. To fill this gap, we present a sea level model that emulates global-mean long-term process-based model projections for all major sea level components. Thermal expansion estimates are calculated with the hemispheric upwelling-diffusion ocean component of the simple carbon-cycle climate model MAGICC, which has been updated and calibrated against CMIP5 ocean temperature profiles and thermal expansion data. Global glacier contributions are estimated based on a parameterization constrained by transient and equilibrium process-based projections. Sea level contribution estimates for Greenland and Antarctic ice sheets are derived from surface mass balance and solid ice discharge parameterizations reproducing current output from ice-sheet models. The land water storage component replicates recent hydrological modeling results. For 2100, we project 0.35 to 0.56m (66% range) total SLR based on the RCP2.6 scenario, 0.45 to 0.67m for RCP4.5, 0.46 to 0.71m for RCP6.0, and 0.65 to 0.97m for RCP8.5. These projections lie within the range of the latest IPCC SLR estimates. SLR projections for 2300 yield median responses of 1.02m for RCP2.6, 1.76m for RCP4.5, 2.38m for RCP6.0, and 4.73m for RCP8.5. The MAGICC sea level model provides a flexible and efficient platform for the analysis of major scenario, model, and climate uncertainties underlying long-term SLR projections. It can be used as a tool to directly investigate the SLR implications of different mitigation pathways and may also serve as input for regional SLR assessments via component-wise sea level pattern scaling

Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0

Sea level rise (SLR) is one of the major impacts of global warming; it will threaten coastal populations, infrastructure, and ecosystems around the globe in coming centuries. Well-constrained sea level projections are needed to estimate future losses from SLR and benefits of climate protection and adaptation. Process-based models that are designed to resolve the underlying physics of individual sea level drivers form the basis for state-of-the-art sea level projections. However, associated computational costs allow for only a small number of simulations based on selected scenarios that often vary for different sea level components. This approach does not sufficiently support sea level impact science and climate policy analysis, which require a sea level projection methodology that is flexible with regard to the climate scenario yet comprehensive and bound by the physical constraints provided by process-based models. To fill this gap, we present a sea level model that emulates global-mean long-term process-based model projections for all major sea level components. Thermal expansion estimates are calculated with the hemispheric upwelling-diffusion ocean component of the simple carbon-cycle climate model MAGICC, which has been updated and calibrated against CMIP5 ocean temperature profiles and thermal expansion data. Global glacier contributions are estimated based on a parameterization constrained by transient and equilibrium process-based projections. Sea level contribution estimates for Greenland and Antarctic ice sheets are derived from surface mass balance and solid ice discharge parameterizations reproducing current output from ice-sheet models. The land water storage component replicates recent hydrological modeling results. For 2100, we project 0.35 to 0.56m (66% range) total SLR based on the RCP2.6 scenario, 0.45 to 0.67m for RCP4.5, 0.46 to 0.71m for RCP6.0, and 0.65 to 0.97m for RCP8.5. These projections lie within the range of the latest IPCC SLR estimates. SLR projections for 2300 yield median responses of 1.02m for RCP2.6, 1.76m for RCP4.5, 2.38m for RCP6.0, and 4.73m for RCP8.5. The MAGICC sea level model provides a flexible and efficient platform for the analysis of major scenario, model, and climate uncertainties underlying long-term SLR projections. It can be used as a tool to directly investigate the SLR implications of different mitigation pathways and may also serve as input for regional SLR assessments via component-wise sea level pattern scaling

Crystal Structure of T7 Gene 4 Ring Helicase Indicates a Mechanism for Sequential Hydrolysis of Nucleotides

AbstractWe have determined the crystal structure of an active, hexameric fragment of the gene 4 helicase from bacteriophage T7. The structure reveals how subunit contacts stabilize the hexamer. Deviation from expected six-fold symmetry of the hexamer indicates that the structure is of an intermediate on the catalytic pathway. The structural consequences of the asymmetry suggest a “binding change” mechanism to explain how cooperative binding and hydrolysis of nucleotides are coupled to conformational changes in the ring that most likely accompany duplex unwinding. The structure of a complex with a nonhydrolyzable ATP analog provides additional evidence for this hypothesis, with only four of the six possible nucleotide binding sites being occupied in this conformation of the hexamer. This model suggests a mechanism for DNA translocation

Multi-gas Emissions Pathways to Meet Climate Targets

So far, climate change mitigation pathways focus mostly on CO2 and a limited number of climate targets. Comprehensive studies of emission implications have been hindered by the absence of a flexible method to generate multi-gas emissions pathways, user-definable in shape and the climate target. The presented method ‘Equal Quantile Walk' (EQW) is intended to fill this gap, building upon and complementing existing multi-gas emission scenarios. The EQW method generates new mitigation pathways by ‘walking along equal quantile paths' of the emission distributions derived from existing multi-gas IPCC baseline and stabilization scenarios. Considered emissions include those of CO2 and all other major radiative forcing agents (greenhouse gases, ozone precursors and sulphur aerosols). Sample EQW pathways are derived for stabilization at 350 ppm to 750 ppm CO2 concentrations and compared to WRE profiles. Furthermore, the ability of the method to analyze emission implications in a probabilistic multi-gas framework is demonstrated. The probability of overshooting a 2 ∘C climate target is derived by using different sets of EQW radiative forcing peaking pathways. If the probability shall not be increased above 30%, it seems necessary to peak CO2 equivalence concentrations around 475 ppm and return to lower levels after peaking (below 400 ppm). EQW emissions pathways can be applied in studies relating to Article 2 of the UNFCCC, for the analysis of climate impacts, adaptation and emission control implications associated with certain climate targets. See http://www.simcap.org for EQW-software and dat

Poor body condition is associated with lower hippocampal plasticity and higher gut methanogen abundance in adult laying hens from two housing systems

It is still unclear which commercial housing system provides the best quality of life for laying hens. In addition, there are large individual differences in stress levels within a system. Hippocampal neurogenesis or plasticity may provide an integrated biomarker of the stressors experienced by an individual. We selected 12 adult hens each with good and poor body condition (based on body size, degree of feather cover and redness of the comb) from a multi-tier free range system containing H&N strain hens, and from an enriched cage system containing Hy-Line hens (n = 48 total). Immature neurons expressing doublecortin (DCX) were quantified in the hippocampus, contents of the caecal microbiome were sequenced, and expression of inflammatory cytokines was measured in the spleen. DCX(+) cell densities did not differ between the housing systems. In both systems, poor condition hens had lower DCX(+) cell densities, exhibited elevated splenic expression of interleukin-6 (IL6) mRNA, and had a higher relative caecal abundance of methanogenic archea Methanomethylophilaceae. The findings suggest poor body condition is an indicator that individual hens have experienced a comparatively greater degree of cumulative chronic stress, and that a survey of the proportion of hens with poor body conditions might be one way to evaluate the impact of housing systems on hen welfare

Advanced Nuclear Power Systems to Mitigate Climate Change

Abstract Fossil fuels currently supply about 80% of humankind's primary energy. Given the imperatives of climate change, pollution, energy security and dwindling supplies, and enormous technical, logistical and economic challenges of scaling up coal or gas power plants with carbon capture and storage to sequester all that carbon, we are faced with the necessity of a nearly complete transformation of the world's energy systems. Objective analyses of the inherent constraints on wind, solar, and other less-mature renewable energy technologies inevitably demonstrate that they will fall far short of meeting today's energy demands, let alone the certain increased demands of the future. Nuclear power, however, is capable of providing all the carbon-free energy that mankind requires, although the prospect of such a massive deployment raises questions of uranium shortages, increased energy and environmental impacts from mining and fuel enrichment, and so on. These potential roadblocks can all be dispensed with, however, through the use of fast neutron reactors and fuel recycling. The Integral Fast Reactor (IFR), developed at U.S. national laboratories in the latter years of the last century, can economically and cleanly supply all the energy the world needs without any further mining or enrichment of uranium. Instead of utilizing a mere 0.6% of the potential energy in uranium, IFRs capture all of it. Capable of utilizing troublesome waste products already at hand, IFRs can solve the thorny spent fuel problem while powering the planet with carbon-free energy for nearly a millennium before any more uranium mining would even have to be considered. Designed from the outset for unparalleled safety and proliferation resistance, with all major features proven out at the engineering scale, this technology is unrivaled in its ability to solve the most difficult energy problems facing humanity in the 21 st century

Cytokine responses in birds challenged with the human food-borne pathogen Campylobacter jejuni implies a Th17 response

Development of process orientated understanding of cytokine interactions within the gastrointestinal tract during an immune response to pathogens requires experimentation and statistical modelling. The immune response against pathogen challenge depends on the specific threat to the host. Here, we show that broiler chickens mount a breed-dependent immune response to Campylobacter jejuni infection in the caeca by analysing experimental data using frequentist and Bayesian structural equation models (SEM). SEM provides a framework by which cytokine interdependencies, based on prior knowledge, can be tested. In both breeds important cytokines including pro-inflammatory interleukin (IL)-1β, , IL-4, IL-17A, interferon (IFN)-γ and anti-inflammatory IL-10 and transforming growth factor (TGF)-β4 were expressed post-challenge. The SEM revealed a putative regulatory pathway illustrating a T helper (Th)17 response and regulation of IL-10, which is breed-dependent. The prominence of the Th17 pathway indicates the cytokine response aims to limit the invasion or colonization of an extracellular bacterial pathogen but the time-dependent nature of the response differs between breeds