Origin of regional climate differences: role of boundary conditions and model formulation in two GCMs

Model differences in projections of extratropical regional climate change due to increasing greenhouse gases are investigated using two atmospheric general circulation models (AGCMs): ECHAM4 (Max Planck Institute, version 4) and CCM3 (National Center for Atmospheric Research Community Climate Model version 3). Sea-surface temperature (SST) fields calculated from observations and coupled versions of the two models are used to force each AGCM in experiments based on time-slice methodology. Results from the forced AGCMs are then compared to coupled model results from the Coupled Model Intercomparison Project 2 (CMIP2) database. The time-slice methodology is verified by showing that the response of each model to doubled CO2 and SST forcing from the CMIP2 experiments is consistent with the results of the coupled GCMs. The differences in the responses of the models are attributed to (1) the different tropical SST warmings in the coupled simulations and (2) the different atmospheric model responses to the same tropical SST warmings. Both are found to have important contributions to differences in implied Northern Hemisphere (NH) winter extratropical regional 500 mb height and tropical precipitation climate changes. Forced teleconnection patterns from tropical SST differences are primarily responsible for sensitivity differences in the extratropical North Pacific, but have relatively little impact on the North Atlantic. There are also significant differences in the extratropical response of the models to the same tropical SST anomalies due to differences in numerical and physical parameterizations. Differences due to parameterizations dominate in the North Atlantic. Differences in the control climates of the two coupled models from the current climate, in particular for the coupled model containing CCM3, are also demonstrated to be important in leading to differences in extratropical regional sensitivity.

The TGR5 receptor mediates bile acid-induced itch and analgesia

Patients with cholestatic disease exhibit pruritus and analgesia, but the mechanisms underlying these symptoms are unknown. We report that bile acids, which are elevated in the circulation and tissues during cholestasis, cause itch and analgesia by activating the GPCR TGR5. TGR5 was detected in peptidergic neurons of mouse dorsal root ganglia and spinal cord that transmit itch and pain, and in dermal macrophages that contain opioids. Bile acids and a TGR5-selective agonist induced hyperexcitability of dorsal root ganglia neurons and stimulated the release of the itch and analgesia transmitters gastrin-releasing peptide and leucine-enkephalin. Intradermal injection of bile acids and a TGR5-selective agonist stimulated scratching behavior by gastrin-releasing peptide- and opioid-dependent mechanisms in mice. Scratching was attenuated in Tgr5-KO mice but exacerbated in Tgr5-Tg mice (overexpressing mouse TGR5), which exhibited spontaneous pruritus. Intraplantar and intrathecal injection of bile acids caused analgesia to mechanical stimulation of the paw by an opioid-dependent mechanism. Both peripheral and central mechanisms of analgesia were absent from Tgr5-KO mice. Thus, bile acids activate TGR5 on sensory nerves, stimulating the release of neuropeptides in the spinal cord that transmit itch and analgesia. These mechanisms could contribute to pruritus and painless jaundice that occur during cholestatic liver diseases.

Decadal climate prediction: an update from the trenches

This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about six to nine years. Recent multi-model results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multi-model ensemble decadal hindcasts than in single model results, with multi-model initialized predictions for near term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6-9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño-Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.

On the lack of stratospheric dynamical variability in low-top versions of the CMIP5 models

We describe the main differences in simulations of stratospheric climate and variability by models within the fifth Coupled Model Intercomparison Project (CMIP5) that have a model top above the stratopause and relatively fine stratospheric vertical resolution (high-top), and those that have a model top below the stratopause (low-top). Although the simulation of mean stratospheric climate by the two model ensembles is similar, the low-top model ensemble has very weak stratospheric variability on daily and interannual time scales. The frequency of major sudden stratospheric warming events is strongly underestimated by the low-top models with less than half the frequency of events observed in the reanalysis data and high-top models. The lack of stratospheric variability in the low-top models affects their stratosphere-troposphere coupling, resulting in short-lived anomalies in the Northern Annular Mode, which do not produce long-lasting tropospheric impacts, as seen in observations. The lack of stratospheric variability, however, does not appear to have any impact on the ability of the low-top models to reproduce past stratospheric temperature trends. We find little improvement in the simulation of decadal variability for the high-top models compared to the low-top, which is likely related to the fact that neither ensemble produces a realistic dynamical response to volcanic eruptions.

Review: the predictability of the extra-tropical stratosphere on monthly timescales and its impact on the skill of tropospheric forecasts

Extreme variability of the winter- and spring-time stratospheric polar vortex has been shown to affect extratropical tropospheric weather. Therefore, reducing stratospheric forecast error may be one way to improve the skill of tropospheric weather forecasts. In this review, the basis for this idea is examined. A range of studies of different stratospheric extreme vortex events shows that they can be skilfully forecasted beyond five days and into the sub-seasonal range (0-30 days) in some cases. Separate studies show that typical errors in forecasting a stratospheric extreme vortex event can alter tropospheric forecasts skill by 5-7% in the extratropics on sub-seasonal timescales. Thus understanding what limits stratospheric predictability is of significant interest to operational forecasting centres. Both limitations in forecasting tropospheric planetary waves and stratospheric model biases have been shown to be important in this context.

Nontraditional employment: the careers of temporary workers

This chapter focuses upon the careers of temporary workers. Temporary employment for many workers presents a route to permanent employment. Other workers, however, get trapped into temporary employment or cycle between unstable jobs and spells of unemployment. Predictors of such transitions are multiple. We selected two broad categories, namely perceived employability from the area of career research and health and well-being from the area of occupational health and well-being research. The overall conclusion is that the association between temporary employment and both perceived employability and health and well-being is inconclusive. This suggests that there are boundary conditions that may make some temporary workers successful and others not. Risk factors include dynamics related to the dual labor market, including lower job quality, lower investments on the part of employers, and negative stereotyping of temporary workers as second-class citizens. On the positive side, many temporary workers have learned to manage their careers in the sense that they invest in training and in continuous job search.

Examining the predictability of the Stratospheric Sudden Warming of January 2013 using multiple NWP systems

The first multi-model study to estimate the predictability of a boreal Sudden Stratospheric Warming (SSW) is performed using five NWP systems. During the 2012-2013 boreal winter, anomalous upward propagating planetary wave activity was observed towards the end of December, which followed by a rapid deceleration of the westerly circulation around 2 January 2013, and on 7 January 2013 the zonal mean zonal wind at 60°N and 10 hPa reversed to easterly. This stratospheric dynamical activity was followed by an equatorward shift of the tropospheric jet stream and by a high pressure anomaly over the North Atlantic, which resulted in severe cold conditions in the UK and Northern Europe. In most of the five models, the SSW event was predicted 10 days in advance. However, only some ensemble members in most of the models predicted weakening of westerly wind when the models were initialized 15 days in advance of the SSW. Further dynamical analysis of the SSW shows that this event was characterized by the anomalous planetary wave-1 amplification followed by the anomalous wave-2 amplification in the stratosphere, which resulted in a split vortex occurring between 6 January 2013 and 8 January 2013. The models have some success in reproducing wave-1 activity when initialized 15 days in advance, they but generally failed to produce the wave-2 activity during the final days of the event. Detailed analysis shows that models have reasonably good skill in forecasting tropospheric blocking features that stimulate wave-2 amplification in the troposphere, but they have limited skill in reproducing wave-2 amplification in the stratosphere.

The hecklers

Group Exhibition curated by Cedar Lewisohn. Contributors: Caroline Achaintre, Edwin Burdis, Slawomir Czajkowski (Zbiok), Shaun Doyle and Mally Mallinson, Ruth Ewan, Andrew Gilbert, Joel Gray, Tod Hanson, Geoffrey Ireland, kennardphillipps, Cedar Lewisohn (curator), Kieron Livingstone and Ian Allison, Alexis Milne, Laura Oldfield Ford, Max Reeves, Clunie Reid, John Russell, Francis Thorburn, Vicky Wright.

Distribution of fungi and aflatoxins in a stored peanut variety

The objective of the present study was to evaluate the mycoflora and occurrence of aflatoxins in stored peanut samples (hulls and kernels) from Tupa, State of Sao Paulo, Brazil. The samples were analyzed monthly over a period of one year. The results showed a predominance of Fusarium spp. (67.7% in hulls and 25.8% in kernels) and Aspergillus spp. (10.3% in hulls and 21.8% in kernels), and the presence of five other genera. The growth of Aspergillus flavus was mainly influenced by temperature and relative humidity. Analysis of hulls showed that 6.7% of the samples were contaminated with AFB(1) (mean levels 15-23.9 mu g/kg) and AFB(2) (mean levels = 3.3-5.6 mu g/kg); in kernels, 33.3% of the samples were contaminated with AFB(1) (mean levels 7.0-116 mu g/kg) and 28.3% were contaminated with AFB(2) (mean levels = 3.3-45.5 mu g/kg). Analysis of the toxigenic potential revealed that 93.8% of the A. flavus strains isolated were producers of AFB(1) and AFB(2). mu 2007 Elsevier Ltd. All rights reserved.

Mycoflora and fumonisin contamination in Brazilian sorghum from sowing to harvest

BACKGROUND: The aim of this study was to characterise the mycoflora and the presence of fumonisin in sorghum grains, correlating the results with the environment and abiotic factors. RESULTS: Fifty samples (five collections of ten samples each) of sorghum were analysed. All samples were found to be contaminated with fungi, with higher frequencies of Cladosporium spp. (61.8%) and Helminthosporium spp. (33.4%). Fusarium verticillioides was isolated from 15.1% of the samples, with 38% of them being contaminated with fumonisin B(1) (FB(1)) at levels ranging from 50 to 368.78 ng g(-1). Regarding abiotic factors, temperature, water activity and rainfall showed a positive correlation with the frequency of F. verticillioides and FB(1) production. There was a significant positive correlation between relative air humidity and FB(1) production. The results obtained from sexual crosses between standard F mating tester strains and the isolated strains confirmed that the strains isolated were F. verticillioides. CONCLUSION: It can be concluded that the decrease in F. verticillioides and fumonisin contamination occurred owing to atypical climatic factors during the period of sorghum cultivation, when there was any occurrence of rain and the level of water activity of grains did not reach 0.58. (C) 2010 Society of Chemical Industry

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.