Inferred changes in El Niño–Southern Oscillation variance over the past six centuries

It is vital to understand how the El Niño–Southern Oscillation (ENSO) has responded to past changes in natural and anthropogenic forcings, in order to better understand and predict its response to future greenhouse warming. To date, however, the instrumental record is too brief to fully characterize natural ENSO variability, while large discrepancies exist amongst paleo-proxy reconstructions of ENSO. These paleo-proxy reconstructions have typically attempted to reconstruct ENSO's temporal evolution, rather than the variance of these temporal changes. Here a new approach is developed that synthesizes the variance changes from various proxy data sets to provide a unified and updated estimate of past ENSO variance. The method is tested using surrogate data from two coupled general circulation model (CGCM) simulations. It is shown that in the presence of dating uncertainties, synthesizing variance information provides a more robust estimate of ENSO variance than synthesizing the raw data and then identifying its running variance. We also examine whether good temporal correspondence between proxy data and instrumental ENSO records implies a good representation of ENSO variance. In the climate modeling framework we show that a significant improvement in reconstructing ENSO variance changes is found when combining information from diverse ENSO-teleconnected source regions, rather than by relying on a single well-correlated location. This suggests that ENSO variance estimates derived from a single site should be viewed with caution. Finally, synthesizing existing ENSO reconstructions to arrive at a better estimate of past ENSO variance changes, we find robust evidence that the ENSO variance for any 30 yr period during the interval 1590–1880 was considerably lower than that observed during 1979–2009

Perceived health and quality of life: the effect of exposure to atmospheric pollution

International audienc

The impact of sea surface temperature biases on North American precipitation in a high-resolution climate model

Positive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic subtropical high and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts

Frailty among older adults and its distribution in England

Background: Information on the spatial distribution of the frail population is crucial to inform service planning in health and social care. Objectives: To estimate small-area frailty prevalence among older adults using survey data. To assess whether prevalence differs between urban, rural, coastal and inland areas of England. Design: Using data from the English Longitudinal Study of Ageing (ELSA), ordinal logistic regression was used to predict the probability of frailty, according to age, sex and area deprivation. Probabilities were applied to demographic and economic information in 2020 population projections to estimate the district-level prevalence of frailty. Results: The prevalence of frailty in adults aged 50+ (2020) in England was estimated to be 8.1 [95% CI 7.3–8.8]%. We found substantial geographic variation, with the prevalence of frailty varying by a factor of 4.0 [3.5–4.4] between the most and least frail areas. A higher prevalence of frailty was found for urban than rural areas, and coastal than inland areas. There are widespread geographic inequalities in healthy ageing in England, with older people in urban and coastal areas disproportionately frail relative to those in rural and inland areas. Conclusions: Interventions aimed at reducing inequalities in healthy ageing should be targeted at urban and coastal areas, where the greatest benefit may be achieved

Is late-life dependency increasing or not? A comparison of the Cognitive Function and Ageing Studies (CFAS)

Background: Little is known about how dependency levels have changed between generational cohorts of older people. We estimated years lived in different care states at age 65 in 1991 and 2011 and new projections of future demand for care. Methods: Two population-based studies of older people in defined geographical areas conducted two decades apart (the Cognitive Function and Ageing Studies) provided prevalence estimates of dependency in four states: high (24-hour care); medium (daily care); low (less than daily); independent. Years in each dependency state were calculated by Sullivan’s method. To project future demand, the proportions in each dependency state (by age group and sex) were applied to the 2014 England population projections. Findings: Between 1991 and 2011 there were significant increases in years lived from age 65 with low (men:1·7 years, 95%CI 1·0-2·4; women:2·4 years, 95%CI 1·8-3·1) and high dependency (men:0·9 years, 95%CI 0·2-1·7; women:1·3 years, 95%CI 0·5-2·1). The majority of men’s extra years of life were independent (36%) or with low dependency (36%) whilst for women the majority were spent with low dependency (58%), only 5% being independent. There were substantial reductions in the proportions with medium and high dependency who lived in care homes, although, if these dependency and care home proportions remain constant in the future, further population ageing will require an extra 71,000 care home places by 2025. Interpretation: On average older men now spend 2.4 years and women 3.0 years with substantial care needs (medium or high dependency), and most will live in the community. These findings have considerable implications for older people’s families who provide the majority of unpaid care, but the findings also supply valuable new information for governments and care providers planning the resources and funding required for the care of their future ageing populations

Eccrine porocarcinoma of the head: An important differential diagnosis in the elderly patient

Background: Eccrine porocarcinoma is a rare malignant tumor of the sweat gland, characterized by a broad spectrum of clinicopathologic presentations. Surprisingly, unlike its benign counterpart eccrine poroma, eccrine porocarcinoma is seldom found in areas with a high density of eccrine sweat glands, like the palms or soles. Instead, eccrine porocarcinoma frequently occurs on the lower extremities, trunk and abdomen, but also on the head, resembling various other skin tumors, as illustrated in the patients described herein. Observations: We report 5 cases of eccrine porocarcinoma of the head. All patients were initially diagnosed as having epidermal or melanocytic skin tumors. Only after histopathologic examination were they classified as eccrine porocarcinoma, showing features of epithelial tumors with abortive ductal differentiation. Characteristic clinical, histopathologic and immunohistochemical findings of eccrine porocarcinomas are illustrated. Conclusion: Eccrine porocarcinomas are potentially fatal adnexal malignancies, in which extensive metastatic dissemination may occur. Porocarcinomas are commonly overlooked, or misinterpreted as squamous or basal cell carcinomas as well as other common malignant and even benign skin tumors. Knowledge of the clinical pattern and histologic findings, therefore, is crucial for an early therapeutic intervention, which can reduce the risk of tumor recurrence and serious complications. Copyright (c) 2008 S. Karger AG, Basel

Microextensive Chaos of a Spatially Extended System

By analyzing chaotic states of the one-dimensional Kuramoto-Sivashinsky equation for system sizes L in the range 79 <= L <= 93, we show that the Lyapunov fractal dimension D scales microextensively, increasing linearly with L even for increments Delta{L} that are small compared to the average cell size of 9 and to various correlation lengths. This suggests that a spatially homogeneous chaotic system does not have to increase its size by some characteristic amount to increase its dynamical complexity, nor is the increase in dimension related to the increase in the number of linearly unstable modes.Comment: 5 pages including 4 figures. Submitted to PR

Characterizing unforced multi-decadal variability of ENSO:a case study with the GFDL CM2.1 coupled GCM

Large multi-decadal fluctuations of El Niño-Southern Oscillation (ENSO) variability simulated in a 4000-year pre-industrial control run of GFDL CM2.1 have received considerable attention due to implications for constraining the causes of past and future changes in ENSO. We evaluated the mechanisms of this low-frequency ENSO modulation through analysis of the extreme epochs of CM2.1 as well as through the use of a linearized intermediate-complexity model of the tropical Pacific, which produces reasonable emulations of observed ENSO variability. We demonstrate that the low-frequency ENSO modulation can be represented by the simplest model of a linear, stationary process, even in the highly nonlinear CM2.1. These results indicate that CM2.1’s ENSO modulation is driven by transient processes that operate at interannual or shorter time scales. Nonlinearities and/or multiplicative noise in CM2.1 likely exaggerate the ENSO modulation by contributing to the overly active ENSO variability. In contrast, simulations with the linear model suggest that intrinsically-generated tropical Pacific decadal mean state changes do not contribute to the extreme-ENSO epochs in CM2.1. Rather, these decadal mean state changes actually serve to damp the intrinsically-generated ENSO modulation, primarily by stabilizing the ENSO mode during strong-ENSO epochs. Like most coupled General Circulation Models, CM2.1 suffers from large biases in its ENSO simulation, including ENSO variance that is nearly twice that seen in the last 50 years of observations. We find that CM2.1’s overly strong ENSO variance directly contributes to its strong multi-decadal modulation through broadening the distribution of epochal variance, which increases like the square of the long-term variance. These results suggest that the true spectrum of unforced ENSO modulation is likely substantially narrower than that in CM2.1. However, relative changes in ENSO modulation are similar between CM2.1, the linear model tuned to CM2.1, and the linear model tuned to observations, underscoring previous findings that relative changes in ENSO variance can robustly be compared across models and observations

Optimization Strategies for Interactive Classification of Interstitial Lung Disease Textures

For computerized analysis of textures in interstitial lung disease, manual annotations of lung tissue are necessary. Since making these annotations is labor intensive, we previously proposed an interactive annotation framework. In this framework, observers iteratively trained a classifier to distinguish the different texture types by correcting its classification errors. In this work, we investigated three ways to extend this approach, in order to decrease the amount of user interaction required to annotate all lung tissue in a computed tomography scan. First, we conducted automatic classification experiments to test how data from previously annotated scans can be used for classification of the scan under consideration. We compared the performance of a classifier trained on data from one observer, a classifier trained on data from multiple observers, a classifier trained on consensus training data, and an ensemble of classifiers, each trained on data from different sources. Experiments were conducted without and with texture selection (ts). In the former case, training data from all eight textures was used. In the latter, only training data from the texture types present in the scan were used, and the observer would have to indicate textures contained in the scan to be analyzed. Second, we simulated interactive annotation to test the effects of (1) asking observers to perform ts before the start of annotation, (2) the use of a classifier trained on data from previously annotated scans at the start of annotation, when the interactive classifier is untrained, and (3) allowing observers to choose which interactive or automatic classification results they wanted to correct. Finally, various strategies for selecting the classification results that were presented to the observer were considered. Classification accuracies for all possible interactive annotation scenarios were compared. Using the best-performing protocol, in which observers select the textures that should be distinguished in the scan and in which they can choose which classification results to use for correction, a median accuracy of 88% was reached. The results obtained using this protocol were significantly better than results obtained with other interactive or automatic classification protocols

Extensive Chaos in the Nikolaevskii Model

We carry out a systematic study of a novel type of chaos at onset ("soft-mode turbulence") based on numerical integration of the simplest one dimensional model. The chaos is characterized by a smooth interplay of different spatial scales, with defect generation being unimportant. The Lyapunov exponents are calculated for several system sizes for fixed values of the control parameter $\epsilon$. The Lyapunov dimension and the Kolmogorov-Sinai entropy are calculated and both shown to exhibit extensive and microextensive scaling. The distribution functional is shown to satisfy Gaussian statistics at small wavenumbers and small frequency.Comment: 4 pages (including 5 figures) LaTeX file. Submitted to Phys. Rev. Let