Aerosol total mass concentration and optical depth during IPY 2007-2008 at Maitri and Larsemann Hills Station

The properties of background aerosols and their dependence on meteorological, geographical and human influence are examined using measured spectral aerosol optical depth (AOD), total mass concentration (Mt) and derived number size distribution (NSD) over two distinct coastal locations of Antarctica; Maitri (70°S, 12°E, 123 m m.s.l.) and Larsemann Hills (LH; 69°S, 77°E, 48 m m.s.l.) during southern hemispheric summer of 2007-2008 as a part of the 27th Indian Scientific Expedition to Antarctica (ISEA) during International Polar Year (IPY). Our investigations showed comparable values for the mean columnar AOD at 500 nm over Maitri (0.034±0.005) and LH (0.032±0.006) indicating good spatial homogeneity in the columnar aerosol properties over the coastal Antarctica. Estimation of Angstrom exponent a showed accumulation mode dominance at Maitri (alpha ~1.2±0.3) and coarse mode dominance at LH (0.7±0.2). On the other hand, mass concentration (M(T)) of ambient aerosols showed relatively high values (~8.25±2.87 µg/m**3) at Maitri in comparison to LH (6.03±1.33 µg/m**3).

Environmental context of selected samples from the Tara Oceans Expedition (2009-2013)

The present data set provides contextual environmental data for samples from the Tara Oceans Expedition (2009-2013) that were selected for publication in a special issue of the SCIENCE journal (see related references below). The data set provides calculated averages of mesaurements made at the sampling location and depth, calculated averages from climatologies (AMODIS, VGPM) and satellite products.

One size fits all: stability of metabolic scaling under warming and ocean acidification in echinoderms

Responses by marine species to ocean acidification (OA) have recently been shown to be modulated by external factors including temperature, food supply and salinity. However the role of a fundamental biological parameter relevant to all organisms, that of body size, in governing responses to multiple stressors has been almost entirely overlooked. Recent consensus suggests allometric scaling of metabolism with body size differs between species, the commonly cited 'universal' mass scaling exponent (b) of ¾ representing an average of exponents that naturally vary. One model, the Metabolic-Level Boundaries hypothesis, provides a testable prediction: that b will decrease within species under increasing temperature. However, no previous studies have examined how metabolic scaling may be directly affected by OA. We acclimated a wide body-mass range of three common NE Atlantic echinoderms (the sea star Asterias rubens, the brittlestars Ophiothrix fragilis and Amphiura filiformis) to two levels of pCO2 and three temperatures, and metabolic rates were determined using closed-chamber respirometry. The results show that contrary to some models these echinoderm species possess a notable degree of stability in metabolic scaling under different abiotic conditions; the mass scaling exponent (b) varied in value between species, but not within species under different conditions. Additionally, we found no effect of OA on metabolic rates in any species. These data suggest responses to abiotic stressors are not modulated by body size in these species, as reflected in the stability of the metabolic scaling relationship. Such equivalence in response across ontogenetic size ranges has important implications for the stability of ecological food webs.

Registry of all samples from the Tara Oceans Expedition (2009-2013)

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. Data sets in this collection provide methodological and environmental context to all samples collected during the Tara Oceans Expedition (2009-2013).

Environmental context of all samples from the Tara Oceans Expedition (2009-2013), about depth specific features

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set provides environmental context to all samples from the Tara Oceans Expedition (2009-2013), including calculated averages of mesaurements made concurrently at the sampling location and depth, and calculated averages from climatologies (AMODIS, VGPM) and satellite products.

Environmental context of all samples from the Tara Oceans Expedition (2009-2013), about mesoscale features

The Tara Oceans Expedition (2009-2013) sampled the world oceans on board a 36 m long schooner, collecting environmental data and organisms from viruses to planktonic metazoans for later analyses using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set provides environmental context to all samples from the Tara Oceans Expedition (2009-2013), about mesoscale features related to the sampling date, time and location. Includes calculated averages of mesaurements made concurrently at the sampling location and depth, and calculated averages from climatologies (AMODIS, VGPM) and satellite products.

Turbulence models of gravitational clustering.

Large-scale structure formation can be modeled as a nonlinear process that transfers energy from the largest scales to successively smaller scales until it is dissipated, in analogy with Kolmogorov’s cascade model of incompressible turbulence. However, cosmic turbulence is very compressible, and vorticity plays a secondary role in it. The simplest model of cosmic turbulence is the adhesion model, which can be studied perturbatively or adapting to it Kolmogorov’s non-perturbative approach to incompressible turbulence. This approach leads to observationally testable predictions, e.g., to the power-law exponent of the matter density two-point correlation function.

Significado empírico de la multifractalidad de la precipitación

El objetivo central de la presente investigación es profundizar la interpretación de los parámetros multifractales en el caso de las series de precipitación. Para ello se aborda, en primer lugar, la objetivación de la selección de la parte lineal de las curvas log-log que se encuentra en la base de los métodos de análisis fractal y multifractal; y, en segundo lugar, la generación de series artificiales de precipitación, con características similares a las reales, que permitan manipular los datos y evaluar la influencia de las modificaciones controladas de las series en los resultados de los parámetros multifractales derivados. En cuanto al problema de la selección de la parte lineal de las curvas log-log se desarrollaron dos métodos: a. Cambio de tendencia, que consiste en analizar el cambio de pendiente de las rectas ajustadas a dos subconjuntos consecutivos de los datos. b. Eliminación de casos, que analiza la mejora en el p-valor asociado al coeficiente de correlación al eliminar secuencialmente los puntos finales de la regresión. Los resultados obtenidos respecto a la regresión lineal establecen las siguientes conclusiones: - La metodología estadística de la regresión muestra la dificultad para encontrar el valor de la pendiente de tramos rectos de curvas en el procedimiento base del análisis fractal, indicando que la toma de decisión de los puntos a considerar redunda en diferencias significativas de las pendientes encontradas. - La utilización conjunta de los dos métodos propuestos ayuda a objetivar la toma de decisión sobre la parte lineal de las familias de curvas en el análisis fractal, pero su utilidad sigue dependiendo del número de datos de que se dispone y de las altas significaciones que se obtienen. En cuanto al significado empírico de los parámetros multifratales de la precipitación, se han generado 19 series de precipitación por medio de un simulador de datos diarios en cascada a partir de estimaciones anuales y mensuales, y en base a estadísticos reales de 4 estaciones meteorológicas españolas localizadas en un gradiente de NW a SE. Para todas las series generadas, se calculan los parámetros multifractales siguiendo la técnica de estimación de la DTM (Double Trace Moments - Momentos de Doble Traza) desarrollado por Lavalle et al. (1993) y se observan las modificaciones producidas. Los resultados obtenidos arrojaron las siguientes conclusiones: - La intermitencia, C1, aumenta al concentrar las precipitaciones en menos días, al hacerla más variable, o al incrementar su concentración en los días de máxima, mientras no se ve afectado por la modificación en la variabilidad del número de días de lluvia. - La multifractalidad, α, se ve incrementada con el número de días de lluvia y la variabilidad de la precipitación, tanto anual como mensual, así como también con la concentración de precipitación en el día de máxima. - La singularidad probable máxima, γs, se ve incrementada con la concentración de la lluvia en el día de precipitación máxima mensual y la variabilidad a nivel anual y mensual. - El grado no- conservativo, H, depende del número de los días de lluvia que aparezcan en la serie y secundariamente de la variabilidad general de la lluvia. - El índice de Hurst generalizado se halla muy ligado a la singularidad probable máxima. ABSTRACT The main objective of this research is to interpret the multifractal parameters in the case of precipitation series from an empirical approach. In order to do so the first proposed task was to objectify the selection of the linear part of the log-log curves that is a fundamental step of the fractal and multifractal analysis methods. A second task was to generate precipitation series, with real like features, which allow evaluating the influence of controlled series modifications on the values of the multifractal parameters estimated. Two methods are developed for selecting the linear part of the log-log curves in the fractal and multifractal analysis: A) Tendency change, which means analyzing the change in slope of the fitted lines to two consecutive subsets of data. B) Point elimination, which analyzes the improvement in the p- value associated to the coefficient of correlation when the final regression points are sequentially eliminated. The results indicate the following conclusions: - Statistical methodology of the regression shows the difficulty of finding the slope value of straight sections of curves in the base procedure of the fractal analysis, pointing that the decision on the points to be considered yield significant differences in slopes values. - The simultaneous use of the two proposed methods helps to objectify the decision about the lineal part of a family of curves in fractal analysis, but its usefulness are still depending on the number of data and the statistical significances obtained. Respect to the empiric meaning of the precipitation multifractal parameters, nineteen precipitation series were generated with a daily precipitation simulator derived from year and month estimations and considering statistics from actual data of four Spanish rain gauges located in a gradient from NW to SE. For all generated series the multifractal parameters were estimated following the technique DTM (Double Trace Moments) developed by Lavalle et al. (1993) and the variations produced considered. The results show the following conclusions: 1. The intermittency, C1, increases when precipitation is concentrating for fewer days, making it more variable, or when increasing its concentration on maximum monthly precipitation days, while it is not affected due to the modification in the variability in the number of days it rained. 2. Multifractility, α, increases with the number of rainy days and the variability of the precipitation, yearly as well as monthly, as well as with the concentration of precipitation on the maximum monthly precipitation day. 3. The maximum probable singularity, γs, increases with the concentration of rain on the day of the maximum monthly precipitation and the variability in yearly and monthly level. 4. The non-conservative degree, H’, depends on the number of rainy days that appear on the series and secondly on the general variability of the rain. 5. The general Hurst index is linked to the maximum probable singularity.

A non-perturbative renormalization group study of the stochastic NavierStokes equation

We study the renormalization group flow of the average action of the stochastic Navier-Stokes equation with power-law forcing. Using Galilean invariance, we introduce a nonperturbative approximation adapted to the zero-frequency sector of the theory in the parametric range of the Hölder exponent 4−2 ɛ of the forcing where real-space local interactions are relevant. In any spatial dimension d, we observe the convergence of the resulting renormalization group flow to a unique fixed point which yields a kinetic energy spectrum scaling in agreement with canonical dimension analysis. Kolmogorov's −5/3 law is, thus, recovered for ɛ=2 as also predicted by perturbative renormalization. At variance with the perturbative prediction, the −5/3 law emerges in the presence of a saturation in the ɛ dependence of the scaling dimension of the eddy diffusivity at ɛ=3/2 when, according to perturbative renormalization, the velocity field becomes infrared relevant.

Analytical properties of horizontal visibility graphs in the Feigenbaum scenario

Time series are proficiently converted into graphs via the horizontal visibility (HV) algorithm, which prompts interest in its capability for capturing the nature of different classes of series in a network context. We have recently shown [B. Luque et al., PLoS ONE 6, 9 (2011)] that dynamical systems can be studied from a novel perspective via the use of this method. Specifically, the period-doubling and band-splitting attractor cascades that characterize unimodal maps transform into families of graphs that turn out to be independent of map nonlinearity or other particulars. Here, we provide an in depth description of the HV treatment of the Feigenbaum scenario, together with analytical derivations that relate to the degree distributions, mean distances, clustering coefficients, etc., associated to the bifurcation cascades and their accumulation points. We describe how the resultant families of graphs can be framed into a renormalization group scheme in which fixed-point graphs reveal their scaling properties. These fixed points are then re-derived from an entropy optimization process defined for the graph sets, confirming a suggested connection between renormalization group and entropy optimization. Finally, we provide analytical and numerical results for the graph entropy and show that it emulates the Lyapunov exponent of the map independently of its sign.

A non-perturbative Kolmogorov turbulence approach to the cosmic web structure

The Kolmogorov approach to turbulence is applied to the Burgers turbulence in the stochastic adhesion model of large-scale structure formation. As the perturbative approach to this model is unreliable, here a new, non-perturbative approach, based on a suitable formulation of Kolmogorov's scaling laws, is proposed. This approach suggests that the power-law exponent of the matter density two-point correlation function is in the range 1–1.33, but it also suggests that the adhesion model neglects important aspects of the gravitational dynamics.

Influence of delay time on regularity estimation for voice pathology detection

The employment of nonlinear analysis techniques for automatic voice pathology detection systems has gained popularity due to the ability of such techniques for dealing with the underlying nonlinear phenomena. On this respect, characterization using nonlinear analysis typically employs the classical Correlation Dimension and the largest Lyapunov Exponent, as well as some regularity quantifiers computing the system predictability. Mostly, regularity features highly depend on a correct choosing of some parameters. One of those, the delay time �, is usually fixed to be 1. Nonetheless, it has been stated that a unity � can not avoid linear correlation of the time series and hence, may not correctly capture system nonlinearities. Therefore, present work studies the influence of the � parameter on the estimation of regularity features. Three � estimations are considered: the baseline value 1; a � based on the Average Automutual Information criterion; and � chosen from the embedding window. Testing results obtained for pathological voice suggest that an improved accuracy might be obtained by using a � value different from 1, as it accounts for the underlying nonlinearities of the voice signal.