Observational and theoretical studies of the evolving structure of baroclinic waves

Dynamical processes involved in comma cloud formation, and passive tracer evolution in a baroclinic wave are discussed. An analytical solution was obtained demonstrating the complex nongeostrophic flow pattern involved in the redistribution of low level constituents in a finite amplitude baroclinic wave, and in the formation of the typical humidity and cloud distributions in such a wave. Observational and theoretical studies of blocking weather patterns in middle latitude flows were studied. The differences in the energy and enstrophy cascades in blocking and nonblocking situations were shown. It was established that pronounced upscale flow of both of these quantities, from intermediate to planetary scales, occurs during blocking episodes. The upscale flux of enstrophy, in particular, suggests that the persistence of blocking periods may be due to reduced dissipation of the large scale circulation and therefore entail some above normal predictability

A Numerical Evaluation of the Stochastic Completeness of the Kinetic Coagulation Equation

The stochastic completeness of the kinetic coagulation equation depends on the extent of correlations between particle properties. Such correlations are induced by the coalescence process that causes spatial inhomogeneities in the number concentration of the particles, and are particularly strong in poorly mixed suspensions. A Monte Carlo simulation of the coalescence process is used to evaluate the suitability of the kinetic coagulation equation to simulate the coalescence process using Brownian diffusion, fluid shear and differential sedimentation collision kernels. It is demonstrated that the outcome of the kinetic equation matches well the true stochastic averages, unless the number concentration of particles involved is very small. In that case, the discrepancies between the two approaches are substantial in the large end of the particle size spectrum

Oscillators and relaxation phenomena in Pleistocene climate theory

Ice sheets appeared in the northern hemisphere around 3 million years ago and glacial-interglacial cycles have paced Earth's climate since then. Superimposed on these long glacial cycles comes an intricate pattern of millennial and sub-millennial variability, including Dansgaard-Oeschger and Heinrich events. There are numerous theories about theses oscillations. Here, we review a number of them in order to draw a parallel between climatic concepts and dynamical system concepts, including, in particular, the relaxation oscillator, excitability, slow-fast dynamics and homoclinic orbits. Namely, almost all theories of ice ages reviewed here feature a phenomenon of synchronisation between internal climate dynamics and the astronomical forcing. However, these theories differ in their bifurcation structure and this has an effect on the way the ice age phenomenon could grow 3 million years ago. All theories on rapid events reviewed here rely on the concept of a limit cycle in the ocean circulation, which may be excited by changes in the surface freshwater surface balance. The article also reviews basic effects of stochastic fluctuations on these models, including the phenomenon of phase dispersion, shortening of the limit cycle and stochastic resonance. It concludes with a more personal statement about the potential for inference with simple stochastic dynamical systems in palaeoclimate science. Keywords: palaeoclimates, dynamical systems, limit cycle, ice ages, Dansgaard-Oeschger eventsComment: Published in the Transactions of the Philosophical Transactions of the Royal Society (Series A, Physical Mathematical and Engineering Sciences), as a contribution to the Proceedings of the workshop on Stochastic Methods in Climate Modelling, Newton Institute (23-27 August). Philosophical Transactions of the Royal Society (Series A, Physical Mathematical and Engineering Sciences), vol. 370, pp. xx-xx (2012); Source codes available on request to author and on http://www.uclouvain.be/ito

Using eddy covariance to measure the dependence of air–sea CO2 exchange rate on friction velocity

Parameterisation of the air–sea gas transfer velocity of CO2 and other trace gases under open-ocean conditions has been a focus of air–sea interaction research and is required for accurately determining ocean carbon uptake. Ships are the most widely used platform for air–sea flux measurements but the quality of the data can be compromised by airflow distortion and sensor cross-sensitivity effects. Recent improvements in the understanding of these effects have led to enhanced corrections to the shipboard eddy covariance (EC) measurements. Here, we present a revised analysis of eddy covariance measurements of air–sea CO2 and momentum fluxes from the Southern Ocean Surface Ocean Aerosol Production (SOAP) study. We show that it is possible to significantly reduce the scatter in the EC data and achieve consistency between measurements taken on station and with the ship underway. The gas transfer velocities from the EC measurements correlate better with the EC friction velocity (u*) than with mean wind speeds derived from shipboard measurements corrected with an airflow distortion model. For the observed range of wind speeds (u10 N = 3–23 m s−1), the transfer velocities can be parameterised with a linear fit to u*. The SOAP data are compared to previous gas transfer parameterisations using u10 N computed from the EC friction velocity with the drag coefficient from the Coupled Ocean–Atmosphere Response Experiment (COARE) model version 3.5. The SOAP results are consistent with previous gas transfer studies, but at high wind speeds they do not support the sharp increase in gas transfer associated with bubble-mediated transfer predicted by physically based models

Air/sea DMS gas transfer in the North Atlantic: evidence for limited interfacial gas exchange at high wind speed

Shipboard measurements of eddy covariance dimethylsulfide (DMS) air–sea fluxes and seawater concentration were carried out in the North Atlantic bloom region in June/July 2011. Gas transfer coefficients (k660) show a linear dependence on mean horizontal wind speed at wind speeds up to 11 m s−1. At higher wind speeds the relationship between k660 and wind speed weakens. At high winds, measured DMS fluxes were lower than predicted based on the linear relationship between wind speed and interfacial stress extrapolated from low to intermediate wind speeds. In contrast, the transfer coefficient for sensible heat did not exhibit this effect. The apparent suppression of air–sea gas flux at higher wind speeds appears to be related to sea state, as determined from shipboard wave measurements. These observations are consistent with the idea that long waves suppress near-surface water-side turbulence, and decrease interfacial gas transfer. This effect may be more easily observed for DMS than for less soluble gases, such as CO2, because the air–sea exchange of DMS is controlled by interfacial rather than bubble-mediated gas transfer under high wind speed conditions

Modeling the dynamics of glacial cycles

This article is concerned with the dynamics of glacial cycles observed in the geological record of the Pleistocene Epoch. It focuses on a conceptual model proposed by Maasch and Saltzman [J. Geophys. Res.,95, D2 (1990), pp. 1955-1963], which is based on physical arguments and emphasizes the role of atmospheric CO2 in the generation and persistence of periodic orbits (limit cycles). The model consists of three ordinary differential equations with four parameters for the anomalies of the total global ice mass, the atmospheric CO2 concentration, and the volume of the North Atlantic Deep Water (NADW). In this article, it is shown that a simplified two-dimensional symmetric version displays many of the essential features of the full model, including equilibrium states, limit cycles, their basic bifurcations, and a Bogdanov-Takens point that serves as an organizing center for the local and global dynamics. Also, symmetry breaking splits the Bogdanov-Takens point into two, with different local dynamics in their neighborhoods

Genetic and environmental influences on eating behavior - a study of twin pairs reared apart or reared together

This study examined the relative influence of genetic versus environmental factors on specific aspects of eating behavior. Adult monozygotic twins (22 pairs and 3 singleton reared apart, 38 pairs and 9 singleton reared together, age 18-76 years, BMI 17-43 kg/m2) completed the Three Factor Eating Questionnaire. Genetic and environmental variance components were determined for the three eating behavior constructs and their subscales using model-fitting univariate and multivariate analyses. Unique environmental factors had a substantial influence on all eating behavior variables (explaining 45-71% of variance), and most strongly influenced external locus for hunger and strategic dieting behavior of restraint (explaining 71% and 69% of variance, respectively). Genetic factors had a statistically significant influence on only 4 variables: restraint, emotional susceptibility to disinhibition, situational susceptibility to disinhibition, and internal locus for hunger (heritabilities were 52%, 55%, 38% and 50%, respectively). Common environmental factors did not statistically significantly influence any variable assessed in this study. In addition, multivariate analyses showed that disinhibition and hunger share a common influence, while restraint appears to be a distinct construct. These findings suggest that the majority of variation in eating behavior variables is associated with unique environmental factors, and highlights the importance of the environment in facilitating specific eating behaviors that may promote excess weight gain.R01 AR046124 - NIAMS NIH HHS; R01 MH065322 - NIMH NIH HHS; T32 HL069772 - NHLBI NIH HHS; R37 DA018673 - NIDA NIH HHS; R01 DK073321 - NIDDK NIH HHS; R01 DA018673 - NIDA NIH HH

Effect of body composition methodology on heritability estimation of body fatness

Heritability estimates of human body fatness vary widely and the contribution of body composition methodology to this variability is unknown. The effect of body composition methodology on estimations of genetic and environmental contributions to body fatness variation was examined in 78 adult male and female monozygotic twin pairs reared apart or together. Body composition was assessed by six methods - body mass index (BMI), dual energy x-ray absorptiometry (DXA), underwater weighing (UWW), total body water (TBW), bioelectric impedance (BIA), and skinfold thickness. Body fatness was expressed as percent body fat, fat mass, and fat mass/height2 to assess the effect of body fatness expression on heritability estimates. Model-fitting multivariate analyses were used to assess the genetic and environmental components of variance. Mean BMI was 24.5 kg/m2 (range of 17.8-43.4 kg/m2). There was a significant effect of body composition methodology (p<0.001) on heritability estimates, with UWW giving the highest estimate (69%) and BIA giving the lowest estimate (47%) for fat mass/height2. Expression of body fatness as percent body fat resulted in significantly higher heritability estimates (on average 10.3% higher) compared to expression as fat mass/height2 (p=0.015). DXA and TBW methods expressing body fatness as fat mass/height2 gave the least biased heritability assessments, based on the small contribution of specific genetic factors to their genetic variance. A model combining DXA and TBW methods resulted in a relatively low FM/ht2 heritability estimate of 60%, and significant contributions of common and unique environmental factors (22% and 18%, respectively). The body fatness heritability estimate of 60% indicates a smaller contribution of genetic variance to total variance than many previous studies using less powerful research designs have indicated. The results also highlight the importance of environmental factors and possibly genotype by environmental interactions in the etiology of weight gain and the obesity epidemic.R01 AR046124 - NIAMS NIH HHS; R01 MH065322 - NIMH NIH HHS; T32 HL069772 - NHLBI NIH HHS; R21 DK078867 - NIDDK NIH HHS; R37 DA018673 - NIDA NIH HHS; R01 DK076092 - NIDDK NIH HHS; R01 DK079003 - NIDDK NIH HHS; F32 DK009747 - NIDDK NIH HHS; R01 DA018673 - NIDA NIH HH