Characterizing, modelling and understanding the climate variability of the deep water formation in the North-Western Mediterranean Sea

Observing, modelling and understanding the climate-scale variability of the deep water formation (DWF) in the North-Western Mediterranean Sea remains today very challenging. In this study, we first characterize the interannual variability of this phenomenon by a thorough reanalysis of observations in order to establish reference time series. These quantitative indicators include 31 observed years for the yearly maximum mixed layer depth over the period 1980–2013 and a detailed multi-indicator description of the period 2007–2013. Then a 1980–2013 hindcast simulation is performed with a fully-coupled regional climate system model including the high-resolution representation of the regional atmosphere, ocean, land-surface and rivers. The simulation reproduces quantitatively well the mean behaviour and the large interannual variability of the DWF phenomenon. The model shows convection deeper than 1000 m in 2/3 of the modelled winters, a mean DWF rate equal to 0.35 Sv with maximum values of 1.7 (resp. 1.6) Sv in 2013 (resp. 2005). Using the model results, the winter-integrated buoyancy loss over the Gulf of Lions is identified as the primary driving factor of the DWF interannual variability and explains, alone, around 50 % of its variance. It is itself explained by the occurrence of few stormy days during winter. At daily scale, the Atlantic ridge weather regime is identified as favourable to strong buoyancy losses and therefore DWF, whereas the positive phase of the North Atlantic oscillation is unfavourable. The driving role of the vertical stratification in autumn, a measure of the water column inhibition to mixing, has also been analyzed. Combining both driving factors allows to explain more than 70 % of the interannual variance of the phenomenon and in particular the occurrence of the five strongest convective years of the model (1981, 1999, 2005, 2009, 2013). The model simulates qualitatively well the trends in the deep waters (warming, saltening, increase in the dense water volume, increase in the bottom water density) despite an underestimation of the salinity and density trends. These deep trends come from a heat and salt accumulation during the 1980s and the 1990s in the surface and intermediate layers of the Gulf of Lions before being transferred stepwise towards the deep layers when very convective years occur in 1999 and later. The salinity increase in the near Atlantic Ocean surface layers seems to be the external forcing that finally leads to these deep trends. In the future, our results may allow to better understand the behaviour of the DWF phenomenon in Mediterranean Sea simulations in hindcast, forecast, reanalysis or future climate change scenario modes. The robustness of the obtained results must be however confirmed in multi-model studies.

The role of the Indian Ocean sector and sea surface salinity for prediction of the coupled Indo-Pacific system

The purpose of this dissertation is to evaluate the potential downstream influence of the Indian Ocean (IO) on El Niño/Southern Oscillation (ENSO) forecasts through the oceanic pathway of the Indonesian Throughflow (ITF), atmospheric teleconnections between the IO and Pacific, and assimilation of IO observations. Also the impact of sea surface salinity (SSS) in the Indo-Pacific region is assessed to try to address known problems with operational coupled model precipitation forecasts. The ITF normally drains warm fresh water from the Pacific reducing the mixed layer depths (MLD). A shallower MLD amplifies large-scale oceanic Kelvin/Rossby waves thus giving ~10% larger response and more realistic ENSO sea surface temperature (SST) variability compared to observed when the ITF is open. In order to isolate the impact of the IO sector atmospheric teleconnections to ENSO, experiments are contrasted that selectively couple/decouple the interannual forcing in the IO. The interannual variability of IO SST forcing is responsible for 3 month lagged widespread downwelling in the Pacific, assisted by off-equatorial curl, leading to warmer NINO3 SST anomaly and improved ENSO validation (significant from 3-9 months). Isolating the impact of observations in the IO sector using regional assimilation identifies large-scale warming in the IO that acts to intensify the easterlies of the Walker circulation and increases pervasive upwelling across the Pacific, cooling the eastern Pacific, and improving ENSO validation (r ~ 0.05, RMS~0.08C). Lastly, the positive impact of more accurate fresh water forcing is demonstrated to address inadequate precipitation forecasts in operational coupled models. Aquarius SSS assimilation improves the mixed layer density and enhances mixing, setting off upwelling that eventually cools the eastern Pacific after 6 months, counteracting the pervasive warming of most coupled models and significantly improving ENSO validation from 5-11 months. In summary, the ITF oceanic pathway, the atmospheric teleconnection, the impact of observations in the IO, and improved Indo-Pacific SSS are all responsible for ENSO forecast improvements, and so each aspect of this study contributes to a better overall understanding of ENSO. Therefore, the upstream influence of the IO should be thought of as integral to the functioning of ENSO phenomenon.

Phosphoglucose isomerase variability of Cerastoderma glaucum as a model for testing the influence of environmental conditions and dispersal patterns through quantitative ecology approaches

Extreme conditions of coastal lagoons could directly modify the genetic patterns of species. The aim of this work was to investigate the influence of environmental conditions and small scale dispersal patterns on the phosphoglucose isomerase (PGI*) genetic variability of Cerastoderma glaucum from the Mar Menor coastal lagoon. For this purpose, 284 cockles were collected around the perimeter of the lagoon. Vertical polyacrylamide gel electrophoresis was used to scan for PGI* polymorphisms, giving a total of seven alleles. The spatial genetic distribution of the PGI* variability, which seems to be marked by the main circulation in the lagoon, discriminates four hydrological basins. In the central basin, a gradient of allelic composition reflects the circulation forced by the dominant winds and the main channel communicated to the open sea. This result is well supported by the salinity GAM model that defines this gradient. The other three basins are defined by the distribution of fine sand in a more complex model that tries to explain the isolation of the three sites localized inside these basins. The southern, western and northern basins show the lowest degree of interconnection and are considered the most confined areas of the Mar Menor lagoon. This situation agrees with the confinement theory for benthic assemblages in the lagoon. The greater degree of differentiation seen in the Isla del Ciervo population is probably due to recent human intervention on the nearby Marchamalo channel, which has been drained in recent years thus altering the influence of the Mediterranean Sea on the southern basin.

Variability of thermohaline properties in Pearl Lagoon, Nicaragua (ESP)

Several hydrographic surveys were carried out in Pearl Lagoon, Nicaragua between april 1995 and december 1997 under the DIPAL (Proyecto para el Desarrollo Integral de la Pesca Artesanal en la Región Autónoma del Atlántico Sur) project. Surface temperature, salinity, dissolved oxygen and turbidity have been measured in 88 hydrographic campaigns. The annual cycle shows maximum and minimum temperatures in May (29.4 °C) and December (25.6 °C) respectively, maximum salinity (25.6 °C) in April, one month before the thermal peak, and minimum salinities (2‰) between July and August, when the annual precipitation index attains its seasonal maximum in the study area. In the case of dissolved O2 the maximum values of oxygen saturation were observed between March and May (90%), when the water turbidity in the lagoon is at its lowest and freshwater contributions from the rivers attains its minimum value. During the rainy season, in the second half of the year, there is an important decrease in oxygen contents, mainly as a consequence of the degradation of organic matter of riverine origin.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

On analysing and interpreting variability in motor output

A recent article in the Journal of Science and Medicine in Sport by Chapman et al.1 reported data from an empirical investigation comparing lower extremity joint motions, joint coordination and muscle recruitment in expert and novice cyclists. 3D kinematic and intramuscular electromyographic (EMG) analyses revealed no differences between expert and novice cyclists for normalised joint angles and velocities of the pelvis, hip, knee and ankle. However, significant differences in the strength of sagittal plane kinematics for hip–ankle and knee–ankle joint couplings were reported, with expert cyclists displaying tighter coupling relationships than novice cyclists. Furthermore, significant differences between expert and novice cyclists for all muscle recruitment parameters, except timing of peak EMG amplitude, were also reported.

Magnitude and variability of loading on the osseointegrated implant of transfemoral amputees during walking

This study directly measured the load acting on the abutment of the osseointegrated implant system of transfemoral amputees during level walking, and studied the variability of the load within and among amputees. Twelve active transfemoral amputees (age: 54±12 years, mass:84.3±16.3 kg, height: 17.8±0.10 m) fitted with an osseointegrated implant for over 1 year participated in the study. The load applied on the abutment was measured during unimpeded, level walking in a straight line using a commercial six-channel transducer mounted between the abutment and the prosthetic knee. The pattern and the magnitude of the three-dimensional forces and moments were revealed. Results showed a low step-to-step variability of each subject, but a high subject-to-subject variability in local extrema of body-weight normalized forces and moments and impulse data. The high subject-to-subject variability suggests that the mechanical design of the implant system should be customized for each individual, or that a fit-all design should take into consideration the highest values of load within a broad range of amputees. It also suggests specific loading regime in rehabilitation training are necessary for a given subject. Thus the loading magnitude and variability demonstrated should be useful in designing an osseointegrated implant system better able to resist mechanical failure and in refining the rehabilitation protocol.

Within-session variability modelling for factor analysis speaker verification

This work presents an extended Joint Factor Analysis model including explicit modelling of unwanted within-session variability. The goals of the proposed extended JFA model are to improve verification performance with short utterances by compensating for the effects of limited or imbalanced phonetic coverage, and to produce a flexible JFA model that is effective over a wide range of utterance lengths without adjusting model parameters such as retraining session subspaces. Experimental results on the 2006 NIST SRE corpus demonstrate the flexibility of the proposed model by providing competitive results over a wide range of utterance lengths without retraining and also yielding modest improvements in a number of conditions over current state-of-the-art.

Within- and between-day repeatability and variability in children's physiological responses during submaximal treadmill exercise

The purpose of this study was to verify within- and between-day repeatability and variability in children's oxygen uptake (VO^sub 2^), gross economy (GE; VO^sub 2^ divided by speed) and heart rate (HR) during treadmill walking based on self-selected speed (SS). Fourteen children (10.1 ± 1.4 years) undertook three testing sessions over 2 days in which four walking speeds, including SS were tested. Within- and between-day repeatability were assessed using the Bland and Altman method, and coefficients of variability (CV) were determined for each child across exercise bouts and averaged to obtain a mean group CV value for VO^sub 2^, GE, and HR per speed. Repeated measures analysis of variance showed no statistically significant differences in within- or between-day CV for VO^sub 2^, GE, or HR at any speed. Repeatability within- and between-day for VO^sub 2^, GE, and HR for all speeds was verified. These results suggest that submaximal VO^sub 2^ during treadmill walking is stable and reproducible at a range of speeds based on children's SS.

Weather variability, sunspots, and the blooms of cyanobacteria

The roles of weather variability and sunspots in the occurrence of cyanobacteria blooms, were investigated using cyanobacteria cell data collected from the Fred Haigh Dam, Queensland, Australia. Time series generalized linear model and classification and regression (CART) model were used in the analysis. Data on notified cell numbers of cyanobacteria and weather variables over the periods 2001 and 2005 were provided by the Australian Department of Natural Resources and Water, and Australian Bureau of Meteorology, respectively. The results indicate that monthly minimum temperature (relative risk [RR]: 1.13, 95% confidence interval [CI]: 1.02-1.25) and rainfall (RR: 1.11; 95% CI: 1.03-1.20) had a positive association, but relative humidity (RR: 0.94; 95% CI: 0.91-0.98) and wind speed (RR:0.90; 95% CI: 0.82-0.98) were negatively associated with the cyanobacterial numbers, after adjustment for seasonality and auto-correlation. The CART model showed that the cyanobacteria numbers were best described by an interaction between minimum temperature, relative humidity, and sunspot numbers. When minimum temperature exceeded 18%C and relative humidity was under 66%, the number of cyanobacterial cells rose by 2.15-fold. We conclude that the weather variability and sunspot activity may affect cyanobacterial blooms in dams.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Spatial variability of soil carbon in forested and cultivated sites: Implications for change detection

The potential to sequester atmospheric carbon in agricultural and forest soils to offset greenhouse gas emissions has generated interest in measuring changes in soil carbon resulting from changes in land management. However, inherent spatial variability of soil carbon limits the precision of measurement of changes in soil carbon and hence, the ability to detect changes. We analyzed variability of soil carbon by intensively sampling sites under different land management as a step toward developing efficient soil sampling designs. Sites were tilled crop-land and a mixed deciduous forest in Tennessee, and old-growth and second-growth coniferous forest in western Washington, USA. Six soil cores within each of three microplots were taken as an initial sample and an additional six cores were taken to simulate resampling. Soil C variability was greater in Washington than in Tennessee, and greater in less disturbed than in more disturbed sites. Using this protocol, our data suggest that differences on the order of 2.0 Mg C ha(-1) could be detected by collection and analysis of cores from at least five (tilled) or two (forest) microplots in Tennessee. More spatial variability in the forested sites in Washington increased the minimum detectable difference, but these systems, consisting of low C content sandy soil with irregularly distributed pockets of organic C in buried logs, are likely to rank among the most spatially heterogeneous of systems. Our results clearly indicate that consistent intramicroplot differences at all sites will enable detection of much more modest changes if the same microplots are resampled.