Statistical modelling of grapevine yield in the Port Wine region under present and future climate conditions

The impact of projected climate change on wine production was analysed for the Demarcated Region of Douro, Portugal. A statistical grapevine yield model (GYM) was developed using climate parameters as predictors. Statistically significant correlations were identified between annual yield and monthly mean temperatures and monthly precipitation totals during the growing cycle. These atmospheric factors control grapevine yield in the region, with the GYM explaining 50.4% of the total variance in the yield time series in recent decades. Anomalously high March rainfall (during budburst, shoot and inflorescence development) favours yield, as well as anomalously high temperatures and low precipitation amounts in May and June (May: flowering and June: berry development). The GYM was applied to a regional climate model output, which was shown to realistically reproduce the GYM predictors. Finally, using ensemble simulations under the A1B emission scenario, projections for GYM-derived yield in the Douro Region, and for the whole of the twenty-first century, were analysed. A slight upward trend in yield is projected to occur until about 2050, followed by a steep and continuous increase until the end of the twenty-first century, when yield is projected to be about 800 kg/ha above current values. While this estimate is based on meteorological parameters alone, changes due to elevated CO2 may further enhance this effect. In spite of the associated uncertainties, it can be stated that projected climate change may significantly benefit wine yield in the Douro Valley.

Estimation of wind storm impacts over Western Germany under future climate conditions using a statistical-dynamical downscaling approach

A statistical–dynamical regionalization approach is developed to assess possible changes in wind storm impacts. The method is applied to North Rhine-Westphalia (Western Germany) using the FOOT3DK mesoscale model for dynamical downscaling and ECHAM5/OM1 global circulation model climate projections. The method first classifies typical weather developments within the reanalysis period using K-means cluster algorithm. Most historical wind storms are associated with four weather developments (primary storm-clusters). Mesoscale simulations are performed for representative elements for all clusters to derive regional wind climatology. Additionally, 28 historical storms affecting Western Germany are simulated. Empirical functions are estimated to relate wind gust fields and insured losses. Transient ECHAM5/OM1 simulations show an enhanced frequency of primary storm-clusters and storms for 2060–2100 compared to 1960–2000. Accordingly, wind gusts increase over Western Germany, reaching locally +5% for 98th wind gust percentiles (A2-scenario). Consequently, storm losses are expected to increase substantially (+8% for A1B-scenario, +19% for A2-scenario). Regional patterns show larger changes over north-eastern parts of North Rhine-Westphalia than for western parts. For storms with return periods above 20 yr, loss expectations for Germany may increase by a factor of 2. These results document the method's functionality to assess future changes in loss potentials in regional terms.

Statistical uncertainty of changes in winter storms over the North Atlantic and Europe in an ensemble of transient climate simulations

Winter storms are among the most important natural hazards affecting Europe. We quantify changes in storm frequency and intensity over the North Atlantic and Europe under future climate scenarios in terms of return periods (RPs) considering uncertainties due to both sampling and methodology. RPs of North Atlantic storms' minimum central pressure (CP) and maximum vorticity (VOR) remain unchanged by 2100 for both the A1B and A2 scenarios compared to the present climate. Whereas shortened RPs for VOR of all intensities are detected for the area between British Isles/North-Sea/western Europe as early as 2040. However, the changes in storm VOR RP may be unrealistically large: a present day 50 (20) year event becomes approximately a 9 (5.5) year event in both A1B and A2 scenarios by 2100. The detected shortened RPs of storms implies a higher risk of occurrence of damaging wind events over Europe.

A closer look at chaotic advection in the stratosphere: part II: statistical diagnostics

Statistical diagnostics of mixing and transport are computed for a numerical model of forced shallow-water flow on the sphere and a middle-atmosphere general circulation model. In particular, particle dispersion statistics, transport fluxes, Liapunov exponents (probability density functions and ensemble averages), and tracer concentration statistics are considered. It is shown that the behavior of the diagnostics is in accord with that of kinematic chaotic advection models so long as stochasticity is sufficiently weak. Comparisons with random-strain theory are made.

A statistical model for sea surface diurnal warming driven by numercial weather predictions fluxes and winds

A statistical model is derived relating the diurnal variation of sea surface temperature (SST) to the net surface heat flux and surface wind speed from a numerical weather prediction (NWP) model. The model is derived using fluxes and winds from the European Centre for Medium-Range Weather Forecasting (ECMWF) NWP model and SSTs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In the model, diurnal warming has a linear dependence on the net surface heat flux integrated since (approximately) dawn and an inverse quadratic dependence on the maximum of the surface wind speed in the same period. The model coefficients are found by matching, for a given integrated heat flux, the frequency distributions of the maximum wind speed and the observed warming. Diurnal cooling, where it occurs, is modelled as proportional to the integrated heat flux divided by the heat capacity of the seasonal mixed layer. The model reproduces the statistics (mean, standard deviation, and 95-percentile) of the diurnal variation of SST seen by SEVIRI and reproduces the geographical pattern of mean warming seen by the Advanced Microwave Scanning Radiometer (AMSR-E). We use the functional dependencies in the statistical model to test the behaviour of two physical model of diurnal warming that display contrasting systematic errors.

Comparative genomics suggests that an ancestral polyploidy event leads to enhanced root nodule symbiosis in the Papilionoideae

Root nodule symbiosis (RNS) is one of the most efficient biological systems for nitrogen fixation and it occurs in 90% of genera in the Papilionoideae, the largest subfamily of legumes. Most papilionoid species show evidence of a polyploidy event occurred approximately 58 million years ago. Although polyploidy is considered to be an important evolutionary force in plants, the role of this papilionoid polyploidy event, especially its association with RNS, is not understood. In this study, we explored this role using an integrated comparative genomic approach and conducted gene expression comparisons and gene ontology enrichment analyses. The results show the following: (1) approximately a quarter of the papilionoid-polyploidy-derived duplicate genes are retained; (2) there is a striking divergence in the level of expression of gene duplicate pairs derived from the polyploidy event; and (3) the retained duplicates are frequently involved in the processes crucial for RNS establishment, such as symbiotic signalling, nodule organogenesis, rhizobial infection and nutrient exchange and transport. Thus, we conclude that the papilionoid polyploidy event might have further refined RNS and induced a more robust and enhanced symbiotic system. This conclusion partly explains the widespread occurrence of the Papilionoideae.

An overview of mixed-effects statistical models for second language researchers

As in any field of scientific inquiry, advancements in the field of second language acquisition (SLA) rely in part on the interpretation and generalizability of study findings using quantitative data analysis and inferential statistics. While statistical techniques such as ANOVA and t-tests are widely used in second language research, this review article provides a review of a class of newer statistical models that have not yet been widely adopted in the field, but have garnered interest in other fields of language research. The class of statistical models called mixed-effects models are introduced, and the potential benefits of these models for the second language researcher are discussed. A simple example of mixed-effects data analysis using the statistical software package R (R Development Core Team, 2011) is provided as an introduction to the use of these statistical techniques, and to exemplify how such analyses can be reported in research articles. It is concluded that mixed-effects models provide the second language researcher with a powerful tool for the analysis of a variety of types of second language acquisition data.

Statistical computations with AstroGrid and the Grid

We outline our first steps towards marrying two new and emerging technologies; the Virtual Observatory (e.g, Astro- Grid) and the computational grid. We discuss the construction of VOTechBroker, which is a modular software tool designed to abstract the tasks of submission and management of a large number of computational jobs to a distributed computer system. The broker will also interact with the AstroGrid workflow and MySpace environments. We present our planned usage of the VOTechBroker in computing a huge number of n–point correlation functions from the SDSS, as well as fitting over a million CMBfast models to the WMAP data.

Ken Thompson's 6-man Statistical Graphs

This note reviews Ken Thompson's statistics on 6-man White wins with Black to move and explains the way in which the statistics have been graphed logarithmically.

Assimilation of sea-ice concentration in a global climate model — physical and statistical aspects

We investigate the initialization of Northern-hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates significantly reduces assimilation error both in identical-twin experiments and when assimilating sea-ice observations, reducing the concentration error by a factor of four to six, and the thickness error by a factor of two. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that the strong dependence of thermodynamic ice growth on ice concentration necessitates an adjustment of mean ice thickness in the analysis update. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that proportional mean-thickness updates are superior to the other two methods considered and enable us to assimilate sea ice in a global climate model using simple Newtonian relaxation.

Assimilation of sea-ice concentration in a global climate model — physical and statistical aspects

We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates leads to good assimilation performance for sea-ice concentration and thickness, both in identical-twin experiments and when assimilating sea-ice observations. The simulation of other Arctic surface fields in the coupled model is, however, not significantly improved by the assimilation. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that an adjustment of mean ice thickness in the analysis update is essential to arrive at plausible state estimates. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that assimilation with proportional mean-thickness updates outperforms the other two methods considered. The method described here is very simple to implement, and gives results that are sufficiently good to be used for initialising sea ice in a global climate model for seasonal to decadal predictions.

Comparative genomics of Salmonella enterica serovars Derby and Mbandaka, two prevalent serovars associated with different livestock species in the UK

Background Despite the frequent isolation of Salmonella enterica sub. enterica serovars Derby and Mbandaka from livestock in the UK and USA little is known about the biological processes maintaining their prevalence. Statistics for Salmonella isolations from livestock production in the UK show that S. Derby is most commonly associated with pigs and turkeys and S. Mbandaka with cattle and chickens. Here we compare the first sequenced genomes of S. Derby and S. Mbandaka as a basis for further analysis of the potential host adaptations that contribute to their distinct host species distributions. Results Comparative functional genomics using the RAST annotation system showed that predominantly mechanisms that relate to metabolite utilisation, in vivo and ex vivo persistence and pathogenesis distinguish S. Derby from S. Mbandaka. Alignment of the genome nucleotide sequences of S. Derby D1 and D2 and S. Mbandaka M1 and M2 with Salmonella pathogenicity islands (SPI) identified unique complements of genes associated with host adaptation. We also describe a new genomic island with a putative role in pathogenesis, SPI-23. SPI-23 is present in several S. enterica serovars, including S. Agona, S. Dublin and S. Gallinarum, it is absent in its entirety from S. Mbandaka. Conclusions We discovered a new 37 Kb genomic island, SPI-23, in the chromosome sequence of S. Derby, encoding 42 ORFS, ten of which are putative TTSS effector proteins. We infer from full-genome synonymous SNP analysis that these two serovars diverged, between 182kya and 625kya coinciding with the divergence of domestic pigs. The differences between the genomes of these serovars suggest they have been exposed to different stresses including, phage, transposons and prolonged externalisation. The two serovars possess distinct complements of metabolic genes; many of which cluster into pathways for catabolism of carbon sources.

The global effect of follicle-stimulating hormone and tumour necrosis factor α on gene expression in cultured bovine ovarian granulosa cells

Background Oocytes mature in ovarian follicles surrounded by granulosa cells. During follicle growth, granulosa cells replicate and secrete hormones, particularly steroids close to ovulation. However, most follicles cease growing and undergo atresia or regression instead of ovulating. To investigate the effects of stimulatory (follicle-stimulating hormone; FSH) and inhibitory (tumour necrosis factor alpha; TNFα) factors on the granulosa cell transcriptome, bovine ovaries were obtained from a local abattoir and pools of granulosa cells were cultured in vitro for six days under defined serum-free conditions with treatments present on days 3–6. Initially dose–response experiments (n = 4) were performed to determine the optimal concentrations of FSH (0.33 ng/ml) and TNFα (10 ng/ml) to be used for the microarray experiments. For array experiments cells were cultured under control conditions, with FSH, with TNFα, or with FSH plus TNFα (n = 4 per group) and RNA was harvested for microarray analyses. Results Statistical analysis showed primary clustering of the arrays into two groups, control/FSH and TNFα/TNFα plus FSH. The effect of TNFα on gene expression dominated that of FSH, with substantially more genes differentially regulated, and the pathways and genes regulated by TNFα being similar to those of FSH plus TNFα treatment. TNFα treatment reduced the endocrine activity of granulosa cells with reductions in expression of FST, INHA, INBA and AMH. The top-ranked canonical pathways and GO biological terms for the TNFα treatments included antigen presentation, inflammatory response and other pathways indicative of innate immune function and fibrosis. The two most significant networks also reflect this, containing molecules which are present in the canonical pathways of hepatic fibrosis/hepatic stellate cell activation and transforming growth factor β signalling, and these were up regulated. Upstream regulator analyses also predicted TNF, interferons γ and β1 and interleukin 1β. Conclusions In vitro, the transcriptome of granulosa cells responded minimally to FSH compared with the response to TNFα. The response to TNFα indicated an active process akin to tissue remodelling as would occur upon atresia. Additionally there was reduction in endocrine function and induction of an inflammatory response to TNFα that displays features similar to immune cells.

The cross-currency hedging performance of implied versus statistical forecasting models

This article examines the ability of several models to generate optimal hedge ratios. Statistical models employed include univariate and multivariate generalized autoregressive conditionally heteroscedastic (GARCH) models, and exponentially weighted and simple moving averages. The variances of the hedged portfolios derived using these hedge ratios are compared with those based on market expectations implied by the prices of traded options. One-month and three-month hedging horizons are considered for four currency pairs. Overall, it has been found that an exponentially weighted moving-average model leads to lower portfolio variances than any of the GARCH-based, implied or time-invariant approaches.