1000 resultados para Simulation methodologies


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[ 1] A rapid increase in the variety, quality, and quantity of observations in polar regions is leading to a significant improvement in the understanding of sea ice dynamic and thermodynamic processes and their representation in global climate models. We assess the simulation of sea ice in the new Hadley Centre Global Environmental Model (HadGEM1) against the latest available observations. The HadGEM1 sea ice component uses elastic-viscous-plastic dynamics, multiple ice thickness categories, and zero-layer thermodynamics. The model evaluation is focused on the mean state of the key variables of ice concentration, thickness, velocity, and albedo. The model shows good agreement with observational data sets. The variability of the ice forced by the North Atlantic Oscillation is also found to agree with observations.

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We perform a numerical study of the evolution of a Coronal Mass Ejection (CME) and its interaction with the coronal magnetic field based on the 12 May 1997, CME event using a global MagnetoHydroDynamic (MHD) model for the solar corona. The ambient solar wind steady-state solution is driven by photospheric magnetic field data, while the solar eruption is obtained by superimposing an unstable flux rope onto the steady-state solution. During the initial stage of CME expansion, the core flux rope reconnects with the neighboring field, which facilitates lateral expansion of the CME footprint in the low corona. The flux rope field also reconnects with the oppositely orientated overlying magnetic field in the manner of the breakout model. During this stage of the eruption, the simulated CME rotates counter-clockwise to achieve an orientation that is in agreement with the interplanetary flux rope observed at 1 AU. A significant component of the CME that expands into interplanetary space comprises one of the side lobes created mainly as a result of reconnection with the overlying field. Within 3 hours, reconnection effectively modifies the CME connectivity from the initial condition where both footpoints are rooted in the active region to a situation where one footpoint is displaced into the quiet Sun, at a significant distance (≈1R ) from the original source region. The expansion and rotation due to interaction with the overlying magnetic field stops when the CME reaches the outer edge of the helmet streamer belt, where the field is organized on a global scale. The simulation thus offers a new view of the role reconnection plays in rotating a CME flux rope and transporting its footpoints while preserving its core structure.

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A model of sugarcane digestion was applied to indicate the suitability of various locally available supplements for enhancing milk production of Indian crossbred dairy cattle. Milk production was calculated according to simulated energy, lipogenic, glucogenic and aminogenic substrate availability. The model identified the most limiting substrate for milk production from different sugarcane-based diets. For sugarcane tops/urea fed alone, milk production was most limited by amino acid followed by long chain fatty acid availability. Among the protein-rich oil cake supplements at 100, 200 and 300 g supplement/kg total DM, cottonseed oil cake proved superior with a milk yield of 5.5, 7.3 and 8.3 kg/day, respectively. This was followed by mustard oil cake with 5.1, 6.5 and 7.6 kg/day, respectively. In the case of a protein-rich supplement (fish meal), milk yield was limited to 6.6 kg/day due to a shortage of long chain fatty acids. However, at 300 g of supplementation, energy became limiting, with a milk yield of 6.7 kg/day. Supplementation with rice bran and rice polishings at 100, 200 and 300 g restricted milk yield to 4.3, 4.9 and 5.5 and 4.5, 5.3 and 6.1 kg/day, respectively, and amino acids became the factor limiting milk production. The diet comprising basal sugarcane tops supplemented by leguminous fodder, dry fodder (e.g. rice or wheat straw) and concentrates at levels of 100, 200 and 300 g supplements/kg total diet DM proved to be the most balanced with a milk yield of 5.1, 6.7 and 9.0 kg/day, respectively.

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The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (λ, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966–1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of λ near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

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Brief periods of high temperature which occur near flowering can severely reduce the yield of annual crops such as wheat and groundnut. A parameterisation of this well-documented effect is presented for groundnut (i.e. peanut; Arachis hypogaeaL.). This parameterisation was combined with an existing crop model, allowing the impact of season-mean temperature, and of brief high-temperature episodes at various times near flowering, to be both independently and jointly examined. The extended crop model was tested with independent data from controlled environment experiments and field experiments. The impact of total crop duration was captured, with simulated duration being within 5% of observations for the range of season-mean temperatures used (20-28 degrees C). In simulations across nine differently timed high temperature events, eight of the absolute differences between observed and simulated yield were less than 10% of the control (no-stress) yield. The parameterisation of high temperature stress also allows the simulation of heat tolerance across different genotypes. Three parameter sets, representing tolerant, moderately sensitive and sensitive genotypes were developed and assessed. The new parameterisation can be used in climate change studies to estimate the impact of heat stress on yield. It can also be used to assess the potential for adaptation of cropping systems to increased temperature threshold exceedance via the choice of genotype characteristics. (c) 2005 Elsevier B.V. All rights reserved.

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Reanalysis data provide an excellent test bed for impacts prediction systems. because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM. when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields "were simulated well across much of India. Correlations between observed and modeled yields, where these are significant. are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales. and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

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The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (lambda, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of lambda near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

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Technical efficiency is estimated and examined for a cross-section of Australian dairy farms using various frontier methodologies; Bayesian and Classical stochastic frontiers, and Data Envelopment Analysis. The results indicate technical inefficiency is present in the sample data. Also identified are statistical differences between the point estimates of technical efficiency generated by the various methodologies. However, the rank of farm level technical efficiency is statistically invariant to the estimation technique employed. Finally, when confidence/credible intervals of technical efficiency are compared significant overlap is found for many of the farms' intervals for all frontier methods employed. The results indicate that the choice of estimation methodology may matter, but the explanatory power of all frontier methods is significantly weaker when interval estimate of technical efficiency is examined.

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Current gas-based in vitro evaluation systems are extremely powerful research techniques. However they have the potential to generate a great deal more than simple fermentation dynamics. Details from four experiments are presented in which adaptation, and novel application, of an in vitro system allowed widely differing objectives to be examined. In the first two studies, complement methodologies were utilised. In such assays, an activity or outcome is inferred through the occurrence of a secondary event rather than by direct observation. Using an N-deficient incubation medium, the increase in starch fermentation, when supplemented with individual amino acids (i.e., known level of N) relative to that of urea (i.e., known quantity and N availability), provided an estimate of their microbial utilisation. Due to the low level of response observed with some arnino acids (notably methionine and lysine), it was concluded, that they may not need to be offered in a rumen-inert form to escape rumen microbial degradation. In another experiment, the extent to which degradation of plant cell wall components was inhibited by lipid supplementation was evaluated using fermentation gas release profiles of washed hay. The different responses due to lipid source and level of inclusion suggested that the degree of rumen protection required to ameliorate this depression was supplement dependent. That in vitro inocula differ in their microbial composition is of little interest per se, as long as the outcome is the same (i.e., that similar substrates are degraded at comparable rates and end-product release is equivalent). However where a microbial population is deficient in a particular activity, increasing the level of inoculation will have no benefit. Estimates of hydrolytic activity were obtained by examining fermentation kinetics of specific substrates. A number of studies identified a fundamental difference between rumen fluid and faecal inocula, with the latter having a lower fibrolytic activity, which could not be completely attributed to microbial numbers. The majority of forage maize is offered as an ensiled feed, however most of the information on which decisions such as choice of variety, crop management and harvesting date are made is based on fresh crop measurements. As such, an attempt was made to estimate ensiled maize quality from an in vitro analysis of the fresh crop. Fermentation profiles and chemical analysis confirmed changes in crop composition over the growing season, and loss of labile carbohydrates during ensiling. In addition, examination of degradation residues allowed metabolizable energy (ME) contents to be estimated. Due to difficulties associated with starch analysis, the observation that this parameter could be predicted by difference (together with an assumed degradability), allowed an estimate of ensiled maize ME to be developed from fresh material. In addition, the contribution of the main carbohydrates towards ME showed the importance of delaying harvest until maximum starch content has been achieved. (c) 2005 Elsevier B.V. All rights reserved.

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Polycondensation of 2,6-dihydroxynaphthalene with 4,4'-bis(4"-fluorobenzoyl)biphenyl affords a novel, semicrystalline poly(ether ketone) with a melting point of 406 degreesC and glass transition temperature (onset) of 168 degreesC. Molecular modeling and diffraction-simulation studies of this polymer, coupled with data from the single-crystal structure of an oligomer model, have enabled the crystal and molecular structure of the polymer to be determined from X-ray powder data. This structure-the first for any naphthalene-containing poly(ether ketone)-is fully ordered, in monoclinic space group P2(1)/b, with two chains per unit cell. Rietveld refinement against the experimental powder data gave a final agreement factor (R-wp) of 6.7%.