A singular vector perspective of 4DVAR: The spatial structure and evolution of baroclinic weather systems

The extent to which the four-dimensional variational data assimilation (4DVAR) is able to use information about the time evolution of the atmosphere to infer the vertical spatial structure of baroclinic weather systems is investigated. The singular value decomposition (SVD) of the 4DVAR observability matrix is introduced as a novel technique to examine the spatial structure of analysis increments. Specific results are illustrated using 4DVAR analyses and SVD within an idealized 2D Eady model setting. Three different aspects are investigated. The first aspect considers correcting errors that result in normal-mode growth or decay. The results show that 4DVAR performs well at correcting growing errors but not decaying errors. Although it is possible for 4DVAR to correct decaying errors, the assimilation of observations can be detrimental to a forecast because 4DVAR is likely to add growing errors instead of correcting decaying errors. The second aspect shows that the singular values of the observability matrix are a useful tool to identify the optimal spatial and temporal locations for the observations. The results show that the ability to extract the time-evolution information can be maximized by placing the observations far apart in time. The third aspect considers correcting errors that result in nonmodal rapid growth. 4DVAR is able to use the model dynamics to infer some of the vertical structure. However, the specification of the case-dependent background error variances plays a crucial role.

The simulation of peak and delayed ENSO teleconnections

There is much evidence that El Niño and La Niña lead to significant atmospheric seasonal predictability across much of the globe. However, despite successful predictions of tropical Pacific SSTs, atmospheric seasonal forecasts have had limited success. This study investigates model errors in the Hadley Centre Atmospheric Model version 3 (HadAM3) by analyzing composites of similar El Niño and La Niña events at their peak in December–January–February (DJF) and through their decay in March–April–May (MAM). The large-scale, tropical ENSO teleconnections are modeled accurately by HadAM3 during DJF but the strongest extratropical teleconnection, that in the North Pacific during winter, is modeled inaccurately. The Aleutian low is frequently observed to shift eastward during El Niño but the modeled response always consists of a deepening of the low without a shift. This is traced to small errors in the sensitivity of precipitation to SST in the tropical Pacific, which does not display enough variability so that the precipitation is always too high over the warmest SSTs. This error is reduced when vertical resolution is increased from 19 to 30 levels but enhanced horizontal resolution does not improve the simulation further. In MAM, following the peak of an El Niño or La Niña, atmospheric anomalies are observed to decay rapidly. The modeled ENSO response in DJF persists into MAM, making the extratropical anomalies in MAM too strong. This inaccuracy is again likely to be due to the high modeled sensitivity of tropical Pacific precipitation to SST, which is not significantly improved with enhanced vertical or horizontal resolution in MAM.

Evaluation of a large-eddy model simulation of a mixed-phase altocumulus cloud using microwave radiometer, lidar and Doppler radar data

Using the Met Office large-eddy model (LEM) we simulate a mixed-phase altocumulus cloud that was observed from Chilbolton in southern England by a 94 GHz Doppler radar, a 905 nm lidar, a dual-wavelength microwave radiometer and also by four radiosondes. It is important to test and evaluate such simulations with observations, since there are significant differences between results from different cloud-resolving models for ice clouds. Simulating the Doppler radar and lidar data within the LEM allows us to compare observed and modelled quantities directly, and allows us to explore the relationships between observed and unobserved variables. For general-circulation models, which currently tend to give poor representations of mixed-phase clouds, the case shows the importance of using: (i) separate prognostic ice and liquid water, (ii) a vertical resolution that captures the thin layers of liquid water, and (iii) an accurate representation the subgrid vertical velocities that allow liquid water to form. It is shown that large-scale ascents and descents are significant for this case, and so the horizontally averaged LEM profiles are relaxed towards observed profiles to account for these. The LEM simulation then gives a reasonable. cloud, with an ice-water path approximately two thirds of that observed, with liquid water at the cloud top, as observed. However, the liquid-water cells that form in the updraughts at cloud top in the LEM have liquid-water paths (LWPs) up to half those observed, and there are too few cells, giving a mean LWP five to ten times smaller than observed. In reality, ice nucleation and fallout may deplete ice-nuclei concentrations at the cloud top, allowing more liquid water to form there, but this process is not represented in the model. Decreasing the heterogeneous nucleation rate in the LEM increased the LWP, which supports this hypothesis. The LEM captures the increase in the standard deviation in Doppler velocities (and so vertical winds) with height, but values are 1.5 to 4 times smaller than observed (although values are larger in an unforced model run, this only increases the modelled LWP by a factor of approximately two). The LEM data show that, for values larger than approximately 12 cm s(-1), the standard deviation in Doppler velocities provides an almost unbiased estimate of the standard deviation in vertical winds, but provides an overestimate for smaller values. Time-smoothing the observed Doppler velocities and modelled mass-squared-weighted fallspeeds shows that observed fallspeeds are approximately two-thirds of the modelled values. Decreasing the modelled fallspeeds to those observed increases the modelled IWC, giving an IWP 1.6 times that observed.

Numerical simulation of the 12 May 1997 CME Event: The role of magnetic reconnection

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.

Simulation of milk production by dairy cows fed sugarcane top-based diets with locally available supplements under Indian conditions

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.

Assessing the vulnerability of food crop systems in Africa to climate change

Africa is thought to be the region most vulnerable to the impacts of climate variability and change. Agriculture plays a dominant role in supporting rural livelihoods and economic growth over most of Africa. Three aspects of the vulnerability of food crop systems to climate change in Africa are discussed: the assessment of the sensitivity of crops to variability in climate, the adaptive capacity of farmers, and the role of institutions in adapting to climate change. The magnitude of projected impacts of climate change on food crops in Africa varies widely among different studies. These differences arise from the variety of climate and crop models used, and the different techniques used to match the scale of climate model output to that needed by crop models. Most studies show a negative impact of climate change on crop productivity in Africa. Farmers have proved highly adaptable in the past to short- and long-term variations in climate and in their environment. Key to the ability of farmers to adapt to climate variability and change will be access to relevant knowledge and information. It is important that governments put in place institutional and macro-economic conditions that support and facilitate adaptation and resilience to climate change at local, national and transnational level.

Quantifying uncertainty in the simulation of crop-climate processes

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.

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates

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.

The costs and benefits of digital dermatitis control on UK dairy farms

Digital dermatitis is a significant problem in UK dairy herds and the cost to farmers has not yet been fully quantified. Under the current farm health planning initiative there is a need to demonstrate to farmers the costs and benefits of disease control. This paper presents the first attempt to quantify the costs and benefits of digital dermatitis control on UK dairy farms through the use of an interactive model.

Genotypic and environmental influences on the performance of wheat root systems

Genetic and environmental factors interact to determine the growth and activity of crop root systems. This paper examines the effects of agronomic management and genotype on wheat root systems in the UK and Australia, and suggests ways in which root limitations to crop performance might be alleviated. In a field study in the UK which examined late-season growth and activity, fungicide maintained the size of the root system during early grain-filling, and there were significant differences between cultivars in root distribution with depth below 0.3 m. Shamrock had a longer root system below 0.3 m than varieties such as Hereward and Consort. Fungicide significantly increased root growth at 0.1-0.2 m in one season. In Australia, a wheat line selected for high shoot vigour had associated root vigour during early seedling growth but the effect on root growth did not persist. The results provide examples of genotypic differences in wheat root growth under field conditions which interact with agronomic management in ways which can be exploited to benefit growth and yield in diverse environments.

Recent advances in the application of in vitro systems to plant improvement

The horticultural industry was instrumental in the early development and exploitation of genetic techniques over a century ago. This review will describe recent advances in a range of in vitro methods and their application to plant breeding, with especial emphasis on horticultural crops. These methods include improvements in the efficiency of haploid breeding techniques in many fruit and vegetable species using either microspore-derived or ovule-derived plants. Significant molecular information is now available to supplement these essentially empirical approaches and this may enable the more predictable application of these technologies in previously intransigent crops. Similarly there are now improved techniques for isolation of somatic hybrids, by application of either in vitro fertilisation or the culture of excised ovules from interspecific crosses. In addition to examples taken from the traditional scientific literature, emphasis will also be given to the use of patent databases as a valuable source of information on recent novel technologies developed in the commercial world.