Evaluation of reanalysis rainfall estimates over Ethiopia

There is a pressing need for good rainfall data for the African continent both for humanitarian and climatological purposes. Given the sparseness of ground-based observations, one source of rainfall information is Numerical Weather Prediction (NWP) model outputs. The aim of this article is to investigate the quality of two NWP products using Ethiopia as a test case. The two products evaluated are the ERA-40 and NCEP reanalysis rainfall products. Spatial, seasonal and interannual variability of rainfall have been evaluated for Kiremt (JJAS) and Belg (FMAM) seasons at a spatial scale that reflects the local variability of the rainfall climate using a method which makes optimum use of sparse gauge validation data. We found that the spatial pattern of the rainfall climatology is captured well by both models especially for the main rainy season Kiremt. However, both models tend to overestimate the mean rainfall in the northwest, west and central regions but underestimate in the south and east. The overestimation is greater for NCEP in Belg season and greater for ERA-40 in Kiremt Season. ERA-40 captures the annual cycle over most of the country better than NCEP, but strongly exaggerates the Kiremt peak in the northwest and west. The overestimation in Kiremt appears to have been reduced since the assimilation of satellite data increased around 1990. For both models the interannual variability is less well captured than the spatial and seasonal variability. Copyright © 2008 Royal Meteorological Society

A geostatistical analysis of soil, vegetation, and image data characterizing land surface variation

The elucidation of spatial variation in the landscape can indicate potential wildlife habitats or breeding sites for vectors, such as ticks or mosquitoes, which cause a range of diseases. Information from remotely sensed data could aid the delineation of vegetation distribution on the ground in areas where local knowledge is limited. The data from digital images are often difficult to interpret because of pixel-to-pixel variation, that is, noise, and complex variation at more than one spatial scale. Landsat Thematic Mapper Plus (ETM+) and Satellite Pour l'Observation de La Terre (SPOT) image data were analyzed for an area close to Douna in Mali, West Africa. The variograms of the normalized difference vegetation index (NDVI) from both types of image data were nested. The parameters of the nested variogram function from the Landsat ETM+ data were used to design the sampling for a ground survey of soil and vegetation data. Variograms of the soil and vegetation data showed that their variation was anisotropic and their scales of variation were similar to those of NDVI from the SPOT data. The short- and long-range components of variation in the SPOT data were filtered out separately by factorial kriging. The map of the short-range component appears to represent the patterns of vegetation and associated shallow slopes and drainage channels of the tiger bush system. The map of the long-range component also appeared to relate to broader patterns in the tiger bush and to gentle undulations in the topography. The results suggest that the types of image data analyzed in this study could be used to identify areas with more moisture in semiarid regions that could support wildlife and also be potential vector breeding sites.

Buffering of recovery from acidification by organic acids

In the United Kingdom, as in other regions of Europe and North America, recent decreases in surface water sulphate concentrations, due to reduced sulphur emissions, have coincided with marked increases in dissolved organic carbon (DOC) concentrations. Since many of the compounds comprising DOC are acidic, the resulting increases in organic acidity may have the potential to offset the benefits of a decrease in mineral (sulphate) acidity. To test this, we used a triprotic model of organic acid dissociation to estimate the proportional organic acid buffering of reduced mineral acidity as measured in the 22 lakes and streams monitored by the UK Acid Waters Monitoring Network. For an average non-marine sulphate decrease of 30 μeq l− 1 over 15 years from 1988–2003, we estimate that around 28% was counterbalanced by rising strong organic acids, 20% by rising alkalinity (partly attributable to an increase in weak organic acids), 11% by falling inorganic aluminium and 41% by falling non-marine base cations. The situation is complicated by a concurrent decrease in marine ion concentrations, and the impact this may have had on both DOC and acidity, but results clearly demonstrate that organic acid increases have substantially limited the amount of recovery from acidification (in terms of rising alkalinity and falling aluminium) that have resulted from reducing sulphur emissions. The consistency and magnitude of sulphate and organic acid changes are consistent with a causal link between the two, possibly due to the effects of changing acidity, ionic strength and aluminium concentrations on organic matter solubility. If this is the case, then organic acids can be considered effective but partial buffers to acidity change in organic soils, and this mechanism needs to be considered in assessing and modelling recovery from acidification, and in defining realistic reference conditions. However, large spatial variations in the relative magnitude of organic acid and sulphate changes, notably for low-deposition sites in northwestern areas where organic acid increases apparently exceed non-marine sulphate decreases, suggest that additional factors, such as changes in sea-salt deposition and climatic factors, may be required to explain the full magnitude of DOC increases in UK surface waters.

The relationship between convection and sea surface temperature on intraseasonal timescales

The relationship between tropical convection, surface fluxes, and sea surface temperature (SST) on intraseasonal timescales has been examined as part of an investigation of the possibility that the intraseasonal oscillation is a coupled atmosphere–ocean phenomenon. The unique feature of this study is that 15 yr of data and the whole region from the Indian Ocean to the Pacific Ocean have been analyzed using lag-correlation analysis and compositing techniques. A coherent relationship between convection, surface fluxes, and SST has been found on intraseasonal timescales in the Indian Ocean, Maritime Continent, and west Pacific regions of the Tropics. Prior to the maximum in convection, there are positive shortwave and latent heat flux anomalies into the surface, followed by warm SST anomalies about 10 days before the convective maximum. Coincident with the convective maximum, there is a minimum in the shortwave flux, followed by a cooling due to increased evaporation associated with enhanced westerly wind stress, leading to negative SST anomalies about 10 days after the convection. The relationships are robust from year to year, including both phases of the El Niño–Southern Oscillation (ENSO) although the eastward extent of the region over which the relationship holds varies with the phase of ENSO, consistent with the variations in the eastward extent of the warm pool and westerly winds. The spatial scale of the anomalies is about 60° longitude, consistent with the scale of the intraseasonal oscillation. The spatial and temporal characteristics of the surface flux and SST perturbations are consistent with the surface flux variations forcing the ocean, and the magnitudes of the anomalies are consistent with mixed-layer depths appropriate to the Indian Ocean and west Pacific

The observed relationships between wheat and climate in China

Recent changes in climate have had a measurable impact on crop yield in China. The objective of this study is to investigate how climate variability affects wheat yield in China at different spatial scales. First the response of wheat yield to the climate at the provincial level from 1978 to 1995 for China was analysed. Wheat yield variability was only correlated with climate variability in some regions of China. At the provincial level, the variability of precipitation had a negative impact on wheat yield in parts of southeast China, but the seasonal mean temperature had a negative impact on wheat yield in only a few provinces, where significant variability in precipitation explained about 23–60% of yield variability, and temperature variability accounted for 37–41% of yield variability from 1978 to 1995. The correlation between wheat yield and climate for the whole of China from 1985 to 2000 was investigated at five spatial scales using climate data. The Climate Research Unit (CRU) and National Centers for Environmental Prediction (NCEP) proportions of the grid cells with a significant yield–precipitation correlation declined progressively from 14.6% at 0.5° to 0% at 5° scale. In contrast, the proportion of grid cells significant for the yield–temperature correlation increased progressively from 1.9% at 0.5° scale to 16% at 5° scale. This indicates that the variability of precipitation has a higher association with wheat yield at small scales (0.5°, 2°/2.5°) than at larger scales (4°/5.0°); but wheat yield has a good association with temperature at all levels of aggregation. The precipitation variable at the smaller scales (0.5°, 2°/2.5°) is a dominant factor in determining inter-annual wheat yield variability more so than at the larger scales (4°/5°). We conclude that in the current climate the relationship between wheat yield and each of precipitation and temperature becomes weaker and stronger, respectively, with an increase in spatial scale.

An ocean modelling and assimilation guide to using GOCE geoid products

We review the procedures and challenges that must be considered when using geoid data derived from the Gravity and steady-state Ocean Circulation Explorer (GOCE) mission in order to constrain the circulation and water mass representation in an ocean 5 general circulation model. It covers the combination of the geoid information with timemean sea level information derived from satellite altimeter data, to construct a mean dynamic topography (MDT), and considers how this complements the time-varying sea level anomaly, also available from the satellite altimeter. We particularly consider the compatibility of these different fields in their spatial scale content, their temporal rep10 resentation, and in their error covariances. These considerations are very important when the resulting data are to be used to estimate ocean circulation and its corresponding errors. We describe the further steps needed for assimilating the resulting dynamic topography information into an ocean circulation model using three different operational fore15 casting and data assimilation systems. We look at methods used for assimilating altimeter anomaly data in the absence of a suitable geoid, and then discuss different approaches which have been tried for assimilating the additional geoid information. We review the problems that have been encountered and the lessons learned in order the help future users. Finally we present some results from the use of GRACE geoid in20 formation in the operational oceanography community and discuss the future potential gains that may be obtained from a new GOCE geoid.

Local management and landscape drivers of pollination and biological control services in a Kenyan agro-ecosystem

Arthropods that have a direct impact on crop production (i.e. pests, natural enemies and pollinators) can be influenced by both local farm management and the context within which the fields occur in the wider landscape. However, the contributions and spatial scales at which these drivers operate and interact are not fully understood, particularly in the developing world. The impact of both local management and landscape context on insect pollinators and natural enemy communities and on their capacity to deliver related ecosystem services to an economically important tropical crop, pigeonpea was investigated. The study was conducted in nine paired farms across a gradient of increasing distance to semi-native vegetation in Kibwezi, Kenya. Results show that proximity of fields to semi-native habitats negatively affected pollinator and chewing insect abundance. Within fields, pesticide use was a key negative predictor of pollinator, pest and foliar active predator abundance. On the contrary, fertilizer application significantly enhanced pollinator and both chewing and sucking insect pest abundance. At a 1 km spatial scale of fields, there were significant negative effects of the number of semi-native habitat patches within fields dominated by mass flowering pigeonpea on pollinators abundance. For service provision, a significant decline in fruit set when insects were excluded from flowers was recorded. This study reveals the interconnections of pollinators, predators and pests with pigeonpea crop. For sustainable yields and to conserve high densities of both pollinators and predators of pests within pigeonpea landscapes, it is crucial to target the adoption of less disruptive farm management practices such as reducing pesticide and fertilizer inputs.

A complete atomistic model of molten polyethylene from neutron scattering data: a new methodology for polymer structure

A new approach to the study of the local organization in amorphous polymer materials is presented. The method couples neutron diffraction experiments that explore the structure on the spatial scale 1–20 Å with the reverse Monte Carlo fitting procedure to predict structures that accurately represent the experimental scattering results over the whole momentum transfer range explored. Molecular mechanics and molecular dynamics techniques are also used to produce atomistic models independently from any experimental input, thereby providing a test of the viability of the reverse Monte Carlo method in generating realistic models for amorphous polymeric systems. An analysis of the obtained models in terms of single chain properties and of orientational correlations between chain segments is presented. We show the viability of the method with data from molten polyethylene. The analysis derives a model with average C-C and C-H bond lengths of 1.55 Å and 1.1 Å respectively, average backbone valence angle of 112, a torsional angle distribution characterized by a fraction of trans conformers of 0.67 and, finally, a weak interchain orientational correlation at around 4 Å.

Root phenomics of crops: opportunities and challenges

Reliable techniques for screening large numbers of plants for root traits are still being developed, but include aeroponic, hydroponic and agar plate systems. Coupled with digital cameras and image analysis software, these systems permit the rapid measurement of root numbers, length and diameter in moderate ( typically <1000) numbers of plants. Usually such systems are employed with relatively small seedlings, and information is recorded in 2D. Recent developments in X-ray microtomography have facilitated 3D non-invasive measurement of small root systems grown in solid media, allowing angular distributions to be obtained in addition to numbers and length. However, because of the time taken to scan samples, only a small number can be screened (typically<10 per day, not including analysis time of the large spatial datasets generated) and, depending on sample size, limited resolution may mean that fine roots remain unresolved. Although agar plates allow differences between lines and genotypes to be discerned in young seedlings, the rank order may not be the same when the same materials are grown in solid media. For example, root length of dwarfing wheat ( Triticum aestivum L.) lines grown on agar plates was increased by similar to 40% relative to wild-type and semi-dwarfing lines, but in a sandy loam soil under well watered conditions it was decreased by 24-33%. Such differences in ranking suggest that significant soil environment-genotype interactions are occurring. Developments in instruments and software mean that a combination of high-throughput simple screens and more in-depth examination of root-soil interactions is becoming viable.

Axonal velocity distributions in neural feld equations

By modelling the average activity of large neuronal populations, continuum mean field models (MFMs) have become an increasingly important theoretical tool for understanding the emergent activity of cortical tissue. In order to be computationally tractable, long-range propagation of activity in MFMs is often approximated with partial differential equations (PDEs). However, PDE approximations in current use correspond to underlying axonal velocity distributions incompatible with experimental measurements. In order to rectify this deficiency, we here introduce novel propagation PDEs that give rise to smooth unimodal distributions of axonal conduction velocities. We also argue that velocities estimated from fibre diameters in slice and from latency measurements, respectively, relate quite differently to such distributions, a significant point for any phenomenological description. Our PDEs are then successfully fit to fibre diameter data from human corpus callosum and rat subcortical white matter. This allows for the first time to simulate long-range conduction in the mammalian brain with realistic, convenient PDEs. Furthermore, the obtained results suggest that the propagation of activity in rat and human differs significantly beyond mere scaling. The dynamical consequences of our new formulation are investigated in the context of a well known neural field model. On the basis of Turing instability analyses, we conclude that pattern formation is more easily initiated using our more realistic propagator. By increasing characteristic conduction velocities, a smooth transition can occur from self-sustaining bulk oscillations to travelling waves of various wavelengths, which may influence axonal growth during development. Our analytic results are also corroborated numerically using simulations on a large spatial grid. Thus we provide here a comprehensive analysis of empirically constrained activity propagation in the context of MFMs, which will allow more realistic studies of mammalian brain activity in the future.

Simulating maize yield in sub-tropical conditions of southern Brazil using Glam model

The objective of this work was to evaluate the feasibility of simulating maize yield in a sub‑tropical region of southern Brazil using the general large area model (Glam). A 16‑year time series of daily weather data were used. The model was adjusted and tested as an alternative for simulating maize yield at small and large spatial scales. Simulated and observed grain yields were highly correlated (r above 0.8; p<0.01) at large scales (greater than 100,000 km2), with variable and mostly lower correlations (r from 0.65 to 0.87; p<0.1) at small spatial scales (lower than 10,000 km2). Large area models can contribute to monitoring or forecasting regional patterns of variability in maize production in the region, providing a basis for agricultural decision making, and Glam‑Maize is one of the alternatives.

Sea surface temperature from a geostationary satellite by optimal estimation

Optimal estimation (OE) is applied as a technique for retrieving sea surface temperature (SST) from thermal imagery obtained by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on Meteosat 9. OE requires simulation of observations as part of the retrieval process, and this is done here using numerical weather prediction fields and a fast radiative transfer model. Bias correction of the simulated brightness temperatures (BTs) is found to be a necessary step before retrieval, and is achieved by filtered averaging of simulations minus observations over a time period of 20 days and spatial scale of 2.5° in latitude and longitude. Throughout this study, BT observations are clear-sky averages over cells of size 0.5° in latitude and longitude. Results for the OE SST are compared to results using a traditional non-linear retrieval algorithm (“NLSST”), both validated against a set of 30108 night-time matches with drifting buoy observations. For the OE SST the mean difference with respect to drifter SSTs is − 0.01 K and the standard deviation is 0.47 K, compared to − 0.38 K and 0.70 K respectively for the NLSST algorithm. Perhaps more importantly, systematic biases in NLSST with respect to geographical location, atmospheric water vapour and satellite zenith angle are greatly reduced for the OE SST. However, the OE SST is calculated to have a lower sensitivity of retrieved SST to true SST variations than the NLSST. This feature would be a disadvantage for observing SST fronts and diurnal variability, and raises questions as to how best to exploit OE techniques at SEVIRI's full spatial resolution.

High resolution simulation of the variability of surface energy balance fluxes across central London with urban zones for energy partitioning

The parameterization of surface heat-flux variability in urban areas relies on adequate representation of surface characteristics. Given the horizontal resolutions (e.g. ≈0.1–1km) currently used in numerical weather prediction (NWP) models, properties of the urban surface (e.g. vegetated/built surfaces, street-canyon geometries) often have large spatial variability. Here, a new approach based on Urban Zones to characterize Energy partitioning (UZE) is tested within a NWP model (Weather Research and Forecasting model;WRF v3.2.1) for Greater London. The urban land-surface scheme is the Noah/Single-Layer Urban Canopy Model (SLUCM). Detailed surface information (horizontal resolution 1 km)in central London shows that the UZE offers better characterization of surface properties and their variability compared to default WRF-SLUCM input parameters. In situ observations of the surface energy fluxes and near-surface meteorological variables are used to select the radiation and turbulence parameterization schemes and to evaluate the land-surface scheme

The population dynamics of disease on short and long time-scales

Observational evidence is scarce concerning the distribution of plant pathogen population sizes or densities as a function of time-scale or spatial scale. For wild pathosystems we can only get indirect evidence from evolutionary patterns and the consequences of biological invasions.We have little or no evidence bearing on extermination of hosts by pathogens, or successful escape of a host from a pathogen. Evidence over the last couple of centuries from crops suggest that the abundance of particular pathogens in the spectrum affecting a given host can vary hugely on decadal timescales. However, this may be an artefact of domestication and intensive cultivation. Host-pathogen dynamics can be formulated mathematically fairly easily–for example as SIR-type differential equation or difference equation models, and this has been the (successful) focus of recent work in crops. “Long-term” is then discussed in terms of the time taken to relax from a perturbation to the asymptotic state. However, both host and pathogen dynamics are driven by environmental factors as well as their mutual interactions, and both host and pathogen co-evolve, and evolve in response to external factors. We have virtually no information about the importance and natural role of higher trophic levels (hyperpathogens) and competitors, but they could also induce long-scale fluctuations in the abundance of pathogens on particular hosts. In wild pathosystems the host distribution cannot be modelled as either a uniform density or even a uniform distribution of fields (which could then be treated as individuals). Patterns of short term density-dependence and the detail of host distribution are therefore critical to long-term dynamics. Host density distributions are not usually scale-free, but are rarely uniform or clearly structured on a single scale. In a (multiply structured) metapopulation with coevolution and external disturbances it could well be the case that the time required to attain equilibrium (if it exists) based on conditions stable over a specified time-scale is longer than that time-scale. Alternatively, local equilibria may be reached fairly rapidly following perturbations but the meta-population equilibrium be attained very slowly. In either case, meta-stability on various time-scales is a more relevant than equilibrium concepts in explaining observed patterns.

Recovery based on plot experiments is a poor predictor of landscape-level population impacts of agricultural pesticides

Current European Union regulatory risk assessment allows application of pesticides provided that recovery of nontarget arthropods in-crop occurs within a year. Despite the long-established theory of source-sink dynamics, risk assessment ignores depletion of surrounding populations and typical field trials are restricted to plot-scale experiments. In the present study, the authors used agent-based modeling of 2 contrasting invertebrates, a spider and a beetle, to assess how the area of pesticide application and environmental half-life affect the assessment of recovery at the plot scale and impact the population at the landscape scale. Small-scale plot experiments were simulated for pesticides with different application rates and environmental half-lives. The same pesticides were then evaluated at the landscape scale (10 km × 10 km) assuming continuous year-on-year usage. The authors' results show that recovery time estimated from plot experiments is a poor indicator of long-term population impact at the landscape level and that the spatial scale of pesticide application strongly determines population-level impact. This raises serious doubts as to the utility of plot-recovery experiments in pesticide regulatory risk assessment for population-level protection. Predictions from the model are supported by empirical evidence from a series of studies carried out in the decade starting in 1988. The issues raised then can now be addressed using simulation. Prediction of impacts at landscape scales should be more widely used in assessing the risks posed by environmental stressors.