ENSO at 6ka and 21ka from ocean–atmosphere coupled model simulations

We analyze how the characteristics of El Niño-Southern Oscillation (ENSO) are changed in coupled ocean–atmosphere simulations of the mid-Holocene (MH) and the Last Glacial Maximum (LGM) performed as part of the Paleoclimate Modeling Intercomparison Project phase 2 (PMIP2). Comparison of the model results with present day observations show that most of the models reproduce the large scale features of the tropical Pacific like the SST gradient, the mean SST and the mean seasonal cycles. All models simulate the ENSO variability, although with different skill. Our analyses show that several relationships between El Niño amplitude and the mean state across the different control simulations are still valid for simulations of the MH and the LGM. Results for the MH show a consistent El Niño amplitude decrease. It can be related to the large scale atmospheric circulation changes. While the Northern Hemisphere receives more insolation during the summer time, the Asian summer monsoon system is strengthened which leads to the enhancement of the Walker circulation. Easterlies prevailing over the central eastern Pacific induce an equatorial upwelling that damps the El Niño development. Results are less conclusive for 21ka. Large scale dynamic competes with changes in local heat fluxes, so that model shows a wide range of responses, as it is the case in future climate projections.

Improving flood models using LiDAR: progress and problems

Airborne scanning laser altimetry (LiDAR) is an important new data source for river flood modelling. LiDAR can give dense and accurate DTMs of floodplains for use as model bathymetry. Spatial resolutions of 0.5m or less are possible, with a height accuracy of 0.15m. LiDAR gives a Digital Surface Model (DSM), so vegetation removal software (e.g. TERRASCAN) must be used to obtain a DTM. An example used to illustrate the current state of the art will be the LiDAR data provided by the EA, which has been processed by their in-house software to convert the raw data to a ground DTM and separate vegetation height map. Their method distinguishes trees from buildings on the basis of object size. EA data products include the DTM with or without buildings removed, a vegetation height map, a DTM with bridges removed, etc. Most vegetation removal software ignores short vegetation less than say 1m high. We have attempted to extend vegetation height measurement to short vegetation using local height texture. Typically most of a floodplain may be covered in such vegetation. The idea is to assign friction coefficients depending on local vegetation height, so that friction is spatially varying. This obviates the need to calibrate a global floodplain friction coefficient. It’s not clear at present if the method is useful, but it’s worth testing further. The LiDAR DTM is usually determined by looking for local minima in the raw data, then interpolating between these to form a space-filling height surface. This is a low pass filtering operation, in which objects of high spatial frequency such as buildings, river embankments and walls may be incorrectly classed as vegetation. The problem is particularly acute in urban areas. A solution may be to apply pattern recognition techniques to LiDAR height data fused with other data types such as LiDAR intensity or multispectral CASI data. We are attempting to use digital map data (Mastermap structured topography data) to help to distinguish buildings from trees, and roads from areas of short vegetation. The problems involved in doing this will be discussed. A related problem of how best to merge historic river cross-section data with a LiDAR DTM will also be considered. LiDAR data may also be used to help generate a finite element mesh. In rural area we have decomposed a floodplain mesh according to taller vegetation features such as hedges and trees, so that e.g. hedge elements can be assigned higher friction coefficients than those in adjacent fields. We are attempting to extend this approach to urban area, so that the mesh is decomposed in the vicinity of buildings, roads, etc as well as trees and hedges. A dominant points algorithm is used to identify points of high curvature on a building or road, which act as initial nodes in the meshing process. A difficulty is that the resulting mesh may contain a very large number of nodes. However, the mesh generated may be useful to allow a high resolution FE model to act as a benchmark for a more practical lower resolution model. A further problem discussed will be how best to exploit data redundancy due to the high resolution of the LiDAR compared to that of a typical flood model. Problems occur if features have dimensions smaller than the model cell size e.g. for a 5m-wide embankment within a raster grid model with 15m cell size, the maximum height of the embankment locally could be assigned to each cell covering the embankment. But how could a 5m-wide ditch be represented? Again, this redundancy has been exploited to improve wetting/drying algorithms using the sub-grid-scale LiDAR heights within finite elements at the waterline.

Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid.

Uncertainties associated with the representation of various physical processes in global climate models (GCMs) mean that, when projections from GCMs are used in climate change impact studies, the uncertainty propagates through to the impact estimates. A complete treatment of this ‘climate model structural uncertainty’ is necessary so that decision-makers are presented with an uncertainty range around the impact estimates. This uncertainty is often underexplored owing to the human and computer processing time required to perform the numerous simulations. Here, we present a 189-member ensemble of global river runoff and water resource stress simulations that adequately address this uncertainty. Following several adaptations and modifications, the ensemble creation time has been reduced from 750 h on a typical single-processor personal computer to 9 h of high-throughput computing on the University of Reading Campus Grid. Here, we outline the changes that had to be made to the hydrological impacts model and to the Campus Grid, and present the main results. We show that, although there is considerable uncertainty in both the magnitude and the sign of regional runoff changes across different GCMs with climate change, there is much less uncertainty in runoff changes for regions that experience large runoff increases (e.g. the high northern latitudes and Central Asia) and large runoff decreases (e.g. the Mediterranean). Furthermore, there is consensus that the percentage of the global population at risk to water resource stress will increase with climate change.

Bis(hydroxy-isoindolinone)s: synthesis, stereochemistry, polymer chemistry, and supramolecular assembly

Pseudoacid chlorides of 2,5-bis(4-fluorobenzoyl) terephthalic acid and 4,6-bis(4-fluorobenzoyl) isophthalic acid condense with primary amines to afford diastereomeric bis(hydroxyindolinone)s in good isolated yields and with diamines to give high molecular weight poly(hydroxyindolinone)s. Bis-N-pyrenemethyl bis(hydroxyindolinone)s assemble, even in dipolar solvents such as DMSO, with macrocyclic diimide-sulfones to give [3]pseudorotaxanes stabilized by electronically complementary aromatic π−π-stacking and shape-complementary van der Waals interactions.

Multi-model assessment of stratospheric ozone return dates and ozone recovery in CCMVal-2 models

Projections of stratospheric ozone from a suite of chemistry-climate models (CCMs) have been analyzed. In addition to a reference simulation where anthropogenic halogenated ozone depleting substances (ODSs) and greenhouse gases (GHGs) vary with time, sensitivity simulations with either ODS or GHG concentrations fixed at 1960 levels were performed to disaggregate the drivers of projected ozone changes. These simulations were also used to assess the two distinct milestones of ozone returning to historical values (ozone return dates) and ozone no longer being influenced by ODSs (full ozone recovery). The date of ozone returning to historical values does not indicate complete recovery from ODSs in most cases, because GHG-induced changes accelerate or decelerate ozone changes in many regions. In the upper stratosphere where CO2-induced stratospheric cooling increases ozone, full ozone recovery is projected to not likely have occurred by 2100 even though ozone returns to its 1980 or even 1960 levels well before (~2025 and 2040, respectively). In contrast, in the tropical lower stratosphere ozone decreases continuously from 1960 to 2100 due to projected increases in tropical upwelling, while by around 2040 it is already very likely that full recovery from the effects of ODSs has occurred, although ODS concentrations are still elevated by this date. In the midlatitude lower stratosphere the evolution differs from that in the tropics, and rather than a steady decrease in ozone, first a decrease in ozone is simulated from 1960 to 2000, which is then followed by a steady increase through the 21st century. Ozone in the midlatitude lower stratosphere returns to 1980 levels by ~2045 in the Northern Hemisphere (NH) and by ~2055 in the Southern Hemisphere (SH), and full ozone recovery is likely reached by 2100 in both hemispheres. Overall, in all regions except the tropical lower stratosphere, full ozone recovery from ODSs occurs significantly later than the return of total column ozone to its 1980 level. The latest return of total column ozone is projected to occur over Antarctica (~2045–2060) whereas it is not likely that full ozone recovery is reached by the end of the 21st century in this region. Arctic total column ozone is projected to return to 1980 levels well before polar stratospheric halogen loading does so (~2025–2030 for total column ozone, cf. 2050–2070 for Cly+60×Bry) and it is likely that full recovery of total column ozone from the effects of ODSs has occurred by ~2035. In contrast to the Antarctic, by 2100 Arctic total column ozone is projected to be above 1960 levels, but not in the fixed GHG simulation, indicating that climate change plays a significant role.

Sewage effluent clean-up reduces phosphorus but not phytoplankton in lowland chalk stream (River Kennet, UK) impacted by water mixing from adjacent canal

Information is provided on phosphorus in the River Kennet and the adjacent Kennet and Avon Canal in southern England to assess their interactions and the changes following phosphorus reductions in sewage treatment work (STW) effluent inputs. A step reduction in soluble reactive phosphorus (SRP) concentration within the effluent (5 to 13 fold) was observed from several STWs discharging to the river in the mid-2000s. This translated to over halving of SRP concentrations within the lower Kennet. Lower Kennet SRP concentrations change from being highest under base-flow to highest under storm-flow conditions. This represented a major shift from direct effluent inputs to a within-catchment source dominated system characteristic of the upper part to the catchment. Average SRP concentrations in the lower Kennet reduced over time towards the target for good water quality. Critically, there was no corresponding reduction in chlorophyll-a concentration, the waters remaining eutrophic when set against standards for lakes. Following the up gradient input of the main water and SRP source (Wilton Water), SRP concentrations in the canal reduced down gradient to below detection limits at times near its junction with the Kennet downstream. However, chlorophyll concentrations in the canal were in an order of magnitude higher than in the river. This probably resulted from long water residence times and higher temperatures promoting progressive algal and suspended sediment generations that consumed SRP. The canal acted as a point source for sediment, algae and total phosphorus to the river especially during the summer months when boat traffic disturbed the canal's bottom sediments and the locks were being regularly opened. The short-term dynamics of this transfer was complex. For the canal and the supply source at Wilton Water, conditions remained hypertrophic when set against standards for lakes even when SRP concentrations were extremely low.

The economic impact of genetically modified cotton on South African smallholders: Yield, profit and health effects

Results of a large-scale survey of resource-poor smallholder cotton farmers in South Africa over three years conclusively show that adopters of Bt cotton have benefited in terms of higher yields, lower pesticide use, less labour for pesticide application and substantially higher gross margins per hectare. These benefits were clearly related to the technology, and not to preferential adoption by farmers who were already highly efficient. The smallest producers are shown to have benefited from adoption of the Bt variety as much as, if not more than, larger producers. Moreover, evidence from hospital records suggests a link between declining pesticide poisonings and adoption of the Bt variety.

Training to address attitudes and behaviour of rural bank managers in Madhya Pradesh, India: A programme to facilitate financial inclusion

Given their physical presence in India, banks are arguably well-placed to improve financial inclusion in rural areas. However, uncertain repayment capacities and high transaction costs mean formal financial institutions are often reluctant to lend to the rural poor. Conversely, high transaction costs in dealing with banks are also incurred by clients, through, for example, lengthy, cumbersome and potentially ignominious procedures. Negative attitudes towards poor clients can be an important component of such transaction costs. An applied research project funded by the Enterprise Development Innovation Fund (EDIF-DFID) developed an innovative training programme designed to encourage more positive attitudes of bank staff towards poor clients, and towards their own role in rural poverty alleviation and development. This paper examines the development of the training programme, its implementation, and the results of its evaluation. It is shown that training can bring about attitudinal change, which in turn is reflected in behaviour and social impact.

Inequality and GM crops: a case study of Bt cotton in India

Critics of genetically modified (GM) crops often contend that their introduction enhances the gap between rich and poor farmers, as the former group are in the best position to afford the expensive seed as well as provide other inputs such as fertilizer and irrigation. The research reported in this paper explores this issue with regard to Bt cotton (cotton with the endotoxtin gene from Bacillus thuringiensis conferring resistance to some insect pests) in Jalgaon, Maharashtra State, India, spanning the 2002 and 2003 seasons. Questionnaire–based survey results from 63 non–adopting and 94 adopting households of Bt cotton were analyzed, spanning 137 Bt cotton plots and 95 non–Bt cotton plots of both Bt adopters and non–adopters. For these households, cotton income accounted for 85 to 88% of total household income, and is thus of vital importance. Results suggest that in 2003 Bt adopting households have significantly more income from cotton than do non–adopting households (Rp 66,872 versus Rp 46,351) but inequality in cotton income, measured with the Gini coefficient (G), was greater amongst non–adopters than adopters. While Bt adopters had greater acreage of cotton in 2003 (9.92 acres versus 7.42 for non–adopters), the respective values of G were comparable. The main reason for the lessening of inequality amongst adopters would appear to be the consistency in the performance of Bt cotton along with the preferred non–Bt cultivar of Bt adopters—Bunny. Taking gross margin as the basis for comparison, Bt plots had 2.5 times the gross margin of non–Bt plots of non–adopters, while the advantage of Bt plots over non–Bt plots of adopters was 1.6 times. Measured in terms of the Gini coefficient of gross margin/acre it was apparent that inequality was lessened with the adoption of Bunny (G = 0.47) and Bt (G = 0.3) relative to all other non–Bt plots (G = 0.63). Hence the issue of equality needs to be seen both in terms of differences between adopters and non–adopters as well as within each of the groups.

Inbreeding depression and founder diversity among captive and free-living populations of the endangered pink pigeon Columba mayeri

The endemic pink pigeon has recovered from less than 20 birds in the mid-1970s to 355 free-living individuals in 2003. A major concern for the species' recovery has been the potential genetic problem of inbreeding. Captive pink pigeons bred for reintroduction were managed to maximise founder representation and minimise inbreeding. In this paper, we quantify the effect of inbreeding on survival and reproductive parameters in captive and wild populations and quantify DNA sequence variation in the mitochondrial d-loop region for pink pigeon founders. Inbreeding affected egg fertility, squab, juvenile and adult survival, but effects were strongest in highly inbred birds (F≥0.25). Inbreeding depression was more apparent in free-living birds where even moderate levels of inbreeding affected survival, although highly inbred birds were equally compromised in both captive and wild populations. Mitochondrial DNA haplotypic diversity in pink pigeon founders is low, suggesting that background inbreeding is contributing to low fertility and depressed productivity in this species, as well as comparable survival of some groups of non-inbred and nominally inbred birds. Management of wild populations has boosted population growth and may be required long-term to offset the negative effects of inbreeding depression and enhance the species' survival.

Bayesian design and conduct of phase II single-arm clinical trials with binary outcomes: a tutorial

The aim of phase II single-arm clinical trials of a new drug is to determine whether it has sufficient promising activity to warrant its further development. For the last several years Bayesian statistical methods have been proposed and used. Bayesian approaches are ideal for earlier phase trials as they take into account information that accrues during a trial. Predictive probabilities are then updated and so become more accurate as the trial progresses. Suitable priors can act as pseudo samples, which make small sample clinical trials more informative. Thus patients have better chances to receive better treatments. The goal of this paper is to provide a tutorial for statisticians who use Bayesian methods for the first time or investigators who have some statistical background. In addition, real data from three clinical trials are presented as examples to illustrate how to conduct a Bayesian approach for phase II single-arm clinical trials with binary outcomes.