Forest fire plumes over the North Atlantic: p-TOMCAT model simulations with aircraft and satellite measurements from the ITOP/ICARTT campaign

Intercontinental Transport of Ozone and Precursors (ITOP) (part of International Consortium for Atmospheric Research on Transport and Transformation (ICARTT)) was an intense research effort to measure long-range transport of pollution across the North Atlantic and its impact on O3 production. During the aircraft campaign plumes were encountered containing large concentrations of CO plus other tracers and aerosols from forest fires in Alaska and Canada. A chemical transport model, p-TOMCAT, and new biomass burning emissions inventories are used to study the emissions long-range transport and their impact on the troposphere O3 budget. The fire plume structure is modeled well over long distances until it encounters convection over Europe. The CO values within the simulated plumes closely match aircraft measurements near North America and over the Atlantic and have good agreement with MOPITT CO data. O3 and NOx values were initially too great in the model plumes. However, by including additional vertical mixing of O3 above the fires, and using a lower NO2/CO emission ratio (0.008) for boreal fires, O3 concentrations are reduced closer to aircraft measurements, with NO2 closer to SCIAMACHY data. Too little PAN is produced within the simulated plumes, and our VOC scheme's simplicity may be another reason for O3 and NOx model-data discrepancies. In the p-TOMCAT simulations the fire emissions lead to increased tropospheric O3 over North America, the north Atlantic and western Europe from photochemical production and transport. The increased O3 over the Northern Hemisphere in the simulations reaches a peak in July 2004 in the range 2.0 to 6.2 Tg over a baseline of about 150 Tg.

Model selection for the mixed logit with Bayesian estimation

In this paper, the mixed logit (ML) using Bayesian methods was employed to examine willingness-to-pay (WTP) to consume bread produced with reduced levels of pesticides so as to ameliorate environmental quality, from data generated by a choice experiment. Model comparison used the marginal likelihood, which is preferable for Bayesian model comparison and testing. Models containing constant and random parameters for a number of distributions were considered, along with models in ‘preference space’ and ‘WTP space’ as well as those allowing for misreporting. We found: strong support for the ML estimated in WTP space; little support for fixing the price coefficient a common practice advocated and adopted in the environmental economics literature; and, weak evidence for misreporting.

Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments

MOTIVATION: The accurate prediction of the quality of 3D models is a key component of successful protein tertiary structure prediction methods. Currently, clustering or consensus based Model Quality Assessment Programs (MQAPs) are the most accurate methods for predicting 3D model quality; however they are often CPU intensive as they carry out multiple structural alignments in order to compare numerous models. In this study, we describe ModFOLDclustQ - a novel MQAP that compares 3D models of proteins without the need for CPU intensive structural alignments by utilising the Q measure for model comparisons. The ModFOLDclustQ method is benchmarked against the top established methods in terms of both accuracy and speed. In addition, the ModFOLDclustQ scores are combined with those from our older ModFOLDclust method to form a new method, ModFOLDclust2, that aims to provide increased prediction accuracy with negligible computational overhead. RESULTS: The ModFOLDclustQ method is competitive with leading clustering based MQAPs for the prediction of global model quality, yet it is up to 150 times faster than the previous version of the ModFOLDclust method at comparing models of small proteins (<60 residues) and over 5 times faster at comparing models of large proteins (>800 residues). Furthermore, a significant improvement in accuracy can be gained over the previous clustering based MQAPs by combining the scores from ModFOLDclustQ and ModFOLDclust to form the new ModFOLDclust2 method, with little impact on the overall time taken for each prediction. AVAILABILITY: The ModFOLDclustQ and ModFOLDclust2 methods are available to download from: http://www.reading.ac.uk/bioinf/downloads/ CONTACT: l.j.mcguffin@reading.ac.uk.

A little learning is a dangerous thing: an experimental demonstration of ignorance-driven inference

Studies of ignorance-driven decision making have been employed to analyse when ignorance should prove advantageous on theoretical grounds or else they have been employed to examine whether human behaviour is consistent with an ignorance-driven inference strategy (e. g., the recognition heuristic). In the current study we examine whether-under conditions where such inferences might be expected-the advantages that theoretical analyses predict are evident in human performance data. A single experiment shows that, when asked to make relative wealth judgements, participants reliably use recognition as a basis for their judgements. Their wealth judgements under these conditions are reliably more accurate when some of the target names are unknown than when participants recognize all of the names (a "less-is-more effect"). These results are consistent across a number of variations: the number of options given to participants and the nature of the wealth judgement. A basic model of recognition-based inference predicts these effects.

Robust background model for pixel based people counting using a single uncalibrated camera

Several pixel-based people counting methods have been developed over the years. Among these the product of scale-weighted pixel sums and a linear correlation coefficient is a popular people counting approach. However most approaches have paid little attention to resolving the true background and instead take all foreground pixels into account. With large crowds moving at varying speeds and with the presence of other moving objects such as vehicles this approach is prone to problems. In this paper we present a method which concentrates on determining the true-foreground, i.e. human-image pixels only. To do this we have proposed, implemented and comparatively evaluated a human detection layer to make people counting more robust in the presence of noise and lack of empty background sequences. We show the effect of combining human detection with a pixel-map based algorithm to i) count only human-classified pixels and ii) prevent foreground pixels belonging to humans from being absorbed into the background model. We evaluate the performance of this approach on the PETS 2009 dataset using various configurations of the proposed methods. Our evaluation demonstrates that the basic benchmark method we implemented can achieve an accuracy of up to 87% on sequence ¿S1.L1 13-57 View 001¿ and our proposed approach can achieve up to 82% on sequence ¿S1.L3 14-33 View 001¿ where the crowd stops and the benchmark accuracy falls to 64%.

The price of risk in construction projects: contingency approximation model (CAM)

Little attention has been focussed on a precise definition and evaluation mechanism for project management risk specifically related to contractors. When bidding, contractors traditionally price risks using unsystematic approaches. The high business failure rate our industry records may indicate that the current unsystematic mechanisms contractors use for building up contingencies may be inadequate. The reluctance of some contractors to include a price for risk in their tenders when bidding for work competitively may also not be a useful approach. Here, instead, we first define the meaning of contractor contingency, and then we develop a facile quantitative technique that contractors can use to estimate a price for project risk. This model will help contractors analyse their exposure to project risks; and help them express the risk in monetary terms for management action. When bidding for work, they can decide how to allocate contingencies strategically in a way that balances risk and reward.

Model-based life cycle cost and assessment tool for sustainable building design decision

There is a growing concern in reducing greenhouse gas emissions all over the world. The U.K. has set 34% target reduction of emission before 2020 and 80% before 2050 compared to 1990 recently in Post Copenhagen Report on Climate Change. In practise, Life Cycle Cost (LCC) and Life Cycle Assessment (LCA) tools have been introduced to construction industry in order to achieve this such as. However, there is clear a disconnection between costs and environmental impacts over the life cycle of a built asset when using these two tools. Besides, the changes in Information and Communication Technologies (ICTs) lead to a change in the way information is represented, in particular, information is being fed more easily and distributed more quickly to different stakeholders by the use of tool such as the Building Information Modelling (BIM), with little consideration on incorporating LCC and LCA and their maximised usage within the BIM environment. The aim of this paper is to propose the development of a model-based LCC and LCA tool in order to provide sustainable building design decisions for clients, architects and quantity surveyors, by then an optimal investment decision can be made by studying the trade-off between costs and environmental impacts. An application framework is also proposed finally as the future work that shows how the proposed model can be incorporated into the BIM environment in practise.

Evaluating the potential for statistical decadal predictions of sea surface temperatures with a perfect model approach

We explore the potential for making statistical decadal predictions of sea surface temperatures (SSTs) in a perfect model analysis, with a focus on the Atlantic basin. Various statistical methods (Lagged correlations, Linear Inverse Modelling and Constructed Analogue) are found to have significant skill in predicting the internal variability of Atlantic SSTs for up to a decade ahead in control integrations of two different global climate models (GCMs), namely HadCM3 and HadGEM1. Statistical methods which consider non-local information tend to perform best, but which is the most successful statistical method depends on the region considered, GCM data used and prediction lead time. However, the Constructed Analogue method tends to have the highest skill at longer lead times. Importantly, the regions of greatest prediction skill can be very different to regions identified as potentially predictable from variance explained arguments. This finding suggests that significant local decadal variability is not necessarily a prerequisite for skillful decadal predictions, and that the statistical methods are capturing some of the dynamics of low-frequency SST evolution. In particular, using data from HadGEM1, significant skill at lead times of 6–10 years is found in the tropical North Atlantic, a region with relatively little decadal variability compared to interannual variability. This skill appears to come from reconstructing the SSTs in the far north Atlantic, suggesting that the more northern latitudes are optimal for SST observations to improve predictions. We additionally explore whether adding sub-surface temperature data improves these decadal statistical predictions, and find that, again, it depends on the region, prediction lead time and GCM data used. Overall, we argue that the estimated prediction skill motivates the further development of statistical decadal predictions of SSTs as a benchmark for current and future GCM-based decadal climate predictions.

Comparing the UK Met Office climate prediction system DePreSys with idealized predictability in the HadCM3 model

The initial condition effect on climate prediction skill over a 2-year hindcast time-scale has been assessed from ensemble HadCM3 climate model runs using anomaly initialization over the period 1990–2001, and making comparisons with runs without initialization (equivalent to climatological conditions), and to anomaly persistence. It is shown that the assimilation improves the prediction skill in the first year globally, and in a number of limited areas out into the second year. Skill in hindcasting surface air temperature anomalies is most marked over ocean areas, and is coincident with areas of high sea surface temperature and ocean heat content skill. Skill improvement over land areas is much more limited but is still detectable in some cases. We found little difference in the skill of hindcasts using three different sets of ocean initial conditions, and we obtained the best results by combining these to form a grand ensemble hindcast set. Results are also compared with the idealized predictability studies of Collins (Clim. Dynam. 2002; 19: 671–692), which used the same model. The maximum lead time for which initialization gives enhanced skill over runs without initialization varies in different regions but is very similar to lead times found in the idealized studies, therefore strongly supporting the process representation in the model as well as its use for operational predictions. The limited 12-year period of the study, however, means that the regional details of model skill should probably be further assessed under a wider range of observational conditions.

The determination of total nitrogen and total phosphorus concentrations in freshwaters from land use, stock headage and population data: testing of a model for use in conservation and water quality management

1. Nutrient concentrations (particularly N and P) determine the extent to which water bodies are or may become eutrophic. Direct determination of nutrient content on a wide scale is labour intensive but the main sources of N and P are well known. This paper describes and tests an export coefficient model for prediction of total N and total P from: (i) land use, stock headage and human population; (ii) the export rates of N and P from these sources; and (iii) the river discharge. Such a model might be used to forecast the effects of changes in land use in the future and to hindcast past water quality to establish comparative or baseline states for the monitoring of change. 2. The model has been calibrated against observed data for 1988 and validated against sets of observed data for a sequence of earlier years in ten British catchments varying from uplands through rolling, fertile lowlands to the flat topography of East Anglia. 3. The model predicted total N and total P concentrations with high precision (95% of the variance in observed data explained). It has been used in two forms: the first on a specific catchment basis; the second for a larger natural region which contains the catchment with the assumption that all catchments within that region will be similar. Both models gave similar results with little loss of precision in the latter case. This implies that it will be possible to describe the overall pattern of nutrient export in the UK with only a fraction of the effort needed to carry out the calculations for each individual water body. 4. Comparison between land use, stock headage, population numbers and nutrient export for the ten catchments in the pre-war year of 1931, and for 1970 and 1988 show that there has been a substantial loss of rough grazing to fertilized temporary and permanent grasslands, an increase in the hectarage devoted to arable, consistent increases in the stocking of cattle and sheep and a marked movement of humans to these rural catchments. 5. All of these trends have increased the flows of nutrients with more than a doubling of both total N and total P loads during the period. On average in these rural catchments, stock wastes have been the greatest contributors to both N and P exports, with cultivation the next most important source of N and people of P. Ratios of N to P were high in 1931 and remain little changed so that, in these catchments, phosphorus continues to be the nutrient most likely to control algal crops in standing waters supplied by the rivers studied.

Real-time multi-model decadal climate predictions

We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.

A model of the three-dimensional evolution of Arctic melt ponds on first-year and multiyear sea ice

During winter the ocean surface in polar regions freezes over to form sea ice. In the summer the upper layers of sea ice and snow melts producing meltwater that accumulates in Arctic melt ponds on the surface of sea ice. An accurate estimate of the fraction of the sea ice surface covered in melt ponds is essential for a realistic estimate of the albedo for global climate models. We present a melt-pond–sea-ice model that simulates the three-dimensional evolution of melt ponds on an Arctic sea ice surface. The advancements of this model compared to previous models are the inclusion of snow topography; meltwater transport rates are calculated from hydraulic gradients and ice permeability; and the incorporation of a detailed one-dimensional, thermodynamic radiative balance. Results of model runs simulating first-year and multiyear sea ice are presented. Model results show good agreement with observations, with duration of pond coverage, pond area, and ice ablation comparing well for both the first-year ice and multiyear ice cases. We investigate the sensitivity of the melt pond cover to changes in ice topography, snow topography, and vertical ice permeability. Snow was found to have an important impact mainly at the start of the melt season, whereas initial ice topography strongly controlled pond size and pond fraction throughout the melt season. A reduction in ice permeability allowed surface flooding of relatively flat, first-year ice but had little impact on the pond coverage of rougher, multiyear ice. We discuss our results, including model shortcomings and areas of experimental uncertainty.

Are clinical documents optimised for patient safety? A critical analysis of patient safety outcomes using the EDA error model

Iatrogenic errors and patient safety in clinical processes are an increasing concern. The quality of process information in hardcopy or electronic form can heavily influence clinical behaviour and decision making errors. Little work has been undertaken to assess the safety impact of clinical process planning documents guiding the clinical actions and decisions. This paper investigates the clinical process documents used in elective surgery and their impact on latent and active clinical errors. Eight clinicians from a large health trust underwent extensive semi- structured interviews to understand their use of clinical documents, and their perceived impact on errors and patient safety. Samples of the key types of document used were analysed. Theories of latent organisational and active errors from the literature were combined with the EDA semiotics model of behaviour and decision making to propose the EDA Error Model. This model enabled us to identify perceptual, evaluation, knowledge and action error types and approaches to reducing their causes. The EDA error model was then used to analyse sample documents and identify error sources and controls. Types of knowledge artefact structures used in the documents were identified and assessed in terms of safety impact. This approach was combined with analysis of the questionnaire findings using existing error knowledge from the literature. The results identified a number of document and knowledge artefact issues that give rise to latent and active errors and also issues concerning medical culture and teamwork together with recommendations for further work.

Origin of regional climate differences: role of boundary conditions and model formulation in two GCMs

Model differences in projections of extratropical regional climate change due to increasing greenhouse gases are investigated using two atmospheric general circulation models (AGCMs): ECHAM4 (Max Planck Institute, version 4) and CCM3 (National Center for Atmospheric Research Community Climate Model version 3). Sea-surface temperature (SST) fields calculated from observations and coupled versions of the two models are used to force each AGCM in experiments based on time-slice methodology. Results from the forced AGCMs are then compared to coupled model results from the Coupled Model Intercomparison Project 2 (CMIP2) database. The time-slice methodology is verified by showing that the response of each model to doubled CO2 and SST forcing from the CMIP2 experiments is consistent with the results of the coupled GCMs. The differences in the responses of the models are attributed to (1) the different tropical SST warmings in the coupled simulations and (2) the different atmospheric model responses to the same tropical SST warmings. Both are found to have important contributions to differences in implied Northern Hemisphere (NH) winter extratropical regional 500 mb height and tropical precipitation climate changes. Forced teleconnection patterns from tropical SST differences are primarily responsible for sensitivity differences in the extratropical North Pacific, but have relatively little impact on the North Atlantic. There are also significant differences in the extratropical response of the models to the same tropical SST anomalies due to differences in numerical and physical parameterizations. Differences due to parameterizations dominate in the North Atlantic. Differences in the control climates of the two coupled models from the current climate, in particular for the coupled model containing CCM3, are also demonstrated to be important in leading to differences in extratropical regional sensitivity.

Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity

Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate–carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2 × and 4 × CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate–carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from palaeoclimate reconstructions. This in turn could be a result of unforced variability within the climate system, uncertainty in the reconstructions of temperature and CO2, errors in the reconstructions of forcing used to drive the models, or the incomplete representation of certain processes within the models. Given the forcing datasets used in this study, the models calculate significant land-use emissions over the pre-industrial period. This implies that land-use emissions might need to be taken into account, when making estimates of climate–carbon feedbacks from palaeoclimate reconstructions.