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This paper shows the impact on global cooling demand of moving from a 1.5ºC to 2.0ºC temperature increase. African countries have the highest increase in cooling requirements. The United Kingdom, Switzerland, and Norway (traditionally unprepared for extreme heat) will suffer the largest relative cooling demand surges.

Air-sea coupling is critical in influencing atmospheric temperature and precipitation. The effect of greenhouse gases has influenced atmospheric variability and extreme events. Understanding and quantifying the effect of air-sea feedback on atmospheric variability and extremes remains unknown.In this work we show results obtained from two numerical experiments. We use the HadSM4 configuration that couples the HadAM4 model at N144 resolution with a Slab Ocean to generate a large ensemble (~1000 members) of realizations of the 2013-14 October-March winter season, forced with a calibrated ocean heat convergence flux.A twin experiment is performed by forcing HadAM4 with the diagnosed SST and sea ice from the ensemble, yielding a new ensemble with identical realizations of SST and sea ice. The only difference between the two ensembles is the enabling or disabling of the feedback of air-sea heat fluxes on SST.While the impact of the feedback on the mean climate is relatively small, we show that its influence has important consequences for the variability of many important quantities, including air-sea fluxes and return periods of extreme events.

Multi-thousand member climate model simulations are highly valuable for showing characteristics of extreme weather events in historical and future climates. However, until now, studies using such a physically-based approach have been limited to using models with a resolution much coarser than the most modern systems. We have developed a global atmospheric model with ~60km resolution that can be run in the climateprediction.net distributed computing system to produce such large datasets. This resolution is finer than that of many current global climate models and sufficient for good simulation of extratropical synoptic features such as storms. It also allows many extratropical extreme weather events to be simulated without requiring regional downscaling. We will show that this model's simulation of extratropical winter weather is competitive with that in other state-of-the-art models. We will also present the first results generated by this system. One application has been the production of ~2000 member simulations based on sea surface temperatures in severe future winters produced in the UK Climate Projections 2018 dataset, generating large numbers of examples of plausible extreme wet and warm UK seasons. Another is showing the increasing spatial extent of precipitation extremes in the Northern Hemisphere extratropics.

Attribution of an extreme magnitude 1-day rainfall event in Hobart is inhibited by small sample size. For moderate magnitude Hobart daily rainfall extremes, models suggest that the associated extratropical lows will deliver more rainfall with weaker pressure anomalies in a warmer world.

Predicting the electricity demand of new supermarkets will help with design, planning, and future energy management. Instead of creating complex site-specific thermal engineering models, simplified statistical energy prediction models as we propose can be useful to energy managers. We have designed and implemented a data-driven method to predict the ‘electricity daily load profile’ (EDLP) for new stores. Our preliminary work exploits a data-set of hourly electricity meter readings for 196 UK supermarkets from 2012 to 2015. Our method combines the most similar stores on a feature space (floor area split by usage such as general merchandise, food retail and offices and geographical location) to obtain a prediction of the EDLP of a new store. Computational experiments were performed separately for subsets of supermarkets that consume only electricity, both electricity and gas, and by season. The best results were obtained when predicting Summer EDLPs with stores using electricity only. In this case, the average Manhattan difference and the percentage difference are 234 kWh and 16%, respectively. We aim to develop an application tool for supermarket energy managers to automatically generate EDLP for potential new stores. EPSRC Impact Acceleration Account - University of Oxford EP/R511742/1

Modified frequencies and magnitudes of extreme events due to climate change can have large impacts on societies and are therefore a key area of current research. Large model ensembles are required to quantify and attribute changes to extreme events. Until now, the large ensembles used for such studies are commonly atmosphere-only models forced with time-varying sea surface temperatures (SST) and sea ice. This approach is very powerful but presents problems of internal physical consistency. In an SST-forced model, the ocean acts as having an infinite heat capacity whereas in the real-world SST emerges dynamically from the interaction of atmospheric and oceanic processes (Dong et al., 2020). This is particularly relevant for the North Atlantic where ocean processes, in particular meridional heat transport, are key drivers of air-sea coupling. A long-standing challenge, however, is the computational cost of spinning up fully coupled atmosphere-ocean models that hinders their application to large-ensemble, high-resolution simulations required to quantify changing hazard frequencies of low-probability events. In this work we combine the HadAM4 (Webb et al., 2001) atmospheric model at N144 resolution with a Slab ocean (Hewitt & Mitchell, 1997; Williams et al., 2003), which includes a simple sea ice model, to yield the atmosphere-Slab Ocean model HadSM4. The Slab Ocean is forced with diagnosed heat convergence (Q-Flux) and surface currents for sea ice advection (a useful model-development finding for this kind of experiment is that including sea ice velocity information from reanalyses in the surface current field yields a substantially improved spatial pattern of sea ice). We are therefore able to directly compare SST-forced atmosphere-only runs with Q-Flux-forced runs where SST is an emergent property of the model, specifically accounting for the passive response of SSTs in the North Atlantic. Using the distributed infrastructure of climateprediction.net (Guillod et al., 2017; Massey et al., 2015) we run large ensembles to compare extreme statistics and quantify the importance of fast ocean-atmosphere coupling for extreme event statistics.We further use this large ensemble setup to investigate the dynamics that drive extreme events from the ocean through air-sea interaction to atmospheric processes. We address is whether and how the slope of a return-time plot (related to the scale parameter of a GEV distribution) is affected by atmosphere-ocean interactions, since this statistic plays a central role in determining relative-risk estimates in event attribution studies. We then investigate how a perturbation to the Q-Flux, representing a change in ocean heat transport, propagates through the system and alters the statistics of extreme events.Dong et al., 2020, Climate Dynamics, 55(5–6), 1225–1245. Guillod et al., 2017, Geoscientific Model Development, 10(5), 1849–1872. Hewitt & Mitchell, 1997, Climate Dynamics, 13(11), 821–834.Massey et al., 2015, Quarterly Journal of the Royal Meteorological Society, 141(690), 1528–1545. Webb et al., 2001, Climate Dynamics, 17(12), 905–922. Williams et al., 2003, Climate Dynamics, 20(7–8), 705–721.

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organisations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales, by running these models in high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of tropical cyclone Karl from September 2016, studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet-streak near Scotland and heavy rainfall over Norway. For the validation we use a two thousand member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF’s forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts as well as discussing the use of large ensembles in the context of forecasting extreme events.

For public, scientists and policy-makers, it is important to know to what extent human-induced climate change played (or did not play) a role behind changing risks of extreme weather events. Probabilistic event attribution (PEA) can provide scientific information regarding this association and reveal whether and to what extent external drivers of climate change have influenced the probability of high-impact weather events. To date, most of the PEA-based studies have focused on extreme events of mid-latitudes and predominantly events that have occurred in the developed countries. Developing countries located at the tropical monsoon regions are underrepresented in this field of research, despite that fact that these countries are highly climate vulnerable, often experience extreme weather events that cause severe damages and have the least capacity to adapt. Bangladesh, a South Asian country with tropical monsoon climate, is a hotspot of climate change impacts as it is vulnerable to a combination of increasing challenges from record-breaking temperatures, extreme rainfall events, more intense river floods, tropical cyclones, and rising sea levels. The unique geographical location of this country particularly exposes it to high risks of flooding and landslides caused by heavy rainfall events. Observation based studies indicate that the frequency of high-intensity rainfall events may have already increased, with significant repercussions for agriculture, health, ecosystems and economic development.Using high resolution regional climate model (RCM) simulations from weather@home, here we quantify the risks of extreme rainfall events in Bangladesh under pre-industrial, present-day and future climate scenarios of the Paris Agreement temperature targets of 1.5°C and 2°C warming. Additionally, we assess the risks under greenhouse gas (GHG)-only climate scenario where anthropogenic aerosols are reduced to pre-industrial levels. In order to test the robustness of the RCM results, available four atmosphere only global circulation model (AGCM) simulations from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project are analysed. This enabled for the first time, a multi-model assessment of the changing risks of extreme rainfall events in Bangladesh considering anthropogenic climate change drivers.Findings suggest that both a 1.5°C and 2.0°C warmer world is poised to experience increased seasonal mean and, to a lesser extent, increased extreme rainfall events. The risk of a 1 in 100 year rainfall event under current climate condition has already increased significantly compared with pre-industrial levels. Substantial reduction in the impacts resulting from 1.5°C compared with 2°C warming is reported in this study; however the difference is spatially and temporally variable across Bangladesh. This paper highlights that reduction in the anthropogenic aerosols play an important role in determining the overall future climate change impacts; by exacerbating the effects of GHG induced global warming and thereby increasing the rainfall intensity. The policy-makers therefore need to take stronger climate actions to avoid impacts of 2°C warmer world and consider future changes in the risks of extreme rainfall events in the face of changeable GHG and aerosol impacts.

A very large ensemble is used to identify subgrid-scale parameter settings for the HadCM3 model that are capable of best simulating the ocean state over the recent past (1980–2010). A simple particle filtering technique based upon the agreement of basin mean sea surface temperature (SST) and upper 700-m ocean heat content with EN3 observations is applied to an existing perturbed physics ensemble with initial conditions perturbations. A single set of subgrid-scale parameter values was identified from the wide range of initial parameter sets that gave the best agreement with ocean observations for the period studied. The parameter set, different from the standard model parameters, has a transient climate response of 1.68 K. The selected parameter set shows an improved agreement with EN3 decadal-mean SST patterns and the Atlantic meridional overturning circulation (AMOC) at 26°N as measured by the Rapid Climate Change (RAPID) array. Particle filtering techniques as demonstrated here could have a useful role in improving the starting point for traditional model-tuning exercises in coupled climate models.

The impacts of sea surface temperature (SST) anomalies and anthropogenic greenhouse gases on the likelihood of extreme drought occurring in the central United States in the year 2012 were investigated using large-ensemble simulations from a global atmospheric climate model. Two sets of experiments were conducted. In the first, the simulated hydroclimate of 2012 was compared to a baseline period (1986–2014) to investigate the impact of SSTs. In the second, the hydroclimate in a world with 2012-level anthropogenic forcing was compared to five “counterfactual” versions of a 2012 world under preindustrial forcing. SST anomalies in 2012 increased the simulated likelihood of an extreme summer precipitation deficit (e.g., the deficit with a 2% exceedance probability) by a factor of 5. The likelihood of an extreme summer soil moisture deficit increased by a similar amount, due in great part to a large spring soil moisture deficit carrying over into summer. An anthropogenic impact on precipitation was detectable in the simulations, doubling the likelihood of what would have been a rainfall deficit with a 2% exceedance probability under preindustrial-level forcings. Despite this reduction in rainfall, summer soil moisture during extreme drought was essentially unaffected by anthropogenic forcing because of 1) evapotranspiration declining roughly one-to-one with a decrease in precipitation due to severe water supply constraint and despite higher evaporative demand and 2) a decrease in stomatal conductance, and therefore a decrease in potential transpiration, with higher atmospheric CO2 concentrations.

With the deployments of fifth generation mobile networks (5G), rapid development of mobile internet, continued growth in mobile traffic and increased adoption of the internet of things, Multi-access Edge Computing (MEC) remains an inevitable critical future radio access network artefact for an optimized network. Network operators as part of their optimization exercise are also adopting Cloud Radio Access Network (C-RAN) to further improve network operation performance. Combining MEC and C-RAN makes the economics (OPEX + CAPEX) of both technologies more attractive and also enables network operators to support key 5G applications. This research investigates a major challenge of combining MEC + C-RAN: how to efficiently orchestrate cloud resources as a user moves within the edge environment without compromising the overall quality of service. This ongoing research adopts a machine learning technique in orchestrating resources efficiently in order for applications and services to adhere to stringent performance requirements even at geographically dispersed "edge" locations.

As mobile traffic continues to grow, network operators are constantly on a journey, looking for ways to optimize their network. The aim is to improve network operation performance as well as cut down cost without compromising on the overall quality of service. Many network operators as part of their optimization exercise are adopting Cloud Radio Access Network (C-RAN) and it is necessary to address the challenges that this technology poses in an edge environment. One of the major challenges or an area of improvement is how tasks are scheduled as a user moves within the edge environment. This ongoing research adopts a machine learning technique in scheduling tasks efficiently in order for applications and services to adhere to stringent performance requirements even at geographically dispersed "edge" locations.

A method for quantifiably estimating the deforestation risk exposure of agricultural Forest Risk Commodities in commercial supply chains is presented. The model consists of a series of equations applied using end-to-end data representing quantitative descriptors of the supply chain and its effect on deforestation. A robust penalty is included for historical deforestation and a corresponding reward for reductions in the rate of deforestation. The INternational FOrest Risk Model (INFORM) is a method for data analysis that answers a particular question for any Forest Risk Commodity in a supply chain: what is its cumulative deforestation risk exposure? To illustrate the methodology a case study of a livestock producer in France who sources soya-based animal feed from Brazil and wishes to document the deforestation risk associated with the product is described and calculated. Building on this example a discussion of the future applicability of INFORM within emerging supply-chain transparency initiatives is made including describing clear shortcomings in the method and how it may also be used to motivate the production of better data by those that may be subject of its analysis.

Predicting the electricity consumption of proposed new supermarkets is helpful to design and plan future energy management. Instead of creating complex site-specific thermal engineering models, data-driven energy prediction models can be useful to energy managers. We have designed and implemented a data-driven method to predict the future ’electricity daily load profile’ (EDLP) of new supermarkets using historical EDLPs of existing supermarkets of the same type. The supermarket features used for the prediction are 10 types of floor areas divided by usage ( m 2 ) and its location. Four data-driven regression models are used and compared to predict EDLPs: Artificial Neural Networks, Support Vector Machines, k-Nearest Neighbours and OLS. Prediction computational experiments were performed over 1-h electricity readings of 213 UK supermarkets gathered during six years. Prediction error mainly varies between 12 and 20% depending on method, year, supermarket type, and division of the data (season or temperature intervals). EDLPs computed over warm periods are better predicted than over cold periods and supermarkets only with electricity are better predicted than supermarkets with electricity and gas. The three features with more weight in the prediction are Food, Chilled produce and Cafeteria areas. The limitations of machine learning methods to solve this problem are discussed.

The year 2014 broke the record for the warmest yearly average temperature in Europe. Attributing how much this was due to anthropogenic climate change and how much it was due to natural variability is a challenging question but one that is important to address. In this study, we compare four event attribution methods. We look at the risk ratio (RR) associated with anthropogenic climate change for this event, over the whole European region, as well as its spatial distribution. Each method shows a very strong anthropogenic influence on the event over Europe. However, the magnitude of the RR strongly depends on the definition of the event and the method used. Across Europe, attribution over larger regions tended to give greater RR values. This highlights a major source of sensitivity in attribution statements and the need to define the event to analyze on a case-by-case basis.

Cloud computing is a mature technology that has already shown benefits for a wide range of academic research domains that, in turn, utilize a wide range of application design models. In this paper, we discuss the use of cloud computing as a tool to improve the range of resources available for climate science, presenting the evaluation of two different climate models. Each was customized in a different way to run in public cloud computing environments (hereafter cloud computing) provided by three different public vendors: Amazon, Google and Microsoft. The adaptations and procedures necessary to run the models in these environments are described. The computational performance and cost of each model within this new type of environment are discussed, and an assessment is given in qualitative terms. Finally, we discuss how cloud computing can be used for geoscientific modelling, including issues related to the allocation of resources by funding bodies. We also discuss problems related to computing security, reliability and scientific reproducibility. European Regional Development Fund | Ref. ED431C 2017/64-GRC Ministerio de Economía y Competitividad | Ref. RYC-2013-14560

This study investigates the future changes in dangerous extreme precipitation event in South America, using the multi-model ensemble simulations from the HAPPI experiments. The risks of dangerous extreme precipitation events occurrence, and changes in area and population exposure are quantified. Our results show that the likelihood of dangerous extreme precipitation increases in large parts of South America under future warming; changes in extreme precipitation are nonlinear with increasing global mean temperatures; and exposure plays a minor role compared to hazard. In all the models, limiting warming to 1.5 °C as opposed to 2 °C shows a general reduction in both area and population exposure to dangerous extreme precipitation throughout South America. The southeast region of South America exhibited the highest multi-model median percentage of avoided area exposure at 13.3%, while the southwest region shows the lowest percentage at 3.1%. Under all shared socioeconomic pathways, South America Monsoon region and southern South America region yielded the highest multi-model median percentage of avoided population exposure (>10%). The strong spatial heterogeneity in projected changes in all the models highlights the importance of considering location-specific information when designing adaptation measures and investing in disaster preparedness.

During July 2017, an unprecedentedly intense heat wave struck Central-Eastern China, resulting in drastically-increased human morbidity/mortality, steeply-reduced agriculture productivity, and serious shortage of electricity and water supply (China Climate Bulletin of 2017). Many meteorological stations registered 15–25 hot days (daily maximum temperature over 35°C), and some even had their record-high July temperatures, such as a new record of 40.9°C amongst historical observations since 1873 in Xu-Jia-Hui station in Shanghai (China Climate Bulletin of 2017). The China Meteorological Administration issued 10 high-level warnings against hot weather during 21st–25th July. Such unprecedentedly frequent alarms within only 5 days attracted intense scrutiny from policy-makers, media, and the public on the relationship between this heat wave and global warming. Previous studies usually conducted attribution analyses on seasonal warmth in Central-Eastern China (e.g. the 2013 record-breaking summer, Sun et al. 2014), leaving attribution statements for short-term (synoptic) hot extremes sparsely reported. This study therefore attempts to answer whether and to what extent anthropogenic warming has increased the likelihood of 5-day heat waves as hot or hotter than the 21st–25th July 2017 case over Central-Eastern China.

On 21st-25thJuly 2017 a record breaking heatwave occurred in Central Eastern China affecting nearly half of the national population and causing severe impacts on public health, agriculture and infrastructure. Here, we compare attribution results from two UK Met Office Hadley Centre models, HadGEM3-GA6 and weather@home (HadAM3P driving 50km HadRM3P). Within HadGEM3-GA6 July 2017-like heatwaves were unequaled in the ensemble representing the world without human influences. Such heatwaves became approximately a 1 in 50 year event and increased by a factor of 4.8 (5-95% range of 3.1 to 8.0) in weather@home as a result of human activity. Considering the risk ratio (RR) for the full range of return periods shows a discrepancy at all return times between the two model results. Within weather@home a range of different counterfactual Sea Surface Temperature (SST) patterns were used whereas HadGEM3-GA6 used a single estimate. The global mean difference in SST (between factual and counterfactual simulations) is shown to be related to the Generalised Extreme Value (GEV) location parameter and consequently the RR, especially for return periods less than 50 years. It is suggested that a suitable range of SST patterns are used for future attribution studies to ensure that this source of uncertainty is represented within the simulations and subsequent attribution results. It is shown that the risk change between factual and counterfactual simulations is not purely a simple shift in the distribution (i.e. change in GEV location parameter). For return periods greater than 50 years the GEV shape parameter is found to strongly influence the RR determined with the GEV scale parameter affecting only the most severe events.

This study investigates the potential influences of anthropogenic forcings and natural variability on the risk of summer extreme temperatures over China. We use three multi-thousand-member ensemble simulations with different forcings (with or without anthropogenic greenhouse gases and aerosol emissions) to evaluate the human impact, and with sea surface temperature patterns from three different years around the El Niño–Southern Oscillation (ENSO) 2015/16 event (years 2014, 2015 and 2016) to evaluate the impact of natural variability. A generalized extreme value (GEV) distribution is used to fit the ensemble results. Based on these model results, we find that, during the peak of ENSO (2015), daytime extreme temperatures are smaller over the central China region compared to a normal year (2014). During 2016, the risk of nighttime extreme temperatures is largely increased over the eastern coastal region. Both anomalies are of the same magnitude as the anthropogenic influence. Thus, ENSO can amplify or counterbalance (at a regional and annual scale) anthropogenic effects on extreme summer temperatures over China. Changes are mainly due to changes in the GEV location parameter. Thus, anomalies are due to a shift in the distributions and not to a change in temperature variability.

In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents, for the first time, an attribution of this precipitation-induced flooding to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2 ∘C warming since pre-industrial times. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed. In all three observational precipitation datasets the climate change trends for extreme precipitation similar to that observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than in precipitation, but the 95 % confidence intervals still encompass no change in risk. Extending the analysis to the future, all models project an increase in probability of extreme events at 2 ∘C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation and being more likely by a factor of about 1.5 for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: we find the change in risk to be greater than 1 and of a similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions. Besides this, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols.

WOS: 000432635500001, …

This paper provides a brief introduction into emerging user requirements and expectations in the context of large cloud-based infrastructures. Observed cloud usage patterns are used to demonstrate the need for large federated cloud infrastructures, in particular cloud federations bridging the gap between private cloud infrastructures and large commercial public cloud providers. The paper proposes the use of open standards as the best possible way to achieve a working user-friendly large-scale cloud federation and discusses the challenges in assembling and managing such a federation whilst focusing on the differences between private-private and public-private cloud federations in key areas of virtual machine management and authentication, authorization, and identity management.

The development of state estimation (SE) tools for distribution networks is receiving increased attention as the traditionally passive distribution systems have become far more active. The high dimensionality of a distribution system model may lead to very high computation times. This paper addresses these issues and proposes an SE algorithm that analyzes a network which has been split into overlapping zones, enabling parallel application of the algorithm. The algorithm is developed to provide compatibility with distribution system requirements. The novelty of the algorithm lies in its capability to generate feasible solutions for the voltage estimation with a much smaller number of real measurements. The proposed algorithm is referred to as an overlapping zone approach, and has been tested on 356 and 711 node networks.

As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g. See white papers by Senseye.io, and output of the NASA Prognostics Center of Excellence (PCoE).) However, as noted by Agrawal and Choudhary 'Our ability to collect "big data" has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery.' In order to fully utilize the potential of Industrial Big Data we need data-driven techniques that operate at scales that process models cannot. Here we present a prototype technique for data-driven anomaly detection to operate at industrial scale. The method generalizes to application with almost any multivariate dataset based on independent ordinations of repeated (bootstrapped) partitions of the dataset and inspection of the joint distribution of ordinal distances., Comment: 9 pages; 11 figures

The NIST definition of cloud computing has been accepted by the majority of the community as the best available description to fully capture the variety of factors which determine how different stakeholders create, use or interact with cloud computing. With the breadth of the cloud computing landscape there is a need being expressed from within different cloud activities to consider how it may be best segmented so that the diversity might be more easily understood by the different stakeholders. The NIST definition considers four different deployment models (Private, Public, Hybrid, Community Cloud), three different service models (IaaS, PaaS, SaaS), and a number of characteristics (five in the final published version, but 13 in previous unpublished drafts). Exploring the definition further, this study aims to answer two questions: first, how can we use the affinity that different activities have with the definition’s characteristics and second, how well does the definition describe the whole cloud ecosystem? We find that utilising a quantitative methodology shows a clustering of different cloud projects and activities that are technically aligned and therefore likely to benefit from interactions and shared learning, and that the final (short-list) definition is more robust than the draft (long-list) definition. Finally, we present a segmentation of the cloud landscape that we believe can best support a sharing of learning between projects in individual clusters.

By using smart meters, more data about how businesses use energy is becoming available to energy retailers (providers). This is enabling innovation in the structure and type of tariffs on offer in the energy market. We have applied Artificial Neural Networks, Support Vector Machines, and Naive Bayesian Classifiers to a data set of the electrical power use by 12,000 businesses (in 44 sectors) to investigate predicting which businesses will gain or lose by switching between tariffs (a two-classes problem). We have used only three features of each company: their business sector, load profile category, and mean power use. We are particularly interested in the switch between a static tariff (fixed price or timeof-use) and a dynamic tariff (half-hourly pricing). We have extended the two-classes problem to include a price elasticity factor (a three-classes problem). We show how the classification error for the two- and three-classes problems varies with the amount of available data. Furthermore, we used Ordinary Least Squares and Support Vector Regression models to compute the exact values of the amount gained or lost by a business if it switched tariff types. Our analysis suggests that the machine learning classifiers required less data to reach useful performance levels than the regression models. 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://

Effectively establishing trust in Cloud Computing is a critical requirement for achieving wider adoption of hybrid and public cloud. Although a number of Trusted Cloud concepts have been proposed, they suffer from limitations in resilience, scalability and dynamism. We tackle these limitations with the creation of a distributed attestation service, Porridge. Porridge achieves resiliency, as multiple attestation workers are employed and redundant workers assigned for attesting each Virtual Machine (VM); scalability, as the attestation load and responsibility is automatically distributed evenly among workers; adaptivity to cloud dynamism, as each VM’s virtual Trusted Platform Module (vTPM) is mapped to a stable set of physical Trusted Platform Modules (TPM) in the host and then the workers TPMs. Overall the attestation scheme enables flexible vTPM-TPM bindings while hiding details of cloud infrastructure, with the root-of-trust for the VM not bound to its underlying host’s TPM, but to its managing workers. This concept can be extended to support more advanced cloud security through the introduction of Trusted Service Providers providing Cloud Attestation as a Service (CAaaS).

Climateprediction.net has traditionally been an activity that requires a large amount of computing resources from its volunteer network, whilst allowing a time-frame of weeks to months for simulations to be returned for each project. However, there is an increasing trend of projects requiring results in shorter and shorter timescales. Under no project is this clearer than in the World Weather Attribution (WWA) initiative, where we are aiming to provide in near to real-time an answer to how anthropogenic climate change has altered the frequency of occurrence of a particular type of extreme weather event, either as it happens or as soon after as is practical. As such we need the ability to run simulations on alternate resources when volunteer resources will not provide results within the necessary timeframe. This paper describes a workflow to distribute ensembles of climateprediction.net simulations in the Amazon Elastic Compute Cloud, to provide urgent compute capability for projects such as WWA. We propose a method of optimizing the use of cloud resources to minimize cost while maximising throughput. A case study is presented to provide a proof of concept of this methodology. As such, this is a clear example of beneficial utilisation of cloud resources to supplement those available through our volunteer community.

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the Half a degree Additional warming, Projections, Prognosis and Impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 °C and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of integrated assessment models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slice experiments are proposed, each a decade in length; the first being the most recent observed 10-year period (2006–2015), the second two being estimates of the a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the Representative Concentration Pathways 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

The OptIPuter microscopy demonstrator project has been designed to enable concurrent and remote usage of world-class electron microscopes located in Oxford and San Diego. The project has constructed a network consisting of microscopes and computational and data resources that are all connected by a dedicated network infrastructure using the UK Lightpath and US Starlight systems. Key science drivers include examples from both materials and biological science. The resulting system is now a permanent link between the Oxford and San Diego microscopy centres. This will form the basis of further projects between the sites and expansion of the types of systems that can be remotely controlled, including optical, as well as electron, microscopy. Other improvements will include the updating of the Microsoft cluster software to the high performance computing (HPC) server 2008, which includes the HPC basic profile implementation that will enable the development of interoperable clients.

The cloud computing ecosystem is in urgent need of effective and practical trust establishment schemes. Cloud customers currently lack approaches to effectively verify the genuine behaviours of cloud services. They can only blindly believe that the Cloud Service Providers (CSPs) are honest enough to not tamper with their data, while many others have avoided using the cloud entirely. Trust establishment schemes, such as cloud auditing and cloud attestation systems, lack controls and transparency over their trust building processes, which only blur the effectiveness of the proclaimed trustworthiness. We argue that these problems ultimately result from the CSPs' autocratic governance over all the activities inside the cloud. In this paper, we present a Separation-of-Powers (SoP) model by referencing the similar concepts from the discipline of the political philosophy. We define three independent roles to separate the powers of definition, enforcement, and inspection from the CSPs. These roles form the collaborative-restrictive relationship to facilitate trustworthy cloud services and achieve the balance-of-powers. We believe a model of this kind will open new opportunities for achieving trustworthy and open cloud ecosystem governance.

Changes in near surface air temperature (ΔT) in response to anthropogenic greenhouse gas forcing are expected to show spatial heterogeneity because energy and moisture fluxes are modulated by features of the landscape that are also heterogeneous at these spatial scales. Detecting statistically meaningful heterogeneity requires a combination of high spatial resolution and a large number of simulations. To investigate spatial variability of projected ΔT, we generated regional, high-resolution (25-km horizontal), large ensemble (100 members per year), climate simulations of western United States (US) for the periods 1985 – 2014 and 2030 – 2059, the latter with atmospheric constituent concentrations from the Representative Concentration Pathway 4.5. Using the large ensemble, 95% confidence interval sizes for grid-cell-scale temperature responses were on the order of 0.1 °C, compared to 1 °C from a single ensemble member only. In both winter and spring, the snow-albedo feedback statistically explains roughly half of the spatial variability in 'T. Simulated decreases in albedo exceed 0.1 in places, with rates of change in T per 0.1 decrease in albedo ranging from 0.3 to 1.4 °C. In summer, ΔT pattern in the northwest US is correlated with the pattern of decreasing precipitation. In all seasons, changing lapse rates in the low-to-middle troposphere may account for up to 0.2 °C differences in warming across the western US. Near the coast, a major control of spatial variation is the differential warming between sea and land.

A new climate modelling project has been developed for regional climate simulation and the attribution of weather and climate extremes over Australia and New Zealand. The project, known as weather@home Australia-New Zealand, uses public volunteers' home computers to run a moderate-resolution global atmospheric model with a nested regional model over the Australasian region. By harnessing the aggregated computing power of home computers, weather@home is able to generate an unprecedented number of simulations of possible weather under various climate scenarios. This combination of large ensemble sizes with high spatial resolution allows extreme events to be examined with more robust estimates of uncertainty. This paper provides an overview of the weather@home Australia-New Zealand project, including initial evaluation of the regional model performance. The model is seen to be capable of resolving many climate features that are important for the Australian and New Zealand regions, including the influence of El Niño-Southern Oscillation on driving natural climate variability. To date, 75 model simulations of the observed climate have been successfully integrated over the period 1985–2014 in a time-slice manner. In addition, multi-thousand member ensembles have also been generated for the years 2013, 2014 and 2015 under climate scenarios with and without the effect of human influences. All data generated by the project is freely available to the broader research community.

The HiPerDNO project is a three-year EU-funded research projects which studies novel applications for Distribution Network Operations (DNOs). These applications (distributed state estimation, power restoration, monitoring of assets) emerge from the large-scale deployment of sensors and instrumentation devices, responsive loads and embedded generation. Their introduction requires HPC strategy. A HiPerDNO HPC platform, which takes into account DNOs' and applications requirements, has been built and several applications were developed and tested on it. In this paper we report on the results achieved.

Within the Environmental 'omics community Bio-Linux is a widely used tool. This has the advantage of providing in a single deliverable package all necessary software and tools to support common analyses. With the growth in data volumes within the community and increasing constraints on user access and control over their own desktops an alternative delivery method of Bio-Linux and, in future, the Docker container environment is necessary. Within the EOS Cloud project we have constructed a Desktop as a Service system to centrally host virtual machines with these tools preconfigured and maintained. To enable efficient use of the resources we have enabled user controlled resource scaling so that users are able to utilise small scale VMs for task configuration and data manipulation and boost to a larger scale to run analysis applications all the while maintaining the user environment in a consistent manner. Alongside this within the project we have been developed tools to simplify the increasingly popular Docker software usage model. This includes ensure uniformity of behaviour between the host system and the running Docker container. Within the invitation only trial user community we identify two different exemplars groups and explain their usage and how the products and services developed within the project are useful for them. We conclude discussing the useful nature of Desktop as a Service, how it is of great benefit to the bioinformatics community but could also be of great use elsewhere, where the need for a stable user environment with applications already available that do not rely on local ICT support.

This paper details the state of the art, the design, development and deployment of the EGI Federated Cloud platform, an e-infrastructure offering scalable and flexible models of utilization to the European research community. While continuing support for the traditional High Throughput Computing model, the EGI Cloud Platform extends its reach to other models of utilization such as long-lived services and on demand computation. Following a two-year period of development, the EGI Federated Cloud platform was officially launched in May 2014 offering resources provided by trusted academic and research organisations from within the user communities and consistently with their standard funding regime. Since then, the use cases supported have significantly increased both in total number and diversity of model of service required, validating both the choice of enforcing cloud technology agnosticism and of supporting service mobility and portability by means of open standards. These design choices have also allowed for the inclusion of commercial cloud providers into an infrastructure previously supported only by academic institutions. This contributes to a wider goal of funding agencies to create economic and social impact from supported research activities.

This paper describes the framework for modelling a multi-agent approach for assessing dynamic pricing of electricity and demand response. It combines and agent-based model with decision-making data, and a standard load-flow model. The multi-agent model described here represents a tool in investigating not only the relation between different dynamic tariffs and consumer load profiles, but also the change in behaviour and impact on low-voltage electricity distribution networks. The authors acknowledge the contribution of the EPSRC Transforming Energy Demand Through Digital Innovation Programme, grant agreement numbers EP/I000194/1 and EP/I000119/1, to the ADEPT project.

This article has been made available through the Brunel Open Access Publishing Fund. The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predetermined by the user. We used the so-called 'Chinese restaurant process' method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships.

We present measurements of time-dependent CP-violating asymmetries in neutral B decays to several CP eigenstates. The measurement uses a data sample of 23x10(6) Upsilon(4S)-->BbarB decays collected by the BABAR detector at the PEP-II asymmetric B Factory at SLAC. In this sample, we find events in which one neutral B meson is fully reconstructed in a CP eigenstate containing charmonium and the flavor of the other neutral B meson is determined from its decay products. The amplitude of the CP-violating asymmetry, which in the standard model is proportional to sin2beta, is derived from the decay time distributions in such events. The result is sin2beta = 0.34+/-0.20 (stat)+/-0.05 (syst).

This paper proposes a new information model of smart consumers where the appliances are not regarded independently such as in traditional scheduling algorithms or home energy management systems, but through a set of common attributes that make up the generic appliance model. Each appliance is generalized by its set of particular attributes. The novel vision is introduced and the attributes of the generic appliance model are explained. The functionality of the system is described with emphasis on the interactions between system components. Under the novel vision, home energy management systems could be developed into widely used practical implementations that would allow better management and user interaction within the home environment.

To improve the integration of distributed energy resources on distribution networks, new DMS functionalities need to be developed. To run near-t o-real time DMS applications novel high performance computing and communications technology solutions c an be adopted by DNO’s. Therefore, this paper presents the work carried out in the HiPerDNO project regarding the development of a new generation of DMS applications and its integration with scalable and secure high performance ICT infrastructures.