Search

The UK has centered itself in the global race to mitigate the impacts of climate change with ambitious plans announced in 2019, committing to the elimination of the UK's net contribution to greenhouse gas emissions by 2050. These targets will require a transition of a historically inelastic energy system within a closing window of time. Our paper presents a whole system approach which argues that decentralized and distributed energy resources operating at grid-edge need an agile trial framework with multi-perspective and experienced stakeholders to develop balanced and well-supported network flexibility services. Incubating these new grid-edge network services require innovative market, operators and digital infrastructure, particularly at local scales. Based on preliminary findings from a UK perspective, Project LEO, we propose a framework around the restructuring of local energy systems which reduce uncertainty through an innovative 'ecosystem' that gives room to failure while minimizing risk to extract value and learnings.

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 organizations, 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 at 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 (https://www.climateprediction.net/, last access: 1 June 2021) 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 2000-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 and discuss the use of large ensembles in the context of forecasting extreme events.

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.

Summer 2010 was characterized by two contemporaneous extreme events: the Russian heat wave and the Pakistan flood. Several studies have shown a link between the two events, and Quasi-Resonant Amplification (QRA) has been suggested as an atmosphere-dynamic mechanism leading to the anomalous wavy circulation pattern which connected both extremes. Here, we aim at reproducing the 2010 circulation conditions in the Northern Hemisphere by obtaining a large ensemble of simulations from the Weather@home project within climateprediction.net (CPDN). We identify those ensemble members exhibiting a specific latitudinal temperature profile characterised by amplified high-latitude land warming (QRA - fingerprint) and investigate their surface temperature and upper level circulation properties. We show that when the QRA - fingerprint is present, the mid-latitude circulation bears similar characteristics to those observed in the 2010 summer: hot temperatures over European Russia and a wavy pattern in the upper-tropospheric meridional winds. As temperature profiles are projected to become increasingly similar to the QRA-fingerprint under future emission scenarios, these results provide further evidence that high latitude warming might favour persistent surface weather in the mid-latitudes.

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.

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.

Volunteer or crowd computing is becoming increasingly popular for solving complex research problems from an increasingly diverse range of areas. The majority of these have been built using the Berkeley Open Infrastructure for Network Computing (BOINC) platform, which provides a range of different services to manage all computation aspects of a project. The BOINC system is ideal in those cases where not only does the research community involved need low-cost access to massive computing resources but also where there is a significant public interest in the research being done.We discuss the way in which cloud services can help BOINC-based projects to deliver results in a fast, on demand manner. This is difficult to achieve using volunteers, and at the same time, using scalable cloud resources for short on demand projects can optimize the use of the available resources. We show how this design can be used as an efficient distributed computing platform within the cloud, and outline new approaches that could open up new possibilities in this field, using Climateprediction.net (http://www.climateprediction.net/) as a case study.

Anthropogenic climate change is likely to increase the frequency of extreme weather events in future. Previous studies have robustly shown how and where climate change has already changed the risks of weather extremes. However, developing countries have been somewhat underrepresented in these studies, despite high vulnerability and limited capacities to adapt. How additional global warming would affect the future risks of extreme rainfall events in Bangladesh needs to be addressed to limit adverse impacts. Our study focuses on understanding and quantifying the relative risks of seasonal extreme rainfall events in Bangladesh under the Paris Agreement temperature goals of 1.5 °C and 2 °C warming above pre-industrial levels. In particular, we investigate the influence of anthropogenic aerosols on these risks given their likely future reduction and resulting amplification of global warming. Using large ensemble regional climate model simulations from weather@home under different forcing scenarios, we compare the risks of rainfall events under pre-industrial (natural), current (actual), 1.5 °C, and 2.0 °C warmer and greenhouse gas only (anthropogenic aerosols removed) conditions. We find that the risk of a 1 in 100 year rainfall event has already increased significantly compared with pre-industrial levels across parts of Bangladesh, with additional increases likely for 1.5 and 2.0 degree warming (of up to 5.5 times higher, with an uncertainty range of 3.5 to 7.8 times). Impacts were observed during both the pre-monsoon and monsoon periods, but were spatially variable across the country in terms of the level of impact. Results also show that reduction in anthropogenic aerosols plays 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. We highlight that the net aerosol effect varies from region to region within Bangladesh, which leads to different outcomes of aerosol reduction on extreme rainfall statistics, and must therefore be considered in future risk assessments. Whilst there is a substantial reduction in the impacts resulting from 1.5 °C compared with 2 °C warming, the difference is spatially and temporally variable, specifically with respect to seasonal extreme rainfall events.

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.

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.

Atmospheric modes of variability relevant for extreme temperature and precipitation events are evaluated in models currently being used for extreme event attribution. A 100 member initial condition ensemble of the global circulation model HadAM3P is compared with both the multi-model ensemble from the Coupled Model Inter-comparison Project, Phase 5 (CMIP5) and the CMIP5 atmosphere-only counterparts (AMIP5). The use of HadAM3P allows for huge ensembles to be computed relatively fast, thereby providing unique insights into the dynamics of extremes. The analysis focuses on mid Northern Latitudes (primarily Europe) during winter, and is compared with ERA-Interim reanalysis. The tri-modal Atlantic eddy-driven jet distribution is remarkably well captured in HadAM3P, but not so in the CMIP5 or AMIP5 multi-model mean, although individual models fare better. The well known underestimation of blocking in the Atlantic region is apparent in CMIP5 and AMIP5, and also, to a lesser extent, in HadAM3P. Pacific blocking features are well produced in all modeling initiatives. Blocking duration is biased towards models reproducing too many short-lived events in all three modelling systems. Associated storm tracks are too zonal over the Atlantic in the CMIP5 and AMIP5 ensembles, but better simulated in HadAM3P with the exception of being too weak over Western Europe. In all cases, the CMIP5 and AMIP5 performances were almost identical, suggesting that the biases in atmospheric modes considered here are not strongly coupled to SSTs, and perhaps other model characteristics such as resolution are more important. For event attribution studies, it is recommended that rather than taking statistics over the entire CMIP5 or AMIP5 available models, only models capable of producing the relevant dynamical phenomena be employed. ISSN:0930-7575 ISSN:1432-0894

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 HiPerDNO project aims to develop new applications to enhance the operational capabilities of Distribution Network Operators (DNO). Their delivery requires an advanced computational strategy. This paper describes a High Performance Computing (HPC) platform developed for these applications whilst also being flexible enough to accommodate new ones emerging from the gradual introduction of smart metering in the Low Voltage (LV) networks (AMI: Advanced Metering Infrastructures). Security and reliability requirements for both data and computations are very stringent. Our proposed architecture would allow the deployment of computations and data access as services, thus achieving independence on the actual hardware and software technologies deployed, and hardening against malicious as well as accidental corruptions. Cost containment and reliance on proven technologies are also of paramount importance to DNOs. We suggest an architecture that fulfills these needs, which includes the following components for the HPC and Data Storage systems: • Hadoop Distributed File System • a federation of loosely coupled computational clusters • the PELICAN computational application framework

Grid computing enables the access of heterogeneous computational resources across a dynamic set of physical organisations seamlessly. A campus grid is an example of the grid solution which is located within a single administrative domain. In the majority of installations these enable connection of all institutionally owned computational resources, making them transparently available to appropriately registered members of staff. In this paper we introduce the design and implementation of a lightweight campus grid framework. It manages a set of heterogeneous computational resources, using virtual organisation based policies for each individual grid resource and enables user's to transparently access resources through a unified user-friendly interface. The proposed framework can discover, aggregate and broker all of these heterogeneous resources with low management costs that matches users’ requirements. With this framework, a campus grid enables university-wide, regional-wide, national and international grid resources become transparently available to university members through their university account without knowing how to access each individual resource. Such a framework allows scalable grid resource sharing, virtual organisation based resource management, dynamical and autonomous resource brokering with improved usability and manageability in a campus grid environment.

Cloud computing has been increasingly adopted by users and providers to promote a flexible, scalable and tailored access to computing resources. Nonetheless, the consolidation of this paradigm has uncovered some of its limitations. Initially devised by corporations with direct control over large amounts of computational resources, cloud computing is now being endorsed by organizations with limited resources or with a more articulated, less direct control over these resources. The challenge for these organizations is to leverage the benefits of cloud computing while dealing with limited and often widely distributed computing resources. This study focuses on the adoption of cloud computing by higher education institutions and addresses two main issues: flexible and on-demand access to a large amount of storage resources, and scalability across a heterogeneous set of cloud infrastructures. The proposed solutions leverage a federated approach to cloud resources in which users access multiple and largely independent cloud infrastructures through a highly customizable broker layer. This approach allows for a uniform authentication and authorization infrastructure, a fine-grained policy specification and the aggregation of accounting and monitoring. Within a loosely coupled federation of cloud infrastructures, users can access vast amount of data without copying them across cloud infrastructures and can scale their resource provisions when the local cloud resources become insufficient.

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.

It has been argued that climate change is the biggest global health threat of the 21st century. The extreme high temperatures of the summer of 2003 were associated with up to seventy thousand excess deaths across Europe. Previous studies have attributed the meteorological event to the human influence on climate, or examined the role of heat waves on human health. Here, for the first time, we explicitly quantify the role of human activity on climate and heat-related mortality in an event attribution framework, analysing both the Europe-wide temperature response in 2003, and localised responses over London and Paris. Using publicly-donated computing, we perform many thousands of climate simulations of a high-resolution regional climate model. This allows generation of a comprehensive statistical description of the 2003 event and the role of human influence within it, using the results as input to a health impact assessment model of human mortality. We find large-scale dynamical modes of atmospheric variability remain largely unchanged under anthropogenic climate change, and hence the direct thermodynamical response is mainly responsible for the increased mortality. In summer 2003, anthropogenic climate change increased the risk of heat-related mortality in Central Paris by ~70% and by ~20% in London, which experienced lower extreme heat. Out of the estimated ~315 and ~735 summer deaths attributed to the heatwave event in Greater London and Central Paris, respectively, 64 (±3) deaths were attributable to anthropogenic climate change in London, and 506 (±51) in Paris. Such an ability to robustly attribute specific damages to anthropogenic drivers of increased extreme heat can inform societal responses to, and responsibilities for, climate change., Environmental Research Letters, 11 (7), ISSN:1748-9326, ISSN:1748-9318

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.

A succession of storms reaching Southern England in the winter of 2013/2014 caused severe floods and £451 million insured losses. In a large ensemble of climate model simulations, we find that, as well as increasing the amount of moisture the atmosphere can hold, anthropogenic warming caused a small but significant increase in the number of January days with westerly flow, both of which increased extreme precipitation. Hydrological modelling indicates this increased extreme 30-day-average Thames river flows, and slightly increased daily peak flows, consistent with the understanding of the catchment’s sensitivity to longer-duration precipitation and changes in the role of snowmelt. Consequently, flood risk mapping shows a small increase in properties in the Thames catchment potentially at risk of riverine flooding, with a substantial range of uncertainty, demonstrating the importance of explicit modelling of impacts and relatively subtle changes in weather-related risks when quantifying present-day effects of human influence on climate.

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.

Kopf, Anna ... et. al.-- 5 pages, 1 figure.-- This manuscript is NOAA-GLERL contribution number 1763, Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world’s oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits, This work was supported by the Micro B3 project, which is funded from the European Union’s Seventh Framework Programme (FP7; Joint Call OCEAN.2011‐2: Marine microbial diversity – new insights into marine ecosystems functioning and its biotechnological potential) under the grant agreement no 287589

The EGI Federated Cloud is a standards-based, open cloud system as well as its enabling technologies that federates institutional clouds to offer a scalable computing platform for data and/or compute driven applications and services. The EGI Federated Cloud is based on open standards and open source Cloud Management Frameworks and offers to its users IaaS, PaaS and SaaS capabilities and interfaces tuned towards the needs of users in research and education. The federation enables scientific data, workloads, simulations and services to span across multiple administrative locations, allowing researchers and educators to access and exploit the distributed resources as an integrated system. The EGI Federated Cloud collaboration established a user support model and a training infrastructure to raise visibility of this service within European scientific communities with the overarching goal to increase adoption and, ultimately increase the usage of e-infrastructures for the benefit of the whole European Research Area. The paper describes this scalable user support and training infrastructure models. The training infrastructure is built on top of the production sites to reduce costs and increase its sustainability. Appropriate design solutions were implemented to reduce the security risks due to the cohabitation of production and training resources on the same sites. The EGI Federated Cloud educational program foresees different kind of training events from basic tutorials to spread the knowledge of this new infrastructure to events devoted to specific scientific disciplines teaching how to use tools already integrated in the infrastructure with the assistance of experts identified in the EGI community. The main success metric of this educational program is the number of researchers willing to try the Federated Cloud, which are steered into the EGI world by the EGI Federated Cloud Support Team through a formal process that brings them from the initial tests to fully exploit the production resources. © 2015 IEEE.

Many production Grid and e-Science infrastructures have begun to offer services to end-users during the past several years with an increasing number of scientific applications that require access to a wide variety of resources and services in multiple Grids. Therefore, the Grid Interoperation Now-Community Group of the Open Grid Forum-organizes and manages interoperation efforts among those production Grid infrastructures to reach the goal of a world-wide Grid vision on a technical level in the near future. This contribution highlights fundamental approaches of the group and discusses open standards in the context of production e-Science infrastructures. © 2009 John Wiley and Sons, Ltd.