The roles of aerosol, water vapor and cloud in future global dimming/brightening

Observational evidence indicates significant regional trends in solar radiation at the surface in both all-sky and cloud-free conditions. Negative trends in the downwelling solar surface irradiance (SSI) have become known as ‘dimming’ while positive trends have become known as ‘brightening’. We use the Met Office Hadley Centre HadGEM2 climate model to model trends in cloud-free and total SSI from the pre-industrial to the present-day and compare these against observations. Simulations driven by CMIP5 emissions are used to model the future trends in dimming/brightening up to the year 2100. The modeled trends are reasonably consistent with observed regional trends in dimming and brightening which are due to changes in concentrations in anthropogenic aerosols and, potentially, changes in cloud cover owing to the aerosol indirect effects and/or cloud feedback mechanisms. The future dimming/brightening in cloud-free SSI is not only caused by changes in anthropogenic aerosols: aerosol impacts are overwhelmed by a large dimming caused by increases in water vapor. There is little trend in the total SSI as cloud cover decreases in the climate model used here, and compensates the effect of the change in water vapor. In terms of the surface energy balance, these trends in SSI are obviously more than compensated by the increase in the downwelling terrestrial irradiance from increased water vapor concentrations. However, the study shows that while water vapor is widely appreciated as a greenhouse gas, water vapor impacts on the atmospheric transmission of solar radiation and the future of global dimming/brightening should not be overlooked.

Understanding lake and catchment history as a tool for integrated lake management

Sustainable lake management for nutrient-enriched lakes must be underpinned by an understanding of both the functioning of the lake, and the origins of changes in nutrient loading from the catchment. To date, limnologists have tended to focus on studying the impact of nutrient enrichment on the lake biota, and the dynamics of nutrient cycling between the water column, biota and sediments within the lake. Relatively less attention has been paid to understanding the specific origins of nutrient loading from the catchment and nutrient transport pathways linking the lake to its catchment. As such, when devising catchment management strategies to reduce nutrient loading on enriched lakes, assumptions have been made regarding the relative significance of non-point versus point sources in the catchment. These are not always supported by research conducted on catchment nutrient dynamics in other fields of freshwater science. Studies on nutrient enrichment in lakes need to take account of the history of catchment use and management specific to each lake in order to devise targeted and sustainable management strategies to reduce nutrient loading to enriched lakes. Here a modelling approach which allows quantification of the relative contribution of nutrients from each specific point and non-point catchment source over the course of catchment history is presented. The approach has been applied to three contrasting catchments in the U.K. for the period 1931 to present. These are the catchment of Slapton Ley in south Devon, the River Esk in Cumbria and the Deben Estuary in Suffolk. Each catchment showed marked variations in the nature and intensity of land use and management. The model output quantifies the relative importance of point source versus non-point livestock and land use sources in each of the catchments, and demonstrates the necessity for an understanding of site-specific catchment history in devising suitable management strategies for the reduction of nutrient loading on enriched lakes.

Demand response — a different form of distributed storage?

Reduced flexibility of low carbon generation could pose new challenges for future energy systems. Both demand response and distributed storage may have a role to play in supporting future system balancing. This paper reviews how these technically different, but functionally similar approaches compare and compete with one another. Household survey data is used to test the effectiveness of price signals to deliver demand responses for appliances with a high degree of agency. The underlying unit of storage for different demand response options is discussed, with particular focus on the ability to enhance demand side flexibility in the residential sector. We conclude that a broad range of options, with different modes of storage, may need to be considered, if residential demand flexibility is to be maximised.

Scaling up data mining techniques to large datasets using parallel and distributed processing

Advances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets.

Quantifying the importance of galactic cosmic rays in cloud microphysical processes

Galactic Cosmic Rays are one of the major sources of ion production in the troposphere and stratosphere. Recent studies have shown that ions form electrically charged clusters which may grow to become cloud droplets. Aerosol particles charge by the attachment of ions and electrons. The collision efficiency between a particle and a water droplet increases, if the particle is electrically charged, and thus aerosol-cloud interactions can be enhanced. Because these microphysical processes may change radiative properties of cloud and impact Earth's climate it is important to evaluate these processes' quantitative effects. Five different models developed independently have been coupled to investigate this. The first model estimates cloud height from dew point temperature and the temperature profile. The second model simulates the cloud droplet growth from aerosol particles using the cloud parcel concept. In the third model, the scavenging rate of the aerosol particles is calculated using the collision efficiency between charged particles and droplets. The fourth model calculates electric field and charge distribution on water droplets and aerosols within cloud. The fifth model simulates the global electric circuit (GEC), which computes the conductivity and ionic concentration in the atmosphere in altitude range 0–45 km. The first four models are initially coupled to calculate the height of cloud, boundary condition of cloud, followed by growth of droplets, charge distribution calculation on aerosols and cloud droplets and finally scavenging. These models are incorporated with the GEC model. The simulations are verified with experimental data of charged aerosol for various altitudes. Our calculations showed an effect of aerosol charging on the CCN concentration within the cloud, due to charging of aerosols increase the scavenging of particles in the size range 0.1 µm to 1 µm.

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals.