A mechanism of internal decadal atlantic ocean variability in a high-resolution coupled climate model

The North Atlantic Ocean subpolar gyre (NA SPG) is an important region for initialising decadal climate forecasts. Climate model simulations and palaeo climate reconstructions have indicated that this region could also exhibit large, internally generated variability on decadal timescales. Understanding these modes of variability, their consistency across models, and the conditions in which they exist, is clearly important for improving the skill of decadal predictions — particularly when these predictions are made with the same underlying climate models. Here we describe and analyse a mode of internal variability in the NA SPG in a state-of-the-art, high resolution, coupled climate model. This mode has a period of 17 years and explains 15–30% of the annual variance in related ocean indices. It arises due to the advection of heat content anomalies around the NA SPG. Anomalous circulation drives the variability in the southern half of the NA SPG, whilst mean circulation and anomalous temperatures are important in the northern half. A negative feedback between Labrador Sea temperatures/densities and those in the North Atlantic Current is identified, which allows for the phase reversal. The atmosphere is found to act as a positive feedback on to this mode via the North Atlantic Oscillation which itself exhibits a spectral peak at 17 years. Decadal ocean density changes associated with this mode are driven by variations in temperature, rather than salinity — a point which models often disagree on and which we suggest may affect the veracity of the underlying assumptions of anomaly-assimilating decadal prediction methodologies.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

Revisiting trends in wetness and dryness in the presence of internal climate variability and water limitations over land

A theoretically expected consequence of the intensification of the hydrological cycle under global warming is that on average, wet regions get wetter and dry regions get drier (WWDD). Recent studies, however, have found significant discrepancies between the expected pattern of change and observed changes over land. We assess the WWDD theory in four climate models. We find that the reported discrepancy can be traced to two main issues: (1) unforced internal climate variability strongly affects local wetness and dryness trends and can obscure underlying agreement with WWDD, and (2) dry land regions are not constrained to become drier by enhanced moisture divergence since evaporation cannot exceed precipitation over multiannual time scales. Over land, where the available water does not limit evaporation, a “wet gets wetter” signal predominates. On seasonal time scales, where evaporation can exceed precipitation, trends in wet season becoming wetter and dry season becoming drier are also found.

Capillary assay device with internal hydrophilic coating

The present invention provides assay devices having a unitary body with an exterior surface, the unitary body being substantially transparent to visible light and formed from a material having a refractive index in the range 1.26 to 1.40, the refractive index being measured at 20 °C with light of wavelength 589 nm, and wherein the unitary body is formed from a hydrophobic material, and at least two capillary bores extending internally along the unitary body, wherein at least a portion of the surface of each capillary bore includes a hydrophilic layer for retaining an assay reagent, and wherein the hydrophilic layer is also substantially transparent to visible light to allow optical interrogation of the capillary bores through the capillary wall. The present invention also provides assay systems including such assay devices, methods of performing an assay using such assay devices and method of method for manufacturing such assay devices.

Internal nanoparticle structure of temperature-responsive self-assembled PNIPAM-b-PEG-b-PNIPAM triblock copolymers in aqueous solutions: NMR, SANS and light scattering studies

In this study we report detailed information on the internal structure of PNIPAM-b-PEG-b-PNIPAM nanoparticles formed from self-assembly in aqueous solutions upon increase in temperature. NMR spectroscopy, light scattering and small-angle neutron scattering (SANS) were used to monitor different stages of nanoparticle formation as a function of temperature, providing insight into the fundamental processes involved. The presence of PEG in a copolymer structure significantly affects the formation of nanoparticles, making their transition to occur over a broader temperature range. The crucial parameter that controls the transition is the ratio of PEG/PNIPAM. For pure PNIPAM, the transition is sharp; the higher the PEG/PNIPAM ratio results in a broader transition. This behavior is explained by different mechanisms of PNIPAM block incorporation during nanoparticle formation at different PEG/PNIPAM ratios. Contrast variation experiments using SANS show that the structure of nanoparticles above cloud point temperatures for PNIPAM-b-PEG-b-PNIPAM copolymers is drastically different from the structure of PNIPAM mesoglobules. In contrast with pure PNIPAM mesoglobules, where solid-like particles and chain network with a mesh size of 1-3 nm are present; nanoparticles formed from PNIPAM-b-PEG-b-PNIPAM copolymers have non-uniform structure with “frozen” areas interconnected by single chains in Gaussian conformation. SANS data with deuterated “invisible” PEG blocks imply that PEG is uniformly distributed inside of a nanoparticle. It is kinetically flexible PEG blocks which affect the nanoparticle formation by prevention of PNIPAM microphase separation.