Low-momentum interactions in three- and four-nucleon scattering

Low momentum two-nucleon interactions obtained with the renormalization group method and the similarity renormalization group method are used to study the cutoff dependence of low energy 3N and 4N scattering observables. The residual cutoff dependence arises from omitted short-ranged 3N (and higher) forces that are induced by the renormalization group transformations, and may help to estimate the sensitivity of various 3N and 4N scattering observables to short-ranged many-body forces.Comment: 5 pages, 8 figures, to be published in Phys. Rev.

Importance subsampling: Improving power system planning under climate-based uncertainty

Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time, modelling renewable generation such as solar and wind requires high temporal resolution to capture fluctuations in output levels. In many realistic power system models, using long samples at high temporal resolution is computationally unfeasible. This paper introduces a novel subsampling approach, referred to as importance subsampling, allowing the use of multiple decades of demand & weather data in power system planning models at reduced computational cost. The methodology can be applied in a wide class of optimisation-based power system simulations. A test case is performed on a model of the United Kingdom created using the open-source modelling framework Calliope and 36 years of hourly demand and wind data. Standard data reduction approaches such as using individual years or clustering into representative days lead to significant errors in estimates of optimal system design. Furthermore, the resultant power systems lead to supply capacity shortages, raising questions of generation capacity adequacy. In contrast, importance subsampling leads to accurate estimates of optimal system design at greatly reduced computational cost, with resultant power systems able to meet demand across all 36 years of demand & weather scenarios

Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain

With a rapidly increasing fraction of electricity generation being sourced from wind, extreme wind power generation events such as prolonged periods of low (or high) generation and ramps in generation, are a growing concern for the efficient and secure operation of national power systems. As extreme events occur infrequently, long and reliable meteorological records are required to accurately estimate their characteristics. Recent publications have begun to investigate the use of global meteorological “reanalysis” data sets for power system applications, many of which focus on long-term average statistics such as monthly-mean generation. Here we demonstrate that reanalysis data can also be used to estimate the frequency of relatively short-lived extreme events (including ramping on sub-daily time scales). Verification against 328 surface observation stations across the United Kingdom suggests that near-surface wind variability over spatiotemporal scales greater than around 300 km and 6 h can be faithfully reproduced using reanalysis, with no need for costly dynamical downscaling. A case study is presented in which a state-of-the-art, 33 year reanalysis data set (MERRA, from NASA-GMAO), is used to construct an hourly time series of nationally-aggregated wind power generation in Great Britain (GB), assuming a fixed, modern distribution of wind farms. The resultant generation estimates are highly correlated with recorded data from National Grid in the recent period, both for instantaneous hourly values and for variability over time intervals greater than around 6 h. This 33 year time series is then used to quantify the frequency with which different extreme GB-wide wind power generation events occur, as well as their seasonal and inter-annual variability. Several novel insights into the nature of extreme wind power generation events are described, including (i) that the number of prolonged low or high generation events is well approximated by a Poission-like random process, and (ii) whilst in general there is large seasonal variability, the magnitude of the most extreme ramps is similar in both summer and winter. An up-to-date version of the GB case study data as well as the underlying model are freely available for download from our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/

Connection between sea surface anomalies and atmospheric quasi-stationary waves

Large scale, quasi-stationary atmospheric waves (QSWs) are known to be strongly connected with extreme events and general weather conditions. Yet, despite their importance, there is still a lack of understanding about what drives variability in QSW. This study is a step towards this goal, and identifies three statistically significant connections between QSWs and sea surface anomalies (temperature and ice cover) by applying a maximum covariance analysis technique to reanalysis data (1979-2015). The two most dominant connections are linked to the El Ni\~no Southern Oscillation and the North Atlantic Oscillation. They confirm the expected relationship between QSWs and anomalous surface conditions in the tropical Pacific and the North Atlantic, but they cannot be used to infer a driving mechanism or predictability from the sea surface temperature or the sea ice cover to the QSW. The third connection, in contrast, occurs between late winter to early spring Atlantic sea ice concentrations and anomalous QSW patterns in the following late summer to early autumn. This new finding offers a pathway for possible long term predictability of late summer QSW occurrence

Effective theories of scattering with an attractive inverse-square potential and the three-body problem

A distorted-wave version of the renormalisation group is applied to scattering by an inverse-square potential and to three-body systems. In attractive three-body systems, the short-distance wave function satisfies a Schroedinger equation with an attractive inverse-square potential, as shown by Efimov. The resulting oscillatory behaviour controls the renormalisation of the three-body interactions, with the renormalisation-group flow tending to a limit cycle as the cut-off is lowered. The approach used here leads to single-valued potentials with discontinuities as the bound states are cut off. The perturbations around the cycle start with a marginal term whose effect is simply to change the phase of the short-distance oscillations, or the self-adjoint extension of the singular Hamiltonian. The full power counting in terms of the energy and two-body scattering length is constructed for short-range three-body forces.Comment: 19 pages (RevTeX), 2 figure

The impact of monsoon intraseasonal variability on renewable power generation in India

India is increasingly investing in renewable technology to meet rising energy demands, with hydropower and other renewables comprising one-third of current installed capacity. Installed wind-power is projected to increase 5-fold by 2035 (to nearly 100GW) under the International Energy Agency’s New Policies scenario. However, renewable electricity generation is dependent upon the prevailing meteorology, which is strongly influenced by monsoon variability. Prosperity and widespread electrification are increasing the demand for air conditioning, especially during the warm summer. This study uses multi-decadal observations and meteorological reanalysis data to assess the impact of intraseasonal monsoon variability on the balance of electricity supply from wind-power and temperature-related demand in India. Active monsoon phases are characterised by vigorous convection and heavy rainfall over central India. This results in lower temperatures giving lower cooling energy demand, while strong westerly winds yield high wind-power output. In contrast, monsoon breaks are characterised by suppressed precipitation, with higher temperatures and hence greater demand for cooling, and lower wind-power output across much of India. The opposing relationship between wind-power supply and cooling demand during active phases (low demand, high supply) and breaks (high demand, low supply) suggests that monsoon variability will tend to exacerbate fluctuations in the so-called demand-net-wind (i.e., electrical demand that must be supplied from non-wind sources). This study may have important implications for the design of power systems and for investment decisions in conventional schedulable generation facilities (such as coal and gas) that are used to maintain the supply/demand balance. In particular, if it is assumed (as is common) that the generated wind-power operates as a price-taker (i.e., wind farm operators always wish to sell their power, irrespective of price) then investors in conventional facilities will face additional weather-volatility through the monsoonal impact on the length and frequency of production periods (i.e. their load-duration curves)

The relationship between wind power, electricity demand and winter weather patterns in Great Britain

Wind power generation in Great Britain has increased markedly in recent years. However due to its intermittency its ability to provide power during periods of high electricity demand has been questioned. Here we characterise the winter relationship between electricity demand and the availability of wind power. Although a wide range of wind power capacity factors is seen for a given demand, the average capacity factor reduces by a third between low and high demand. However, during the highest demand average wind power increases again, due to strengthening easterly winds. The nature of the weather patterns affecting Great Britain are responsible for this relationship. High demand is driven by a range of high pressure weather types, each giving cold conditions, but variable wind power availability. Offshore wind power is sustained at higher levels and offers a more secure supply compared to that onshore. However, during high demand periods in Great Britain neighbouring countries may struggle to provide additional capacity due to concurrent low temperatures and low wind power availability

Response of the North Atlantic storm track to climate change shaped by ocean–atmosphere coupling

A poleward shift of the mid-latitude storm tracks in response to anthropogenic greenhouse-gas forcing has been diagnosed in climate model simulations1, 2. Explanations of this effect have focused on atmospheric dynamics3, 4, 5, 6, 7. However, in contrast to storm tracks in other regions, the North Atlantic storm track responds by strengthening and extending farther east, in particular on its southern flank8. These adjustments are associated with an intensification and extension of the eddy-driven jet towards western Europe9 and are expected to have considerable societal impacts related to a rise in storminess in Europe10, 11, 12. Here, we apply a regression analysis to an ensemble of coupled climate model simulations to show that the coupling between ocean and atmosphere shapes the distinct storm-track response to greenhouse-gas forcing in the North Atlantic region. In the ensemble of simulations we analyse, at least half of the differences between the storm-track responses of different models are associated with uncertainties in ocean circulation changes. We compare the fully coupled simulations with both the associated slab model simulations and an ocean-forced experiment with one climate model to establish causality. We conclude that uncertainties in the response of the North Atlantic storm track to anthropogenic emissions could be reduced through tighter constraints on the future ocean circulation

Rossby wave dynamics of the North Pacific extra-tropical response to El Niño: importance of the basic state in coupled GCMs

The extra-tropical response to El Nino in a "low" horizontal resolution coupled climate model, typical of the Intergovernmental Panel on Climate Change fourth assessment report simulations, is shown to have serious systematic errors. A high resolution configuration of the same model has a much improved response that is similar to observations. The errors in the low resolution model are traced to an incorrect representation of the atmospheric teleconnection mechanism that controls the extra-tropical sea surface temperatures (SSTs) during El Nino. This is due to an unrealistic atmospheric mean state, which changes the propagation characteristics of Rossby waves. These erroneous upper tropospheric circulation anomalies then induce erroneous surface circulation features over the North Pacific. The associated surface wind speed and direction errors create erroneous surface flux and upwelling anomalies which finally lead to the incorrect extra-tropical SST response to El Nino in the low resolution model. This highlights the sensitivity of the climate response to a single link in a chain of complex climatic processes. The correct representation of these processes in the high resolution model indicates the importance of horizontal resolution in resolving such processes