The Storm-Track Response to Idealized SST Perturbations in an Aquaplanet GCM

The tropospheric response to midlatitude SST anomalies has been investigated through a series of aquaplanet simulations using a high-resolution version of the Hadley Centre atmosphere model (HadAM3) under perpetual equinox conditions. Model integrations show that increases in the midlatitude SST gradient generally lead to stronger storm tracks that are shifted slightly poleward, consistent with changes in the lower-tropospheric baroclinicity. The large-scale atmospheric response is, however, highly sensitive to the position of the SST gradient anomaly relative to that of the subtropical jet in the unperturbed atmosphere. In particular, when SST gradients are increased very close to the subtropical jet, then the Hadley cell and subtropical jet is strengthened while the storm track and eddy-driven jet are shifted equatorward. Conversely, if the subtropical SST gradients are reduced and the midlatitude gradients increased, then the storm track shows a strong poleward shift and a well-separated eddy-driven jet is produced. The sign of the SST anomaly is shown to play a secondary role in determining the overall tropospheric response. These findings are used to provide a new and consistent interpretation of some previous GCM studies concerning the atmospheric response to midlatitude SST anomalies

An aggregated wind power generation model based on MERRA reanalysis data: MATLAB model and example data for the April 2014 wind farm distribution of Great Britain.

The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014

Implications of the North Atlantic Oscillation for a UK–Norway renewable power system

UK wind-power capacity is increasing and new transmission links are proposed with Norway, where hydropower dominates the electricity mix. Weather affects both these renewable resources and the demand for electricity. The dominant large-scale pattern of Euro-Atlantic atmospheric variability is the North Atlantic Oscillation (NAO), associated with positive correlations in wind, temperature and precipitation over northern Europe. The NAO's effect on wind-power and demand in the UK and Norway is examined, focussing on March when Norwegian hydropower reserves are low and the combined power system might be most susceptible to atmospheric variations. The NCEP/NCAR meteorological reanalysis dataset (1948–2010) is used to drive simple models for demand and wind-power, and ‘demand-net-wind’ (DNW) is estimated for positive, neutral and negative NAO states. Cold, calm conditions in NAO− cause increased demand and decreased wind-power compared to other NAO states. Under a 2020 wind-power capacity scenario, the increase in DNW in NAO− relative to NAO neutral is equivalent to nearly 25% of the present-day average rate of March Norwegian hydropower usage. As the NAO varies on long timescales (months to decades), and there is potentially some skill in monthly predictions, we argue that it is important to understand its impact on European power systems

Importance subsampling for power system planning under multi-year demand and weather uncertainty

This paper introduces a generalised version of importance subsampling for time series reduction/aggregation in optimisation-based power system planning models. Recent studies indicate that reliably determining optimal electricity (investment) strategy under climate variability requires the consideration of multiple years of demand and weather data. However, solving planning models over long simulation lengths is typically computationally unfeasible, and established time series reduction approaches induce significant errors. The importance subsampling method reliably estimates long-term planning model outputs at greatly reduced computational cost, allowing the consideration of multi-decadal samples. The key innovation is a systematic identification and preservation of relevant extreme events in modeling subsamples. Simulation studies on generation and transmission expansion planning models illustrate the method’s enhanced performance over established "representative days" clustering approaches. The models, data and sample code are made available as open-source software

Hourly historical and near-future weather and climate variables for energy system modelling

Energy systems are becoming increasingly exposed to the impacts of weather and climate due to the uptake of renewable generation and the electrification of the heat and transport sectors. The need for high-quality meteorological data to manage present and near-future risks is urgent. This paper provides a comprehensive set of multi-decadal, time series of hourly meteorological variables and weather-dependent power system components for use in the energy systems modelling community. Despite the growing interest in the impacts of climate variability and climate change on energy systems over the last decade, it remains rare for multi-decadal simulations of meteorological data to be used within detailed simulations. This is partly due to computational constraints, but also due to technical barriers limiting the use of meteorological data by non-specialists. This paper presents a new European-level dataset which can be used to investigate the impacts of climate variability and climate change on multiple aspects of near-future energy systems. The datasets correspond to a suite of well-documented, easy-to-use, self-consistent, hourly- and nationally aggregated, and sub-national time series for 2 m temperature, 10 m wind speed, 100 m wind speed, surface solar irradiance, wind power capacity factor, solar power factor, and degree days spanning over 30 European countries. This dataset is available for the historical period 1950–2020 and is accessible from https://doi.org/10.17864/1947.000321 (Bloomfield and Brayshaw, 2021a). As well as this a companion dataset is created where the ERA5 reanalysis is adjusted to represent the impacts of near-term climate change (centred on the year 2035) based on five high-resolution climate model simulations. These data are available for a 70-year period for central and northern Europe. The data are accessible from https://doi.org/10.17864/1947.000331 (Bloomfield and Brayshaw, 2021b). To the authors’ knowledge, this is the first time a comprehensive set of high-quality hourly time series relating to future climate projections has been published, which is specifically designed to support the energy sector. The purpose of this paper is to detail the methods required for processing the climate model data and illustrate the importance of accounting for climate variability and climate change within energy system modelling from the sub-national to European scale. While this study is therefore not intended to be an exhaustive analysis of climate impacts, it is hoped that publishing these data will promote greater use of climate data within energy system modelling.</p

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

The contribution of North Atlantic atmospheric circulation shifts to future wind speed projections for wind power over Europe

Wind power accounts for a large portion of the European energy mix (~17% of total power capacity). European power systems therefore have a significant - and growing - exposure to near-surface wind speed changes. Despite this, future changes in European wind climate remain relatively poorly studied (compared to, e.g., temperature or precipitation), and there is limited understanding of the differences shown by different general and regional circulation models (GCMs and RCMs). This study provides a step towards a process-based understanding of European wind speed changes by isolating the component associated with `large-scale' atmospheric circulation changes in the CMIP5 simulations. The component associated with the large-scale atmospheric circulation is found to explain cold season windiness projections in the free troposphere over Western Europe, with the changes reflecting the poleward shift of the North Atlantic jet. However, in most GCMs the projected wind speed changes near the surface are more negative than would be expected from the large-scale circulation alone. Thus, while the spread in CMIP5 21st century near surface wind speed projections is associated with divergent projections for the large-scale atmospheric circulation, there is a remarkably good agreement concerning a relative reduction in near-surface wind speeds. This analysis suggests that projected 21st century wind speed changes over Western Europe are the result of two distinct processes. The first is associated with changes in the large-scale atmospheric circulation, while the second is likely to be more local in its connection to the near-surface boundary layer. An improved process-based understanding of both is needed for enhancing confidence in wind-power projections on multi-decadal timescales

Meteorological information to support the transition to clean energy

Professor David Brayshaw has led the University’s Energy Meteorology Group since its formation in 2012. Sharing open data over a period of several years has enabled it to bridge the fields of meteorology and renewable energy research, leading to operational uses of their data and several research collaborations. Professor Brayshaw and Dr Hannah Bloomfield were finalists in the University of Reading Open Research Award 2021

Evaluating Multiple Arthropod Taxa as Indicators of Invertebrate Diversity in Old Fields

Biodiversity, often quantified by species richness, is commonly used to evaluate and monitor the health of ecosystems and as a tool for conservation planning. The use of one or more focal taxa as surrogates or indicators of larger taxonomic diversity can greatly expedite the process of biodiversity measurement. This is especially true when studying diverse and abundant invertebrate fauna. Before indicator taxa are employed, however, research into their suitability as indicators of greater taxonomic diversity in an area is needed. We sampled invertebrate diversity in old fields in southern Michigan using pitfall trapping and morphospecies designations after identification to order or family. Correlation analysis was used to assess species richness relationships between focal arthropod taxa and general invertebrate diversity. Relationships were assessed at two fine spatial scales: within sampling patches, and locally across four sampling patches. Cumulative richness of all assessed taxa increased proportionately with cumulative invertebrate richness as sampling intensity increased within patches. At the among-patch scale, we tentatively identified Hemiptera and Coleoptera as effective indicator taxa of greater invertebrate richness. Although Hymenoptera, Araneae and Diptera exhibited high species richness, their total richness within patches was not associated with overall invertebrate richness among patches. Increased sampling throughout the active season and across a greater number of habitat patches should be conducted before adopting Hemiptera and Coleoptera as definitive indicators of general invertebrate richness in the Great Lakes region. Multiple sampling techniques, in addition to pitfall trapping, should also be added to overcome capture biases associated with each technique

Verification of European subseasonal wind speed forecasts

Analysis of the forecasts and hindcasts from the ECMWF 32-day forecast model reveals that there is statistically significant skill in predicting weekly mean wind speeds over areas of Europe at lead times of at least 14–20 days. Previous research on wind speed predictability has focused on the short- to medium-range time scales, typically finding that forecasts lose all skill by the later part of the medium-range forecast. To the authors’ knowledge, this research is the first to look beyond the medium-range time scale by taking weekly mean wind speeds, instead of averages at hourly or daily resolution, for the ECMWF monthly forecasting system. It is shown that the operational forecasts have high levels of correlation (~0.6) between the forecasts and observations over the winters of 2008–12 for some areas of Europe. Hindcasts covering 20 winters show a more modest level of correlation but are still skillful. Additional analysis examines the probabilistic skill for the United Kingdom with the application of wind power forecasting in mind. It is also shown that there is forecast “value” for end users (operating in a simple cost/loss ratio decision-making framework). End users that are sensitive to winter wind speed variability over the United Kingdom, Germany, and some other areas of Europe should therefore consider forecasts beyond the medium-range time scale as it is clear there is useful information contained within the forecast