Expansion of wind energy installed capacity is poised to play a key role in climate change mitigation. However, wind energy is also susceptible to global climate change. Some changes associated with ...climate evolution will likely benefit the wind energy industry while other changes may negatively impact wind energy developments, with such ‘gains and losses’ depending on the region under consideration. Herein we review possible mechanisms by which global climate variability and change may influence the wind energy resource and operating conditions, summarize some of the tools that are being employed to quantify these effects and the sources of uncertainty in making such projections, and discuss results of studies conducted to date. We present illustrative examples of research from northern Europe. Climate change analyses conducted for this region, which has shown considerable penetration of wind energy, imply that in the near-term (i.e. to the middle of the current century) natural variability exceeds the climate change signal in the wind energy resource and extreme wind speeds, but there will likely be a decline in icing frequency and sea ice both of which will tend to benefit the wind energy industry. By the end of the twenty-first century there is evidence for small magnitude changes in the wind resource (though the sign of the change remains uncertain), for increases in extreme wind speeds, and continued declines in sea ice and icing frequencies. Thus the current state-of-the-art suggests no detectable change in the wind resource or other external conditions that could jeopardize the continued exploitation of wind energy in northern Europe, though further research is needed to provide greater confidence in these projections.
This work quantitatively evaluates the fidelity with which the northern annular mode (NAM), southern annular mode (SAM), Pacific–North American pattern (PNA), El Niño–Southern Oscillation (ENSO), ...Pacific decadal oscillation (PDO), Atlantic multidecadal oscillation (AMO), and the first-order mode interactions are represented in Earth system model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families, and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climatemodes. Substantial variability in skill scoresmanifests across realizations fromindividual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM–PNA first-order interaction and underestimate the NAM–AMO connection. These results suggest that the choice of ESMand ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near term.
SIGNIFICANCE STATEMENT: Internal climate variability occurs over multiple spatial and temporal scales and is encapsulated in a series of internal climate modes. The representation of such modes in climate models is a critically important aspect of model fidelity. Analyses presented herein uses several skill scores to evaluate both the spatial and temporal manifestations of these climate modes in the CMIP6 generation of Earth system models (ESMs). There is marked variability in model fidelity for these modes and this variability in credibility within the current climate has important implications for the choice of specific ESMs and ESM realizations in making climate projections.
The energy sector comprises approximately two-thirds of global total greenhouse gas emissions. For this and other reasons, renewable energy resources including wind power are being increasingly ...harnessed to provide electricity generation potential with negligible emissions of carbon dioxide. The wind energy resource is naturally a function of the climate system because the "fuel" is the incident wind speed and thus is determined by the atmospheric circulation. Some recent articles have reported historical declines in measured near-surface wind speeds, leading some to question the continued viability of the wind energy industry. Here we briefly articulate the challenges inherent in accurately quantifying and attributing historical tendencies and making robust projections of likely future wind resources. We then analyze simulations from the current generation of regional climate models and show, at least for the next 50 years, the wind resource in the regions of greatest wind energy penetration will not move beyond the historical envelope of variability. Thus this work suggests that the wind energy industry can, and will, continue to make a contribution to electricity provision in these regions for at least the next several decades.
Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and ...innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that finescale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.
ABSTRACT
Temporal trends (1971–2007) in 10‐m wind speeds from homogeneous observational data sets from 540 weather stations and reanalysis data sets are quantified and compared. Also, possible ...physical cause of inconsistencies between the data sets and temporal trends and variability in wind speeds are investigated. Annual mean wind speeds from the observational data exhibit pronounced downward trends especially in the upper percentiles and during spring. The NCEP/NCAR reanalysis reproduces the observed wind speeds, seasonality and temporal trends better than the ERA‐40 even though it shows larger interannual fluctuations. The warm and cold AO and ENSO phases have significant influence on probability distribution of wind speeds, thus internal climate variability is a major source of both interannual and long‐term variability.
The ability of nine current generation (Coupled Model Intercomparison Project Phase 5, CMIP‐5) coupled atmosphere‐ocean general circulation models (AOGCMs) to accurately simulate the near‐surface ...wind climate over China is evaluated by comparing output from the historical period (1971–2005) with an observational data set and reanalysis output. Results suggest the AOGCMs show substantial positive bias in the mean 10 m wind speed relative to observations and the ERA‐40, National Centers for Environmental Prediction–Department of Energy, and National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis. Given that the models generally produce the upper level geopotential height gradients comparatively well, it is postulated that one major reason for the discrepancy between observed and modeled wind fields is the surface characterization used in the AOGCMs. All models exhibit lower interannual variability than reanalysis data and observations, and none of the models reproduce the recent decline in wind speed that is manifest in the near‐surface observations. The wind speed of individual model runs during the historical period does not exhibit much influence from the initial atmospheric conditions. The output for the current century from seven of the AOGCMs is examined relative to the historical wind climate. The results indicate that spatial fields of wind speed at the end of the 21st century are very similar to those of the last 35 years with comparatively little response to the precise representative concentration pathway scenario applied.
Key Points
Nine CMIP‐5 AOGCMs display positive bias in near‐surface wind speeds over China
The AOGCMs do not indicate any long‐term tendency over the historical period
Projected wind climates over China are insensitive to the RCP scenario applied
Impacts from current and future wind turbine (WT) deployments necessary to achieve 20% electricity from wind are analyzed using high resolution numerical simulations over the eastern USA. Theoretical ...scenarios for future deployments are based on repowering (i.e. replacing with higher capacity WTs) thus avoiding competition for land. Simulations for the contemporary climate and current WT deployments exhibit good agreement with observed electricity generation efficiency (gross capacity factors (CF) from simulations = 45-48%, while net CF for WT installed in 2016 = 42.5%). Under the scenario of quadrupled installed capacity there is a small decrease in system-wide efficiency as indicated by annual mean CF. This difference is approximately equal to that from the two simulation years and may reflect saturation of the wind resource in some areas. WT modify the local near-surface climate in the grid cells where they are deployed. The simulated impact on near-surface climate properties at both the regional and local scales does not increase with increasing WT installed capacity. Climate impacts from WT are modest compared to regional changes induced by historical changes in land cover and to the global temperature perturbation induced by use of coal to generate an equivalent amount of electricity.
In the UK market, the total price of renewable electricity is made up of the Renewables Obligation Certificate and the price achieved for the electricity. Accurate forecasting improves the price if ...electricity is traded via the power exchange. In order to understand the size of wind farm for which short-term forecasting becomes economically viable, we develop a model for wind energy. Simulations were carried out for 2003 electricity prices for different forecast accuracies and strategies. The results indicate that it is possible to increase the price obtained by around £5/MWh which is about 14% of the electricity price in 2003 and about 6% of the total price. We show that the economic benefit of using short-term forecasting is also dependant on the accuracy and cost of purchasing the forecast. As the amount of wind energy requiring integration into the grid increases, short-term forecasting becomes more important to both wind farm owners and the transmission/distribution operators.
This manuscript describes the energy and water components of a new community land surface model called the Joint UK Land Environment Simulator (JULES). This is developed from the Met Office Surface ...Exchange Scheme (MOSES). It can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model. The JULES model has been coupled to the Met Office Unified Model (UM) and as such provides a unique opportunity for the research community to contribute their research to improve both world-leading operational weather forecasting and climate change prediction systems. In addition JULES, and its forerunner MOSES, have been the basis for a number of very high-profile papers concerning the land-surface and climate over the last decade. JULES has a modular structure aligned to physical processes, providing the basis for a flexible modelling platform.
Dynamical downscaling of ECHAM5 using HIRHAM5 and RCA3 for a northern European domain focused on Scandinavia indicates sustained extreme wind speeds with long recurrence intervals (50 years) and ...intense winds are not likely to evolve out of the historical envelope of variability until the end of C21st. Even then, significant changes are indicated only in the SW of the domain and across the central Baltic Sea where there is some evidence for relatively small magnitude increases in the 50 year return period wind speed (of up to 15%). There are marked differences in results based on the two Regional Climate Models. Additionally, internal (inherent) variability and initial conditions exert a strong impact on projected wind climates throughout the twenty-first century. Simulations of wind gusts by one of the RCMs (RCA3) indicate some evidence for increased magnitudes (of up to +10%) in the southwest of the domain and across the central Baltic Sea by the end of the current century. As in prior downscaling of ECHAM4, dynamical downscaling of ECHAM5 indicates a tendency towards increased energy density and thus wind power generation potential over the course of the C21st. However, caution should be used in interpreting this inference given the high degree of wind climate projection spread that derives from the specific AOGCM and RCM used in the downscaling.
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