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- Author or Editor: Anna C. Fitch x
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Abstract
The local, regional, and global climate impacts of a large-scale global deployment of wind power in regionally high densities over land are investigated for a 60-yr period. Wind farms are represented as elevated momentum sinks as well as enhanced turbulence to represent turbine blade mixing in the Community Atmosphere Model, version 5 (CAM5), a global climate model. For a total installed capacity of 2.5 TW, to provide 16% of the world’s projected electricity demand in 2050, minimal impacts are found both regionally and globally on temperature, sensible and latent heat fluxes, cloud, and precipitation. A mean near-surface warming of 0.12 ± 0.07 K is seen within the wind farms, with a global-mean temperature change of −0.013 ± 0.015 K. Impacts on wind speed and turbulence are more pronounced but largely confined within the wind farm areas. Increasing the wind farm areas to provide an installed capacity of 10 TW, or 65% of the 2050 electricity demand, causes further impacts; however, they remain slight overall. Maximum temperature changes are less than 0.5 K in the wind farm areas. To provide 20 TW of installed capacity, or 130% of the 2050 electricity demand, impacts both within the wind farms and beyond become more pronounced, with a doubling in turbine density. However, maximum temperature changes remain less than 0.7 K. Representing wind farms instead as an increase in surface roughness generally produces similar mean results; however, maximum changes increase, and influences on wind and turbulence are exaggerated. Overall, wind farm impacts are much weaker than those expected from greenhouse gas emissions, with very slight global-mean climate impacts.
Abstract
The local, regional, and global climate impacts of a large-scale global deployment of wind power in regionally high densities over land are investigated for a 60-yr period. Wind farms are represented as elevated momentum sinks as well as enhanced turbulence to represent turbine blade mixing in the Community Atmosphere Model, version 5 (CAM5), a global climate model. For a total installed capacity of 2.5 TW, to provide 16% of the world’s projected electricity demand in 2050, minimal impacts are found both regionally and globally on temperature, sensible and latent heat fluxes, cloud, and precipitation. A mean near-surface warming of 0.12 ± 0.07 K is seen within the wind farms, with a global-mean temperature change of −0.013 ± 0.015 K. Impacts on wind speed and turbulence are more pronounced but largely confined within the wind farm areas. Increasing the wind farm areas to provide an installed capacity of 10 TW, or 65% of the 2050 electricity demand, causes further impacts; however, they remain slight overall. Maximum temperature changes are less than 0.5 K in the wind farm areas. To provide 20 TW of installed capacity, or 130% of the 2050 electricity demand, impacts both within the wind farms and beyond become more pronounced, with a doubling in turbine density. However, maximum temperature changes remain less than 0.7 K. Representing wind farms instead as an increase in surface roughness generally produces similar mean results; however, maximum changes increase, and influences on wind and turbulence are exaggerated. Overall, wind farm impacts are much weaker than those expected from greenhouse gas emissions, with very slight global-mean climate impacts.
Abstract
For assessing the impacts of wind farms on regional climate, wind farms may be represented in climate models by an increase in aerodynamic roughness length. Studies employing this method have found near-surface temperature changes of 1–2 K over wind farm areas. By contrast, mesoscale and large-eddy simulations (LES), which represent wind farms as elevated sinks of momentum, generally showed temperature changes of less than 0.5 K. This study directly compares the two methods of representing wind farms in simulations of a strong diurnal cycle. Nearly the opposite wake structure is seen between the two methods, both during the day and at night. The sensible heat fluxes are generally exaggerated in the enhanced roughness approach, leading to much greater changes in temperature. Frequently, the two methods display the opposite sign in temperature change. Coarse resolution moderates the sensible heat fluxes but does not significantly improve the near-surface temperatures or low-level wind speed deficit. Since wind farm impacts modeled by the elevated momentum sink approach are similar to those seen in observations and from LES, the authors conclude that the increased surface roughness approach is not an appropriate option to represent wind farms or explore their impacts.
Abstract
For assessing the impacts of wind farms on regional climate, wind farms may be represented in climate models by an increase in aerodynamic roughness length. Studies employing this method have found near-surface temperature changes of 1–2 K over wind farm areas. By contrast, mesoscale and large-eddy simulations (LES), which represent wind farms as elevated sinks of momentum, generally showed temperature changes of less than 0.5 K. This study directly compares the two methods of representing wind farms in simulations of a strong diurnal cycle. Nearly the opposite wake structure is seen between the two methods, both during the day and at night. The sensible heat fluxes are generally exaggerated in the enhanced roughness approach, leading to much greater changes in temperature. Frequently, the two methods display the opposite sign in temperature change. Coarse resolution moderates the sensible heat fluxes but does not significantly improve the near-surface temperatures or low-level wind speed deficit. Since wind farm impacts modeled by the elevated momentum sink approach are similar to those seen in observations and from LES, the authors conclude that the increased surface roughness approach is not an appropriate option to represent wind farms or explore their impacts.
Abstract
Large wind farms are expected to influence local and regional atmospheric circulations. Using a mesoscale parameterization of the effects of wind farms that includes a momentum sink and a wind speed–dependent source of turbulent kinetic energy, simulations were carried out to quantify the impact of a wind farm on an atmospheric boundary layer throughout a diurnal cycle. The presence of a wind farm covering 10 km × 10 km is found to have a significant impact on the local atmospheric flow and on regions up to 60 km downwind at night. Daytime convective conditions show little impact of the wind farm on wind speeds, as the momentum deficits generated by the wind farm rapidly mix through the depth of the boundary layer. At night, the stable layer within the rotor area inhibits turbulent mixing of the momentum deficit, leading to a shallower wake and a greater reduction in the wind speed within the wake. Although a low-level jet forms at altitudes within the rotor area in the hours before dawn, it is completely eliminated within the wind farm. At night, a maximum warming of 1 K is seen at the bottom of the rotor area. Near the surface, there is less warming (0.5 K). Downwind, the surface temperature perturbation is small, with a cooling of up to 0.3 K. Over the simulation period, the mean temperature change over the wind farm area at 2 m is a very slight warming (0.2 K). Mean temperature changes downwind are negligible. Other influences on turbulent kinetic energy, surface heat fluxes, and boundary layer height, are discussed.
Abstract
Large wind farms are expected to influence local and regional atmospheric circulations. Using a mesoscale parameterization of the effects of wind farms that includes a momentum sink and a wind speed–dependent source of turbulent kinetic energy, simulations were carried out to quantify the impact of a wind farm on an atmospheric boundary layer throughout a diurnal cycle. The presence of a wind farm covering 10 km × 10 km is found to have a significant impact on the local atmospheric flow and on regions up to 60 km downwind at night. Daytime convective conditions show little impact of the wind farm on wind speeds, as the momentum deficits generated by the wind farm rapidly mix through the depth of the boundary layer. At night, the stable layer within the rotor area inhibits turbulent mixing of the momentum deficit, leading to a shallower wake and a greater reduction in the wind speed within the wake. Although a low-level jet forms at altitudes within the rotor area in the hours before dawn, it is completely eliminated within the wind farm. At night, a maximum warming of 1 K is seen at the bottom of the rotor area. Near the surface, there is less warming (0.5 K). Downwind, the surface temperature perturbation is small, with a cooling of up to 0.3 K. Over the simulation period, the mean temperature change over the wind farm area at 2 m is a very slight warming (0.2 K). Mean temperature changes downwind are negligible. Other influences on turbulent kinetic energy, surface heat fluxes, and boundary layer height, are discussed.
Abstract
A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy.
Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.
Abstract
A new wind farm parameterization has been developed for the mesoscale numerical weather prediction model, the Weather Research and Forecasting model (WRF). The effects of wind turbines are represented by imposing a momentum sink on the mean flow; transferring kinetic energy into electricity and turbulent kinetic energy (TKE). The parameterization improves upon previous models, basing the atmospheric drag of turbines on the thrust coefficient of a modern commercial turbine. In addition, the source of TKE varies with wind speed, reflecting the amount of energy extracted from the atmosphere by the turbines that does not produce electrical energy.
Analyses of idealized simulations of a large offshore wind farm are presented to highlight the perturbation induced by the wind farm and its interaction with the atmospheric boundary layer (BL). A wind speed deficit extended throughout the depth of the neutral boundary layer, above and downstream from the farm, with a long wake of 60-km e-folding distance. Within the farm the wind speed deficit reached a maximum reduction of 16%. A maximum increase of TKE, by nearly a factor of 7, was located within the farm. The increase in TKE extended to the top of the BL above the farm due to vertical transport and wind shear, significantly enhancing turbulent momentum fluxes. The TKE increased by a factor of 2 near the surface within the farm. Near-surface winds accelerated by up to 11%. These results are consistent with the few results available from observations and large-eddy simulations, indicating this parameterization provides a reasonable means of exploring potential downwind impacts of large wind farms.