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, 101 (46), 16115-20

The Influence of Large-Scale Wind Power on Global Climate

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The Influence of Large-Scale Wind Power on Global Climate

David W Keith et al. Proc Natl Acad Sci U S A.

Abstract

Large-scale use of wind power can alter local and global climate by extracting kinetic energy and altering turbulent transport in the atmospheric boundary layer. We report climate-model simulations that address the possible climatic impacts of wind power at regional to global scales by using two general circulation models and several parameterizations of the interaction of wind turbines with the boundary layer. We find that very large amounts of wind power can produce nonnegligible climatic change at continental scales. Although large-scale effects are observed, wind power has a negligible effect on global-mean surface temperature, and it would deliver enormous global benefits by reducing emissions of CO(2) and air pollutants. Our results may enable a comparison between the climate impacts due to wind power and the reduction in climatic impacts achieved by the substitution of wind for fossil fuels.

Figures

Fig. 1.
Fig. 1.
Wind-farm array and temperature response. Data are surface (2 m) air temperature, experiment minus control. Drag perturbation, δCD, was 0.005 over the A wind-farm array outlined in black. Points that are significant at P > 0.9 by using a binary t test on annual/seasonal means are indicated (×). NCAR data are 37 yr of perturbed run composed of two runs with differing initial conditions and 108 yr of control composed of five independent runs. GFDL perturbed and control runs are both 20 yr long. NCAR (A) and GFDL (B) annual means are given, as well as NCAR (C) and GFDL (D) winter (December-February) means.
Fig. 5.
Fig. 5.
Surface-temperature response (δT2-m air) to various configurations of wind-farm array and δCD. (A) The B array covered 2.5% of global land surface. The roughness length z0 was set to 5 m everywhere within the array, equivalent to δCD ≅ 0.016 at the original 0.12-m areal-mean-roughness length of the array. Data are given for 50 yr of integration, δP = 15 TW. (B) Same as for A, but for the C array, with δCD = 0.0006 globally (excepting Antarctica), 30 yr of integration, and δP = 30 TW.
Fig. 6.
Fig. 6.
Zonal measurements of climatic response. (A) Torque. Data are given from the NCAR model as described in Fig. 1 A. [The plotted quantity is F(θ)cos2(θ), which is torque per radian of latitude divided by formula image, where RE is the earth's radius and F(θ) is the zonal stress.] Note how the torque added by the wind-farm drag at ≈30-60°N is redistributed so that total torque remains at zero. (B) Zonal and annual mean δT2-m air over land. Black lines show response to the A array shown in Fig. 1. Red and blue lines show data from the experiments using different wind farm configurations shown in Fig. 5. All lines correspond to single-model runs except the thick black line, which is derived from the linear response data of Fig. 3A scaled with an arbitrary 25 TW δP. (C) Same as for B, but for zonal means of the absolute magnitudes.
Fig. 2.
Fig. 2.
Energy dissipation versus drag. Statistical uncertainty in δP is negligible. The ensemble of seven NCAR “linearity” model runs are shown (there are two points at δCD = 0.0006 and δCD = 0.005). The change in global-mean surface dissipation (experiment control) is <1% of the control mean of 1.7 W·m-2, or 850 TW.
Fig. 3.
Fig. 3.
Linear coefficient of climatic response in NCAR-linearity ensemble. In all plots, the magnitude at each point is the slope of a least-squares linear fit of the deviation in the given variable with respect to the global δP values using one datum from each of the seven linearity runs shown in Fig. 2. The y intercepts are constrained to zero. Points at which the correlation between the variable and δP was significant at P > 0.9 are indicated (×). (A) Annual mean δT2-m air in mK·TW-1. (B) Ratio change in annual mean precipitation in % TW-1. (C) Annual mean change in zonal wind in mm·sec-1·TW-1. Note that the dipole corresponds to a shift toward the pole of the northern-hemisphere jet.
Fig. 4.
Fig. 4.
Mean climatic response over various masks versus δP. In each plot, the x axis is δP, corresponding to the y axis of Fig. 2. For each point, the seasonal means of a given model run are first integrated over a mask, and differences and standard errors are then computed by using the set of mask integrals for all model years in the experiment and control runs. Results from 10 model runs are shown, all of which use the A array shown in Fig. 1. ○, Data from the seven elements of the NCAR ensemble; □, NCAR drag physics run; and ⋄, data from the two GFDL runs in which the 13 and 18 TW points indicate the roughness length and drag physics runs, respectively. (A) Relative decrease in intensity of the northern-hemisphere jet over a mask that extends from 40-60°N and 100-30 kPa. (B) Annual mean δT2-m air averaged over two separate masks. The red and blue points use a mask defined by the points that are positive and negative, respectively, as well as significant in Fig. 3A. (C) Annual mean δT2-m air over zonal land-surface masks at 25-45°N (black) and 55-65°N (blue). (D) Summer (June-August) δT2-m air for the North American (black) and European (blue) areas of the A wind-farm array shown in Fig. 1. (E) Same as for D, but for winter (December-February).

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