1. Trends in Surface Wind Speed over the Last 30 Years Gene Takle Analyses done primarily by Sara Pryor, University of Indiana Additional analyses by Theresa Andersen Gene Takle Analyses done primarily by Sara Pryor, University of Indiana Additional analyses by Theresa Andersen Based on manuscript submitted to Journal of Geophysical Research: Pryor, S. C., R. J. Barthelmie, D. T. Young, E. S. Takle, R. W. Arritt, D. Flory, W. J. Gutowski, Jr., and A. Nunes, 2008: Wind speed trends over the contiguous USA Atmospheric Science Seminar Series Iowa State University 28 November 2008

2. Outline  Why should we care?  Emerging importance of wind power  Trends in US wind speed - observations  Trends in US wind speed - modeling studies  Trends in winds elsewhere  Role of wind in the global energy balance  Future studies  Why should we care?  Emerging importance of wind power  Trends in US wind speed - observations  Trends in US wind speed - modeling studies  Trends in winds elsewhere  Role of wind in the global energy balance  Future studies

3. Trends in Surface Wind Speeds: Why should we care?  Surface winds drive ocean circulation  Surface winds regulate exchanges of heat and moisture with the surface  Surface winds transport pollen, seeds, insects, etc.  Surface winds can be harnessed for wind power  Surface winds drive ocean circulation  Surface winds regulate exchanges of heat and moisture with the surface  Surface winds transport pollen, seeds, insects, etc.  Surface winds can be harnessed for wind power Note: I am not considering storm-scale winds

4. Wind Power  Wind is a clean (i.e., carbon-free, pollution-free) source of renewable energy  The US Department of Energy has a goal of producing 20% of our energy supply from wind by 2030.  This will require 300 GW by 2030 ( US installed capacity as of Sept 2008 was 20 GW)  Wind is a clean (i.e., carbon-free, pollution-free) source of renewable energy  The US Department of Energy has a goal of producing 20% of our energy supply from wind by 2030.  This will require 300 GW by 2030 ( US installed capacity as of Sept 2008 was 20 GW) Wind power, P ~  V 3

8. Iowa’s Contribution  Iowa ranks third among all states in wind generation with 1.375 GW of operating capacity  An additional 1.586 GW of capacity is under construction and will be available in 18 months, according to the American Wind Energy Association. (DM Register, 24 October 2008).  Iowa ranks third among all states in wind generation with 1.375 GW of operating capacity  An additional 1.586 GW of capacity is under construction and will be available in 18 months, according to the American Wind Energy Association. (DM Register, 24 October 2008).

11. “Given that a 1% error in wind speed estimates for a 100 MW wind generation facility can lead to losses approaching $12,000,000 over the lifetime of that plant, a better understanding of the physical and dynamic processes across the range of scales that create a particular wind climate is needed.” Draft recommendations, DOE Workshop on Research Needs for Wind Resource Characterization, 14-16 Jan 2008, Broomfield, CO Iowa will have 3,000 MW installed by 2010, so a 1% error is $360 million over its lifetime. Need Better Estimates of Wind Speed for Forecasting Wind Power

12. Two Datasets of Observed Wind  Subset of NCDC-6421 dataset, homogenized to 10-m height, 800+ stations, 1973-2000 (Groisman, 2000)  Subset of NCDC DS3505 dataset, homogenized to 10-m height, 193 stations, 1973- 2005  Only data from 00 and 12 UTC are used  Subset of NCDC-6421 dataset, homogenized to 10-m height, 800+ stations, 1973-2000 (Groisman, 2000)  Subset of NCDC DS3505 dataset, homogenized to 10-m height, 193 stations, 1973- 2005  Only data from 00 and 12 UTC are used

13. Problems with Observations  Changes in instrumentation  Station moves  Changes in land-use around the station  Missing data  Data resolution for computing robust temporal trends  Changes in instrumentation  Station moves  Changes in land-use around the station  Missing data  Data resolution for computing robust temporal trends

14. Mean Wind Speeds as Represented in Observations, Reanalyses, and Regional Climate Models

15. Analysis Method  Focus on 50 th and 95 th percentiles of wind speed distribution (annual time scale)  Analyzed for trends using linear regression and bootstrapping techniques  Interannual variability computed from a 7-year window for annual means of each station, with value assigned to the central year (for assessing trends in interannual variability)  Focus on 50 th and 95 th percentiles of wind speed distribution (annual time scale)  Analyzed for trends using linear regression and bootstrapping techniques  Interannual variability computed from a 7-year window for annual means of each station, with value assigned to the central year (for assessing trends in interannual variability)

16. Trends is Surface Winds - Observations Transition to ASOS wind speed change of -0.2 (-0.65 to +0.15) m/s)

17. Observed Wind Speeds

18. Trends in Observed Surface Winds (% per year) 00 UTC

19. Trends in Observed Surface Winds (% per year) 12 UTC

20. Modeled Trends in Surface Winds  NCEP/NCAR Reanalysis 1  NCEP/DOE Reanalysis 2  ERA-40 Reanalysis  North American Regional Reanalysis (NARR)  MM5  RSM  NCEP/NCAR Reanalysis 1  NCEP/DOE Reanalysis 2  ERA-40 Reanalysis  North American Regional Reanalysis (NARR)  MM5  RSM

21. Trends in Reanalysis Surface Winds (% per year) NCEP/NCAR R 1 and NCEP/DOE R 2 00 UTC

22. Trends in Reanalysis Surface Winds (% per year) NCEP/NCAR R 1 (truncated to NCDC periods) 00 and 12 UTC combined

23. Trends in Reanalysis Surface Winds (% per year) ERA-40 and NARR 00 UTC

24. Trends in Regional Climate Models Surface Winds (% per year) MM5 and RSM 00 UTC

25. Trends in Regional Climate Models Surface Winds (% per year) NARR 00 UTC 12 UTC

26. Trends in Regional Climate Models Surface Winds (% per year) MM5 12 UTC 00 UTC

27. Trends in Regional Climate Models Surface Winds (% per year) RSM 00 UTC 12 UTC

28. Summary of Analysis of Observed and Modeled Surface Wind Speeds  Observed winds show substantial decreasing trends (up to 1%/yr at many stations)  NCEP-1 (but not NCEP-2) Reanal show increasing trend over much of the US, especially the Midwest  ERA-40 has regions in the western US that are evenly divided between increases and decreases. Not much change in eastern US  NARR show little change in eastern US but conflicting changes (increases at 50 th and decreases at 90 th percentile; decreases at 00 UTC and increases at 12 UTC)  Period of observation is important in assessing trends  MM5 shows decreasing trend  RSM shows increasing trend (larger trends at 00 UTC)  Observed winds show substantial decreasing trends (up to 1%/yr at many stations)  NCEP-1 (but not NCEP-2) Reanal show increasing trend over much of the US, especially the Midwest  ERA-40 has regions in the western US that are evenly divided between increases and decreases. Not much change in eastern US  NARR show little change in eastern US but conflicting changes (increases at 50 th and decreases at 90 th percentile; decreases at 00 UTC and increases at 12 UTC)  Period of observation is important in assessing trends  MM5 shows decreasing trend  RSM shows increasing trend (larger trends at 00 UTC)

29. Statistically Significant Changes in Mean and Variability of Wind Speed: Observations mean mean & IV IV

30. Statistically Significant Changes in Mean and Variability of Wind Speed: NCEP-1 and NCEP -2

31. Statistically Significant Changes in Mean and Variability of Wind Speed: ERA-40 and NARR

32. Statistically Significant Changes in Mean and Variability of Wind Speed: MM5 and RSM

33. Wind Speed (m/s) departures from monthly means from 70-m tall towers in Minnesota Klink, K., 2007: J. Appl. Meteor. Clim. 46, 446 Seasonal and Interannual Variability of Wind Speeds

34. Wind Speed (m/s) departures from monthly means from 70-m tall towers in Minnesota Klink, K., 2007: J. Appl. Meteor. Clim. 46, 446 Seasonal and Interannual Variability of Wind Speeds

38. Does MM5 recognize an SOI signal in the Upper Midwest from Jan 79 – May 04?

39. Does MM5 recognize an NAO signal in the Upper Midwest from Jan 79 – May 04?

40. Wind Speed Trends Elsewhere  China: “From 1969-2000, the annual mean wind speed over China has decreased steadily by 28%, and the prevalence of windy days (daily mean wind speed > 5 m/s) has decreased by 58%”. (Xu et. al., JGR 111, 2006)  Australia: “Recent observations of near-surface wind speed trends measured by terrestrial anemometers have shown declines between -0.004 m/s per year to -0.017 m/s per year over the last 30-50 years in Australia, China, Europe, North America, and Tibet.” McVicar et al., GRL 35, 2008)  China: “From 1969-2000, the annual mean wind speed over China has decreased steadily by 28%, and the prevalence of windy days (daily mean wind speed > 5 m/s) has decreased by 58%”. (Xu et. al., JGR 111, 2006)  Australia: “Recent observations of near-surface wind speed trends measured by terrestrial anemometers have shown declines between -0.004 m/s per year to -0.017 m/s per year over the last 30-50 years in Australia, China, Europe, North America, and Tibet.” McVicar et al., GRL 35, 2008)

41. McVicar et al., 2008: GRL 35. “…the Australian -averaged u- trend for 1975- 2006 was -0.009 m/s per year … over 88% of the land surface.”

42. Impact on Pan Evaporation Roderick et al., 2007: GRL 34

43. Summary  Evidence seems to be growing that wind speeds globally are declining  Observing and analysis challenges (NOA, SOI) make it difficult to define trends, however  Reanalyses and regional climate models are not consistent in simulating trends  Impacts of trends on wind power production demands better answers  Evidence seems to be growing that wind speeds globally are declining  Observing and analysis challenges (NOA, SOI) make it difficult to define trends, however  Reanalyses and regional climate models are not consistent in simulating trends  Impacts of trends on wind power production demands better answers