Climate Dynamics

, Volume 52, Issue 7–8, pp 4891–4906 | Cite as

Wind energy variability and links to regional and synoptic scale weather

  • Dev MillsteinEmail author
  • Joshua Solomon-Culp
  • Meina Wang
  • Paul Ullrich
  • Craig Collier


The accurate characterization of seasonal and inter-annual site-level wind energy variability is essential during wind project development. Understanding the features and probability of low-wind years is of particular interest to developers and financers. However, a dearth of long-term, hub-height wind observations makes these characterizations challenging, and thus techniques to improve these characterizations are of great value. To improve resource characterization, we explicitly link wind resource variability (at hub-height, and at specific sites) to regional and synoptic scale wind regimes. Our approach involves statistical clustering of high-resolution modeled wind data, and is applied to California for a period covering 1980–2015. With this approach, we investigate the unique meteorological patterns driving low and high wind years at five separate wind project sites. We also find wind regime changes over the 36-year period consistent with global warming: wind regimes associated with anomalously hot summer days increased at half a day per year and stagnant conditions increased at one-third days per year. Despite these changes, the average annual resource potential remained constant at all project sites. Additionally, we identify correlations between climate modes and wind regime frequency, a linkage valuable for resource characterization and forecasting. Our general approach can be applied in any location and may benefit many aspects of wind energy resource evaluation and forecasting.


Wind energy Wind resource inter-annual variability Regional climate 



This work was funded by the California Energy Commission under the Electric Program Investment Charge Grant, “EPC-15-068: Understanding and Mitigating Barriers to Wind Energy Expansion in California.” We would like to thank Chris Hayes and Daran Rife of DNV GL for helpful discussions. And from Lawrence Berkeley National Laboratory, we thank Aditya Murthi for early input into this research and Samir Touzani for helpful discussion about the methodological approach. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231.

Supplementary material

382_2018_4421_MOESM1_ESM.pdf (5 mb)
Supplementary material 1 (PDF 5089 KB)


  1. Albadi M, El-Saadany E (2010) Overview of wind power intermittency impacts on power systems. Electr Power Syst Res 80:627–632CrossRefGoogle Scholar
  2. Archer CL, Jacobson MZ (2013) Geographical and seasonal variability of the global “practical” wind resources. Appl Geogr 45:119–130. CrossRefGoogle Scholar
  3. Archer CL, Simão HP, Kempton W, Powell WB, Dvorak MJ (2017) The challenge of integrating offshore wind power in the U.S. electric grid. Part I: wind forecast error. Renew Energy 103:346–360. CrossRefGoogle Scholar
  4. Bailey B, Kunkel J (2015) The financial implications of resource assessment uncertainty. North American Windpower, vol 12Google Scholar
  5. Barthelmie R, Pryor S (2014) Potential contribution of wind energy to climate change mitigation. Nat Clim Change 4:684–688CrossRefGoogle Scholar
  6. Beaver S, Palazoglu A (2009) Influence of synoptic and mesoscale meteorology on ozone pollution potential for San Joaquin Valley of California. Atmos Environ 43:1779–1788CrossRefGoogle Scholar
  7. Berg N, Hall A, Capps SB, Hughes M (2013) El Niño-Southern oscillation impacts on winter winds over Southern California. Clim Dyn 40:109–121CrossRefGoogle Scholar
  8. Bolinger M (2017) Using probability of exceedance to compare the resource risk of renewable and gas-fired generation. Lawrence Berkeley National Laboratory Report, LBNL-1007269Google Scholar
  9. Carta JA, Velázquez S, Cabrera P (2013) A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site. Renew Sustain Energy Rev 27:362–400CrossRefGoogle Scholar
  10. Chadee XT, Clarke RM (2015) Daily near-surface large-scale atmospheric circulation patterns over the wider Caribbean. Clim Dyn 44:2927–2946CrossRefGoogle Scholar
  11. Clifton A, Lundquist JK (2012) Data clustering reveals climate impacts on local wind phenomena. J Appl Meteorol Climatol 51:1547–1557CrossRefGoogle Scholar
  12. Cochran J, Mai T, Bazilian M (2014) Meta-analysis of high penetration renewable energy scenarios. Renew Sustain Energy Rev 29:246–253CrossRefGoogle Scholar
  13. Cochrane D, Orcutt GH (1949) Application of least squares regression to relationships containing auto-correlated error terms. J Am Stat Assoc 44:32–61. Google Scholar
  14. Conil S, Hall A (2006) Local regimes of atmospheric variability: a case study of southern California. J Clim 19:4308–4325CrossRefGoogle Scholar
  15. Cullen J (2013) Measuring the environmental benefits of wind-generated electricity. AEJ Econ Policy 5:107–133Google Scholar
  16. Darby LS (2005) Cluster analysis of surface winds in Houston, Texas, and the impact of wind patterns on ozone. J Appl Meteorol 44:1788–1806CrossRefGoogle Scholar
  17. Dawson JP, Bloomer BJ, Winner DA, Weaver CP (2014) Understanding the meteorological drivers of US particulate matter concentrations in a changing climate. Bull Am Meteor Soc 95:521–532CrossRefGoogle Scholar
  18. Delle Monache L, Nipen T, Liu Y, Roux G, Stull R (2011) Kalman filter and analog schemes to postprocess numerical weather predictions. Mon Weather Rev 139:3554–3570CrossRefGoogle Scholar
  19. Delle Monache L, Eckel FA, Rife DL, Nagarajan B, Searight K (2013) Probabilistic weather prediction with an analog ensemble. Mon Weather Rev 141:3498–3516CrossRefGoogle Scholar
  20. Draxl C, Clifton A, Hodge B-M, McCaa J (2015) The wind integration national dataset (WIND) Toolkit. Appl Energy 151:355–366CrossRefGoogle Scholar
  21. Duffy PB, Bartlett J, Dracup J, Freedman J, Madani K, Waight K (2014) Climate change impacts on generation of wind, solar, and hydropower in California. California Energy Commission Report, CEC‐500‐2014‐111Google Scholar
  22. Gelaro R et al (2017) The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J Clim 30:5419–5454CrossRefGoogle Scholar
  23. GES (2017) Goddard Earth Sciences Data and Information Services Center. MERRA-2 inst3 3d asm Np: 3d,3-Hourly,Instantaneous,Pressure- Level,Assimilation,Assimilated Meteorological Fields V5.12.4.
  24. Gibson PB, Cullen NJ (2015) Synoptic and sub-synoptic circulation effects on wind resource variability—a case study from a coastal terrain setting in New Zealand. Renew Energy 78:253–263CrossRefGoogle Scholar
  25. Goddard SD, Genton MG, Hering AS, Sain SR (2015) Evaluating the impacts of climate change on diurnal wind power cycles using multiple regional climate models. Environmetrics 26:192–201CrossRefGoogle Scholar
  26. GWEC (2017) Global Wind Report: Annual Market Update 2016. Global Wind Energy Council.
  27. Haupt SE, Copeland J, Cheng WY, Zhang Y, Ammann C, Sullivan P (2016) A method to assess the wind and solar resource and to quantify interannual variability over the united states under current and projected future climate. J Appl Meteorol Climatol 55:345–363CrossRefGoogle Scholar
  28. Horton DE, Skinner CB, Singh D, Diffenbaugh NS (2014) Occurrence and persistence of future atmospheric stagnation events. Nat Clim Change 4:698–703CrossRefGoogle Scholar
  29. Jacob DJ, Winner DA (2009) Effect of climate change on air quality. Atmos Environ 43:51–63CrossRefGoogle Scholar
  30. Jiménez PA, González-Rouco JF, Montávez JP, García-Bustamante E, Navarro J (2009) Climatology of wind patterns in the northeast of the Iberian Peninsula. Int J Climatol 29:501–525CrossRefGoogle Scholar
  31. Jin L, Harley RA, Brown NJ (2011) Ozone pollution regimes modeled for a summer season in California’s San Joaquin Valley: a cluster analysis. Atmos Environ 45:4707–4718CrossRefGoogle Scholar
  32. Kaffine DT, McBee BJ, Lieskovsky J (2013) Emissions savings from wind power generation in Texas. Energy J 34:155CrossRefGoogle Scholar
  33. Karnauskas KB, Lundquist JK, Zhang L (2017) Southward shift of the global wind energy resource under high carbon dioxide emissions. Nat Geosci. Google Scholar
  34. Leung LR, Gustafson WI (2005) Potential regional climate change and implications to US air quality. Geophys Res Lett 32Google Scholar
  35. Li X, Zhong S, Bian X, Heilman W (2010) Climate and climate variability of the wind power resources in the Great Lakes region of the United States. J Geophys Res Atmos 115Google Scholar
  36. Luderer G et al (2014) The role of renewable energy in climate stabilization: results from the EMF27 scenarios. Clim Change 123:427–441. CrossRefGoogle Scholar
  37. Ludwig FL, Horel J, Whiteman CD (2004) Using EOF analysis to identify important surface wind patterns in mountain valleys. J Appl Meteorol 43:969–983CrossRefGoogle Scholar
  38. McVicar TR, Van Niel TG, Li LT, Roderick ML, Rayner DP, Ricciardulli L, Donohue RJ (2008) Wind speed climatology and trends for Australia, 1975–2006: Capturing the stilling phenomenon and comparison with near-surface reanalysis output. Geophys Res Lett 35Google Scholar
  39. Mickley LJ, Jacob DJ, Field B, Rind D (2004) Effects of future climate change on regional air pollution episodes in the United States. Geophys Res Lett 31Google Scholar
  40. Millstein D, Wiser R, Bolinger M, Barbose G (2017) The climate and air-quality benefits of wind and solar power in the United States. Nat Energy 2:17134CrossRefGoogle Scholar
  41. NOAA (2017a) Arctic oscillation (AO). National Oceanic and Atmospheric Administration.
  42. NOAA (2017b) Historical El Nino/La Nina episodes (1950-present) National Oceanic and Atmospheric Administration.
  43. NOAA (2017c) North Atlantic Oscillation (NAO). National Oceanic and Atmospheric Administration.
  44. NOAA (2017d) Pacific Decadal Oscillation (PDO). National Oceanic and Atmospheric Administration.
  45. NOAA (2017e) Pacific-North American (PNA). National Oceanic and Atmospheric Administration.
  46. Olauson J, Edström P, Rydén J (2017) Wind turbine performance decline in Sweden. Wind Energy 20:2049–2053CrossRefGoogle Scholar
  47. Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Google Scholar
  48. Pryor S, Barthelmie R (2011) Assessing climate change impacts on the near-term stability of the wind energy resource over the United States. Proc Natl Acad Sci USA 108:8167–8171CrossRefGoogle Scholar
  49. Pryor S, Barthelmie R (2013) Assessing the vulnerability of wind energy to climate change and extreme events. Clim Change 121:79–91CrossRefGoogle Scholar
  50. Pryor S, Barthelmie RJ, Schoof J (2006) Inter-annual variability of wind indices across Europe. Wind Energy 9:27–38CrossRefGoogle Scholar
  51. Pryor S et al (2009) Wind speed trends over the contiguous United States. J Geophys Res Atmos 114Google Scholar
  52. Seefeldt MW, Cassano JJ, Parish TR (2007) Dominant regimes of the Ross Ice Shelf surface wind field during austral autumn 2005. J Appl Meteorol Climatol 46:1933–1955CrossRefGoogle Scholar
  53. Sherman P, Chen X, McElroy MB (2017) Wind-generated electricity in china: decreasing potential, inter-annual variability and association with changing climate. Sci Rep 7:16294. CrossRefGoogle Scholar
  54. Siler-Evans K, Azevedo IL, Morgan MG, Apt J (2013) Regional variations in the health, environmental, and climate benefits of wind and solar generation. Proc Natl Acad Sci USA 110:11768–11773CrossRefGoogle Scholar
  55. Staffell I, Green R (2014) How does wind farm performance decline with age? Renew Energy 66:775–786CrossRefGoogle Scholar
  56. Sun W, Hess P, Liu C (2017) The impact of meteorological persistence on the distribution and extremes of ozone. Geophys Res Lett 44:1545–1553CrossRefGoogle Scholar
  57. Tindal A (2011) Financing wind farms and the impacts of P90 and P50 Yields. In: EWEA Wind Resource Assessment Workshop.
  58. Vautard R, Cattiaux J, Yiou P, Thépaut J-N, Ciais P (2010) Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness. Nat Geosci 3:756CrossRefGoogle Scholar
  59. Wang M, Ullrich P (2018) Marine air penetration in California’s Central Valley: Meteorological drivers and the impact of climate change. J Appl Meteorol Climatol 57:137–154CrossRefGoogle Scholar
  60. Wang M, Ullrich P, Millstein D (2018) The future of wind energy in California: future projections with the variable-resolution CESM. Renew Energy 127:242–257CrossRefGoogle Scholar
  61. Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244CrossRefGoogle Scholar
  62. Wiser R, Jenni K, Seel J, Baker E, Hand M, Lantz E, Smith A (2016) Expert elicitation survey on future wind energy costs. Nat Energy 1:16135CrossRefGoogle Scholar
  63. Wiser HR, Bolinger M (2017) 2016 Wind Technologies Market Report. U.S. Department of Energy.
  64. Xie L, Carvalho PM, Ferreira LA, Liu J, Krogh BH, Popli N, Ilic MD (2011) Wind integration in power systems: operational challenges and possible solutions. Proc IEEE 99:214–232CrossRefGoogle Scholar
  65. Yu L, Zhong S, Bian X, Heilman WE (2015) Temporal and spatial variability of wind resources in the United States as derived from the climate forecast system reanalysis. J Clim 28:1166–1183CrossRefGoogle Scholar
  66. Yu L, Zhong S, Bian X, Heilman WE (2016) Climatology and trend of wind power resources in China and its surrounding regions: a revisit using climate forecast system reanalysis data. Int J Climatol 36:2173–2188CrossRefGoogle Scholar
  67. Zaremba LL, Carroll JJ (1999) Summer wind flow regimes over the Sacramento Valley. J Appl Meteorol 38:1463–1473CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Lawrence Berkeley National LaboratoryBerkeleyUSA
  2. 2.University of California, DavisDavisUSA
  3. 3.DNV GLSan DiegoUSA

Personalised recommendations