Climate Dynamics

, Volume 53, Issue 1–2, pp 41–66 | Cite as

Multidecadal to centennial surface wintertime wind variability over Northeastern North America via statistical downscaling

  • Etor E. Lucio-EceizaEmail author
  • J. Fidel González-Rouco
  • Elena García-Bustamante
  • Jorge Navarro
  • Hugo Beltrami


The variability of the surface wind field over Northeastern North America was analysed through a statistical downscaling (SD) approach, using the relationships among the main large-scale and observed wind circulation modes. The large-scale variables were provided by 12 global reanalyses. The observed zonal and meridional wind components come from a database of 525 sites spanning over 1953–2010. A large percentage of the regional variability was explained in terms of three major large- and regional/local-scale coupled circulation patterns, accounting for 55.3% (59.3%) of the large (regional/local) scale variability. The method delivered robust results regardless of the SD model configuration, albeit with sensitivity to the number of retained circulation modes and the large-scale window size, but not to the reanalysis chosen for the large-scale variables. The methodological uncertainty was larger for sites/wind components with larger variability. A parameter configuration chosen for yielding the best possible SD estimations showed high correlation values between these estimations and the observations for the majority of the sites (0.6–0.9, significant at \(p<0.05\)), and a realistic wind variance (standard deviation ratios between 0.6 and 1.0), with similar results regardless of the reanalysis. The reanalysis direct wind outputs showed higher correlations than the SD estimates (0.7–0.97, also significant). The skill in reproducing observational variance differed considerably from model to model (ratios between 0.5 and 3). The regional wind climatology was reconstructed back to 1850 with the help of century long reanalyses and two additional SLP gridded datasets allowing to estimate the variability at decadal and multidecadal timescales. Recent trends in the wind components are not unusual in the context of century-long reconstructed variability. Extreme values in both components tend to appear associated with high values in the first two modes of variability.


Surface wind Statistical downscaling Spatial and temporal variability Sensitivity Reanalysis intercomparison Past reconstructions Extreme events 



EELE was supported by the Agreement of Cooperation 4164281 between the UCM and St. Francis Xavier University, and the ILMODELS (CGL2014-59644-R) and NEWA (PCIN-2014-017-C07-06) projects of the MINECO (Spain). Funding for 4164281 was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC DG 140576948), the Canada Research Chairs Program (CRC 230687), and the Atlantic Innovation Fund (AIF-ACOA). HB holds a Canada Research Chair in Climate Dynamics. EGB and JFGR held James Chair visiting Professorships (2017) at StFX University. JN, EGB and JFGR were supported by NEWA (PCIN-2014-017-C07-03, PCIN-2014-017-C07-06) and NEWA2 (PCIN-2016-176, PCIN-2016-009) projects of the MINECO (Spain). This research has been conducted under the Joint Research Unit between UCM and CIEMAT, by the Colaboration Agreement 7158/2016. pecial thanks to Douglas Schuster for information regarding the assimilation of surface wind land observations in NCAR-R1, DOE-R2 and CFSR reanalyses.

Note: a version of the observational data used in this study will be made to the public. Likewise, the wind climatologies showed in the manuscript will also be available. Potential users interested in having the data are invited to contact the corresponding author.


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Copyright information

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

Authors and Affiliations

  1. 1.Facultad de CC. FísicasUniversidad Complutense MadridMadridSpain
  2. 2.Instituto de Geociencias (UCM-CSIC)MadridSpain
  3. 3.Dpto. Física de la Tierra y Astrofísica, Fac. CC. FísicasUniversidad Complutense MadridMadridSpain
  4. 4.División de Energías RenovablesCIEMATMadridSpain
  5. 5.Climate and Atmospheric Sciences InstituteSt. Francis Xavier UniversityAntigonishCanada

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