Publication: Summertime surface wind variability over Northeastern North America at multidecadal to centennial time scales via statistical downscaling
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2020-03
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Amer Meteorological Soc.
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Abstract
The variability of the surface zonal and meridional wind components over northeastern North America during June–October is analyzed through a statistical downscaling (SD) approach that relates the main wind and large-scale circulation modes. An observational surface wind dataset of 525 sites over 1953–2010 provides the local information. Twelve global reanalyses provide the large-scale information. The large-to-local variability of the wind field can be explained, to a large extent, in terms of four coupled modes of circulation explaining a similar amount of variance. The SD method is mostly sensitive to the number of retained modes and subregionally to the large-scale information variable, but not to the reanalysis source. The SD methodological uncertainty based on the use of multiple configurations is directly related to the variability of the wind, similar in relative terms for both components.With an adequate choice of parameters the SD estimates provide more realistic variances than the reanalysis wind, although their correlations with respect to observations are lower than the latter. Additionally, while these different SD estimations are very similar on the reanalysis used, the various reanalysis wind fields show noticeable differences, especially in their variances. The wind variability is reconstructed back to 1850, making use of century-long reanalyses and two additional SLP gridded datasets, which allows estimating the variability at decadal to multidecadal time scales. Recent negative (significant) trends in the zonal component do not stand out in the multidecadal context, but they are consistent with a global stilling process, and are partially attributable to changes in the large-scale dynamics.
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© 2020 American Meteorological Society. This research has been conducted under the Joint Research Unit between UCM and CIEMAT, by the Collaboration Agreement 7158/2016. Part of the financial support for some of the authors involved in this work was provided by the Agreement of Cooperation 4164281 (funded 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) between the UCM and St. Francis Xavier University, the ILMODELS (CGL2014-59644-R), NEWA (PCIN-2014-017-C07-03, PCIN-2014-017-C07-06) and NEWA2 (PCIN-2016-176, PCIN-2016-009) projects of MINECO (Spain), and GLOBAL Forecasters S.L. Special thanks to Douglas Schuster for information regarding the assimilation of surface wind land observations in NCAR-R1, DOE-R2 and CFSR reanalyses.