## 1. Introduction

[2] An earlier paper [*Pryor et al.*, 2009] reports linear trends in annual 50th and 90th percentile 10 m wind speeds at 0000 and 1200 UTC over approximately 30 years for nearly 1000 measurement time series collected across the continental United States. The data analyzed were drawn from two data sets available from the National Climate Data Center (NCDC): NCDC-6421 [*Groisman*, 2002] (from which data were selected for 1973–2001) and DS3505 (http://www.ncdc.noaa.gov/oa/mpp/freedata.html, DS3505 surface data, global hourly) (from which data were selected for 1973–2005). Data from a given source, station and reporting time were deemed sufficient for analysis if over 300 observations are present in every year of record and more than two thirds of valid observations are available in each climatological season of each year. After computing the yearly summary statistics (i.e., median and 90th percentile wind speed) at a station, *Pryor et al.* [2009] fitted a regression model on time

The response *y*_{t} is the summary statistic (either median or 90th percentile wind speed) at a particular measurement station in year *t* (*t* = 1 for year = 1973, until *t* = *n* = 33 for year = 2005), and *β*_{0}, *β*_{1} are the regression coefficients with *β*_{1} expressing the yearly change (i.e., trend). Assuming independent errors *a*_{t}, *Pryor et al.* [2009] used ordinary least squares to determine the trend estimate _{1}. Using a bootstrap resampling technique and a confidence level of 90%, they found evidence for a statistically significant reduction in the wind speed percentiles for over half of the stations considered in each of the data sets, sampling times and both percentile values.

[3] Following publication of the aforementioned article it has been suggested that the study may have been biased because it did not address possible temporal autocorrelation in the annual wind speed statistics. This assumption of independence among the errors in model (1) may be incorrect if the regression is carried out on time series data. Positive autocorrelations in the time series lead to a negative bias in the standard error of the usual regression estimate, which implies “spurious” regressions [*Box and Newbold*, 1971; *Granger and Newbold*, 1974; *Abraham and Ledolter*, 2006, chap. 10]. A model that ignores positive autocorrelation is likely to find a significant effect of a regressor variable, despite the fact that no relationship is present. Since annual statistics of wind speeds may exhibit positive autocorrelations, we use the same data time series as *Pryor et al.* [2009] to address whether either the trend magnitudes and/or trend significance reported in that earlier publication were in error due to temporal autocorrelation in the time series. Specifically, we check whether (1) autocorrelation among the residuals is present, (2) the presence of such autocorrelation led the investigators to conclude too often that the estimated decreases in wind speed are statistically significant, and (3) an analysis that incorporates autocorrelated errors into the model would find different magnitudes for the trend reductions.

[4] We also present analyses focused on (1) determining whether temporal trends evident in the 10 m wind speeds are also evident in direct observations of wind speed at 850 or 700 hPa and how sensitive they are to the specific period of record and (2) providing a first assessment of possible causes of the temporal autocorrelation in annual time series of 50th and 90th percentile wind speeds. For these analyses, twice-daily radiosonde data for 1950–2008 were obtained from the Integrated Global Radiosonde Archive (http://www.ncdc.noaa.gov/oa/climate/igra/index.php), and are presented for all stations (23) from which the time series have over 365 data points present in every year of record. The pressure level from which wind speeds were analyzed varies as a function of longitude to account for the higher terrain west of −103°E. Thus data are presented for the 850 hPa level east of −103°E, and the 700 hPa level west of that longitude.

[5] While there is considerable interannual variability in wind climates [*Klink*, 2002; *Petersen et al.*, 1998], as described herein, there is also substantial temporal autocorrelation. Near-surface wind climates at many midlatitude locations are strongly linked to extratropical cyclone activity and hence are a function of cyclone frequency, intensity or tracking, which in turn are linked to persistent large-scale climatic patterns or regimes as manifest in teleconnection indices [*Enloe et al.*, 2004; *Klink*, 2007; *Schoof and Pryor*, 2006]. Thus herein we analyze annual 90th percentile wind speeds in the context of three dominant key teleconnection indices: El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and the Pacific North American (PNA) index.