Homogenization and Trend Analysis of Canadian Near-Surface Wind Speeds

Abstract

Near-surface wind speeds recorded at 117 stations in Canada for the period from 1953 to 2006 were analyzed in this study. First, metadata and a logarithmic wind profile were used to adjust hourly wind speeds measured at nonstandard anemometer heights to the standard 10-m level. Monthly mean near-surface wind speed series were then derived and subjected to a statistical homogeneity test, with homogeneous monthly mean geostrophic wind (geowind) speed series being used as reference series. Homogenized monthly mean near-surface wind speed series were obtained by adjusting all significant mean shifts, using the results of the statistical test and modeling along with all available metadata, and were used to assess the long-term trends. This study shows that station relocation and anemometer height change are the main causes for discontinuities in the near-surface wind speed series, followed by instrumentation problems or changes, and observing environment changes. It also shows that the effects of artificial mean shifts on the results of trend analysis are remarkable, and that the homogenized near-surface wind speed series show good spatial consistency of trends, which are in agreement with long-term trends estimated from independent datasets, such as surface winds in the United States and cyclone activity indices and ocean wave heights in the region. These indicate success in the homogenization of the wind data. During the period analyzed, the homogenized near-surface wind speed series show significant decreases throughout western Canada and most parts of southern Canada (except the Maritimes) in all seasons, with significant increases in the central Canadian Arctic in all seasons and in the Maritimes in spring and autumn.

Changes in terrestrial near-surface wind speed and their possible causes: an overview

“…, and the declines in annual mean SWS were matched by increases in the percentage of calm conditions and by decreases in high wind speeds (Tuller 2004). Based on the credibility of the raw wind dataset from Canada, Wan et al (2010) homogenized monthly mean SWS series by adjusting all significant mean shifts and identified significant reductions in SWS throughout western Canada and most parts of southern Canada from 1953 to 2006 (Fig. 3b).…”

“…A significant decrease in SWS has been observed in the Western Plains region, where the SWS during the spring has decreased by 20% over the past 30 years (Green et al 2012). Furthermore, the most significant decrease in SWS from 1989 to 1998 occurred in Wisconsin Wan et al (2010)) and reached − 0.51 m s −1 decade −1 , whereas weak declines (smaller than − 0.09 m s −1 decade −1 ) occurred in the southwestern region of Idaho (Reba et al 2011), the American Midwest (Abhishek et al 2010), the southern region of California (Rasmussen et al 2011). Malloy et al (2015) reported a clear slowdown in SWS in the southeastern United States, whereas Mahowald et al (2007) reported an SWS reduction of only − 0.08 m s −1 decade −1 in the southern United States.…”

“…Changes in these factors can cause large discontinuities in the wind data series. However, such changes are often inevitable, especially in a long-term record (Wan et al 2010). DeGaetano (1998) investigated the observational biases in hourly SWS from 41 stations in the northeastern United States and found that the wind speed biases were greater than the wind direction biases.…”

Wind speed trends over China: quantifying the magnitude and assessing causality

“…The lack of correspondence between observational and reanalysis data sets and between observational data sets may derive from differences in time-series duration (and the short time-series of wind speed that are available for analysis), changes in station location, measurement height, data recording procedures and instrumentation deployed, in addition to difficulties in developing homogeneous observed records of near-surface wind speed (Pryor et al, 2009;Fu et al, 2011;Wan et al, 2010). Further, mid-latitude wind speeds exhibited high variability at interannual to interdecadal time scales linked to seasonal variation of the atmospheric circulation and internal modes of climate variability (Enloe et al, 2004;Pryor et al, 2005, Park et al, 2010Pryor and Ledolter, 2010) which further confounds identification of robust trends and attribution thereof.…”

Comparison of storm damage functions and their performance

“…with wind velocity v, anemometer height h, and an exponent λ = 1/7 as discussed in Wan et al (2010). Inhomogeneities in meteorological times-series can be identified by finding an optimal solution to the multiple breakpoint problem.…”