1.1. Temporal trends in near-surface wind speeds
A number of recent studies have reported declines in near-surface observed wind speeds during the past 30–50 years over parts of North America (Klink, 1999; Tuller, 2004; Pryor et al., 2009; Pryor and Ledolter, 2010), China (Xu et al., 2006; Jiang et al., 2010), regions of Europe (Pirazzoli and Tomasin, 2003; Brazdil et al., 2009) and Australia (McVicar et al., 2008). However, converse trends toward increasing wind speeds have been reported over the global oceans (Young et al., 2011). For example, based on data from passive microwave satellites, Wentz et al. (2007) found that wind speeds averaged over the tropics (30°S–30°N) increased by 0.04 m s–1/decade (0.6%/decade) over the period 1987–2006, while over all oceans the average trend was +0.08 m s–1/decade (1.0%/decade).
Temporal trends derived based on observed near-surface wind speeds are not always consistent with tendencies manifest either in other observational data or in reanalysis data sets. For example, in the analysis of daily mean wind speed data over Australia, measurements from terrestrial anemometers showed declines (a ‘stilling’) of −0.13 m s–1/decade when averaged over the entire country, but two gridded wind speed datasets (including the NCEP/NCAR reanalysis output) did not exhibit temporal tendencies (McVicar et al., 2008). Further, comparisons of 10-m wind speeds from observational data sets, reanalysis products and Regional Climate Model (RCM) simulations over North America showed trends in reanalysis data sets and RCM output were generally of lesser magnitude, and frequently of opposite sign, to those manifest in observational data sets (Pryor et al., 2009). Smits et al. (2005) also reported that the apparent decrease in storminess over the Netherlands based on station data was inconsistent with that based on reanalysis data, which suggested increased storminess during the same 41-years period.
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., 2010; Pryor and Ledolter, 2010) which further confounds identification of robust trends and attribution thereof.
The discrepancies between temporal trends derived from observational data sets and from reanalysis or RCM simulations and over land surface areas and the oceans may also reflect, at least in part, a physical cause of the trends. Specifically, changes in surface roughness over the land surfaces have led to an increase in surface drag and a reduction in near-surface wind speeds. For example, model simulations over Eurasia using MM5 suggested that the recent increase in surface roughness (due to land-over change) explained 25–60% of the reported decline in 10-m wind speeds (Vautard et al., 2010). Since land-surface characteristics are not variable with year in the reanalysis data sets, changes in roughness length would not be characterized by the boundary data sets used within the reanalysis systems (Pryor et al., 2009). This mechanism, if confirmed, would also account for the discrepancy between temporal trends over land and water surfaces.
1.2. Atmospheric circulation and wind speed trends over China
The seasonal and interannual variability of Chinese climate is largely due to the summer and winter monsoons (Ding, 1994), which are major components of the global large-scale circulation and is linked to a number of internal climate modes as manifest in several teleconnection indices. For example, El Niño–Southern Oscillation (ENSO) is an important factor in the East Asian monsoon variability (Wu et al., 2003; Lim and Kim, 2007; Zhou and Wu, 2010). Further, Gong et al. (2001) found that the Arctic oscillation (AO) influenced the East Asian winter monsoon through the Siberian High. Later, Gong and Ho (2003) indicated that the AO significantly impacted the year-to-year variations in the East Asian summer monsoon rainfall via changes in the large-scale atmospheric circulation patterns. However, these prior studies have focused on linking the phase and magnitude of these teleconnection indices to variability in temperature and/or precipitation. Here we analyze relationships between intra- and interannual wind speeds and indices of the AO and ENSO.
Prior analyses of in situ daily average wind speed data from China have indicated declining values over the last few decades (Table 1). For example, Guo et al. (2010) estimated the spatially averaged trend in annual mean observed wind speed over China from 1969 to 2005 to be −0.18 m s–1/decade. Xu et al. (2006) reported a spatially averaged decline in annual mean wind speed (again computed from daily mean measurements) between 1969 and 2000 of 0.18–0.20 m s–1/decade. Using a similar data set, Jiang et al. (2010) reported a decline between 1956 and 2004 of 0.124 m s–1/decade, and most recently Fu et al. (2011) found an average decline in annual mean wind speed of 0.13 m s–1/decade between 1961 and 2007. A further study reported that over eastern Asia the annual mean surface wind speed changed by approximately −0.12 m s–1/decade between 1979 and 2008 (Vautard et al., 2010). Here we extend these prior analyses by specifically comparing temporal trends and seasonality as derived from the in situ data with those from global reanalysis data sets. We further seek to identify causality for any discrepancies identified and temporal trends and interannual variability.
|Original paper||Time-series duration||Number of stations||Trend (m s–1/decade)|
|Cong et al. (2009)||1956–2005||317||−0.11|
Jiang et al. (2010)
|Yin et al. (2010)||1961–2008||595||−0.09|
|Fu et al. (2011)||1961–2007||597||−0.13|
|Guo et al. (2010)||1969–2005||652||−0.18|
|Xu et al. (2006)||1969–2000||305||−0.2|