Evaluating the effects of land use and cover change on the decrease of surface wind speed over China in recent 30 years using a statistical downscaling method

Abstract

V10m-SDM has better performance in the spatial-temporal changes than does V10m-ERA with respect to V10m-OBV. The impact of LUCC on the SWS was pronounced, the SWSD was −0.24 m s −1 in 1993, and the SWSD reached −0.56 m s −1 in 2011. LUCC could induce a 0.17 m s −1 wind speed decrease per 10 year in the ECP region during 1993-2011. Furthermore, each 10 % rise of the urbanization rate could cause an approximately 0.12 m s −1 decrease in wind speed. Additionally, pressure-gradient force was eliminated as the primary cause of the observed longterm decrease of SWS in ECP by composite analysis and temporal serial analysis, and the rise of the surface drag force caused by LUCC was identified as an important factor inducing the SWS decrease during 1993-2011 in ECP region. Moreover, the results from different time lengths used to construct SDM were compared, and the uncertainties in SWSD were evaluated by different SDMs established by different time periods. The causes of these observed decreases in SWS remain unclear.Abstract The long-term decrease of surface wind speed (SWS) has been revealed by previous studies in China in recent decades, but the reasons for the SWS decrease remain uncertain. In this paper, we evaluated the effects of land use and cover change (LUCC) on the SWS decrease during 1980-2011 over the Eastern China Plain (ECP) region using a combined method of statistical downscaling and observation minus reanalysis data, which was used to improve the climate prediction of general circulation models and to evaluate the influence of LUCC on climate change. To exclude the potential influence of LUCC on SWS observation, a statistical downscaling model (SDM) was established during 1980-1992 because a lower extent of LUCC occurred during this period than in later periods. The skill of the SDM was checked by comparing the results of different predictor combinations. Then, SDM was used to improve the wind speed data at 10 m above the surface in the ERA-Interim reanalysis data (V10m-ERA) during 1993-2011, which decreased the error in the reanalysis wind speed as far as possible. Then, the difference between the station observed SWS (V10m-OBV) and the downscaled SWS (V10m-SDM) during 1993-2011 (SWSD) was considered the quantitative estimation of the influence of the LUCC on SWS in this period. The V10m-SDM can capture both the large-scale and local characteristics in the observation, and their patterns are very similar.

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

“…18). However, the differences in the observed SWS values between years with stronger and weaker PGF conditions are not statistically significant, whereas the differences in the wind speeds at 925 and 850 hPa between years with stronger and weaker PGF conditions are statistically significant (Wu et al 2017). These results suggest that the effects of PGF on the SWS include distinctly regional differences and that the changes in SWS cannot be explained by changes in the PGF everywhere.…”

“…General circulation models and RCMs are designed to simulate the present climate and project future climate. Hence, they can effectively predict the principal features of wind speeds at the global-scale but are unable to present the historically observed magnitude and spatial variability of wind speeds at the regionaland local-scale, because of their coarse spatial resolution (Fan et al 2011(Fan et al , 2013Wu et al 2017). Therefore, the downscaling technique is needed to further study longterm changes in SWS.…”

“…The observed decrease in SWS in the Eastern China Plain (ECP) is best explained by an estimation that used a frictional wind model with an increase in the drag coefficient related to LUCC . A quantitative evaluation that used a downscaling method showed an SWS decreasing rate between − 0.17 and − 0.23 m s −1 decade −1 in the ECP over the past 30 years (Wu et al 2017). More information about averaged trends in regional SWS in China can be found in Table 2.…”

“…17); therefore, Guo et al (2011) argued that the decrease in the observed SWS in recent decades is mainly attributable to changes in the lower-tropospheric PGF. To investigate the actual influences of PGF on SWS, Wu et al (2017) compared the wind speeds near the surface, at 925 hPa, and at 850 hPa over the ECP region and found different spatio-temporal characteristics at different heights. The authors also found inconsistent spatial characteristics of the wind speed variances at these levels: much greater variance was observed in the SWS data than in the 925 and 850 hPa data (Wu et al 2017).…”

“…To investigate the actual influences of PGF on SWS, Wu et al (2017) compared the wind speeds near the surface, at 925 hPa, and at 850 hPa over the ECP region and found different spatio-temporal characteristics at different heights. The authors also found inconsistent spatial characteristics of the wind speed variances at these levels: much greater variance was observed in the SWS data than in the 925 and 850 hPa data (Wu et al 2017). They compared the PGF values at these three levels and found that the PGF has increased significantly at the near-surface level (corresponding to a significant decrease in observed SWS values) and that the PGF has increased slightly at 925 and 850 hPa (Fig.…”