Changes And Their Impact Factors Of Wind Speed(Energy)Over China Under The Background Of Climate Warming

First of all, temporal trends (1971to2007) in10-m wind speeds from homogeneous observational data sets from540weather stations and reanalysis data sets are quantified and compared. Then, the probability distribution of daily average wind speed and average energy density in mainland of China are studied by using Weibull distribution model. Thirdly, possible physical causes of multi-time scale changes in near surface wind speeds are investigated in terms of large-scale atmospheric circulation primarily. Finally, The ability of current generation coupled Atmosphere-Ocean General Circulation Models (AOGCMs), released by Coupled Model Intercomparison Project Phase5(CMIP5), to accurately simulate the near-surface wind climate over China is evaluated by comparing output from the historical period (1971-2005) with an observational data set and reanalysis output. Also, Outputs for the current century from the same AOGCMs are examined relative to the contemporary wind climate. The main conclusions are summed up as follows:(1) Annual mean wind speeds computed from in situ measurements of daily mean wind speeds exhibit declining trends over much of the country during the period1971-2007. The trend is of larger magnitude in the upper percentiles of the wind speed probability distribution and during the spring months. The downward trend of wind speed is greast in the south-east coastal islands and the Northeast. It is worthy of note that there will be different temporal trends if homogeneity test is not performed for observerd wind speeds. Intercomparison of direct in situ observations and those from the NCEP/NCAR and ERA-40reanalysis data sets indicates the patterns exhibit a high degree of spatial similarity. However, data from the ERA-40reanalysis reanalysis are negatively biased and exhibit little trend.(2) Weibull distribution is better to describe the skewness and kurtosis of wind speed probability than normal distribution, overcoming the inevitable random oscillation caused by sample frequency. The geographical distribution of the average wind power density and its variation is consistent with wind speed correspondingly. The declining trends of annual and seasonal wind energy are weaker before rejecting inhomogeneous wind speed data sets.(3) The decreased wind speed over China is mainly due to the change of lare-scale atmospheric circulation because:a) the wind speed in mid-low troposphere consistently show weakening trend and the spatial distribution of the trend is highly consistent with the near-surface wind speed; b) as the major driver of wind speed, the pressure gradient in surface and mid-low troposphere exhibit downward trend; c) Asian monsoon and Asian meridional circulation index are weakened; d) decreasing (increasing) sea level pressure in Eurasia in winter (summer) leads to the declining of zonal pressure gradient between the land and sea; e) in north China, weakend vertical zonal wind shear results in increased atmospheric stability and further air momentum down from upper levels is becoming weak. (4) In terms of the interannual scale, the Arctic Oscillation (AO) has significant influence on wind speed over north of30°N. During wintertime, under the background of positive AO phases, the patterns of East Asia westerly jet,500hPa height field, wind field at lower levels and sea-level pressure are favorable to weaker wind speeds. During winter season, average of500hPa height field over (80～100°E,50～65°N) has negative impact on near-surface wind speed.(5) The AOGCM initialization condition has little influence on the simulated wind speed and temporal variability during1971to2005, however each of the CMIP-5generation AOGCMs considered display both positive bias in mean near-surface wind speeds and negative bias in the inter-annual variability relative to reanalysis output and observations and no AOGCM exhibit uniformly best agreement with either the reanalysis products or the in situ observations. Some AOGCMs reproduce at least some aspects of the seasonality of wind speeds over China. The spatially averaged and spatial fields of seasonal wind speeds for2066-2100exhibit very close accord with simulations from the AOGCM for the historical period (1971-2005). The mean wind speeds from each model computed for2066to2100do not show a substantial, consistent dependence on the degree of radiative forcing or the simulated mean wind speed during the historical period. Thus, any climate change signal is considerably smaller than the discrepancies between the different AOGCMs during the historical period.