Studies On The Low-Levels Winds Obtained By Dynamical Downscaling Over Heterogeneous Surfaces

The general circulation models could represent the large-scale atmospheric conditions, but the mesoscale and smaller scale processes could not be captured due to coarse resolution. Dynamical downscaling introduces new scales through the non-linear interaction between the large-scale atmospheric circulation and the local forcing in limited-area models. Dynamical downscaling has been extensively used in regional climate modeling and climate change projection of precipitation and temperature, but is not sufficiently disscued in respect of low-levels winds which are essential for wind resource assessment and air quanlity studies. Therefore, this work is focus on the mesoscale low-levels winds obtained by dynamical downscaling. Firstly, discussions are presented based on the downscaled local winds over the urban agglomeration and coastal complex topography, respectively. Then, validation and projection of wind climate in China is studies under IPCC A1B scenario. The main results and conclusions are summarized as follows.Two 1-year experiments with different land use conditions were performed by WRF/Noah/UCM modeling system over the Pearl River Delta region in Chapter 2. With new MODIS land use data, the urban distribution was well represented in the control experiment (hereafter CTRL). The diurnal variation of wind speed observed by the wind-profiling radar was reproduced in CTRL. The simulated wind speed was overestimated below about 1 km, and underestimated above about 1 km. In addition, a sensitive experiment (hereafter NO-URBAN) with urban land use changed to cropland was performed in order to determine the impact of urbanization on the local circulation. Compared to NO-URBAN, CTRL produced smaller latent heat and larger ground heat fluxes during daytime, later more heat was released from the ground during nighttime, and then transferred to the atmosphere through positive sensible heat fluxes. Correspondingly, the surface wind speed decreased during daytime and increased during nighttime (excluding in winter). The wind speed increased about 1 m/s during nighttime in summer.In Chapter 3, the results from a hybrid approach that combines a mesoscale meteorological model with a diagnostic model to produce high-resolution wind fields in complex coastal topography are evaluated. The diagnostic wind model (California Meteorological Model, hereafter CALMET) with 100-m horizontal spacing was driven with outputs from the Weather Research and Forecasting (WRF) model to obtain near-surface winds for the 1-year period from 12 September 2003 to 11 September 2004. Results were compared with wind observations at 7 sites. Correlation coefficients indicate that the WRF-simulated winds were produced reasonably well. The correlation coefficients were ranging from 0.5 to 0.7 for the zonal wind component and from 0.75 to 0.85 for the meridional wind component. The variation of seasonally-averaged wind field and sea-breezes were reproduced. Moreover, combining the CALMET model with WRF significantly improves the spatial variability of the simulated wind fields. It can be concluded that the WRF/CALMET modeling system is capable of providing a detailed near-surface wind field, but the physics in the diagnostic CALMET model need to be further improved.In Chapter 4, surface wind climate in China in the late 20th century (1981-2000) and in the middle 21st century (2041-2060) was simulated by the regional climate model MM5, based on the multi-model datasets of 20C3M and the IPCC A1B scenario projection provided by ECHAM5, respectively. The simulated present wind climate was evaluated against the daily wind speed observations obtained from surface meteorological stations, and then future change of wind climate features was investigated in respect of climatological distributions and strong wind events. Results show that MM5 can reproduce the climatological patterns of both mean wind speed and 90th percentile wind speed. However, MM5 did not present the stilling trend which is obvious in the observations. Under the A1B scenario, Annual mean wind speed for 2041-2060 will be less than those for 1981-2000 by about 0.1 m/s in most area of China. Greater wind speed appears in northeast China, areas surrounding Bo Sea, and southwest China. Seasonal mean wind speed will increase over the eastern part of China, and decrease over the western part of China in warm seasons (summer and autumn). On the other hand, it will increase over the northern part of China, and decrease over the southern part of China in cold seasons (winter and spring). Moreover, it was found that the strong wind events will decrease under A1B scenario. It can be concluded that MM5 has certain capabilities of simulating surface wind climate in China, and the wind climate change projected based on this model is reliable to some extent.