Li, D. (2016): Added value of high-resolution regional climate model: selected cases over the Bohai Sea and the Yellow Sea areas. International Journal of Climatology, doi:10.1002/joc.4695
Added value (AV) from dynamical downscaling has long been a crucial and debatable issue in regional climate studies. To assess AV generated by COSMO Climate Local Model (CCLM), a model-reconstructed hindcast with 7 km grid resolution was compared against the forcing data set ERA-Interim (ERA-I) over the Bohai Sea and the Yellow Sea, with satellite and in situ observation as reference. Both quantitative metrics and qualitative assessments have been used in the investigation of AV by CCLM. Land surface winds, extreme winds, a typhoon, a cold surge and a vortex street have been selected in the assessment process. Statistical analysis on surface winds reveals that high-resolution CCLM hindcast can add value to ERA-I in reproducing wind intensities and direction, probability distribution and extreme winds mainly at mountain areas, which may be related to the highly resolved and accurate description of terrain roughness length and orographic barriers in CCLM. With respect to atmospheric processes, CCLM outperforms ERA-I in resolving detailed temporal and spatial structures for phenomena of a typhoon and of a cold surge; CCLM generates some orography-related phenomena such as a vortex street, which is not captured by ERA-I. These AVs demonstrate the utility of the 7-km resolution CCLM for regional and local climate studies and applications.
Li, D.; Geyer, B.; Bisling, P. (2016): A model-based climatology analysis of wind power resources at 100-m height over the Bohai Sea and the Yellow Sea. Applied Energy, Volume 179, Pages 575–589, doi:10.1016/j.apenergy.2016.07.010
China has set ambitious goals for the development of offshore wind energy to meet the increasing energy demand of coastal provinces. Many studies have assessed the potential offshore wind energy in Chinese territorial waters. However, few studies have focused on the climatology, variability, and extreme climate of wind speeds and wind power, especially at hub height in this area. This type of study is important for selecting promising sites for offshore wind farms. In the present study, a 35-year (1979–2013) high-resolution (7 km) wind hindcast over the Bohai Sea and the Yellow Sea (BYS) at 100-m height was constructed using the regional climate model COSMO-CLM (CCLM) driven by the ERA-Interim reanalysis dataset. The quality of wind speeds reconstructed by CCLM was assessed by a comparison with observation data at several stations. After verification, the climatology, variability, and extreme climate of winds over the BYS were spatially and temporally investigated. The results show that the 35-year mean wind speed is mostly between 7.0 and 7.5 m/s; in the coastal areas of the BYS, the mean is less than 7.0 m/s, and in the remote offshore areas, the mean is greater than 7.5 m/s. The daily mean wind speed is stronger (weaker) in winter (summer) half year, with stronger (weaker) spatial variability. Wind power density is mainly 300–500 W/m2. The interannual variability of annual mean wind speed and the wind power are in the range of 0.1–0.3 m/s and 10–40 W/m2, respectively. Decadal variances of the mean wind speed and the wind power are roughly within ±2% and ±5%, respectively, with a stronger variability along the southwestern coasts of the Yellow Sea. The distribution patterns of extreme winds (i.e., 5, 10, 30, and 50-year return values) are generally similar, with strength increasing from the northwest to the southeast. The wind energy characteristics for water areas and potential wind farm sites are summarized.
Schubert-Frisius, M., F. Feser, H. von Storch, and S. Rast (2016): Optimal spectral nudging for global dynamical downscaling. Monthly Weather Review, doi:10.1175/MWR-D-16-0036.1
This study analyzes a method to construct a homogeneous, high-resolution global atmospheric hindcast. The method is the spectral nudging technique which was applied to a state-of-the-art general circulation model (ECHAM6, T255L95). Large spatial scales of the global climate model prognostic variables were spectrally nudged towards a reanalysis data set (NCEP1, T62L28) for the last decades. The main idea is the addition of dynamically consistent regional weather details to the coarse grid NCEP1 reanalysis. A large number of sensitivity experiments were performed, using different nudging e-folding times, vertical profiles, wave numbers, and variables. Comparisons with observations and several reanalyses showed a high dependency on the variations of the nudging configuration. At the global scale, the accordance is very high for extra-tropical regions and lower in the tropics. A wave number truncation of 30, a relatively short e-folding time of 50 min and a plateau-shaped nudging profile applied only to divergence and vorticity generally yielded the best results. This is one of the first global spectral nudging hindcast studies and the first applying an altitude-dependent profile to selected prognostic variables. The method can be applied to reconstruct the history of extreme events such as intense storms in the context of ongoing climate change.