DOI: 10.1175/JAMC-D-19-0048.1
论文题名: Comparison of statistical and dynamic downscaling techniques in generating high-resolution temperatures in China from CMIP5 GCMs
作者: Zhang L.E.I. ; Xu Y. ; Meng C. ; Li X. ; Liu H. ; Wang C.
刊名: Journal of Applied Meteorology and Climatology
ISSN: 15588424
出版年: 2020
卷: 59, 期: 2 语种: 英语
Scopus关键词: Climate change
; Finite difference method
; Mean square error
; Diurnal temperature ranges
; Global climate model
; Minimum temperatures
; Regional climate modeling
; Root mean square errors
; Spatial correlation coefficients
; Spatial disaggregation
; Temperature simulations
; Climate models
; air temperature
; climate modeling
; CMIP
; comparative study
; downscaling
; global climate
; regional climate
; statistical analysis
; trend analysis
; China
英文摘要: In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information. © 2020 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159601
Appears in Collections: 气候变化与战略
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作者单位: Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China; Met Office Hadley Centre, Exeter, United Kingdom
Recommended Citation:
Zhang L.E.I.,Xu Y.,Meng C.,et al. Comparison of statistical and dynamic downscaling techniques in generating high-resolution temperatures in China from CMIP5 GCMs[J]. Journal of Applied Meteorology and Climatology,2020-01-01,59(2)