DOI: 10.1007/s00382-016-3378-y
Scopus记录号: 2-s2.0-84990831670
论文题名: Influence of reanalysis datasets on dynamically downscaling the recent past
作者: Moalafhi D.B. ; Evans J.P. ; Sharma A.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2017
卷: 49, 期: 4 起始页码: 1239
结束页码: 1255
语种: 英语
英文关键词: Dynamical downscaling
; Lateral boundary conditions
; RCM
; Reanalyses
; Southern Africa
英文摘要: Multiple reanalysis datasets currently exist that can provide boundary conditions for dynamic downscaling and simulating local hydro-climatic processes at finer spatial and temporal resolutions. Previous work has suggested that there are two reanalyses alternatives that provide the best lateral boundary conditions for downscaling over southern Africa. This study dynamically downscales these reanalyses (ERA-I and MERRA) over southern Africa to a high resolution (10 km) grid using the WRF model. Simulations cover the period 1981–2010. Multiple observation datasets were used for both surface temperature and precipitation to account for observational uncertainty when assessing results. Generally, temperature is simulated quite well, except over the Namibian coastal plain where the simulations show anomalous warm temperature related to the failure to propagate the influence of the cold Benguela current inland. Precipitation tends to be overestimated in high altitude areas, and most of southern Mozambique. This could be attributed to challenges in handling complex topography and capturing large-scale circulation patterns. While MERRA driven WRF exhibits slightly less bias in temperature especially for La Nina years, ERA-I driven simulations are on average superior in terms of RMSE. When considering multiple variables and metrics, ERA-I is found to produce the best simulation of the climate over the domain. The influence of the regional model appears to be large enough to overcome the small difference in relative errors present in the lateral boundary conditions derived from these two reanalyses. © 2016, Springer-Verlag Berlin Heidelberg. 资助项目: BIUST, Botswana International University of Science and Technology
; CRAC, Criminology Research Advisory Council, Australian Institute of Criminology
; CRAC, Criminology Research Advisory Council, Australian Institute of Criminology
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53145
Appears in Collections: 过去全球变化的重建
There are no files associated with this item.
作者单位: School of Civil and Environmental Engineering, University of New South Wales, High St., Kensington, Sydney, NSW, Australia; Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
Recommended Citation:
Moalafhi D.B.,Evans J.P.,Sharma A.. Influence of reanalysis datasets on dynamically downscaling the recent past[J]. Climate Dynamics,2017-01-01,49(4)