Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1007/s00382-016-3378-y |
Influence of reanalysis datasets on dynamically downscaling the recent past | |
Moalafhi, Ditiro B.1; Evans, Jason P.2; Sharma, Ashish1 | |
2017-08-01 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2017 |
卷号 | 49期号:4 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia |
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. |
英文关键词 | Reanalyses Dynamical downscaling Lateral boundary conditions RCM Southern Africa |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000407247200006 |
WOS关键词 | GLOBAL PRECIPITATION ; SPATIAL VARIABILITY ; SOUTH-AFRICA ; RAINFALL ; DROUGHT |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/35581 |
专题 | 气候变化 |
作者单位 | 1.Univ New South Wales, Sch Civil & Environm Engn, High St, Sydney, NSW 2052, Australia; 2.Univ New South Wales, Climate Change Res Ctr, Sydney, NSW, Australia |
推荐引用方式 GB/T 7714 | Moalafhi, Ditiro B.,Evans, Jason P.,Sharma, Ashish. Influence of reanalysis datasets on dynamically downscaling the recent past[J]. CLIMATE DYNAMICS,2017,49(4). |
APA | Moalafhi, Ditiro B.,Evans, Jason P.,&Sharma, Ashish.(2017).Influence of reanalysis datasets on dynamically downscaling the recent past.CLIMATE DYNAMICS,49(4). |
MLA | Moalafhi, Ditiro B.,et al."Influence of reanalysis datasets on dynamically downscaling the recent past".CLIMATE DYNAMICS 49.4(2017). |
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