Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1002/joc.5043 |
Large-scale ocean-atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts | |
Tozer, C. R.1,2; Kiem, A. S.1; Verdon-Kidd, D. C.3 | |
通讯作者 | Tozer, C. R. |
来源期刊 | INTERNATIONAL JOURNAL OF CLIMATOLOGY
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ISSN | 0899-8418 |
EISSN | 1097-0088 |
出版年 | 2017 |
卷号 | 37页码:861-877 |
英文摘要 | Seasonal rainfall forecasts are an important tool for risk management across many sectors. However, significant challenges arise in the development of skilful and practically useful seasonal forecasts for regions where the temporal and spatial variability of rainfall is large and/or knowledge about what causes this variability is in its infancy. This is evident in the state of South Australia (SA), where seasonal rainfall currently has low predictive skill. The key climate processes have yet to be fully identified in SA and therefore may not be adequately represented in forecast models. The aim of this paper is to identify and quantify relationships between large-scale ocean-atmospheric processes and seasonal rainfall variability across SA. We identify two distinct climate zones: (1) the arid northern region, where rainfall is mostly influenced by climate processes stemming from the tropical Indian and/or Pacific Oceans and (2) southern SA, which is dominated by Southern Ocean processes. The average percent of variability of SA rainfall accounted for by any single large-scale climate process (i.e. linear regression using a single predictor) is 8% in summer, 19% in autumn, 33% in winter and 24% in spring. However, when two or more processes are considered in combination (through multiple linear regression), this rises to 13, 26, 46, and 33%, respectively, highlighting the importance of capturing the interaction among multiple climate processes. Importantly, the findings from this study provide a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and highlight processes that should be focused on in order to improve (or develop new) forecasting schemes. The study also recommends the need for further investigations into non-linear relationships between rainfall and large-scale ocean-atmospheric processes and the development of more objective methods for determining which climate process, or combination of processes, are most important for a certain season or location. |
英文关键词 | rainfall variability seasonal forecasting ocean-atmospheric ENSO IOD SAM IPO PDO ENSO Modoki STR blocking climate |
类型 | Article |
语种 | 英语 |
国家 | Australia |
收录类别 | SCI-E |
WOS记录号 | WOS:000417298600058 |
WOS关键词 | SEA-SURFACE TEMPERATURE ; ARTIFICIAL NEURAL-NETWORKS ; MULTI-DECADAL VARIABILITY ; WATER-SUPPLY MANAGEMENT ; SUBTROPICAL RIDGE ; ANNULAR MODE ; PREDICTOR IDENTIFICATION ; PROBABILISTIC FORECASTS ; WESTERN-AUSTRALIA ; EASTERN AUSTRALIA |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/199735 |
作者单位 | 1.Univ Newcastle, CWCL, Callaghan, NSW, Australia; 2.Univ Tasmania, Antarctic Climate & Ecosyst Cooperat Res Ctr, Private Bag 80, Hobart, Tas 7001, Australia; 3.Univ Newcastle, Fac Sci & IT, Sch Environm & Life Sci, Environm & Climate Change Res Grp, Callaghan, NSW, Australia |
推荐引用方式 GB/T 7714 | Tozer, C. R.,Kiem, A. S.,Verdon-Kidd, D. C.. Large-scale ocean-atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts[J],2017,37:861-877. |
APA | Tozer, C. R.,Kiem, A. S.,&Verdon-Kidd, D. C..(2017).Large-scale ocean-atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts.INTERNATIONAL JOURNAL OF CLIMATOLOGY,37,861-877. |
MLA | Tozer, C. R.,et al."Large-scale ocean-atmospheric processes and seasonal rainfall variability in South Australia: potential for improving seasonal hydroclimatic forecasts".INTERNATIONAL JOURNAL OF CLIMATOLOGY 37(2017):861-877. |
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