Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JCLI-D-17-0279.1 |
Spatial Variability in Seasonal Prediction Skill of SSTs: Inherent Predictability or Forecast Errors? | |
Kumar, Arun1; Zhu, Jieshun1,2 | |
2018 | |
发表期刊 | JOURNAL OF CLIMATE |
ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2018 |
卷号 | 31期号:2页码:613-621 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Seasonal prediction skill of SSTs from coupled models has considerable spatial variations. In the tropics, SST prediction skill in the tropical Pacific clearly exceeds prediction skill over the Atlantic and Indian Oceans. Such skill variations can be due to spatial variations in observing system used for forecast initializations or systematic errors in the seasonal prediction systems, or they could be a consequence of inherent properties of the coupled ocean-atmosphere system leaving a fingerprint on the spatial structure of SST predictability. Out of various alternatives, the spatial variability in SST prediction skill is argued to be a consequence of inherent characteristics of climate system. This inference is supported based on the following analyses. SST prediction skill is higher over the regions where coupled air-sea interactions (or Bjerknes feedback) are inferred to be stronger. Coupled air-sea interactions, and the longer time scales associated with them, imprint longer memory and thereby support higher SST prediction skill. The spatial variability of SST prediction skill is also consistent with differences in the ocean-atmosphere interaction regimes that distinguish between whether ocean drives the atmosphere or atmosphere drives the ocean. Regions of high SST prediction skill generally coincide with regions where ocean forces the atmosphere. Such regimes correspond to regions where oceanic variability is on longer time scales compared to regions where atmosphere forces the ocean. Such regional differences in the spatial characteristics of ocean-atmosphere interactions, in turn, also govern the spatial variations in SST skill, making spatial variations in skill an intrinsic property of the climate system and not an artifact of the observing system or model biases. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000425164800008 |
WOS关键词 | EL-NINO ; TEMPERATURE VARIABILITY ; TROPICAL ATLANTIC ; ENSO PREDICTION ; OCEAN ; MODEL ; SPECIFICATION ; PACIFIC |
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/20490 |
专题 | 气候变化 |
作者单位 | 1.NOAA NWS NCEP, Climate Predict Ctr, College Pk, MD 20740 USA; 2.Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA |
推荐引用方式 GB/T 7714 | Kumar, Arun,Zhu, Jieshun. Spatial Variability in Seasonal Prediction Skill of SSTs: Inherent Predictability or Forecast Errors?[J]. JOURNAL OF CLIMATE,2018,31(2):613-621. |
APA | Kumar, Arun,&Zhu, Jieshun.(2018).Spatial Variability in Seasonal Prediction Skill of SSTs: Inherent Predictability or Forecast Errors?.JOURNAL OF CLIMATE,31(2),613-621. |
MLA | Kumar, Arun,et al."Spatial Variability in Seasonal Prediction Skill of SSTs: Inherent Predictability or Forecast Errors?".JOURNAL OF CLIMATE 31.2(2018):613-621. |
条目包含的文件 | 条目无相关文件。 |
个性服务 |
推荐该条目 |
保存到收藏夹 |
查看访问统计 |
导出为Endnote文件 |
谷歌学术 |
谷歌学术中相似的文章 |
[Kumar, Arun]的文章 |
[Zhu, Jieshun]的文章 |
百度学术 |
百度学术中相似的文章 |
[Kumar, Arun]的文章 |
[Zhu, Jieshun]的文章 |
必应学术 |
必应学术中相似的文章 |
[Kumar, Arun]的文章 |
[Zhu, Jieshun]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论