Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018JD028267 |
Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China | |
Zhao, Tongtiegang1,2,3; Chen, Xiaohong1; Liu, Pan2; Zhang, Yongyong4; Liu, Bingjun1; Lin, Kairong1 | |
2018-06-16 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
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ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2018 |
卷号 | 123期号:11页码:6039-6052 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China; Australia |
英文摘要 | The skill of global climate model (GCM) forecasts is usually indicated by the anomaly correlation between ensemble mean and observation. For GCM forecasts, anomaly correlation does not steadily improve with decreasing lead time but oscillates instead. This paper aims to address the oscillation and illustrate the relationship between anomaly correlation and lead time. We formulate the anomaly correlation of forecasts at different initialization times as a vector and pool anomaly correlation vectors across grid cells in the analysis. We propose two patterns to characterize the spatial and temporal variation of anomaly correlation in the three-dimensional space of latitude, longitude, and initialization time. The first pattern suggests that the anomaly correlation at different initialization times is at a similar level. The second pattern indicates that the anomaly correlation linearly increases with decreasing lead time. These two patterns are tested using the eigenvectors through principal component analysis. They are first illustrated using the GFDL-CM2p1-aer04 forecasts of summer precipitation in China. They are further verified by another nine sets of North-American Multi-Model Ensemble (NMME) forecasts. Overall, the first pattern explains more variation than the second pattern. In total, the two patterns explain 42% of the variation of anomaly correlation for CanCM3, 59% for CanCM4, 42% for COLA-RSMAS-CCSM3), 45% for COLA-RSMAS-CCSM4, 59% for GFDL-CM2p1, 67% for GFDL-CM2p1-aer04, 65% for GFDL-CM2p5-FLOR-A06, 57% for GFDL-CM2p5-FLOR-B01, 48% for NCAR-CESM1, and 60% for NCEP-CFSv2. The percentage of explained variation demonstrates the effectiveness of the two patterns as exploratory tools to analyze the predictive performance of GCM forecasts. |
英文关键词 | global climate model seasonal forecasts precipitation anomaly correlation spatial and temporal variation |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000436110800017 |
WOS关键词 | STREAMFLOW FORECASTS ; GLOBAL PRECIPITATION ; PREDICTION SYSTEM ; CLIMATE MODEL ; RESOLUTION ; RAINFALL ; MONSOON ; SKILL ; CFSV2 ; PREDICTABILITY |
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/32486 |
专题 | 气候变化 |
作者单位 | 1.Sun Yat Sen Univ, Dept Water Resources & Environm, Guangzhou, Guangdong, Peoples R China; 2.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Hubei, Peoples R China; 3.Univ Melbourne, Dept Infrastruct Engn, Melbourne, Vic, Australia; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Zhao, Tongtiegang,Chen, Xiaohong,Liu, Pan,et al. Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2018,123(11):6039-6052. |
APA | Zhao, Tongtiegang,Chen, Xiaohong,Liu, Pan,Zhang, Yongyong,Liu, Bingjun,&Lin, Kairong.(2018).Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,123(11),6039-6052. |
MLA | Zhao, Tongtiegang,et al."Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 123.11(2018):6039-6052. |
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