气候变化领域集成服务门户(CCPortal): How much of monthly mean precipitation variability over global land is associated with SST anomalies?

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DOI	10.1007/s00382-019-05023-5
	How much of monthly mean precipitation variability over global land is associated with SST anomalies?
	Hu Z.-Z.; Kumar A.; Jha B.; Huang B.
发表日期	2020
ISSN	0930-7575
起始页码	701
结束页码	712
卷号	54 期号:2020-01-02
英文摘要	The role of sea surface temperature (SST) in determining the predictability of monthly mean precipitation over the global land is assessed by analyzing the Atmospheric Model Intercomparison Project (AMIP)-like simulations forced by observed SST, which provides a benchmark for the impact of SST on the precipitation. The correlations of monthly mean precipitation anomalies between the ensemble mean of the AMIP simulations and observations are dominated by positive values with maxima around 0.3–0.4 in the tropical North Africa along 15° N and northeastern Brazil. The SST forcing for the precipitation variability is mainly associated with the El Niño-Southern Oscillation (ENSO) and in the tropical Indian Ocean. Statistically, positive and negative SST anomalies associated with an ENSO cycle have a comparable influence on precipitation variability over the land. In addition to the spatial variations, the precipitation responses to SST also vary with season and decade. Pattern correlations are larger in boreal winter than in boreal summer in the Northern Hemisphere, and relatively larger in April-June and September–November in the Southern Hemisphere. The global average of correlation is lower during 1957–1980 and 2000–2018, and higher in between. The interdecadal fluctuation of the pattern correlations is coherent with the interdecadal variation of the amplitude of ENSO. © 2019, This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
英文关键词	ENSO; Global land precipitation; Predictability; Temporal and spatial variations of the SST influence
语种	英语
scopus关键词	benchmarking; climate prediction; correlation; El Nino-Southern Oscillation; ensemble forecasting; precipitation assessment; sea surface temperature; spatiotemporal analysis; Indian Ocean; Indian Ocean (Tropical)
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145754
作者单位	Climate Prediction Center, NCEP/NWS/NOAA, 5830 University Research Court, College Park, MD 20740, United States; Innovim, Greenbelt, MD, United States; National Centers for Environmental Information (NCEI), NOAA, Asheville, NC, United States
推荐引用方式 GB/T 7714	Hu Z.-Z.,Kumar A.,Jha B.,et al. How much of monthly mean precipitation variability over global land is associated with SST anomalies?[J],2020,54(2020-01-02).
APA	Hu Z.-Z.,Kumar A.,Jha B.,&Huang B..(2020).How much of monthly mean precipitation variability over global land is associated with SST anomalies?.Climate Dynamics,54(2020-01-02).
MLA	Hu Z.-Z.,et al."How much of monthly mean precipitation variability over global land is associated with SST anomalies?".Climate Dynamics 54.2020-01-02(2020).