气候变化领域集成服务门户(CCPortal): Metrics for understanding large-scale controls of multivariate temperature and precipitation variability

CCPortal

DOI	10.1007/s00382-019-04749-6
	Metrics for understanding large-scale controls of multivariate temperature and precipitation variability
	O’Brien J.P.; O’Brien T.A.; Patricola C.M.; Wang S.-Y.S.
发表日期	2019
ISSN	0930-7575
起始页码	3805
结束页码	3823
卷号	53 期号:2020-07-08
英文摘要	Two or more spatio-temporally co-located meteorological/climatological extremes (co-occurring extremes) place far greater stress on human and ecological systems than any single extreme could. This was observed during the California drought of 2011–2015 where multiple years of negative precipitation anomalies occurred simultaneously with positive temperature anomalies resulting in California’s worst drought on observational record. The large-scale drivers which modulate the occurrence of extremes in two or more variables remains largely unexplored. Using California wintertime (November–April) temperature and precipitation as a case study, we apply a novel, nonparametric conditional probability distribution method that allows for evaluation of complex, multivariate, and nonlinear relationships that exist among temperature, precipitation, and various indicators of large-scale climate variability and change. We find that multivariate variability and statistics of temperature and precipitation exhibit strong spatial variation across scales that are often treated as being homogeneous. Further, we demonstrate that the multivariate statistics of temperature and precipitation are highly non-stationary and therefore require more robust and sophisticated statistical techniques for accurate characterization. Of all the indicators of the large-scale climate conditions we studied, the dipole index explains the greatest fraction of multivariate variability in the co-occurrence of California wintertime extremes in temperature and precipitation. © 2019, The Author(s).
英文关键词	AMO; California; Climate variability; El Niño; ENSO; Global change; Joint extremes; La Niña; Non-stationarity; PDO; Precipitation extremes; Teleconnections
语种	英语
scopus关键词	air temperature; climate variation; El Nino; El Nino-Southern Oscillation; extreme event; global change; La Nina; multivariate analysis; precipitation (climatology); teleconnection; California; United States
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/145991
作者单位	Department of Earth and Planetary Sciences, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, United States; Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, MS74R-316C, Berkeley, CA 94720, United States; Department of Plants, Soils and Climate, Utah State University, 4820 Old Main Hill, Logan, UT 84322, United States
推荐引用方式 GB/T 7714	O’Brien J.P.,O’Brien T.A.,Patricola C.M.,et al. Metrics for understanding large-scale controls of multivariate temperature and precipitation variability[J],2019,53(2020-07-08).
APA	O’Brien J.P.,O’Brien T.A.,Patricola C.M.,&Wang S.-Y.S..(2019).Metrics for understanding large-scale controls of multivariate temperature and precipitation variability.Climate Dynamics,53(2020-07-08).
MLA	O’Brien J.P.,et al."Metrics for understanding large-scale controls of multivariate temperature and precipitation variability".Climate Dynamics 53.2020-07-08(2019).