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Title: Metrics for understanding large-scale controls of multivariate temperature and precipitation variability

Abstract

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.

Authors:
ORCiD logo; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1572852
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Climate Dynamics
Additional Journal Information:
Journal Volume: 53; Journal Issue: 7-8; Journal ID: ISSN 0930-7575
Publisher:
Springer-Verlag
Country of Publication:
United States
Language:
English

Citation Formats

O’Brien, John P., O’Brien, Travis A., Patricola, Christina M., and Wang, S. -Y. Simon. Metrics for understanding large-scale controls of multivariate temperature and precipitation variability. United States: N. p., 2019. Web. doi:10.1007/s00382-019-04749-6.
O’Brien, John P., O’Brien, Travis A., Patricola, Christina M., & Wang, S. -Y. Simon. Metrics for understanding large-scale controls of multivariate temperature and precipitation variability. United States. doi:10.1007/s00382-019-04749-6.
O’Brien, John P., O’Brien, Travis A., Patricola, Christina M., and Wang, S. -Y. Simon. Tue . "Metrics for understanding large-scale controls of multivariate temperature and precipitation variability". United States. doi:10.1007/s00382-019-04749-6.
@article{osti_1572852,
title = {Metrics for understanding large-scale controls of multivariate temperature and precipitation variability},
author = {O’Brien, John P. and O’Brien, Travis A. and Patricola, Christina M. and Wang, S. -Y. Simon},
abstractNote = {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.},
doi = {10.1007/s00382-019-04749-6},
journal = {Climate Dynamics},
issn = {0930-7575},
number = 7-8,
volume = 53,
place = {United States},
year = {2019},
month = {4}
}

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