Metrics for Understanding Large-Scale Controls of Multivariate Temperature and Precipitation Variability

TitleMetrics for Understanding Large-Scale Controls of Multivariate Temperature and Precipitation Variability
Publication TypeJournal Article
Year of Publication2019
AuthorsO'Brien, John P., O'Brien Travis A., Patricola Christina M., and Wang S-Y Simon
JournalClimate Dynamics
Date Published04/2019
Abstract / Summary

Two or more spatiotemporally 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 5 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 remain 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.

URLhttps://doi.org/10.1007/s00382-019-04749-6
DOI10.1007/s00382-019-04749-6
Journal: Climate Dynamics
Year of Publication: 2019
Date Published: 04/2019

Two or more spatiotemporally 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 5 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 remain 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
Citation:
O'Brien, JP, TA O'Brien, CM Patricola, and SS Wang.  2019.  "Metrics for Understanding Large-Scale Controls of Multivariate Temperature and Precipitation Variability."  Climate Dynamics.  https://doi.org/10.1007/s00382-019-04749-6.