North American wintertime temperature anomalies: the role of El Niño diversity and differential teleconnections
El Niño–Southern Oscillation (ENSO) teleconnections induced wintertime surface air temperature (SAT) anomalies over North America show inter-event variability, asymmetry, and nonlinearity. This diagnostic study appraises the assumption that ENSO-induced teleconnections are adequately characterized as symmetric shifts in the SAT probability distributions for North American locations. To this end, a new conditional quantile functional estimation approach presented here incorporates: (a) the detailed nature of location and amplitude of SST anomalies—in particular the Eastern Pacific (EP), Central Pacific (CP) ENSO events—based on its two leading principal components, and (b) over the entire range of SATs, characterize the differential sensitivity to ENSO. Statistical significance is assessed using a wild bootstrap approach. Conditional risk at upper and lower quartile SAT conditioned on archetypical ENSO states is derived. There is marked asymmetry in ENSO effects on the likelihood of upper and lower quartile winter SATs for most North American regions. CP El Niño patterns show 20–80% decrease in the likelihood of lower quartile SATs for Canada and US west coast and a 20–40% increase across southeastern US. However, the upper quartile SAT for large swathes of Canada shows no sensitivity to CP El Niño. Similarly, EP El Niño is linked to a 40–80% increase in the probability of upper quartile winter SATs for Canada and northern US and a 20% decrease for southern US and northern Mexico regions; however, little or no change in the risk of lower quartile winter temperatures for southern parts of North America. Localized estimate of ENSO-related risk are also presented.
NCEP Reanalysis data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, from their website at http://www.cdc.noaa.gov/. This study is supported by National Science Foundation Awards 0904155 and 1055934, and NOAA award NA14OAR4320158.
- Koenker R (2016) quantreg: quantile regression. R package version 5.29. https://CRAN.R-project.org/package=quantreg. Accessed 10 Mar 2016
- Livezey RE, Chen WY (1983) Statistical field significance and its determination by Monte Carlo techniques. Mon Weather Rev 111(1):46–59. doi: 10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2
- NOAA CPC/National Oceanic and Atmospheric Administration Climate Prediction Center (2016) http://www.cpc.ncep.noaa.gov/data/indices/. Accessed 7 Dec 2016