Tyler W. Ruff and J. David Neelin Geophys. Res. Lett., in Press.
Preprint (PDF 223 KB),
Auxiliary Material (PDF 123 KB)
Abstract
Prior work has shown that probability distributions of column water vapor and several
passive tropospheric chemical tracers exhibit longer-than-Gaussian (approximately
exponential) tails. The tracer-advection prototypes explaining the formation of
these long-tailed distributions motivate exploration of observed surface temperature
distributions for non-Gaussian tails. Stations with long records in various climate
regimes in National Climatic Data Center Global Surface Summary of Day (GSOD)
observations are used to examine tail characteristics for daily average, maximum and
minimum surface temperature probability distributions. Each is examined for departures
from a Gaussian fit to the core (here approximated as the portion of the distribution
exceeding 30% of the maximum). While the core conforms to Gaussian for most distributions,
roughly half the cases exhibit non-Gaussian tails in both winter and summer seasons.
Most of these are asymmetric, with a long, roughly exponential, tail on only one side.
The shape of the tail has substantial implications for potential changes in extreme event
occurrences under global warming. Here the change in the probability of exceeding a given
threshold temperature is quantified in the simplest case of a shift in the present-day
observed distribution. Surface temperature distributions with long tails have a much
smaller change in threshold exceedances (smaller increases for high-side exceedances,
and smaller decreases for low-side exceedances relative to exceedances in current climate)
under a given warming than do distributions that are close to Gaussian. This implies that
models used to estimate changes in extreme event occurrences due to global warming should
be verified regionally for accuracy of simulations of probability distribution tails.
Citation Ruff, T. W. and J. D. Neelin, Long tails in regional surface temperature probability distributions with implications for extremes under global warming. Geophys. Res. Lett., submitted, Dec. 2011.