Environmental Engineering Reference
In-Depth Information
sistencies likely result from the rather large biases in the climatology of the
atmosphere and ocean in the tropical Pacific in current generation of global
climate models (e.g., the double Intertropical Convergence Zone (ITCZ)
common to many of the models). Whether or not the spatial, temporal and
amplitude characteristics of ENSO change in a warmer world, however, the
associated far-field impacts of ENSO will be different for several reasons. For
example, the pattern and amplitude of the mid-latitude wintertime climate
anomalies associated with ENSO will change as the mid-latitude jets shift
poleward; places that experience drought during the warm (Australia) or
cold (central United States) phase of ENSO and that are projected to dry with
increasing global average temperature will experience enhanced drought
conditions associated with identical ENSO cycles.
4.5 TEMPERATURE EXTREMES
The literature on temperature (as well as that on precipitation) extremes
clearly suggests that any method trying to link changes in extremes of
maximum and/or minimum temperature, and their consequent effects on
the number of very hot or very cold days, and on the duration of hot and
cold spells, to global or even local average temperature changes would
fall short. As a recent indication of this, a paper by Ballester et al. (2010)
shows how an accurate scaling of temperature extremes would have to
involve not only average temperature change under future scenarios, but
also change in temperature variability and in the skewness of its distribu-
tion. No paper has addressed explicitly the quantitative differences across
multiple scenarios (or stabilization targets) of temperature extremes that we
could straightforwardly utilize to describe expected changes under different
atmospheric concentrations.
We chose here to adopt the perspective offered by Battisti and Naylor
(2009)—BN09 from now on—on the expected changes in the likelihood
of experiencing extremely hot or unprecedented
average summer tempera-
tures
. This way of characterizing warming from the perspective of the tail of
the distribution of
average temperature
has the strength of utilizing model
output (seasonal averages of surface temperature, or TAS) whose reliability
has been more extensively corroborated than other parameters representing
more traditional definitions of extreme or rare events (e.g., daily tempera-
tures exceeding high thresholds). We adapt BN09's analysis to our report's
focus on different stabilization targets and make it consistent with its reli-
ance on pattern scaling in order to infer geographically detailed projections
of temperature changes as a linear function of changes in global average
temperature (see Methods, Section 4.5).
