Aspects of model emergent behavior and uncertainty in regional and
small-scale effects of increasing CO2 on seasonal (JJA) precipitation
are explored. 19 different climate models are studied. New methods of
comparing multiple climate models reveal a clearer and more
impact-relevant view of precipitation projections for the current
century. First, the importance of small spatial scales in multi-model
projections is demonstrated. Local trends can be much larger than or
even have an opposing sign to the large-scale regional averages used in
previous studies. Small-scale effects of increasing CO2 and natural
internal variability both play important roles here. These small-scale
features make multi-model comparisons difficult for precipitation. New
methods are presented, which allow information from small spatial scales
to be usefully compared across an ensemble of multiple models. Our
analysis philosophy works with statistical distributions of small-scale
variations within climatological regions. A major result of this work
is a set of emergent relationships coupling the small and regional-scale
effects of CO2 on precipitation trends. Within each region, a single
relationship fits the ensemble of 19 different climate models. Using
these relationships, a surprisingly large part of the inter-model
variance in small-scale effects of CO2, is explainable simply by the
inter-model variance in the regional mean (a form of pattern scaling).
Different regions show distinctly different relationships. These
relationships imply that regional mean results are still useful, as long
as the inter-regional variation in their relationship with
impact-relevant extreme trends is recognized. These relationships are
used to present a clear but rich picture of an aspect of model
uncertainty, characterized by the inter-model spread in seasonal
precipitation trends, including information from small spatial scales.

Last Updated: 2006-03-17