GFDL - Geophysical Fluid Dynamics Laboratory

Publication 1801

John R. Lanzante, Mary Jo Nath, Carolyn E. Whitlock, Keith W. Dixon, and
Dennis Adams-Smith:

Evaluation and Improvement of Tail Behavior in the Cumulative Distribution
Function Transform (CDFt) Downscaling Method.

Submitted to International Journal Of Climatology.

Abstract:

The cumulative distribution function transform (CDFt) downscaling method has
been used widely to provide local-scale information and bias correction to
output from physical climate models. The CDFt approach is one from the category
of statistical downscaling methods that operates via transformations between
statistical distributions. Although numerous studies have demonstrated that
such methods provide value overall, much less effort has focused on their
performance with regard to values in the tails of distributions. We evaluate
the performance of CDFt-generated tail values based on four distinct
approaches, two native to CDFt and two of our own creation, in the context of a
“Perfect Model” setting in which global climate model output is used as a proxy
for both observational and model data. We find that the native CDFt approaches
can have sub-optimal performance in the tails, particularly with regard to the
maximum value. However, our alternative approaches provide substantial
improvement.