GFDL - Geophysical Fluid Dynamics Laboratory

Publication 1701

Lanzante, J. R., K. W. Dixon, M. J. Nath, C. E. Whitlock and
D. Adams-Smith, 2017:
Some Pitfalls in Statistical Downscaling of Future Climate.

Accepted by Bulletin of the American Meteorological Society.


Statistical downscaling (SD) is commonly used to provide information for the
assessment of climate change impacts. Using as input the output from
large-scale dynamical climate models and observation-based data products, it
aims to provide finer grain detail and also to mitigate systematic biases. It
is generally recognized as providing added value. However, one of the key
assumptions of SD is that the relationships used to train the method during a
historical time period are unchanged in the future, in the face of climate
change. The validity of this assumption is typically quite difficult to assess
in the normal course of analysis, as observations of future climate are
lacking. We approach this problem using a “Perfect Model” experimental design
in which high-resolution dynamical climate model output is used as a surrogate
for both past and future observations. 

We find that while SD in general adds considerable value, in certain
well-defined circumstances it can produce highly erroneous results.
Furthermore, the breakdown of SD in these contexts could not be foreshadowed
during the typical course of evaluation based only on available historical
data. We diagnose and explain the reasons for these failures in terms of
physical, statistical and methodological causes. These findings highlight the
need for caution in the use of statistically downscaled products as well as the
need for further research to consider other hitherto unknown pitfalls, perhaps
utilizing more advanced “Perfect Model” designs than the one we have