GFDL - Geophysical Fluid Dynamics Laboratory

 

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.

Abstract:

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 employed.