U.S. Department of Commerce


Date of this Version



In: Climate Science Special Report: A Sustained Assessment Activity of the U.S. Global Change Research Program [Wuebbles, D.J., D.W. Fahey, K.A. Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington, DC, USA (2017), pp. 642-651.


U.S. government work.


This document briefly describes a weighting strategy for use with the Climate Model Intercomparison Project, Phase 5 (CMIP5) multimodel archive in the 4th National Climate Assessment. This approach considers both skill in the climatological performance of models over North America and the interdependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as long-term metrics representing long-term statistics of weather extremes. The weights, once computed, can be used to simply compute weighted mean and significance information from an ensemble containing multiple initial condition members from co dependent models of varying skill.

Our methodology is based on the concepts outlined in Sanderson et al. (2015), and the specific application to the Fourth National Climate Assessment (NCA4) is also described in that paper. The approach produces a single set of model weights that can be used to combine projections into a weighted mean result, with significance estimates which also treat the weighting appropriately.

The method, ideally, would seek to have two fundamental characteristics:

• If a duplicate of one ensemble member is added to the archive, the resulting mean and significance estimate for future change computed from the ensemble should not change.

• If a demonstrably unphysical model is added to the archive, the resulting mean and significance estimates should also not change