Skill and independence weighting for multi-model
Benjamin Sanderson1, Michael Wehner2, and Reto Knutti3,11National Center for Atmospheric Research, Boulder CO, USA 2Lawrence Berkeley National Laboratory, CA, USA 3ETH Zurich, Switzerland
Received: 18 Nov 2016 – Accepted for review: 04 Dec 2016 – Discussion started: 21 Dec 2016
Abstract. We present a weighting strategy for use with the CMIP5 multi-model archive in the 4th National Climate Assessment which considers both skill in the climatological performance of models over North America as well as the inter-dependency 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 metrics representing long term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from co-dependent models of varying skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended with respect to the Assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.
Sanderson, B., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model
assessments, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-285, in review, 2016.