An evaluation of surface temperature variability in CMIP5 simulations

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Thursday, 6 February 2014: 11:45 AM
Room C102 (The Georgia World Congress Center )
Eugene Cordero, San Jose State Univ., San Jose, CA; and S. A. Mauget

A statistical technique that highlights a model's skill in reproducing inter- to multi-decadal (IMD) variability has been successfully used to rank and evaluate models from the Coupled Model Intercomparison Project 3 (CMIP3) dataset. This technique is now applied to the CMIP5 dataset and in particular to both the 20th century historical simulations and the 30-year decadal hindcast simulations of the 20th and 21st century. Models are then ranked by their ability to simulate observed IMD surface temperature variability and through an analysis using uncertainty estimates. The results suggest that certain models are better able to simulate observed variability compared to others. An examination of the rankings from decadal hindcasts compared to historical simulations provides additional insights into the ability of models to predict climate variations on decadal timescales.