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A New Method for Evaluating Skill in Predicting the IPO in Initialized Decadal Climate Prediction Hindcasts in CESM1 and E3SMv1 Using a Small Set of Start Years

Presentation Date
Wednesday, January 11, 2023 at 4:30pm - Wednesday, January 11, 2023 at 4:45pm
Colorado Convention Center - 210/212



A big challenge in decadal climate prediction involves having to run a large set of hindcasts with many ensemble members to evaluate skill of the hindcasts. Establishing hindcast skill is a necessary step to quantify credibility of initialized multi-year predictions. However, the computation burden of running such large hindcast data sets is daunting and, consequently, only occasionally is there enough computer time available to perform such a set of simulations. This limits the ability to experiment with different initialization methods, alternate model configurations and resolutions, and a myriad of other science problems that need to be addressed to improve decadal climate predictions. One reason to run such large hindcast data sets is to have enough samples of start years to form a drifted climatology from which to compute anomalies used to compare to observations to quantify skill of the hindcasts. Here we run a set of hindcasts with CESM1 and E3SMv1 for a limited set of start years, and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the model drifts from the observed initial states in the hindcasts are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatologies. The anomalies from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 4 out to lead year 10. There is comparable skill for predicting the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with large hindcast data sets.

Funding Program Area(s)