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Published June 27, 2014 | Supplemental Material + Published
Journal Article Open

Process-based analysis of climate model ENSO simulations: Intermodel consistency and compensating errors

Abstract

Systematic and compensating errors can lead to degraded predictive skill in climate models. Such errors may be identified by comparing different models in an analysis of individual physical processes. We examine model simulations of El Niño–Southern Oscillation (ENSO) in five Coupled Model Intercomparison Project (CMIP) models, using transfer functions to analyze nine processes critical to ENSO's dynamics. The input and output of these processes are identified and analyzed, some of which are motivated by the recharge oscillator theory. Several errors and compensating errors are identified. The east-west slope of the equatorial thermocline is found to respond to the central equatorial Pacific zonal wind stress as a damped driven harmonic oscillator in all models. This result is shown to be inconsistent with two different formulations of the recharge oscillator. East Pacific sea surface temperature (SST) responds consistently to changes in the thermocline depth in the eastern Pacific in the five CMIP models examined here. However, at time scales greater than 2 years, this consistent model response disagrees with observations, showing that the SST leads thermocline depth at long time scales. Compensating errors are present in the response of meridional transport of water away from the equator to SST: two different models show different response of the transport to off-equatorial wind curl and wind curl response to East Pacific SST. However, these two models show the same response of meridional transport to East Pacific SST. Identification of errors in specific physical processes can hopefully lead to model improvement by focusing model development efforts on these processes.

Additional Information

© 2014 American Geophysical Union. Received 23 DEC 2013; Accepted 22 MAY 2014; Accepted article online 26 MAY 2014; Published online 30 JUN 2014. We thank four anonymous reviewers for their constructive and very helpful comments. We are grateful to Gabriel Vecchi for providing the GFDL CM 2.1 model output. We acknowledge the World Climate Research Programme's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups listed in Table 1 for producing and making available their model output. The U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work was funded by grant DE-SC0004984 from the DoE Climate and Environmental Sciences Division, Office of Biological and Environmental Research, and by grant NA13OAR4310130 from NOAA. E.T. thanks the Weizmann Institute of Science for its hospitality during parts of this work.

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Supplemental Material - SupplementaryinformationD2.pdf

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