ENSO feedbacks and their relationships with the mean state in a flux adjusted ensemble
Springer Verlag (Germany)
© 2016 The Author(s)
The El Niño Southern Oscillation (ENSO) is governed by a combination of amplifying and damping ocean–atmosphere feedbacks in the equatorial Pacific. Here we quantify these feedbacks in a flux adjusted HadCM3 perturbed physics ensemble under present day conditions and a future emissions scenario using the Bjerknes Stability Index (BJ index). Relationships between feedbacks and both the present day biases and responses under climate change of the mean equatorial Pacific climate are investigated. Despite minimised mean sea surface temperature biases through flux adjustment, the important dominant ENSO feedbacks still show biases with respect to observed feedbacks and inter-ensemble diversity. The dominant positive thermocline and zonal advective feedbacks are found to be weaker in ensemble members with stronger mean zonal advection. This is due to a weaker sensitivity of the thermocline slope and zonal surface ocean currents in the east Pacific to surface wind stress anomalies. A drier west Pacific is also found to be linked to weakened shortwave and latent heat flux damping, suggesting a link between ENSO characteristics and the hydrological cycle. In contrast to previous studies using the BJ index that find positive relationships between the index and ENSO amplitude, here they are weakly or negatively correlated, both for present day conditions and for projected differences. This is caused by strong thermodynamic damping which dominates over positive feedbacks, which alone approximate ENSO amplitude well. While the BJ index proves useful for individual linear feedback analysis, we urge caution in using the total linear BJ index alone to assess the reasons for ENSO amplitude biases and its future change in models.
This research is supported by an EPSRC studentship. We thank Eric Guilyardi and two anonymous reviewers for helpful comments and feedback on this work. 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 for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and leads development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.
Published online 1 November 2016