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Annular modes and apparent eddy feedbacks in the Southern Hemisphere
Nicholas J Byrne1, Theodore G Shepherd1, Tim Woollings2
1Department of Meteorology University of Reading Reading UK.
Geophysical Research Letters
|September 27, 2016
Summary
Nonstationarity in Southern Hemisphere wind spectra challenges lagged correlation analysis. Findings suggest external variability, not eddy feedback, drives observed correlations, impacting climate model interpretation.
Area of Science:
- Atmospheric Science
- Climate Dynamics
- Meteorology
Background:
- Lagged correlation analysis is a common tool for studying intraseasonal climate dynamics.
- This method is susceptible to nonstationarity, where statistical properties change over time.
- A quasi-biennial oscillation (QBO) in Southern Hemisphere wind spectra indicates significant nonstationarity.
Purpose of the Study:
- To investigate the impact of nonstationarity on Southern Annular Mode (SAM) and eddy momentum flux convergence.
- To re-evaluate the interpretation of positive lagged correlations in climate dynamics.
- To understand the mechanisms behind zonal wind variations.
Main Methods:
- Analysis of power spectra for anomalous zonal wind and eddy momentum flux convergence.
- Examination of nonstationarity's influence on the Southern Annular Mode.
- Investigating lagged correlations in the context of identified nonstationary signals.
Main Results:
- A prominent quasi-biennial peak was identified in Southern Hemisphere wind and eddy momentum flux convergence spectra.
- This nonstationarity challenges previous attributions of lagged correlations to eddy feedback mechanisms.
- Positive lagged correlations are more likely explained by external interannual variability.
Conclusions:
- The study questions the reliability of lagged correlation analysis in the presence of nonstationarity.
- Findings suggest that observed correlations may stem from external climate variability rather than internal feedback processes.
- Implications for climate model evaluation and understanding zonal wind variability are significant.
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