Uncertainty in the ocean–atmosphere feedbacks associated with ENSO in the reanalysis products
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The evolution of El Niño-Southern Oscillation (ENSO) variability can be characterized by various ocean–atmosphere feedbacks, for example, the influence of ENSO related sea surface temperature (SST) variability on the low-level wind and surface heat fluxes in the equatorial tropical Pacific, which in turn affects the evolution of the SST. An analysis of these feedbacks requires physically consistent observational data sets. Availability of various reanalysis data sets produced during the last 15 years provides such an opportunity. A consolidated estimate of ocean surface fluxes based on multiple reanalyses also helps understand biases in ENSO predictions and simulations from climate models. In this paper, the intensity and the spatial structure of ocean–atmosphere feedback terms (precipitation, surface wind stress, and ocean surface heat flux) associated with ENSO are evaluated for six different reanalysis products. The analysis provides an estimate for the feedback terms that could be used for model validation studies. The analysis includes the robustness of the estimate across different reanalyses. Results show that one of the “coupled” reanalysis among the six investigated is closer to the ensemble mean of the results, suggesting that the coupled data assimilation may have the potential to better capture the overall atmosphere–ocean feedback processes associated with ENSO than the uncoupled ones.
KeywordsReanalysis products ENSO feedback Dynamical and thermodynamical processes Uncertainty Multi-reanalysis ensemble Coupled and uncoupled reanalyses
We appreciate the comments of Drs. Wanqiu Wang, Yan Xue, D. G. DeWitt, and two reviewers. Thanks also go to Dr. Li Zhang for managing the reanalysis data sets at CPC.
- IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S et al (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
- Lloyd J, Guilyardi E, Weller H (2011) The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part II: using AMIP runs to understand the heat flux feedback mechanisms. Clim Dyn. doi: 10.1007/s00382-010-0895-y (published on-line)
- National Research Council (2010) Assessment of intraseasonal to interannual climate prediction and predictability. The National Academies Press, Washington, 192 pp, ISBN-10: 0-309-15183-XGoogle Scholar
- Philander SGH (1990) El Niño, La Niña and the southern oscillation. Academic Press, San Diego, 293 pp, ISBN 0125532350Google Scholar
- Rienecker MM et al (2011) MERRA—NASA’s modern-era retrospective analysis for research and applications. J Clim (submitted)Google Scholar
- Wang C, Picaut J (2004) Understanding ENSO physics—a review. In: Wang C, Xie S-P, Carton JA (eds) Earth’s climate: the ocean–atmosphere interaction. Geophysical Monograph Series, vol 147. AGU, Washington, pp 21–48Google Scholar
- Xue Y, Huang B, Hu Z-Z, Kumar A, Wen C, Behringer D, Nadiga S (2011) An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Clim Dyn. doi: 10.1007/s00382-010-0954-4 (published online)
- Zhang Y, Rossow W, Lacis A, Oinas V, Mishchenko M (2004) Calculation of radiative flux profiles from the surface to top-of atmosphere based on ISCCP and other global data sets: refinements of the radiative transfer model and input data. J Geophys Res 109:D19105. doi: 10.1029/2003JD004457