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Atmospheric Feedbacks Over the Pacific Cold-tongue: Results From Models and Observations

Atmospheric Feedbacks Over the Pacific Cold-tongue: Results From Models and Observations. De-Zheng Sun and Tao Zhang CIRES/Climate Diagnostics Center & ESRL/Physical Science Division http://www.cdc.noaa.gov/~ds In collaboration with Curt Covey and Steve Klein

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Atmospheric Feedbacks Over the Pacific Cold-tongue: Results From Models and Observations

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  1. Atmospheric Feedbacks Over the Pacific Cold-tongue: Results From Models and Observations De-Zheng Sun and Tao Zhang CIRES/Climate Diagnostics Center & ESRL/Physical Science Division http://www.cdc.noaa.gov/~ds In collaboration with Curt Covey and Steve Klein Lawrence Livermore National Laboratory Bill Collins, Jim Hack, Jeff Kiehl, and Jerry Meehl National Center for Atmospheric Research Isaac Held Geophysical Fluid Dynamics Laboratory Max Suarez National Aronics and Space Administration

  2. Why feedbacks? • Feedbacks determine the sensitivity of the climate system to an external perturbation • Feedbacks determine the amplitude of the natural variability in the climate system • Feedbacks determine the equilibrium state of the climate system--the time-mean climate

  3. Why feedbacks over the Pacific cold-tongue? • The cold-tongue plays a key role in the heat and carbon balance of the climate system and is a major source region of climate variability • Climate models do not simulate the cold-tongue properly: they have a tendency to develop an excessive cold-tongue.

  4. The Excessive Cold-tongue in coupled models Annual Mean SST from observations and CGCMs

  5. Ideas about the causes of the excessive cold-tongue • Insufficient resolution of the ocean model (Stockdale et al. 1998, Kessler et al. 1998) • Lack of phytoplankton in the ocean model (Murtugudde et al. 2002) • A weaker regulating effect from the model atmosphere (Sun et al. 2003)

  6. The idea of a weaker regulating effect from the model atmosphere? Could it be that the strength of one or more negative feedbacks in the model underestimated? Or alternatively, could it be that the strength of the one or more positive feedbacks in the model is over estimated?

  7. The effect of a less negative net atmospheric feedback on the cold-tongue SST bias Net flux error at T0 SST Bias Ocean feedback Atmospheric Feedbacks M

  8. A further assessment of the effect of feedback errors on the cold-tongue SST Control run Perturbed run with observed feedbacks Perturbed run with feedbacks from CAM1 Coupled model used: NCAR Pacific basin model coupled with an empirical atmosphere (Sun 2003)

  9. Methodology • Use ENSO as the forcing signal and obtain the feedbacks by examining the response of various energy fluxes to SST changes associated with El Nino and La Nina

  10. Data • Observational data: ERBE, NCEP/NCAR reanalysis, ISCCP • Model outputs: AMIP runs of climate models

  11. The Physical Processes

  12. The Water Vapor Feedback

  13. The Dynamical Feedback

  14. The Cloud Feedbacks ERBE CAM1 CAM1

  15. The Cloud Feedbacks

  16. The Cloud Feedbacks

  17. Response of Cs to El Nino warming (W/m2/K)

  18. Response of Precipitation to El Nino Warming

  19. CAM1 Response of Cloud Cover in CAM1

  20. Atmospheric feedbacks in observations and the NCAR models

  21. How are other models doing in simulating the feedbacks?

  22. Precipitation Response to El Nino Warming

  23. Summary and Conclusion • These results confirm the suspicion that an underestimate of the regulating effect from the equatorial atmosphere is a prevalent problem in climate models. • Most models underestimate the strength of the negative feedbacks of cloud albedo and atmospheric transport. All models overestimate of the positive feedback from water vapor. • The results highlight the need to validate the feedback from the ocean transport.

  24. Water vapor response

  25. Oceanic Issues • What’s the effect of ENSO on the time-mean upper ocean temperature?

  26. Atmospheric Issues • What are the processes that control the equatorial precipitation? • How is the vertical distribution of clouds determined? • Why does convection in the models have a stronger moistening effect on the upper troposphere?

  27. El Nino in climate models

  28. La Nina in Climate Models

  29. The Asymmetry between El Nino and La Nina Sk.=-0.10 Sk.=0.62 Sk.=-0.20 Sk.=0.30 Sk.=0.05 Sk.=0.29

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