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Theoretical and Applied Climatology

, Volume 115, Issue 1–2, pp 355–364 | Cite as

Influence of non-feedback variations of radiation on the determination of climate feedback

  • Yong-Sang ChoiEmail author
  • Heeje Cho
  • Chang-Hoi Ho
  • Richard S. Lindzen
  • Seon Ki Park
  • Xing Yu
Original Paper

Abstract

Recent studies have estimated the magnitude of climate feedback based on the correlation between time variations in outgoing radiation flux and sea surface temperature (SST). This study investigates the influence of the natural non-feedback variation (noise) of the flux occurring independently of SST on the determination of climate feedback. The observed global monthly radiation flux is used from the Clouds and the Earth's Radiant Energy System (CERES) for the period 2000–2008. In the observations, the time lag correlation of radiation and SST shows a distorted curve with low statistical significance for shortwave radiation while a significant maximum at zero lag for longwave radiation over the tropics. This observational feature is explained by simulations with an idealized energy balance model where we see that the non-feedback variation plays the most significant role in distorting the curve in the lagged correlation graph, thus obscuring the exact value of climate feedback. We also demonstrate that the climate feedback from the tropical longwave radiation in the CERES data is not significantly affected by the noise. We further estimate the standard deviation of radiative forcings (mainly from the noise) relative to that of the non-radiative forcings, i.e., the noise level from the observations and atmosphere–ocean coupled climate model simulations in the framework of the simple model. The estimated noise levels in both CERES (>13 %) and climate models (11–28 %) are found to be far above the critical level (~5 %) that begins to misrepresent climate feedback.

Keywords

Climate Sensitivity Radiative Forcings Feedback Process Climate Feedback Global Surface Temperature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This study is supported by the Korea Meteorological Administration Research and Development Program under grant CATER 2012–3064 and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2009-83527).

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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Yong-Sang Choi
    • 1
    Email author
  • Heeje Cho
    • 2
  • Chang-Hoi Ho
    • 2
    • 3
  • Richard S. Lindzen
    • 4
  • Seon Ki Park
    • 1
  • Xing Yu
    • 5
  1. 1.Department of Environmental Science and EngineeringEwha Womans UniversitySeoulKorea
  2. 2.Computational Science and TechnologySeoul National UniversitySeoulKorea
  3. 3.School of Earth and Environmental SciencesSeoul National UniversitySeoulKorea
  4. 4.Department of Earth, Atmospheric and Planetary SciencesMassachusetts Institute of TechnologyCambridgeUSA
  5. 5.Tropical Marine Science InstituteNational University of SingaporeSingaporeSingapore

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