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Water Vapor Feedback

Water Vapor Feedback.

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Water Vapor Feedback

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  1. Water Vapor Feedback [W]ater vapor, confessedly the greatest thermal absorbent in the atmosphere, is dependent on temperature for its amount, and if another agent, as CO2, not so dependent, raises the temperature of the surface, it calls into function a certain amount of water vapor which further absorbs heat, raises the temperature and calls forth more vapor ... TC Chamberlin (1905) as quoted in Held and Soden (2000)

  2. Other References on Climate Feedbacks Held and Soden (2000), Ann. Rev. Energy and Environment. IPCC (2001) Third Assessment Report, Chapter 7. Wentz and Schnabel (2001), Nature Soden et al. (2002), Science. Santer et al. (2005), Science. Soden et al. (2005), Science. Soden and Held (2006), J. Climate. Bony et al. (2006), J. Climate. Primer in realclimate.org writen by Brian Soden in June 2006.

  3. Climate Feedbacks dX/dt = Q + F(X) X = state of climate system (temperature, etc) Q = Forcing, independent of X. examples: insolation, CO2, etc F = Feedbacks, dependent on X. examples: water vapor, clouds, ocean circulation, etc. Line between feedback and forcing can be unclear: e.g. methane in atmosphere…

  4. Radiative Balance S = Te4 S is incoming solar radiation Stefan-Boltzmann constant Te = effective emission temperature. Surface T ~ 290K, Te ~ 255K. Te = T-Ze Implies emission coming from ~5km up.

  5. Fig 1. Held and Soden (2000)

  6. Radiative equilibrium with no feedbacks No feedbacks:

  7. With water vapor feedback

  8. Water vapor feedback increases response to forcing • Without water vapor feedback: ∂T/ ∂(logCO2)= o ~ 1°C • With water vapor feedback: ∂T/∂(logCO2) = o·(1-H20)-1 H20 provides measure of water vapor feedback. Models indicate ~0.4 for fixed relative humidity. So: ∂T/∂(logCO2) ~ 1.7°C If H20 > 1 runaway warming (cooling).

  9. Figure 2, Held and Soden (2000) Greenhouse Effect Largest greenhouse effect <- Most water vapor <- Warmest SSTs Water Vapor Surface Temp.

  10. Figure 3, Held and Soden (2000)

  11. H2O Feedback interacts with other feedbacks

  12. Observed moisture changes follow temperature at ~ constant RH Wentz and Schabel (2001) Nature.

  13. Using Mt. Pinatubo to test model water vapor feedback Mt. Pinatubo errupted in 1991, cooling Earth from aerosols. Can a model represent the satellite-observed moisture changes? From Soden et al (2002, Science)

  14. Water Vapor Feedback

  15. Temperature Change Feedback

  16. Current uncertainty in Feedbacks from Climate Models

  17. Models able to represent many aspects of today’s relative humidity dist’n

  18. Why does relative humidity remain ~constant? • Current generation climate models indicate RH ~ constant. • RH not explicitly set to constant in these models. • Water vapor feedback in models can be approximated with RH constant. • Satellite observations indicate global-mean change in RH is small. • RH tied to strength of atmospheric circulation: circulation acts to dry air (adiabatic cooling produces rainfall with ascent, adiabatic warming reduces RH in subsidence)

  19. Major Sources of Water Vapor Feedback Uncertainty • Complexity of Tropics • Convective Outflow Temperatures • Condensate • Precipitation Efficiency All linked to some degree with clouds processes.

  20. Potential Problems With Models: Representation of Tropical Moist Convection

  21. Increasing uncertainty with time… From IPCC Reports: • 1990: “The best understood feedback mechanism is water vapor feedback, and this is intuitively easy to understand” (63). • 1992: “There is no compelling evidence that water vapor feedback is anything other than positive—although there may be difficulties with upper tropospheric water vapor” (64). • 1995: “Feedback from the redistribution of water vapor remains a substantial source of uncertainty in climate models—Much of the current debate has been addressing feedback from the tropical upper troposphere” (65).

  22. Third Assessment Report - 2001

  23. Fourth Assessment Report • 2007:? Current observational estimates (bars) Current model estimates (open histogram - 82 models) (shade - Normal fit) From Bony et al (2006), from Forster and Collins (2004)

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