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Predicted and Observed Histograms of Free Tropospheric Water vapor

Predicted and Observed Histograms of Free Tropospheric Water vapor. Steven Sherwood, Yale University Robert Kursinski, JPL William Read, JPL (Also thks to A. Dessler) CGU/AGU 05/2004. Water vapor feedback. GCM’s show RH distributions not changing much as climate warms --> positive WVF

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Predicted and Observed Histograms of Free Tropospheric Water vapor

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  1. Predicted and Observed Histograms of Free Tropospheric Water vapor Steven Sherwood, Yale University Robert Kursinski, JPL William Read, JPL (Also thks to A. Dessler) CGU/AGU 05/2004

  2. Water vapor feedback • GCM’s show RH distributions not changing much as climate warms --> positive WVF • Can we trust them? Why do they do this?

  3. The “cold trap” model of Relative Humidity • Water vapor near saturation in small moist convective regions; • Water vapor mixing ratio conserved as air leaves; • Dynamics maintains constant (small) difference between temperatures in convective and elsewhere; • Horizontal extent and organization of convective regions, RH within, and transport therefrom are known (…??)

  4. Simulation of vapor from known dynamics Pierrehumbert and Roca, 1998. (See also Sherwood 1996; Salathe and Hartmann 1997)

  5. Stochastic implementation From Clausius Clapeyron eq: Integrated through time: dry is a few days. This gives RH as a function of parcel “age” t. Parcels age until swept into another convective system, where t is reset to zero. If we additionally suppose remoistening is a Poisson process, then Which finally gives

  6. Observed distributions • Upper troposphere: MLS (UARS) v4.9 retrievals from 450-150 hPa (FY 1993) • 3 km resolution, microwave limb emission • Partial cloud penetration • Lower+middle troposphere: GPS (CHAMP) occulations (O ‘91, JAJ ‘92) • 200 m resolution, radio refraction • Full cloud penetration • Diffraction-corrected (C. Ao, R. Mastaler) • These data are preliminary!!

  7. Predicted vs. observed distribution (MLS, 30S-30N) of RH  Cloud contamination RH0

  8. Predicted vs. observed distribution (GPS, 30S-30N) of RH  RH0

  9. Eulerian implementation (II) • Prefer theory that predicts RH0,  ratio, and  vs. height, and that accounts for convective ceiling. • Energy + mass conservation constrain dry • A simple, 2-parameter model gets 3/4!: • Simple overturning circulation in energy balance • Precipitation efficiency and/or mixing constant in convective region • Horizontal mixing constant • Still have to prescribe  but results not sensitive to it.

  10. EULERIAN MODEL GPS MLS

  11. Model sensitivity RH mean Microphysical parameter  RH range Horizontal mixing rate 

  12. Conclusions • A comprehensible model of relative humidity does exist • It explains observations of very dry air, convergent histograms, bimodality, and RH min at 400 hPa indicated by MLS and GPS data • It predicts that RH distributions are not very sensitive to cloud microphysical effects, but are somewhat sensitive to how frequently air parcels encounter convection • Further tests of the theory are needed

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