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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 - PowerPoint PPT Presentation
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Predicted and Observed Histograms of Free Tropospheric
Water vapor
Steven Sherwood, Yale UniversityRobert 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
• Can we trust them? Why do they do this?
The “cold trap” model of Relative Humidity
1. Water vapor near saturation in small moist convective regions;
2. Water vapor mixing ratio conserved as air leaves;
3. Dynamics maintains constant (small) difference between temperatures in convective and elsewhere;
4. Horizontal extent and organization of convective regions, RH within, and transport therefrom are known (…??)
QuickTime™ and aNone decompressor
are needed to see this picture.
Simulation of vapor from known dynamics
Pierrehumbert and Roca,1998.
(See also Sherwood 1996;Salathe and Hartmann 1997)
Stochastic implementation
€
dqsdt
= qswΓ
RvT2
⎛
⎝ ⎜
⎞
⎠ ⎟
q
qs= RH ≈ RH0e
−t /τ dry ,
τ dry =RvT
2
wΓ
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 intoanother convective system, where t is reset to zero.
If we additionally suppose remoistening is a Poisson process, then
€
Pt (t) = τ moist−1 e
−tτ moist
Which finally gives
€
PRH (RH)∝ RHτ dry
τ moist−1
,RH < RH0
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!!
Predicted vs. observed distribution (MLS, 30S-30N) of RH
RH0
Cloudcontamination
Predicted vs. observed distribution (GPS, 30S-30N) of RH
RH0
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.
GPS MLS
EULERIAN MODEL
Horizontal mixing rate
Mic
roph
ysic
al p
aram
eter
RH mean
RH range
Model sensitivity
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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