Predicted and Observed Histograms of Free Tropospheric Water vapor
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Transcript of Predicted and Observed Histograms of Free Tropospheric Water vapor
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