Uncertainties in Water vapor:Uncertainties in Water vapor: … · 2009-09-28 · Uncertainties in...
Transcript of Uncertainties in Water vapor:Uncertainties in Water vapor: … · 2009-09-28 · Uncertainties in...
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Uncertainties in Water vapor:Uncertainties in Water vapor:Budgets in two versions of CAM(Track 1 and Track 5)( )
C OCécile Hannay, Dave Williamson, Jerry Olson, Rich Neale, Sungsu Park, Phil Rasch*,
Andrew Gettelman and Hugh Morrisson.
National Center for Atmospheric Research, Boulder*Pacific Northwest National Laboratory, Richland
Climate Feedback workshop, Keck Inst
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What are Track 1 and Track 5 ?
• Track 1 = cam3.5.1- Deep convection: Neale-Richter (2008)
- Microphysics: Rasch-Kristjansson (1998)
- Boundary layer: Holtslag-Boville (1993)
- Shallow convection: Hack (1993)
- Bulk Aerosol Model (BAM, Barth, Rasch, Mahowald … 2000-2009)
- Radiation: CAMRT (Ramanathan, Kiehl, Collins,…1970s-2006)
T k 5 4• Track 5 = cam4- Deep convection: Neale-Ritcher (2008)
- Microphysics: Morrison and Gettelman (2008)p y ( )
- Boundary layer: Bretherton and Park (2009)
- Shallow convection: Park and Bretherton (2009)
M h i P k R h B th t (2009)-Macrophysics: Park, Rasch, Bretherton (2009)
- Modal Aerosol Model (MAM): Ghan and Liu
- Radiation: RRTMG: Iacono et al (2008)
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Water vapor biases
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Clear Sky OLR
Model values too lowModel values too lowPartly explained by
Emissions above top of modelmodelDifferences in Sampling methodology btw models and CERESToo much water vapor
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Motivation
• Analysis of the budget for temperature, moisture and condensate
• Climate runs:- understand the balance that controls the climate
• Forecast runs:- understand how we attain this balance
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Water vapor budget
∂q∂t
= −V •∇q −ω∂q∂p
+ Qphysics
Physics tendencies
∂t ∂pAdvective tendencies
Qpbl + Qdeep convection+ Qshallow convection + Qmacro+micro
Convective mass flux
Shallow convection Evap & Prod of Evaporationflux
Evaporation convective
precipitation
convection tendency
Evaporation
Evap & Prod of condensate
Evaporation falling
precipitation
Evaporation ofprecipitationconvective
precipitationEvaporation of
sedimenting cloud water
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Global annual means: q profiles and tendencies
Moisture tendenciesMoisture profile
− Total− Advection− Physics
Track 1 = plainTrack 5 = dash− ERA40
− JRA25− Track 1 Track 1− Track 5
MoisteningDrying
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Global annual mean: physics tendencies break-up
Track 1 Track 5Drying of upper troposphere- by Hack in Track 1- by microphysics in Track 5
− PBL− Micro+Macro− Deep− Shallow
In Track 1: MoisteningMoistening by deep convection around 500 mb
Shallow convection is stronger in Track 5
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Budget terms in various regimes
•Stratocumulus•Transition•ITCZITCZ•Bay of Bengal•Storm Tracks•Arctic•Continental US•Tropical land
http // cgd car ed /cms/hanna /internal/cam4 de /b dgets/t1 t5/B dget t1 t5 htmlhttp // cgd car ed /cms/hanna /internal/cam4 de /b dgets/t1 t5/B dget t1 t5 htmlhttp://www.cgd.ucar.edu/cms/hannay/internal/cam4_dev/budgets/t1_t5/Budget_t1_t5.htmlhttp://www.cgd.ucar.edu/cms/hannay/internal/cam4_dev/budgets/t1_t5/Budget_t1_t5.html
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Stratocumulus
Track 1 Track 5
− PBL− Micro+Macro− Deep
Shallow− Shallow
Shallow convection is stronger in Track 1
PBL moves moisture higher in the atmosphere
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Transition region
Track 1 Track 5
− PBL− Micro+Macro− Deep
Shallow− Shallow
Shallow convection is stronger in Track 5Deep convection is
stronger in Track 1
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Conclusion and future work
• Budgets help to understand the balance that controls the climate
• Water vapor tendencies are very different in Track 1 and 5• Water vapor tendencies are very different in Track 1 and 5, even when the states in the two tracks are similar.
A major difference is in the shallow convective tendencies, j ,which are globally stronger in Track 5 than Track 1
The drying of the upper troposphere is driven by different processes:- Track1: Hack convection scheme (unrealistic)- Track5: macro+micro (reflects new ice microphysics)
In the stratocumulus regions, there is an unrealistically large contribution from the shallow convection scheme in Track 1
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Future work
Next , we will look at :the temperature, liquid and ice budgets forecast runs (CAPT framework):
understand how we attain this balanceallows direct comparison of the parameterized variables (e.g. p p ( gclouds) with observations from field campaigns
How do we use observations to help us understand the balances in the real world for these regimes, and achieve g ,similar balance in models?•