what we can learn from the midlatitudes – intercomparison ... talks/barth.pdf · what we can...
TRANSCRIPT
![Page 1: What We Can Learn from the Midlatitudes – Intercomparison ... talks/Barth.pdf · What We Can Learn from the Midlatitudes – Intercomparison Studies Mary C. Barth Mesoscale and](https://reader031.vdocuments.us/reader031/viewer/2022041016/5ec78aef5641e14d14486eb7/html5/thumbnails/1.jpg)
What We Can Learn from the Midlatitudes –Intercomparison Studies
Mary C. BarthMesoscale and Microscale Meteorology and
Atmospheric Chemistry DivisionsNational Center for Atmospheric Research
Si-Wan Kim, NCAR; now at NOAA/ESRL/CSDChien Wang, MITKen Pickering, NASA/GSFCLesley Ott, Univ. MarylandG. Stenchikov, Rutgers Univ.Maud Leriche, Sylvie Cautenet, CNRS/U. Blaise-PascalAnn Fridlind, Andy Ackerman, NASA/GISSJean-Pierre Pinty, Celine Mari, Lab. D’Aerologie, CNRSVlado Spiridonov, Hydrological Inst., MacedoniaJohn Helsdon, Richard Farley, SDSMT
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Motivation
• Convective processing of chemical species isimportant to– Moving pollutants to upper troposphere
– Cleansing the atmosphere (rain out)
• Large-scale models produce inconsistentresults for convective transport of scalars
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Results from the NCAR CCSM UsingDifferent Convection Parameterizations
Mixing ratio of surface tracer averaged over the TOGA-COARE region as afunction of day (December 18 – January 8) and pressure
From Phil Rasch, EGS talk, 2003
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Motivation
• Convective processing of chemical species isimportant to– Moving pollutants to upper troposphere
– Cleansing the atmosphere (rain out)
• Large-scale models produce inconsistentresults for convective transport of scalars
• Convective-scale models produce reasonablyrepresent convective transport
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Results From the COMMAS ConvectiveCloud Model Coupled With Chemistry
From Skamarock et al. (2000)
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Motivation
• To improve sub-grid convective transport andwet deposition in large-scale models– multiple convective-scale models can be used to
obtain general characteristics of these processes.
• The Chemistry Transport in Deep ConvectionIntercomparison– means to calibrate a variety of convective-scale
models coupled with chemistry.
• Determine what the variability amongreputable cloud chemistry convective modelsis for a given storm.
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Acknowledgment
The Chemistry Transport in Deep ConvectionIntercomparison
6th International Cloud Modeling Workshop
July 2004Hamburg, Germany
WMO
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Simulate the 10 July 1996 STERAOstorm
Colorado
NebraskaWyoming
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Chemistry Transport by Deep Convection
Primary Species:
Ozone (O3) – tracer
Carbon monoxide (CO) – tracer
Nitrogen oxides (NOx = NO + NO2) – enhanced by lightning
Soluble species:
Nitric acid (HNO3)
Hydrogen peroxide (H2O2)
Formaldehyde (CH2O)
affected by microphysics parameterization
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Initialization
• Soundings for T, qv, u, and v
• Initiation via 3 warm bubbles
• Aircraft vertical profiles for chemical species
Initial profile
MOZART profile
Points from aircraftobservations
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360x328x25 km
2x2 km horiz
0.500 km vert
3-30 s timestep
Offline chemistry transport
Gas chemistry on
Lightning-NOx param
Predict Mass only:
cw, rain, ice, snow, hail (Tao and Simpson, 1993)
No aerosols
3d GCE Model
(Tao and Simpson, 93)
U. Md.
Pickering, Ott
Stenchikov
120x120x20 km
1x1 km horiz
Aqueous chemistryPredict Mass only:
cw, rain, ice, snow, hail,
(Lin et al 83)
3d
No radiation
Spiridonov
120x120x20 km1x1 km horiz
51 vert levels
5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hail
No aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km
1x1 km horiz
50 vert levels
Scavenge soluble species
Electrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelastic
MPDATA advec.
Meso-NH
Pinty, Mari
120x120x20 km
1x1 km horiz
0.250 km vert
No chemistryPredict Number, Mass & size
Prognostic CCN and IN
3d,
Interactive radiation
DHARMA
Fridlind, Ackerman
120x120x20 km
1x1 km horiz
0.4 km vert
Chemistry onPredict Number and Mass
Prognostic CCN and IN
3d pseudo-elastic
Interactive radiation
C. Wang
160x160x20 km
1x1 km horiz
50 vert levels
10 s timestep
Chemistry onPredict Mass only:
cw, rain, ice, snow, hail
(Lin et al, 83)
No aerosols
3d, flux form Runge-Kutta
No radiation
WRF-AqChem
Barth, Kim
ConfigurationChemistryCloud Microphysics and AerosolsDynamics, thermodynamics, radiationModel
360x328x25 km
2x2 km horiz
0.500 km vert
3-30 s timestep
Offline chemistry transport
Gas chemistry on
Lightning-NOx param
Predict Mass only:
cw, rain, ice, snow, hail (Tao and Simpson, 1993)
No aerosols
3d GCE Model
(Tao and Simpson, 93)
U. Md.
Pickering, Ott
Stenchikov
120x120x20 km
1x1 km horiz
Aqueous chemistryPredict Mass only:
cw, rain, ice, snow, hail,
(Lin et al 83)
3d
No radiation
Spiridonov
120x120x20 km1x1 km horiz
51 vert levels
5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hail
No aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km
1x1 km horiz
50 vert levels
Scavenge soluble species
Electrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelastic
MPDATA advec.
Meso-NH
Pinty, Mari
120x120x20 km
1x1 km horiz
0.250 km vert
No chemistryPredict Number, Mass & size
Prognostic CCN and IN
3d,
Interactive radiation
DHARMA
Fridlind, Ackerman
120x120x20 km
1x1 km horiz
0.4 km vert
Chemistry onPredict Number and Mass
Prognostic CCN and IN
3d pseudo-elastic
Interactive radiation
C. Wang
160x160x20 km
1x1 km horiz
50 vert levels
10 s timestep
Chemistry onPredict Mass only:
cw, rain, ice, snow, hail
(Lin et al, 83)
No aerosols
3d, flux form Runge-Kutta
No radiation
WRF-AqChem
Barth, Kim
ConfigurationChemistryCloud Microphysics and AerosolsDynamics, thermodynamics, radiationModel
360x328x25 km
2x2 km horiz
0.500 km vert
3-30 s timestep
Offline chemistry transport
Gas chemistry on
Lightning-NOx param
Predict Mass only:
cw, rain, ice, snow, hail (Tao and Simpson, 1993)
No aerosols
3d GCE Model
(Tao and Simpson, 93)
U. Md.
Pickering, Ott
Stenchikov
120x120x20 km
1x1 km horiz
Aqueous chemistryPredict Mass only:
cw, rain, ice, snow, hail,
(Lin et al 83)
3d
No radiation
Spiridonov
120x120x20 km1x1 km horiz
51 vert levels
5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hail
No aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km
1x1 km horiz
50 vert levels
Scavenge soluble species
Electrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelastic
MPDATA advec.
Meso-NH
Pinty, Mari
120x120x20 km
1x1 km horiz
0.250 km vert
No chemistryPredict Number, Mass & size
Prognostic CCN and IN
3d,
Interactive radiation
DHARMA
Fridlind, Ackerman
120x120x20 km
1x1 km horiz
0.4 km vert
Chemistry onPredict Number and Mass
Prognostic CCN and IN
3d pseudo-elastic
Interactive radiation
C. Wang
160x160x20 km
1x1 km horiz
50 vert levels
10 s timestep
Chemistry onPredict Mass only:
cw, rain, ice, snow, hail
(Lin et al, 83)
No aerosols
3d, flux form Runge-Kutta
No radiation
WRF-AqChem
Barth, Kim
ConfigurationChemistryCloud Microphysics and AerosolsDynamics, thermodynamics, radiationModel
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km2x2 km horiz0.500 km vert3-30 s timestep
Offline chemistrytransportGas chemistry onLightning-NOx param
Predict Mass only:cw, rain, ice, snow, hail(Tao and Simpson, 1993)No aerosols
3d GCE Model(Tao and Simpson, 93)
U. Md.Pickering, OttStenchikov
120x120x20 km1x1 km horiz
Aqueous chemistryPredict Mass only:cw, rain, ice, snow, hail,(Lin et al 83)
3dNo radiation
Spiridonov
120x120x20 km1x1 km horiz51 vert levels5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hailNo aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km1x1 km horiz50 vert levels
Scavenge soluble speciesElectrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelasticMPDATA advec.
Meso-NHPinty, Mari
120x120x20 km1x1 km horiz0.250 km vert
No chemistryPredict Number, Mass &sizePrognostic CCN and IN
3d,Interactive radiation
DHARMAFridlind, Ackerman
120x120x20 km1x1 km horiz0.4 km vert
Chemistry onPredict Number and MassPrognostic CCN and IN
3d pseudo-elasticInteractive radiation
C. Wang
160x160x20 km1x1 km horiz50 vert levels10 s timestep
Chemistry onPredict Mass only:cw, rain, ice, snow, hail(Lin et al, 83)No aerosols
3d, flux form Runge-KuttaNo radiation
WRF-AqChemBarth, Kim
ConfigurationChemistryCloud Microphysics andAerosols
Dynamics,thermodynamics,radiation
Model
360x328x25 km
2x2 km horiz
0.500 km vert
3-30 s timestep
Offline chemistry transport
Gas chemistry on
Lightning-NOx param
Predict Mass only:
cw, rain, ice, snow, hail (Tao and Simpson, 1993)
No aerosols
3d GCE Model
(Tao and Simpson, 93)
U. Md.
Pickering, Ott
Stenchikov
120x120x20 km
1x1 km horiz
Aqueous chemistryPredict Mass only:
cw, rain, ice, snow, hail,
(Lin et al 83)
3d
No radiation
Spiridonov
120x120x20 km1x1 km horiz
51 vert levels
5 s timestep
Chemistry onLightning-NOx param
Predict Mass only,cw, rain, ice, snow, hail
No aerosols
3d, RAMSLeriche, Cautenet
120x120x20 km
1x1 km horiz
50 vert levels
Scavenge soluble species
Electrical scheme
Predict Mass only:6 hydrometeor classesNo aerosols
3d anelastic
MPDATA advec.
Meso-NH
Pinty, Mari
120x120x20 km
1x1 km horiz
0.250 km vert
No chemistryPredict Number, Mass & size
Prognostic CCN and IN
3d,
Interactive radiation
DHARMA
Fridlind, Ackerman
120x120x20 km
1x1 km horiz
0.4 km vert
Chemistry onPredict Number and Mass
Prognostic CCN and IN
3d pseudo-elastic
Interactive radiation
C. Wang
160x160x20 km
1x1 km horiz
50 vert levels
10 s timestep
Chemistry onPredict Mass only:
cw, rain, ice, snow, hail
(Lin et al, 83)
No aerosols
3d, flux form Runge-Kutta
No radiation
WRF-AqChem
Barth, Kim
ConfigurationChemistryCloud Microphysics and AerosolsDynamics, thermodynamics, radiationModel
Mary Barth and Si-Wan Kim (NCAR) – WRF–AqChem sensitivity sim.
Chien Wang (MIT) – C.Wang sensitivity sim.
Ken Pickering and Lesley Ott (U. Maryland), and Georgiy Stenchikov(Rutgers Univ.) – UMd/GCE
Ann Fridlind and Andy Ackerman (NASA/Ames) – DHARMA
Jean-Pierre Pinty and Celine Mari (CNRS--Toulouse) – Meso-NH
Maud Leriche and Sylvie Cautenet, (LaMP, U. B-P, Clermont-Ferrand)–Leriche or RAMS
Vlado Spiridonov, (Hydrometeor. Inst., Macedonia) – Spiridonov
John Helsdon, Richard Farley (South Dakota School M&T) – SDMST
Participants and their Models
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Formulation of Models for the Convective-ScaleSimulations
• 3-d, fully compressible, non-hydrostatic– WRF-AqChem – WRF dynamics (flux form)– C.Wang – pseudo-elastic– UMd/GCE – GCE dynamics (anelastic)– DHARMA –Large Eddy Simulation– Meso-NH – anelastic, MPDATA advection– Leriche/RAMS – RAMS dynamics (anelastic)– Spiridonov – based on Klemp and Wilhelmson– SDMST – modified Clark-Hall model, MPDATA
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Microphysics and Chemistry
vapor
ice
rain snow
cloudwater
Bulk water method:
qv, qaer, Naer, size of aerosols (16 bins)
qliq, Nliq, size of drops (16 bins)
qice, Nice, size of ice (16 bins)
Sectional method:
vapor
ice
rain snow
cloudwater
Two Moment method:
Nc
Nr Ns
Ni
NCCN, IN
WRF-aqchem, Meso-NH, RAMS,Spiridonov, UMd/GCE, SDSMT
C. Wang
DHARMA
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Models Configured for an IdealizedConvective Case
• 160 km x 160 km x 20 km Domain: WRF-AqChem• 120 km x 120 km x 20 km Domain: C.Wang, DHARMA,
Meso-NH, RAMS/Leriche, Spiridonov, SDSMT• 360 km x 328 km x 25 km Domain: UMd/GCE
• Resolution: Δx = Δy = 1 km
stretched vertical grid (50 grid points): WRF-AqChem,Meso-NH, RAMS/Leriche
Δz = 500 m: Spiridonov, UMd/GCEΔz = 400 m: C. WangΔz = 250 m: DHARMA, SDSMT
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Sample of the Results Analyzed for theIntercomparison
STERAO-1996From Dye et al. (2000)
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Radar Reflectivityz = 10.5 km m.s.l.
t = 1 hr
observations
Model results
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Radar Reflectivityalong-axis cross section
t = 1 hr
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Maximum Updraft
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Transectsacross Anvil
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NOx production from Lightning
DeCaria et al. (2005)• Lightning interferometer data as input• Finds region of reflectivity > 20 dBZ• Distributes NO verticallyWRF-AqChem, UMd/GCE
Parameterized flash rate based on max updraftsRAMS/Leriche
Parameterized electric field based on microphysicsC. Wang
Predicts charge density in modelMeso-NH, SDSMT
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Transectsacross Anvil
Left side has linearplots, Right side haslog plots
NO, NOxLinear scale
NO, NOxLinear scale
NO, NOxLog scale
NO, NOxLog scale
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Cross sectionsCO
50 km from coret = 6000 s
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Cross sectionsNOx
50 km from coret = 6000 s
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Cross sectionsO3
50 km from coret = 6000 s
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*Skamarock et al.(2003) JGR
Flux Through Anvil Average values through vertical cross-sections from t = 1 h to t = 2 h
6.58 +/- 3.492.24 +/- 0.536.86 +/- 1.30344.3 +/- 133.0avg +/- std dev
13.041.936.59196.9SDSMT(Helsdon et al.)
8.452.549.06274.0U. Md / GCE(Pickering et al.)
3.23.35.00444.0V. Spiridonov
5.302.297.68332.7RAMS(Leriche et al.)
2.841.595.41n/aMeso-NH(Pinty, Mari)
n/a2.397.69531.9DHARMA(Fridlind et al.)
5.971.946.72442.7C. Wang
7.231.946.75187.7WRF-AqChem(Barth, Kim)
5.81.905.9315Observations*
NOx Flux(10-8 mol m-2 s)
CO Flux(10-5 mol m-2 s-1)
Mass Flux(kg m-2 s-1)
Anvil Area(106 m2)
Model
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Simulations of HNO3, H2O2, and CH2O
Initial profile
MOZART profile
Points from aircraftobservations
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Chemistry and Aerosols
• CO, O3, NOx, H2O2, CH2O, HNO3
Chemistry simulated: WRF-AqChem, C. Wang,RAMS/Leriche, UMd/GCE
• Aerosols simulated:C. Wang
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Microphysics and Chemistry
Species are transferredamong hydrometeorsaccording to the microphysics
Liquid to ice, snow, or hail: WRF-AqChem, C. Wang
Liquid to gas: UMd/GCE*, RAMS
vapor
ice
rain snow
cloudwater
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Observations
WRF-AqChem(NCAR)
Chien Wang
U.Md/GCE
RAMS/Leriche
Transects acrossAnvil
Gas-phase mixing ratios
Observations
WRF-AqChem(NCAR)
Chien Wang
U.Md/GCE
RAMS/Leriche
No lightning – dotted lines
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Liquid to ice, snow, or hail: WRF-AqChem, C. Wang
Liquid to gas: UMd/GCE*, RAMS
degas
adsorption
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Transects acrossAnvil
Gas-phase mixing ratios
Observations
WRF-AqChem(NCAR)
Chien Wang
U.Md/GCE
RAMS/Leriche
Microphysical effects
Degassing during dropfreezing
No adsorption of gasesonto ice
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Conclusions
• Tracer transport (CO and O3) are similaramong models and similar to observations.
• NOx is consistently underestimated when nolightning is included.
• Lightning-NOx parameterizations performreasonably well.
• Comparison of soluble species HNO3, H2O2,and CH2O shows we have much more toevaluate.
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What’s next?
Observations Measurements of HOx precursors in both
the inflow air and the convective outflow arelacking.
Intercomparison of tropical convection andchemistry? Impact of aerosols?
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Planning: Deep Convective Clouds andChemistry (DC3) Field Experiment
When: Summer 2009
Where: Central U.S.
Major Facilities:Aircraft (high altitude and low altitudeplanes),Radar (Doppler and polarimetric)Lightning Mapping Array
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Initialization
Convection initiatedwith 3 warm bubbles
Sounding data camefrom Skamarock et al.(2000)
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Initialization of Chemical Species
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flux) mass air for 1 ( species of ratio mixing C
sectioncrossincellgridoflengthhorizontalwhere
==
!="
""
""
=#
# $
l
l
l
sanvil cell
cellsanvilflux
z
zCU%
Calculation is done on grid cells that contain cloud particles.The area of the anvil is the denominator.
!
z
x
y
Δx
l!
Δz
Flux Calculation
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transport
washout and rainout
NO production from lightning
Processes in deep convection that affectchemical species
high photolysis rates
low photolysis rates
cloudchemistry
icechemistry
Phase of cloud particles¬cloud microphysics and
chemical species
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Spatial Scales and Time Scales
1 s 100 s 1 hr 1 day 1 week 1 mon
Synoptic-scalestorms
1 yr
1 m
100 m
1 km
100 km
1,000 km
Short-lived speciesCH3OO
HO2
NO3OH
Moderately long-lived speciesCO
O3
SO2 H2O2
NOx
CH2O C3H6
C5H8
10 km
Fair weathercumulus
Cumulonimbus
Adapted from Brasseur et al. (1999)