Coupling of Convective Motions and Cloud Macrophysics in Climate Model CMDV-CM4

 
   
     CMDV-CM
4
 Overview
 
- Who we are
- What we are trying to do
- Summary of observational techniques
- Summary of parameterization development
 
CMDV-CM
4
  Collaborators
 
David Romps
     
Lead PI
   
LBNL
Andrew Vogelmann
   
Co-PI
   
BNL
Christopher Bretherton
  
Co-PI
   
U Washington
Charles Jackson
    
  
 
Co-PI
   
UT Austin
Michael Jensen
     
Co-I
   
BNL
Pavlos Kollias
     
Co-I
   
BNL
Rusen Oktem
     
Co-I
   
LBNL
 
What we are trying to do
 
 
CM4 will use new ARM observations of shallow
clouds and the lower-tropospheric state to
evaluate the current CLUBB representation of
shallow clouds in ACME, develop a shallow-
cloud scheme for the SPM, and tune and
validate those shallow-cloud schemes in single-
column-model (SCM) simulations.
 
Obs
 
Param
 
ACME
 
Obs
 
Subcloud turbulence structure from Doppler
lidar (BNL)
 
Obs
 
Thermodynamic structure and stability from
AERI/MWR and Raman lidar (BNL)
Cloud-base mass flux from KAZR Doppler
(BNL)
In-cloud w and TKE from KAZR (BNL)
Liquid water from KAZR and MWR (BNL)
 
Obs
 
Bulk convective entrainment rate (BNL)
 
Obs
 
Gridded binary cloud field from stereo
cameras (LBNL)
 
Detection of shallow clouds by 
stereo cameras 
and 
lidar
 
“Obs”
 
CM4 LES, borrowing LASSO methodologies, at
~25-m and ~5-min resolutions (BNL)
 
“Obs”
 
CRM cloud-radar simulator; CR-SIM (BNL)
 
Param
 
Stochastic Parcel Model (SPM) convective
parameterization (LBNL)
 
Param
 
Framework for evaluating ACME shallow
convection against RACORO (U Washington)
 
Param
 
UQ infrastructure for ACME in single-column
mode (UT Austin)
 
Critical RH for low clouds
 
Critical RH for high clouds
 
|Model – Obs|
 
What we could use
 
Updated ACME SCM code
Any upgrades to CLUBB from other projects
Help with tuning and evaluation in global
ACME simulations
Help investigating of process-coupling errors
 
CMDV-CM
4
  Collaborators
 
David Romps
     
Lead PI
   
LBNL
Andrew Vogelmann
   
Co-PI
   
BNL
Christopher Bretherton
  
Co-PI
   
U Washington
Charles Jackson
    
  
 
Co-PI
   
UT Austin
Michael Jensen
     
Co-I
   
BNL
Pavlos Kollias
     
Co-I
   
BNL
Rusen Oktem
     
Co-I
   
LBNL
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This project led by Lawrence Berkeley National Laboratory aims to mechanistically couple convective motions and cloud macrophysics in the CMDV-CM4 climate model. Through observational techniques and parameterization development, they seek to evaluate and enhance current cloud representations, develop new cloud schemes, and validate them through simulations. The project involves collaboration with various institutions and utilizes observations such as Doppler lidar data and cloud structure analysis to improve understanding of shallow clouds and lower-tropospheric conditions.

  • Climate model
  • Convective motions
  • Cloud macrophysics
  • Parameterization development
  • Observational techniques

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  1. Coupling M echanistically the Convective M otions and Cloud M acrophysics in a Climate M odel CMDV-CM4 Overview Cover Page - Who we are - What we are trying to do - Summary of observational techniques Project Title Coupling Mechanistically the Convective Motions and Cloud Macrophysics in a Climate Model A pplicant/ Institution - Summary of parameterization development Lawrence Berkeley National Laboratory Postal A ddress One Cyclotron Road, Berkeley, CA 94720 A dministrative Point of Contact Rosie Davis, 510-486-7496, brdavis@ lbl.gov Lead PI David Romps, 510-486-7175, dromps@ lbl.gov DOE National Laboratory A nnouncement Number LAB 16-1530 DOE/ Office of Science Program Office Office of Biological and Environmental Research DOE/ Office of Science Program Office Technical Contact Dr. Dorothy Koch PA M S Letter of Intent Tracking Number LOI-0000014513

  2. CMDV-CM4 Collaborators David Romps Andrew Vogelmann Christopher Bretherton Charles Jackson Michael Jensen Pavlos Kollias Rusen Oktem Lead PI Co-PI Co-PI Co-PI Co-I Co-I Co-I LBNL BNL U Washington UT Austin BNL BNL LBNL

  3. What we are trying to do CM4 will use new ARM observations of shallow clouds and the lower-tropospheric state to evaluate the current CLUBB representation of shallow clouds in ACME, develop a shallow- cloud scheme for the SPM, and tune and validate those shallow-cloud schemes in single- column-model (SCM) simulations. ACME Obs Param

  4. Obs Subcloud turbulence structure from Doppler lidar (BNL)

  5. Obs Thermodynamic structure and stability from AERI/MWR and Raman lidar (BNL) Cloud-base mass flux from KAZR Doppler (BNL) In-cloud w and TKE from KAZR (BNL) Liquid water from KAZR and MWR (BNL)

  6. Obs Bulk convective entrainment rate (BNL)

  7. Obs Gridded binary cloud field from stereo cameras (LBNL)

  8. Detection of shallow clouds by stereo cameras and lidar

  9. Obs CM4 LES, borrowing LASSO methodologies, at ~25-m and ~5-min resolutions (BNL)

  10. Obs CRM cloud-radar simulator; CR-SIM (BNL)

  11. 20 20 20 liquid LES ice LES purity LES 15 15 15 Height (km) Height (km) Height (km) 10 10 10 5 5 5 0 0 0 Param ice 0.000 LES updraft liquid mass fraction 20 liquid 0.002 0.004 0e+00 LES updraft ice mass fraction 20 0.0 0.2 LES updraft purity 0.4 0.6 0.8 1.0 4e 04 8e 04 20 SPM SPM purity SPM 15 15 15 Height (km) Height (km) Height (km) Stochastic Parcel Model (SPM) convective parameterization (LBNL) 0.000 0.002 0.004 SPM updraft liquid mass fraction SPM updraft ice mass fraction 20 theta e LES buoyancy 10 10 10 5 5 5 0 0 0 0e+00 0.0 0.2 SPM updraft purity 0.4 0.6 0.8 1.0 4e 04 8e 04 20 20 LES velocity LES 15 15 15 Height (km) Height (km) Height (km) 10 10 10 5 5 5 0 0 0 325 335 345 355 0.00 0.05 0.10 0.15 0 5 10 15 20 0.05 LES updraft buoyancy(m s2) 20 buoyancy LES updraft theta e (K) LES updraft velocity (m/s) 20 20 SPM SPM velocity SPM theta e 15 15 15 Height (km) Height (km) Height (km) 10 10 10 5 5 5 0 0 0 325 335 345 355 0.00 0.05 0.10 0.15 0 5 10 15 20 0.05 SPM updraft buoyancy(m s2) SPM updraft theta e (K) SPM updraft velocity (m/s) 0 1

  12. 20 20 20 liquid LES ice LES purity LES 15 15 15 Height (km) Height (km) Height (km) 10 10 10 5 5 5 0 0 0 0.000 LES updraft liquid mass fraction 20 liquid 0.002 0.004 0e+00 LES updraft ice mass fraction 20 ice 0.0 0.2 LES updraft purity 0.4 0.6 0.8 1.0 4e 04 8e 04 20 SPM SPM purity SPM 15 15 15 Height (km) Height (km) Height (km) 10 10 10 5 5 5 0 0 0 0.000 SPM updraft liquid mass fraction 20 theta e 0.002 0.004 0e+00 SPM updraft ice mass fraction 20 buoyancy 0.0 0.2 SPM updraft purity 0.4 0.6 0.8 1.0 4e 04 8e 04 20 LES LES velocity LES 15 15 15 Height (km) Height (km) Height (km) 10 10 10 5 5 5 0 0 0 325 335 345 355 0.00 0.05 0.10 0.15 0 5 10 15 20 0.05 LES updraft buoyancy(m s2) 20 buoyancy LES updraft theta e (K) LES updraft velocity (m/s) 20 20 SPM SPM velocity SPM theta e 15 15 15 Height (km) Height (km) Height (km) 10 10 10 5 5 5 0 0 0 325 335 345 355 0.00 0.05 0.10 0.15 0 5 10 15 20 0.05 SPM updraft buoyancy(m s2) SPM updraft theta e (K) SPM updraft velocity (m/s) 0 1

  13. Param Framework for evaluating ACME shallow convection against RACORO (U Washington)

  14. Param UQ infrastructure for ACME in single-column mode (UT Austin) |Model Obs| Critical RH for low clouds Critical RH for high clouds

  15. What we could use Updated ACME SCM code Any upgrades to CLUBB from other projects Help with tuning and evaluation in global ACME simulations Help investigating of process-coupling errors

  16. CMDV-CM4 Collaborators David Romps Andrew Vogelmann Christopher Bretherton Charles Jackson Michael Jensen Pavlos Kollias Rusen Oktem Lead PI Co-PI Co-PI Co-PI Co-I Co-I Co-I LBNL BNL U Washington UT Austin BNL BNL LBNL

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