The Performance of CAM5 Physics Modules at High Spatial Resolution
P.J. Rasch, J.D. Fast, W.I. Gustafson Jr., B. Singh, R.C. Easter, P.-L. Ma, and M. Shrivastava - PNNL G. Pfister, S. Walters, and J.-F. Lamarque - National Center for Atmospheric Research
Motivation
Acknowledgements: This research was supported by the U.S. DOE’s ESM Program under contract DE-AC06-76RCO 1830 at PNNL and uses software tools developed under the PNNL LDRD program.
Concept for Earth System Modeling Project on Arctic Processes
Objectives
Next Steps Comparing Aerosol Predictions
xx
Weather Research & Forecasting (WRF)
Aerosol Modeling Testbed
high resolution
low resolution medium resolution
typical CAM grid cell 2.5 x 1.9 degrees
Multi-Scale Simulations There has been relatively little interaction between the cloud-resolving / mesoscale and global modeling communities
CAM will likely be run at Δx = 10 – 20 km in the future (5 – 10 years from now), but the performance of the current suite of physics modules at those scales are not known
Rapid development and evaluation of the next generation suite for CAM requires the ability to isolate processes as well the ability to test parameterizations across a range of scales
Current computing capabilities do not allow global models to be run routinely at mesoscale resolutions, anticipated for use 5 – 10 years from now
Incorporate the CAM5 parameterization suite into WRF Use the Aerosol Modeling Testbed to evaluate the CAM5
parameterization suite Evaluate CAM5 physics suite at higher spatial resolution
more compatible with data Compare CAM5 physics modules against more complex
and expensive representations using systematic and consistent methodology
Use performance metrics to identify more desirable parameterization choices for both models
Increase communication between cloud-resolving / mesoscale and global scale modeling communities
Downscaling for MILAGRO Testbed
Community Atmosphere Model (CAM5)
module
CLM
Park & Bretherton
MOZART
Morrison & Gettleman
RRTMG
MAM
ZM deep & UW shallow
trace gas chemistry
convection
microphysics
aerosols
boundary layer
land surface
radiation
trace gas chemistry
convection
microphysics
aerosols
boundary layer
land surface
radiation
Philosophy: Several parameterizations for each atmospheric process for short-term simulations using range of grid spacings
Philosophy: Single parameterization for each atmospheric process for long-term climate simulations using a coarse grid
CAM5 “package”
downscaling with consistent physics
boundary conditions Use field campaign data to evaluate how performance varies as a function of resolution
Simulations with CAM5 package and individual CAM5 modules coupled with more complex treatments
Utilize Data from MILAGRO Field Campaign and Tools from Aerosol Modeling Testbed to Evaluate CAM5 Downscaling
Simulated PM2.5, excluding dust
Run WRF at high resolution, downscaling from CAM5, for the ISDAC / ARCTAS testbed case
Compare performance of CAM5 simulations with high-resolution simulations from WRF
Clouds from ARSCL
Clouds from Morrison Microphysics Scheme
C-130 Flight on March 10, 2006 T0 Site in Mexico City
Average PBL Depth at T1 Site observed – radar wind profilers YSU scheme from WRF UW scheme from CAM5
conc
entr
atio
n (µ
g m
-3)
Organic Matter (OM)
Primary + Biomass Burning OM
Secondary OM
Sulfate
Nitrate
mean mean downwind city
CAM5 aerosol model neglects NO3
under-prediction of downwind aerosols
from CAM5 related to its PBL scheme
SOA scheme coupled to MOSAIC aerosols
produced too much SOA
Emissions identical for red and blue lines, so differences above due to differences in aerosol (MOSAIC vs MAM) and boundary layer (YSU vs UW) treatments
IPCC POM surface emissions ~10% lower over Mexico, but 3.7 times lower over Mexico City and do not vary diurnally
WRF simulations at Δx ~ 5 km over Barrow
‘clean’ ‘dirty’
aerosols entrained
into clouds
conc
entr
atio
n (µ
g m
-3)
Organic Matter (OM)
Primary + Biomass Burning OM
Secondary OM
Sulfate
Nitrate CAM5 aerosol model
neglects NO3
SOA scheme in CAM5 produced too much SOA
downwind
city
transport and mixing simulated reasonably well
mean
AMS organic aerosol data from MILAGRO very useful in evaluating SOA treatment in CAM5 compared to more sophisticated ones in WRF-Chem
CAM5 treatment too low during afternoon
CAM5 (2.5 x 1.9o) + IPCC AR5 emissions
SW ambient winds µg m-3
WRF (Δx = 12 km) + CAM5 Physics + CAM5 (IPCC AR5) emissions
CAM CAM
CAM CAM
mostly dust
no dust emissions
observed AMS data (provided by Jose Jimenez, Allison
Aiken, and Pete de Carlo)
‘default’ WRF-Chem, local emissions CAM5 Physics, local emissions CAM5 Physics, IPCC emissions
20
15
10
5
0 6a 20a 10 29 20b 22a 19a 18a 15a 7a
OM Percentiles for All Flights
G-1 C-130
observed (5/25/50/75/95 percentiles) ‘default’ WRF CAM5 Physics
Δx = 3 km domain
Mexico City
PM2.5 at 700 hPa Aerosol Optical Depth
WRF (Δx = 12 km) + CAM5 Physics + local emissions (hi-res)
extract results to compare with data
extract results to compare with data
C-130 Flight Path
How well will MAM aerosols (MAM in CAM5, MAM in WRF-Chem, MOSAIC in WRF-Chem) compare with aircraft measurements in the Arctic
In contrast to aerosols, the complexity of the Morrison & Gettleman microphysics scheme from CAM5 is comparable to those in WRF. But the performance as scales change needs to be quantified in addition to comparing the performance with other schemes.
red = 4 µg m-3
18 UTC March 19
2006
(city) (downwind)