Upload
chandler
View
26
Download
0
Embed Size (px)
DESCRIPTION
Using A-train observations to evaluate clouds in CAM. Jennifer Kay (NCAR/CSU) Andrew Gettelman (NCAR) Thanks to Hugh Morrison (NCAR). Spaceborne radar and lidar 101. Active instruments such as radar or lidar emit a pulse . - PowerPoint PPT Presentation
Citation preview
Using A-train observations to evaluate clouds in CAM
Jennifer Kay (NCAR/CSU)
Andrew Gettelman (NCAR) Thanks to Hugh Morrison (NCAR)
Active instruments such as radar or lidar
emit a pulse.
The pulse is either reflected back to the instrument, continues
downward, or is absorbed and lost.
The reflected signal is a measure of vertical cloud and aerosol
structure.
CloudSat’s 94 GHz (3 mm) radar measures cloud particles, raindrops, and snowflakes.
CALIPSO’s 532/1064 nm lidar measures aerosols and thin clouds.
Spaceborne radar and lidar 101
CloudSat and CALIPSO Data Sampling
Example radar ‘quicklook’ showing tropical convection - February 7, 2008
1400 km
30 km
Global Zonal Mean Cloud Fraction(CloudSat+CALIPSO)
QuickTime™ and a decompressor
are needed to see this picture.
More data plots: http://www.cgd.ucar.edu/cms/jenkay/
The A-train satellite data provide a unique view of Arctic clouds.
CloudSat+CALIOP(radar+lidar)
ISCCP D2(infrared)
Warren(surface obs.)
DJF Low Cloud Maps
How do we use CloudSat data to evaluate CAM’s clouds?
Important factors to consider:- How do we define a cloud? (radar sensitivity)- Are these data representative? (short data record)
Clear advantages of CloudSat data:- first measure of global cloud vertical structure- measured cloud quantities such as dBZ can be directly compared to simulated model cloud quantities (w/MG microphysics)
The importance of cloud definition
ISCCP D2Warren Surface Obs.
CloudSat/CALIOP CAM 3.6 (CAM 3.5 + MG)
JJA low cloud cover
Variability in the short CloudSat record
Western Arctic cloud reductions from 2007 to 2006 are associated with differing
atmospheric circulation patterns. New record sea ice
extent minimum, Sept. 16 2007
Credit: NSIDC
2007 cloud reductions contributed to dramatic sea ice loss.
Kay et al. (submitted to GRL)
Overall Goal:Apple-to-Apple Comparisons
CloudSat vs. “CAM-dev”
TODAY, preliminary comparisons of:- Global low cloud cover- Global high cloud cover- dBZ-ht histograms, cloud profiles in specific regions
Some important cloud definitions…cloud -30 dBZ < cloud < 10 dBZcloud fraction cloud #/ total # -cloud fraction can be “by-profile” or “by-height”
“CAM-dev” CAM 3.6 CAM 3.5 + MG microphysics + empirical radar reflectivity simulator; 3 years, 6-hourly output
JJA Low Cloud Fraction Maps
CloudSat Observations (1-3 km, “by-profile”)
CAM 3.6 (1-3 km, “by-profile”)
CAM 3.6 (from standard diagnostics)
CAM 3.6 (7-22 km, “by-profile”)
DJF High Cloud Fraction Maps
CloudSat Observations (7-22 km, “by-profile”)
CAM 3.6 (from standard diagnostics)
Tropical Comparison(CFAD, Cloud fraction “by-height”)
Sub-tropics Comparison(CFAD, Cloud fraction “by-height”)
Mid-Latitude Storm Track (CFAD, Cloud fraction “by-height”)
Future Plans
- Add CFMIP ISCCP/CloudSat/CALIPSO simulator to CAM - Use DART to constrain CAM dynamics, look at clouds- Actively engage with model evaluation efforts for CAM4
PLUG: Does your work incorporate model-obs cloud comparisons?I can provide cloud data to help you evaluate model performance…E-mail me ([email protected]) or Talk to me later.
Conclusions- CloudSat data are a unique tool for evaluating the representation of clouds in next-generation climate models.- Cloud definition is key to useful comparisons.- Much more work to be done…