1. Using A-train observations to evaluate clouds in CAM
CloudSat/CALOP data - June 2006 to present.
Jennifer Kay (NCAR/CSU)
Andrew Gettelman (NCAR)
Thanks to Hugh Morrison (NCAR)

2. Spaceborne radar and lidar 101
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.
Together, CloudSat and CALIPSO actively profile most clouds in the atmosphere!
CloudSat’s 94 GHz (3 mm) radar measures cloud particles, raindrops, and snowflakes.
CALIPSO’s 532/1064 nm lidar measures aerosols and thin clouds.

3. CloudSat and CALIPSO Data Sampling
Example radar ‘quicklook’ showing tropical convection - February 7, 2008
1400 km
30 km
Each data granule segment is 1400 km long, 30 km high.
16 day repeat cycle on the ground - fixed orbit. CloudSat has a very narrow field of view ~1.5 km. cross section through the atmosphere.
In a 2x2 degree box at the equator - 6 overpasses per month. In a 2x2 degree box in the Arctic Ocean, 29 overpasses per month.
Note: attenuation in the radar data, especially once you get into the heavy rain.

4. Global Zonal Mean Cloud Fraction (CloudSat+CALIPSO)
cloud mask from radar (2B-GEOPROF) and lidar (2B-GEOPROF-LIDAR)
More data plots:

5. The A-train satellite data provide a unique view of Arctic clouds.
DJF Low Cloud Maps
Looking down at the North Pole. Observational estimates of low cloud fraction in the Arctic ISCCP D , Warren Cloud Surface.
Spatial pattern not too different (START HERE), but amounts very different. Large discrepancies - this is what modelers have to evaluate their clouds!
CloudSat/Calipso seeing a lot more clouds than existing observations. Some differences in spatial patterns, but this might be because CS+C are only
one year.
Lead into the next slide ----> The vertical structure…. that’s what the A-train provides and that is what is new.
Remote sensing instruments on NASA’s A-train satellite platforms are uniquely capable of measuring Arctic clouds and constraining radiative flux calculations. We use CloudSat’s 94-GHz radar (Stephens et al., 2002) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation)’s 532-nm and 1064-nm lidar to document the vertical Arctic summertime cloud structure during 2006 and Combined radar-lidar cloud masks from CloudSat standard products (2B-GEOPROF and 2B-GEOPROF-LIDAR) provide a highly accurate measurement of cloud fraction profiles (Mace et al., 2007). Unlike cloud detection techniques based on passive radiances, combined radar-lidar cloud detection does not rely on the weak thermal and albedo contrast between clouds and the often ice-covered Arctic surface. Instead, cloud detection relies on the amount of energy backscattered by cloud particles.
ISCCP D2
(infrared)
Warren
(surface obs.)
CloudSat+CALIOP
(radar+lidar)

6. 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)

7. The importance of cloud definition
JJA low cloud cover
Warren Surface Obs.
ISCCP D2
CloudSat/CALIOP
CAM 3.6 (CAM MG)
JJA low cloud cover from several global estimates.

8. Variability in the short CloudSat record
New record sea ice extent minimum, Sept
Credit: NSIDC
2007 cloud reductions contributed to dramatic sea ice loss.
Kay et al. (submitted to GRL)
Western Arctic cloud reductions from 2007 to 2006 are associated with differing atmospheric circulation patterns.
JJA low cloud cover from several global estimates.

9. Overall Goal: Apple-to-Apple Comparisons CloudSat vs. “CAM-dev”
“CAM-dev”  CAM 3.6  CAM MG microphysics + empirical radar reflectivity simulator; 3 years, 6-hourly output
Some important cloud definitions…
cloud  -30 dBZ < cloud < 10 dBZ
cloud fraction  cloud #/ total #
-cloud fraction can be “by-profile” or “by-height”
Climate Model Description: We are comparing CloudSat R04 dBZ observations to a development version of NCAR’s community atmosphere model (“CAM-dev”). CAM-dev is CAM3 (Collins et al., 2006) with modifications to the deep convection scheme (Neale and Richter (200x)) and a new two-moment stratiform microphysics scheme (Morrison & Gettelman (2008); Gettelman et al. (2008)). We use an empirical simulator to convert the modeled microphysical parameters to radar reflectivity for direct comparison with CloudSat observations. The empirical simulator is based on microphysics-Z relationships used operationally at NOAA (Matthew Shupe, personal communication) and Matrosov (2007).
Methodology For Comparison: We compared three years of CAM-dev instantaneous dBZ values saved at 6 hourly intervals to the CloudSat dBZ observations, which are available from June 2006 to present. Here, we present comparisons during the cloudiest season for two regions in the Arctic: Fall (SON) in the Beaufort Sea and Winter (DJF) in the Barents Sea. The diurnal sampling of the observations and the model output are not the same, but this isn’t a concern because there is not a strong diurnal cycle in Arctic cloudiness. Given our height sampling resolution of 0.48 km, comparisons below 0.96 km are not valid because the CloudSat dBZ data are contaminated by surface clutter. Microphysical comparisons are presented as normalized contoured frequency by height diagrams (CFADs). The normalization is done over each CFAD individually. Comparison between CFADs shows the relative amounts of cloud types as a function of dBZ and height. Because the normalized CFADs cannot be used to compare absolute cloud amounts, we also present vertical cloud fraction profiles. We define a volume as “cloudy” if it has a dBZ value between -30 dBZ and 10 dBZ.
TODAY, preliminary comparisons of:
Global low cloud cover
Global high cloud cover
dBZ-ht histograms, cloud profiles in specific regions

10. JJA Low Cloud Fraction Maps
CAM 3.6 (from standard diagnostics)
CloudSat Observations (1-3 km, “by-profile”)
bin-by-profile comparison, require 2 bins with low cloud cover (1-3 km) using CloudSat cloud definition (-30 to 10 dBZ)
CAM 3.6 (1-3 km, “by-profile”)

11. DJF High Cloud Fraction Maps
CAM 3.6 (from standard diagnostics)
CloudSat Observations (7-22 km, “by-profile”)
bin-by-profile comparison, require 2 bins with high cloud cover (7+ km) using CloudSat cloud definition (-30 to 10 dBZ)
CAM 3.6 (7-22 km, “by-profile”)

12. Tropical Comparison (CFAD, Cloud fraction “by-height”)
Darwin - CFAD and cloud fraction profile
work_analyze_geoprof_stats_dbz.pro for CloudSat
work_analyze_ccsm_dbz.pro for CAM-dev

13. Sub-tropics Comparison (CFAD, Cloud fraction “by-height”)
Off Coast of CA - CFAD and cloud fraction profile
work_analyze_geoprof_stats_dbz.pro for CloudSat
work_analyze_ccsm_dbz.pro for CAM-dev

14. Mid-Latitude Storm Track (CFAD, Cloud fraction “by-height”)
North Pacific - CFAD and cloud fraction profile
work_analyze_geoprof_stats_dbz.pro for CloudSat
work_analyze_ccsm_dbz.pro for CAM-dev

15. Conclusions Future Plans
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…
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
ISCCP D2 dataset - low cloud cover
CloudSat/CALIOP low cloud
PLUG: Does your work incorporate model-obs cloud comparisons?
I can provide cloud data to help you evaluate model performance…
me or Talk to me later.