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Using A-train observations to evaluate clouds in CAM

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 .

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Using A-train observations to evaluate clouds in CAM

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  1. Using A-train observations to evaluate clouds in CAM 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. 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. 30 km 1400 km Example radar ‘quicklook’ showing tropical convection - February 7, 2008 CloudSat and CALIPSO Data Sampling

  4. Global Zonal Mean Cloud Fraction(CloudSat+CALIPSO) More data plots: http://www.cgd.ucar.edu/cms/jenkay/

  5. The A-train satellite data provide a unique view of Arctic clouds. DJF Low Cloud Maps ISCCP D2 (infrared) CloudSat+CALIOP (radar+lidar) Warren (surface obs.)

  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. Warren Surface Obs. ISCCP D2 CloudSat/CALIOP CAM 3.6 (CAM 3.5 + MG) The importance of cloud definition JJA low cloud cover

  8. 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) Variability in the short CloudSat record Western Arctic cloud reductions from 2007 to 2006 are associated with differing atmospheric circulation patterns.

  9. Overall Goal:Apple-to-Apple ComparisonsCloudSat vs. “CAM-dev” “CAM-dev”  CAM 3.6  CAM 3.5 + 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” • TODAY, preliminary comparisons of: • Global low cloud cover • Global high cloud cover • dBZ-ht histograms, cloud profiles in specific regions

  10. CAM 3.6 (from standard diagnostics) JJA Low Cloud Fraction Maps CloudSat Observations (1-3 km, “by-profile”) CAM 3.6 (1-3 km, “by-profile”)

  11. CAM 3.6 (from standard diagnostics) DJF High Cloud Fraction Maps CloudSat Observations (7-22 km, “by-profile”) CAM 3.6 (7-22 km, “by-profile”)

  12. Tropical Comparison(CFAD, Cloud fraction “by-height”)

  13. Sub-tropics Comparison(CFAD, Cloud fraction “by-height”)

  14. Mid-Latitude Storm Track (CFAD, Cloud fraction “by-height”)

  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… • 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 (jenkay@ucar.edu) or Talk to me later.

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