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CFMIP-UBC-June-2009

Cloud Vertical Structure along the GPCI transect over the Northeastern Pacific as exhibited by CloudSat , ECMWF Analysis and two Climate Prediction Models Jui -Lin F Li, J. Teixeira, D. Waliser , C. Woods, T. Kubar , D. G. Vane / JPL J.D. Chern,W .-K. Tao, J. Bacmeister / GSFC

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CFMIP-UBC-June-2009

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  1. Cloud Vertical Structure along the GPCI transect over the Northeastern Pacific as exhibited by CloudSat, ECMWF Analysis and two Climate Prediction Models Jui-Lin F Li, J. Teixeira, D. Waliser, C. Woods, T. Kubar, D. G. Vane/JPLJ.D. Chern,W.-K. Tao, J. Bacmeister/GSFC A. Tompkins, R. Forbes, M. Koehler/ECMWF CFMIP-UBC-June-2009

  2. Motivation Present-day shortcomings in the representation of clouds in general circulation models (GCMs) lead to errors in weather and climate forecasts, as well as account for a source of uncertainty in climate change projections. For example,

  3. IPCC Model Uncertainties: “Cloud Ice” Mean IWP from 16 IPCC Contributions of 20th Century Climate (Waliser and Li et. el., JGR 2009)

  4. IPCC Model Uncertainties: “Cloud Liquid” Mean LWP from 16 IPCC Contributions of 20th Century Climate (Li and Waliser et al, 2008a)

  5. Uncertainty of cloud feedbacks from IPCC Models For 2x CO2, net change, in particular, in stratocumulus (Sc) clouds amount: • Decreasing in GFDL AM2 (positive albedo feedback) • Increasing in NCAR CAM2 (negative albedo feedback) (From Bretherton et al, 2003 CPT)

  6. These raise Uncertainty about IPCC Cloud Feedback Representation This level of disagreement must be reduced.

  7. Cloud Ice Water Path: Passive Measurements Annual Mean MODIS - Courtesy S. Platnick Annual Mean IWP CERES/MODIS - Courtesy P. Minnis Annual Mean IWP NOAA/Microwave - Courtesy H. Meng Annual Mean IWP ISCCP - Courtesy W. Rossow

  8. Cloud liquid Water Path: Passive Measurements (a) CERES/MODIS LWP (b) SSM/I LWP (c) ISCCP LWP

  9. Cloud Liquid Water Path : Values from GCMs (a) (b) (c) GMAO/MERRA ECMWF R30 GEOS5 (d) (e) CAM3 fvMMF Mean=122.8 Figure 3. Multi-year mean values of cloud liquid water path (LWP; g m-2) from (a) NASA GMAO/MERRA ( 01/1979-10/1979), (b) ECMWF R30 analysis (Annual: 08/2005-07/2006), (c) GEOS5 AGCM (01/1999-12/2002), (d) NCAR CAM3 (1979-1999) and (e) fvMMF (01/2005-12/2006). (Li and Waliser et al, 2008a)

  10. Vertical-resolved profile of cloud hydrometers  With the MLS/CloudSat ice water content (IWC) and liquid water content (LWC) A-Train retrieval data sets, more robust model-data evaluation is possible MLS: Microwave Limb Sounder Aura Parasol Calipso CloudSat Aqua MLS

  11. Cloud Ice Water Path : Values from CloudSat Annual Mean IWP CloudSat RO4 Aug 2006-Jul 2007 Annual Mean MODIS - Courtesy S. Platnick Annual Mean IWP CERES/MODIS - Courtesy P. Minnis Annual Mean IWP NOAA/Microwave - Courtesy H. Meng Annual Mean IWP ISCCP - Courtesy W. Rossow

  12. Cloud Liquid Water Path : Values from CloudSat (a) CERES/MODIS LWP (b) SSM/I LWP (c) ISCCP LWP (d) CloudSat total LWP

  13. OBJECTIVES: In here, we utilize the vertically resolved A-Train CloudSat estimates of IWC/LWC for GCM performance evaluation over regional cloud vertical structures such as: - GCSS GPIC cross section – Hadley Circulation (Li, Teixeira, Waliser, et. el.. 2009b) This requires knowledge of the retrieval process for CloudSat product and how the product relates to modeled quantities.

  14. CloudSat IWC/LWC Retrieval ICE ICE ICE CloudSat measurements are sensitive to multiple particle types:  cloud ice (~small particle), snow, graupel  cloud liquid (~small particle), rain SNOW MIXED GRAUPEL LIQUID LIQUID LIQUID RAIN Note that: The Micro Wave Limb Sounder (MLS) provides IWC estimates described as small ice particles at levels in the upper-troposphere

  15. More Complex Model Typical GCM e.g. fvMMF, DARE/RAVE e.g., ECMWF, GEOS5, NCAR/CAM Riming GCM hydrometeor representations: Depends on the level of sophistication of the model’s physics parameterizations Typically represents a ‘cloud’ ice and ‘cloud’ liquid field that remains quasi-suspended between model time-steps, while allowing other ice/liquid particles to be realized as precipitation.

  16. Annual Mean IWP CloudSat RO4 Aug 2006-Jul 2007 Mean IWP fvMMF July99 & Jan98 Chern & Tao graupel All ice snow

  17. fvMMF R04 Aug06-Jul07 Can CloudSat Be Used as a Preliminary Estimate of the Total IWC Field to compare to GCMs? graupel ice Can we judiciously sample/filter Cloudsat to use for GCM Cloud IWC? snow all

  18. Conventional GCM IWC/LWC Representation ICE ICE • BUT Most conventional GCMs represent: • non-precipitating (~small particle) and/or non-convectiveIWC • non-precipitating (~small particle) LWC • All large particles fall as surface precipitation ICE SNOW MIXED GRAUPEL LIQUID LIQUID LIQUID • A-Train CloudSat IWC and LWC data, AS IS, CANNOT be used to validate/evaluate most GCM cloud ice and liquid fields typical output. RAIN

  19. CloudSat GCM Cloud Ice Water Content (IWC) Annual Mean Values CAM3 GEOS5 ECMWF fvMMF DARE UCLA (Waliser and Li et al., 2009)

  20. NP_IWC NC_IWC This is considered a preliminary estimate of cloud IWC for GCMs evaluation NP C_IWC CloudSat Cloud Ice Water Content (IWC) Annual Mean Values NP - Non-Precipitating at Surface NC - Non-Convective CloudSat has cloud classification and surface precipitation flags we put to use to get an estimate of cloud iwc for GCM evaluation Total

  21. IWC @215 hPa IWC MLS Aug04~Jul06 CloudSat NP+NC Aug06~Jul07 The agreement between the MLS IWC estimates and CloudSat sampled IWC estimates in the upper-troposphere is remarkable!!

  22. CloudSat GCM Cloud Ice Water Content (IWC) Annual Mean Values CAM3 GEOS5 ECMWF fvMMF DARE NP & NC (Waliser and Li et al., 2009) UCLA

  23. GCM Cloud Liquid Water Content (LWC) Annual Mean Values GMAO/MERRA ECMWF R30 GEOS5 CloudSat non-precipitating CAM3 fvMMF CloudSat total (a) UCLA

  24. Alternative way… Partitioning CloudSat Ice Water Content for Comparison with Upper-Tropospheric Cloud Ice in Global Atmospheric Models

  25. IWC<100μm IWC>100μm 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Use CloudSat retrievals of the PSD parameters to reconstruct the ice PSD and partition mass according to size by integration: 2.0 1010 108 1.6 106 1.2 104 Number concentration (m-4) Mass (mg m-3) 102 0.8 100 0.4 10-2 0 0 Particle size (mm)

  26. Partitioned CloudSat IWC estimates to Small & Large Particles 215 hPa CloudSat IWCTOTAL (mg m-3) CloudSat IWC>100μm CloudSat IWC<100μm 215 hPa 215 hPa ECMWF C31r 215 hPa MLS 215 hPa (Woods, Li and Waliser et. al., JGR, 2009)

  27. Regional Cloud Structures: viewed from CloudSat, ECMWF Analysis and GCMs In here, we utilize the vertically resolved A-Train CloudSat estimates and ECMWF analyses of IWC/LWC for GCM Cloud representation performance evaluation, over, for example, - GCSS GPIC cross section – Hadley Circulation (Li, Teixeira, Waliser, et. el.. 2009b)

  28. GCSS GPIC cross section – Hadley Circulation (a) IWP (b) LWP (Li, Teixeira, Waliser, et. el.. 2009b)

  29. Hadley Circulation Jet Stream Boundary Layer top Santa Monica Warm/humid Cold/dry Eq. Upwelling precipitating deep convections trade-wind cumuli Stratocumulus

  30. CloudSat NP/NPC ECMWF fvMMF GEOS5 b1 NP-LWC c1 LWC d1 LWC e1 LWC CloudSat Total a1 LWC c2 IWC d2 IWC e2 b2 NPC-IWC IWC a2 IWC d3 NP-LWC+NPC-IWC LWC+IWC LWC+IWC e3 LWC+IWC b3 c3 a3 LWC+IWC

  31. (a) CloudSat (b) ECMWF (c) GEOS5 (d) fvMMF Note: fvMMF cloud fraction includes ice, snow and graupel.

  32. CloudSat Cloudiness along the cross section JJA 2006 Total Cloud Frequency Ci Sc As Ac Cu Deep Cu

  33. Issues regarding the Cloudsat Profile Product in general: • The minimum detectable signal of the Cloud Profiling Radar is approximately -31 dBZe.  high thin cirrus, and non precipitating water cloud such as altocumulus and continental stratus will be below the detection threshold of the CPR • Due to reflection from the surface and the 1 km pulse length of the CPR, •  no or reduced sensitivity. Caveats associated with CloudSat LWC retrieval:  CloudSat LWC retrieval often fails for profiles containing high radar reflectivities due to the presence of precipitation-sized particles.

  34. We are currently using collocated data from measurements/RA including CloudSat, Calipso, MODIS, AMSR, ECMWF, TRMM, AIRS…….  to identify/study the above issues and uncertainties For example, Inversion top CloudSat MODIS Lidar CloudSat (Kubar, Waliser, Li et al., in preparation, 2009)

  35. Thank You

  36. GCM Vertical velocity along the cross section for JJA 2006 ECMWF GEOS5 fvMMF (a) (b) (c) All models reasonably exhibit the Hadley circulation, with a narrow area of upward motion in tropical region and rather broader subsidence branch in the subtropical area.

  37. Cloud Liquid Water Path : Values from GCMs CloudSat total LWP (a) (b) (c) GMAO/MERRA ECMWF R30 GEOS5 (d) (e) CAM3 fvMMF Mean=122.8 Figure 3. Multi-year mean values of cloud liquid water path (LWP; g m-2) from (a) NASA GMAO/MERRA ( 01/1979-10/1979), (b) ECMWF R30 analysis (Annual: 08/2005-07/2006), (c) GEOS5 AGCM (01/1999-12/2002), (d) NCAR CAM3 (1979-1999) and (e) fvMMF (01/2005-12/2006). (Li and Waliser et al, 2008a)

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