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Cloud Vertical Structure along the GPCI transect over the Northeastern Pacific as exhibited by CloudSat, ECMWF Analysis and two Climate Prediction Models.

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Presentation on theme: "Cloud Vertical Structure along the GPCI transect over the Northeastern Pacific as exhibited by CloudSat, ECMWF Analysis and two Climate Prediction Models."— Presentation transcript:

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, /JPL J.D. Chern,W.-K. Tao, J. Bacmeister/GSFC 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 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 (Li and Waliser et al, 2008a) IPCC Model Uncertainties: “Cloud Liquid” Mean LWP from 16 IPCC Contributions of 20th Century Climate

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

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

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

8 (a)(b)(c)CERES/MODIS LWPISCCP LWPSSM/I LWP Cloud liquid Water Path: Passive Measurements

9 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), (b) ECMWF R30 analysis (Annual: 08/ /2006), (c) GEOS5 AGCM (01/ /2002), (d) NCAR CAM3 ( ) and (e) fvMMF (01/ /2006). (a)(b) (c) (d)(e) GMAO/MERRAECMWF R30GEOS5 CAM3fvMMF (Li and Waliser et al, 2008a) Cloud Liquid Water Path : Values from GCMs

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 Aura CloudSat Aqua Parasol Calipso MLS MLS: Microwave Limb Sounder

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

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

13 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: Teixeira, Waliser, et. el b) - GCSS GPIC cross section – Hadley Circulation (Li, Teixeira, Waliser, et. el b) This requires knowledge of the retrieval process for CloudSat product and how the product relates to modeled quantities. OBJECTIVES:

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

15 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. Typical GCM More Complex Model e.g., ECMWF, GEOS5, NCAR/CAM e.g. fvMMF, DARE/RAVE Riming

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

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

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

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

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

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

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

23 (a) CAM3fvMMF GMAO/MERRAECMWF R30GEOS5 CloudSat total CloudSat non-precipitating GCM Cloud Liquid Water Content (LWC) Annual Mean Values UCLA

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

25 Particle size (mm) Number concentration (m -4 ) Mass (mg m -3 ) IWC >100μm IWC <100μm Use CloudSat retrievals of the PSD parameters to reconstruct the ice PSD and partition mass according to size by integration:

26 CloudSat IWC >100μm CloudSat IWC <100μm 215 hPa ECMWF C31r MLS 215 hPa CloudSat IWC TOTAL (mg m -3 ) 215 hPa Partitioned CloudSat IWC estimates to Small & Large Particles (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 Teixeira, Waliser, et. el b) (Li, Teixeira, Waliser, et. el b)

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

29 Cold/dryWarm/humid Boundary Layer top Santa Monica Eq. Upwelling Stratocumulustrade-wind cumuliprecipitating deep convections Jet Stream Hadley Circulation

30 CloudSat Total LWC LWC+IWC IWC NP-LWC+NPC-IWC CloudSat NP/NPC NP-LWCb1 NPC-IWCb2 b3 ECMWF LWCc1 c2IWC LWC+IWC c3 GEOS5 e1LWC IWC e2 LWC+IWCe3 fvMMF LWCd1 IWCd2 LWC+IWC d3 a1 a2 a3

31 ECMWF fvMMFGEOS5 (a)(b) (c)(d) CloudSat Note: fvMMF cloud fraction includes ice, snow and graupel.

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

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 CloudSat (Kubar, Waliser, Li et al., in preparation, 2009) CloudSat Lidar MODIS Inversion top 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,

35 Thank You

36 ECMWFfvMMFGEOS5 (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. GCM Vertical velocity along the cross section for JJA 2006

37 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), (b) ECMWF R30 analysis (Annual: 08/ /2006), (c) GEOS5 AGCM (01/ /2002), (d) NCAR CAM3 ( ) and (e) fvMMF (01/ /2006). (a)(b) (c) (d)(e) GMAO/MERRAECMWF R30GEOS5 CAM3fvMMF (Li and Waliser et al, 2008a) CloudSat total LWP Cloud Liquid Water Path : Values from GCMs

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