Jet Propulsion Laboratory/NASA, CalTech

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Jet Propulsion Laboratory/NASA, CalTech An Observationally-Based Evaluation of Cloud Ice and Liquid Water in CMIP3 and CMIP5 GCMs and Reanalysis Using Contemporary Satellite Data Jui-Lin (Frank) Li Jet Propulsion Laboratory/NASA, CalTech CloudSat (Cloud) CERES (Radiation) CALIPSO (Cloud) MLS (Cloud) Contributors Duane Waliser/JPL Wei-Ting Chen/NTU Min Deng/U. Wyomin Seungwon Lee/JPL Tristan L’Ecuyer/UW-Madison Graeme Stephens/JPL Matthew Lebsock/JPL Matt Christensen/JPL Bin Guan/JPL J. Jiang/JPL J. Teixeira/JPL And many others….. AGU-INVITED-A44B-02-2013

Cloud Water Measurement From Satellites using Passive Techniques Passive Techniques such as those used in the ceres/modis, isccp, modis, and noaa/mw products can provide Total ice water path (IWP) estimates - challenging in multi-layer, mixed and thick clouds. MLS - a limb sounder - can probe the upper troposphere to estimate IWC (but not Total IWP)

Satellite data usingactive techniques from CloudSat/CALIPSO provide an opportunity for validating and constraining vertical cloud hydrometeors profiles for models CloudSat/CALIPSO Uncertainties & Applications of the Retrived Cloudsat/CALIPSO “Cloud Ice and Liquid”

All the IPCC AR4/CMIP3 and most IPCC AR5/CMIP5 models…..

All the IPCC AR4/CMIP3 and most IPCC AR5/CMIP5 models….. Assuming deep cumulus clouds fraction is very small, and rain/snow fall down onto the surface Assuming big drop of falling snow scatters very little radiation compared to the same mass in tiny little droplets These assumptions to date haven't been too bad with coarser resolution (e.g., 4 latitude by 5 Longitude).

Model Hydrometeors for Radiation Real World Hydrometeors and Radiation Representation of Ice Water Content (IWC) for Radiation Calculation in GCMs Model Hydrometeors for Radiation GRAUPEL Real World Hydrometeors and Radiation ICE MIXED SNOW “Real World” All CMIP3 and most CMIP5 Models Few GCMs such as NASS-GISS model, NCAR-CAM5, GFDL-AM3 and CSIRO etc include diagnostic falling snow and/or convective ice (or snow) in their models (Li et al., 2012)

Available CloudSat/CALISO IWC Products: 2CWC - CloudSat Radar Only (Standard CloudSat product) DARDAR - CloudSat Radar +CALIPSO Lidar combined products (Delanoe et al., 2010)]. 2CICE - CloudSat Radar +CALIPSO Lidar combined products (Deng, 2011) These data are sensitive to “falling” and “floating cloud” particles For a meaningful model-data comparison Discriminate observed “cloud only” IWC data or Include large ice particle in a GCM Cloud simulator is another good choice (Li, J.-L. F., D. E. Waliser, S. Lee, Guan, T. Kubar, G. Stephens, H-Y Ma, (2012a): An Observational Evaluation of Cloud Ice Water in CMIP3 and CMIP5 GCMs and Contemporary Analyses, J. Geophys. Res., doi:10.1029/2012JD017640.)

Discrimination of Observed Cloud Ice Water Content Methods to estimate observed cloud ice water content (CIWC) and cloud liquid water content (CLWC) from CloudSat and/or Calipso: FLAG method - filter out cloud hydrometers using flags with convective & precipitation cases to get ballpark estimates of CIWC & CLWC (mg kg-1) Total IWC CONV IWC Cloud IWC Filtering out convective clouds and precipitating cases, we can get as a preliminary estimate of ice in clouds (albeit this has shortcomings) Precip IWC P&C IWC

PSD method - Using CloudSat Specified PSD information Separate Cloud only ice (CIWC) and Precipitating Ice (“large”) in CloudSat Total IWC Dc= cut-off threshold between small and large ice particle dN(D)/dD D Dc IWC<Dc = “Small” Ice Mass (cloud ice) IWC>Dc = “Large” Ice Mass (precipitating ice) (Chen et al., 2011) 9

Observed Ice Water Content (IWC) for Model-Data Evaluation Total IWC PC IWC Cloud IWC Ens.-Mean CWC FLAG Total DARDAR FLAG 2CICE FLAG Cloud CWC PSD (Li et al., 2012)

IPCC CMIP5 Model Uncertainties: “Cloud Ice Water Content- CIWC” CMIP5 – Cloud only IWC GFDL TIWC Ens Mean Obs. Total IWC Ens Mean Obs. Cloud Only IWC (mg kg-1)

Model Hydrometeors for Radiation Real World Hydrometeors and Radiation Representation of Liquid Water Content (LWC) for Radiation Calculation in GCMs Model Hydrometeors for Radiation RAIN GRAUPEL Real World Hydrometeors and Radiation LIQUID RAIN & DRIZZLE MIXED “Real World” All CMIP3 and most CMIP5 Models (Li et al., 2013, under revision)

CloudSat LWC/LWP Retrieval: Major Uncertainties & Caveats Failure of LWC retrieval below about 800 meters (~950-900 hPa) above the surface due to surface clutter. But the standard CloudSat retrieval assumes the entire PSD follows one functional PSD. BUT, CloudSat radar is more sensitive to large-size particles, and the water droplet particle size distribution (PSD) for cloud particles (small-size) is different from the PSD for rain particles (large-size). Cloud Particles Number Concentration Rain Particles Water Droplet Radius

Extimate Liquid Water Content (LWC) Data Precipitating/Convective LWP (g m-2) Precipitating/Convective LWP Total LWP Cloud only LWP CloudSat MODIS AMSR-E Ensemble Mean

CMIP3/CMIP5 CWLP Mean Bias vs CloudSat+MODIS-based “Cloud only” LWP No improvement from CMIP3 to CMIP5 for CLWP estimation. Most models significantly overestimate CLWP.

Bias of CMIP Ensemble Mean CLWP vs AMSRE-”Total LWP” The ensemble CMIP3 and CMIP5 LWP have similar biases relative to ensemble mean AMSR-E LWP Underestimate “total” LWP over heavy rainfall regions

LWC/LWP Major Retrieval Uncertainties and Caveats It is imperative to consider the issues presented here to properly utilize the CloudSat (LWC/LWP) and other passive LWP data (MODIS, AMSR-E) for model comparison and validation. Use these retrieved LWC and/or LWP for model evaluation cautiously…..

Bias of CMIP Ensemble Mean Cloud Only IWP vs Obs. Total CloudSat IWP Total IWP

Model Hydrometeors and Radiation Real World Hydrometeors and Radiation Representation of Cloud Water Content (CWC) for Radiation Calculation in GCMs More RSDS Model Hydrometeors and Radiation RAIN GRAUPEL More RLUT Real World Hydrometeors and Radiation Less RSUT LIQUID RAIN & DRIZZLE RSUT RLUT ICE MIXED SNOW RSDS Real World Model

Bias of CMIP5 Ensemble Mean Radiation vs. Water Vapor - Total PW (CMIP5 Model fluxes) – (CERES Fluxes) Reflected Shortwave at TOA Downward Shortwave at SFC Outgoing Longwave at TOA CMIP5 Total Precipitable Water (mm)

Result Highlights Caution must be taken into account when making model-data comparisons related to cloud ice/liquid water content and their radiative fields if precipitating/convective core cloud hydrometeors are not represented in the models (Known as conventional GCM including All CMIP3 & most CMIP5). With filtering out convective clouds and precipitating cases from the observations, we can get a first order estimate of ice/liquid in clouds for conventional GCM use (albeit this has shortcomings) Regional excessive OLR and net surface shortwave fluxes are evident over convective active regions against CERES data, consistent with what was suggested in Waliser et. al. (2011) & Li et al. (2013a;b) that such a bias might be caused by not treating the interaction of precipitation and/or convective core and with radiation in the models.

Jet Propulsion Laboratory/NASA, CalTech The impacts of Cloud-Radiation Bias on Circulations, Water Vapor Simulations in CMIP5 and NCAR CESM Sensitivity Experiments Jui-Lin (Frank) Li Jet Propulsion Laboratory/NASA, CalTech Friday: A53I. Cloud, Convection, Radiation, Water and Energy Cycles II 2:25 PM - 2:40 PM @3010 (Moscone West) AGU-A35I-2013