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Cloud/Aerosol Group Progress

Cloud/Aerosol Group Progress. goal: identify and fix issues in CAM which limit our ability to predict Arctic climate change.

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Cloud/Aerosol Group Progress

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  1. Cloud/Aerosol Group Progress goal: identifyand fix issues in CAM which limit our ability to predict Arctic climate change Team: N. Barton, D. Bergmann, J. Boyle, P. Caldwell, G. de Boer, R. Easter, J. Fan, J. Fast, D. Ganguly, S. Ghan, X. Liu, C. Long, P.-L. Ma, Y. Qian, M. Ovchinnikov, P. Rasch, J.-H. Yoon, S. Klein, B. Singh, H. Wang, S. Xie, Y. Zhang, C. Zhao Outline: • Progress in identifying issues in cloud/aerosol • Progress in fixing issues in aerosol/clouds Clouds over Barrow on ISDAC Golden Day (Apr 20, 2008) from G. McFarquhar

  2. Tools for Evaluation: COSP Kay, J., B. Hillman, S. A. Klein, Y. Zhang, …, J.Boyle, …(JClim, 2012). • Objective: • Incorporate the CFMIP Observation Simulator Package (COSP) into CAM & use to evaluate clouds and precip • Value: • Facilitates fair model/satellite observation comparison • Key Accomplishments: • Integrated COSP into CAM4 & CAM5 • Added COSP output to AMWG diagnostics package, making it a standard metric for model development Fig: CAM5 significantly reduces cloud biases relative to CAM4. Default CAM5 +Caldwell consistency fixes Fig: CALIPSO ANN low cloud fraction bias for default and polar-project funded modifications.

  3. Tools for Evaluation: ARM Obs Qian, Y., Long, C. N., Wang, H., Comstock, J. M., McFarlane, S. A., and Xie, S. (ACP, 2012; joint funding with ASR) • Objective: • Analyze ARM long-term cloud fraction (CF) and radiation measurements for evaluating climate models • Value: • Unique radar/lidar measurements of CF seasonal variation and vertical structures; along with simultaneous surface radiation to diagnose problems of GCMs in representing other cloud optical properties than CF. • Key Accomplishments: • Developed the datasets • Evaluated CMIP3 GCMs including CAM3/CCSM3. Fig: CMIP3 GCMs vs. ARSCL, TSI and TSK total CF. Fig: CAM5 vs. ARM ARSCL cloud fraction at Barrow.

  4. Tools for Evaluation: WRF-CAM P. Rasch, J. Fast, P.-L. Ma, W. Gustafson, B. Singh, R. Easter (to be submitted, 2012; Joint funding with LDRD) • Objective: • Run WRF regional model with CAM physics as a cheap way to test resolution dependency of CAM parameterizations • Value: • Future releases must be credible at very high resolution but testing/developing at these scales is too expensive • WRF-CAM connects mesoscale and global modelers • Insight from high-resolution runs can guide coarse-resolution parameterization development • Results: • CAM & WRF look similar @ coarse resolution • Underprediction is reduced by increased dx CAM5 (dx=1.9x2.5o) Aircraft obs WRF (dx=10 km) Aircraft obs 200 hPa 500 hPa 850 hPa Fig: Median (vert. lines), ±25% (boxes), and ±45% (horiz. lines) Black Carbon (BC) mass over Alaska during ARCTAS/ISDAC. Fig: Snapshot of BC from CAM and WRF @ low & high resolution (remapped to CAM grid)

  5. Tools for Evaluation: Offline CAM P.-L. Ma, P. Rasch, et al. (to be submitted, 2012) • Objective: • Run CAM with dynamics (U, V, T, P) specified from reanalysis, clouds & aerosol computed by the model • Value: • splits physics issues from dynamics & feedbacks • Results: • CAM radically underpredicts BC transport. Polar Project-funded changes greatly improve this. • Issue is transport, not midlat concentration AMIP w/ aerosol fixes Default AMIP 4.8 3.3 45.5 7.1 Offline ERA + fixes Offline MERRA + fixes 7.2 6.5 a bit high Too low 51.5 50 Fig: BC transport across 65.5N for various runs. Colorbar units = ng/m3 m/s. Red bars divide lower and mid/upper troposphere with #s indicating averages above/below the bar (in μm/m2 m/s). Fig: Ratio of offline+fix/AMIP+fix zonal average BC (left) and meridional BC transport (right).

  6. Offline CAM Cont’d Table: Difference in BC transport components (in 103 kg/day) across 65.6N from AMIP vsave of offline CAM runs. • Results: • Underpredicted transport is due to eddy kinetic energy (EKE) being 20% too weak in default runs, resulting in weak transient eddy flux • The Arctic Oscillation (AO) explains ~20% of Arctic surface pressure variability and is well-simulated by default CAM though the Pacific maximum is too far west. • AO/BC correlation is reasonable except over NE Asia (due to translated Pacific max) and over W Russia (the location of max transport in offline runs) Fig: AO pattern (left) and AO/BC correlation in AMIP and offline runs.

  7. America Asia Arctic Aerosol Source Attribution H. Wang, P. J. Rasch, N. Beagley et al., 2012 (in preparation) • Objective: • Use emission-tagging to identify Arctic BC source • Value: • Our other tests have held emissions fixed and haven’t totally removed low bias • Allows us to quantify the impact of regional emission changes on Arctic aerosols • Results: Yr 2000 emission Yr 1980 emission Fig: Yr 2000 BC emissions with source regions identified. • The seasonal cycle of relative contribution is weak • Asia is currently the largest source of Arctic BC, but in 1980 Europe shared this honor Europe Asia Europe Africa America Arctic Rest Africa Fig: Relative contribution to monthly mean Arctic BC from various regions; Yr1980 inventory has more BC emission from EU than 2000.

  8. Tools for Evaluation: Aerosol Testbed Fast, J., W. Gustafson, E. Chapman, R. Easter, J. Rishel, R. Zaveri, G. Grell, M. Barth, (BAMS, 2011; joint funding with ASR) Objective: Assess the impact of potential CAM aerosol parameterization changes using Aerosol Modeling Testbed in WRF, CAM physics suite, and plug-and-play WRF aerosol modules Results: Treatment of secondary aerosols makes BIG difference – lots of uncertainty here! MAM (from CAM5) modal – 3 modes, 18 species ’simple’ MADE/SORGAM modal – 3 modes, 38 species MOSAIC sectional – 4 bins, 164 species ‘complex’ 9 times more species ~ 1.2 times slower 3 times slower MOSAIC > MAM 40 30 20 10 0 mg m-3 MAM > MOSAIC Fig: simulations of fine PM (< 2.5 mm) @ ~1800 m AGL in Gulf of Mexico (excluding dust) for “offline” WRF-CAM runs with differing aerosol parameterizations.

  9. Liquid-Only Ice-Only Mixed-Phase Total Cloud Phase at Obs Sites (preliminary) G. de Boer, M. Shupe, D. Bergmann, P. Caldwell Altitude Barrow CAM5 • Objective: • Compare CAM5 cloud phase against obsclimatologies at available locations. • Value: • Broadens perspective beyond Barrow • Provides more specific info on parameterization errors • Results: • CAM5 liquid- and ice-only cloud fraction is reasonably simulated. • Mixed-phase is under-predicted(?) • Over-predicted CF at Barrow is an anomaly(?) Altitude Altitude Eureka CAM5 Altitude 15 15 15 15 15 15 10 10 10 10 10 10 Altitude 5 5 5 5 5 5 SHEBA CAM5 2 2 2 2 4 4 4 4 6 6 6 6 8 8 8 8 10 10 10 10 Altitude Jan Dec Jan Dec Jan Dec Jan Dec Month

  10. CRM Simulations of Mixed-Phase Clouds (Fan, Ghan, Ovchinnikov, Liu, Rasch, and Korolev; JGR 2011; joint funding with ASR) Vert. Veloc (contours) and Regime (colors) • Objective: • Reproduce MPACE and ISDAC obs with bin-resolved Cloud Resolving Model (CRM) • Value: • Provides data and insight difficult to obtain from measurements. • Results: • Ice grows by depleting liquid (Bergeron process) only in downdrafts (panel a). • Liquid and ice are spatially uncorrelated: pure ice is found near cloud base and areas of pure liquid exist near cloud top. • Subgrid variations in vertical veloc and total water content are Gaussian, liquid and ice content fit truncated Gaussian or Gamma distributions. • Fig: Snapshot showing vertical cross sections of cloud properties for ISDAC run. updraft downdraft Liq Water Content Ice Water Content

  11. First-Indirect Effect (FIE) in CAM Evaluated at ARM sites C. Zhao, S. Klein, S. Xie, X. Liu, J. Boyle, and Y. Zhang (GRL, 2012) • Objective: • Use short-range CAPT forecasts to compare CAM’s aerosol First Indirect Effect (FIE) for low-level non-precipitating liquid clouds against ARM obs • Value: • Aerosol effects are critical, uncertain, and thought to be overly strong in CAM5 • Results: • FIE matches observations and generally decreases with increased LWC • Sensitivity to location and season is weak. Fig:2 FIE stratified by LWC and season for 3 ARM sites. FIE is calculated using accumulation-mode aerosol (left) and CCN @ 0.1% supersaturation (right) Fig: CAM reffvs CCN (@ 0.1% supersaturation) at NSA binned by LWC gives FIE values similar to observed values from previous studies

  12. Understanding the 2nd Indirect Effect (SIE) in CAM5M. Wang, S. Ghan, et al. (submitted, 2012; joint funding with NASA) • Objective: • Relate 2nd indirect effect (SIE, aka lifetime effect) to specific aspects of model behavior • Value: • CAM5 is unusually sensitive to aerosol changes. FIE seems ok – is SIE a problem? • Results: • SIE scales with the fraction of precip associated with autoconversion rather than accretion • Unlike obs (Rob Wood) and MMF (Minghuai Wang), most precip in CAM5 is from autoconversion • Current plans to implement prognostic precip or add memory to diagnostic precip may help this, decreasing SIE. Fig: Fraction of non-convective precip from autoconversion (x-axis) versus SIE (y-axis) for runs with CAM and other models.

  13. Summary: Model Interrogation • New tools are operational: COSP, WRF-CAM, Offline CAM, Aerosol Modeling Testbed, tagging • Arctic BC is too low • transport is too weak due to under-predicted EKE • cloud processing is poor (more later) • increasing resolution helps a bit • Secondary aerosol treatment is important/uncertain • Mixed-phase clouds are underpredicted by CAM, but our CRM study gives us ideas • CAM5’s strong aerosol sensitivity comes from SIE • FIE seems correct • SIE scales with autoconversion:accretion ratio (which seems wrong in CAM)

  14. Testing Cloud Microphysics Parameterizations in NCAR CAM5 with ISDAC and M-PACE Observations (X. Liu, S. Xie, J. Boyle, S. Klein,…, S. Ghan, … (JGR, 2011; joint funding with ASR) • Objective: • Run CAM in short-term forecast and single column modes for ARM ISDAC and MPACE Arctic obs to test proposed microphysical changes. • Value: • Provides detailed guidance about value of proposed changes. • Results: • CAM5 significantly underestimates LWC in the Arctic in both Spring and Fall and cloud fraction in the Spring season. • surface downward longwaveradiative fluxes are underpredicted by 20-40 W m-2. • Changing homogeneous freezing temperature of rain from -5 to -40 C has a substantial impact on modeled LWC • Underestimation of Arctic aerosol concentration also plays an important role in LWC bias. Fig: Time series of (a) liquid water path and (b) total ice water path (cloud ice plus snow) from two CAM5 simulations (control and a run with the Phillips et al. (2008) ice nucleation scheme) compared with ECMWF reanalysis and remote-sensing retrievals) during the ISDAC period.

  15. Sensitivity of CAM5 Arctic Cloud Simulation to Ice-Nucleation Parameterization (S. Xie, X. Liu, C. Zhao, Y. Zhang) Meyers et al (1992) DeMott et al (2010) • Objective: • Two ice nucleation schemes were tested in 6-year AMIP runs: • Meyers et al. (1992) scheme: Nd linked to ice super-saturation, based on a limited # of mid-latitude obs (default CAM5 scheme). • DeMottet al. (2010) scheme: Nd linked to aerosol properties and temperature, based on 9 separate field studies over many regions of globe (scheme similar to Phillips from prev slide) . • Value: • Ice nucleation is very important but poorly understood. • Results: • Ice nuclei (IN) number concentrations depend strongly on scheme. • Less IN -> larger LWP and smaller IWP, more mid-level and optically thick clouds (brightens) Fig: IN concentrations using Meyers scheme are much larger than those using DeMott. Annual Mean Cloud Fraction (60N-90N) Optically thin Optically intermediate Optically thick High Top Middle Top Low Top Fig: Climatological cloud fraction stratified by optical depth and cloud-top level.

  16. Testing Aerosol Resuspension Assumptions in CAM5 D. Ganguly, R.C. Easter, H. Wang, P.J. Rasch (2012, in preparation) Objective: CAM5 currently increases aerosol # from evaporated precipitation at a rate ∝ fractional rain evaporation. Replace this with a more realistic approach of releasing 1 large aerosol particle per evaporated raindrop. No Resuspension – Control (CAM5.1) 26.9 /cm3 -24.9 % • Value: • Aerosol resuspension is a critical part of wet removal which is not well captured in CAM5 • Results: • Default CAM5 significantly overestimates global-mean lower-tropospheric CCN (using no-resuspension as a proxy for 1 particle/droplet) • Implementation of new scheme is in the works Fig: Effect of resuspension on annual avg. lower-tropospheric CCN number concentration (at 0.1% supersaturation)

  17. Improving Cloud Processing of BC transported to the Arctic H. Wang, P. Rasch, R. Easter, M. Wang, X. Liu, S.Ghan, Y. Qian, J.-H. Yoon, P.-L. Ma, V. Velu, (ACPD, 2012) • Objective: • Improve aerosol wet removal, BC aging, and convective treatment of aerosol in CAM5 to improve Arctic BC simulation • Value: • Mid- to high-latitude liquid-water containing clouds were identified as a source of poor aerosol transport to Arctic in CAM5. Overpredicted upper-level BC required improved convective treatment. • Results: • Freeze-Dry + wet removal changes are effective at increasing Arctic BC • New convective treatment + bugfix modestly improves surfaceBC • 7 mode increases BC and improves the seasonal cycle by delaying insoluble -> soluble BC aging Jan Mar May Jul Sep Nov Observed values Multi-Scale Modeling Framework Default CAM5 new convective treatment + cldfracbugfix CONV with 7-mode aerosol CONV + Freeze-Dry + weaker wetremoval NEW_m3 + 7-mode aerosol NEW_m7 + 1980 emissions (Europe higher) Caldwell consistency fixes (more later) Fig: Observed and modeled monthly BC surface mixing ratio (ng/kg) at Barrow, Alaska

  18. Cloud Processing of BC Cont’d • CONV Details: • wet removal and vertical transport are treated simultaneously • updraft cloud-borne aerosols and aerosol activation are treated explicitly • wet removal is applied to updraft aerosols • (Optional, identified with “_sact”) Second chance for aerosol activation above cloud base • Results: • Changes which increase surface BC tend to increase upper-troposphere bias • CONV and particularly secondary aerosol activation improves vertical BC gradient Fig: Observed (HIPPO1;black dots=mean, gray regions=1σ bounds) and modeled (colored lines) vertical profiles of BC for various areas of the globe.

  19. Improving Consistency between Cloud Processes P. Caldwell, S. Klein, D. Bergmann, S. Park, H. Morrison, X. Liu, P. Rasch Fig: Example PDF from ASTEX (dots) with Gaussian fit (line) and cloud fraction (shaded area). • Objective: • Implement consistent PDF-based subgrid assumptions within all cloud physics and fix problematic coupling between processes • Value: • Inconsistencies between processes are thought to be a major source of model deficiencies. Fixing these are a quick path to model improvement! • Details: • Unified Gaussian PDF scheme for cloud mass and fraction (macrophysics) • Consistent subgrid macro- and microphysics assumptions • Fixed Inconsistency between aerosol activation and microphysics • Fixed poor macro/microphysics coupling • Consistent implementation of radiation subcolumns(in progress) • Results: • LWP looks better • NSA BC a bit better (prev. slide) • Cloud fraction is better globally (prev slide) • Cloud fraction too low, has poor seasonal cycle at NSA Default Fixes ARM Obs Fig: Seasonal cycle of cloud fraction and LWP at NSA

  20. Prescribed Aerosol J.-H. Yoon, P. Rasch, and S. Ghan (to be submitted, 2012) Objective: Create a specified-aerosol version of CAM5 with results similar to the default. Two methods: aerosol=(in-cldclimo value)*cldfrac + (all-sky climo value)*(1-cldfrac) aerosol randomly selected from log-normal distribution Value: Specified-aerosol runs much faster, will be useful for sensitivity studies and SCM Results: Conditional sampling underpredicts Arctic aerosol (Panels B vs C) due to log-normal aerosol distribution in CAM5 Arctic (panel A) . Stochastic sampling fixes this (Panel D). PDF(Aerosol Number) north of 80N mean Fig: Aerosol # concentrations (#/cm3) for various runs (B-D) and Arctic-region PDF from prognostic run (A). Prognostic Aerosol Conditional Sampling Stochastic Sampling

  21. Summary: Model Development • Model is sensitive to ice nucleation scheme and rain-freezing T – improvements are ongoing • CAM5 over-predicts cloud fraction and under-predicts LWP and BC at Barrow. Our fixes help. • A viable method for prescribed aerosol runs has been identified. This capability should be available soon.

  22. Thanks Team! Peter Caldwell caldwell19@llnl.gov Phil Raschphiliprasch@pnnl.gov Po-Lun Ma po-lun.ma@pnnl.gov Rahul Zaverirahul.zaveri@pnnl.gov Richard Easter richard.easter@pnnl.gov Scott Elliott sme@lanl.gov Steve Ghansteve.ghan@pnnl.gov ShaochengXiexie2@llnl.gov Steve Klein klein21@llnl.gov Steven Smith ssmith@pnnl.gov VinojVeluvinoj.velu@pnnl.gov Xiaohong Liu xiaohong.liu@pnnl.gov Yun Qianyun.qian@pnnl.gov Yuying Zhang zhang24@llnl.gov BalwinderSingh balwinder.singh@pnnl.gov ChuanfengZhaozhao6@llnl.gov Dan Bergmann bergmann@llnl.gov David Romps davidromps@gmail.com DilipGangulydilip.ganguly@pnnl.gov Gijs de Boer gdeBoer@lbl.gov Hailong Wang hailong.wang@pnnl.gov Jerome Fast jerome.fast@pnnl.gov Chuck Long chuck.long@pnnl.gov Jim Boyle boyle5@llnl.gov Jin-ho Yoon jin-ho.yoon@pnnl.gov Jiwen Fan jiwen.fan@pnnl.gov Joel Rowland jrowland@lanl.gov Mikhail Ovchinnikovmikhail.ovchinnikov@pnnl.gov Neil Barton barton30@llnl.gov

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