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AM and ChemClim WG - February, 2008

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AM and ChemClim WG - February, 2008

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  1. Aerosol-Climate Interactions inthe CAM-Oslo atmospheric GCM and Investigation of Associated Basic Shortcomings *ØyvindSeland1TrondIversen, 1,2Alf Kirkevåg, 1TrudeStorelvmo, 2,31) Norwegian Meteorological Institute2) University of Oslo3) ETH ZurichThanks to: Jon EgillKristjansson,Phil J. Rasch, Steve Ghan, Richard Easter AM and ChemClim WG - February, 2008 * In press in Tellus 60A (2008) (RegClim Special issue) Also CAM-Oslo with slab ocean: Kirkevåg et al (2008)

  2. First:Under development: Norwegian GCM/ESMBjerknes Centre / met.no / Univ. Of Oslo CAM4 ? • ”CAM-Oslo” based on CAM3 + aerosols • CLM3 • CICE4 • BCM version of MICOM isopycnic ocean model • Prism (Oasis 4) coupling (alternative to CPL6/7) • Later: • Photochemistry (simplified) • Carbon cycle emphasizing on ocean processes

  3. This Talk • Aerosol-cloud-climate in ”CAM-Oslo” • Briefonvalidation • Directradiativeforcing (DRF) • Is absorption in CAM-Osloabnormal? • Indirectradiativeforcing (IRF) and theroleofprognosticcloud droplet number • Influenceofnatural aerosols on IRF

  4. Aerosol-cloud-climate in ”CAM-Oslo” Major extensions to NCAR CAM3: • Aerosol lifecycling and physical properties • Sea-Salt, Dust, SO4, OM, BC • Size-modes of emitted primary particles are presumed • Concentrations are tagged to production and size mode • Process-specific mixing state • Tables for size, optical, and physical properties • Aerosol interactions with radiation • Refractive index according to mixing state and size • Optical Mie scattering and absorption • Aerosol interaction with clouds • CCN activation by realized super-saturations • Cloud droplet aging and autoconversion

  5. Aerosol-cloud-climate in ”CAM-Oslo” Other changes and adjustments of CAM3 physics • Vavrus ”freeze-dry” parameterisation: Cloud fraction reduced for low specific humidities • Critical mean volume radius for onset of warm-cloud auto-conversion is set to: r3lcrit =15 mm (not 10 mm); • Critical precipitation for reduction of auto-conversion collection efficiency by a factor 10: Paut,crit=5 mm/day (not 0.5 mm/day)

  6. BriefonvalidationRegularSurface Measurement verification: Modeled vs. Measuredannualconcentrations SO4 BC OM SS DU SO4: over-estimated remotely BC: under-estimated SS: over-estimated DU: under-estimated

  7. CAM-Oslo Model calculated clear sky (0.120) Briefonvalidation:AODAerosolOpticalDepth CAM-Oslo Modis + MISR Satellite Retrievals (0.137) Modis + MISR Satellite Retrievals (0.137) Aeronet • General: under-estimate • Tropics (biomass reg.): under-estimate • Extratropics ocean: over-estimate

  8. BriefonvalidationÅngström parameter:Indicating dominating particle size Accumulation Mode particles CAM-Oslo Coarse particles Aeronet Coarse particles Accumulation Mode particles Slightly too large particles in CAM-Oslo

  9. Directradiativeforcing (DRF): • CAM-Oslo DRF • Year 2000 vs. ”pre-industrial” (1750) Top of the Atmosphere +0.03W/m2 Ground Surface -1.18 W/m2 TOA global DRF is approximately zero.

  10. Directradiativeforcing (DRF)AerosolDirectRadiativeForcing (since 1750)Our aerosol absorbes more thanaverage Aerocom average: Total: -0.22 Wm-2; SO4: -0.35 W m-2; OM: -0.17 W m-2 BC: 0.25 Wm-2

  11. Is absorption in CAM-Osloabnormal?Absorption part of AOD CAM-Oslo CAM-Oslo Aeronet ECHAM5-HAM Stier et al 2005 The aerosol does not appear to be too absorptive - If anything it absorbs too little.

  12. Is absorption in CAM-Osloabnormal?Assumptionsinfluencing aerosol absorption Assuming external mixing has serious implications for climate impacts of BC

  13. Directradiativeforcing (DRF) Surfacealbedo CAM-Oslo (0.147) Aerocom aver.(0.170) Modis + SSMI (0.153) (S. Kinne) CAM-Oslo surface albedo large at high latitudes and in some mid-latitude areas

  14. IRF and theroleofprognostic CDNC • Simulated TOA IRF; • 1st indirecteffect (cloudalbedo). Prognostic CDNC: -1.13 W/m2 CDNC=CCN: -1.78 W/m2 Compensating effects in cloud droplet generation and aging are important

  15. Influenceofnatural aerosols on IRFSensitivityof 1st and 2nd IRFto increasedpre-ind CDNC ( +15 cm-3 to account for missing natural aerosols; e.g. SOA)

  16. Points ofConcern • AOD 10-25% underestimated  Missing aerosols ? • Non-desert mineral dust • Nitrate • Natural and anthropogenic SOA • Bio-aerosols • Slightly positive direct aerosol forcing; • Internal mixing increases absorption efficiency • Surface albedo and ABL cloudiness • Water cloud indirect forcing is sensitive to • Cloud droplet number concentrations (CDNC) in pristine areas • Compensating effects in CCN and CDNC modeling

  17. Thank You

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