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Simulation of Below-cloud and In-cloud Aerosol Scavenging in ECHAM5-HAM

Simulation of Below-cloud and In-cloud Aerosol Scavenging in ECHAM5-HAM. Betty Croft, Ulrike Lohmann, Philip Stier, Sabine Wurzler, Sylvaine Ferrachat, Hans Feichter, Randall Martin, and Ulla Heikkil ä ETH Group Retreat Presentation Einsiedeln, Switzerland

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Simulation of Below-cloud and In-cloud Aerosol Scavenging in ECHAM5-HAM

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  1. Simulation of Below-cloud and In-cloud Aerosol Scavenging in ECHAM5-HAM Betty Croft, Ulrike Lohmann, Philip Stier, Sabine Wurzler, Sylvaine Ferrachat, Hans Feichter, Randall Martin, and Ulla Heikkilä ETH Group Retreat Presentation Einsiedeln, Switzerland February 6, 2008

  2. Motivation : • Below-cloud scavenging should depend on aerosol size and • precipitation rates, as opposed to fixed scavenging ratios for • each aerosol mode. • In-cloud scavenging should be linked to the cloud microphysics • and depend on cloud droplet (or ice crystal) number concentrations, • cloud droplet size and aerosol size, as opposed to fixed scavenging • ratios for each aerosol mode. • Project Goals: • Size-dependent below-cloud scavenging • Microphysically-dependent in-cloud scavenging

  3. Wet scavenging of aerosols: • In-cloud scavenging processes • (aerosol  droplets or crystals) • Nucleation • Impaction • Below-cloud scavenging processes (precipitation-aerosol collisions) • Inertial impaction and interception • Brownian motion • Thermophoresis and diffusionphoresis • Turbulence • Electrostatic attraction

  4. ECHAM5-HAM has 7 lognormal aerosol modes and includes black carbon, particulate organic matter, sulfate, sea salt and dust. All results shown are from 5-year simulations after 3-month spin-up.

  5. Below-cloud scavenging coefficients for rain: Present-day GCMs use typically mean scavenging coefficients (solid red steps). This study selects mass (solid lines) and number (dashed lines) below-cloud scavenging coefficients from a look-up table based on aerosol size and rainfall rate.

  6. The scavenging coefficients are found assuming both a raindrop (or cloud droplet) distribution and a log-normal aerosol distribution Below-cloud:N(Dp) = Marshall-Palmer distribution In-cloud:N(Dp)= Gamma distribution Then,

  7. Below-cloud scavenging coefficients for snow (Slinn, 1984) : (normalized by precipitation rate) Previously, ECHAM5-HAM used 5x10-3 mm-1 for all aerosol sizes (dashed line).

  8. Global and annual mean deposition budgets (black carbon): Below-cloud scavenging (BCS) is increased with the new parameterization.

  9. Simulated Geographic distribution of wet deposition (SO4): Changes in the annual mean wet deposition near source regions can be above 10% as compared to the simulation with mean coefficients. Scavenging is increased for rain rates near 1mm/hr and higher, but decreased for rain rates below 1mm/hr . All scavenging by snow is increased.

  10. Validation with MODIS-MISR: (global zonal mean optical depth comparison) Compare the control simulation (MEANC) and revised below cloud scavenging (ASDS-RS) with solid red (observations from MODIS-MISR).

  11. Validation with NADP data: (observed sulfate wet deposition from US) Sea salt deposition has improved correlation coefficients and slope-offset parameters in simulation ASDS-RS as opposed to the MEANC simulations. Sulfate deposition is more within factor of 2.

  12. Part II - In-cloud scavenging: 1) Impaction scavenging (aerosol-cloud droplet collisions) Project goal: Introduce aerosol size dependent in-cloud impaction scavenging. Look-up table is a function of mean cloud droplet size, aerosol size and CDNC. Mass (dashed) and number (solid) coefficients

  13. In-cloud scavenging: 2) Nucleation scavenging parameterization: Standard ECHAM5-HAM uses fixed in-cloud stratiform scavenging ratios for each of the 7 modes. These are 0.1, 0.25, 0.85, 0.99 for the NS, KS, AS, and CS modes, respectively. Revised scavenging parameterization is consistent with the Lin and Leaitch cloud droplet activation scheme. Assume CDNC = total number of aerosols to be scavenged . Scavenging ratio for ith mode is, Where Na is the sum over all soluble modes of the number of aerosols > 35nm, and xfracniis the fraction of aerosol number >35 nm in the ith mode.

  14. We use the cumulative lognormal function to find a critical radius where Ci is the number in the lognormal tail if r > rcrit. Scavenge all aerosol mass above rcrit. For mixed clouds, same approach but CDNC+ICNC = total number of aerosols scavenged. Ice clouds, do not use same activation, so assume ICNC = total number scavenged and scavenge from largest to smallest mode progressively and find rcrit for the partially scavenged mode. Alternatively, Tost et al., 2006 gave the scavenging ratio as a function of aerosol radius. We also tested this parameterization.

  15. Predicted scavenging ratios: (normalized frequency of occurrence) Warm stratiform clouds Warm convective clouds Generally, unity for AS and CS modes and greatest variability in KS mode, zero for NS mode. Greater variability in predicted convective cloud scavenging ratios.

  16. Example: Dust deposition budgets - Higher stratiform in-cloud scavenging and lower convective in-cloud scavenging, comparing IC-ALL with CTL.

  17. Validation: (global SO4 wet deposition – Dentener et al, 2006) Correlation coefficients are improved by revisions to in-cloud scavenging.

  18. Summary and future work: • Aerosol size-dependent below-cloud scavenging was introduced to ECHAM5-HAM and is a more physical representation of below-cloud scavenging • Microphysically dependent in-cloud scavenging was implemented in all stratiform, and warm convective clouds and results are comparable with simulations using fixed coefficients and the method of Tost et al. (2005). This approach is desirable since the scavenging physics are now more consistent with the cloud parameterizations. • Convective ice cloud scavenging will be implemented. • The sensitivity of the below-cloud scavenging to the assumptions about the raindrop distribution will be investigated. • Global validation of vertical profiles of extinction will be conducted with CALIPSO data to better examine influence of the scavenging parameterizations on the vertical aerosol profiles.

  19. Simulations: MEANC – uses the existing mean below-cloud scavenging coefficients ASDS-RS – revised below-cloud scavenging by both rain and snow (aerosol size dependent scavenging). ASDS-R – revisions only for rain ASDS-RS-PF – as ASDS-RS but uses the old precipitation fraction parameterization labelled as method 1 in the subsequent slides. ASDS-RT – as ASDS-R but add the thermophoretic effects – so that scavenging also depends on the below-cloud relative humidity IC-ALL – revised in-cloud impaction and nucleation scavenging IC-WARM – revised in-cloud scavenging only in warm clouds IC-WARM-T – applies the parameterization of Tost et al (2006) for warm clouds IC-STRAT – revised in-cloud scavenging only for stratiform clouds

  20. Collection efficiency for snow (Slinn 1984): where, Scavenging coefficient, normalized by precipitation rate is, Parameters are varied for different types of snow (powder, rimed and dendrites).

  21. Precipitation fraction parameterization: Methods for finding the fraction of grid box that is raining (PF), using cloud fraction (CF) – all simulations use method 2 except ASDS-RS-PF: Method 1 (Stratiform): Where CF(k) follows Tompkins, 2001 Method 2 (Stratiform): If then else Weighting similar to method 1 only if CF(k) > PF (k-1).

  22. Sensitivity to precipitation fraction parameterization: Method 1 (Convective): Based on updraft mass flux and velocity. Method 2 (Convective): Kiehl et al. 1996; Xu and Krueger (1991) PF (k) is limited to be within the range of 0.05 to 0.8

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