1. Influences of In-cloud Scavenging Parameterizations on Aerosol Concentrations and Deposition in the ECHAM5-HAM GCM Betty Croft - Dalhousie University, Halifax, Canada Ulrike Lohmann - ETH Zurich, Zurich, Switzerland Randall Martin - Dalhousie University, Halifax, Canada Philip Stier - University of Oxford, Oxford, U.K. Johann Feichter - MPI for Meteorology, Hamburg, Germany Sabine Wurzler – LANUV, Recklinghausen, Germany Corinna Hoose - University of Oslo, Oslo, Norway Ulla Heikkilä - Bjerknes Centre for Climate Research, Bergen, Norway Aaron van Donkelaar –Dalhousie University, Halifax, Canada --------------------------------------------------------------------------------------------------------------------------- CAFC Winter Meeting, Toronto, February 1, 2010

2. Do global models agree on predicted aerosol profiles? Koch et al. (2009), ACP Black carbon profiles differ by 2 orders of magnitude among global models.  Deficits in our understanding of the processes involved and their interactions

3. AEROSOLSCLOUDSPRECIPITATION The aerosol-cloud-precipitation interaction puzzle: This problem involves many processes, and isolating the effects of one or the other is difficult.

4. Aerosol Scavenging Processes: (Figure adapted from Hoose et al. (2008)) Wet scavenging accounts for 50-95% of aerosol deposition, and strongly controls aerosol 3-dimensional distributions, which influence climate both directly and indirectly. Sedimentation and dry deposition

5. Aerosols  Cloud Droplets / Ice Crystals  Precipitation Processes: 1)Nucleation of droplets/crystals 2)Impaction with droplets/crystals Processes: In-cloud: (tuning parameters) 1)Autoconversion 2)Accretion 3)Aggregation Below-cloud: 1) Impaction with rain/snow Aerosol wet scavenging processes: We examine the relative contributions of nucleation and impaction to in-cloud scavenging.

6. Modeling of Aerosol In-Cloud Scavenging: Methodologies - 1)Prescribed scavenging ratios (e.g., Stier et al. (2005)) 2)Diagnostic - cloud droplet and ice crystal number concentrations are used to diagnose nucleation scavenging + size-dependent impaction scavenging (e.g. Croft et al. (2009)) 3) Prognostic - in-droplet and in-crystal aerosol concentrations are prognostic species that are passed between model time-steps (e.g., Hoose et al. (2008)) Using the ECHAM5-HAM GCM, we compare the strength/weaknesses of these 3 fundamental approaches, and examine the sensitivity of predicted aerosol profiles to differences in the parameterization of in-cloud scavenging.

7. All results shown are for a 1-year simulation of the ECHAM5-HAM global aerosol-climate model, at T42 resolution, nudged to the meteorological conditions of the year 2001, and following a 3 months spin-up period. SU:sulfate; BC:black carbon; POM:particulate organic matter; DU:dust; SS:sea salt ECHAM5 GCM coupled to HAM (Hamburg Aerosol Module): (Stier et al. (2005))

8. NSKSASCSKIAICI 1) Prescribed in-cloud scavenging ratios: standard ECHAM5-HAM (nucleation+impaction) T>273K 238<T<273K T<238K

9. 2) Diagnostic scheme: Size-Dependent Nucleation Scavenging Assume each cloud droplet or ice crystal scavenges 1 aerosol by nucleation, and apportion this number between the j=1-4 soluble modes, based on the fractional contribution of each mode to the total number of soluble aerosols having radii >35 nm, which are the aerosols that participate in the Ghan et al. (1993) activation scheme. From the cumulative lognormal size-distribution, Scavenge all mass above this radius for nucleation scavenging. Thus, we typically scavenge a higher fraction of the mass versus number distribution. Find r crit that contains N scav,j in the lognormal tail.

10. Size-Dependent Impaction Scavenging by Cloud Droplets: Solid lines: Number scavenging coefficients Dashed lines: Mass scavenging coefficients Data sources described in Croft et al. (2009) Example for CDNC 40 cm -3, assuming a gamma distribution Prescribed coefficients of Hoose et al. (2008) prognostic scheme are shown with red steps

11. Impaction Scavenging by Column and Plate Ice Crystals: Prescribed coefficients of Hoose et al. (2008) (red steps) Assume columns for T<238.15KAssume plates for 238.15<T<273.15 K (Data from Miller and Wang, (1991), and following Croft et al. (2009))

12. 3) Prognostic scheme: Aerosol-cloud processing approach (Hoose et al. (2008)) Stratiform in-droplet and in-crystal aerosol concentrations are additional prognostic variables. Two new aerosol modes  In-droplet (CD) In-crystal (IC)

13. Histograms of diagnosed vs. prescribed scavenging ratios: Aitken mode  Accumulation mode  Coarse mode  T>273 K238<T<273 KT<238 K

14. Uncertainty in global and annual mean mass burdens: [%] SO4BCPOMDUSTSS

15. Uncertainties in Aerosol Mass Mixing Ratios: Zonal and annual mean black carbon mass is increased by near to one order of magnitude in regions of mixed and ice phase clouds relative to the simulation with prescribed scavenging ratios.

16. Uncertainties in Accumulation Mode Number: Assuming 100% of the in-cloud aerosol is cloud –borne reduces the accumulation mode number burden by up to 0.7, but the diagnostic and prognostic scheme give increases up to 2 and 5 times, respectively relative to the prescribed fractions.

17. Uncertainties for Nucleation Mode Number: Increased new particle nucleation is found for the simulation that assumes 100% of the in-cloud aerosol is cloud- borne.

18. Uncertainties in Aerosol Size: The size of the accumulation mode particles changes by up to 100%. (nm)

19. Contributions of nucleation vs. impaction to annual and global mean stratiform in-cloud scavenging: Diag. scheme [%] SO4BCPOMDustSSNumber >90% of mass scavenging by nucleation (dust:50%); >90% of number scavenging by impaction.

20. Influence of impaction on dust scavenged mass:

21. Influence of impaction on black carbon scavenged mass:

22. Observed black carbon profiles from aircraft (Koch et al. 2009)

23. Observations of MBL size distributions (Heintzenberg et al. (2000))

24. Observations of AOD from MODIS MISR composite (van Donkelaar et al., subm.)

25. Observations of sulfate wet deposition (Dentener et al. (2006))

26. Observed 210Pb and 7Be concentrations and deposition (Heikkilä et al. (2008))

27. Current work: Coupled Stratiform-Convective Aerosol Processing: CD CV IC CV Stratiform CloudsConvective Clouds Detrainment CDVC and ICCV will not be prognostic variables since the convective clouds entirely evaporate or sublimate after the above processes for each GCM timestep.

28. Aerosol Processing by Convective Clouds: CCN 0.6 /CN Red: 12 hours before convective system Blue: 12 hours after convective system Figure from Crumeyrolle et al. (2008), ACP - case study from Niger. CN: Solid CCN 0.6 : Dotted Evidence for dust coating by sulfate above the boundary layer as a result of cloud processing.

29. Summary and Outlook: 1)Mixed /ice phase cloud scavenging was most uncertain between the parameterizations. Middle/upper troposphere black carbon concentrations differed by more than 1 order of magnitude between the scavenging schemes. Recommend:  understanding nucleation and impaction processes for cloud temperatures T<273K. 2)In stratiform clouds, number scavenging is primarily (>90%) by impaction, and largely in mixed and ice phase clouds (>99%). Mass scavenging is primarily (>90%) by nucleation, except for dust (50%). Recommend:  understanding of impaction processes for cloud temperatures <273K, and for dust at all cloud temperatures. 3)Better agreement with black carbon profiles for diagnostic and prognostic schemes.  ↓ prescribed ratios for mixed phase clouds. 4)Recommend diagnostic and prognostic schemes over the prescribed ratio scheme, which can not represent variability of scavenging ratios. 5) Recommend further development of the prognostic aerosol cloud processing approach for convective clouds. Acknowledgements: Thanks! Questions?