1. Towards Parameterization of Atmospheric Aerosol in Regional Forecast Models David B. Mechem, and Yefim L. Kogan Collaborators: Yi Lan, Paul Robinson, Yuri Shprits The University of Oklahoma Seminar presented at NRL, Monterey, 7 January 2005 Acknowledgements. This research was supported by the Office of Naval Research and the U. S. Department of Energy Atmospheric Radiation Measurement Program.

2. Aerosol dramatically influences the radiative characteristics of PBL clouds First indirect effect: cloud droplet radius and concentration influences albedo => ship tracks

3. Ship tracks in the eastern Atlantic Photo credit: Robert Wood, University of Washington

4. Aerosol affects the thermodynamic structure and persistence of PBL clouds Second indirect effect: drizzle may lead to cloud breakup=> Pockets of Open Cells (POCs)

5. POCs Photo credit: Robert Wood, University of Washington

6. Regional simulations of aerosol-cloud-drizzle interactions using the COAMPS mesoscale model coupled with the CIMMS drizzle parameterization

7. Model setup COAMPS v2.0.14 18/6/2 km grid. Vertical grid spacing stretched from 10 to 800 m 1.5-order subgrid closure (“Level 2.5” Mellor and Yamada 1982) 24 h simulation. Two 12 h cycled pre-forecasts establish a reasonable boundary layer structure Bulk drizzle parameterization (Khairoutdinov and Kogan, 2000) Prognostic equations for q c, N c, q r, N r, and N CCN Initial and boundary condition CCN value of 45 cm -3 Compare drizzling (KK) and nondrizzling (ND) runs to evaluate the effect of drizzle on a mesoscale forecast. Goal

8. 5-moment scheme (KK) Operational microphysics LWP [g m -2 ] 1800 UTC COAMPS LWP comparison of 5-moment drizzle parameterization (KK) and the operational (Kessler) microphysics scheme (18 km grid) Three significant improvements of KK drizzle scheme: 1 3 2 1 3 2 2 3 1 Reduced entrainment from drizzle-stabilization leads to a further northern extent of cloud wedge Reduction in LWP and cloud coverage south and east of Point Conception. Open oceanic LWP is better match with climatology

9. 1800 UTC COAMPS LWP comparison of 5-moment drizzle parameterization (KK) and the operational (Kessler) microphysics scheme (2 km grid) LWP [g m -2 ] q c [kg kg -1 ] 5-moment scheme (KK) A1′A1 A1′ Operational microphysics B1 B1′ B1 B1′ Significant improvements from the KK drizzle scheme, inferred by LES results: More realistic cloud base structures and variability Improves ability of COAMPS to represent broken PBL cumulus fields Represents the transition from unbroken stratocumulus to PBL cumulus A1-A1′ cuts across banded cloud structures with weak resolved vertical velocity. We take these bands to represent ensembles of PBL cumulus.

10. Comparison of surface bulk CCN concentration using the 5-moment drizzle parameterization (KK) [cm -3 ] 1200 UTC 25 July12 h later…

11. In these previous COAMPS simulations, aerosol characteristics were represented by a single parameter N CCN. Attempting to distill aerosol characteristics to a single parameter often gives an incomplete and sometimes incorrect portrayal of aerosol properties

12. LWC [g m -3 ] Droplet spectra at each grid point Giant aerosol above the inversion: Enhance drizzle production Attenuate PBL turbulence Accelerate stratocumulus breakup When pollution above the inversion is predominantly fine-mode, drizzle production is suppressed. Background sulfate plus giant aerosol Background sulfate only Effect of coarse mode/giant aerosols

13. Drop conc. [cm -3 ] Sulfate + sea salt Sulfate aerosol only The presence of sea salt results in: Significant drizzle formation Reduction in mean drop concentration Large variations in cloud base Greater variability in cloud top More complex internal cloud structure Significant differences in overall cloud geometry — implying possible future breakup of cloud field Effects of surface winds=sea salt aerosols

14. Susceptibility of cloud drop concentration to sea-salt addition S=(Nss-N)/N 3 LES simulations: clean=low background concentration polluted with low and high Aitken nuclei concentrations Sea salt effect depends on the sulfate aerosol concentration, N: When N is low, the effect of sea-salt is to significantly increase cloud drop concentration. When N is high, the effect depends on the concentration of Aitken nuclei

15. Advanced prediction of aerosol-cloud- drizzle feedbacks should include 3 main aerosol parameters: Coarse mode (giant) aerosols Background (fine mode) sulfate aerosols Aitken nuclei and Parameterization of the effects of surface winds – sea-salt aerosols

16. Full system of equations describing coupled aerosol-cloud interactions Equations for cloud drop parameters (4 equations in KK approach) need to be complemented by 3 equations for major aerosol parameters

17. Cloud microphysics formulation

18. i=1,2,3 Prediction of Aerosol Parameters Parameterization of cloud parameter conversion rates (for example): Parameterization of aerosol-aerosol, aerosol-cloud conversion rates: yet TBD S i,ccn represents (interstitial) source and sink terms of aerosol, e.g. transformation, sedimentation, production from DMS, sea- spray

19. What components are required for an accurate mesoscale forecast of aerosol-cloud-drizzle system? Specification of aerosol field (initial and boundary conditions) Size characteristics Spatial distribution Cloud processing Activation Coagulation, rainout, diffusiophoresis Regeneration Specification of sources and sinks Urban sources Sea salt Heterogeneous chemistry Transformation rates (fine↔coarse mode) Transport Advection Sedimentation Turbulent mixing (entrainment)

20. Where we are now? Specification of aerosol field Observations and data assimilation necessary for all 3 aerosol parameters Cloud processing Processing via coagulation represented in some cloud physics schemes Recent activation parameterizations not yet linked to model SGS energetics Specification of sources and sinks Simple parameterizations exist for sea-spray aerosol source Parameterizations of aerosol transformation rates have yet to be developed Transport As accurate as the model’s advection scheme Depends on how accurately the model SGS represents entrainment More understanding is needed of the relative role and importance of these various processes, sources, and sinks.

21. Control experiments with different initial CCN concentrations Sensitivity runs with various CCN source mechanisms and magnitudes Example of parameterization/formulation of cloud processing

22. Model setup — idealized Δx = Δy = 2 km; Δz = 25 m; Δt = 10 s Domain size 100  100  1.5 km Periodic horizontal boundary conditions Imposed large scale divergence 5.0  10 -6 s -1 Sensible and latent heat fluxes (10 and 25 Wm -2 ) Longwave only KK bulk drizzle parameterization Activation by Martin et al. (1994) and O’Dowd et al. (1996) Thermodynamic initial conditions from ASTEX A209 Various initial CCN profiles and magnitudes

23. Time-height representation of q c and N t q c [g kg -1 ] N ccn + N c [cm -3 ]

24. Cloud top/base LWP Drizzle rate Statistics for different initial CCN concentrations Smaller values of CCN result in: Reduced entrainment and lower mean cloud top height Higher mean cloud base Reduced mean LWP Larger drizzle rates Increased variability

25. COAMPS aerosol budget PBL aerosol budget is calculated in terms of total particle concentration (CCN + droplet): “Cloud processing” Calculate entrainment term from change of inversion height and magnitude of imposed divergence Any additional source/sink terms are known (i.e. imposed) → We can back-out the cloud processing rate

26. Cloud processing for two different CCN concentrations Cloud processing Cloud processing + dilution

27. Sensitivity experiments — Entrainment source Assume N CCN = 200 cm -3 for z z i. As PBL entrains free tropospheric air, this CCN is mixed down into the boundary layer Free-tropospheric concentration When entrained into the PBL, free tropospheric CCN can: Suppress drizzle Counteract depletion via cloud processing Increase PBL N t, given sufficient entrainment and free tropospheric CCN concentration

28. Validation and parameterization of cloud processing Cloud processing (depletion) is correlated to drizzle rates by simple power laws and largely independent of initial conditions Depletion can also be related to other model parameters (e.g. N c, not shown) These relationships might serve as nexus of aerosol-cloud interactions in large- scale models Validation of COAMPS cloud processing Results from LES show similar behavior Hoell et al. (2000) give larger cloud processing for given drizzle rates Albrecht’s estimate (1989) is for a strongly-drizzle, highly-depleting example

29. Summary of COAMPS cloud processing results Results respond predictably to changes in initial CCN Idealized COAMPS runs gauge the relative importance of various components of a mesoscale aerosol forecast Magnitude of the entrainment source is greater than any reasonable values of in-situ or surface sources… yet we know that sea-spray can play a vital role in PBL clouds Specification of vertical aerosol profile and species may be more vital than detailed knowledge of in-situ source rates — Importance of remote sensing.

30. Conclusions The general requirements of how to treat aerosol- cloud-drizzle interactions are becoming clear Absolute magnitudes of sources/sinks are poorly constrained Major effort to estimate these quantities and develop parameterizations, either from observations or process models (LES, CRM) Aerosol-cloud parameterization could be implemented gracefully (?) into the COAMPS aerosol-tracer module

31. What components are required are required for an accurate mesoscale forecast of cloud-aerosol system? Specification of aerosol field (initial and boundary conditions) Size characteristics Spatial distribution Cloud processing Activation Coagulation, rainout, diffusiophoresis Regeneration Specification of sources and sinks Urban sources Sea salt Heterogeneous chemistry Transformation rates (fine↔coarse mode) Transport Advection Turbulent mixing (entrainment)

33. Improvement of cloud physics parameterization in NWP: Parameterization of Sub Grid Scale (SGS) processes Closing the scale gap Cloud physics processes scale: ~ 100 m NWP model grid: ~ 1-10 km

34. 15Z 16Z17Z 15Z 16Z17Z Control SGS Condensation Sub-grid scale condensation in COAMPS Top: GOES-9 visible imagery of San Francisco Bay region Middle: Control simulation (no SGS parameterization) Bottom: Forecast with the SGS condensation parameterization. Satellite imagery shows nearly complete clearing by 17 UTC. The control simulation remains cloudy until 2000 UTC. The Bay area is nearly cleared of cloud by 1800 UTC in the SGS condensation simulation 1 1 3 2 2 3