1. Numerical diffusion in sectional aerosol modells Stefan Kinne, MPI-M, Hamburg stefan.kinne@zmaw.de DATA in global modeling aerosol climatologies & impact of clouds

2. MODELING needs DATA data to initialize modeling data to evaluate modeling INPUT MODEL OUTPUT DATA

3. MODELING needs DATA data to initialize modeling AEROSOL REPRESENTATION data to evaluate modeling INPUT MODEL OUTPUT EMEM DATA

4. aerosol – complexity to modeling aerosol (‘small atmos.particles’) many sources short lifetime diff. magnitudes in size changing over time  aerosol  clouds  aerosol  chemistry  aerosol  biosphere  aerosol  aerosol ocean desert industry cities volcano forest rapid atmospheric ‘cycling’ highly variable in space and time !

5. modeling shortcut needs for radiative transfer simulation single scattering properties at all model spect.bands aerosol optical depth attentuation (scatter +absorption) single scattering albedo scattered fraction asymmetry-factor scattering behavior concept improve ensemble average ‘ssp’ monthly fields from global modeling* with quality local stats ** * median of 20 global models (with detailed aerosol modules) participating in AeroCom excercises **AERONET: global sun-/sky- photometer network extend data spectrally with ‘smart’ assumptions samples at 0.55  m (visible) and 11.2  m (IR-window) adopt vertical distribution from global modeling

6. aerosol opt. properties AOD aerosol optical depth annual fields SSA single scattering albedo (…of monthly data) ASY asymmetry-factor hhhh

7. natural and anthropogenic previous fields are based on yr 2000 emissions AOD can be split into those of coarse sizes (> 1  m) and those of accumulation mode sizes (< 1  m) assuming a bi-modal size-distribution shape use the AOD spectral dependence (by pre-defining a fine mode Angstrom parameter as function of low cloud cover) coarse mode AOD is assumed to be all natural  no anthropogenic IR effect (anthropogenic dust neglected ) distinction between SEASALT and DUST via visible SSA accumulation mode AOD is partly natural and partly anthropogenic AOD fraction estimates are derived from comparisons of simulationed accumulation mode AODs with yr1750 and yr 2000 emissions (AeroCom excercises)

8. annual fields of monthly data

9. summary what these data can do for you simple method to include aerosol in simulations not just amount … but also size and absorption monthly (seasonal) variations are considered typical environmental conditions are considered separation into natural and anthrop. components what these data can NOT do no interaction with simulated dynamics humidity, clouds … no response to unusual emissions surface wind speed anomaly scaling ? where to get the data contact stefan.kinne@zmaw.destefan.kinne@zmaw.de anonymous ftp ftp-projects.zmaw.de

10. MODELING needs DATA data to initialize modeling data to evaluate modeling CLOUD IMPACT on broadband radiative fluxes INPUT MODEL OUTPUT DATA

11. model - validation testing the impact (on the radiative budget) of CLOUDS major impact, highly variable  the main modulators of climate how well are clouds simulated in ECHAM5 ? no atmosphere

12. validation approach global modeling is ‘tuned’ to the ToA impact how well is the surface impact simulated? reductions to the solar down flux (opt.depth info) increases to the IR down flux (altitude/cover info) ‘participants’ SRB / ISCCP cloud climatology products (1984-2004) (cloud data based on satellite observations) cloud climatologies applied in RT modeling TOVS, HIRS, MODIS, ISCCP IPCC (1980-2000) (20 models … including ECHAM5) focus: ( monthly) statistics of 1984-1995 average

13. ECHAM5 - IPCC Sdt solar dn all-sky flux at top-of-atmosphere Sut solar up all-sky flux at top-of-atmosphere Sds solar dn all-sky flux at surface Lds longwave dn all-sky flux at surface

14. ECHAM5 - IPCC cloud effect = ‘all-sky flux’ minus ‘clear-sky flux’ on surface fluxes solar (shortwave) dn all-sky flux at surface ’Sds’ minus ’sds’ IR (longwave) dn all-sky flux at surface ’Lds’ minus ‘lds’ solarIR

15. ‘data-tied’ Cloud Effect References SRB surface radiation budget (GEWEX) ISCCP intern. satellite cloud climatology project NO certain reference ! all-sky

16. SRBECHAM5ISCCP 12 year average (1984 -1995)

17. ECHAM5 solar diff. to SRB

18. IR monthly diff. to SRB

19. initial assessment deviations of cloud-effect at surface SOLAR  info on cloud optical depth more negative  more cloud opt. depth / cover IR  info on altitude of lower clouds more negative  higher clouds or less opt.depth /cover MPI has overall higher cloud optical depth esp. May-August higher opt. depth: at high-latitudes in (NH) summer lower opt. depth: off-coastal stratus, ITCZ, Asia overall higher altitude / lower fract of low clouds e.g.: less re- radiation to surface in (sub-) tropics despite more re- radiation to surf. at high latitudes

20. final thoughts useful data are collected on an opportunity basis e.g. http://disc.sci.gsfc.nasa.gov/techlab/giovanni/ http://disc.sci.gsfc.nasa.gov/techlab/giovanni/ near-term focus on Calipso / A-train data clues for parameterization in global modeling data quality must be explored (are data useful ?) e.g. are the satellite cloud climatology products of SRB and ISCCP consistent ? support by institute and MPG is appreciated !

21. EXTRAS

22. cloud effect - solar dn ECHAM5

23. cloud effect - IR dn ECHAM5

25. LOGO 1 COSMOS

26. LOGO 2 CO MO S

27. LOGO 3 COS MOS