Numerical diffusion in sectional aerosol modells

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

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

3. MODELING needs DATA • data to initialize modeling • AEROSOL REPRESENTATION • data to evaluate modeling MODEL INPUT OUTPUT EM 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 highly variable in space and time ! rapid atmospheric ‘cycling’ ocean industry cities forest desert volcano

5. modeling shortcut • needs for radiative transfer simulation • single scattering properties at all model spect.bands • aerosol optical depth attentuation (scatter +absorption) • single scattering albedoscattered 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 0.55mm (visible) and 11.2mm (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 h h h h

7. natural and anthropogenic • previous fields are based on yr 2000 emissions • AOD can be split into those of coarse sizes (> 1mm) and those of accumulation mode sizes (< 1mm) • 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 • a 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.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 MODEL INPUT OUTPUT DATA

11. model - validation testing the impact (on the radiative budget) of • CLOUDS • major impact, highly variable a 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’ solar IR

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

16. 12 year average (1984 -1995) SRB ISCCP ECHAM5

17. ECHAM5 solar diff. to SRB

18. IR monthly diff. to SRB

19. initial assessment • deviations of cloud-effect at surface • SOLAR a info on cloud optical depth • more negative a more cloud opt. depth / cover • IR a info on altitude of lower clouds • more negative a 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/ • 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