1. aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

2. overview aerosol in (global) climate modeling (tropospheric) aerosol climatology concept column optical properties altitude distribution changes in time link to clouds some applications further improvements

3. aerosols ! 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 !

4. aerosols ? the radiation budget is modulated by clouds –so, what is all the fuss about aerosol ? aerosol has a role in cloud formations –many clouds exist because of avail. aerosols aerosol has an anthropogenic component – the impact on climate is highly uncertain and at times speculative (for indirect effects)

5. complex aerosol modules they do exist - even in Hamburg ECHAM/HAM they are useful … but often too expensive size  optically active and relevant sizes HAM calculations carry 27 tracers !

6. simplifications ? simplified methods have been tried in the past ignore aerosol compensate with a radiation correction simplify by ignoring fine-mode absorption improve on the Tanre-scheme used in ECHAM IPCC 4AR simulations –a highly absorbing dust bubble over Africa –lack in seasonal feature (e.g. biomass) now better simplifications are possible matured complex aerosol module output improved obs. constrains by remote sensing

7. new aero climatology goal provide a simplified aerosol representation in global models for rapid estimates on aerosol direct & indirect effects on the radiation budget establish aerosol optical properties (as they required for atmospheric radiative transfer) at sufficient resolution … in order to resolve regional variability and seasonality whenever possible, tie climatology data to (quality) observations establish via CCN (cloud condensation nuclei) or IN (ice nuclei) links to cloud properties

8. data sources the available observables ground based in-situ samples satellite guesses sun-/sky- photometry the modeling world data-constrained simulations ensemble output

9. ground samples ? issues ground composition is not that of the column samples are usually heated (less or no water) –monthly statistics example up quartile median low quartile absorption  size 

10. satellite data ? issues ancillary data needed –indirect reflection info requires information on aerosol absorption (usually assumed) and reflection contribution by other sources (such as surface or clouds) at high accurcay limited information content –only AOD is retrieved and via AOD spectral dependency some information on size limited coverage –land?, not with clouds, daytime, not polar, … positives spatial distribution statistics

11. seasonal AOD by satellites combining MODIS, MISR, AVHRR data

12. ground-based sun-photometry advantages –direct AOD measurements AOD and fine-mode AOD –other aerosol properties with sky-radiance data size distribution (0.05  m < size bins < 15  m) single scatt. albedo (noise issue at low AOD) issues –local … may fail to represent its region –daytime and cloud-free ‘bias’

13. global modeling advantages complete consistent (no data-gaps) issues as good as assumptions … with ‘tuning’ satellite pattern modeling all relevant aerosol optical properties - aerosol optical depth (AOD) - single scattering albedo - Angstrom  asymmetry factor annual aerosol optical depth at 550nm

14. climatology concept merge –quality statistics of AERONET onto –consistent background statistics by global modeling NO satellite NO grd in-situ AERONET modeling

15. aerosol climatology resolution and data content processing steps essential properties temporal variations link to clouds first applications further improvements

16. climatology products global monthly 1x1 lat-lon maps –fine mode (radius < 0.5  m) properties column AOD, SSA, g (… for each ) altitude distribution anthropogenic AOD fraction (1850 till 2100) pre-industrial AOD fraction –coarse mode (radius > 0.5  m) properties column AOD, SSA, g (… for each ) altitude distribution –cloud relevant properties CCN concentration(kappa approach) IN concentration (coarse mode dust)

17. climatology details / steps define global monthly maps of mid-visible aerosol column prop (AOD, SSA & Ang  g) spread data spectrally to define AOD, SSA & g needed for radiative transfer simulations add altitude information using active remote sensing from space and/or modeling account for decadal (anthrop.) change using scaling data from aerosol component modeling establish a (needed) link to cloud properties by extracting associated CCN and IN

18. column optical properties concept combine –for mid-visible aerosol properties (0.55  m) –for monthly (multi-annual) data –for a (1 o x1 o lon/lat) horizontal resolution AeroCom model median maps –consistent and complete maps –median of a 15 model ensemble removes extreme behavior of individual models quality data by sun-/sky-photometry networks –data of ~400 AERONET and ~10 Skynet sites

19. the merging –combine point data on regular grid (1 o x1 o lon/lat) –extend spatial influence of identified sites with better regional representation –spatially spread valid grid ratios and combine –decaying over +/- 180 (lon) and +/- 45 (lat) –apply ratio field values (A) at site domains with distance decaying weights (B)  corr. factors example: correction to model suggested AOD site weight ratio fieldcorrection factor AA*BB

20. merging results 0.55  m AOD SSA Ang model  AERONETmerged

21. merging note: low ssa at low AOD are unreliable

22. merging results 0.55  m AOD SSA Ang other.. model  AERONETmerged dust size kappa fine-mode anthropogenic fraction

23. spectral extension (1) concept –split AOD contribution by small & large sizes –fine-mode sizes (radii < 0.5  m) –coarse-mode sizes (radii > 0.5  m) apply the Angstrom for total aerosol … with –pre-defined fine-mode Angstrom at function of ‘low clouds’: Ang,f = [1.6 moist … 2.2 dry] –fixed coarse-mode Angstrom: Ang,c = 0.0 –coarse mode (spectr. dep. properties) prescribed apply the single-scatt. albedo for total aerosol –sea-salt (re=2.5  m / no vis. absorption: SSA = 1.0) –dust (re=1.5, 2.5, 4.5, 6.5, 10  m / SSA =.97,.95,.92,.89,.86

24. spectral extension (2) concept –fine-mode spectral dependence by property –only adjustments for solar spectrum needed AOD spectral dependence is defined by the fine-mode Angstrom parameter [1.6-2.2] range SSA spectral dependence considers lower values in the near-IR (Rayleigh scattering limit) asymmetry-factor and its spectral dependence is linked with fine mode Angstrom parameter (sharing common information on aerosol size) g = 0.72 - 0.14*Ang,f *sqrt[ (  m) -0.25] g,min=0.1

25. spectral samples – annual avg maps UV 0.4  m VIS 0.55  m n-IR 1.0  m IR 10  m AODSSA g

26. altitude distribution concept … tied to the mid-visible AOD –define altitude distribution for total aerosol use CALIPSO (vers.2) multi-annual statistics or use ECHAM5 / HAM simulation data –define altitude distribution separately for fine- mode and for coarse-mode aerosol use ECHAM5 / HAM simulation data –pre-defined layer boundaries 0 - 0.5 - 1.0 - 1.5 - 2.0 - 2.5 - 3.0 - 3.5 - 4.0 - 4.5 - 5 - 6 - 7 - 8 - 9 - 10 - 11 - 12 - 13 (- 15 - 20) km (if surface height > 0.5km  fewer atmospheric layers)

27. total AOD (550nm) by layer ECHAM 5 / HAM (model) CALIPSO vers.2 (obs)

28. temporal change concept –assume that the coarse mode is all natural – –assume that the coarse mode does NOT change with time –assume that the pre-industrial fine-mode aerosol fraction does NOT change with time –the only aerosol to change over time is the anthropogenic fraction of the fine-mode (which is by definition zero before 1850)

29. from the past – from 1860 concept –use results ECHAM/HAM simulations with NIES historic emissions –aggregate simulated fine-mode monthly AOD patterns in 10 year steps for local monthly decadal trends –linearly interpolate between 10 year steps to get data for individual years –(at this stage) only changes to AOD are allowed … no changes to composition »could be an issues as there was a higher BC before WWII and a higher sulfate fraction after WWII

31. into the future – until 2100 concept –simulate with ECHAM/HAM AOD pattern changes by modifying suggested future emissions according to RCP 2.6 IPCC% scenario RCP 4.5 IPCC 5 scenario RCP 8.5 IPCC 5 scenario –simulate changes for sulfate and carbon emissions stratified by selected regions –quantify the associated changes to fine- mode AOD pattern –sum impacts from all regions

33. link to clouds aerosol provide CCN and IN to clouds –changes to aerosol concentration can modify cloud properties and the hydrological cycle –critical parameters are aerosol concentration  from climatology aerosol composition  from modeling super-saturation temperature

34. aerosol concentrations …are defined by the AOD vertical distribution and assumed log-normal size-distributions coarse-mode log-normal properties for sea- salt or dust are prescribed (see above) fine-mode log-normal width is fixed (std.dev 1.8) and the mode radius is linked to the Angstrom of the fine-mode (which depends on low cloud cover) – larger aerosol radii at moister conditions – smaller aerosol radii at drier conditions define concentrations

35. define CCN / IN all coarse mode particles are CCN … plus a fraction of fine-mode particles are CCN –for fine-mode concentrations: a critical cut-off size is determined as function –super-saturation –kappa (‘humidification’) - maps are based on ECHAM /HAM simulations for fine-mode AOD »kappa weights: SS =1.0, SU =0.6, OC =0.1, DU/BC =0) only sizes larger that the cut-off are considered as fine-mode CCN coarse mode dust particles are IN

36. critical fine-mode radius SS 0.1% SS 0.2% SS 0.4% SS 1.0% 1km8km3km

37. CCN – natural vs anthrop IN – coarse dust SS 0.1% SS 0.2% SS 0.4% SS 1.0% natural CCN(dust) INanthrop.CCN 3 = 10 3 scale 

38. applications aerosol direct forcing –in global numbers (vs +2.8 W/m2 by greenhouse) –global direct forcing pattern (highly uneven) –strong seasonality (snow, clouds, aerosol type) –sensitivity to input (define uncertainty range) –temporal change (explore climate sensitivity) CCN application –first tests for impacts on clouds

39. direct effects by the numbers global averages for direct radiative forcing - 0.5 W/m2 anthropogenic at ToA all-sky » + 0.3 W/m2 for anthropogenic BC –compare to +2.8 W/m2 by greenhouse gases - 1.1 W/m2 anthropogenic at ToA clear-sky - 4.7 W/m2 solar at ToA clear-sky »as seen by satellite sensors - 8.6 W/m2 solar at surface clear-sky »as seen by ground flux radiometers

40. direct forcing/effects global patterns clear ToA clear surface all-sky ToA all-sky surface solar + IRsolaranthrop. solar  cooling warming  ‘seen’ by satellite ‘seen’ by ground flux-radiometer ‘climate’ impact

41. direct ToA forcing – seasonality  cooling warming 

42. direct forcing - sensitivity tests ToA all-sky forcing uncertainty –examine impact by changing individual prop. to aerosol or environment +/- 0.3 W/m2 lower low clouds no AERONET AOD by satellite AOD uncertainty SSA uncertainty higher low clouds higher surf. albedo higher surf. albedo g uncertainty 25% more absorption fine-mode = anthropogen higher max. for fine mode absorption

43. anthrop. direct forcing in time changing pattern –1940: -.16 W/m 2 –1945: -.17 W/m 2 –1950: -.19 W/m 2 –1955: -.21 W/m 2 –1960: -.24 W/m 2 –1965: -.27 W/m 2 –1970: -.31 W/m 2 –1975: -.35 W/m 2 –1980: -.38 W/m 2 –1985: -.41 W/m 2 –1990: -.43 W/m 2 –1995: -.44 W/m 2 strongest increase

44. CCN – climatology in use CCN /ccm3 at 1km 0.1% super-saturation liquid water path comp. stronger aerosol indirect effect with the climatology climatology ECHAM 5.5 /HAM by U.Lohmann ETHZ

45. future improvements update AOD vertical distribution with CALIPSO version 3 data (multi-annual averages) include compositional change over time for anthropogenic aerosol (BC vs sulfate content) allow responses to relative humidity changes in the boundary layer explore smarter ways for data merging of trusted point data onto background data allow natural aerosol (sea-salt and dust) to vary according to met-data and/or to soil-data

46. extras

47. data access ftp ftp-projects.zmaw.de cd aerocom/climatology/2010/yr2000 look at ‘README_nc’ –ss-properties for the 14 solar-bands of ECHAM6 g30_sol.nctotal aerosol (yr 2000) –g30_coa.nccoarse mode aerosol –g30_pre.ncfine-mode pre-industrial (yr 1850) –g30_ant.ncfine-mode anthropogenic (yr 2000) –ss-properties for the 16 IR-bands of ECHAM6 g30_fir.nctotal (=coarse mode) aerosol –CCN and IN fields g_CCN_km20.nc for supersat. of 0.1, 0.2, 0.4 and 1% –anthrop. CCN is changing along with anthrop. AOD year 2000 properties

48. data access ftp ftp-projects.zmaw.de cd aerocom/climatology/2010/ant_historic –based on decadal change by ECHAM/HAM simulations for fine mode aerosol with NIES historic emissions aeropt_kinne_550nm_fin_anthropAOD_yyyy.nc »simulations by Silvia Kloster, prep. by Declan O’Donnell cd aerocom/climatology/2010/ant_future –based on sulfate and carbon emission related regional changes to fine-mode AOD patterns with ECHAM/HAM for IPCC RCP 2.6, RCP 4.5 and RCP 8.5 scenarios till 2100 aeropt_kinne_550nm_fin_anthropAOD_rcp ??_ yyyy.nc »simulations by Kai Zhang, concepts by Hauke and Bjorn anthropogenic change

49. data access ftp ftp-projects.zmaw.de cd aerocom/climatology/2010/altitude –based on CALIPSO version2 multi-annual data –fine/coarse mode altitude differences via ECHAM/HAM calipso_fc.nc »CALIPSO data via Dave Winker (NASA_LaRC) »ECHAM/HAM altitude data from Philip Stier cd aerocom/climatology/2010/ccn_historic –applying anthropogenic change to CCN concentrations CCN_1km_yyyy.ncat 1km above ground CCN_3km_yyyy.ncat 3km altitude CCN_8km_yyyy.ncat 8km altitude altitude

51. extra plots assumed environmental data mid-visible aerosol ss-properties current anthropogenic AOD … by month solar spectral ant aerosol properties variations vertical spread of fine mode AOD by ECHAM vert. spread of coarse mode AOD by ECHAM

52. environmental data albedo (MODIS + ice, snow) clouds (ISCCP) snow fraction ice fraction temperature

53. AOD / SSA / ASY at 550nm

58. looking for trends with stratospheric aerosol until 1999