aerosol

1. aerosol an ancillary data-set in many applications How best to describe global aerosol optical properties for the last 30 years?

2. aerosols ! • 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

3. needs • global consistent data for all aerosol properties • AOD, SSA, g (amount, absorption, size) • satellite data … only deliver in part • limited spatial coverage (non-polar, darker) • usually only AOD … with assumptions • modeling … can deliver • but can it be trusted ? (even if constrained by satellite AOD pattern & component treatment) • data assimilation ? ! • MACC is not ready yet / hindcasts are too few

4. climatology • the next best thing • monthly 1x1 climatology for mid-visible aerosol properties of AOD, SSA and Angstrom ( g) • start with ‘one year’ (year 2000 or ‘current’) • median of 15 component models (no extremes) • enhance with quality AERONET data (merging) • extend spectrally • use simulation to scale back/forward in time • add altitude information (modeling or CALIPSO)

5. start with modeling • advantages • complete consistent (no data-gaps) • issues • as good as assumptions … the ‘tuning’ to observations • all relevant aerosol optical properties • aerosol optical depth (AOD) • single scattering albedo • Angstrom  asymmetry factor annual aerosol optical depth at 550nm modeling

6. merge in AERONET data • merge • quality statistics of AERONET • onto • consistent background statistics by global modeling • NO satellite • NO grd in-situ AERONET modeling

7. the merging • combine point data on regular grid (1ox1o 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 A B A*B ratio field site weight correction factor

8. merging results model  merged AERONET 0.55mm • AOD • SSA • Ang

9. spectral extension • use the Angstrom to statify AOD into • AOD, f (fine-mode … particles > 0.5mm radius) • AOD, c (coarse-mode … part. < 0.5mm radius) • use SSA to identify dust size • when fine mode SSA falls below threshold • coarse mode spectral dependence is defined • sea-salt or dust (4 different sizes) • fine mode spectral dependence only for solar • g (from Angstrom) and SSA drop in near-IR

10. spectral samples – annual avg maps g AOD SSA • UV 0.4mm • VIS 0.55mm • n-IR 1.0mm • IR 10mm

11. extension in time • does NOT change with time • coarse mode • pre-industrial fraction of fine mode • use info on what is ‘anthropo’ from modeling • changes with time • stratospheric aerosol • anthropogenic fraction of the fine mode • back: ECHAM simulation with NIES emissions • forward: ECHAM runs with rcp scenarios

14. altitude • current distributions • total aerosol • CALIPSO version 2 • ECHAM • Separately for fine and coarse mode • ECHAM • next … CALIPSO version 3 • use (non-sphere) (=dust) for coarse • use total-(non-sphere) for fine

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

16. looking for trends

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

19. 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

20. aerosols ! • 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

21. 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)

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

23. 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

24. 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

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

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

27. 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

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

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

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

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

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

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

34. climatology details / steps • define global monthly maps of mid-visible aerosol column prop (AOD, SSA & Ang) • 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

35. column optical properties • concept combine • for mid-visible aerosol properties (0.55 mm) • for monthly (multi-annual) data • for a (1ox1o 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

36. the merging • combine point data on regular grid (1ox1o 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 A B A*B ratio field site weight correction factor

37. merging results model  merged AERONET 0.55mm • AOD • SSA • Ang

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

39. merging results model  merged AERONET 0.55mm • AOD • SSA • Ang other .. fine-mode anthropogenic fraction kappa dust size

40. spectral extension (1) • concept • split AOD contribution by small & large sizes • fine-mode size (radii < 0.5um) • coarse-mode size (radii > 0.5um) • 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=2.5mm / no vis. absorption: SSA = 1.0) • dust (re=1.5, 2.5, 4.5, 6.5, 10mm / SSA =.97, .95, .92, .89, .86

41. 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[l(mm) -0.25] g,min=0.1

42. spectral samples – annual avg maps g AOD SSA • UV 0.4mm • VIS 0.55mm • n-IR 1.0mm • IR 10mm

43. 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  less atmos. layers)

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

45. 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)

46. 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

48. 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

50. 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