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aerosol. an ancillary data-set in many applications How best to describe global aerosol optical properties for the last 30 years?. aerosols !. aerosol (‘small atmos.particles’) many sources short lifetime diff. magnitudes in size changing over time aerosol  clouds

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Aerosol

aerosol

an ancillary data-set in many applications

How best to describe global aerosol optical properties for the last 30 years?


Aerosols

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


Needs

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


  • Climatology

    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)


    Start with modeling

    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


    Merge in aeronet data

    merge in AERONET data

    • merge

      • quality statistics of AERONET

    • onto

      • consistent background statistics by global modeling

        • NO satellite

        • NO grd in-situ

    AERONET

    modeling


    The merging

    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


    Merging results

    merging results

    model 

    merged

    AERONET

    0.55mm

    • AOD

    • SSA

    • Ang


    Spectral extension

    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


    Spectral samples annual avg maps

    spectral samples – annual avg maps

    g

    AOD

    SSA

    • UV 0.4mm

    • VIS 0.55mm

    • n-IR 1.0mm

    • IR 10mm


    Extension in time

    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


    Altitude

    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


    Total aod 550nm by layer

    total AOD (550nm) by layer

    ECHAM 5 / HAM (model)

    CALIPSO vers.2 (obs)


    Looking for trends

    looking for trends


    Aerosol climatology

    aerosol climatology

    S. Kinne

    and colleagues

    MPI-Meteorology

    with support by

    AeroCom and AERONET


    Overview

    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


    Aerosols1

    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


    Aerosols2

    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)


    Complex aerosol modules

    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


    Simplifications

    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


  • New aero climatology goal

    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


    Data sources

    data sources

    • the available observables

      • ground based in-situ samples

      • satellite guesses

      • sun-/sky- photometry

  • the modeling world

    • data-constrained simulations

    • ensemble output


  • Ground samples

    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


    Satellite data

    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


  • Seasonal aod by satellites

    seasonal AOD by satellites

    combining MODIS, MISR, AVHRR data


    Ground based sun photometry

    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’


    Global modeling

    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


    Climatology concept

    climatology concept

    • merge

      • quality statistics of AERONET

    • onto

      • consistent background statistics by global modeling

        • NO satellite

        • NO grd in-situ

    AERONET

    modeling


    Aerosol climatology1

    aerosol climatology

    • resolution and data content

    • processing steps

    • essential properties

    • temporal variations

    • link to clouds

    • first applications

    • further improvements


    Climatology products

    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)


    Climatology details steps

    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


    Column optical properties

    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


    The merging1

    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


    Merging results1

    merging results

    model 

    merged

    AERONET

    0.55mm

    • AOD

    • SSA

    • Ang


    Merging

    merging

    note: low ssa at low

    AOD are unreliable


    Merging results2

    merging results

    model 

    merged

    AERONET

    0.55mm

    • AOD

    • SSA

    • Ang

      other ..

    fine-mode

    anthropogenic

    fraction

    kappa

    dust size


    Spectral extension 1

    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


    Spectral extension 2

    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


    Spectral samples annual avg maps1

    spectral samples – annual avg maps

    g

    AOD

    SSA

    • UV 0.4mm

    • VIS 0.55mm

    • n-IR 1.0mm

    • IR 10mm


    Altitude distribution

    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)


    Total aod 550nm by layer1

    total AOD (550nm) by layer

    ECHAM 5 / HAM (model)

    CALIPSO vers.2 (obs)


    Temporal change

    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)


    From the past from 1860

    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


    Into the future until 2100

    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


    Link to clouds

    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


    Define concentrations

    define concentrations

    • 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 ccn in

    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


    Critical fine mode radius

    critical fine-mode radius

    1km

    3km

    8km

    • SS 0.1%

    • SS 0.2%

    • SS 0.4%

    • SS 1.0%


    Critical fine mode radius1

    critical fine-mode radius

    natural CCN

    anthrop.CCN

    (dust) IN

    • SS 0.1%

    • SS 0.2%

    • SS 0.4%

    • SS 1.0%


    Applications

    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


    Direct effects by the numbers

    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


  • Direct forcing effects global patterns

    direct forcing/effects global patterns

    solar + IR

    solar

    anthrop. solar

    • clear ToA

    • clear surface

    • all-sky ToA

    • all-sky surface

    ‘seen’ by satellite

    ‘seen’ by ground

    flux-radiometer

    ‘climate’ impact

    coolingwarming


    Direct toa forcing seasonality

    direct ToA forcing – seasonality

    coolingwarming


    Direct forcing sensitivity tests

    direct forcing - sensitivity tests

    • ToA all-sky forcing uncertainty

      • examine impact by changing individual prop. to aerosol or environment

        • +/- 0.3 W/m2

    no

    AERONET

    lower

    low clouds

    AOD

    uncertainty

    higher

    low clouds

    AOD

    by satellite

    SSA

    uncertainty

    fine-mode =

    anthropogen

    higher

    surf. albedo

    g

    uncertainty

    higher max.

    for fine mode

    absorption

    higher

    surf. albedo

    25% more

    absorption


    Anthrop direct forcing in time

    anthrop. direct forcing in time

    • changing pattern

      • 1940: -.16 W/m2

      • 1945: -.17 W/m2

      • 1950: -.19 W/m2

      • 1955: -.21 W/m2

      • 1960: -.24 W/m2

      • 1965: -.27 W/m2

      • 1970: -.31 W/m2

      • 1975: -.35 W/m2

      • 1980: -.38 W/m2

      • 1985: -.41 W/m2

      • 1990: -.43 W/m2

      • 1995: -.44 W/m2

    strongest

    increase


    Ccn climatology in use

    CCN – climatology in use

    • CCN /ccm3 at 1km 0.1% super-saturation

    • liquid water path comp.

      • stronger aerosol

        indirect effect with

        the climatology

    climatology

    ECHAM5.5/HAM

    by U.Lohmann ETHZ


    Future improvements

    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


    Aerosol

    • extras


    Data access ftp ftp projects zmaw de

    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


    Data access ftp ftp projects zmaw de1

    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


    Data access ftp ftp projects zmaw de2

    data access ftp ftp-projects.zmaw.de

    altitude

    • 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


  • Extra plots

    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


    Environmental data

    environmental data

    albedo (MODIS + ice, snow) clouds (ISCCP)

    snow fraction

    ice fraction

    temperature


    Aod ssa asy at 550nm

    AOD / SSA / ASY at 550nm


    Looking for trends1

    looking for trends


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