aerosol climatology s. kinne and colleagues mpi-meteorology with support by aerocom and aeronet

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

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Page 1: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

aerosol climatology

S. Kinneand colleagues

MPI-Meteorology

with support by

AeroCom and AERONET

Page 2: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 3: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

desertindustry cities

volcano forest

rapidatmospheric

‘cycling’

highly variablein space and time !

Page 4: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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)

Page 5: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

complex aerosol modules

• they do exist - even in Hamburg ECHAM/HAM

• they are useful … but often too expensive

size

optically activeand relevant sizes

Page 6: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 7: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 8: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

data sources

• the available observables

• ground based in-situ samples• satellite guesses• sun-/sky- photometry

• the modeling world

• data-constrained simulations• ensemble output

Page 9: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

ground samples ?

• issues• ground composition is not that of the column• samples are usually heated (less or no water)

– monthly statistics example

up quartile medianlow quartile

abso

rptio

n

size

Page 10: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 11: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

seasonal AOD by satellitescombining MODIS, MISR, AVHRR data

Page 12: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

ground-based sun-photometry

• advantages

– direct AOD measurements• AOD and fine-mode AOD

– other aerosol properties with sky-radiance data• size distribution (0.05m < 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’

Page 13: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

global modeling

• advantages• completeconsistent(no data-gaps)

• issues• as good asassumptionsor the ‘tuning’

AERONET

modeling

Page 14: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

climatology concept

• merge

– quality statistics of AERONET

• onto

– consistent background statistics by global modeling• NO satellite • NO grd in-situ

AERONET

modeling

Page 15: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

aerosol climatology

• resolution and data content

• processing steps

• essential properties

• temporal variations

• link to clouds

• first applications

• further improvements

Page 16: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

climatology products

• global monthly 1x1 lat-lon maps – fine mode (radius < 0.5m) properties

• column AOD, SSA, g (… for each ) • altitude distribution• anthropogenic AOD fraction (1850 till 2100)• pre-industrial AOD fraction

– coarse mode (radius > 0.5m) properties• column AOD, SSA, g (… for each )

• altitude distribution

– cloud relevant properties• CCN concentration (kappa approach)• IN concentration (coarse mode dust)

Page 17: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 18: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

column optical properties

• concept

combine– for mid-visible aerosol properties (0.55 m)– for monthly (multi-annual) data– for a (1ox1o lon/lat) horizontal resolution

• consistent and complete maps from modeling – use AeroCom (15 model ensemble) median

composite to remove any extreme behavior

• quality data by sun-/sky-photometry networks– data of ~400 AERONET and ~10 Skynet sites

Page 19: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

site weight ratio field correction factor

A A*BB

Page 20: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

merging results

0.55m

• AOD

• SSA

• Ang

other ..

model AERONETmerged

dust sizekappa

fine-modeanthropogenicfraction

Page 21: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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=2.5m / no vis. absorption: SSA = 1.0)

– dust (re=1.5, 2.5, 4.5, 6.5, 10m / SSA =.97, .95, .92, .89, .86

Page 22: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 23: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

spectral samples – annual avg maps

• UV

0.4m

• VIS

0.55m

• n-IR

1.0m

• IR

10m

AOD SSA g

Page 24: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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)

Page 25: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

total AOD (550nm) by layer

• ECHAM 5 / HAM CALIPSO vers.2

Page 26: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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)

Page 27: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 28: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET
Page 29: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 30: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET
Page 31: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 32: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

• 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

Page 33: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 34: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

critical fine-mode radius

• SS

0.1%

• SS

0.2%

• SS

0.4%

• SS

1.0%

1km 8km3km

Page 35: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

critical fine-mode radius

• SS

0.1%

• SS

0.2%

• SS

0.4%

• SS

1.0%

natural CCN (dust) INanthrop.CCN

Page 36: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 37: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 38: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

direct forcing – global patterns

• clear ToA

• clear surface

• all-sky ToA

• all-sky surface

solar + IR solar anthrop. solar

‘seen’ by satellite

‘seen’ by ground flux-radiometer

climate signal

cooling warming

Page 39: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

direct forcing – seasonality

cooling warming

Page 40: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

noAERONET

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

Page 41: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

stro

ng

es

tin

crea

se

Page 42: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

CCN – climatology in use

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

• liquid water path comp.• stronger aerosol

indirect effect withthe climatology

climatology ECHAM5.5/HAM

by U.Lohmann ETHZ

Page 43: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 44: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET
Page 45: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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.nc total aerosol (yr

2000)– g30_coa.nc coarse mode aerosol – g30_pre.nc fine-mode pre-industrial (yr 1850) – g30_ant.nc fine-mode anthropogenic (yr 2000)

– ss-properties for the 16 IR-bands of ECHAM6• g30_fir.nc total (=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 2000properties

Page 46: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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

Page 47: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

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.nc at 1km above ground

• CCN_3km_yyyy.nc at 3km altitude• CCN_8km_yyyy.nc at 8km altitude

altitude

Page 48: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET
Page 49: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

extra plots

• 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

Page 50: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET
Page 51: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

anthropogenic (fine) AOD

Page 52: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET
Page 53: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET
Page 54: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET
Page 55: Aerosol climatology S. Kinne and colleagues MPI-Meteorology with support by AeroCom and AERONET

looking for trends