aerosol climatology s. kinne and colleagues mpi-meteorology with support by aerocom and aeronet
TRANSCRIPT
aerosol climatology
S. Kinneand 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
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 !
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
• they do exist - even in Hamburg ECHAM/HAM
• they are useful … but often too expensive
size
optically activeand relevant sizes
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
• 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
• the available observables
• ground based in-situ samples• satellite guesses• sun-/sky- photometry
• the modeling world
• data-constrained simulations• ensemble output
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
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 satellitescombining MODIS, MISR, AVHRR data
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’
global modeling
• advantages• completeconsistent(no data-gaps)
• issues• as good asassumptionsor the ‘tuning’
AERONET
modeling
climatology concept
• merge
– quality statistics of AERONET
• onto
– consistent background statistics by global modeling• NO satellite • NO grd in-situ
AERONET
modeling
aerosol climatology
• resolution and data content
• processing steps
• essential properties
• temporal variations
• link to clouds
• first applications
• further improvements
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)
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
• 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
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
merging results
0.55m
• AOD
• SSA
• Ang
other ..
model AERONETmerged
dust sizekappa
fine-modeanthropogenicfraction
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
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
spectral samples – annual avg maps
• UV
0.4m
• VIS
0.55m
• n-IR
1.0m
• IR
10m
AOD SSA g
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 layer
• ECHAM 5 / HAM CALIPSO vers.2
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
• 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
• 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
• 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
• 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
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
• SS
0.1%
• SS
0.2%
• SS
0.4%
• SS
1.0%
1km 8km3km
critical fine-mode radius
• SS
0.1%
• SS
0.2%
• SS
0.4%
• SS
1.0%
natural CCN (dust) INanthrop.CCN
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
• 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 – 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
direct forcing – seasonality
•
cooling warming
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
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
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
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
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
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.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
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
anthropogenic (fine) AOD
looking for trends