BIOGENIC AEROSOL: SECONDARY ORGANIC AEROSOL (SOA) PRIMARY BIOLOGICAL AEROSOL PARTICLES (PBAP)
Numerical diffusion in sectional aerosol modells Stefan Kinne, MPI-M, Hamburg [email protected]...
-
Upload
elvin-wade -
Category
Documents
-
view
215 -
download
0
Transcript of Numerical diffusion in sectional aerosol modells Stefan Kinne, MPI-M, Hamburg [email protected]...
Numerical diffusion in sectional aerosol
modells
Stefan Kinne, MPI-M, Hamburg
DATA in global modeling
aerosol climatologies&
impact of clouds
MODELING needs DATA
data to initialize modeling
data to evaluate modeling
INPUT MODEL OUTPUT
DATADATA
MODELING needs DATA
data to initialize modeling AEROSOL REPRESENTATION
data to evaluate modeling
INPUT MODEL OUTPUT
EM
DATA
aerosol – complexity to modeling
aerosol (‘small atmos.particles’)
many sources short lifetime diff. magnitudes in size changing over time
aerosol cloudsaerosol chemistryaerosol biosphereaerosol aerosol
ocean
desertindustry cities
volcano forest
rapidatmospheric
‘cycling’
highly variablein space and time !
modeling shortcut needs for radiative transfer simulation
single scattering properties at all model spect.bands aerosol optical depth attentuation (scatter +absorption) single scattering albedo scattered fraction asymmetry-factor scattering behavior
concept improve ensemble average ‘ssp’ monthly fields
from global modeling* with quality local stats *** median of 20 global models (with detailed aerosol
modules) participating in AeroCom excercises **AERONET: global sun-/sky- photometer network
extend data spectrally with ‘smart’ assumptions samples at 0.55m (visible) and 11.2m (IR-window)
adopt vertical distribution from global modeling
aerosol opt. properties AOD aerosol optical depth annual fields SSA single scattering albedo (…of monthly data) ASY asymmetry-factor
h h h h
natural and anthropogenic previous fields are based on yr 2000 emissions
AOD can be split into those of coarse sizes (> 1m) and those of accumulation mode sizes (< 1m) assuming a bi-modal size-distribution shape use the AOD spectral dependence (by pre-defining a fine
mode Angstrom parameter as function of low cloud cover)
coarse mode AOD is assumed to be all natural no anthropogenic IR effect (anthropogenic dust neglected) distinction between SEASALT and DUST via visible SSA
accumulation mode AOD is partly natural and partly anthropogenic AOD fraction estimates are derived from comparisons of
simulationed accumulation mode AODs with yr1750 and yr 2000 emissions (AeroCom excercises)
annual fields ofmonthly data
summary what these data can do for you
simple method to include aerosol in simulations not just amount … but also size and absorption monthly (seasonal) variations are considered typical environmental conditions are considered separation into natural and anthrop. components
what these data can NOT do no interaction with simulated dynamics
humidity, clouds … no response to unusual emissions
surface wind speed anomaly scaling ?
where to get the data contact [email protected] anonymous ftp ftp-projects.zmaw.de
MODELING needs DATA
data to initialize modeling
data to evaluate modeling CLOUD IMPACT on broadband radiative fluxes
INPUT MODEL OUTPUT
DATA
model - validationtesting the impact (on the radiative budget) of CLOUDS
major impact, highly variable the main modulators of climate
how well are clouds simulated in ECHAM5 ?
no atmosphere
validation approach
global modeling is ‘tuned’ to the ToA impact
how well is the surface impact simulated? reductions to the solar down flux (opt.depth info) increases to the IR down flux (altitude/cover info)
‘participants’ SRB / ISCCP cloud climatology products (1984-2004)
(cloud data based on satellite observations)
cloud climatologies applied in RT modeling TOVS, HIRS, MODIS, ISCCP
IPCC (1980-2000) (20 models … including ECHAM5)
focus: (monthly) statistics of 1984-1995 average
ECHAM5 - IPCC
Sdt solar dn all-sky flux at top-of-atmosphere Sut solar up all-sky flux at top-of-atmosphere Sds solar dn all-sky flux at surface Lds longwave dn all-sky flux at surface
ECHAM5 - IPCC
cloud effect = ‘all-sky flux’ minus ‘clear-sky flux’ on surface fluxes
solar (shortwave) dn all-sky flux at surface ’Sds’ minus ’sds’ IR (longwave) dn all-sky flux at surface ’Lds’ minus ‘lds’
solar IR
‘data-tied’ Cloud Effect References
SRB surface radiation budget (GEWEX)
ISCCP intern. satellite cloud climatology project
NO certain reference !
all-sky all-sky
all-sky
SRB ECHAM5ISCCP
12 year average (1984 -1995)
ECHAM5 solar diff. to SRB
IR monthly diff. to SRB
initial assessment deviations of cloud-effect at surface
SOLAR info on cloud optical depth more negative more cloud opt. depth / cover
IR info on altitude of lower clouds more negative higher clouds or less opt.depth /cover
MPI has overall higher cloud optical depth esp. May-August
higher opt. depth: at high-latitudes in (NH) summer lower opt. depth: off-coastal stratus, ITCZ,
Asia
overall higher altitude / lower fract of low clouds e.g.: less re- radiation to surface in (sub-) tropics despite more re- radiation to surf. at high latitudes
final thoughts
useful data are collected on an opportunity basis e.g. http://disc.sci.gsfc.nasa.gov/techlab/giovanni/
near-term focus on Calipso / A-train data clues for parameterization in global modeling
data quality must be explored (are data useful ?)
e.g. are the satellite cloud climatology products of SRB and ISCCP consistent ?
support by institute and MPG is appreciated !
EXTRAS
cloud effect - solar dn ECHAM5
cloud effect - IR dn ECHAM5
LOGO 1
COSMOS
LOGO 2
CO MO
S
LOGO 3
COS MOS